f2m Automation Book by landmagd

AUTOMATION SCIENCE AND TECHNOLOGY

www.bakingbiscuit.com

INTRODUCTION

Futuristic concepts Ethical debates concerning robots were crystallized into what has arguably become the world’s most famous – yet fictional – laws of robotics. Isaac Asimov first imagined them in 1942, before ‘science fiction’ was even coined as a term. After 80 years of scientific advances nothing short of the ingenuity in our favorite futuristic literature, robotics take part in the daily routines of many production facilities. And in doing so, they drastically change the manufacturing scene for the better, with significant improvements in efficiency,

++ Catalina Mihu, Editor-in-chief Your commments or suggestions are always appreciated: E-mail: mihu@foodmultimedia.de

safety, speed and consistency in results. Real-life ethical debates still surround robotics today. However, the robots performing tedious, repetitive and often demanding tasks (and flawlessly, every time) open opportunities to upscale work for people who can better devote their efforts to new tasks. ‘New’ is the key to this entire equation, from the novelty of emerging and fast-developing technology to innovation in automation and ensuing shifts in processes they transform; new discoveries, new adjustments, new results. A look at careers that are said to dominate the future of work speaks volumes about embracing the ‘new’, in the bakery industry and elsewhere: artificial intelligence specialist, robotics engineering, data scientist, data engineering, cyber-security specialist. It also paves the way for people and technology to work together, as opposed to one instead of the other. Looking back at 1942 again, it was also the year when the Federation of Bakers was formed. It was founded in an effort to assist in organizing wartime production and the distribution of bread. Following this timeline in advances that improved the bread-making process is just as inspiring and thought-provoking as ‘I, Robot’ or any great book you might like. Fields such as artificial intelligence, robotics and machine learning amount to tangible, reliable scientific applications in automated baking. A welcomed increase in production efficiency is just the beginning of what automation can do to support baking. Large-scale operations are the first to benefit, but small and medium manufacturers are also finding new access to useful, smart tools. Increasing automation also counts towards sustain-

INTRODUCTION

ability and minimizing the environmental impact of manufacturing, both ‘hard skills’ that are urgently needed.

INTRODUCTION

New, smart tools can take many shapes. Yum China, the owner of brands including Pizza Hut and KFC, recently inaugurated a Digital R&D Center, a hub for the development of new solutions and services using technologies in big data, artificial intelligence, middle office and digital Software as a Service (SaaS), to drive end-to-end digitalization. Here is the view from Joey Wat, Yum China’s CEO, on the new solutions the giant is spearheading: “Digitalization is one of the key enablers behind Yum China's resiliency and long-term development as we move toward our next milestone of 20,000 stores.” We hope this book will help inspire your automation journey and provide the information to spark

INTRODUCTION

the research for new solutions.

CONTENTS

++ Science 08 Software: Manufacturing Execution Systems in bakeries 16 Robotics: Autonomous performance 22 Smart stores: The search for answers is on 32 Rheology: Bread dough rheology 40 Baking line audit: Metrology on baking and freezing lines 50 Mixing: Dough mixing supervision: an overview 60 Production planning: Production and selling optimizations for bakeries 70 Digital twins: Digital twins in baking process automation 78 Digitization: Digitizing food supply chains 86 Design thinking: Using design thinking to facilitate automation 96 Image processing: Image processing applications for baking process monitoring 104 Artifical intelligence: The role of artificial intelligence in designing baking ovens 112 3D printing: Will we 3D print the bread of the future?

CONTENTS

122 Cybersecurity: Safe and smart bakery production

CONTENTS

++ Company reports 130 American Pan: Pan design and handling for automated bakery systems 134 AMF Bakery Systems: Future-smart technology arrives 138 Bakon: The key is knowledge! 140 Cetravac: Fast, flexible and sustainable 142 Diosna: Everything from a single source 144 Ernst Böcker: Why sourdough plays a decisive role 148 FRITSCH Group: Progress in the world of bakery 152 Heuft Industry: Energy savings at the end of the tunnel oven 156 Kaak: Bring time on your side 160 Koenig Group Baking Equipment: The future of the baking industry is automation 162 MECATHERM: The human must remain the pilot 166 Rademaker B.V.: Training is money well spent 170 Sugden: Baking for joy 172 TECNOPOOL S.p.A.: Complete spiral system control 174 WP BAKERYGROUP: Connected processes

178 f2m: Portfolio

CONTENTS

180 Imprint

Research The science resource to understand, inspire and improve processes

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

RESEARCH

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

MES in bakeries Every bakery is familiar with software terms such as cash register system, ERP, merchandise management, recipe management, shipping system and a few more. In the meantime, the term MES has also entered the vocabulary of innovative bakery businesses.

What is the difference between ERP systems, MES and recipe management? ERP (Enterprise Resource Planning) systems, also known as merchandise management systems, regulate the business processes in companies. In the ERP system, suppliers are managed in food companies, orders are triggered at suppliers, branches or other customers’ orders are accepted or invoices are created. These are functions that map the commercial process. MES systems regulate the production process. In MES systems three main resources are managed: Personnel, Material and Equipment. These resources are also used in ERP systems to calculate products. Production plans are also created in the ERP system, but ERP systems lack real-time reference to production. MES systems are closer to production. In the MES system, detailed production planning is carried out on the basis of the actual availability of resources. Compliance with the production plan can be monitored in real-time and short-term measures can be taken to reschedule in the event

of deviations. Qualitative production data acquisition in real-time in the form of continuous quality checks by tapping machine data or recording random checks by employees are also typical features of MES systems. In contrast to ERP systems, the data in MES systems is recorded promptly and punctually made available to the employee as support in production. In this way, the production manager or line supervisor has a view of the actual situation of production quality and performance at all times and can react immediately in the event of deviations. Recipe management is a module of a MES system. Recipes are of course also required in ERP systems for product costing. In MES systems, recipes are referred to as processes. Processes take into account all three resources: material, personnel and equipment. In ERP systems, only the material list, the so-called Bill of Material (BOM), is required for price and nutritional value calculation. In MES systems, the entire production process can be described in the recipes. In this way, technical steps such as kneading times, stirring times for sourdough production, temperature setpoints for production processes, etc. are also defined. These steps are then passed on to the plant or plant section in the correct sequence at the correct time. The actual measured values are returned by the machines as feedback. In this way, it is

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MES, Manufacturing Execution System is a software solution that manages, controls and monitors a production process. An MES system can map the entire production process from planning to quality assurance, effectiveness measurement and much more, from goods receipt to goods issue.

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

possible to determine whether production has actually run as planned. In ERP systems, especially in those that offer modules for food companies, the creation of process parameters in their software is also possible, but the reference to the production plant is missing. What is the interface between the ERP system and MES? A distinction is made here between the type of data transmission and the content of an interface. In the type of data transmission, different methods have been established in the past. Previously, data was sent from one system to another in file form. Today, data is exchanged in real-time using secure protocols. However, this is basically just the physics of how data gets from one system to the other. Much more important is the content of the interface.

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Who is the master of which data? The article master is basically created and managed in ERP systems. This includes raw materials, semi-finished products, doughs, production items, sales items, etc. Also, product prices, data about different suppliers of raw materials are in the master data of ERP systems. The so-called master data is synchronized with the MES system via the interface. Data is maintained in only one system at a time. Information and properties of this article master data are added in the MES system. Additions include the specific weight, tolerances for raw material dosing and preferred storage location. Here, it is not possible to define a clear boundary,

since ERP systems can also take over MES tasks, and vice versa. Two examples of this are warehouse management and batch tracking. These functionalities are provided in both ERP and MES systems. This raises the question of which should do what. Warehouse management – ERP or MES Here we want to look primarily at the raw material warehouses, from the contents of which sales items are produced. This module is available in almost all ERP systems. However, MES systems also cover this function. There are also pure warehouse management systems that are advertised on the market. Maybe the following will guide your decision. The warehouse management starts with the raw material acceptance in the goods receipt area. The information about delivery date, delivery quantity and the supplier are available in the ERP system. This would lead to the conclusion that logically the goods receipt process and the warehouse process are also carried out by the ERP system. Further questions are in the room, such as who does continuous batch tracing, how are silos filled with raw material etc.. In the latter case, an interface to the MES system or the silo/ recipe administrations is always up for discussion. Information about the total stock of raw materials is a must in ERP systems. These order from suppliers on the basis of various criteria such as minimum stock, calculated stock range or sales forecasts. However, this does not require detailed information about the respective storage location, but only the total.

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

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For continuous and, above all, relatively accurate batch tracing from the raw material to the sales item, not only are the goods movements in the warehouse necessary, but also very often information from machines. Let's take the example of a flour dosing point with manual feeding of the kettle to the kneader and then on to the lifting tipper. The kettles can be automatically identified at the individual processing points. This requires reading the RFID information of the respective boilers. Here it could happen that the order of

the boilers changes. When the boiler is then handed over at the lifting tipper for processing, the MES system knows exactly the time, the boiler number and the contents of the boiler with the raw materials contained therein that were handed over for processing. This example can be continued up to the sales item. MES systems usually find it easier to collect and process this kind of information, since the integrated interfaces to machines and plants are a feature of such systems. For this reason, the part upstream of production, i.e. the raw materials warehouse, is often managed by the MES system, especially in automated production operations. This means that the data on the movement of raw materials through the individual warehouses to the dough, as well as the data for further processing in kettles or even continuously to the sales item, are recorded and stored in one system. The alternative to managing the raw materials warehouse in the ERP system or even in a separate warehouse

When deciding which system to use for detailed warehouse management based on the section and the location of the storage, the following questions make the decision easier. + Which system guarantees continuous batch tracing? Which functions does the ERP or MES system + offer? + Which do I feel more comfortable handling?

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

management system is of course also state of the art. Intelligent data transfer of raw material data from the ERP to the MES and back again also enables traceability in one system.

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If warehouse management is controlled via a MES system, the delivery note data must be transferred from the ERP system to the MES system for the delivery of raw materials. In the goods receipt area, qualitative and quantitative control of the goods takes place. In the process, article master data transferred from the ERP system is enriched with properties such as inspection parameters. Likewise, data on the minimum shelf life (best before date), lot ID of the goods are added via manual entry or scanning of a barcode. The flexibility to add properties and parameters to articles is predominantly higher in MES systems and a standard. In ERP systems it is very product-dependent. Some ERP systems offer very flexible solutions and have integrated MES functionality, others focus on pure ERP functionalities. For example, ERP vendors that serve the bakery segment and want to cover production control, in particular, have added MES functionalities to their products. Other products focus on their very own merchandise management functions and have developed the product in the direction of store management. Some systems use Microsoft products as a base system and develop their own solutions. Depending on the ERP system used, information must therefore be added to one or the other system. Very often, customers wish to keep the ERP system as standard as possible and to map processes that require interaction with production staff in the MES system. The possibility of adapting the operator interface to the warehouse worker according to the customer's wishes and making it as user-friendly as possible is higher in MES systems.

sponding silo must be released for filling and the batch data must be carried in the silo. This task can also be solved in both systems. Conclusion: Both systems provide solutions and there are no significant differences. The decision lies in the price/performance ratio of the functionality on offer. Dosing control – Recipe management Dough production is an essential sub-process in bakeries. We are not only talking about dosing, but the complete process up to the transfer of the dough to processing, either via continuous conveying or batch production. Manufacturers of silo plants very often offer – especially for bakeries – their own software solution or software solutions from partners who work almost exclusively for the plant manufacturer. The solutions are in a proven combination – recipe control – silo plant, which is often tested and matured in use. In other industries, e.g. in construction chemistry, this is not common The advantage of procuring everything from one source, however, also has disadvantages. Software solutions that are specifically tailored to the plant construction of one or more manufacturers

Further bookings in different storage areas, block storage and single place storage are covered by mobile terminals; this is the same in ERP as well as MES systems. When goods are received into the silo, there is a technical interface to the silo system. The corre-

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If you want to digitally map the entire process up to the finished product now or in the future, it also pays to talk to MES system manufacturers both for control system upgrades and for new plant construction. Above all, it is important to keep the number of software interfaces and the number of different software systems to a minimum. Higher maintenance and servicing costs will come with more different systems. It also should not be forgotten that software systems have to be updated cyclically. A completely independent software solution also offers the advantage of being able to combine different system designs depending on their strengths. If one decides in favor of an independent solution, there are a few things to consider. In the implementation, there is another interface, namely the one from the software manufacturer to one or more plant constructors. As a rule, the trades are commissioned separately. This can save costs, but the customer also has to coordinate the systems. Here it must be clearly defined in advance who is responsible for what. It is advisable to take a close look at the service, product functionality and manpower of the software company before making a decision. Important questions are: Does the company offer 24/7 support? Who picks up the phone? How many people take turns on the hotline? Do the support staff understand the system? Take your time making your selection and check carefully. Mechanics are easier to change and replace than the heart of the system, the software!

Quality assurance – a topic for you? From the processing of the dough to the produced article and the article on sale, quality tests are usually carried out by every company today. Recording results is still done manually in many companies. The data is later transferred to Excel lists and evaluated again later. This is time-consuming and error-prone – both when recording the data and when transferring it to electronic systems. Often the sampling control is done via the software of scale manufacturers. This in turn has the disadvantage of adding a further interface to an ERP system in which the master data is transferred – unless one manages data twice, which in itself is the most cumbersome and inefficient way. MES systems have a QA module on board. As mentioned at the beginning of this article, the entire production process can be mapped in MES systems. Thus, quality measuring points can be defined for the reprocessing lines. The production or sales articles from the article master, which is synchronized with the ERP system, are only enriched with information, such as in which cycle the various parameters are to be checked and at which quality measuring points. Weighing data is recorded directly via scales and, of course, does not have to be entered separately. Since MES systems are designed to connect machines and systems, data that is valuable for product quality, such as the humidity and temperature in fermenters, can be recorded automatically. OEE Not only is quality decisive for success! But also efficiency paired with top quality is the desired combination, whether in industrial or craft enterprises. MES systems provide a module – OEE – (Overall Equipment Effectiveness) for this purpose. The use of OEE solutions has been common practice in the automotive industry for decades. In the bakery industry, the demand for corresponding software solutions is increasing in order

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usually do not cover the upstream and downstream processes. Although these systems have been enriched with functionalities in recent years so that the process can be controlled up to the point of transfer to reprocessing, more advanced functions such as quality assurance, efficiency analysis, labeling, batch tracing through to the finished article are usually not available.

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

Real-time display of production progress with deviations

to be able to produce even more efficiently and with higher quality. A frequent challenge in the bakery industry is the collection of data (production data acquisition – PDA). This can be done automatically or manually. Anyone who believes that everything works automatically is mistaken!

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How does the data automatically get from the machine to the software? Modern machine controls offer the possibility of exchanging data via a standardized interface (OPC UA). Attention! A standard interface is a means to an end The content of the interface must be defined. Not every machine can transmit the same data. For example, in the case of a spiral freezer, the temperature setpoint and actual value are primarily relevant, and in the case of a dough divider, the number of pieces. The data to be exchanged must be provided by the machine manufacturer. In the case of older systems, it is sometimes necessary to install additional sensors if it is not possible to tap the data automatically. In particular, if spare parts are no longer available for the existing control system, consideration should be given to upgrading the system to acquire additional data when replacing the control system. (The data acquisition is mostly an interplay of automatically acquired data, which is then added manually). Essentially, the following information is important. Performance data: How many pieces are currently being produced on the machine? If more pieces are produced than specified, the power factor can also be over 100 %. If the machine

does not provide this data, this can be realized by mounting additional sensors. Quality data: For some machines, the temperature, relative humidity or other measured parameters influence the product quality. In any case, such data should also be transmitted and recorded. The data is displayed in trends in relation to a product and allows conclusions to be drawn about quality deviations in a simple manner. Fault messages: Ideally, all faults are transmitted to the MES system with their priority defined in the MES system. Many small faults do not affect production and are therefore not interesting for an effectiveness analysis. Is it possible to record staff time? Yes, and quite simply. The employee logs on to an order that is displayed on a touch panel, e.g. via RFID or barcode. Personnel times are then added up for the order according to the current order status. If the status ‘Setup’ is selected, personnel times are booked to ‘Setup’. Does the data acquisition function fully automatically? No. Machines only provide partial information. An example: The information as to whether a machine is ready for operation, switched off or in operation is usually supplied by the machine. Information about rejects or whether maintenance is currently being carried out is very important for evaluations, but must be entered manually or recorded semi-automatically. In particular, any scrap must be able to be recorded easily. After all, no one counts the individual pieces that are selected from a conveyor belt. Usually, the best

M A N U FAC T U R I N G E X E C U T I O N SYST E M S

Isn’t manual entry too complicated for an operator? Ease of use is a key requirement in bakery operations. Here the suppliers of MES systems differ significantly, as different industries also have different requirements. Experience has shown that in the food industry, less is more. The input screen must be clearly designed and easy to use. The screens on the line can also be used for multiple purposes, such as quality data acquisition or weighing. Who benefits from an MES system? The data from MES systems are useful for the user on the line up to the company manager. The user on the line receives the order progress and the effectiveness from the OEE module via a large display. For example, it can be seen whether the planned order will be completed earlier or later than planned. The QA module shows the line operator whether there are deviations in the quality. For example, continuous or random weight measurements after the dough divider are useful in order to maintain the required product weight and not to exceed – and above all not to fall below – it. The technician can see via an evaluation what faults have occurred, how often they have happened and the duration of the interruptions to operations caused by them. The analysis facilitates prioritization in the elimination of defects. All events, including their causes, are documented. All activities of the respective technicians in their shifts can also be recorded via a shift book. The production manager receives an overall view of the manufacturing process. In the event of changes to machine settings or the production process, it can be very clearly displayed whether an improvement in performance or quality could be achieved as a result.

At the push of a button, the quality manager has a continuous log of the production process from goods receipt to the finished product. In case of deviations in the final product, the data of the individual production steps can be analyzed to find the causes – and this is from the raw material quality and batch number of a used raw material at delivery. The evaluations can be adapted to the needs of users in MES systems. Not everyone requires the same information. This is done by adapting tables such as showing and hiding columns and saving filters and queries, configuring dashboards and much more. By assigning rights, the respective user only sees the information that is important to them. Also, a report generator is on board, providing PDF reports at certain intervals or when an event is set to generate one (i.e., when an order is completed). These reports can then be sent automatically or stored on a file system to make information from the system available to employees who do not work with the MES system. Conclusion In many industries, MES systems are already indispensable support for managing the production process. Bakery companies are constantly working to increase the efficiency of their production processes and the quality of their products. The software industry has recognized this need, and in the meantime manufacturers of MES systems have also adapted their solutions to this market or developed their own modules. In the experience of AutomationX GmbH, solutions implemented in the bakery industry have shown that the benefits far outweigh the costs of introducing the system. The systems have established themselves as an indispensable part of production control. +++

Author Thomas Mühlheimer, AutomationX. The company develops solutions ranging from the complete digitization of individual components to plant-wide production optimization.

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way to do this is to collect the scrap in containers and then weigh it. If the rejects are added to a dough batch again (rework), the batch tracing can also be mapped in the system.

RESEARCH 16 ROBOTICS

ROBOTICS

Autonomous performance Baking automation is poised for transformation driven by robotic solutions, as they are increasingly specializing in new applications and expanding their uses out of the packaging stations and into the manufacturing areas. screens. The restaurant is fully digital, from placing the order to collecting the pizza – a timely solution concerning safety in distancing times.

AUTONOMOUS PERFORMANCE

The owners had the ambition of changing ‘fast-food’ into ‘fast-good-food’, by perfecting the process and selecting quality ingredients. “We are in a very fast process, with perfect control of time, and control of quality since we have a constancy offered by robotics, and also an environment that is quite cool and relaxed,” said Roverso, one of the inventors of the Pazzi robot. The challenging aspect the robot had to master was the dough. Thierry Graffagnino, a chef consultant at Pazzi and triple world pizza champion, pinpointed the challenge of working

The Braubourg district in Paris became the fitting home to a technological first with the opening of Pazzi in July 2021, a pizza restaurant where the production, handling, packaging and delivery are exclusively in the ‘hands’ of a robot with artificial intelligence, developed by a team of 30 engineers and developers. Eight years of R&D and five patents filled later, Cyril Hamon and Sébastien Roverso opened the autonomous 120 sqm restaurant in Paris, after the launch of the pilot project in a shopping center in 2019. Pazzi can prepare up to 80 pizzas per hour, to be consumed at the restaurant or via delivery or click & collect: one pizza is ready every 45 seconds and it can bake six at a time. The production progress is displayed on

ROBOTICS

Pazzi pizza robot with fresh dough: the machine had to constantly adapt: they had to give the robot the means to make these corrections on its own, and given the fact that some pizza makers can't even manage that themselves. Pazzi aims to expand with two new openings in Paris by the end of 2021 and abroad, with an opening in Switzerland scheduled for the first quarter of 2022.

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Hand-to-eye-to-brain coordination The pandemic has been a catalyst to the adoption of automation features of all kinds, robotics included. Soft Robotics is among the specialists that expanded their operations to meet the demand fueled by pandemic-related changes in manufacturing processes. The company’s SoftAI solution enables industrial robots to have handto-eye coordination similar to human beings. This is achieved with 3D vision and artificial intelligence technologies and it enables the automation of bulk picking. It is the next step for industrial robots to be able to handle product or workspace variations, both commonly found in food processing, baking industry included. The vulnerabilities in the food supply chain were highlighted by the pandemic. It made it “Clear that automation has graduated from a nice-to-have to a must-have across all large-scale food production operations,” said Jeff Beck, Soft Robotics

+ 6 patents filed + 7 years into development + 80 pizzas/hour can be prepared + 29cm is the circumference available + 2,000+ parts are needed to assemble these robots

CEO. The company reported the demand for both its hardware and SoftAI software solutions is increasing at an unprecedented pace. In response, the company is investing in automation solutions that can help safeguard the food supply against disruptions. Robotics is well underway to transform packaging entirely. Flexible robot technology and fast format changes have already established their usefulness in packaging, especially in connection with handling fragile goods such as biscuits or cookies. They answer needs for diversity in packaging materials as well as formats. Schubert’s flowwrapping machine is an example in this regard, designed to handle plastic and paper-based films, trays made of cardboard and plastic, and U-boards. One of the features in demand is the gentle processing of a wide variety of biscuit shapes, starting right after the production process, through quality control and packaging into flowpack bags, both with and without trays. Such an integrated system can include de-stackers, pick & place F4 robots and the Flowmodul flow-wrapping unit.

ROBOTICS

A clear vision to going mainstream In the next few years, it is predicted that robotic functionality will increase with richer algorithms, dynamic maneuverability, force control and inherent safety [1], all features rendering robots more adaptable along the processing line and improving their skills in co-working with humans. R&D work is focusing on making robots easier to use, with improved sensory functions for autonomous motion. As advancements are being made, their cost is also declining as estimates are made that the industry will grow by doubledigits in the next years. The tasks robots should excel in include increasing throughput, reducing manual labor, especially work consisting of repetitive tasks, improving staff safety, increasing product quality and consistency, and gaining in manufacturing flexibility with advances in AI and self-learning robots. Automated changeovers and shorter product runs also rank high in the operational improvements they bring. Applications that stand to benefit from deploying robots are also increasing, driven by their functionality improvements. Bakeries will have more choices in robotic advances to automate anything from cutting, picking, stacking, sorting,

packaging, shipping, to carton making, stacking and de/palletizing. Current and future applications in bakeries The British Association of Robot Automation (BARA) promotes the use of industrial robots and automation in the UK‘s industry and supports their advancement. The association also represents the interests of its British member companies overseas as a partner of the International Federation of Robotics. The association can advise on sources of independent advice and/or help identify potential suppliers. Robotic solutions have been assisting British bakeries in production for several years, especially high-volume operations. Some of the newest innovations that constitute useful tools in bakery manufacturing, in particular, are improvements in vision capabilities and gripping techniques. Grippers can range from magnets (for tins), mechanical grippers for boxes, pneumatic for products and packages, pins for muffins and something similar to soft fingers for delicate products. Moreover, advances in the control and feeding of icing materials are making robot automation a viable option in the decorating process, traditionally a time-consuming and skill-dependant step. A possible long-term evolution, based on t r be hu Sc ongoing trends in their cost and © ease of use, would see robots being used more and entering artisan and lowervolume facilities. BARA is represented on the PPMA Board by Mark Stepney, managing director of Schubert UK. He evaluates that the bakery industry is one of the food production sectors that benefit from a higher level of automation currently, over a multitude of operations, particularly applications in tin handling, packing and palletizing. He highlights how it influences productivity and will increasingly do so: “There has been a significant impact on productivity because the robot systems are highly efficient, do not stop for food breaks

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To rule out damaged goods entirely, each product also passes through an incident-light scanner. Schubert’s image processing system only passes on the data of qualitatively faultless products to the robots’ control system, rejecting defective units. The baked goods passing the inspection are then either packed directly into a flowpack or stacked into trays or cardboard U-boards, which are in turn packed into flowpacks. Sealing technology also contributes to the efficiency of the robot’s performance. Ultrasonic sealing closes the flowpack longitudinally, followed by a heat-sealing system with a flying cross-sealing unit that adjusts itself automatically to the speed of the upstream pick & place robots for each flowpack.

ROBOTICS

and can operate continuously across multiple shifts thus reducing manual labor requirements.”

be a major challenge, assuming the original program was well defined and annotated in the first place. Various communication interfaces are available for robots to communicate with the automated systems in the facility, including discrete IO or Profinet, to name a few. Synchronization may entail anything from program selection for different products, to production status and error information.

He also estimates that at present, most robots in bakeries are deployed in the packing stage, generally creating the packs before these are fed onto flow wrappers. However, robots are also in use in all manufacturing stages, e.g., cutting pastry, decorating cakes, and more. The best choice in the structure of the robotic solution to be implemented in bakeries depends on the process to be automated. Stepney recommends: “Packing is often a delta robot where high-speed movements are required, whereas palletizing is typically a four-axis articulated arm robot. In circumstances where a product or package needs to be re-orientated as well as re-positioned, a five- or six-axis robot may well be selected.”

Outlook and opportunities There was a record 3 million industrial robots operating in factories around the world in 2021, an increase of 10%. Positive developments in China supported sales to rise, despite a slight contraction elsewhere, as countries began experiencing their COVID-19 low points at different times. Almost all Southeast Asian markets are expected to grow by double-digit rates in 2021. The Chinese manufacturing industry began surging in the second quarter of 2020, while the North American economy started to recover in the second half of 2020, and Europe followed suit not long after that. This is the third most successful year in history for the robotics industry, following 2018 and 2017 [2]. “Global robot installations are expected to rebound strongly and grow by 13% to 435,000 units in 2021, thus exceeding the record level achieved in 2018,” according to estimations announced by Milton Guerry, president of the International Federation of Robotics.

When planning programming/updating for robots working in bakeries, offline tools are often used to simulate their actions and initially offline program the robots, thus reducing downtime during programming. Programming can become more complex in the case of packing systems that utilize vision systems and systems that have product moving on conveyors, for example.

Figure 1: Unit growth in the number of robots shipped, North America [3]

Reprogramming can be necessary when new products are introduced, or when dealing with different pallet layouts, in the case of palletizing. If the original set-up was programmed offline, such changes can be made through the existing simulation. If not, it could require modifications to an existing program. However, this should not

Source: RIA 2018, Statistics for North America

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Robots for food and consumer goods

The upward trend for the adoption of robotics in manufacturing is shown in their sales increase. While food and consumer goods account for about 8% of the total robotics implementation, their utilization recorded a significant increase, by almost 50%, in North America between 2018 and 2019 alone, for example. Only 3% of these sales were collaborative robots in 2017, but this ratio is expected to be well over 30% by 2025. Asia remains the world’s largest market for industrial robots, with 71% of all newly deployed robots in 2020 installed on the continent, up from 67% in 2019. Installations for the region´s largest adopter China grew strongly by 20% with 168,400 units shipped, the highest value ever

In Europe, industrial robot installations were down by 8% to 67,700 units in 2020. This was the second year of decline, following a peak of 75,560 units in 2018. Demand from the automotive industry dropped by 20%, while demand from the general industry was up by 14%. Germany, one of the top five major robot markets in the world (China, Japan, USA, Korea, Germany) had a share of 33% of the total installations in Europe, followed by Italy with 13% and France with 8%. The German robotics industry is recovering, driven by strong overseas business. The ‘boom after the crisis’ is expected to fade slightly in 2022 worldwide, IFR anticipates, expecting average annual growth rates in the medium singledigit range from 2021 to 2024. Robotics and enhanced automation Robotics greatly contribute to building smart production facilities. The IFR identifies five scenarios in which robots are connected within broader automation strategies [4], which also apply to the baking industry: automated production (streamlining process steps), optimizing performance (by connecting equipment and robots), digital twins (virtual representations and simulations), robots as a service (robots on a pay-per-

Top five advancements that will make robotics easier to use 1. Dexterity

4. Connectivity

2. Vision

5. Safety

3. Mobility

use basis), and sense and respond (sensors, vision systems help robots react in real-time). R&D for innovation in robotics for the food industry is not without challenges. Currently, the occasional issue with robots in bakeries will come from either the client or the supplier misunderstanding the needs of the application or the complexity of the solution. Other issues can be caused by wear, damage, deformation or the inconsistency of items the robot system might be dealing with, such as tins or trolleys for transporting trays, in Mark Stepney’s experience. Cyber Physical Systems (CPS) can offer solutions, merging theories of cybernetics, mechatronics, design and process science. This interdisciplinary field of research is based on IoT and can help streamline the supply chain and can greatly contribute to the communication of smart food labels and traceability. The food serving sector presents great potential for R&D in robotics, sustained by advancements in sensor fusion, CPS design, HMI, robot learning, vision systems, innovation in altering robot structural configurations. New ideas are emerging in tandem with new technologies. +++

Author f2m, editorial team

References [1] The Association for Packaging and Processing Tech-

[3] RIA 2018, Statistics for North America

nologies (PMMI) – Business Intelligence Webinars, 2019

[4] https://ifr.org/industrial-robots

Robotics Webinar; Donna Ritson, DDR Communications

[5] Prospects of robotics in food industry - Food Science and

President, May 2019 [2] World Robotics 2021, Industrial Robots and Service Robots – International Federation of Robotics

Technology (Campinas), by Jamshed Iqbal, Zeashan H. Khan, Azfar Khalid, April 2017; 37(2):159-165

I NUTTEORNVOI EMW A OUS PERFORMANCE

recorded for a single country, indicating the rapid speed of robotization in China. Japan follows China as the largest market for industrial robots, though the Japanese economy was hit hard by the pandemic, as shown by the sales decline by 23% in 2020, with 38,653 units installed. The Japanese robotics market is expected to grow by 7% in 2021 and continue to do so by 5% in 2022. The Republic of Korea was the fourth largest robot market in terms of annual installations, following Japan, China and the US. Robot installations decreased by 7% to 30,506 units in 2020.

Source: PMMI, 2019 Robotics Webinar

ROBOTICS

SMART STORES

RESEARCH

The movable robot, flexible in three axes, is able to handle the goods from the frozen food warehouse to the output and can even drive around the curve (page 29)

SMART STORES

The search for answers is on Who will sell baked goods in the future and how? This is a question that interests not only marketing people but also cost accountants. New solutions are emerging.

In European food retailing, attempts are currently underway to digitize the recording of sales at self-service bakery stores using cameras and scales, thus generating faster responses and lowering personnel costs. A development from Bavaria goes much further, employing the use of artificial intelligence and robotics for automation solutions in bakeries. If needed, it can automate all processes, from the removal of the dough pieces from the frozen storage facility and the baking process to the presentation of the goods and the care/cleaning of the output trays. The user can decide whether partial or total automation is used, and what and how much of this process is visible to the buyer of the baked goods. This makes the system interesting not only for supermarkets but also for chain bakeries, which can use parts of it to relieve staff. But it can also be used to think of a pure vending machine solution that offers freshly baked goods 24 hours a day, for example, at transport hubs or in the cafeterias of large companies.

One thing is certain, the pandemic has kicked off the change processes with full force. Baking shop in food retail: camera or scales? The majority of baking stations in food retailing are filled with goods that are delivered deep-frozen and baked in the ovens on-site or just defrosted. The stocking of the self-service shelves is calculated in advance with a great deal of data from empirical values, past checkout events, weather forecasts, the expected influence of special events, organized sales promotions, etc. The data is then used to calculate the number of baked goods on the shelves. But no matter how well the algorithms predict demand, weaknesses remain and they commonly arise from: 1. Despite all the planning, the supply does not meet demand. 2. The presentation of the goods is anything but tempting. 3. The personnel, who are supposed to implement the presentation plans, are overwhelmed with other tasks, poorly paid and/or unmotivated. The result: sales are not closed and the pull effect of the baking station, whose attractiveness is supposed to draw customers into the stores and generate other sales, suffers. There is an increased risk that customers will leave or switch to online delivery services because, in case of doubt, they have time to bake the desired product fresh in their

THE SEARCH FOR ANSWERS IS ON

Which plus points that sales concepts offer today will consumers still perceive as benefits in the future? How quickly will customers react if they don’t get what they want or when they want it, or if the act of sale is not convenient or fast enough? How important will new hygiene concepts be for the delivery and payment of the goods? All these questions are becoming increasingly urgent in light of online competition that can deliver.

SMART STORES

distribution center and thus deliver exactly what customers want, crisp and fragrant, to their door. The fact that these weak points exist has something to do with the product itself. Baked goods age quickly, change their appearance, crumble or leave grease and other stains, which does not exactly make them more tempting for the customer. In fact, it would be necessary to keep a permanent eye on the bakery shelf and its sales, especially since in supermarkets they are often placed at the beginning of the customer’s run and it can take a long time before the sale is recorded at the checkout, if it gets there at all. Customer behavior is another problem that stands in the way of precise sales control, on the one hand, and the permanent presence of the products at the aisle, on the other. Merchandise may be positioned in the back, in the right or wrong compartment, with or without packaging or the tongs or glove that should actually be used to lift the item.

RESEARCH

In the past, having full shelves until the closing time was considered a customer magnet and, because of low acquisition costs, items discarded after closing time were not considered a painful cost driver. It wasn’t until the advent of public campaigns against food waste that the perspective turned. Nevertheless, having staff permanently

The dashboard of the SES Imagotag solution

checking the shelves is expensive and hard to find given the working conditions. There are, therefore, two trial arrangements currently in the retail sector to digitally capture what is happening in the retailer’s output compartments. Cameras from SES-Imagotag One option comes from the French SES Group, whose Ettenheim, Germany-based subsidiary SESImagotag launched a camera-based solution in early 2021 as part of a shelf management system. It reports that this is now in use in several supermarkets in France, the UK and Spain. The store operator defines the number of compartments to be monitored as well as the quantity and duration of the desired availability of goods. Every half hour, the cameras take a picture of the goods in the output compartments. Special software compares the captured images with predefined ones. The software, which evaluates the recorded images, recognizes not the number of items, but the percentage fill level of the compartment, and also anomalies – i.e. incorrect or foreign products in the compartment – based on learned images, and alerts the responsible employee if necessary. The software’s continuous learning process ensures ever greater precision and optimization of the comparison.

SMART STORES

Discounters and supermarkets in German-

with local artisans. In northern Germany, the

speaking Europe are increasingly trying to

organic bakery Bahde, based in Seevetal, is one

make their baked goods ranges more attrac-

of those that have dipped into the market and

tive by offering shelves for regional chain

has had its own shelves in Edeka and Rewe

stores. It is not uncommon for the goods to be

supermarkets for some time. In Switzerland,

delivered fresh, eliminating the need for bak-

Lidl cooperates with bakeries under the

ing. The name of the chain store instills con-

heading ‘Von Deinem Beck’ and in Austria,

fidence in consumers and higher prices are

too, Lidl maintains regional cooperation with

accepted. How much of this reaches the chain

chain stores. The importance of artisanal chain

store is a matter of negotiation because it is

stores for image and sales in the baked goods

usually the supplier whose influence ends at

market has been appreciated and recognized

the ramp. Only rarely do retailers accept that

by retailers, especially in German-speaking

the baker has the entire value chain in his

countries for a while and the idea has now

hands.

gone global. Aldi Australia, for example, is

The Aldi Süd Group has equipped more than

cooperating in its new convenience stores in

two-thirds of its locations in Germany with

the Sydney area with Sonoma Bakery, a chain

such offers. Aldi Nord is currently following

bakery that operates in New South Wales and

suit. Other retail groups are also cooperating

calls itself Artisan Sourdough Bakers.

THE SEARCH FOR ANSWERS IS ON

Craft bakers as suppliers

SMART STORES

SES camera systems are installed showed an average improvement in merchandise availability from 92% to 98% and a 6% increase in sales. The actual data recording also shows clear indications of a reduction in disposal volumes. A detailed evaluation will be available at the end of 2021.

A small camera regularly transmits a picture of the respective bakery shop shelf

With the appropriate link to the control of oven occupancy and baking process, post-baking processes can also be triggered automatically. Links with the merchandise management system are also possible, as are links with digital price labels that adapt to the inventory according to predefined rules. According to Michael Unmüßig, Managing Director of SES -Imagotag Deutschland GmbH, initial evaluations of nearly 50 stores in which

Scales at Billa Plus Rewe Group in Austria is testing the installation of scales under the goods display areas in Billa Plus stores. Every time a piece of pastry is picked, a computer is notified. The system collects this information and promptly sends a message to the dashboard of the responsible staff member when a predefined threshold is reached. It is envisaged that, in the future, the computer will also be able to trigger the oven when the threshold is reached. There is currently no direct networking of the computer with the checkout system or the merchandise management system, but this is being considered.

RESEARCH

Digitization in retail The digitization strategies of retailers are currently

already offer customers the option of speeding up

in full swing. Most groups are well advanced in

their payment process by scanning the goods using

the area of marketing and communication with

a smartphone or handheld scanner provided by

customers. Social media channels are being used,

the store.

apps are being designed to retain customers, and

The pandemic has boosted interest among con-

it is being made easier for customers to shop online

sumers in online retailing in the food sector. In

and, incidentally, obtain data.

addition to traditional delivery services, so-called

The growing willingness of younger consumers, in

fast delivery services such as Gorillas, Flink and

particular, to pay digitally is arousing retailers’

Getir are currently making headlines in Europe. They

interest in automated store concepts, especially in

promise delivery within 10 minutes of receiving

locations where large numbers of walk-in customers

the order via smartphone. Deliveries are made ex-

want to be served quickly. Valora in Switzerland,

clusively to urban centers by bicycle courier from

for example, is currently testing automated sales

decentralized small warehouses. The assortment

and payment solutions in convenience stores. It says

is based on supermarkets but only includes around

it could also envisage such solutions in the form of

1,000 items. At the beginning of August, the Czech

automated shelving for offices, for example, and

Rohlik Group launched its Knuspr delivery service

automated shop-in-shop concepts. Valora owns

in Munich. The service focuses on daily freshness

convenience stores in high-frequency locations,

and regionality, to offer a wide range of supermarket

pretzel specialists Ditsch and Brezelkönig, and the

and farm shop products that are delivered to the

BackWerk chain of bakery restaurants in Germany,

doorstep within three hours of the order being

Austria, Switzerland and the Netherlands, all to-

placed. The promotional promise for baked goods

gether some 3,000 outlets. Various other retailers

says: “With us, you get oven-fresh baked goods

from our own bakery or from the best bakers and

into the merchandise management system. A rather

confectioners in the region.” According to press

young field is the optimization of the presence of

reports, Gopuff, the forefather of all quick delivery

goods at the point of sale, their recognizability,

services, which has so far only handled deliveries

their accessibility and the question of whether

in the USA, is also in the starting blocks for the

the goods are available where the customer ex-

European market.

pects them. Here, too, cameras and other sensor

A large and highly advanced field of digitization

technology are used, which feed their data to a

and automation in retail is the entire supply chain,

cloud where changes or optimization of merchandise

from the connection to manufacturers’ systems

presence are calculated. At the same time, they are

via logistics and warehousing to the presence of

networked and automatically report inventories to

goods in the store. This now includes self-learning

the merchandise management system.

forecasts that can take more and more factors into

Digitization at the POS naturally also includes

account, automatic inventory management and the

digital price tags, which allow prices to be adjusted

distribution of master data. For packaged goods,

within the day, thus reducing waste. Digital adver-

all of this is normal today, even if huge volumes of

tising spaces, promotional notices and the linking

data are generated in the process, for example for

of all these things with customer communication

products that are on the shelf in various packaging

before, during and after the purchase round off the

sizes and are additionally or seasonally offered in

possibilities for making the range more attractive

special areas with different content quantities and

to customers on an ad hoc basis.

prices. Fresh produce such as fruit and vegetables,

However, the greatest benefit is promised by the

whose shelf life generally extends beyond the day,

networking of all these information systems.

and service counters are also digitally integrated

At Billa Plus in Austria, scales monitor the fill level of the output trays

THE SEARCH FOR ANSWERS IS ON

SMART STORES

Oven

RESEARCH

Smart shelf at Billa in Austria. The Bizerba scales in the output compartments report every sale

The system only calculates in weights and also only recognizes the difference in weight. Therefore, there is a clearly defined plan of the type of pastries that are offered in each compartment. The corresponding average weights of the item are stored in the computer. If an item is deposited in the wrong compartment, the employee recognizes the incorrectly calculated item number based on the display on the dashboard, unless the items have the same weight.

of the threshold value, the baking time, the tray occupancy plan, but also on staff deployment, structural conditions and other site-specific requirements. Currently, the system Rewe is testing at Billa still relies heavily on staff presence and attention. Work is currently underway on automatic baking plans that take into account both sales and markdowns, to account for goods that should theoretically still be available but are not in reality, as the scale shows.

Pastry residues such as crumbs, grains, etc. usually have only small weights that are below the tolerance limit. This is defined as a percentage of the respective average weight of the item. Otherwise, it is the responsibility of the employees to regularly clean the pastry trays and remove tongs, bags, or similar items that have mistakenly landed in the trays. If a customer puts a pastry item back in the wrong compartment, the same applies as in the case of an incorrectly filled compartment. This means that it is only noticed if a difference in weight to the pastry item intended at the location becomes visible.

When asked, Rewe Austria stated that there are currently no further concrete rollout plans. The test has not yet been completed.

The goods delivered to Billa’s baking stations are deep-frozen. Whether a product is baked, when and in what quantity, depends on the definition

Upgrading with AI and robotics – the future of sales Artificial intelligence and robotics bring more efficiency in sales for chain stores and baking stations – and without having to change the market presence. In the Bavarian town of Gallmersgarten, a cooperation of various experts has resulted in a solution that has what it takes to catapult bakery sales into a digitalized and automated age. The good thing about it is that, unlike the failed vending machine solution of a German discounter at the time, it does not represent a break with consumers’ shopping experiences. What’s more,

SMART STORES

The team around EngRoTec Systems GmbH focused primarily on the processes behind the goods issue. Every step was examined for automation possibilities and solutions were developed for various requirements, which can be installed either individually or step by step, building on each other. The result is an automatically loaded shelf for bakery goods that can be varied in many ways, including on oven and cooling capacities. How much a customer standing in front oft he bakery shelf will notice, the supplier decides. Werner Huprich, head of the lead company EngRoTec Systems GmbH: “It was important to us that each bakery supplier decides what runs automatically and what runs manually. A chain store has a completely different need for flexibility and the presentation of its craftsmanship than a retailer at its baking station. We can cater to both. We can even think of a pure vending solution that offers freshly baked goods 24 hours a day, for example at transport hubs or in the canteens of large companies.” The heart of the system is a movable robot with three flexible axes that transport the goods from the warehouse via the oven to the sales outlet and can also move around corners. The goods travel into the oven on belts; otherwise, they lie on trays on which they can also be temporarily stored in buffers. The exact counterpart of the trays in the goods issue area is, in turn, stored on weighing cells and is itself movable via gears so that when the goods are fed in, they are not simply thrown in, but are moved in gently to protect the product. The brain of the plant is an Artificial Intelligence system that controls all the stations involved and processes the information generated there. The system is freely programmable and at the same time self-learning. If a special case occurs at any point, for example, if baguettes are suddenly in extremely high demand because the weather is conducive for people to picnic, the AI is able to

redirect the entire process so that more baguettes are produced until the demand subsides. The solution The solution essentially consists of technology, robots and artificial intelligence. Its charm lies in the fact that the composition and the layout are flexible, making custom solutions possible. The solution is usable for retail bakeries as well as for chain bakeries thar want to automate only part of their presentation. A 24-h-shop running exclusively with a vending machine is also possible. Even manual work on the oven can be integrated, whether on an hourly or permanent basis. Personnel requirements are reduced to a few hours per day for placing the goods and cleaning, with basic cleaning being carried out permanently by the system itself. Unsold products can be disposed of automatically at defined times. The operator can determine which process steps should be in the customer’s field of vision. Technology + Storage capacities with the possibility of automatic unloading for goods delivered chilled, ambient or frozen. + Feeding stations of various designs and capacities, where the goods are either manually placed for baking according to a predefined positioning scheme, or handled fully automatically using grippers, or are prepared for baking in complete sheet loads. Loads prepared in this way can go directly into the oven or into a temperature-controlled buffer. + Different ovens from different manufacturers, including the vacuum oven UDO from Cetravac; their control will be integrated into the main control system so that the baking programs can be adjusted to the fill level of the oven. A vacuum deck oven is currently being worked on. + Product-specific presentation racks in various sizes and with different removal options such as flaps for direct intervention, or the solution where the desired product is maneuvered into a dispensing channel with a rod. Chutes for large quantities are also conceivable. For 24-hour operation, the system is also available

THE SEARCH FOR ANSWERS IS ON

it can be introduced partially and gradually at individual locations and brings greater efficiency right from the start.

SMART STORES

Top: Finished goods are gently pushed into the output compartments by the robot so that damage is largely avoided Left: If required, the robot stores goods in a cold storage unit

RESEARCH

Various dispensing options can be used; here, the customer has to push the desired pastry sideways out of the presentation in order to be able to bag it. Flaps for direct removal are also conceivable

The task can be done manually. A screen specifies how the goods must be placed on the belts. With an automated solution, this step is also left to the robot

The belts on which the goods are transported and presented are not rigid, but independently driven, so that the goods move into the customers' field of vision and do not slide or get piled up

SMART STORES

Robotics The worker in the system is a robot with mobility in the axes x/y/z, which can handle sheets, trays, paper supports or place the goods on belts. The robot itself can rotate if necessary. The position is freely programmable so that any position can be controlled. The robot can rotate and thus also serve an ‘around the corner’ store design. It retrieves occupied trays from the infeed station, transfers their occupancy to the oven, and picks up the goods after baking, transporting them to buffers or directly to the outfeed station. Depending on the height of the shelf to be filled, it can tilt the trays so that the goods move gently into the outfeed compartment without risk of being bumped. An additional function of the robot is the automatic cleaning of storage and oven belts. Artificial Intelligence An industrial control system of the latest generation is used to control the entire process with a self-learning product management system. Artificial intelligence picks up the signals from the weighing cells in the output compartments and sets the production of the replenishment in motion in good time. If the products are placed manually, a visual or acoustic signal is transmitted to the employee. Sensors monitor the docking of the robot with other system parts. Access control to system parameters can be differentiated via various authorization levels. Software changes and support are available via remote maintenance. The control system is networked to the company’s existing networks via an Ethernet interface.

The think tank behind the solution Process optimization through automation and process control through artificial intelligence are the headlines under which the participants came together, from thoroughly heterogeneous backgrounds, at first glance. The linchpin is EngRoTec Systems GmbH, part of the homonymous industrial group whose core competence lies in the development of automated production and goods systems, including the development of hardware and IT. The group is strongly represented by various subsidiaries in the automotive and packaging industries as well as in mechanical engineering. At its helm in Gallmersgarten is Werner Huprich, who not only has experience from various industrial companies, but is also behind the first bakery vending machine, which once marked the entry of discount retailers into the sale of unpackaged baked goods. EngRoTec’s partner is Cetravac AG from Switzerland, which builds equipment for vacuum conditioning of baked goods that is now successfully used in many leading chain stores. Two years ago, the company also introduced a vacuum oven that, according to the manufacturer, uses superheated steam, vacuum and infrared heating to significantly shorten the in-store baking process compared to conventional ovens, stabilizing the goods and cooling them down to ‘touch and cut’ temperature. Third in the group are the specialists from BakeXperts, an international network of professionals specializing in technical and technological consulting for bakery companies worldwide. The focus is on the design, realization or reorganization of production capacities and the optimization of operating processes. +++

Author f2m, editorial team

THE SEARCH FOR ANSWERS IS ON

in a version that is safe against vandalism. The goods lie in the compartments on white belts under which load cells check the fill level. The belts can be moved by sprocket wheels made of hardened steel so that the goods are always in the field of vision and any crumbs are disposed of. The belts can be replaced without tools and are easy to clean. + Buffers at the various interfaces of technology and robots.

RESEARCH 32 RHEOLOGY

RHEOLOGY

Fundamental and empirical measurements

Bread dough rheology Cereal scientists have developed a range of test instruments for measuring the important properties of wheat and flour. However, no single instrument is available to inform the baker fully about the settings required for optimal dough mixing. This article explores the range of fundamental and empirical

It is often stated by bakers that the single most important process in bread making is to mix the dough to its optimal quality. If this is achieved, the downstream processing stages will work smoothly and the baked bread will be of consistently high quality. This means that the dough rheology must be optimal at the end of mixing. To achieve this, firstly it is essential that techniques are available to measure dough rheology and, secondly be able to determine critical settings such as flour water absorption and mixing time. If only it was this simple. Measurement of dough rheology has challenged the milling and baking sectors for around a century. Fundamental rheology methods Instrumental methods for measurement of rheological properties of materials can be divided into two broad classes: + Fundamental tests that measure the inherent properties of the material and do not depend on the geometry and shape of the sample, the conditions of loading or the type of apparatus used. Typically, the properties measured, to name just two include relaxation time and shear modulus. + Empirical tests or imitative tests are those where the mass of sample, geometry and speed of test

will decide the magnitude of the parameters measured. Typical examples include texture profile analysis which uses a compression force to measure parameters such as hardness and springiness. Bread dough is a complex multi-phase material, and, like other structured foods, it displays both viscous and elastic properties, otherwise referred to as a viscoelastic material. Viscoelastic foods lend themselves well to dynamic fundamental tests performed on rheometers. The frequency sweep test in which the frequency of the applied stress or strain frequency is varied is a typical example of a dynamic test. Different types of dynamic tests can be used to study the viscoelastic properties of dough systems. These include creep recovery, stress relaxation and dynamic oscillatory tests. In a creep test a very small but defined shear stress is applied until shear strain increases at a constant rate. Once this constant rate is reached, the applied shear stress is removed and the material is allowed to reach an equilibrium state. There are two primary pieces of information generated by a creep test: + Zero Shear Viscosity – This is the viscosity of the material under very low shear conditions. It is difficult to measure because of the extremely

B R E A D D O U G H R H E O L O G Y M E A S U R E M E N T S – F U N DA M E N TA L A N D E M P I R I C A L

methods for measuring dough rheology and what they can be used for.

RHEOLOGY

low shear conditions and is the subject of much debate about its relationship with yield stress. + Equilibrium Compliance – This is the elastic response of the material to strain and it provides information of the viscoelastic component of a material.

or stress generated in the dough which also varies sinusoidally is measured. The storage modulus (G´) and the viscous modulus (G´´) are defined as follows:

In a stress relaxation test, the material is suddenly brought to a given deformation (strain), and the stress required to hold the deformation constant is measured as a function of time. Even though creep and stress relaxation tests are straightforward to perform, there are two disadvantages in these tests. The first disadvantage is the time it takes to get the complete information about viscoelastic properties of the material, which can be long. For dough systems, this is important because dough is a dynamic system and its properties change continuously with time. For example, yeast and enzymes are continually changing the rheological properties as they generate new materials from the substrates such as starch and protein. The second disadvantage is the difficulty in providing a truly instantaneous application of stress or deformation at the start of the experiment. These disadvantages can be overcome by dynamic tests in which the specimen is deformed by stress which varies sinusoidally with time. Figure 1: The effect of work input in dough development

where τ 0 is shear stress (Pa), θ is the phase shift and γ 0 is the shear strain (s-1). Dough made from wheat flour is sensitive to the mixing energy used to develop the dough. It is vital to develop dough to its optimal level in order to achieve the correct viscoelastic properties that will ensure a good quality bread is produced. Figure 1 shows the dependency of the glutenforming proteins to mixing energy imparted by the mixer. The optimal elastic properties were achieved with a lower mixing energy, with additional mixing energy resulting in a reduction of the elastic modulus. The level of water addition is very important for dough rheology. Too much water leads to soft sticky doughs which can cause processing problems with dough sticking to transfer surfaces and becoming damaged between sheeting rollers. However, a lower level of water produces dough of firmer texture which can also be damaged between rollers, and it will resist expansion during proving and baking. Water addition becomes even more tricky to estimate when water-absorbing components such as fiber are added to the dough. Figure 2 shows the response of the viscoelastic properties of the dough with added fiber for increasing levels of water addition.

In the dynamic oscillatory tests, the dough material is subjected to deformation or stress that varies sinusoidally with time. The deformation

Applied strain (%)

I NETSEERAVRICEHW R

Elastic modulud (Pa)

Effect of work input on dough development

Oscillatory shear measurements are a sensitive way to determine the change in interactions within a dough system when other functional ingredients are added. Enzymes are one example of an ingredient added at low levels but with a disproportionate effect on rheology. Enzymes are frequently added to dough systems to improve the handling properties of dough as well as for

RHEOLOGY

The hydrolytic action of an enzyme such as a xylanase, when acting on the hemicellulose components of flour, breaks down the structure of this substrate and the enzyme action also releases water which lowers the consistency of the dough. Oscillatory measurements can be used to follow the change in the dough with time. In Figure 3, the change in the storage (elastic) modulus of the dough compared with the value obtained of the control dough at time point zero was used to study the activity of two xylanases from different suppliers. Xylanase 2 was found to be considerably more active than xylanase 1 and lowered the storage modulus more rapidly. This indicated that if the same enzymes were used at the same activity level, the dough with xylanase 2 would be considerably softer and may lead to handling problems on the plant with the result that the bread quality may also suffer. Oscillatory rheology measurements can be an effective way to evaluate the activity of a particular ingredient such as an enzyme, oxidizing agent or emulsifier. Results from a rheometer test are sufficiently sensitive to indicate whether

Time (min)

the dough recipe is likely to cause problems during processing. This can reduce the number of mixing and baking experiments required. Empirical tests As shown from the rheological tests, dough is a complex material that exhibits both viscous (fluid-like) and elastic (solid-like) behavior when forces are applied to it. When fully hydrated and developed, gluten-forming proteins and polysaccharides are mostly responsible for this viscoelastic nature. Since the main recipe component in conventional bread dough is wheat flour, this contributes to much of the functionality of dough. Understanding all the functionalities of flour is a challenging subject. Over many years, the wheat and flour sectors have developed and applied a series of unique testing methods that are not found in other sectors. These tests measure the quality of wheat and flour so their suitability for making bakery products can be determined. The measured properties have parallels to the more scientific properties measured from viscometers and rheometers but with the advantage that they can be undertaken by the milling or baking companies. There are many unique instruments developed by the milling and baking sectors, with each measuring different properties of flour. In addition, there are further instruments that measure critical wheat properties such as moisture, protein, kernel weight, hardness and Hagberg Falling Number.

Figure 2 (left): Change in viscoelastic properties of a dough with fiber addition Figure 3 (right): Effect of xylanase addition on the rheological properties of a wholemeal flour dough

I NRTEEARDV IDEOWU G H R H E O L O G Y M E A S U R E M E N T S – F U N D A M E N T A L A N D E M P I R I C A L B

improving the quality of the bread and to increase shelf ife. However, enzymes are employed at low levels, typically parts per million on flour weight. It is not easy to determine the correct level of addition without carrying out time-consuming trials to bake bread containing different concentrations of an enzyme.

ΔG' k Pa

Elastic modulud, k Pa

Wholemeal flour

RHEOLOGY

No single instrument, or test, has yet been invented that provides all the information required on flour properties. This is because flour is multifunctional. The main properties for baking performance of flour are as follows: + Elasticity: This is very important for bread making. The dough needs to have the elasticity to allow the gas bubbles to expand as various gases inflate them during proof and baking. If the bubbles cannot stretch, they will break, resulting in lower bread volume and firmer texture. For biscuits, elasticity is not desirable as it causes spring back that makes shape control difficult during molding or depositing. Viscosity: This is a measure of how much a + material will flow. Dough or batter both need to flow although to a different extent. The dough must be sufficiently viscous so it retains its shape when molded. Batters require lower viscosities than dough so they can flow into their natural shapes or into molds. Extensibility: This is a measure of the ability + of the dough to stretch or deform without breaking. It is largely dependent on the quality

of the network created by the gluten-forming proteins. Dough should have enough extensibility to allow dough expansion during proving and baking. + Resistance to deformation: This concept is equivalent to dough softness and relates to the ease of shape change during sheeting and molding. It must be possible to mold dough without damaging the delicate gas bubbles incorporated during mixing. The following are examples of the many empirical methods used in the baking and milling industry. Table 1 is not an exhaustive list but some of the more commonly used tests in the industry. These tests measure the rheological properties of dough and batter under conditions that simulate, as much as possible, the processing conditions found in commercial plants. Two of the main instruments used by the flour sector are the farinograph and alveograph. These are very different in their mode of operation yet can provide similar information for bakeries to

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Table 1: Commonly used flour testing instruments Instrument

Details

Farinograph or Mixograph

Measures the resistance over time of dough against mixing blades or pins operating at a specific speed (rpm) and temperature. Resistance to deformation is measured by the motor torque in dimensionless values (e.g. Brabender Units, BU). Several parameters are calculated from the farinograph trace.

Extensograph

Measures the extensibility and resistance to extension of a fully mixed, relaxed flour/water dough. Deforming forces are applied via a hook that moves at a constant rate until the dough ruptures. Resistance to deformation is measured in Pa or N/m2, or in dimensionless values (e.g. Brabender Units).

Alveograph

Measures the resistance to deformation of a flour/water dough by inflating the dough piece into a bubble until it ruptures. Several parameters are calculated from the alveograph trace.

Mixolab

Measures the resistance to deformation of a flour/water dough while subjecting it to variable shear forces and heating and cooling cycles. Resistance to deformation is measured as motor torque in N.m.

Amylograph or Rapid Visco Analyser (RVA)

Measures the resistance of a flour and water slurry in a container while it is stirred by a paddle or mixing pins and heated above the starch gelatinization temperature.

Viscometer

Measures the resistance to deformation over time of a flour/water slurry or batter while it is subjected to heating and cooling cycles. Resistance to deformation is measured as viscosity in Pa.s, centipoise (cP), or dimensionless values.

RHEOLOGY

Farinograph traces will identify flour suitable for different bakery applications. A flour suitable for bread making will have different farinograph parameters to one for biscuit making. Figures 4 and 5 show examples of farinograph traces for bread and biscuit flours, respectively. Bread flours have higher levels of stability than biscuit flours and do not show as much reduction in consistency during mixing.

Figure 4: Farinograph for a bread flour

Figure 5: Farinograph for a biscuit flour

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There are several important parameters from the farinograph that define the bread-making ability of a flour. One of the most important is the water absorption of the flour. This is estimated by adjusting the water required to achieve a fixed value of 500 BU at the peak torque. This value is internationally accepted as representative for bread dough but with one main exception. This originates from the UK, where the peak dough consistency is taken at the 600 BU line. This difference is attributed to the high shear style of dough mixing used for bread making using the UK’s Chorleywood Bread Process. Several countries that also use the same process have adopted the 600 line.

Farinograph The farinograph dates to 1928 when Carl Brabender established a method for measuring and predicting the baking qualities of bread flour. It is an instrument that measures the resistance to deformation during the mixing of flour and water into dough. This is similar to the changes expected within a dough mixer in a bakery. Dough resistance is expressed as motor torque, in dimensionless units known as Brabender Units (BU). During the test, the dough is developed past its optimal point until it starts to break down. This is useful information for a bakery to know how a dough will behave through the processing stages from mixing, dividing, molding and proving.

Several flour parameters are derived from the traces: + Water absorption (%). The amount of water added to achieve the 500 BU line, expressed as a percentage of the flour (14% mb). Water absorption is used to adjust the water in commercial dough recipes. + Dough development time, mixing time or peak time. This is the time (in minutes) between time zero and the maximum or peak torque. Dough development time is used to make adjustments during mixing in commercial processes when the flour mixing requirements change. Stability. This is the difference in minutes + between the arrival time (the time at which

help with recipe and process needs. Both instruments have received widespread global use for measuring the properties of bread dough. The key features of these are described in the next sections.

RHEOLOGY

the curve reaches the 500 line and departure time (the time at which the curve falls below the 500 line). It is a measurement of how well a flour resists overmixing. + Degree of softening. This is the reduction in consistency from the maximum peak to the value at a period of time later. There are small differences between ICC and AACC definitions but essentially it is a measure of how much consistency loss there is after a fixed time.

within dough as gases swell the bubbles during proving and oven spring in baking. The elasticity and strength of the bubble are important to know about in order to predict the baking performance of a flour. The test involves mixing flour, water and salt for around eight minutes to form dough for testing. It is extruded and divided into five pieces that are sheeted to a controlled thickness. After resting, the dough sheets are cut into discs and each one inflated by injecting air at constant pressure and flow rate until the resulting bubble bursts. The pressure inside the bubble and time for the bubble to burst are measured for each of the five dough pieces.

Figure 7: Alveograph for a weak biscuit flour

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Figure 6: Alveograph for a strong bread flour

Alveograph The alveograph is a modified version of the extensometer invented by Marcel Chopin in 1920. An alveograph is an instrument to measure the properties of flour for baked products such as bread, noodles, tortillas and biscuits. It does this by injecting air into a thinly stretched sheet of dough to form a bubble, which is inflated and bursts. This simulates the processes taking place

There are various parameters that are derived from the alveograph data: + P (y-axis): Height of the peak when the bubble bursts, which is related to the resistance of the dough to deformation (tenacity). + L (x-axis): Length of the curve, or distance at which the bubble breaks, which is an indication of the dough extensibility. Elasticity Index (Ie): Compares pressure after + 200 mL of air has inflated the bubble versus the maximum pressure (P). + W: Total area of the curve, which equates to the energy required to expand the dough. + P/L ratio: This represents the balance of the elastic and viscous components of the dough. + Swelling index (G): is the square root of the volume of air required to rupture the dough. Figures 6 and 7 show alveographs for strong bread making and weak biscuit flour respectively. Flour for bread making requires elasticity to enable the bubbles to increase in volume, whereas for biscuit making, elasticity is a disadvantage. The ratio of P/L is one of the most used alveograph parameters because it indicates the elasticity and strength of a flour. Bread flour tends to have higher P/L values than biscuit flour because of the need for elasticity. For example, UK flour for export uses P/L targets of 0.9 maximum for ukp (premium bread making wheat) and 0.55 maximum for uks (soft wheat).

RHEOLOGY

There is at least one major challenge with dough rheology testing. Attempts to characterize the rheological properties of wheat flour with a single empirical test have not yet proved successful. The holy grail is the all-encompassing flour test that provides bakers with the information they require for setting the recipes and processing conditions. The industry has used empirical methods for 100 years and is yet to find that single test. Technology and instrumentation advances may provide the answer, but for now it seems likely that several tests are going to be required for the foreseeable future. +++

Authors Gary Tucker and Sarabjit Sahi, Campden BRI Gary Tucker is a Fellow and Sarabjit Sahi manages the Rheology and Texture section

References AACC Approved Methods of Analysis (1999), 11th Edition. AACC

Caballero, P.A., Gomex, M. and Rosell, C.M (2007). Bread qual-

Methods 54-21.02 Rheological Behaviour of Flour by

ity and dough rheology of enzyme-supplemented wheat

Farinograph: Constant Flour Weight Procedure. http://

flour. European Food Research and Technology 224(5):525-

methods.aaccnet.org. AACC Approved Methods of Analysis (1999), 11th Edition. AACC Methods 54-30.02 Alveograph Method for Soft and Hard Wheat Flour. http://methods.aaccnet.org. ICC Standard No. 121 (1992). Method for using the Chopin Alveograph (matrix: wheat flour; analyte: rheological Properties) https://icc.or.at/publications/icc-standards/ standards. Barnes, H.A. and Walters, K. (1985). The yield stress myth? Rheologica Acta, 24:323-326.

534. Faridi, H. and Faubion, J.M. (1990). Dough rheology and baked product texture. Springer US. Millar, S. and Tucker, G. (2012). Controlling bread dough development. Chapter 16 in Breadmaking (Second Edition), Elsevier. Posner, E.S. and Hibbs, A.N. (2011). The Flour Mill Laboratory. Wheat Flour Milling, 2nd printing, American Association of Cereal Chemists, Inc., pp. 47–99.

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Summary The rheological characterization of bread dough is complex and requires both fundamental and empirical methods. Fundamental methods can be used on full bread recipes to investigate the changes in rheology when adding one or more of the minor ingredients. These provide elastic and viscous components of the rheology. Small quantities of enzymes, oxidizing or reducing agents, and emulsifiers can make a measurable difference to the rheology and therefore the bread quality. This can be investigated using fundamental rheology tests. Empirical methods, on the other hand, are designed to work on flour/water systems. They enable the mill to produce flour that meets the baker’s need and for the baker to calculate the correct process settings for that flour. There are many empirical tests available to the miller and baker, and the choice depends on the application. Farinograph and alveograph are described in the article.

RESEARCH 40 BAKING LINE AUDIT

BAKING LINE AUDIT

Metrology on baking and freezing lines The audit of a baking and freezing line is an important step towards the

Improvements may address issues of product quality, productivity and energy consumption, all of which are often interrelated. Other issues may relate to specific risks such as the presence of acrylamide, a chemical contaminant resulting from reactions occurring at high temperatures and involving ingredients contained in the product recipe. This contribution provides a review of the issues encountered in the context of applications in the cooking and then in the refrigeration and freezing processes. The last section focuses on the challenges addressed by energy audits. Baking processes The baking of cereal products under industrial conditions takes place in batch ovens with automated loading or in conveyor ovens. Baking involves high-temperature levels (150/250°C) or even much higher for certain specialties such as flatbreads (up to 600°C) with humidity control in the ovens playing a key role in the final quality of the products; a bakery oven should be considered as much as a drying device as a baking device and many of the elements mentioned in this section can be applied to the case of product drying. Air humidity can be expressed in different ways. The most common is the concept of relative humidity, which expresses, at a given temperature, the ratio between the partial pressure of water vapor and the saturation pressure of water vapor.

This concept is poorly adapted to the context of a bakery oven because of the low values reached. Another quantity used is absolute humidity, which expresses the mass of water per mass of dry air. It is sometimes confused with the specific humidity (water mass per humid air mass) often used in climatology. In general, it is necessary to watch the units because confusions are frequent and are linked to the English and French definitions which use close adjectives (confusion between absolute and specific for example); it is thus advisable to be careful about the units of these quantities. Absolute humidity (expressed in mass of water per mass of dry air for the following) is a relevant quantity for monitoring the hygrometry of a kiln because it expresses a quantity related to a mass of dry air which is a conservative quantity in the sense that the mass of dry air entering is equal to the mass of dry air leaving. The dew point or dew point temperature is also a relevant quantity that expresses the temperature from which the humidity of the air will condense on the products placed in a given environment; this quantity is usually expressed in degrees Celsius and it is easy to link this temperature to the air humidity. The absolute humidity in an oven varies greatly during the baking process. In general, high humidity is sought at the beginning of baking in order to ensure condensation on the surface of the products; this condensation will ensure the plasticization of the envelope of the dough that

METROLOGY ON BAKING AND FREEZING LINES

continuous improvement of production tools.

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enters baking and which will be the future crust of the products. It also plays an important role in the phenomenon of sprouting in the oven (expansion at the beginning of baking which occurs during the first few minutes of baking) and in the structure of the crust. AltamiranoFortoul (2012) has shown that excessive humidity leads to an excessively colored crust, a lack of crispness, and ultimately a non-conforming product. As mentioned by Le-Bail et al. (2010), too high a moisture content will lead to a product with a large volume but not necessarily compliant due to gelatinization and dextrinization of the starch, which can lead to an increased presence of acrylamide (Dessev et al., 2020).

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Cooking technologies can also influence the humidity during cooking. Ovens are most often equipped with gas burners, especially in industrial conditions where high power is required. Combustion can be indirect or direct. In indirect combustion, the heat available in the burner (thermal radiation) and in the combustion fumes is transferred indirectly through a heat exchanger tube. In direct combustion, the burner is located inside the kiln; usually supplied with methane, the combustion products (nitrogen, CO 2 and water vapor) are brought into direct contact with the product. In this type of kiln, the temperature control is adjusted based on the relative humidity of the air in the kiln, which indirectly adjusts the excess air supplied to the combustion. These furnaces have very poor efficiency with excess air of up to 4 to 6 (up to 6 times more air is injected than necessary for complete combustion, which lowers the temperature of the smoke and therefore the firing temperature to 180°C to 200°C. These ovens are used for baking specialty pastries, biscuits or hamburger buns. The following sections present the measurement technologies and associated problems for measuring temperature, humidity and heat flow. Ambient and product temperature metrology This type of on-board metrology is now commonplace and there is a wide range of equipment available, including in microwave environments.

The issues to be addressed are heat shields (insulating enclosure protecting the recorders) and time-temperature resistance. In the case of microwave environments, a suitable metal shield is required with electrical continuity to be ensured to avoid any antenna effect and the risk of electric arcing that could damage sensors or electronics (accumulation of charges). The quality of the construction of the heat shields is important, particularly pertaining to the tightness against the penetration of hot air and the quality of the insulating materials used. While measuring the temperature of a product does not pose any major problem apart from the fixing of the sensor, measuring the temperature of the air in a kiln may be disturbed by the heat radiation transmitted by the burners; a sensor equipped with a thermal shield can correct this problem. Metrology for measuring humidity There are various pieces of equipment on the market that operate essentially on two measurement principles. Wet oxygen technology consists of the dissociation of water molecules present in the atmosphere of the furnace; a comparison between ‘atmospheric’ oxygen and ‘wet oxygen’ makes it possible to determine the humidity of the air. This type of sensor has a low response time (less than one minute) and can be used over a wide temperature range up to 300°C. A circulation of humid air around the sensor must be ensured, whatever the technology, which can modulate the response time and lead to fouling problems. The second technology uses substrates whose electrical properties (capacity, inductance, resistance, etc.) depend on the ambient humidity. This technology has a response time that is sometimes slightly higher than that of wet oxygen sensors. The maximum operating temperatures are comparable and reach 250°C to 300°C. The calibration of these sensors can be carried out in an environment with stable hygrometry, possibly at a lower temperature than the maximum temperature. The humidity value of the air can be measured by a mirror hygrometer; by sampling the air and cooling it while remaining above the dew point, it is possible to perform a calibration (also with a conventional sensor).

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Measurement in the exhaust chimney and overall balance of a baking line Measuring the humidity in the extraction stacks can provide interesting information, particularly in the case of a complete energy and drying balance of a product being baked. However, it is necessary to carry out a prior calibration to obtain the velocity in the duct as a function of the opening angle of the butterfly valve. An exploration of the speed in the section of the pipe carried out upstream makes it possible after integration to obtain a global flow in m 3/s. The knowledge of the static pressure and the hygrometry of the air makes it possible to go back to the density of the air and the mass flow rate of wet air and dry air circulating in the pipe.

Figure 1: Example of the link between valve opening and flow velocity (from ONIRIS data)

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Static metrology (from gas sampling instead of conveying sensors) is also a solution to consider as it is more economical; mirror hygrometers are the most accurate and allow direct determination of the dew point temperature. The maximum operating temperature of static sensors is often limiting. However, the relative humidity of the

air in baking processes in the food industry is often quite low, so that by sampling the air in the atmosphere of an oven or in the stacks and cooling it to a temperature above the dew point temperature, it is possible to use ‘low temperature’ sensors that are much less expensive and often sufficiently accurate.

On-board humidity metrology is rarer, and to our knowledge there are only two companies that offer stand-alone on-board equipment that can be used for conveying in bakery ovens. Static metrology is also a solution to be considered as it is more economical; mirror hygrometers are the most accurate and allow direct determination of the dew point temperature. The maximum operating temperature of static sensors is often limiting. However, the air humidity in the cooking process in the food industry is often quite low, so that by sampling the air in the atmosphere of an oven or in the chimneys and by cooling it to a temperature higher than the dew point temperature, it is possible to use ‘low temperature’ sensors that are much less costly and often sufficiently accurate.

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Measurement of the heat flux received by the products The measurement of the heat flux received by the products is a key point in understanding certain operating anomalies in baking ovens. This information can also be used to facilitate the transfer of production to a new furnace or to define the specifications of a new furnace. The received heat flux is divided into two main components, the convective flux related to the circulation of air in the kiln and the radiative flux received from the burners and the kiln walls. The heat flux sensors consist of multiple thermocouples screens printed on either side of a thin plastic film. The whole assembly is integrated into a sensor shaped like a plate of various geometries (most often square, e.g. 3cm x 3cm, sometimes rectangular or circular) of about 1mm thickness. Sensors measuring

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convective flux are coated with a thin sheet of gold or silver, a metal with a very high radiation reflection capacity. The ‘total’ sensors measuring radiative and convective flux are coated with a high-temperature resistant black paint. These sensors are also equipped with a built-in temperature sensor to monitor their temperature. When a heat flow passes through the sensor, a voltage is delivered by the thermocouples in series. A prior calibration allows the heat flux density (W/m²) to be related to the voltage delivered.

Figure 2: Heat flow sensor. Thermocouples placed on either side of the plastic film amplify the temperature difference that appears on the film when a heat flow passes through the sensor. This type of sensor is placed on a heat sink (copper block) which absorbs the heat received from the outside environment. The measurement is possible as long as the sensor is kept at a compatible temperature. By measuring the ambient temperature and the surface temperature of the sensor (thermocouple inserted in the sensor), the convection coefficient h in W.m-2.K-1 can be determined. h = Heat flux density (W/m²)/(TAMBIENT – T SENSOR SURFACE)

This type of heat flux sensor should be applied to a heat sink as shown in Figure 2 that can absorb the heat passing through the collector. One can imagine a fluid flow system on a static mounting. In the case of a conveying system, the sensor is most often bonded to a copper block, as copper is an affordable metal with the highest specific mass x heat product. When recording, the exposure time of the sensor to the heat flow is a critical point. This exposure time is determined by the maximum temperature of the sensor and is, in fact, a function of the ability of the copper block to absorb the heat received. Commercial devices exist on the market but it is also quite easy to design your own device. A flow sensor can also be placed on the product or glued to a mold. A second type of commercially available device consists of a metal paddle (e.g., 3cm x 3cm in size) coated with gold plating or black high-temperature paint (for convective flow or total flow respectively) connected to a heat sink by a metal bar. This rod is connected to a heat sink and the measurement of the temperature difference between the vane and the heat sink provides a signal proportional to the heat flux received by the vane. The two types of devices are equivalent, with a shorter response time for the device using a heat flux sensor, and a more ‘average’ device for the paddle device. An important point concerning these two types of sensors is that they must be preheated above the dew point temperature to prevent the moisture contained in the furnace air from condensing on them; in the event of condensation, an endothermic phenomenon occurs leading to an overestimation of the actual flux.

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Figure 3: Top left Spiral freezer (Doc. Heinen). A Top right, an example of a convection coefficient reading in an industrial freezer using a heat flow measuring device. Bottom, average values of the convection coefficient at different times during production, T0 defrosted installation and follow-up at +3, +6, +8 and +12 hours during production (ONIRIS internal data)

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graph below. It is possible to detect the passages in front of the fans that propel the air over the conveying device consisting of a plastic belt that winds up in a spiral. The knowledge of the convective exchange coefficient makes it possible to estimate the duration of freezing and also to qualify and optimize the air flow conditions within industrial freezers. The passage of the selfpowered conveying sensor also makes it possible to highlight the reduction of the convection coefficient during production due to the accumulation of frost on the walls of the cold battery and consequently of the air speed. This problem of frosting of exchangers in freezing installations is a major issue in the food industry; a review paper proposed by Badri et al. (2021) proposes an overview on this topic. ONIRIS-GEPEA is supervising the ongoing FOODEFREEZE project (Funded by the French Environment and Energy Management Agency – ADEME), dealing with energy and frosting in freezing equipment of the food industry. Frosting and icing of the evaporators in freezing equipment yields an increase in the required product freezing time and ultimately in non-compliance (insufficiently frozen products) if the line’s output is maintained.

Metrology under freezing conditions The metrology mentioned for ovens can generally be used to evaluate the performance of freezing tunnels. In addition to measuring the temperature of the products, which is a relatively easy measurement, it is most often a question of measuring a convective exchange coefficient between a product and its environment. This coefficient is the result of the air flow in the tunnel, which is often very variable depending on the orientation of the air flow and its level of turbulence in particular. The flow sensor heat flow measuring device can be successfully used by preheating the recorder and its insulating screen as well as the heat flow sensor. When passing through the tunnel, the heat sink acts as a heat source and the limitation will be that the heat flow will cease as the heat sink (copper block) reaches the ambient air temperature. The convective exchange coefficient is calculated from the heat flux value divided by the temperature difference between the collector surface temperature and the ambient air. A metalized collector is a priori recommended. The contribution of heat radiation is very small in freezing conditions due to the low temperature level. An example of a reading taken in a spiral freezer is shown in the

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Figure 4: Aluminum shapes for the measurement of convective exchange coefficients in freezers; various shapes reproduced by machining (pizza, bun, cylinders) and possibly equipped with internal heaters and temperature sensors (surface and ambient temperature) made it possible to determine the convective exchange coefficient by taking into account the real geometry of the food products. (ref. Gibeaud et al., 2010/Pictures and production ONIRIS-GEPEA, L. Guihard, O. Rouaud, A. Le-Bail)

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There has been increasing interest in measuring the degree of freezing of products. The degree of freezing can be determined from the amount of freezable water actually frozen in the product. The resulting function can be obtained from the knowledge of the starting freezing temperature

(from the temperature plateau observed at the beginning of freezing), the ending freezing temperature (from a calorimetric reading) and the amount of freezable water (obtained by calorimetry). Le-Bail (2017) presents these different elements and the figure below shows a function of the amount of freezable water frozen from this reference.

An alternative way of measuring the convective exchange coefficients in situ is to apply the heat fluxmeter to an aluminum form equipped with an internal heater. Such devices are shown in Figure 4 below with a pizza-like plate on the left and a bun in the center (ref. Gibeaud et al., 2010). This type of system has the advantage of being able to operate in a steady-state over a long period of time (several hours) and also of realistically mimicking the air flow conditions (geometry identical to the food product). The main disadvantage is the long stabilization time of up to 30 to 60 minutes; this type of device is, therefore, more suitable for ‘static’ freezing equipment. Another alternative is the use of small aluminum studs of a given geometry. An example is shown in Figure 4 below (right). The dots were drilled and placed on a needle equipped with a thermocouple to record the temperature as a function of time. This device was used to dynamically analyze the local convection coefficient in a conveying tunnel with liquid nitrogen cryogenic fluid. A thermal model with a low Biot number was used (transient heat transfer, lumped analysis model) to determine the convection coefficient (see Gibeaud et al., 2010).

Figure 5: Function describing the evolution of the quantity of freezable water frozen as a function of temperature for a product starting to freeze at -1°C and ending to freeze at -40°C. It can be seen that at -18°C, the quantity of frozen water is 96.9%; the product is therefore not completely frozen at this temperature, which has been adopted as the world standard, as it corresponds to 0°Farenheit

This type of function makes it possible to give a freezing tunnel exit target linked to the freezing rate associated with the average temperature of a product. If the French regulation describing the ‘surgélation’ process (deep freezing) indicates that

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a ‘deep frozen product’ must be cooled “as quickly as possible” to -18°C (MAP-1964, MAP-2011), the interpretation of this regulation addresses issues in the industrial field. ONIRIS-GEPEA has therefore worked with many food companies to assist them in their exchanges with regulatory authorities and control services. The recommendations of the International Institute of Refrigeration (www.iir-iif.org) are that a product is considered as ‘frozen’ when its temperature reaches -10°C or when at least 80% of the freezable water is frozen. A concrete example is presented in the article by Chapleau et al (2005), in the context of a collaboration with the company MARIE-SURGELE (France), showing that, in the case of a model system (pie type), 80% of freezable water being frozen was reached at a temperature of -9.8°C. in such conditions, the target temperature to consider the food as ‘frozen’ according to the IIR criteria was -10°C. One of the difficulties involved in measuring the average temperature of products (pizza, quiches, etc.) at the end of the freezer, which could be achieved using an isothermal calorimetric technique as described below. Indeed, a temperature gradient is present in a product and by grouping several products around a temperature sensor, it is possible to obtain an average temperature of the products after equilibration. An alternative touchless method has been developed by the company in partnership with AQUANTIS company (Nouhin et al., 2020). It is based on a sensor developed by the company

AQUANTIS (Belgium) working in the terahertz domain. The sensor consists of two high-frequency (HF) measuring units (an HF transmitter and an HF receiver) and a computing unit. The two measuring units are aligned and face each other on opposite sides of a non-metallic conveyor belt on which the food products travel. The freezing degree sensor consists of two high-frequency (HF) measuring units (an HF transmitter and an HF receiver) and a computing unit. The two measuring units are aligned and face each other on opposite sides of a non-metallic conveyor belt on which the food products travel. The distance between the measuring units is usually kept as small as possible while ensuring that there is no contact with the product under test. For measurements on individual products (frozen), focusing means are added to the transmitter unit to confine the electromagnetic radiation to a spot size significantly smaller than the characteristic lateral dimension of the product under test. In the latter case, the emitter-product distance is determined by the focal length of the focusing means. A laser-based product thickness sensor is placed next to the HF transmitter and is oriented to measure the product thickness (layer) at the position of the HF measurement axis. Products passing the sensor are exposed to low-power electromagnetic radiation in the millimeter wave range. The wavelength applied can vary depending on the application. It is chosen according to several parameters such as product (layer) thickness, product (layer) density, water content, scattering properties, etc. The electromagnetic radiation

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Figure 6: Example of a calorimetric-isothermal system for obtaining the average temperature of a non-isothermal product at the end of the process. Two or more products coming out of a freezer are placed in an insulated container. After thermal equilibration (the products being colder at the surface than in the core), an average temperature of the product is reached at thermal equilibrium (Chapleau et al., 2005)

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incident on the test product is partially reflected, scattered and absorbed by the food product. A transmission measurement allows the overall electromagnetic loss induced by the test product to be measured and traced back to the amount of frozen water in the product and thus the degree of freezing.

a reverse path had been discussed in the case of cooking ovens where the air sampled is cooled in the exhaust stacks. Energy and mass balance The audit of the energy consumption on a baking line or on a freezing line requires the gathering of a large number of factors and parameters. A mass balance is also needed to be able to determine the moisture lost during baking and ideally all along the baking line (conveying oven) by sampling products at different locations. Very often, for dry cereal products such as rusks or crackers, the oven embeds a gas-powered section for the baking stage and an electric section for the final drying stage. For dry cereal products, the measurement of the moisture content reaches very low values (below 5%) and in such conditions, the moisture must be determined with the Karl Fisher technic; the drying cabinet test is, by far, very imprecise. Overall, the mass balance must be done on a dry basis to have a clear scheme of water loss during the process.

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Low-temperature hygrometry and icing of freezing lines A simple solution to know the hygrometry of the air inside a freezer is to take it with a pump and then recondition it (heated) in order to measure the hygrometry with conventional capacitive sensors at room temperature. The adjustment of the sampling temperature plays a key role here to be able to use inexpensive commercial sensors;

Figure 7: Schematic representation of the information to consider to establish an energy balance for a baking oven (top). H is the enthalpy of the bread and should be considered ideally on a dry basis. Bottom, case of a baking oven with direct combustion. In such a case, the combustion fumes are mixed with the moisture losses from the bread. A precise knowledge of the moisture loss of the bread is needed to unravel the situation. Lambda is the excess air coefficient.

The gas energy counting requires a correction to obtain values of volumic flow rate in Normal cubic meters (at 0°C, Normal atmospheric pressure), in order to determine the gas power. A gas analyzer is needed to assess the excess air of the combustion; this is very important to assess the efficiency of the gas combustion. In the case of a direct oven, the moisture from the product is mixed with the moisture released by the combustion, making the situation very complex. A precise knowledge of the water loss of the product is the key to unravel such a situation. An alternative or complementary approach is to measure the air flow injected into the burner. The schemes below show all parameters to look at; this has to be done for each section of the baking oven. In the case of freezing lines, the electrical energy is the main information to log in addition to moisture losses. However, a refrigeration unit is most of the time feeding several freezers in parallel, making the situation very often extremely complex. Mass flow rate measurement at each evaporator would then be needed to carry out a

BAKING LINE AUDIT

Conclusion This article illustrates, through various configurations, the importance of temperature measurement or measurement systems based on temperature difference measurements, such as flux meters. The measurement of air humidity is also discussed; this physical quantity is difficult to measure and is of great importance, both in cooking and in the case of freezing processes. The development of

on-board systems should continue by integrating connected sensors offering real-time monitoring. In addition, at-line or in-line software sensor systems offer interesting solutions for the supervision of production lines and the detection of operating anomalies such as heat exchanger icing or a steam injection fault at the entrance to a baking oven. +++

Authors Alain Le-Bail, Luc Guihard, Anthony Oge, Michel Havet, Jean-Yves Monteau, Olivier Rouaud, Cyril Toublanc ONIRIS, Université de Nantes, CNRS, GEPEA, UMR 6144, F-44000 France Contacts alain.lebail@oniris-nantes.fr luc.guihard@oniris-nantes.fr anthony.oge@oniris-nantes.fr michel.havet@oniris-nantes.fr jean-yves.monteau@oniris-nantes.fr olivier.rouaud@oniris-nantes.fr cyril.toublanc@oniris-nantes.fr

References Altamirano-Fortoul, R., A. Le-Bail, S. Chevallier and C.M. Rosell, (2012), Effect of the amount of steam during baking on bread crust features and water diffusion Journal of Food Engineering, 2012, 108 (1), p. 128–134. Badri, D., Toublanc, C., Rouaud, O., Havet, M., (2021), Review on frosting, defrosting and frost management techniques in industrial food freezers Renewable and Sustainable Energy Reviews 151 (2021) 111545 Chapleau, N., A. Le-Bail, M. Anon-De Lamballerie and M. Vignolle, (2005), Application de la réglementation des produits surgelés à des produits hétérogènes (plats cuisinés). Revue Générale du Froid, 2005. 1053: p. 41-45. Dessev, T., V. Lalanne, J. Keramat, V. Jury, C. Prost, A. Le-Bail, (2020), Influence of Baking Conditions on Bread Characteristics and Acrylamide Concentration, J Food Sci Nutr Res 2020; 3 (4): 291-310 DOI: 10.26502/jfsnr.2642-11000056 Gibeaud, A., O. Rouaud, A. Le Bail, R. De Pellegrin ‘Étude comparative des performances de deux tunnels de surgélation à l'azote liquide', Revue Générale du Froid & du conditionnement d'air, N° 1107, octobre 2010, pp 43-49.

Le-Bail, A., T. Dessev, D. Leray, T. Lucas, S. Mariani, G. Mottollese and V. Jury (2011), Influence of the amount of steaming during baking on the kinetic of heating and on selected quality attributes of bread. Journal of Food Engineering, 2011. Vol. 105 p. 379-385. Le-Bail, A., Procédés de congélation, de surgélation et procédés émergeants ; vers une évolution de la réglementation ?, RGF – Revue Générale du Froid, Sept-Nov. 2017 MAP-1964, Gouvernement Français, Décret n° 64-949 du 9 septembre 1964 modifié portant application de l'article L.214-1 du code de la consommation pour les produits surgelés. MAP-2011, NOTE DE SERVICE DGAL/SDSSA/N2011-8117 – Date: 23 mai 2011. Nouhin, M., Vandermeiren ,W., Le-Bail, A., (2020), CALLIFREEZE. Inline measurement of the degree of freezing of food products, 6th IIR Conference on Sustainability and the Cold Chain. April 15-17, 2020. Nantes, France, Manuscript ID: 296887, DOI: 10.18462/iir.iccc.2020.2968 87

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precise evaluation of the energy consumption in addition to pressure and temperature levels of the refrigeration fluid and parameters in the freezing area. The energy consumption of the fans is a crucial point since it can make up to 50% of the overall freezing energy in the case of bread freezing. The energy associated to the frosting of the evaporators can also represent a large share of the overall energy consumption; a precise knowledge of the moisture loss of the products is needed to unravel the situation in order to be able to establish a precise pie chart of the energy share. Once again, a balance done on a dry basis of product is of great support for the computations. Numbers can easily be converted on a humid basis of product entering the line once the computations are done.

RESEARCH 50 MIXING

MIXING

Dough mixing supervision: an overview The key parameters governing the mixing phase, their importance and their effects on the final products are described, as well as examples of strategies for monitoring the mixing process.

Figure 1: The manipulation of the dough by a baker is a traditional way to assess the degree of dough mixing. Stretching the dough until reaching a thin and film is the usual criteria to decide on the end of mixing in addition to dough compression and dough stickiness assessed through manual contact

DOUGH MIXING SUPERVISION: AN OVERVIEW

General aspects of dough mixing Dough mixing is a process in which wheat flour, water, and other ingredients like salt, yeast, sugar, oil, enzymes and emulsifiers are blended together, leading to a series of bio-physical-chemical reactions. As the first step in the breadmaking process, it has a strong impact on the quality of the final product. The mixing operation has three main functions: + the hydration of the flour in order to prepare the formation of a viscoelastic network that will ensure the gas-holding capacity during fermentation and baking (Bloksma, 1990) + the creation of a homogeneous network forming the matrix of the bread dough (Figure 1) the + incorporation of air into the cereal matrix to form the gas cells that will later become the cells of the baked product

However, hydration alone does not allow the development of the dough and a certain mechanical energy is necessary to form and to organize the gluten network (Campos et al., 1996). Thus, proper dough development depends on the quality and quantity of ingredients, the mixer geometry, the mixing duration and mixing speed. Any variation in mixing can affect the end-product quality; although it is a decisive step, it is also one which can be most controlled.

Cereal-based foodstuffs are at the base of the food pyramid. Among them, wheat flour is one of the most important food sources in the world, particularly because of its unique property of forming a dough and developing gluten when mixed with water. The mixing process is the first step towards the final bakery product and it plays a major role in the whole breadmaking process. The quality of the dough obtained, in particular its rheological properties, will have a great impact on the behavior of the dough during shaping, fermentation and baking.

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Figure 2: Evolution of the power, the Specific Mechanical Energy and the temperature during mixing. The time to peak tPEAK corresponds to the maximum power consumed by the mixer. It is usually located just after the optimal dough development that is determined by an expert baker

+ Conventional mixing can be defined as slow

Background Over time, mixing technology has evolved according to the wheat grinds, the types of mixers and the breads consumed by the population. Before the 20th century, the bread consumed was very dense and made from firm dough. Mixing was done by the baker without mechanical aids; walking and pressing the dough with the feet was a common way to carry out the mixing, which was based mainly on compression and partly on shearing. The mixing trough was generally a wooden box with a rounded bottom, closed with a hinged lid. The first attempts to mechanize the mixing process were made between 1760 and 1840, but they were not democratized in bakeries, for fear that machines would replace the work of men. The first mechanical mixing machines tried to imitate man’s movement on the dough, by bringing energy by way of a wheel actuated initially by animals or a crank, which reduced the effort needed, then thereafter by an electric motor.

mixing, corresponding mainly to manual mixing, as practiced before mechanization. + Intensified mixing can be defined as mechanized mixing with formulations using oxidizing agents leading to voluminous breads often presenting a white crumb. + Improved mixing can be defined as a compromise between conventional mixing and intensified mixing. It aims to obtain a better balance between the development of the dough and the preservation of its texture, taste and aromas. The mixing phases There are as many ways of mixing bread dough as there are bakers, recipes and mixers. According to Tömösközi and Békés (2015), two conditions must be met in order to achieve the desired breadmaking properties: the formulation of the ingredients must be balanced and the ingredients within the dough must be evenly distributed. The recipe used by the bakers determines the first condition. In order to achieve the second condition, most bakers use a two-stage mixing process, starting with a slow speed and continuing with a faster speed.

At the beginning of the 20th century, hygiene standards caused an increase in the number of mechanical mixers in bakeries, particularly with models with a plunging arm. In parallel, bread made from soft dough became the most consumed. Therefore, the mixing process has evolved between conventional, intensified and improved:

Slow speed mixing The initial phase of mixing is operated at a slow speed; its main purpose is to hydrate the solid ingredients and homogenize the dough. The water ensures the plasticization of the system, promoting molecular mobility. The low speed used and the low resistance of the dough means that the power consumed by the tool to maintain its rotation speed is not very high. The power consumption increases very little. However, this step is marked by a rapid increase in the temperature of the dough (Figure 2). At the end of this step, it is possible to adjust the consistency of the dough by adding water or flour, or depending on the recipe, ingredients such as salt or fat.

High speed mixing The second step begins with the switch to a higher mixing speed, which is usually called kneading. This step leads to the formation of a three-dimensional developed gluten network,

MIXING

During the kneading process, the consistency of the dough reaches a maximum (Figure 2). The time to obtain this consistency is called the ‘optimum dough development time’ or ‘time to peak’ indicated as tPEAK (Sadot et al., 2017). This time does not necessarily correspond to the kneading time to get the best rheological properties of the dough, but is usually very close to it. To successfully knead dough to its maximum level of development, a minimum critical speed must be achieved. Above this value, any speed can be used for kneading; but, it has to be kept in mind that increasing the speed of the mixer leads to an increase in the maximum torque supplied by the motor. The integration over time of the power consumed by the mixer yields the Specific Mechanical Energy (SME) in J/kg of dough, or often determined in kWh/kg dough. Keeping the SME constant is often considered in the industry to adjust the mixing time. Another approach consists in counting the number of tool revolutions. It was observed by Sadot et al. (2017) that the SME is proportional to the number of tool revolutions in a spiral mixer. Therefore, the total kneading time must be decreased if the rotation speed is increased in order to obtain the same SME and possibly the same number of tool revolutions to reach the maximum torque (Chin and Campbell, 2005). However, using too high a speed can lead to a deterioration in bread quality, especially crumb texture (Kilborn and Tipples, 1972).

Beyond the optimum kneading time, the dough weakens, collapses and becomes sticky; the dough qualifies as overmixed. Dough undergoing a lamination process (sheeted dough like puff dough) is often undermixed to prevent an excess of elasticity (springiness), which may address difficulties during sheeting (lack of plasticity). The mechanics of mixing A distinction can be made between batch mixers, generally consisting of one or more tools rotating in a bowl, which are more suited to craft bakers and medium-size industry manufacturers, and continuous mixers, which can produce several tonnes per hour and which are rather adapted to large industry. The geometry of mixers may vary but is always based on the same principle of supplying energy to the dough being formed by the movement of the tool. The capacity to provide the necessary amount of energy in a given time is one of the criteria to be taken into account when choosing a mixer. The different types of batch mixers The four most popular mixers in the European craft industry are plunger arm mixers, fork mixers, vertical (spiral tool) and horizontal kneaders. For a baker, the choice of a mixer will depend on the type of dough they want to obtain. Plunge arm mixers The plunge arm mixer is historically the most recognized but probably the least used today. In order to imitate the mixing movements of a baker’s arms, two linked arms exert a symmetrical action so that the end of each arm folds the ingredients from the center to the outside of the bowl during mixing. As the dough begins to form, this movement lifts it, stretches and folds it, then the rotation of the bowl brings it back towards the tools. Unlike other mixers, such a mixer has only one mixing speed and the mixing action is only slight against the bowl walls. The main advantages are that the cycloidal kneading movement consumes little energy, yielding in a moderate dough warm-up during mixing. The work is mainly done in extension and shearing, which induces a blowing effect that

I NOTUEGRHV I M D EW IXING SUPERVISION: AN OVERVIEW

which gives the dough its viscoelastic properties and its gas holding capacity. Indeed, the mechanical work is more intense, with physical and biochemical consequences. Kneading imposes a stretching, shearing and extension stress on the dough, modifying to a large extent the interactions between the ingredients. Molecular interactions within the dough can be of low energy (hydrophobic, hydrogen bonds) or generate chemical reactions (formation/breakage of covalent bonds), the latter being most often catalyzed by enzymes. With the strengthening of its gluten network, there is an increase in the dough’s capacity to retain the gases incorporated by the tool during mixing.

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Figure 3: From left to right, Spiral mixer (5kg capacity), Fork mixer, Kneadster mixer with double tools; Bottom: Automated mixing systems with suspended shuttle for high capacity lines

contributes to the formation of large air pockets in the dough. Such a mixer is adapted for breadmaking and panettone-type cakes. It is also an expensive and a fairly low productivity piece of equipment.

act like a ‘plow blade’ to bring the dough between the forks of the tool into the mixing area. In this type of movement, the dough does not move continuously, but is rather pushed with each turn of the bowl.

Fork mixers Fork mixers have generated the reputation of French bread throughout the world. This mixer’s tool is fork-shaped with profiled ends and is mounted at a given angle to the bowl’s axis. The center of the bowl usually has a central pivot so that the action of the tool acts between the pivot and the bowl’s wall. This mixer exerts a shearing and extension action on the dough. The initial mixing is achieved by folding the ingredients over each other and then the mixing is done by pinching the oblique axis between the bowl’s wall and the tool. The profiled ends of the tool

Vertical kneaders A general definition of this type of mixer would be a mixing machine equipped with a spiralshaped mixing tool, having a rotary movement along a vertical axis against the inner circumference of a bowl, which also rotates around a vertical axis. Several geometries exist for these mixers; some manufacturers offer a very wide tool that sweeps an area larger than the radius of the bowl in order to eliminate any areas where the dough is not mixed, especially in the center of the bowl. Others use a central pivot which has an effect on dough development. A very wide center

MIXING

Horizontal kneaders Horizontal mixers are widely used for doughs that need to develop a gluten network, such as cracker mixers and hamburger buns. The tools are mounted horizontally in a U-shaped bowl that can discharge the dough after kneading

into a hopper. Several tool geometries are available for this type of mixer: Z, sigma or paddleshaped with scrapers at the ends (Niranjan et al., 1994). Automated batch kneading This involves ensuring continuous dough production from mixers working in a discontinuous manner: the mixing bowls are moved automatically (linear bowl transport system, for example) from one station to another corresponding to the various stages of mixing: introduction of ingredients, mixing, breaking, delayed incorporation of ingredients, etc. Continuous mixers This type of installation has become particularly important with the development of large-capacity industries. In contrast to batch mixing, the dough

I NOTUEGRHV I M D EW IXING SUPERVISION: AN OVERVIEW

pivot will increase the effective kneading area, while a blade-shaped pivot can be used to create a shear zone between it and the spiral tool. A double mixing tool can also be proposed and enables slower kneading. Each geometry has advantages and disadvantages in terms of the homogeneity of the kneading, the structure of the dough, the time to achieve optimum kneading and the energy used to achieve it. This kind of mixer is the most efficient equipment for the production of bread dough, Viennese pastry, brioches and pizza with loads ranging from 80 to 900 kg.

Figure 4: Continuous Mixer installation

MIXING

moves through a mixing line in which the phases of mixing take place: dosing of the powders (flour, salt, improvers), dosing of the liquids (water, leavening, poolish or other possible liquids), premixing and kneading. The dough falls from the pre-mixer into the kneading trough, the latter being designed in such a way that the dough moves from one end of the trough to the other (driven by the rotor blades) while being mixed (Figure 4). Continuous kneading allows an increase in the production rate, a reorientation of production towards products with a long shelf life, and the expansion of distribution areas.

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Factors influencing mixing Some key parameters can significantly improve the kneading process and its effect on the quality of the final product. Indeed, the SME, flour composition, dough temperature, water content, kneading time and mixing speed must be carefully controlled. Energy Mixing involves an intense input of mechanical work through the movement of the dough mixer arm. The force varies according to the geometry of the mixer (shape of the arm and action on the dough), the mass of dough and the properties of the dough (adhesion to the bowl and arm, viscous and elastic resistance). Only a part of the energy supplied by the moving arm is absorbed by the dough and converted to heat resulting from viscous dissipation, causing the dough to warm up. Some of this absorbed energy is stored via the formation of molecular structures (Belton, 2005), while some is converted into thermal energy by viscous dissipation. The thermal energy returned to the dough by viscous dissipation is measured from its temperature rise during kneading (Amjid et al., 2013; Contamine et al., 1995). Some of this energy is exchanged with the mixer bowl and is evacuated to the outside. The same SME input will not necessarily give the same effective work for different materials (type of deformation applied), different conditions of use (speed, duration) or different doughs (consistency, composition). The more resistant

a dough is, the greater the force required to deform it, and the greater the power consumed by the mixer. Therefore, the mixing time will be shortened. Flour composition The composition of the flour and particularly its protein content is a significant factor determining the final quality of the bread. The higher the protein content, the greater the dough’s ability to trap and hold carbon dioxide and the greater the volume of the bread can be. The quality of the protein and the amount of damaged starch also have an impact on the quality of the dough. Indeed, damaged starch granules absorb more water than undamaged ones. It was found that a strong flour, containing more gluten, takes longer to reach the peak consistency (Frazier et al., 1975). Water Water is a mobility enhancer. As the water content in the dough increases, the elastic properties and viscosity of the dough are reduced. A moisture content below 44% does not allow for optimal gluten formation. A moisture content between 44 and 50% does not modify the structure of the dough but has a plasticization effect. The addition of electrolytes can also change the nature and intensity of hydrophobic interactions between the gluten proteins. For example, the addition of salt increases ionic strength and reduces the ability of the proteins to retain water. Temperature Temperature is a process condition that affects physical properties. Dough temperature depends on the temperature of the raw materials, the mechanical energy absorbed during mixing, and the energy exchanged with the mixer bowl. The solubilization and swelling processes also generate heat due to the exothermic hydration reactions. Controlling dough temperature is perhaps the most important parameter that can affect the final consistency of the dough during mixing, regardless of the mixing process. In general, doughs processed at lower temperatures give better dough properties and final bread quality than those

MIXING

Mixing monitoring Real-time detection techniques that do not require manual sampling are of particular interest because of their automatic data acquisition capability. They can therefore be used in control systems to automate processes and improve product quality by monitoring certain critical parameters. Power monitoring Power and torque measurements are simple and inexpensive techniques used to determine in real-time the force required to drive the tool (i.e., spiral in a spiral mixer). The power input is simpler to measure because no equipment needs to be installed inside the bowl. The power input is measured based on the electrical consumption of the motor driving the mixer. During mixing, the power required to maintain a constant tool speed increases to a maximum (Figure 2). The

mixing must therefore be stopped at a fixed energy input, chosen according to a particular rheological state of the dough. Temperature monitoring Mixers are usually equipped with a conventional thermocouple or possibly touchless infrared temperature sensors. When the temperature of the dough is measured in real-time and the energy loss of the mixer is the same for each mixing batch, the temperature at the end of mixing can be used as a control mechanism. Spectroscopic monitoring Just as in milling, where the application of NearInfrared Spectroscopy (NIRS) is widespread for monitoring the composition of raw materials and finished products, the trend of using the benefits of spectroscopic analysis techniques is also increasing in the baking industry, particularly for the continuous monitoring of the dough development process in the mixer. Spectroscopic techniques are based on the light/matter interactions and thus have the potential to provide a non-invasive means of probing the chemical changes that occur during dough development. As the wavelengths of the absorbed and emitted radiation depend on the chemical composition and the intensity depends on the concentration,

Figure 5: PCA analysis of NIR spectra obtained during mixing

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Kneading speed and kneading time Increasing the kneading speed results in a decrease in the time required to achieve optimal dough development (Auger, 2008; Muchová and Žitný, 2010). According to Kilborn and Tipples (1972), mixing is more efficient at higher speeds, as more work is delivered to the dough for each tool rotation. However, working above a maximum mixing time causes overdevelopment and disruption of the dough structure. On the contrary, there is a critical minimum kneading speed below which the dough consistency does not change during mixing. The viscoelastic properties of the dough do not develop, and the final product obtained from such doughs is inconsistent and has a low volume. Below this critical speed, increasing the energy input, i.e. increasing the kneading time, improves the qualities of the product, but without ever reaching those of a product obtained from doughs kneaded above this critical speed.

processed at higher temperatures. A final temperature of about 30°C is generally recommended to obtain doughs with optimal rheological properties for further processing. Such a temperature also matches with the range of temperature used for fermentation.

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they allow for both qualitative and quantitative multi-component chemical identification (water, starch, protein and fat). The measuring probes are generally in a reflectance mode and are placed directly in contact with the dough or possibly in front of a viewing window on the bowl. Noncontact probes exist but are very expensive. NIRS is particularly sensitive to O-H, C-H and N-H bonds, making this technique ideally suited to low moisture content measurements. But it has broad absorption bands that are quite difficult to interpret. The processing of spectra requires the help of chemometric analysis (Aït Kaddour and Cuq, 2011). Chemometrics is the combination of statistics and chemical knowledge to obtain information from chemical systems. Common chemometric methods include Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression. PCA applied to the NIR spectra during dough mixing makes it possible to follow the evolution of hydrogen bonds and the gluten network development (Figure 5).

Acoustic monitoring The use of ultrasonic techniques has also raised some interest in assessing the rheological properties of dough. The speed of sound can be measured by the time of flight of the sound wave, which depends on the density and compressibility of the material. The attenuation of the sound wave is the loss of energy as it travels through the medium due to viscous losses. As the speed of ultrasound is highly sensitive to the presence of air, it can be used to measure the aeration of the dough during mixing. Depending on the frequency used, it is possible to study the distribution of bubbles within the dough during mixing (Leroy et al., 2015) or the porosity of the dough (Petrauskas, 2010). Conclusions This article aimed to provide a general understanding of the mixing process and its relevance in the breadmaking process, the different stages of dough mixing, the main pieces of equipment

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and their applications, the key parameters influencing the process and how real-time monitoring can help to achieve the best quality and determine the end of the process. Indeed, a good knowledge of these key parameters and the choice of the best kneading machine can improve the process and the quality of the final product. +++

Authors Eloïse Lancelot a, Dominique Della-Valle a, Joran Fontainea,b, Adrien Rebillarda,b, Anthony OGEa, José Cheiob, Alain Le-Baila a

ONIRIS, UMR CNRS GEPEA 6144, 44300

Nantes France b

Acknowledgments This project was funded by the ANR project “MIXILAB” (n°15-LCV3-0006-01) and by ONIRIS-GEPEA.

VMI, 85224 St Hilaire de Loulay, France

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

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Aït Kaddour, A., Cuq, B., 2011. Dynamic NIR spectroscopy to

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Kilborn, R.H., Tipples, K.H., 1972. Factors affecting mechanical dough development. I. Effect of mixing intensity and work

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Shoaib, M., 2013. A comprehensive review on wheat flour

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Leroy, V., Fan, Y., Strybulevych, A.L., Bellido, G.G., Page, J.H., Scanlon, M.G., Melorose, J., Perroy, R., Careas, S., 2015.

epjconf/20135605004

Investigating the bubble size distribution in dough using ultrasound. Statew. Agric. L. Use Baseline 2015 1, 1–11. https://doi.org/10.1017/CBO9781107415324.004

gluten au cours du pétrissage de suspensions farine-eau. Belton, P.S., 2005. New approaches to study the molecular

Muchová, Z., Žitný, B., 2010. New approach to the study of dough mixing processes.

basis of the mechanical properties of gluten. J. Cereal Sci. 41, 203–211. https://doi.org/10.1016/j.

Bloksma, A.H., 1990. Rheology of the Breadmaking Process.

Niranjan, K., Smith, D.L.O., Rielly, C.D., Lindley, J.A., Phillips, V.R., 1994. Mixing Processes for Agricultural and Food

Am. Assoc. Cereal Chem. 35, 228–236.

Materials: Part 5, Review of Mixer Types.

Campos, D.T., Steffe, J.F., Ng, P.K.W., 1996. Mixing wheat flour

and ice to form undeveloped dough.

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Petrauskas, A., 2010. The application of the ultrasonic method

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Sadot, M., Cheio, J., Le-Bail, A., 2017. Impact on dough aeration

Tömösközi, S., Békés, F., 2015. Bread: Dough Mixing and Testing Operations.

dough and biscuits. Cereal Chem.

Frazier, P.J., Daniels, N.W.R., Russell Eggitt, P.W., 1975. Rheology

J. Food Eng. 195, 150–157. https://doi.org/10.1016/j.jfoodeng.2016.09.008

Contamine, a. S., Abecassis, J., Morel, M.-H., Vergnes, B., Verel, a., 1995. Effect of mixing conditions on the quality of

Ultragarsas (Ultrasound) 65, 20–27. of pressure change during mixing.

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J. Agric. Eng. Res. https://doi.org/10.1006/jaer.1994.1072 for evaluating the porosity of bread.

Chin, N.L., Campbell, G.M., 2005. Dough aeration and rheology :

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monitor wheat product processing: A short review.

RESEARCH 60 PRODUCTION PLANNING

PRODUCTION PLANNING

Production and selling optimizations for bakeries Bakeries, as all manufacturing industries, need to ensure all production steps are optimized to become as efficient as possible, all changes considered. This will bring financial gains and make processes more eco-friendly, a vastly

The consumption of bread cereals in Germany from 2013 to now has increased from 77.9 kg per person to 83.6 kg with a gradient of 1.1 kg/year [1]. During the same period, the turnover of baker’s craft has increased from EUR1.0 million to EUR 1.4 million as well as the employees per enterprise from 21.5 to 25.1 [2]. However, a further trend can be observed from small bakeries with only a few branches to bakeries with a medium and large number of branches. It seems that smaller bakeries are not competitive compared to larger ones. Most probably the trend has been strengthened due to the COVID-19 pandemic. One reason is the efficiency of bakeries. The more flour is processed, the lower the energy input per ton of final products[3]. In bakeries, there is still a lot of manual labor, which cannot be substituted by machines and robotics efficiently. The labor cost is almost onefifth of the whole production cost in bakeries [4]. Therefore, the labor and its schedule must be organized in an optimal way. This is especially true if small enterprises are to be competitive. In most bakeries, the scheduling of production is done by an experienced production manager, who is usually not an expert in production scheduling theory. However, finding the optimal schedule is unquestionably a challenging task.

Usually, the production is performed in different, roughly nine production stages, such as gathering ingredients, mixing, kneading, resting, dividing, refining, shaping, proving, and baking. Since all products cannot be processed altogether, they must be produced one after the other. This limitation leaves enormous options, one of which can be followed. For example, if 20 different products are considered, then 2,432,902,008,176,640,000 or roughly 2,4 trillion different combinations of production schedules are possible. The only difference between these schedules is the order of the products during production i.e. which product is produced when. However, each of these schedules may lead to a different production efficiency level. Therefore, finding the best one is a really challenging job. Optimizing production processes contributes in many ways to increasing the competitiveness of SMEs. Especially in times of massive increases in energy and raw material costs, a reduction in consumption of the resources is essential. Annually, 372 MWh of energy is used per average bakery, resulting in 101 tons of CO2 emissions [3]. If the energy consumption can be reduced, it will improve the overall competition and consequently, help to reduce CO 2 production. A half-loaded

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important additional benefit.

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oven has a 20% higher energy consumption than an optimally loaded one [4]. Here an optimal schedule of the production process can help significantly. Hecker et al. have shown that an optimized production plan in terms of machine occupancy can lead to a reduction in occupancy time of up to 23% for ovens [5]. Furthermore, the image of the company can be improved, if the resources are consumed efficiently and can therefore improve the reputation among customers and will unload the environment. However, the optimization of production is one point. Another is the selling of the product. Reputation can be lost if at noon most of the products are sold out. Therefore, an optimization of the available products shortly before closing the shop must be performed.

The production process In general, a product in a production plant follows a series of operations or process steps, each with specific machines. In most cases, the products then follow a defined path through the individual production stages, and the machines required for this are usually arranged to build on each other accordingly. Such a production environment is called a flow-shop (FS) and serves as a basic model for the consideration of time flow control problems, which can be adapted to the respective real model by appropriate modifications. In principle, the flow-shop problem is based on the assumption that p products pass through a number of s production stages and that each product is processed exactly once on each machine, and that each machine can only process one product

Table 1: The duration of processing for a simplified production process of 20 products with 5 individual processing stages. Product

Dough

Dough rest

Shaping

Proof

Baking

360

preparation Product 1

Product 2

Product 3

360

Product 4

Product 5

Product 6

Product 7

Product 8

Product 9

Product 10

Product 11

Product 12

Product 13

Product 14

Product 15

Product 16

Product 17

Product 18

Product 19

Product 20

source: Hohenheim University

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Processing time [min]

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How a no-wait flow-shop can be implemented in a digital twin and used for optimization will be discussed by an example presented in Table 1. Here a simplified production plan is shown. Just 20 products with 5 processing stages are presented, as well as the duration necessary for each stage. The task now is to process all products in a way that no overlap of individual stages takes place and to process all the products as early as possible so that the production time is minimal. Of course, it will make no sense to start with a new product, if the predecessor product has been finished (baked). In this way, the production will last longest as the sum of all the durations, which is in this case 2,656 min. As soon as one product has finalized one stage, one can check if the next product can be started, to eliminate the risk that

Optimization using the particle swarm optimization algorithm For optimization, very effective algorithms have been developed using artificial intelligence. Here nature-inspired algorithms are applied, for example, the intelligence of a swarm to solve an optimization task [8]. In this contribution, the particle swarm optimization (PSO) algorithm, which is an algorithm that seeks a solution to an optimization problem based on the model of biological swarm behavior, like bird swarms or fish swarms searching for food [9]. The principle is presented in Figure 1 where the contour-plot of a search space for the determination of two parameters is presented. In the example of the simplified bakery with 20 products, the dimension of the search space would be 20. The color is indicating how good the solution is. Red means the worst, green is in the middle, and blue is the best solution containing the area for a

Figure 1: Search space of a problem with two parameters, where only one particle is presented with the three velocity components (vectors), which are used to calculate the new velocity; color indicates the quality of the position (red < green < blue)

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However, since the actual processes in manufacturing plants are often much more complex than in a flow-shop model, the much more flexible Hybrid Flow-shop (HFS) is used for their modeling. The main difference to the FS is that the production stages in the HFS can consist of more than one machine and a product can skip any number of stages as long as it is processed on at least one stage. Furthermore, an HFS consists of at least two production stages (s ≥ 2) and each stage consists of at least one parallel machine, with m > 1 for at least one stage [6, 7]. Further special cases can be defined by appropriate modifications, such as the permutation flowshop (PFS), which is always used when the product sequence must not change during production, and the no-wait flow-shop (NWFS), in which there must be no unscheduled waiting times for products between two process steps, which is the case in a bakery. Here the activity of the yeast cells in the dough will not just stop consuming glucose and stop producing carbon dioxide until the machine of the next production stage is free. So one stage after the other must be carried out immediately.

it will catch up to the predecessor during any of the following stages (i.e. in the case that the second product must be baked, although the predecessor is still in the oven). Only configurations are allowed where no overlap takes place, although this can be checked by the computer very quickly. The whole production schedule will be fixed and the production time will be calculated by processing all products in this way. Using an optimization procedure, the production time can be minimized.

at a time. Thereby, all products follow the same process sequence of stage 1, stage 2, ..., to stage s. Omitting or skipping a certain production stage is not possible here [6].

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Figure 2: The evolution of the schedule of five products of one particle during the optimization by PSO

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Figure 3: The actual production time of the particle is presented in Figure 2 (black line) as well as the production time of the global best particle (red line)

given problem. Of course, if the optimization starts, the colors are not known. The particles are moving through the search space depending on the global best position of all particles as well as their corresponding personal best position, and inertia. Here just one particle is presented as a black circle. Usually, the particles are much higher in numbers such as 50 or 100 or even more particles. The position of a particle in the coordinate system determines the value of the parameters and therefore its performance, indi-

cated by the color that represents the baking sequence and the order of processing of all products. The position of a particle in search space is defined by floating-point numbers, and to convert them to a schedule for processing the baking goods, the smallest position value rules [10] are applied. At the beginning of the optimization, each particle gets a random position as well as a random velocity. Each particle remembers where its best position was, indicated by the red triangle, as well it knows where the global best position

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from all particles is, indicated by the yellow square in Figure 1. The optimization is performed in iterations, where a new individual velocity is calculated for every particle and then a new position is determined meaning a new production sequence for the baking goods. For the calculation of the new velocity, the actual velocity (red vector), as well as new velocity components in the direction of the personal best position (green vector) and global best position (yellow vector) are multiplied by random weighting factors and summed up which result in the new velocity of the corresponding particle. If the velocity is small, then the particle is more exploiting a region, and if the velocity is high, then more exploration is performed. Using the new velocity the new position is determined and therefore, a new production

sequence. Nature-inspired optimization algorithms are very efficient in finding good solutions, which may not always be the best. However, this is usually much better than any sequence chosen randomly. In this way, the optimization is performed. Application example How an optimization is performed can be seen in Figure 2 for one particle out of 80, which represents one possible schedule at every iteration. Iterations determine for how long the algorithm should search for an optimal schedule. Not all 20 products are presented, because the diagram would be even more confusing. Therefore, only the position of the five products is shown. On the ordinate the position of the product in the schedule is presented, the number 1 means that

Table 2: Optimized schedule obtained by PSO Product

Dough

Dough rest

Shaping

Proof

Baking

preparation 1

Product 3

375

Product 20

326

355

365

375

387

Product 1

359

375

735

Product 17

638

660

675

735

Product 6

696

725

735

745

757

Product 5

725

741

745

750

835

849

Product 18

810

826

830

835

920

934

Product 15

895

911

915

920

1005

1019

Product 10

957

977

992

1005

1080

1098

Product 9

1055

1071

1075

1080

1165

1179

Product 19

1117

1137

1152

1165

1240

1258

Product 12

1215

1231

1235

1240

1325

1339

Product 7

1300

1316

1320

1325

1410

1424

Product 4

1362

1382

1397

1410

1485

1503

Product 8

1460

1476

1480

1485

1570

1584

Product 16

1487

1509

1524

1584

Product 13

1545

1574

1584

1594

1606

Product 11

1574

1590

1594

1599

1684

1698

Product 14

1601

1623

1638

1698

Product 2

1682

1698

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Ready time

source: Hohenheim University

Starting time [min]

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the corresponding product will be processed first and 20 means last. On the abscissa, the iteration number is presented. As can be seen, the position of the products in the schedule changes a lot at the beginning of the iterations and is more relaxed at the end. For instance, after 65 iterations, the particle seems to be near the global best position. Products 1, 5, 15, and 20 do not change their position anymore so their position in the schedule seems optimal. Product 20 which starts on position 20 was placed on position 2 early on at iteration 31 and did not change its position to the end of optimization. Product 10 in particular is flipping from position 19 to 9 back and forth. Some of the other products, not presented in Figure 2, were flipping in a corresponding way to minimize the production time. Following this approach, the algorithm tries to find an optimal position for each product in the processing order. The combination of such optimal positions for 20 products can give the best processing order with minimum production time for this example problem. In Figure 3, the production time of all 20 products of the individual particle from Figure 2 as well as of the global best production time can be seen. It is clear that this algorithm is not a hill-climbing algorithm as the production time of the individual particle is not only decreasing. After 28 iterations a steep increase in production time can be recognized and shortly later on an even more pronounced decrease. After 31 iterations the particle has the same performance as the global best one. But due to inertia and the randomness in the calculation of the velocity, the particle is flying to a position in the search space, which seems not optimal. However, due to this behavior, the particle still searches for better solutions by exploitation and after 16 further iterations, it again became closer to the optimal production time performing an exploration. The best schedule obtained by PSO after 100 iterations is presented in Table 2. The optimal production can be performed in 1,689 min, which is much less time than 2,656 min, representing the worst schedule. However, even an experienced baker would not have used 2,656 min, and if he were to achieve 1,689 min, it might be his secret.

As pointed out, nature-inspired optimization algorithms are very efficient to find a good solution, which may not always be the best. But in most cases, this is usually much better than many other sequences. We have already applied the optimization with real production data from bakeries and could show that the production time can be reduced by 8% and the machine idle time by 18% [11]. Validation of production scheduling results using logistic production twins Optimization algorithms as shown in the previous sections provide a great way for optimizing KPIs like the production time and idle time of the machines in very short time frames. However, naturally, these optimization models ignore or simplify many of the real-world properties of actual bakeries to keep the optimization times short or to keep the mathematical problem solvable, such as stochastic distributions of model properties, e.g. process times, machine failure rates, or scrap rates. Therefore, it can be helpful to validate the initial optimization results against a more detailed model of the bakery that can take these real-life properties into account, e.g. by conducting several simulation runs in a Monte-Carlo approach where several possible values of the statistical quantities are tested. Such models can also rather easily calculate more complex KPIs such as the maximum energetic peak loads caused by a certain production schedule, the power distribution, or the average utilization/work rates of employees. Material flow models based on discrete event simulations provide the features for accomplishing these tasks while also being relatively fast since the simulation engine jumps from event to event through the simulated time and does not require the numeric discretization of time. Several professional and open-source tools exist on the market to conduct such discrete eventbased material flow simulations. In this case for the validation and KPI calculation of the optimized schedule, the tool Tecnomatix Plant

Simulation 2 from Siemens was used of which a screenshot is shown in Figure 4. Here, an exemplary data set from [11] was used. Using Digital Twins in operational applications To utilize optimization algorithms and digital twins in an operative production environment the models need to be provided in a form that is appropriate for employees in a bakery. The corresponding software architecture is shown in Figure 5. Various model parts of the digital twin are integrated on different hierarchical levels of the bakery like the material flow simulation on the bakery level and oven models used for the prediction of energetic load curves of specific ovens, on the machine level. This is done in a web-based framework that allows the integration or the direct connection to relevant data sources (e.g. production orders, production resource availabilities). Also, external data services can be dynamically considered. A prototype was developed using the low code development environment Mendix3 from Siemens that allows the rather quick setup of

scalable web applications that can be connected to various backend services such as in this case the optimization of production schedules, the schedule validation, and the oven models. Optimization of the selling procedure Forecasting is a common industry practice, for example in supply chain management (SCM), it is used to estimate future customer demand, sales or revenue so that adequate planning can be made to prepare for the future. For bakeries, it is desirable to manufacture enough products to meet customer demand without incurring an excess surplus of products and subsequent inventory costs or food wastage. An accurate forecast is critical for an optimal planning process. Bakeries are mainly interested in forecasting the customer demand for different products for the next week at daily resolution and for every baked-good product. To create forecasts in a data-driven manner, first of all data needs to be collected from historical sales in the same or finer time resolution compared to that of the

¹ Planning data taken from [11]; energy data of the machines not real ² www.plm.automation.siemens.com/global/de/products/manufacturing-planning/plant-simulation-throughput-optimization.html ³ www.mendix.com/en

Figure 4: Screenshot from a discrete event material flow simulation showing the detailed power profile of a bakery when using a previously optimized schedule¹

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data models and developing corresponding software adapters is needed.

Figure 5: Software architecture for using digital twins in an operational environment

desired forecasts. Additionally, a common data model needs to be in place such that the different data sources from each bakery can be transformed into a common data format. This is not always available in practice as different bakeries may have different Enterprise Resource Planning (ERP) systems with different data schemas. It is recommended to have a well-documented data model which can be interpreted by the forecasting tool. Otherwise, additional effort for translating

It is common practice to have a forecast model for each product independently. Common methods for forecasting use techniques like ARIMA, Lasso [12], Gradient Boosting, Support Vector Regression, or Neural Networks. However, as the number of products increase, it may be cumbersome to manage and store many independent models with potentially sub-optimal forecast performance. For example, the sales of a product can be highly correlated to those of another product. Then jointly learned models which can forecast several product sales, are a better solution. We developed a web application that predicts sales demand for each product of a particular bakery by using Lasso [12], a linear regression model. Bakeries can specify from when and for how many days in the future, they want to predict the demands of their products (Figure 6). Conclusion For bakeries as for all other enterprises, the optimal processing of their tasks is of utmost importance. Due to strong competition in the market,

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Figure 6: A web application of sales demand forecasting using an independent forecast model

inefficient production will make it very hard for them to survive. This holds especially true for SMEs. On the one hand, efficient production is important from the view of the economy. However, ecology is even more important from the point of view of society, on the other hand. Therefore, efficient utilization of resources such as energy and the decrease of production of carbon dioxide will improve the image of an enterprise. Using a tool to optimize the production schedule in a bakery will improve the efficiency and therefore the competition and save our environment. The production time can be reduced by 8% and the machine idle time by 18%. Therefore, such tools will help the ecology as well as the economy. +++

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Authors Majharulislam Babor 1, Julia Senge1, Bernd Hitzmann¹, Dianna Yee², Yi Huang², Jan Fischer², Rudolf Sollacher² ¹Process Analytics and Cereal Science, Institute of Food Science and Biotechnology, University Hohenheim, Garbenstr. 23, 70599 Stuttgart, Germany ²Siemens AG, Technology, Otto-Hahn-Ring 6, 81739 Munich, Germany

References [1] Pro-Kopf-Konsum von Getreide in Deutschland in den

of Operational Research 205 (1), S. 1–18. [8] Vikhar, P. A. (2016). "Evolutionary algorithms: A critical

Jahren 1950/51 bis 2019/20 (in Kilogramm Mehlwert) BMEL,

review and its future prospects". Proceedings of the

bmel-statistik.de, May 2021

2016 International Conference on Global Trends in Signal

[2] Zentralverband des Deutschen Bäckerhandwerks e. V., Berlin, 2021, www.baeckerhandwerk.de/baeckerhandwerk/zahlen-fakten/downloaded 1907.2021 [3] Auswertung der Ergebnisse der KMU-Scheck- Initiative –

Processing, Information Computing and Communication (ICGTSPICC). Jalgaon: 261–265. doi:10.1109/ICGTSPICC.2016. 7955308 [9] Eberhart, R., and Kennedy, J. (1995). A new optimizer using

Endbericht, Energieinstitut der Wirtschaft GmbH, Wien,

particle swarm theory. MHS’95. Proceedings of the Sixth

September 2011

International Symposium on Micro Machine and Human

[4] EnEff Bäckerei, Netzwerk zur Steigerung der Energieeffizienz in Bäckereien. Leitfaden "Energieeffizienz in

Science, 39–43. https://doi.org/10.1109/MHS.1995.494215 [10] Huang, K.-W.; Girsang, A.S.; Wu, Z.-X.; Chuang, Y.-W. A

Bäckereien – Einsparungen in Backstuben und Filialen".

Hybrid Crow Search Algorithm for Solving Permutation

Bremerhaven, 2014.

Flow Shop Scheduling Problems. Appl. Sci. 2019, 9, 1353.

[5] Hecker, F.; Hussein, W.; Mitzscherling, M.; Becker, T. (2007): Simulation der Produktion in einer Bäckerei unter Berück-

https://doi.org/10.3390/app9071353 [11] Hecker, F. T.; Stanke, M.; Becker, T.; Hitzmann, B. (2014):

sichtigung des Energiebedarfs. Universität Hohenheim.

Application of a modified GA, ACO and a random search

Detmold, 07.11.2007.

procedure to solve the production scheduling of a case

[6] Pinedo, M. L. (2008): Scheduling: Theory, Algorithm, and Systems. 3. Ed. New York: Springer Science+Business Media, LLC [7] Ruiz, Rubén; Vázquez-Rodríguez, José Antonio (2010): The hybrid flow shop scheduling problem. European Journal

study bakery. In: Expert Systems with Applications 41 (13), S. 5882–5891. DOI: 10.1016/j.eswa.2014.03.047. [12] Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267--288.

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Acknowledgments This contribution has been funded by EIT Food for the project entitled “Optimization of bakery processes by a computational tool together with consumer feedback to minimize ecological footprint and food waste: Making baking more efficient”.

RESEARCH 70 D I G I TA L T W I N S

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Digital twins in baking process automation In a digitization project, it is important to first consider several use cases, because they typically require the most effort, while the implementation of additional use cases promises additional revenue at a lower cost.

A digital twin encompasses all possible information about a product, its production and possibly also its performance throughout its complete lifecycle. Let us illustrate this with bread production: + The general digital twin of a batch of bread may consist of the base recipe, the formulation related to a specific production line and any kind of data collected during production, analytics, and finally transport and distribution. It may also include simulation models relevant for product design or production, and even consumer feedback may be part of it. + The production line in a bakery producing this bread also has a digital twin. It is a combi-

nation of digital twins of its stations and machines. These may include 3-dimensional CAD models used for hardware design; simulation models of different granularity ranging from detailed 3-dimensional simulations to 1-dimensional process flow simulations; models of its control to be used in e.g. production design and model-predictive control; a track of data is collected during operation for monitoring and analytics. The benefit of using digital twins is manifold: The design of a product and its production can be made faster and cheaper using simulation instead of prototyping; the degree of automation can be increased up to autonomous production (3); the transparency of production can be increased leading to more informed and optimal decisions; processes in the supply chain or related to audits can be simplified substantially. In most cases, the main hurdle is the collection of sufficient data points and the integration of several, usually isolated data sources. Digital twins can be stored and used in the cloud, on-premise, but can also be embedded (4). A particular decision typically depends on requirements concerning data privacy and real-time performance. Using digital twins in the cloud is becoming more and more attractive, especially for remote monitoring, analysis and computationally extensive

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What is a digital twin? The industry has seen a couple of periods, each of which introduced innovations leading to a substantial increase in productivity. The introduction of mechanical machines, mass production and electrification and finally automation using electronics and information technology were previous such periods. Now we are in the era of digitization, the advent of which had already been foreseen in 1991 (1) and which is a core innovation of Industry 4.0, a high-tech strategy originally developed in Germany (2). Digitization is about creating digital twins of physical objects, maintaining them, and using them for improving processes related to these objects.

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applications1. On-premise infrastructure is not available everywhere; it is typically a choice for those who don’t trust cloud solutions. Using digital twins in embedded systems is recommended for applications where a rapid and reliable reaction is needed; examples are preprocessing key performance indicators (KPIs) for real-time monitoring or image pre-processing. In the following, we will present examples from projects co-funded by EIT Food2, an innovation initiative funded by the European Institute of Innovation and Technology (EIT), and from a customer project at Siemens. Collecting data from production A very important step in a digitization project is the collection of data from production. The effort and time needed for this strongly depend on the existing automation infrastructure. A necessary precondition is accessible communication links to sensors or controllers. Not having that implies an investment in the corresponding infrastructure like controllers and an automation network. Here, we want to present an example showing how fast and seamless the collection of data can be carried out even in brownfield if this precondition is fulfilled.

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In 2018, Nestlé Wagner GmbH asked Siemens for support in enhancing the efficiency of the existing machine park through the changeover to Industry 4.0 in accordance with the company's productivity policies, and at a reasonable cost. A proof of concept was implemented in the plant in Nonnweiler-Otzenhausen, Saarland, Germany, where amongst others the popular Wagner stone-baked pizza specialties are produced. The following requirements had to be fulfilled: + No modifications of machine controller are allowed. This would inevitably result in a change of the machine software and could cause unexpected machine behavior.

+ The new system must comply with the Nestlé

Wagner safety regulations. + No cloud-based solution may be used. All data must be stored locally. + It must be compliant with the General Data Protection Regulation (GDPR). The tool must be integrable into corporate IT. + + It must be quickly integrable within the structure and be able to quickly supply data. + The system must be easily and intuitively usable. The solution is based on SIMATIC WinCC OA IOT Suite3 combining reliable, industrial-grade hardware with proven software and intuitive apps for establishing connections, setting controllers, and displaying information. The proof of concept deployed at the production line of Nestlé Wagner is currently comprised of a central system controller, the so-called On Premise Administration (OPA) and the IOT box. OPA hosts system infrastructure including system administration and apps, equipment, and updates management. The IOT box is connected to a SIEMENS S7 controller through an Ethernet network, and directly processes the collected values and feeds them to the appropriate apps. Two apps are currently in use at Nestlé Wagner: one displaying the machine performance indicators and another, a dashboard app, which is freely configurable. The technician or person in charge of the machine opens the app and can view the current values on their tablet (s. Figure 1). The IOT box can also be connected to different other devices for collecting their data and integrating them into a consistent data model. In most cases, data are collected from PLCs, but there are also drivers available for communicating directly with e.g. sensors. Data collection and pre-processing is directly configured; the corresponding apps providing functionality are downloaded to the IOT box. The apps calculate

1 https://news.microsoft.com/2021/05/13/mars-and-microsoft-work-together-to-accelerate-mars-digital-transformation-and1 reimagine-business-operations-associate-experience-and-consumer-engagement/ 2 www.eitfood.eu/ 3 https://new.siemens.com/global/en/products/automation/industry-software/automation-software/scada/simatic-wincc-oa/ 1 simatic-wincc-oa-iot-suite.html

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Digital twins of product and production A digital twin of production has been used in HealthSnaP⁴, an innovation project co-funded by EIT Food. This project aimed at supporting healthy eating by developing a new food manufacturing technology and B2C business concept for at-site customized production of healthy snacks. The result was a prototype of a machine of the size of a typical vending machine hosting a complete production line for snack production.

4 www.eitfood.eu/projects/health-snap

Figure 2 shows the simplified layout of the machine. On the left in the background are the containers (green) for the ingredients water, oil, premixed dry base ingredients and optional boosts. In the foreground are the mixing and extrusion unit (blue). On the right side in the

Figure 2: Simplified 3D-layout of the machine prototype

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Nestlé Wagner is planning to deploy IOT Suite also on the remaining lines comprising production, packaging and reader equipment. Apart from gaining transparency on their production, a major goal is to reduce machine faults by getting more transparency through the data collection and used apps.

Figure 1: Data retrieved from a production line at Nestlé Wagner GmbH are shown on a tablet

The production process starts with collecting all needed ingredients, mixing them, extruding sticks to a transportation plate, baking the sticks in an oven, and putting them into a cup. The cup – and an additional dip – are collected by the consumer in the end.

different KPIs from e.g. the shift and provide them as aggregated data. Commissioning the IOT box for this application entails the integration of data from different PLCs, setup of the apps and providing them with the appropriate data. Through the ease of use, this process can be done by an electrician in less than one hour.

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Velocity Pressure

Die Angle

60° 90° 120°

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Figure 3: (left) comparison of measured mean extrusion pressure, in brackets, and mean extrusion pressure derived from the numerical simulations; (right) experimental setup for extrusion measurements for an extrusion angle of 90°

1 mm/s

3 mm/s

5 mm/s

kPa

53.0

91.9

119.0

(45.9)

(82.4)

(125.3)

46.8

81.3

105.08

(43.9)

(87.8)

(107.3)

48.6

84.4

109.0

(49.4)

(82.3)

(106.8)

background is the oven (brown) and in front of it is the unit moving snack sticks into and out of the oven and into the cup. The 3D CAD-model not only contains shapes and dimensions of all components, but also a semantic context for them. This imposes constraints on the position of components and on their dimensions. For example, the maximum number and size of the snack sticks impose a minimum size of the mixing cup; this determines the size of the mixer and the extruder and – consequently – the size of the cup for cleaning the mixer and extruder. The 3D CAD-model also allows for simulations of the motions of the components. Any potential collision of components enforces a new arrangement of all components. This can be done automatically using a suitable solver for all these constraint equations. The benefit is a much faster design process. Some components must be designed specifically for the product type. A good example is the mixing and extrusion unit. In the first phase of the design, a single hole extruder has been studied experimentally to evaluate the effect of the extrusion speed and die angle on the resulting dough sticks. The experimental setup is shown in Figure 3 right for an extrusion angle of 90°. A parametric model of a quarter of the extruder was created using the Siemens StarCCM+⁵ simulation solver. The die-angle is parametrized and a new model for a new angle can be generated with a few mouse clicks. The experimental

measurements of extrusion speed and extrusion force were used to calibrate a Power-Law model for non-Newtonian fluids (5). The results obtained for three die angles and three extrusion speeds show a good agreement with the measurements with a maximum error of 15%. The results are displayed in Figure 3 left. The numerical results are used to determine at which exit speed (different from the extrusion speed) the sticks start to sag, and the ‘cylindrical’ shape is compromised. In this way, an optimal exit speed is determined which can be used for evaluating different designs. For more accurate analysis and complex extrusion geometries, precise calibration of the material model is needed. This is achieved using rheology measurements of the dough which was not possible in this project. Simulations also help in determining optimal parameters for production steps. This has been done for the baking process, also with Siemens StarCCM+. To support the production steps, the baking process in the oven has been simulated in three dimensions including models for the baking plate and the bread sticks. To reduce model size only half of the oven was modeled, as a symmetric set-up was assumed. The bread sticks’ properties, as well as the air properties, are temperaturedependent. The sticks are considered solid and no deformation is allowed. The cross-section of the sticks allows for contact between the sticks and the plate. The model setup is presented in Figure 4 (left). The condition for the sticks to be reached in the baking process (=‘baked condition’)

5 www.plm.automation.siemens.com/global/en/products/simcenter/STAR-CCM.html

In the first phase a dummy material for the baking tray was assumed, as the final configuration was not yet specified and time would not be lost in setting up the model. Based on this model the digital twin simulation model was generated and possible scenarios to be investigated were defined. Once the final material for the tray was defined, the simulations were executed in less than one

day. A minimum baking time of 35 seconds was necessary to get the golden color on the sticks. The baking time was driven by the crust conditions, if the aluminum plate inside the oven was already hot (the machine has been working for some time) and by the temperature at the contact between plate and sticks, if the plate was well below oven temperature (first batch after a long pause). Figure 4 (right) presents the configuration a few seconds before the baked condition was reached, the part of the sticks in contact with the plate is the last to reach baked conditions. For all investigated conditions a baked condition could be reached in a maximum of 52 seconds. The baking time could be made dependent on the oven conditions (continuous usage or paused).

Figure 4: (left) Simulation setup. The hot air injection holes are visible at the top. The three bread sticks lay on a removable Teflon plate on top of a fixed aluminum tray. (right) Mid-section of the sticks temperature at reached baked conditions. White areas represent temperatures below 98°C

Finally, 1d-process flow simulations using Tecnomatix Plant Simulation from Siemens allow for the evaluation of the design and investigating

Figure 5: Material flow simulation model of a production process

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has been defined as having a temperature above 98°C everywhere and between 150 and 180°C on the crust to obtain a golden color, but avoiding burning. Using this definition several scenarios were investigated. The aim was to determine the necessary baking time for the sticks under different starting conditions (dough temperature, plate temperature, oven air temperature). A target baking time of under 60 seconds was set from the requirements coming from the whole machine design.

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velocities, as well as variants in the cleaning process such as by an additional mixer or in the dispensing process with several plates, the production time of a specific product can be evaluated. Also, with this, stress tests by a specific

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Figure 6: Some simulation results, e.g. production time and necessary ingredient refill for a given number of orders

Figure 7: Example of some experiments and their interpretation

design options (see Figure 5). Based on the recipes with the individual customized ingredients the production schedule can be dynamically defined and tested. With the configuration of specific process parameters like motor speeds or conveyor

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Results from simulation such as production time or refill chart (see Figure 6) can then be used to improve the process. Figure 7 shows an evaluation of adding a second plate or a second mixer. While a second plate does not improve production time substantially, a second mixer can reduce the order production time by about 8%. This evaluation gives hints for process optimization regarding design decisions: Adding a second mixer shortens the total production time for the orders listed in Figure 7 by about two minutes corresponding to about 8%. Adding a second plate has a minor effect on the total production time but can notably reduce production time for a small portion produced after a large portion; for the marked example in Figure 7 this is a reduction of 10 seconds in the case of one mixer and about 20 seconds in the case of a second mixer, corresponding to about 5%, respectively 10%.

Acknowledgment This contribution has been co-funded by EIT Food for the project entitled “Health SnaP” (www.eitfood.eu/projects/health-snap).

Authors Rudolf Sollacher¹, Wendelin Feiten¹, Birgit Obst¹, Arianna Bosco¹, Stefan Boschert¹, Justina Yoo², Heiko Soehner² ¹Siemens AG, Otto-Hahn-Ring 6, 81739 Munich, Germany ²Siemens AG, Gleiwitzer Str. 555, 90475 Nuremberg, Germany

There are plenty of other applications for such simulations: Examples are (i) determining optimal dimensions of ingredient containers, (ii) handling of production processes with the prediction of production time for a customer's order or (iii) giving alarms in advance e.g. in case of refill.

+++

Literature [1] Gelernter, David Hillel. Mirror Worlds: or the Day Software Puts the Universe in a Shoebox—How It Will Happen and What It Will Mean. Oxford; New York : Oxford University Press, 1991. [2] Henning Kagermann; Wolf-Dieter Lukas; Wolfgang Wahlster. Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. https://web.archive. org/. [Online] April 01, 2011. [Cited: March 04, 2013.] https://web.archive.org/web/20130304101009/http:// www.vdi-nachrichten.com/artikel/Industrie-4-0-Mit-dem-

Georg; Lo, George; Bettenhausen, Kurt D. 2015. IFAC-PapersOnLine. Vol. 48, pp. 567-572. [4] Next Generation Digital Twin. Stefan Boschert; Christoph Heinrich; Roland Rosen. [ed.] I. Horváth, J.P. Suárez Rivero and P.M. Hernández Castellano. Las Palmas de Gran Canaria, : s.n., 2018. Proceedings of TMCE 2018, 7-11 May, 2018. pp. 209-218. [5] H. A. Barnes, J. F. Hutton, K. Walters. An Introduction to Rheology. s.l. : Elsevier Science, 1989. [6] Florian T. Hecker, Marc Stanke, Thomas Becker, Bernd

Internet-der-Dinge-auf-dem-Weg-zur-4-industriellen-

Hitzmann. Application of a modified GA, ACO and a ran-

Revolution/52570/1.

dom search procedure to solve the production scheduling

[3] About The Importance of Autonomy and Digital Twins for the Future of Manufacturing. Rosen, Roland; von Wichert,

of a case study bakery. Expert Systems with Applications. 2014, 41, pp. 5882–5891.

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combination of orders are possible in order to identify ingredient availability and bottlenecks in material and process flow.

RESEARCH 78 D I G I T I Z AT I O N

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Digitizing food supply chains In the following, we focus on a few innovative aspects related to data privacy, supply chain integrity, and data models for digital twins.

The global food supply network that provides us with our daily food is the world’s most extensive and most important critical infrastructure. This implies that we need to trust this infrastructure and its services. Unfortunately, we still see a substantial number of food safety incidents. In the fourth quarter of 2020 alone, the International Food Safety Authorities Network (INFOSAN), developed by the World Health Organization (WHO) and United Nations Food and Agriculture Organization (FAO), was involved in 23 food safety events - about half of them in Europe (1). Unsafe food still has a substantial adverse effect on our health and economy (2): one out of ten persons is affected, and about 420,000 die each year because of unsafe food, with children under 5 carrying 40% of the foodborne disease burden, from which 125,000 die every year. Another threat to consumer trust is food fraud, referring to “the mislabeling, concealment, diversion, dilution, substitution/adulteration, unapproved enhancement, counterfeiting or grey market production/theft/diversion of foods” (3). Annual damages in Europe account for approximately EUR 8 to 12 billion. In 2019, cereal & bakery products appeared among the 10 top product categories in the annual report of the EU Food Fraud Network (4); the supply material category ’fats & oils‘ is leading these statistics and has seen an increase from 29 inquiries in 2018 to 44 in 2019.

A particular type of fraud, organic food fraud, which is driven by the profit from the high price tag of this kind of food, is on the rise. In 2018, the European Commission launched the OPSON VIII operation in collaboration with Europol (5); this operation led to 63 fraud inquiries and flagged 90,000 tons of organic products as suspicious, of which 16,000 tons have been downgraded to conventional food products by the authorities. One can also observe an increasing consumer interest in their food sources (6) and growing demand for individualized products and transparency in food supply chains. The benefit of more transparency is apparent as (7): + Consumers can make better-informed buying decisions + Traceability is improved, thus reducing the impact of recalls, accelerating the analysis of root causes, reducing fraud, and simplifying processes for audits and export control + Sustainability is improved by tracking resource consumption, CO 2 footprint and waste, by identifying critical process steps in the supply chain, and by eliminating them with appropriate measures Technologies enabling transparency in food supply chains include data acquisition technologies, Internet of Things (IoT), platforms for managing IoT generated data and big data technology (7). IoT is used to collect data from supply chain processes, these data are then stored, processed, and analyzed on an IoT platform like

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Why do we need more transparency in supply chains?

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Figure 1: Exemplary bakery supply chain

MindSphere1 In turn, blockchain stores transactions, therefore it must be protected from manipulation. Equally important, especially for automated analysis, are digital twins of food processes (8). They describe processes and products and their relationships in the supply chain and give data points a meaning.

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In the following, we focus on a few innovative aspects related to data privacy, supply chain integrity, and data models for digital twins. Data privacy and supply chain integrity Digitalization is the pre-requisite for a data privacy and integrity concept for organic food supply chains. It may comprise of digital twins of each involved stakeholder, as shown in Figure 1: The bottom half shows the different actors like farmers, bakeries, logistics, retailers and customers, but also suppliers, certification bodies and insurance companies. All of them exchange products or information or both. On top, the digital world is shown with digital twins of equipment and machinery, products, and processes. They are typically stored in databases and part of them can be exchanged with business partners, agencies, and customers. This exchange is usually done via a shared information space shown on the very top of Figure 1.

Ensuring integrity in supply chains typically involves three qualitatively distinct levels: 1) Level 1 ‘Reality gap’: ensures the data in the digital space correctly represents reality 2) Level 2 ‘Hackers’: ensures digital data is authenticated and cannot be changed or removed 3) Level 3 ‘Semantics’: ensures data is interpreted the right way and appropriate action is taken The classical means to enforce Level 1 is a threat of legal consequences for providing and signing false assertions. Therefore we propose electronic signatures that can be checked automatically and thereby create more reliability. Additionally, the use of Biomarker Technology is an important pillar to ensure 1). The Biomarker Technology is a device that can check and assert if a food item is organic or not (10). The biomarker device confirms its measurements directly with an electronic signature of its own, eliminating the risk of somebody reporting embellished results. The system can be further extended by including check sums, such as hash of measurements at different stages, and other checks across tenants (on a trusted cloud, with homomorphic encryption in perspective). Examples are described in a recent paper on olive oil fraud (11): 32 cases of fraud have been reported from September 2016 until December 2019, of which 11 concerned

¹ https://siemens.mindsphere.io/en/industrial-iot?gclid=EAIaIQobChMIl9vf6a-c8gIV2oODBx0QIQhrEAAYASAAEgIBQPD_BwE

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The public Blockchain entry entails no data directly, but only random-looking numbers, i.e. hash values (numeric values of a fixed length that uniquely identifies data). Therefore, it is necessary that at least one of the parties still has the data, usually the owner of the data. To ensure Level 3, the data must be interpreted with standardized ontologies. In the food industry, many different ontology standards have already been developed. Some standards are for particular use cases, but also some generic standards are available that can and should be used for a shared understanding and better exchange within the food industry. So far, none of the existing ontology standards suit the requirements of a digital twin for the supply chain. Therefore the following section introduces a new Ontology.

Ontologies for digital twins Digitizing food supply chains heavily relies on digital twins of supply chain processes and digital twins of products. Examples of supply chain processes are not only transport, processing, and storage of food, but also lab tests of samples for quality management. Digital twins of products typically contain information about the specification of the product and its history along the supply chain. Therefore, digital twins of products and supply chain processes are closely linked. Standardized data models for these digital twins are fundamental for several reasons: + The interpretation of data in digital twins is more effortless and allows for analysis by generic tools which understand the data model; + The exchange of information represented by digital twins between stakeholders along the supply chains is simplified and can be done entirely electronically without manual examination and translation by humans; + Relationships between supply chain processes as well as between products and supply chain processes allow the automated analysis of root causes in case of contamination or fraud. Such standardized models specify entities and their relationships using semantic Web technologies such as RDF (12), RDFS (13) and OWL (14). In a project2 co-funded by EIT Food3 addressing

www.eitfood.eu/projects/the-development-of-organic-supply-chains-that-drive-fair-transparent-and-healthy-options-for-the-consumer

www.eitfood.eu

Figure 2: Basic supply chain integrity and privacy concept

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To ensure Level 2, a concept that relies on hashing, signing, and linking all messages on the sender and receiver’s side is required. With this concept, all directly involved parties can prove (without doubt) mathematically, at any time, that a message was sent/accepted by a specific entity and not changed afterward. Additionally, the generated signature chains can be anchored in the public distributed ledger, such as bitcoin blockchain, at regular intervals (e.g. once a day) or if somebody rejects reception (s. Figure 2). So, if some or even all parties colluded later to change or hide something, they cannot do so without this being automatically revealed. So, the regulator can automate big parts of the audit process and let it happen continuously.

mislabeling, 4 untrue origin, 16 substitution, 6 dilution, 5 intentional distribution of contaminated products/counterfeiting and 1 was related to theft. The true origin, for example, can be detected by establishing a specific databank of isotopic values similar to that for wine. These values denote the ratio of e.g. hydrogen to deuterium or 13C to 12C and can be measured with isotope ratio mass spectroscopy.

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organic supply chains for meat and leeks, several standards were considered in order to create the systematic base for a digital twin: 1. The ISA standard ISA-88 (15) (adopted as IEC 61512-1) was considered as it enables various structuring examples and labeling of unit operations. The classification into enterprise, enterprise site, work area, work centers, work units was applied to design comparable, standardized structures in the generic model. Thus, it becomes possible to create a physical model of each production process where each work unit forms its own digital twin. The classification enables nearly every user to map their partners of the supply chain. The generic model can map from small suppliers, like farmers who supply the grain, to big flour mills and warehouses that belong to large groups or the bakery shop that sells the product in the end. In addition, the overlying corporate structure is also mapped to the standardized model. 2. The descriptions for modeling food products contained in the model are also based on established standards. In concrete terms, the Codex Alimentarius (16), LanguaL (17), and the Food Additive Classification (18) of the European Community were used for this purpose. 3. Concepts of FoodOn (19), which is based on LanguaL (17), guarantee an even broader use. FoodOn currently represents the most powerful and massive ontology for modeling knowledge considering food sources, derived food products and some related processes.

These standards were used to develop a hierarchical approach for digital twins in supply chains (see Figure 3). The first ontology, “Organic Food Supply Chain” (OFSC), is a generic ontology that contains the structure and main concepts about organic supply chains. The other two ontologies are specific for the two different product groups, beef and leek, considered in this project. A similar ontology can be developed for the supply chains of bakeries and their products. The link to the integrity concept for food supply chains described above is achieved by including information, which is stored in the blockchain, also in the digital twin data model. A simple query using the product id allows the organic status of the product to be checked: the ontology helps to identify all instances of supply chain processes related to this particular product; information that is stored in the blockchain is queried from there. Case examples Seamless traceability at Hochdorf Swiss Nutrition AG (9) We describe this case as an example of traceability inside a production facility. Due to regulation, this is mandatory here. However, it is also an example of a highly automated and complex data acquisition process. Similarly automated data acquisition in any production system substantially simplifies the extension of traceability solutions along whole supply chains also involving suppliers and customers. Hochdorf Swiss Nutrition AG4 produces food from milk, oilseeds, and cereals. In their new 30-meterhigh production tower, one of the largest of its kind in Europe, around 75 million liters of milk are processed each year into around 30,000 tons of baby food. Precise process control around the clock guarantees that the milk powder has the right consistency at the end and does not become too dusty or too moist.

Ontology

OBSC

OVSC © Siemens

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OFSC Figure 3: The ontologies OBSC and OVSC specify the ontology OFSC and inherit its basic concepts

www.hochdorf.com

The high hygiene and quality requirements for baby food require reliable 24-hour monitoring of the plant and complete traceability of the entire

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All production data is stored on a long-term basis. Data processing and automation are carried out via 10 Simatic controllers with 64 peripheral stations. The system produces continuously for one to two weeks in a 24-hour operation. All process variables are continuously recorded in the control room, and crucial key data is automatically stored on a central server. This procedure ensures complete traceability of the entire production process – all data can still be retrieved years later. Potential applications of such data in bakeries include rapid detection of problems in the process, and improvement of the production process using data analytics. Potato chips with a certificate show their origin Siemens is working on an intelligent solution to deal with recalls more quickly and specifically in the future. The open, cloud-based MindSphere

Figure 4: Numerous sensors in the production plant record all parameters. Important key data is stored automatically. This ensures complete traceability of the entire baby food production process

IoT system will enable suppliers, distributors, and manufacturers to collect data at every stage of the transport and production chain and store it in the Siemens blockchain. This makes it possible to narrow down a recall to a specific batch or production day. The risk of contaminated food entering the supermarket can thus be minimized as much as the unnecessary effort and costs for a reasonless recall. In a possible scenario, for example, a Frankfurtbased manufacturer of potato chips with an organic certificate who obtains his potatoes from Germany, the salt from France, and the sunflower oil from Italy would have immediate access to all relevant information: from the cultivation of organic potatoes and other ingredients, their storage and transport, processing such as cutting, frying, and seasoning to packaging and distribution at the retailer. At the end of the day, the consumer is provided with a complete information chain that can be viewed at any time and guarantees that his chips consist of, for example, 100% organically grown potatoes and that they have been processed under optimal conditions.

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production process. This is achieved with modern technology from Siemens. A total of 663 digital and 637 analog sensors continuously record parameters such as pressure, temperature, or filling levels. In addition, 300 motors provide the drive, and 1,700 valves regulate the pressure and flow rates of the media and ingredients required for production (see Figure 4).

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The transparent supply chain for food traceability ensures that products and their ingredients are safe and genuine. Wholesalers and supermarkets receive detailed information on where they were last stored. The manufacturer can identify the origin of the ingredients used at any time and obtain detailed information about their producers. The unchangeable time stamps provide all ingredients with a forgery-proof best-before-date. +++

Authors Rudolf Sollacher 1, Aliza Maftun1, Michael Fiegert 1, Alastair Orchard2, Kilian Vernickel 3, Paul Weber4 1

Siemens AG, Technology,

Otto-Hahn-Ring 6, 81739 Munich, Germany 2

Siemens Industry Software S.r.l.,

Via Enrico Melen, 83, 16152 Genova GE, Italy 3

Fraunhofer Institute for Casting,

Composite and Processing Technology IGCV, Am Technologiezentrum 10, 86159 Augsburg, Germany 4

Fraunhofer Institute for Process

Engineering and Packaging IVV,

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Heidelberger Str. 20, 01189 Dresden, Germany

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Bibliography

A review. Mihailova, A., Kelly, S. D., Chevallier, O. P., Elliott, C.

1. INFOSAN. INFOSAN Quarterly Summary, 2020 #4. World Health

T., Maestroni, B. M. & Cannavan, A. Apr 2021, Trends in Food

2021.] www.who.int/news/item/27-01-2021-infosan-quarterlysummary-2020-4. 2. Paul Garwood; Zoie Jones. Food safety is everyone’s business. World Health Organization. [Online] June 6, 2019. [Cited: March 10, 2021.] www.who.int/news/item/06-06-2019-food-safety-iseveryones-business. 3. Organic Food Fraud in the EU: Meaning, Examples & Prevention. foodcircle. [Online] [Cited: March 10, 2021.] www. foodcircle.com/magazine/organic-food-fraud-eu-meaningexamples-prevention.

Science and Technology, Vol. 110, pp. 142-154. 11. Emerging trends in olive oil fraud and possible countermeasures. Casadei, E., et al. s.l.: Elsevier BV, 2021, Food Control, Vol. 124, p. 107902. 12. A comparison of RDF query languages. International Semantic. Haase, P., et al. Berlin: Springer, 2004. International Semantic Web Conference. pp. 502–517. 13. Hendler, Dean Allemang and Jim. Semantic Web for the Working Ontologist – Effective Modeling in RDFS and OWL. s.l.: Elsevier Inc., 2011. 14. S., Bechhofer. OWL: Web Ontology Language. [ed.] ÖZSU M.T. LIU

4. Commission, European. 2019 Annual Report: The EU Food Fraud

L. Encyclopedia of Database Systems. Boston, MA : Springer.

Network and the Administrative Assistance and Cooperation

15. Exchange General, Technical and Business Information. ISA-

System. Luxembourg: Publications Office of the European Union, 2020. ISBN 978-92-76-18809-4. 5. DG SANTE Unit G5; DG AGRI Unit B4. OPSON VIII DEBRIEFING: organic targeted action. [Online] [Cited: March 11, 2021.] https:// ec.europa.eu/food/sites/food/files/safety/docs/food-fraudreports_20191125_pres01.pdf. 6. How consumer demand for transparency is shaping the food industry. [Online] Label Insight, 2016. [Cited: August 6, 2021.]

88. [Online] The International Society of Automation. [Cited: 08 08, 2021.] www.isa-88.com/. 16. Codex Classification Of Foods And Animal. s.l.: CODEX ALIMENTARIUS COMMISSION, 2006. 17. The LanguaL 2017™ Thesaurus – Details for descriptor K0020. [Online] Danish Food Informatics. [Cited: 08 08, 2021.] www. langual.org/langual_thesaurus.asp?termid=K0020&haschildr en=True&o.

www.labelinsight.com/hubfs/Label_Insight-Food-Revolu-

18. GSFA Online. CODEX GENERAL STANDARD FOR FOOD ADDITIVES

tion-Study.pdf?hsCtaTracking=fc71fa82-7e0b-4b05-b2b4-

(GSFA) ONLINE DATABASE. [Online] FAO/WHO. [Cited: 08 08,

de1ade992d33%7C95a8befc-d0cc-4b8b-8102-529d937eb427. 7. Transparency in food supply chains: A review of enabling technology solutions. Astill, J., et al. s.l.: Elsevier BV, 2019, Trends in Food Science & Technology, Vol. 91, pp. 240-247. 8. Pieter Verboven; Thijs Defraeye; Ashim K Datta; Bart Nicolai. Digital twins of food process operations: the next step for food process models? [ed.] Pedro ED Augusto. Current Opinion in Food Science. March 19, 2020, Vol. 35, pp. 79-87. 9. Blockchain in the food and beverage industry. s.l. : Siemens AG, 2019. 10. High-resolution mass spectrometry-based metabolomics for the discrimination between organic and conventional crops:

2021.] www.fao.org/gsfaonline/index.html. 19. Hsiao, William, et al. FoodOn: A farm to fork ontology. [Online] [Cited: 08 08, 2021.] https://foodon.org/. 20. A study on adulteration in cereals and bakery products from Poland including a review of definitions. Kowalska, A.; Soon, J. M.; Manning, L. s.l.: Elsevier BV, 2018, Food Control, Vol. 92, pp. 348-356. 21. FTTO: An example of Food Ontology for traceability purpose. Teresa Pizzuti, Giovanni Mirabelli. 2013. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). pp. 281–286.

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Organization. [Online] January 27, 2021. [Cited: March 10,

RESEARCH 86 DESIGN THINKING

DESIGN THINKING

INDUSTRIAL BAKING

Using design thinking to facilitate automation In a time when change is the only constant, bakeries need to generate new ideas at an increasing pace and solve challenges in a human-centered manner. One of these challenges is the successful implementation of automation in the production process of baked goods. In this chapter, I describe how design thinking could improve this change process in industrial-scale manufacturing environments.

However, there is no one-size-fits-all solution for how automation can be introduced within every bakery. Each implementation rather requires several adjustments to leverage the optimization potential. Furthermore, successful change in a

complex, rapidly transforming environment requires a motivated workforce with sufficient leeway for responding to changes in a self-determined manner. Due to the VUCA environment, prefabricated blueprints for managing change are of limited use. For instance, due to changing customer preferences, such as more emphasis on healthy and sustainable goods, labeling food products becomes more important. However, introducing new labels might require a new quality management system. Thus, bakeries need agile approaches that iteratively integrate new insights into the solution process along the way. One of these new approaches is design thinking, a team-based method that relies on a defined process and takes human needs into account before developing a solution (Endrejat & Kauffeld, 2017; Norman, 2013). Initially created for product and service development (due to its participatory and iterative style), design thinking is also an agile approach to implement organizational change (e.g. Elsbach & Stigliani, 2018; Endrejat et al., 2018). In this chapter, I describe the design thinking mindset and process based on the challenge of “using automation to increase speed and efficiency of the

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In a volatile, uncertain, complex, and ambiguous (VUCA) environment, companies have to continuously adjust their existing processes to changing conditions. Otherwise, they struggle to strive and survive. For instance, comparing the Fortune 500 (the list of the most lucrative organizations in the U.S.) from 1955 and 2019 reveals that only 10.4% of organizations appear on both lists. Naturally, companies in the baking industry are not immune to potential disruption. Structural and technological changes in the baking industry have been (e.g. Storey & Farris, 1964) and still are subject to scholarly attention (e.g. Hecker et al., 2013). One significant innovation in this regard is the automation of processes. Automation is “the use of machines and computers that can operate without needing human control” (Cambridge Dictionary). Automation can optimize the baking and selling process, especially for consistent and repeatable services

DESIGN THINKING

communication and ordering between bakeries and subsidiaries”. Furthermore, I provide general recommendations for introducing this innovative approach into organizational routines. Design thinking Problems often arise from poor design. In this context, design goes beyond aesthetic aspects but refers to how users interact with objects or processes. Take for instance the famous ‘NormanDoors’ (named after the user-experience expert Dan Norman): These poorly designed doors let users guess whether they should push or pull. They might even have a design that tells you to do the opposite of what you are supposed to do and hence often come with a sign to correct the wrong implicit signals (Norman, 2013).

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Next to these ‘simple’ design flaws, problems can also be deeply interwoven into organizational structures. In most cases, we tend to overemphasize the effect of workers on performance, while neglecting the impact of system variables. For instance, if employees have difficulties following up on agreed priorities, it is often not human inability to focus but poor design, such as a matrix organization. The matrix consists of customer segments and a functional structure (marketing, sales, etc.) and hence, employees have to serve authorities with different interests, making it hard to focus on one subject (Carucci, 2019). Serving two supervisors might be the root of the problem, yet organizations try to solve the challenge by surface solutions, such as weekly check-ins, dashboards and the likes. This is because trying to fundamentally understand the problem and frame it into a solvable solution often runs opposite to the predominant organizational culture (‘we have always done it this way’). To overcome the forces of perseverance, more and more organizations are recognizing that a user-centered approach is necessary to design change that promotes desired behavior. Thus, one goal of design thinking for organization development is to systematize the approach of designers and turn it into a process that can be practiced in organizations.

Design thinking rests on an iterative process by which a team continuously gathers new information about a given challenge (a concrete bakery challenge will be discussed below) and integrates the perspectives of relevant stakeholders into the solution process. This is particularly relevant for organizational transformation – like the implementation of automation – because it invites the workforce to co-design a change, using its expertise, and creating commitment to the solution. However, employees’ participation and support do not emerge out of the blue or due to tokenistic surveys. Rather, design thinking involves the ones affected by a change and rests on an actionoriented mindset. As most organizations and employees spend more effort in analyzing than modifying products and processes, this new way of working might run opposite to established work routines. Hence, it is often beneficial, to start with a design thinking intro or speed-run that is based on an artificial challenge (e.g., ‘how to communicate in a country whose language you do not speak’). This provides team members with an overview of how design thinking works and lets them appreciate the value of iterative processes and fast user feedback in a playful manner. The design thinking mindset Design thinking rests on the optimistic, empathic, and action-oriented conviction that a solution is out there and that by keeping focused on human needs – and asking the right questions – the team will get there. Instead of presenting final solutions, design thinking involves employees in a process that helps them to understand the causes or the meaning of a necessary change. In this way, those affected are given the opportunity to contribute their views and wishes. In the common terminology of organizational science, it is a bottom-up approach: The data of the employees are collected, put together and developed into a new solution, which is then carried upwards in the organization. This approach contrasts with the traditional top-down approach in which the management specifies how existing processes are to be improved incrementally and then expects the workforce to adopt these changes.

At first glance, a design thinking team that is confronted with organizational members who are reluctant to change their behaviors might brand them to be ‘resistant’. However, attributing innovation failure to users neglects that changes can be challenging and associated with uncertainty, or indeed be disadvantageous for the change recipients. A design thinking team that responds to reluctance with confrontational language to ‘sell’ an idea might evoke employees’ reactance and thus reduce the chances that a prototype will be accepted by the intended users. In other words, sometimes ‘resistance’ is rather the product of the communication between a design thinking team and employees (Endrejat et al., 2020). Thus, design thinking does not ignore problems and challenges, but also does not demonize ‘resistance’ by change recipients and rather understands it as an expression of needs that have to be acknowledged.

which new products and processes can be developed.

This reinterpretation allows us to work together on solutions – true to the motto ‘energy flows where attention goes’. The decisive criterion is which solutions are sufficient to meet the requirements of the users. Instead of evaluating solutions according to right and wrong, it is about examining which concepts meet the stakeholder needs. In this process, it helps to think about problems without bias and to explore possibility spaces in

Divergent means that new directions of thinking are explored in order to gain as much data as possible about the concerns of the employees. During convergent thinking, this data is structured and, if necessary, reduced to define clear needs and solution ideas. Both ways of thinking can be found in the needs analysis as well as in the solution phase (thus the name ‘double diamond’). In the first step (discover), the design thinking

The design thinking process A design thinking process usually begins with a challenge defined by the management or other relevant stakeholders that see room for improvement or the need for change in a certain area. Such a challenge might be: “using automation to increase speed and efficiency of the communication and ordering between bakeries and subsidiaries”. A design thinking project consists of several phases that are necessary for a successful innovation process, from the initial challenge to the successful implementation, placing a strong focus on a detailed needs analysis. An established process model is the ‘double diamond’ developed by the Design Council (2007) that includes two phases of divergent and convergent thinking, respectively (see figure 1).

Figure 1 The ‘double diamond’ design thinking process by the Design Council (2007)

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Source: The Design Council

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team gathers as much information as possible about the perspectives of their colleagues (divergent thinking) and then focuses on a few central concerns that they would like to solve (define). In the subsequent solution phase, various options and possibilities are thought through to take the needs of the employees into account (develop), and select and develop a promising proposal (deliver). Each of these steps is described in the following sections.

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Discover (divergent thinking) In the first process phase, the design thinking team tries to gain a deep understanding of users’ perspectives, preferably on vivid insights that are gained through observation, interviews with colleagues as well as subject matter experts. During this phase, the team acts like anthropologists that observe a foreign culture and learn about the rites and processes practiced in the organization in a non-judgmental manner. By taking such a role, the design thinking teams question taken-for-granted-beliefs (‘we have always done it this way’) and learn new nonanticipated facts (e.g. sales of baked goods depend on the weather) as well as the concerns of change recipients. One concern might be that employees in the subsidiaries fear that algorithms developed at the headquarters take away their control to order new products spontaneously, for instance, due to a regional holiday. Define (convergent thinking) After collecting a lot of information during the discovery phase, design thinking teams have to focus on the relevant insights. Key needs of employees could be phrased into “How to…” questions, such as “How to make employees feel safe that they keep their jobs?”. After structuring the employees’ needs, the transition from the needs analysis to the solution phase takes place. Often, this goes along with re-framing the challenge or focusing on the most relevant aspects. Usually, the users’ key needs are translated into "How might we ...?" (HMW) questions. A HMW question consists of only three words but these are carefully chosen to guide the team into a creative trajectory.

How. Implies that solutions exist and that there is a possibility the team can find them. Might. Suggests that hypothetical solutions are being developed and gives permission to explore unorthodox avenues (underlined by the subjunctive). We. Assigns responsibility to each team member and facilitates collaboration necessary to generate results. By selecting and merging the most relevant “How to…” questions (convergent thinking), the design thinking team creates a tangible HMW question. In our example the question could be “How might we combine algorithms and the expertise of local employees to increase the efficiency of ordering processes?” After the team has defined the key challenges, it moves from the problem definition to the solution phase (i.e. the second diamond). Develop (divergent thinking) To generate creative user-centered solutions for the defined HMW question, the team is encouraged to think out of the box (what wows?) because employees seldom think up but rather tend to tame down. For this purpose, design thinking teams use various creative techniques such as brainstorming or -writing, the six-thinking hats (a method of approaching the challenge from different perspectives (=wearing different hats), developed by Edward de Bono in 1999), or mash-ups (a method that creates new insights by forming associations between unrelated domains). Conducting a mash-up round usually consists of four stages: 1. Think about a category that is as far away as possible from the situation of your challenge (e.g., your challenge is about a hospital, so for the mash-up you choose ‘amusement park’ as the unrelated category). 2. List as many elements as possible that belong to your chosen category (i.e. amusement park with the elements roller coaster, snack stand, mascots, photo box, etc.). 3. List as many elements as possible that belong to your challenge category (i.e. hospital with

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During this ideation, team members should defer criticism and then build on each other’s ideas to arrive at new and unexpected outcomes. In our example, the team might come up with solutions like: + Customers order via the homepage of the subsidiary which is integrated into the development of ordering algorithms. + There will be training so that the staff of the subsidiaries can write the ordering algorithms. + Introducing a competition between subsidiaries about who produces the least waste. + The engineers of the algorithms will offer a monthly office hour to receive feedback from the employees working in the subsidiaries. Deliver (convergent thinking) In this phase, the design thinking team has to select and focus on ideas it considers promising

to proceed. During this step, the ‘wow’ ideas are rendered into ideas that work and could be integrated into organizational structures. A key aspect of design thinking lies in prototyping in order to quickly generate solutions. The goal of prototyping is to collect feedback from users if the solution path goes towards a desired direction (see figure 2). Prototypes not only test an idea but also generate more knowledge about the needs of the employees. This agile approach takes into account that we cannot predict the effect of measures in complex systems but only understand them retrospectively. For the challenge of combining algorithms for the bakeries ordering processes and the expertise of local employees, the team might decide to progress the idea of “providing training so that the ordering algorithms can be written by the staff of the subsidiaries”. While prototyping the agendas for such types of training, the team might receive feedback that the content of the training would be too detailed and complicated. Instead, the employees might wish to gather a basic understanding of the working modes of algorithms. So the IT department could produce a short video that explains the algorithm modes.

1 Discard

Develop

3 2 Test Source: Endrejat P. C.

Learn

Figure 2 The basic steps during prototyping

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the elements waiting room, chief rounds, hospital beds). 4. Combine the elements by mixing the two columns to create new products, services, and ideas. For instance, a hospital might now offer a VR experience in its waiting room or the healing process is documented as a photo story.

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Furthermore, the design thinking team might learn that this rendered idea fits well with the office hour idea (employees provide feedback to the algorithm engineers) but it would suffice that these take place quarterly. Recommendations for the implementation of design thinking After elaborating on the design thinking mindset and process, I want to conclude with three general recommendations that should be considered while integrating design thinking into organizational routines. These are (1) start with a team of motivated members with interdisciplinary backgrounds (2) have a bias towards actions, and (3) apply design thinking for challenges that offer leeway to generate solutions.

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Start with a team of motivated members It is usually not sufficient for a transfer of the design thinking mindset into an organizational culture if the management decides top-down that employees must now work more ‘innovatively’, for example, by hiring a chief designer and declaring design thinking to be a top priority. Training only leaders within a company in design thinking might even have a negative effect on the teams’ operational capabilities (Kurtmollaiev et al., 2018). Thus, I suggest that the management creates the conditions for designated design thinking teams to change the organization from within. Although managers can participate in the team, design thinking coaches should ensure that hierarchical differences do not hinder the creative work process, e.g., through unevenly distributed speech shares. To avoid potentially obstructive group constellations, I suggest that the management acts as the client that offers feedback for an internal design thinking team, but is not part of the team itself. Attention should be paid so that the design thinking team has an interdisciplinary composition to account for the complexity of organizational challenges. Interdisciplinary teams consisting of members with different professional backgrounds, representatives of different departments, and demographic heterogeneity enable

different perspectives on a task. These open up a new scope for action so that the possibilities for problem solutions become more diverse. Next to an interdisciplinary background, I recommend that challenges are tackled by employees that volunteered – or even applied – to become part of the design thinking team. Design thinking is a new way of solving challenges that requires a lot of tolerance for ambiguity: In the beginning, we do not know what the solution will look like. This way of working might not suit every employee. Further, work-to-rule performance will not suffice, as motivation and self-determination are necessary throughout a project. Hence, it is necessary to select team members carefully to ensure the success of the first design thinking projects. Otherwise, design thinking might become another fad that fails to promote change. According to Rogers’ (2003) curve of diffusion, innovations follow the pattern that the vast majority must first see that something new works. It is a common pattern that new things are first picked up by small groups (early adopters) before the majority follows and ‘skeptical’ people are motivated to try out and implement something new. Working with designated design thinking teams does not mean that the rest of the organization is not involved in the process. On the contrary, the entire design thinking approach relies on change recipients’ information and feedback when defining needs, and verifying that the solution design is moving in a promising direction. To turn all affected employees into co-researchers, the design thinking team can generate different prototypes and let the other employees vote for the best way to change. Digitalization can further promote participation. For instance, digital platforms can be the medium to display storyboards, ideation outcomes, and prototyping results. These outcomes can be shared easily with colleagues to integrate them into the design thinking process. A bias towards action The term design thinking might suggest that there is a focus on ‘thinking’ rather than ‘doing’. However, the opposite is true: like its older

sibling action research (Lewin, 1947), design thinking brings ‘cognition’ (research/thinking) and ‘behavior’ (action/design) together rather than separating them. As mentioned above, prototyping (the iterative process of production, testing, and learning) is an existential component in design thinking. Rather than spending a lot of time designing an idea, prototypes are used to illustrate an idea’s core. These first drafts are presented to stakeholders so that they can give feedback on whether the solution path points in a direction worth striving for. Therefore, design thinking tends to enable ‘doing’ instead of spending a lot of resources developing the perfect plan.

if the final product or process is already clear in the beginning, it will generally not be an innovative solution. A design thinking team must therefore not fear serious consequences if a project does not succeed at the first attempt. To this end, top management has to create a climate in which employees feel psychologically safe and that also allows for mistakes. Organizations are usually rational and numbers-driven. Thus, it makes sense to select change projects for the introduction of the design thinking approach that have not yet been worked on in order to avoid the fear of comparison references, limiting the creativity of the team.

Successful companies combine the ability to create new products and services (exploration) with an administrative counterpole that focuses on revenue (exploitation; March, 1991). Unfortunately, most organizations pay more attention to exploitation than exploration (Endrejat & Kauffeld, 2016). The bias towards action inherent in design thinking shifts this focus towards exploration. Rather than planning and deriving hypotheses, design thinking sets out to test assumptions. Complex challenges cannot be solved like mathematical problems but we can waste much time overanalyzing the pros and cons of every solution, without asking our users. Thus, like a shark that has to keep moving to breathe, a design thinking team has to keep momentum and operate in the field to flourish.

Furthermore, the leeway to generate solutions is necessary to take employees’ concerns and fears into account. Automation can make processes more efficient, especially by taking over repetitive work. Alternatively, automation can lead to a loss of jobs. For instance, ATMs made it redundant for bank employees to count and hand out money. However, the needs of bank customers have become more complex, such as getting financial advice. Hence, automation reduced the repetitive work and enabled bankers to engage in more fulfilling tasks. Finding such new and interesting work areas for bakery employees whose jobs became redundant due to automation is in line with the design thinking mindset.

Apply design thinking for challenges that offer leeway to generate solutions Design thinking can only work if there is some leeway in what the final solution might look like. For instance, if the management already has in mind which software they want to apply, inquiring employees’ opinions without consequences might even have diametrical effects on their motivation and supportiveness (Endrejat & Meinecke, 2021). Design thinking is optimistic, constructive, and experimental. Thus, it is necessary that organizations allow experiments to fail as well. Organizations have to tolerate a certain degree of uncertainty (i.e. tolerance for ambiguity) because

Besides, if concerns by those affected are not taken into consideration, the team will not create a human-centered solution. A design thinking team that is confronted with organizational members who are reluctant to change their behaviors might consider them to be ‘resistant’. Responding to the reluctance with confrontational language to ‘sell’ an idea reduces the chances that a prototype will be accepted by the intended users. Thus, in some cases, ‘resistance’ is the product of communication between a design thinking team and employees rather than resistance to innovation itself (Endrejat et al., 2020). Instead, design thinking teams should use their leeway to work in a collaborative manner with the change recipients towards the desired solution for a given challenge.

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Conclusion Automation is an innovation that has the potential to disrupt the baking industry. To help bakeries cope with challenges proactively, this chapter described the design thinking approach. The design thinking process and mindset help to manage the transformation in an agile and humancentered manner. Furthermore, I offered three recommendations (ensure that (1) the team consists of motivated members, (2) has a bias towards action, and (3) has leeway to generate solutions) to increase the success of design thinking as a tool for organization development. If these recommendations are taken into account, design thinking contributes to an improved problem-solving capability of organizations (Carlgren et al., 2016). Besides ‘superficial’ gains of the design thinking approach (e.g. better process design), the integration of design thinking into organizational structures also leads to employees developing a holistic understanding of the organization, and creates a climate in which different opinions and arguments can be discussed constructively. Not only does that bring a competitive advantage, but it also helps to make employees' work environments more meaningful (Buchanan, 2015). Nevertheless, such an approach also requires a change in employees' thinking: Instead of managers generating solutions for employees, those affected themselves develop concepts on how their needs can be taken into account in the future. In this way, design thinking helps employees become ‘authors’ of their organization as opposed to being simply ‘readers’. +++

Author Dr. Paul C. Endrejat is co-founder of The Why Guys GmbH (www.thewhyguys.de). The Why Guys help organizations adapt to external challenges and create an environment in which employees can grow and work in a self-determined manner. Paul’s research foci

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include innovation processes in teams, eliciting change readiness, and the enhancement of energy conservation behaviors. Contact: paul@thewhyguys.de

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Systems with Applications, 40(17), 6837–6847. https://doi. org/10.1016/j.eswa.2013.06.038 Kurtmollaiev, S., Pedersen, P. e., Fjuk, A., & Kvale, K. (2018).

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Elsbach, K. D., & Stigliani, I. (2018). Design thinking and organizational culture: A review and framework for future research. Journal of Management, 44(6), 2274–2306. https:// doi.org/10.1177/0149206317744252 Endrejat, P. C., & Kauffeld, S. (2016). Über innovationsverhindernde und innovationsfördernde Denkweisen [About innovation impeding and innovation facilitating mindsets]. Gruppe. Interaktion. Organisation, 47(3), 275–282. https:// doi.org/10.1007/s11612-016-0337-3 Endrejat, P. C., & Kauffeld, S. (2017). Der Design Thinking Ansatz als Instrument zur Gestaltung von Veränderungsprozessen. [The design thinking approach as a tool to engineer change processes]. Gruppe. Interaktion. Organisation., 48(2), 143–154. https://doi.org/10.1007/s11612-017-0361-y Endrejat, P. C., Klonek, F. E., Müller-Frommeyer, L. C., & Kauffeld, S. (2020). Turning change resistance into readiness: How change agents’ communication shapes recipient

training. Academy of Management Learning & Education, 17(2), 184–202. https://doi.org/10.5465/amle.2016.0187 Lewin, K. (1947). Frontiers in group dynamics II: Channels of group life; Social planning and action research. Human Relations, 1(2), 143–153. https://doi. org/10.1177/001872674700100201 March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https:// doi.org/10.1287/orsc.2.1.71 Norman, D. A. (2013). The design of everyday things. Basic Books. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. Storey, D. A., & Farris, P. L. (1964). Market performance in the baking industry. Journal of Marketing, 28(1), 19–25. https://doi.org/10.2307/1249221

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References

RESEARCH 96 IMAGE PROCESSING

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Image processing applications for baking process monitoring Computer vision and image processing offer the development and employment of non-invasive, contactless, non-destructive, and fast measurement methods with a wide range of applications within the baking industry. These include both the visualization (qualitative measurement) and the exploration of shape and

As a subcategory of signal processing, image processing is used in satellite imaging, medical imaging, and industrial imaging for process and even product assessment. Image processing uses camera technologies to capture two- or three-dimensional data, followed by data transfer to a processing unit to extract product or process information. The usual captured light range is the visible (VIS) light range (400–800nm) because it is captured by both CMOS (complementary metal-oxidesemiconductor) and CCD (charge-coupled device) cameras, which can also include the initial nearinfrared (NIR) range (800–900nm). Silicon sensors can also detect the extended VIS/NIR range. The NIR range up to 1700nm is captured using InGaAs sensors, e.g., with shortwave infrared (SWIR) cameras. For the mid-infrared range (MIR, 2.5–25.0μm), used to capture thermograms, microbolometers are utilized. Microbolometers can also be used to capture ultraviolet and Xradiation. Other camera types for specific parts of the electromagnetic spectrum are available, and these are constantly being developed and adapted to match changing requirements. Image data capture occurs mainly in line scan mode, in which a dynamic scene is captured line

by line, or in area scan mode, where the whole scene is captured in one shot. For image processing, these single recorded lines are sequentially stitched together for image reconstruction. For example, with conveyer belts, typically line scan cameras are used to capture continuous webs of raw material or products. The obtained image data are available as twodimensional matrices with intensity values. For monochrome cameras, the form is n×m×1, and for RGB cameras, the form is n×m×3. The light or color intensity forms the third dimension for each pixel inside the layers in the n×m matrix. InGaAs-cameras and microbolometers capture n×m×1 monochrome data. By using filters for the separation into individual wavelengths, so-called spectral observations can be carried out. In the resulting spectral cube, the captured data is in the form n×m× ‘number of observed wavelengths/ regions’. The number of captured image data (sampling frequency) is specified by sampling criteria and indicated by frames per second (fps). Image data processing can be applied at different computational levels. First is the description of the image using statistical tools, e.g., the mean, minimum and maximum value of the color, contrast, or histogram analysis. The image data

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surface properties (quantitative measurement).

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Figure 1: Overview of typical image processing applications

can be processed point-wise or in groups, and operators such as thresholding for object isolation can be applied. More sophisticated operations are template matching methods, e.g., the ViolaJones algorithm or neural networks, which can be trained to detect particular objects in scenes. The advantage of these higher-level methods is the robust and real-time object detection. However, the training of the methods in all variations of the object, especially in food manufacturing, where not all objects have the same shape, could be extensive. After the object is detected or recognized, feature extraction can be performed.

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Figure 1 gives an overview of the mentioned image processing methods and typical applications. The VIS image processing can measure properties such as object size, color or simply be used as a presence sensor. When applied over

Figure 2: Camera system for proofing monitoring and captured images of a dough piece from 0° - 90°

time, the evolution of size or discoloration can be measured and kinetics can be established. A spectral imaging system captures three-dimensional spectral data cubes, where the first two dimensions are the spatial information and the third dimension is the wavelength-discretized signal intensity. Chemical information from the region of interest or segmented objects can be measured this way. Dynamic laser speckle imaging can be used for laser speckle contrast or rheology analysis. Current developments in image processing Digital image processing in the VIS range is applied in the baking industry for raw material analysis by scanning single kernels, analyzing the color, and evaluating, e.g., in fungus contaminations, glossy or non-glossy, whole and broken kernels. With high-speed camera technology,

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L, a, and b are the mean color values of the Lab color space of an object and t is the process time.

Figure 3: Segmented dough pieces inside the proofing chamber

the raw materials can be analyzed inline and in real-time. Digital image processing is also applied for product or process assessment. In the project AiF 18123 N (German Federation of Industrial Research Associations) [1], a camera system was applied to capture dough pieces during the proofing process. Figure 2 shows the set-up used to measure the dough pieces over time during the proofing. The camera rotated from 0° to 90° around the dough pieces and captured the front and side view to calculate the three dimensions of the pieces: width, height, and depth. By using segmentation algorithms, single dough pieces were detected automatically. Figure 3 shows the 0° view of single-segmented dough pieces with the measured width and depth (yellow boxes) during proofing. As data was captured over time, the growth kinetics were calculated and are available for process control and automation.

These object features were captured over time throughout the proofing and baking process. Figure 4 shows the results of one batch where the browning index, texture homogeneity, and dimensions (height and width) are calculated over the proofing and baking process. In Figure 4, the texture homogeneity and color changes during proofing and baking, especially the crust browning during the baking process, are shown. Furthermore, the growth due to the proofing and the oven rise during baking is apparent. Image processing in the mid-infrared spectral range was also applied to observe the baking process. One such implementation was in the project AiF 17735 N [4]. By using MIR imaging, the process and products were evaluated during

Figure 4: Mean browning index (BI), homogeneity, and dimensions (height and width) of the captured dough pieces during proofing and baking

Furthermore, object capturing was applied for a consecutive proofing and baking process. Two cameras, one with top view and one with side view, captured the dough pieces. After applied segmentation, the width and depth, browning index (BI) [2] (see Eq. 1), and texture homogeneity [3] (see Eq. 2) of single pieces were analyzed.

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Where i indicates the row and j the column of the gray-level co-occurrence matrix (GLCM), a homogeneity of 1 specifies homogenous, a value of 0 an inhomogeneous texture.

100

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Figure 5: MIR range image capture of bread, in a) pseudocolor representation of the bread inside the oven deck and in b) the segmented bread with indicated height and width

the baking process [5]. Figure 5 a) shows a captured thermogram of bread in a pseudocolor representation during the baking process. Figure 5 b) shows the segmented object and object borders used to determine the object’s width and height. As a reference, the samples were measured with a volumeter laser scanning device. The deviation between the volumeter laser scanning device and MIR-based measurements was 1.01 mm in width and 1.49 mm in height. Furthermore, the growth kinetics, especially the oven rise, can also be calculated based on MIR image capture over the baking process.

Figure 6: a) Schematic representation of a laser-optical system. The laser beam is expanded and directed towards the sample. A camera records the laser speckle images and transfers them to the computer. Depending on the application, the laser speckle images can be used to perform b) Laser speckle contrast analysis or c) Laser speckle rheology.

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The development over recent years of thermal camera techniques and thermal housings has enabled more reliable measurements and more applications to the baking process; also, it has enabled measurement of other parameters such as crust cracks, heat losses, or even thermal distributions within baking plants. Another method for visualizing the surface properties of dough and baked goods is laser speckle imaging – in which coherent laser light illuminates the optically rough surface of a sample and the light is scattered from the surface and within the layers beneath the surface. Interference effects of randomly phased partial waves originating from different scattering sites create a pattern of brighter and darker spots. These randomly distributed grains of light forming high contrast patterns are called laser speckles (see Figure 6) [6]. The fluctuation of the laser speckle intensity is time-dependent and called dynamic speckle. The movement of the speckle pattern provides information about the movement of the scattering centers. A technique called laser speckle contrast

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LASCA is used to monitor the surface properties of baked goods in the context of AiF project 20495 N [8]. Two expanded laser beams, which are at an angle of 90° to each other, are directed onto a dough piece within an oven. Two cameras acquire the speckle pattern, as well as the shape and size of the dough piece. The technology can be used to visualize surface structures, which are invisible to the human eye. It allows the visualization of the gas distribution beneath the surface during proofing and the gas permeability through the crust as well as the crust formation during baking (see Figure 6). These spatial and timeresolved insights contribute to the optimization and intelligent inline control of the proofing and baking process. Alongside mapping of the flow, the thermal motion of endogenous scattering particles can be observed. The fluctuations of the temporal speckle intensity are correlated to the mean square displacement (MSD) of light scattering particles undergoing Brownian motion and thus also to the viscoelastic properties of the surrounding medium. Based on this approach, laser speckle rheology (LSR) was developed to measure the mechanical properties of biological tissue [9, 10]. Compared to LASCA, where contrast reduction is used to distinguish areas within a region of interest in terms of motion, samples in LSR must be homogeneous and uniform. Through careful selection of light source, camera, and image

acquisition specifications, an optimum speckle pattern as needed to accomplish LSR measurements can be achieved. Furthermore, LSR was used to develop an innovative, non-invasive and comparatively inexpensive inline method to detect surface and rheological properties of dough during the kneading process [11]. The state of the gluten network shows a characteristic correlation with the rheological key figures from which the kneading optimum can be derived by analyzing the light scattering at the dough surface. This measurement method reduces both the time of analysis and the sample preparation requirements compared to the reference method. Outlook: automatization based on image processing All the approaches or methods described above are characterized by their process capability. Measuring the product or process parameters online in a non-destructive, non-invasive manner and in real-time enables a process control ability and opens up further application areas. Image processing has been applied in various projects, e.g., one of the last projects concerned the imagebased measurement of product width and depth in a proofing chamber and corrected possible process faults. In detail, a control strategy was developed for the fermentation of dough pieces, which could counteract a lower yeast quantity. A fuzzy strategy was conceived to overcome this challenge based on control of temperature, humidity, and growth rate determined by timedependent measurements of dough depth and width, and in the first step, the process corridor

Figure 7: Fuzzy-control approach from input, fuzzification by fuzzy-sets, fuzzy rule base, defuzzification, and output of the control variables

1. If (BI is normal) and (Temp is low) then (Temp is veryHigh) (1) 2. If (BI is normal) and (Temp is normal) then (Temp is normal) (1) 3. If (BI is high) and (Temp is hight) then (Temp is lowl) (1) 4. If (BI is high) and (Temp is normal) then (Temp is lowl) (1) 5. If (Temp is low) and (Growth is low) then (Temp is veryHigh) (1) 6. If (Temp is normal) and (Growth is low) then (Temp is veryHigh) (1) 7. If (Temp is high) and (Growth is low) then (Temp is high) (1) 8. If (Temp is low) and (Growth is normal) then (Temp is normal) (1) 9. If (Temp is normal) and (Growth is normal) then (Temp is normal) (1) 10. If (Temp is normal) and (Growth is high) then (Temp is normal) (1) 11. If (Temp is hight) and (Growth is high) then (Temp is normal) (1) 12. If (Temp is hight) and (Growth is high) then (Temp is low) (1)

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analysis (LASCA) utilizes this relationship to map flows and regions with different motion patterns and visualizes them directly on the depicted object [7].

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of the ‘normal’ process was captured and designed. Subsequently, based on this data and expert knowledge, a fuzzy rule base was designed to reflect the expert knowledge. Figure 7 shows a more detailed pathway of the fuzzy control system. First, the temperature, humidity, and object growth rate are fuzzified and taken into the fuzzy control. In this step, the input parameters are transformed to fuzzy values by membership functions. Subsequently, the fuzzy values are processed and, if possible, combined by the fuzzy rule base to determine the fuzzy-ciphered

values. In the final step, the concrete output parameters are defuzzified output rules. The investigations were performed with dough pieces for bread rolls with half of the ‘normal’ yeast content. Two scenarios were examined, the first without and the second with the fuzzy system to control the proofing chamber climate. The recorded relative size of the dough pieces shows the fuzzy control was able to move the process slightly towards the optimal growth of the dough pieces.

Authors Ronny Takacs, Stefan Steinhauser, Dominik

Munich, where he graduated in engineering

Geier, Prof. Dr. Thomas Becker: Chair of Brew-

(Dipl.-Ing.) in 2011. He has worked as a member

ing and Beverage Technology, Technical Uni-

of the scientific staff of the Chair of Brewing

versity of Munich, Freising

and Beverage Technology of the Technical University of Munich since 2011. His research

Ronny Takacs has studied brewing and trained

centers on process monitoring and sensor

as a brewer. At the Technical University of

development, especially ultrasonic measuring

Munich, he graduated with a MSc in Brewing

systems and imaging methods. He has also

and Beverage Technology in 2014. He has

headed the BioPAT and Digitization workgroup

worked as a member of the scientific staff in

since 2016. E-mail: dominik.geier@tum.de

the BioPAT and Digitization workgroup of the Chair of Brewing and Beverage Technology of

Thomas Becker studied at the Technical Uni-

the Technical University of Munich since 2014.

versity of Munich, where he obtained his

His research focuses on process monitoring,

doctorate in 1995 with his doctoral thesis

automatization, and sensor development, in

entitled “Development of a computer-assist-

particular image processing and spectral im-

ed enzyme-integrated flow injection system

age processing for the brewing, baking, and

and its use in biotechnology process control

biotechnological industries. E-mail: ronny.

engineering and quality monitoring”. In 2002,

takacs@tum.de

he concluded his habilitation on the topic

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“Management of bioprocesses through modStefan Steinhauser studied Food Technology

eling and cognitive tools” at the Chair of Fluid

and Biotechnology at the Technical University

Mechanics and Process Automation of the

of Munich, where he graduated with a MSc in

Technical University of Munich. From 2004

2016. He has worked as a member of the scien-

to 2009, he was a professor at the Chair of

tific staff in the BioPAT and Digitalization work-

Process Analysis and Cereal Technology at

group of the Chair of Brewing and Beverage

the University of Hohenheim. He became pro-

Technology since 2016. His research focuses on

fessor of the Chair of Brewing and Beverage

the development and application of laser-optical

Technology of the Technical University of

measurement systems for the food industry.

Munich in 2009. He was also Dean of the TUM School of Life Sciences of the Technical

Dominik Geier studied Brewing and Beverage Technology at the Technical University of

University of Munich, from 2016 to 2021.

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103

Acknowledgments The IGF Projects AiF 18123 N, AiF 17735 N, AiF 18906 N, and AiF 20495 N of the FEI were supported via AiF within the program for promoting the Industrial Collective Research (IGF) of the German Ministry of Economic Affairs and Energy (BMWi), based on a resolution of the German Parliament.

References

means of mid-infrared imaging,” Innovative Food Science

[1] AiF 18123 N, Intelligente Gärsteuerung: Entwicklung einer

& Emerging Technologies, vol. 61, p. 102327, 2020, doi:

intelligenten Gärsteuerung zur optimierten Herstellung von Teiglingen mittels digitaler Bildauswertung und erfahrungsbasierter Fuzzyregelung. Bonn: Forschungskreis der Ernährungsindustrie e.V. (FEI), 2014-2016. [2] G. Dadalı, D. Kılıç Apar, and B. Özbek, “Color Change Kinetics of Okra Undergoing Microwave Drying,” Drying Technology, vol. 25, no. 5, pp. 925–936, 2007, doi: 10.1080/07373930701372296. [3] R. M. Haralick, K. Shanmugam, and I.'H. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Syst., Man, Cybern., SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/ TSMC.1973.4309314. [4] AiF 17735 N, Volumetrischer keramischer Brenner: Systematische Untersuchungen zur Einsatzqualifizierung einer innovativen Backofentechnik mit volumetrischem

10.1016/j.ifset.2020.102327. [6] J. W. Goodman, Speckle phenomena in optics: Theory and applications. Englewood, Colo.: Roberts, 2007. [7] D. Briers et al., “Laser speckle contrast imaging: theoretical and practical limitations,” Journal of biomedical optics, vol. 18, no. 6, p. 66018, 2013, doi: 10.1117/1. JBO.18.6.066018. [8] AiF 20495 N, Analyse des Gär- und Backprozesses durch Dynamic Laser Speckle Imaging. Bonn: Forschungskreis der Ernährungsindustrie e.V. (FEI), 2019-2021. [9] T. G. Mason, “Estimating the viscoelastic moduli of complex fluids using the generalized Stokes-Einstein equation,” Rheologica Acta, vol. 39, no. 4, pp. 371–378, 2000, doi: 10.1007/s003970000094. [10] Z. Hajjarian and S. K. Nadkarni, “Evaluation and correction

keramischem Brenner (VKB) einstellbaren Wellenlängens-

for optical scattering variations in laser speckle rheology

pektrums sowie hoher Regeldynamik und Energieeffizienz.

of biological fluids,” PloS one, vol. 8, no. 5, e65014, 2013,

Bonn: Forschungskreis der Ernährungsindustrie e.V. (FEI), 2014-2017. [5] R. Takacs, V. Jovicic, A. Zbogar-Rasic, D. Geier, A. Delgado, and T. Becker, “Evaluation of baking performance by

doi: 10.1371/journal.pone.0065014. [11] AiF 18906 N, Zusammenhang zwischen Teigrheologie und Oberflächeneigenschaften. Bonn: Forschungskreis der Ernährungsindustrie e.V. (FEI), 2016-2018.

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Further developments will address the baking process to monitor more properties such as crust formation, gas permeability, or possible product failures such as crust cracks that are to be detected during the process. In this way, further process control strategies will be developed to continually increase product quality and prevent faulty production. +++

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The role of artificial intelligence in designing baking ovens Investigation on the baking ovens performance are conducted typically either through expensive experiments or by means of numerical methods. taken advantage of machine learning techniques to discover new aspects of science and develop methods that outperform conventional ones.

Specifically, when it comes to modeling of fluid dynamics problems, due to the inherent complexity of fluids dynamics, neural networks and deep learning have found plenty of applications to facilitate the fast and accurate estimations. The novel techniques of the artificial neural network can be employed to modelling and optimization of different parts of the baking oven with lower efforts which was not possible previousely. Fan is one of the critical components of convection ovens that influences the quality of baked products and baking time by controlling the air flow and temperature distributionin in the oven. This chapter reviews the different steps of simulation of fan flow and the use of deep learning in modelling the fan flow from data of simulation. Introduction Optimization of the baking process has been a topic of interest for decades because of its associations with people’s daily life and energy use. This has given rise to a huge demand for baking units with higher performance, which deliver better baking quality while respecting lower energy consumption. Baking is a complex process

involving a set of physicochemical and biological phenomena, such as heat and mass transfer, chemical reactions, and volume expansion. These transformations contribute to important quality features such as color, texture, crumb, and size. Therefore, understanding all aforementioned phenomena and effective terms will provide us with enriched background towards designing efficient baking systems. Computational methods are proposed as an alternative to costly experimental approaches, in which the mathematical approaches are applied to solve the governing equations of the problem. Numerical methods like finite element method(FEM) and finite volume method (FVM), have been widely used by literature due to their strength in facilitating the analysis of the baking process Simulation of foaming in proofing step is one of the application of numerical methods that visualizes the effect of chemical reactions in microscale. Furthermore, it was shown that this methodology can be applied for modelling the two phase phenomenon such as evaporation and moisture transportation that take place during the baking of the bread. However, developing a model that represents the entire process thoroughly demands high computational

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With advancements in data science, many engineering disciplines have

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resources or even might be impossible and that is the reason why researchers usually focus on specific phenomenon at certain scale. More details regarding the estimation of temperature and Brownian index in baking process are found [1], [2] and [3]. As the temperature and moisture are the driving forces in all transformations happening in the baking, the temperature distribution inside ovens plays an important role in the baking process, and the best baking quality is subject to uniform temperature distribution over the surface of baking objects. Investigating the temperature distribution and improving the temperature uniformity appeared as another topic of interest for scientists to push efforts towards novel and optimum oven designs both in domestic and industrial scales. There are some studies that performed a simulation of oven geometry to verify the effect of geometrical characteristics on pre-heating time [4] and temperature uniformity of the drying ovens [5] [6]. Simulation of the baking oven on an industrial scale is another interesting topic in which the U-movement of dough is considered into calculations [7] [8]. It is obvious that excellent performance of simulations task is subject to faithful representation of the system physic, scale and properties. It becomes more challenging when the systems is experiencing a multiscale and multiphysics phenomena, where the first-principle based representations in numerical simulations fail to fulfill all expectation. Fortunately the availability of observation data and recent development of artificial intelligence techniques has prepared a platform to address the issues of traditional computational methods. Artificial Intelligence Artificial intelligence, since its advent, has increasingly drawn scientist attentions from all branches. Especially with the advancement of machine learning and, most prominently, deep learning algorithms, the data-driven approach of computational mechanics became an active area of study. This method soon was employed in many tasks like, solving the partial differential equations and optimization problems. The core of the deep learning algorithm is a neural network

with multiple layers that aim to model a nonlinear relationship between the inputs and outputs through an iterative process, in which the parameters of the network are adapted to minimize the cost function. Since deep learning methods deal with huge amounts of data to extract meaningful pattern, establishing a robust and reliable method has always been challenging. To tackle the problem of handling massive datasets, Physics Informed Neural Network (PINN), firstly proposed by Raissi et al. [9] appeared as a robust framework for solving computational mechanics problems while demanding for a lower amount of data. Afterward, other method has been proposed, which need only limited information of boundary condition and initial conditions [10]. Except some rare works concerning the application of artificial intelligence in baking [11] the literature in this field is insufficient. Considering the current capability of AI in modeling fluid dynamics problems, its applications can be further extended into the food industry, including the baking process and ovens design. Ovens typically consist of certain elements whose functions influence the air circulation accordingly the temperature distribution of the oven. The advantage of using neural network models compared to conventional methods is their ability to accelerate the optimization process of oven design for a set of given parameters. Training a Neural Network and results Training a NN model needs data that can be either experimental measurements or the result of computational methods. CFD study provides a rich resource of information for different problems, which deals with fluids. Performing a CFD study consists of several steps; as a first step, the geometry and domain of the solution should be created. A typical three-dimensional convection oven model is shown in Figure 1, including a fan for circulating the air, a heater that supplies hot air, and a baffle with circular holes. Afterwards, the continuous geometric domain needs to turn into discrete geometric cells for numerical calculations in the next step. Mesh

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relatively straightforward method compared to those abovementioned methods, in which only the angular velocity of the domain is taken into account. The results of simulations of temperature and velocity distribution on a cross-section across the oven without baffle are presented in Figure 3 and Figure 4. As shown, the airflow induced by the fan is pushed through the channel leading to the baking room. Meanwhile, due to exposure to the heater located near the fan, the airflow warms up repeatedly. Once the simulation is done, the results can be fed to the PINN to train a desirable model. Simulation of fan flow using MRF or Mesh motion is more accurate and computationally expensive as well. Figure 1: A representation of a convection oven Figure 2: A cross-section view of generated grids for computational domain

2 © TUM

Figure 3: Temperature distribution in the oven chamber Figure 4: Velocity distribution in the oven chamber

4 © TUM

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generation was done by Ansys Meshing with tetrahedral mesh for the whole domain, as shown in Figure 2. The finite volume method implemented in Ansys Fluent is used to solve conservation equations of continuity, momentum and energy to simulate the fluid dynamics in the geometry of the oven. In this study, the working fluid is assumed to be an incompressible 3D unsteady state fluid flow, and K-ω SST model is applied for modeling the turbulent flow. For simulation of fan flow, typically, there are three ways established in Ansys Fluent. When the airflow characteristics around the blades matter, moving reference frames and sliding mesh methods are suggested, bringing about more computational costs. 3D Fan zone model is a

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It becomes more challenging when the simulation process needs to be repeated several times during the optimization process. The beneficial combination of artificial intelligence, especially PINN with CFD results, makes it possible to avoid high-cost computational methods by substituting the costly methods with a predictive neural network model that can make estimations with an acceptable level of accuracy. Developing a surrogate model for fan flow that provides predictions for the whole baking chamber is the topic of our ongoing research. Here the concentration will be on modeling fan flow itself. For this purpose, the CFD results of fan flow using

the MRF method (shown in Figure 5) are used as training data of PINN (Figure 6). The input of the model is the Spatio-temporal features of each node, and the outputs are velocity components and pressure. The number of hidden layers and neurons are determined in a trial and error way. In current work the neural network consists of 8 hidden layers and 20 neurons for each layer. Adam optimizer is used as an optimization algorithm in the backpropagation phase. Once the model has been trained, it can make predictions of fluid properties for the entire training domain at a different time step. Besides, PINN can predict

Figure 5: Velocity distribution around the fan blade Figure 6: Schematic representation of PINN model

6 © TUM

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Figure 7: Velocity profile prediction by PINN

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variables involved in governing equations, which have not been used during the training. Here we have trained the model using the sparse information of domain velocity, aiming to extract the information of entire domain. Figure 7 shows the result of velocity distribution of PINN model at an arbitrary length far from fan location. In addition, since this model has been integrated with Navier-Stokes equations other parameters like pressure has been obtained, while the information of pressure was not used in training the model. The application of PINN can be further exploited by applying additional parameters, like energy equations for estimation of the entire domain temperature. This helps designers speed

up the process of design optimization by examining different working conditions with no extra computational costs. Conclusion and outlook Applying artificial intelligence methods in CFD problems is a promising framework for monitoring, optimization, and dimensionality reduction of complexity. This article aimed mainly at reviewing possible applications in the baking industry and oven design. Further investigation in this field can be oriented more on the estimation of fluid flow and heat transfer inside the baking room, modeling moisture diffusion and temperature evolution in baking products and geometrical optimization for oven interior design. +++

Literature [1] J. Bikard, T. Coupez, G. Della Valle und B. Vergnes, “Simulation of bread making process using a direct 3D numerical method at microscale: Analysis of foaming phase during proofing”, Journal of Food Engineering, Jg. 85, Nr. 2, S. 259–267, 2008, doi: 10.1016/j.jfoodeng.2007.07.027. [2] A. Mondal und A. K. Datta, “Two-dimensional CFD mod-

air circulation”, Applied Thermal Engineering, Jg. 54, Nr. 2, S. 387–398, 2013, doi: 10.1016/j.applthermaleng.2013.02.014. [7] S.-Y. Wong, W. Zhou und J. Hua, “CFD modeling of an industrial continuous bread-baking process involving U-movement”, Journal of Food Engineering, Jg. 78, Nr. 3, S.

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[8] N. Therdthai, W. Zhou und T. Adamczak, “Three-dimen-

doi: 10.1016/j.jfoodeng.2010.02.015. [3] N. Chhanwal, D. Indrani, K.S.M.S. Raghavarao und C. Anandharamakrishnan, “Computational fluid dynamics modeling of bread baking process”, Food Research International, Jg. 44, Nr. 4, S. 978–983, 2011, doi: 10.1016/j. foodres.2011.02.037. [4] C. O. Díaz-Ovalle, R. Martínez-Zamora, G. González-

sional CFD modelling and simulation of the temperature profiles and airflow patterns during a continuous industrial baking process”, Journal of Food Engineering, Jg. 65, Nr. 4, S. 599–608, 2004, doi: 10.1016/j.jfoodeng.2004.02.026. [9] M. Raissi, P. Perdikaris und G. E. Karniadakis, “Physicsinformed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear

Alatorre, L. Rosales-Marines und R. Lesso-Arroyo, “An

partial differential equations”, Journal of Computa-

approach to reduce the pre-heating time in a convec-

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tion oven via CFD simulation”, Food and Bioproducts Processing, Jg. 102, Suppl. 1, S. 98–106, 2017, doi: 10.1016/j. fbp.2016.12.009. [5] J. Smolka, A. J. Nowak und D. Rybarz, “Improved 3-D

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validation of a CFD model for a heating oven with natural

temperature uniformity in a laboratory drying oven based on experimentally validated CFD computations”, Journal

jcp.2018.10.045. [10] J. Berg und K. Nyström, “A unified deep artificial neural network approach to partial differential equations in complex geometries”, Neurocomputing, Jg. 317, Nr. 9, S. 28–41, 2018, doi: 10.1016/j.neucom.2018.06.056. [11] H. Isleroglu und S. Beyhan, “Prediction of baking quality

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[6] J. Smolka, Z. Bulinski und A. J. Nowak, “The experimental

s00231-020-02837-6.

Authors MSc, Seyedalborz Manavi, Dr. Ehsan Fattahi, Prof. Dr. Thomas Becker Chair of Brewing and Beverage Technology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany Seyedalborz Manavi, master graduated in the field of mechanical engineering at Babol University of Technology, Iran. He is a research assistant in the simulation/modeling unit at the chair of brewing and beverage technology of the Technical University of Munich since 2019. His research field is focused on the application of deep learning in fluid mechanics problems. Dr. Ehsan Fattahi Evati received his Ph.D. in numerical mathematics with a focus on highperformance simulation of porous media from the Technical University of Munich. Currently, he is pursuing his habilitation at the chair of brewing and beverage technology and, focusing on computational fluid dynamic and

artificial intelligence. From 2017 to present, he is the leader of simulation/modeling unit. Prof. Thomas Becker studied at the Technical University of Munich, where he obtained his doctorate in 1995 with his doctoral thesis entitled “Development of a computer-assisted enzyme-integrated flow injection system and its use in biotechnology process control engineering and quality monitoring”. In 2002, he concluded his habilitation on the topic “Management of bioprocesses through modeling and cognitive tools” at the Chair of Fluid Mechanics and Process Automation of the Technical University of Munich. From 2004 to 2009, he was a professor at the Chair of Process Analysis and Cereal Technology at the University of Hohenheim. He became professor of the Chair of Brewing and Beverage Technology of the Technical University of Munich in 2009. He was also Dean of the TUM School of Life Sciences of the Technical University of Munich, from 2016 to 2021.

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Will we 3D print the bread of the future? There are numerous challenges using a 3D printer to produce bread, from adapting the recipe to reproducing its texture and pore distribution. The aim is to have the resulting product taste and smell like bread. To achieve this, the 3D printing process offers the advantage of its ability to react flexibly to all requirements by individually adapting the different process steps. Here are

The manufacturing process of 3D printing has already established itself in various industries over the last few years. In the food sector, however, the application is still in Research and Development. The innovation potential in the food industry is considered to be high and the number of scientific publications has increased disproportionately over the last few years, from 19 in 2010 to 129 in 2015 and 821 in 2020 (sciencedirekt.com) (R. Derossi et al., 2021). In this context, 3D printing technology combines mechanical manufacturing with digital processing, which enables the rapid and conflict-free customization of products. The created objects are transmitted to the 3D printer using appropriate software, which then builds them layer by layer. This allows both the shape and the interior of the printed object to be customized. For example, new geometries, unusual textures and customized nutrient contents can be made possible, thus also reducing food waste (Godoi et al., 2016). The challenges of producing bread with a 3D printer are quite different. First, the recipe of the dough must be adapted so that it can be printed. The viscoelastic properties of the dough play a decisive role here. Then the different textures of the bread (crumb and crust) must be reproduced

in a product-typical manner by means of suitable product design and post-processing. Attention must be paid to the different pore distributions as well as to the different texture impressions between elastic (especially in the crumb) and firm (especially in the crust). Crucial for this is above all a heating step that can be integrated into the 3D printing process in order to stabilize the printing mass after printing and also to improve its digestibility. Finally, it is of course also important that the bread from the 3D printer tastes and smells like real bread. In conventional bread, the aroma is primarily formed by the fermentation of yeast and/or sourdough as well as the formation of roast aromas during baking (Pico et al., 2015). As the use of yeast and other leavening agents in a pressurized process is currently not possible, as this would change the properties of the pressurized mass over time, the aroma must be added elsewhere or created through post-processing. A decisive advantage of the 3D printing process is, as already mentioned above, the ability to react flexibly to all requirements by individually adapting the different process steps. It is important to understand the individual process steps of the 3D printer in order to be able to make the desired settings. If each distinct area of food 3D

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the technical requirements and the current state of research in the field.

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printing is mastered, new and innovative products can be created that revolutionize the food industry. Printing process Among the various printing processes used in additive manufacturing, Fused Deposition Modeling (FDM) has so far been best established in the food sector. In this process, a printing compound is extruded through a nozzle and deposited onto the previous layer by the movement of the print head (Crump, 1992). In this process, the printing material is stored in a reservoir and conveyed through the nozzle by a feed mechanism. The process, originally developed for plastics, metals and ceramics, is ideally suited for printing foodstuffs. The most important prerequisite for the printing material is that it can be extruded. The 3D printing process can also be divided into five steps (see Figure 1), all of which must be controlled and managed to achieve the desired end product. It should be noted that each of these steps is influenced by the others and also influences them itself. A change at the beginning results in a correction in all other steps. The 3D printing process begins with the production of the printing materials, the pre-processing. The individual recipe components are mixed with Figure 1: Schematic representation of the process steps in 3D printing

an agitator or kneader and placed in a storage container, usually a cartridge or syringe, which is then connected to the 3D printer. In this process step, it is important that the rheological properties of the printing materials are adjusted. When selecting the food ingredients and also, above all, when dosing the liquid, care must be taken to ensure that they meet the rheological requirements of the printing process. Once the printing compound has been prepared and the cartridge connected to the 3D printer, the actual printing process can begin. This section is divided into the extrusion and the deposition of the printing material. Extrusion here simply refers to the transport of the printing material through the nozzle opening. In the extruder of the 3D printer, the printing material is neither mixed nor textured yet. In the extrusion process, it is crucial which forces act and thus the flow properties of the printing materials are changed. The volume flow rate as well as the nozzle diameters are the most important factors (Fanli Yang et al., 2019). Once the printing material has passed the nozzle tip, it is deposited onto the printing plate or onto the already printed layers (deposition). During this process, the print head follows the paths previously created on the

Printing process

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Pre-processing

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The FDM printing process with its individual process steps is also suitable for the production of bread. The combination of extrusion and postprocessing can enable complex printed objects with unique textures. However, the scientific literature so far has mainly printed pastries such as biscuits and cookies (Kim et al., 2019; Fan Yang et al., 2018) or doughs with a high-fat content (A. Derossi, Caporizzi, Paolillo, et al., 2020). Thus, the challenge for future research is to develop bread dough suitable for the 3D printing process. Several prerequisites, which will be discussed in the next section, need to be considered.

Material conditions In order to be able to print foodstuffs, certain rheological requirements must be met. Firstly, they must be extrudable. This means that, on the one hand, the consistency of the printing compound must be soft enough to print through a nozzle. On the other hand, it must also be firm enough to retain its shape after printing to minimize deformation (Q. Liu et al., 2020). To meet these conflicting properties, the rheological behavior of the printing materials must be precisely adjusted to the printing process. The extrudability of the material enables the shear-thinning property of many food products. As described in the last section, the printing ink is pressed through the die during the printing process, which subjects it to strong forces (Fanli Yang et al., 2019). However, these forces cause the viscosity of the shear-thinning materials to decrease, making them printable. The reason for this is that the internal structure of the printing materials changes greatly and the individual particles or polymers can slide past each other more easily. This partial destruction is an advantage during extrusion, but weakens the stability of the printing material after it has been deposited on the printing platform or on the previous layer. The internal structure must therefore recover as quickly as possible after leaving the nozzle tip so that the viscosity increases again and provides the necessary stability. This rheological behavior is called thixotropy and describes the decrease in viscosity over time due to external influences and the return to the initial viscosity only after the stress has ended. In addition, a complete regeneration of the internal structure should take place if possible in order to enable the best possible stability of the printed objects. This can be controlled by the viscosity and the yield point of the printing materials. To prevent the print objects from spreading, these two parameters must be high enough (Paxton et al., 2017). In order to be able to print bread or bread-like products in the 3D printer, the bread dough must be simplified. Due to the various ingredients and processing steps during production, the

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computer that result in the desired object shape and infill structure. For an exact print image, it is important that the print material bonds with the previous layer. For a good bond, the properties of the printing materials are crucial (Fahmy et al., 2020a; Li et al., 2017). To increase the stability of the printed objects, another processing step can be added after printing. Post-processing can be used to stabilize during the printing process as well as to finalize the printed objects. It usually involves thermal post-processing such as freezing, baking or even freeze-drying (He et al., 2019). The arrows in Figure 1 indicate that stabilization can also occur during the 3D printing process, e.g., after each printed layer. This increases the possibility of influencing the texture of the printed object. At the end of the printing process, there is the finished printed object, which has all the digital properties, which were set at the beginning by means of digital models on the computer and suitable material selection. The deviations from these specifications determine the success of the food 3D printing. Apart from the individual process steps, there are further possibilities of influencing the printing process by changing the printer settings. Alternatively, the accuracy (resolution) of the print can be improved by selecting the nozzle diameter and the layer height. A reduction of both para-meters usually not only leads to an improvement of the printing accuracy, but also to an increase of the printing time. However, this can be counteracted by adjusting the printing speed. For a good printing image it is necessary to find the optimal settings.

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conventional dough is subject to change over time. The yeast in particular changes the dough properties enormously by expanding the gas pores during proofing (Alpers et al., 2021). These temporal changes are unsuitable for 3D printing, as the dough properties are specifically adjusted to the printing process at the beginning. Therefore, 3D printing requires the omission of ingredients such as yeast, which are essential for traditional bread production. The main components of bread dough are water and flour. By combining these two raw materials in the right proportions, a printable dough can already be produced (Fahmy et al., 2020a; Severini et al., 2016). The addition of water in those doughs made from wheat flour varies from 50g to 80g H2O/100g of flour. In this regard, Fahmy et al. demonstrated that different water contents of the doughs have a strong influence on the printing accuracy. The higher the water content, the more unstable the printed geometries. Furthermore, it was found that the properties of the gluten network strongly influence the 3D printing process. On the one hand, gluten serves as a stabilizing element due to its network formation, which provides an improved printed image especially at a high water content. On the other hand, the elastic behavior of the network causes the dough mass to be ‘pulled along’ by the movements of the print head, especially at a low water content, which decisively deteriorates the printing accuracy

(Fahmy et al., 2020a). In order to circumvent this complex relationship, it seems sensible to use gluten-free doughs for 3D printing. In order to maintain the dough-like properties and nutritional value of printing material, other proteins can be used to replace the gluten in wheat flour. In the study by Fahmy et al. mentioned earlier, egg white protein was used for this purpose. The results showed similar good stability of the printed objects compared to the standard wheat dough (Fahmy et al., 2020a). Another possible recipe for 3D printing is composed of wheat starch, soy protein isolate (SPI) and water. In this case, the ratio between starch and protein is kept at the same ratio as for a wheat flour, as in the study by Fahmy et al. However, due to the high water absorption capacity of SPI, the amount of water must be increased to a 110g H 2O/100g starch-protein mixture. That this printing mass can be printed well can be seen in Figure 2 a). A cube with almost vertical sidewalls, which corresponds to a good stability after extrusion, can be realized without any problems. However, during printing with this formulation, it has been shown that water partly leaks from the extruder (see Figure 2 b)). This means that starch and SPI are not able to bind or hold the water completely during extrusion depending on the water amount in the recipe. Due to the increasing pressure, phase separation

Figure 2: 3D printing of a starch-protein mixture of wheat starch and soy protein isolate. a) Printing of a cube (edge length: 1.5 cm), b) Water leakage from the extruder due to phase separation of the printing mass during the printing process

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By adjusting the recipe using additives and varying the water content, a dough-like mass based on starch and protein can be printed. This is the first step towards bread from the 3D printer. In addition to a suitable printing mass, it is also necessary to simulate the typical bread structures of the crust and crumb using a 3D printer. The resulting requirements are described in the next section. Structure and infill pattern of the printed objects The internal structure of a bread resembles a sponge with a gas phase (pores) and a solid phase (gelatinized starch and denatured protein). A typical pore structure for a wheat bread with smaller and larger cavities can be seen in Figure 3 a). This pore distribution is specific for different bread types and thus also determines the sensory character of a bread type. With a 3D printer, these internal structures (also infill patterns) can be customized and standardized. As mentioned above, the object to be printed is created digitally, e.g. using Computer-Aided Design (CAD) programs. This technique enables the creation of completely new shapes and infill structures that are impossible with a conventional manufacturing process. Thus, 3D printing offers an opportunity for the entire food industry to develop new innovative products. Furthermore, it offers new possibilities for research to examine the structure of the crumb

in more detail. For example, the thickness of the lamellae between two pores can be varied or the behavior of a single pore with a defined shape within a starch-protein matrix can be investigated. For the creation of an infill pattern, the programs with which one can control a 3D printing process (slicer software) already offer some pre-settings. Different patterns can be selected and the density of the infill structure can be set (see Figure3 b)). Using conventional materials for 3D printing (e.g. plastics), these infill patterns serve on the one hand to give the material the necessary stability and on the other hand to save as much material as possible. There are already first studies that show the influence of different infill patterns on the texture of printed food objects. Liu et al. used a formulation of potato flakes, wheat starch, soybean oil and water to print objects with different infill patterns (similar to Figure 3 b)). As expected, the strength of the objects post-treated by microwave vacuum drying increases with increasing infill density. At the same time, a difference in strength between infill patterns was also found (Z. Liu & Zhang, 2021). Severini et al. also found similar results with a mixture of wheat flour and water (Severini et al., 2016). Derossi et al. took aim at creating targeted textures using 3D printing by varying the number of voids. Again, by varying the ratio between void and dough, different texture impressions can be created (A. Derossi, Caporizzi, Paolillo, et al., 2020). With the infill patterns, which have been quite simple so far, it is already possible to influence the texture of the printed objects and to evoke different sensory impressions. However, the patterns still have little in common with the pore structure of a loaf of bread. As can be seen in Figure 3 a), the pores in a bread crumb are round to oval. In addition, there are individual cavities that are completely enclosed by dough (closed pores) and, more frequently, others that are connected to other cavities (open pores). To create a bread crumb with a 3D printer, these closed and open pore structures must be created inside the printed object. That this is possible

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occurs. This could prove to be a problem in the course of printing, especially afterwards, as the properties of the printing material may change as a result of the leaking water. In addition, the nozzle becomes blocked, which means that further printing is no longer possible. One possibility to prevent this is the use of hydrocolloids. The waterbinding or gel-forming properties of these additives prevent water from escaping during the printing process. When selecting hydrocolloids, care must be taken to ensure that they also meet the rheological properties of the printing process described above. This has already been confirmed in the literature with the hydrocolloids xanthan gum, carrageenan, agar and gelatine (GholamipourShirazi et al., 2019).

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Figure 3: a) Crumb structure of a whole wheat bread, b) Infill settings by the slicer software on a 3D printer

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Figure 4: Print of closed pores inside a dough matrix of wheat starch, soy protein isolate and water

with a 3D printer can be seen in Figure 4. In these experiments, a dough matrix of wheat starch, SPI and water was printed and closed pores of different sizes and in different positions were placed inside the printed object. As Fahmy et al. could show, the texture of the printed objects can thus also be specifically influenced and desired strengths of the printed objects can be produced. The position of the closed pores inside does not play a major role. The decisive factor is the ratio between void and dough, the so-called porosity of the printed objects. The greater the porosity, i.e. the more or larger the voids inside, the softer the final product will be (Fahmy et al., 2020b). This shows that crumblike structures are possible with a 3D printer. A

next study investigates more complex pore arrangements with open and closed pores to study the influence of the pore distribution and their arrangement on the texture of the printed objects. There are still limiting factors that are mainly defined by the properties of the 3D printer. Thus, the lamella thickness between two pores can be minimally reduced to the size of the nozzle diameter. For the printed objects shown in Figure 4, a diameter of 0.84mm was used. However, the average pore wall diameter of wheat bread is about 0.2mm (Lassoued et al., 2007). Therefore, there is still a need for further optimization in order to reproduce the crumb structure of traditional breads with the 3D printer.

The current results show that the texture of the printed objects can be influenced by varying the infill pattern. Even typical bread structures such as closed pores can already be printed. However, in order to realistically reproduce a crumb structure on the 3D printer, the printing process must be further optimized. Ultimately, however, it has been shown that it is primarily the porosity of the printed objects that determines the strength of the end products. Texture, however, can be influenced not only by the design of the infill pattern, but also by the mechanical properties of the printing materials. By regulating the water content, which can be influenced by the postprocessing step, the strength of the printed objects can also be influenced (Fahmy et al., 2020b). Post-processing The texture of a bread is largely determined by the difference between the crumb and the crust. The dry and firm crust is clearly distinguished from the moist and elastic crumb. This characteristic profile must be reproduced with the 3D printing process. Post-processing is the decisive step in order to influence the texture of the printed object downstream and at the same time offers the possibility of breaking new ground in terms of texture design. This process step should primarily be used to stabilize the printed objects by inducing phase transitions of the biopolymers starch and protein and reducing the moisture of the printed objects. Furthermore, the sensory character of the printed objects can be changed by Malliard reactions. An important criterion is that the shape of the printed objects does not change as a result of the post-treatment. In traditional bread making, the dough is baked at the end. Hereby the fermentation process is stopped, the starch is gelatinized, protein is denatured and different flavors are created through the Maillard reaction and caramelization. This makes the bread taste like what we are used to and makes it more digestible. Thus baking is an essential step and hence has been used by many scientists in the 3D printing of grain-based products. The most commonly used method, similar to traditional bread making, is baking in

an oven. Temperatures of 150°C-190°C are used and baked for 10-18min (A. Derossi, Caporizzi, Oral, et al., 2020; A. Derossi, Caporizzi, Paolillo, et al., 2020; Kim et al., 2019; Pulatsu et al., 2020; Fan Yang et al., 2019). The studies show that printed objects made of dough containing fat and sugar (cookie dough) tend to collapse due to the heating step. The high temperatures melt the fats and sugars contained in the dough, thus changing the rheological properties. The dough becomes softer and collapses under its own weight (Kim et al., 2019; Pulatsu et al., 2020). However, by using thickening agents such as xanthan gum, this can be counteracted (Kim et al., 2019). Doughs containing only oil (similar to a pizza dough) were studied by Derossi et al. Again, a reduction in the volume of the printed objects after baking was observed. However, this could be attributed primarily to the loss of moisture during heating. It was found that the larger the surface area of the printed objects, due to varying infill patterns, the greater the loss (A. Derossi, Caporizzi, Paolillo, et al., 2020). An alternative method to baking was applied to a potato dough made from potato flakes, wheat flour, soybean oil, and water in the research by Liu et al. The printed objects were dried after the printing process using a microwave vacuum dryer at 70°C and -0.085 MPa. Here, the printed objects also became smaller due to heating, albeit much smaller (Z. Liu & Zhang, 2021). Another major disadvantage of the described methods is that the printed object must be removed from the 3D printer. This decouples the post-processing step from the actual printing process and thus leads to an additional work step during which the printed object can also be damaged. As mentioned at the beginning, the post-processing step should be integrated into the printing process and should not only be applied after the printing has been completed. For example, the printed object can be treated after each printed layer and the texture can be affected differently throughout the printing process. One way to implement this is to use an IR heater that is mounted on the 3D printer's housing. The printed object then simply needs

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to be moved under the emitter by controlling the printing plate. This can then be incorporated into the printing process as many times as required, allowing the printed object to be treated after each layer or even after multiple layers. In the investigations of Fahmy et al., an IR radiator was used which was moved at different speeds over the print object after each layer. By controlling the intensity of the heating, the moisture content of the printed objects could be varied and thus the texture could be influenced (Fahmy et al., 2020b). The texture of the cereal-based printed objects can be influenced by post-treatment. Current research only focuses on the strength and hardness of the printed objects. The elasticity of a bread crumb has not yet been reproduced on a 3D printer. An important factor here is starch gelatinization and protein denaturation, which has also not been the focus of any of the investigations to date. Summary and outlook 3D printing is about to revolutionize food production. Not only is the interest of the food industry in this new innovative technology increasing, but also research is intensively engaged in the development of new recipes and attempts to understand the printing process in detail. It has become clear that the knowledge gained from recipe development, structural design of the infill pattern and post-processing must be combined in order to achieve specific product properties.

important step will be the extension of the printing mass by different flavors. A study by Fahmy and Amann et al. has already experimented with an inhomogeneous distribution of salt for taste in order to specifically influence sensory perception (Fahmy et al., 2021). Through these and similar studies, it will be possible in the future to integrate flavors into products to evoke specific taste sensations. There is no limit to the use of bread-like or product-untypical flavors. Furthermore, the further development of 3D printers will make it possible in the future to create realistic pore distributions like those in a loaf of bread. With the already tested post-processing treatment, the firmness and elasticity of the crumb and crust of conventional breads will be made possible through the gelatinization of starch and the denaturation of proteins, thus laying the foundation for bread from the 3D printer. Research or new innovative approaches from the industry will show which bread imitations or new fancy products can still be realized with this technology. Through the further development of the initial findings and a combination of recipe development, innovative structure design and finalizing post-processing, it is only a matter of time before we can enjoy the first bread from the 3D printer. +++

Authors Martin Heckl1*, Mario Jekle2*, Thomas Becker 1 1

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By combining starch, protein and hydrocolloids, an edible ink can already be produced that is suitable for printing bread-like products. Similarly, it has been shown that by varying the infill pattern, the texture of the printed objects can be influenced. This can furthermore be extended with a post-processing step, whereby heat treatment can also be used to adjust the texture to the desired properties. This development will be continued by constant new approaches and thus a bread from a 3D printer will become more and more realistic. An

Technical University of Munich, Chair of

Brewing and Beverage Technology, Cereal Technology and Process Engineering Unit, Weihenstephaner Steig 20, 85354 Freising, Germany 2

Department of Plant-based Foods, Institute

of Food Science and Biotechnology, University of Hohenheim, Garbenstr. 25, 70599 Stuttgart, Germany. *Corresponding authors: PD Dr. Mario Jekle, mario.jekle@uni-hohenheim.de Martin Heckl, martin.heckl@tum.de

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cookie dough. Lwt, 101, 69-75. https://doi.org/10.1016/j. lwt.2018.11.019 Lassoued, N., Babin, P., Della Valle, G., Devaux, M. F., & Réguerre, A. L. (2007). Granulometry of bread crumb grain: Contributions of 2D and 3D image analysis at different scale. Food Research International, 40(8), 1087-1097. https://doi.org/10.1016/j.foodres.2007.06.004 Li, H., Tan, Y. J., Leong, K. F., & Li, L. (2017). 3D bioprinting of

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highly thixotropic alginate/methylcellulose hydrogel

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gies, 102531. https://doi.org/10.1016/j.ifset.2020.102531 Derossi, A., Caporizzi, R., Paolillo, M., & Severini, C. (2020).

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Liu, Z., & Zhang, M. (2021). Texture properties of microwave

(2021). Drawing the scientific landscape of 3D Food Print-

post-processed 3D printed potato snack with different

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the first 13 years of detailed experiments, from 2007 to 2020. Innovative Food Science & Emerging Technologies. Fahmy, A. R., Amann, L. S., Dunkel, A., Frank, O., Dawid, C., Hof-

https://doi.org/10.1016/j.fufo.2021.100017 Paxton, N., Smolan, W., Böck, T., Melchels, F., Groll, J., & Jungst, T. (2017). Proposal to assess printability of bioinks for

mann, T., Becker, T., & Jekle, M. (2021). Sensory design in

extrusion-based bioprinting and evaluation of rheological

food 3D printing - Structuring , texture modulation , taste

properties governing bioprintability. Biofabrication, 9 (4),

localization , and thermal stabilization. Innovative Food Science and Emerging Technologies, 72, 102743. https:// doi.org/10.1016/j.ifset.2021.102743 Fahmy, A. R., Becker, T., & Jekle, M. (2020a). 3D printing and additive manufacturing of cereal-based materials: quality analysis of starch-based systems using a

044107. https://doi.org/10.1088/1758-5090/aa8dd8 Pico, J., Bernal, J., & Gómez, M. (2015). Wheat bread aroma compounds in crumb and crust: A review. Food Research International, 75, 200-215. https://doi.org/10.1016/j. foodres.2015.05.051 Pulatsu, E., Su, J. W., Lin, J., & Lin, M. (2020). Factors affecting

camera-based morphological approach. Innovative Food

3D printing and post-processing capacity of cookie dough.

Science & Emerging Technologies, 63, 102384. https://doi.

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org/10.1016/j.ifset.2020.102384 Fahmy, A. R., Becker, T., & Jekle, M. (2020b). Design and modu-

102316. https://doi.org/10.1016/j.ifset.2020.102316 Severini, C., Derossi, A., & Azzollini, D. (2016). Variables affect-

lation of food textures using 3D printing of closed-cell

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foams in point lattice systems Hardness targeted design

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of textures in the elastic regime. 63, 85354. Gholamipour-Shirazi, A., Norton, I. T., & Mills, T. (2019). Design-

Technologies 2, 38 (A), 281-291. Yang, Fan, Zhang, M., Fang, Z., & Liu, Y. (2019). Original article

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Impact of processing parameters and post-treatment on

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Godoi, F. C., Prakash, S., & Bhandari, B. R. (2016). 3D printing technologies applied for food design: status and pros-

https://doi.org/10.1111/ijfs.13904 Yang, Fan, Zhang, M., Prakash, S., & Liu, Y. (2018). Physical

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He, C., Zhang, M., & Fang, Z. (2019). 3D printing of food: pretreatment and post-treatment of materials. Critical Reviews in Food Science and Nutrition, 0(0), 1-14. https:// doi.org/10.1080/10408398.2019.1641065 Kim, H. W., Lee, I. J., Park, S. M., Lee, J. H., Nguyen, M. H., & Park, H. J. (2019). Effect of hydrocolloid addition on dimensional stability in post-processing of 3D printable

Emerging Technologies, November 2017. https://doi. org/10.1016/j.ifset.2018.01.001 Yang, Fanli, Guo, C., Zhang, M., Bhandari, B., & Liu, Y. (2019). Improving 3D printing process of lemon juice gel based on fluid flow numerical simulation. Lwt, 102, 89-99. https:// doi.org/10.1016/j.lwt.2018.12.031

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Alpers, T., Tauscher, V., Steglich, T., Becker, T., & Jekle, M. (2021).

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Safe and smart bakery production Networked machines, plants and systems are a milestone on the way to Industry 4.0 in food production. The optimized flow of information increases transparency, reaction speed and efficiency – but also the vulnerability of operations. Currently, for safe food, risks stemming from IT often do not get enough attention.

In the European Union, the same applies to networked machine networks as to conventional systems: they must meet the requirements of the Machinery Directive (2006/42/EC), which was adopted into national law with the respective Product Safety Act. The directive primarily relates to accident prevention (safety) – i.e. occupational health and safety for workers – and thus to risks and hazards that can occur when handling the machine and must be safeguarded against. These include flour dust explosions or collisions with automated guided vehicles (AGVs). Considering cybersecurity These risks are predictable, quantifiable and qualifiable. Risk assessment (RA) is used to identify, analyze and evaluate potential hazards,

which are controlled with suitable countermeasures. The Machinery Directive prescribes such a risk assessment. With the CE declaration of conformity and marking, manufacturers and integrators confirm that the system meets the requirements of the Machinery Directive. However, networked systems in increasingly intelligent factories that implement the Industrial Internet of Things (IIoT) offer new points of attack for deliberate manipulation from the outside. These are temporally unpredictable and can not only have a direct impact on the machine but also on product safety. Hacker attacks could, for example, deliberately manipulate the recipes, packaging or the declarations. This can affect the health of consumers if there are no instructions for allergy sufferers or if, for example, nuts get into a product that is declared nut-free. Cybersecurity is therefore also essential for consumer safety. In many publications, however, the role of IT security in production is sometimes reduced to securing the components of functional safety with cybersecurity measures or making an existing safety risk assessment ‘secure’. However, the usual safety risk assessments do not consider deliberate manipulation.

SAFE AND SMART BAKERY PRODUCTION

In the food industry, manufacturers and operators are increasingly relying on modular plants: they can be quickly reconfigured to flexibly manufacture a different product or optimize capacity utilization. Enterprise resource planning (ERP) software and digital representations – such as digital twins or the asset administration shell (AAS) – also promote transparency, simplify planning tasks and, in combination with new dynamized approaches, increase plant productivity.

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Keeping recipes secret and products safe According to a survey by the German association bitkom, 88% of the companies surveyed were

The SIRI Assessment systematically shows manufacturers their ‘Industry 4.0’ maturity level and helps to efficiently define the next steps

affected by a cyber attack in 2020 and 2021, resulting in damage amounting to more than EUR 220 billion. Hackers pursue different objectives

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Impaired productivity can lead to unwanted downtime. This impact is immediately noticeable. However, industrial espionage or theft of intellectual property can sometimes remain undetected for a long time. For example, recipes could be stolen that represent a unique selling proposition on the market. Manipulated temperature displays of food refrigeration or incorrect product designations impair product quality, possibly generate costly product rejects and endanger the brand image or even the health of consumers. Depending on the type of facility, a cyberattack can also put the environment, employees or other machinery at risk: an airlock that has been tampered with does not close or open in time, or an industrial robot receives incorrect feedback, resulting in a collision. Thus, a cyberattack can affect value creation, competitive advantage, or the integrity of people, capital assets, and employees. Risk analysis with two main ingredients The Machinery Directive and other regulations on plant safety focus on the intended use and reasonably foreseeable misuse, from which unsafe situations must not arise. If an existing safety risk assessment is ‘made secure’ in the sense of the Machinery Directive, for example by securing all defined safety measures against cyber attacks in the event of misuse, then depending on the type of machine or plant, potentially dangerous situations resulting from manipulation can remain undetected. For a comprehensive risk assessment, therefore, both the consequences of possible misuse and the dangers of deliberate cyber manipulation must be considered. With the Enhanced Risk Assessment (ERA), TÜV SÜD has developed a flexible process that can also be adapted to the specific requirements of bakery production. Classic safety assessment

methods, such as risk and hazard assessment or the HAZOP (Hazard and Operability) method, are combined with common cybersecurity assessment methods – for example, in accordance with the IEC 62443 series of standards. The focus is not necessarily only on accident prevention and occupational safety. Other protection goals can be defined depending on the foodstuff, system and environment. Baked goods producers, suppliers and integrators should urgently address and prioritize the topic of IT security. Holistic safety and security are increasingly demanded in new regulations, directives and standards, as current drafts of the Machinery Ordinance show. Necessary assessments should not be postponed against the backdrop of the COVID-19 pandemic, because new security gaps may arise as a result of work in the home office and inadequately secured communication channels. Modularizing the bakery production CE conformity must currently be assessed manually for safety-relevant changes to the machine assembly. For this reason, all the variants likely to be required are often considered and evaluated before commissioning. This is an obstacle to flexible production in the sense of ‘plug & produce’. In an increasingly volatile market environment with frequently changing requirements, it is not possible to predict which system configurations will be needed in the future. Sometimes this means that during short production breaks, machines have to be integrated that were unknown at the time of system planning. Thus, a business conflict has arisen between the goals of automation technology and safety technology. Currently applied safety concepts for the protection of people and capital goods analyze defined processes and secure them with static solutions. This is opposed to the goal of being able to react flexibly to different requirements and to map dynamic processes. This applies in particular to increasingly complex machines, for which cybersecurity, for example, must be evaluated in addition to classic safety.

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when attacking production facilities – ranging from money extortion to industrial espionage or sabotage. The possible effects are just as varied and serious.

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When a module interacts with its environment, different hazardous situations can occur, for example, due to malfunctions or human error. If the hazards and protective measures are described in the safety profile of the management shell, a digital smart safety agent can analyze the possible situations, for example during a simulation, automatically compare the detected hazards with the appropriate protective measures, and evaluate the machine safety. This makes it possible to replace components or machines during operation with very short interruptions: The hazards and protective measures associated with the new compound were automatically updated in advance on the digital level and thus the risk assessment was also renewed. The result of the digital safety assessment can be displayed graphically for approval by the operator. The safety-relevant contents of the AAS do not only concern machine safety but also cybersecurity. After all, the safe interaction of machines,

especially in a dynamic and flexible production environment, depends on the communication between the assets. Determining the I4.0 maturity level In order to be able to offer customers the appropriate service on the road to Industry 4.0, TÜV SÜD uses the Smart Industry Readiness Index (SIRI) to determine the I4.0 maturity level and suggests further steps for the development of the roadmap with regard to the customer-specific Key Performance Indicator (KPI). With the Enhanced Risk Assessment (ERA), a method for holistic safety and security assessment is already available today, which lays the foundation for future dynamic and flexible production environments. This enables bakery manufacturers to achieve smart, safe and economically flexible production.

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Authors Michael Pfeifer, Expert for machine safety and I4.0, TÜV SÜD Industrie Service GmbH +49 151 656 146 95 michael.pfeifer@tuvsud.com Sunanth Venkateshwaran, Certified SIRI assessor, TÜV SÜD Industrie Service GmbH +49 89 579 111 95 sunanth.venkateshwaran@tuvsud.com

I NATF EE RAVNI EDWS M A R T B A K E R Y P R O D U C T I O N S

Increasing flexibility with Smart Safety In the communication of modular systems, the digital twin in the form of a an asset administration shell (AAS) plays a central role. Information relevant from various organizational, technical and event-dependent points of view can be stored in the AAS. The organizational content relates, for example, to Purchasing and Sales, Production, and Plant Maintenance. Alternatively, the functional category includes safety and security or operational characteristics such as reliability and maintenance effort, for example.

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Company reports The latest intelligence from the industry’s leading solution providers

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A M E R I C A N PA N

+ C O M PA N Y R E P O R T S

Automation in bakery systems drives innovation for the design of baking pans in several ways, including features that improve how they run on automated conveyors, how they are stacked, and how coatings are protected. Bakeries are facing unprecedented challenges relating to demand for product and workforce shortages. These two forces combined have resulted in the latest push for automated systems in bakeries. In turn, these automated systems continue to affect the design of baking pans resulting in several innovations including: + Enhancements to run on automated conveyors + Increased pan size and capacity while trying to minimize pan mass

Pan design and handling for automated bakery systems

+ Nesting and stacking designs for safe efficient

storage Designs to protect coatings while running on + automated systems

Designing for automation Many automated oven systems require specific designs to be compatible with their conveyor

American Pan Europe Strada Dunarii Nr.277, Corp C10 Alexandria, Judetul Teleorman, Romania Tel.:

+ 40 374 664 600

E-Mail: apeu@americanpan.com Website: www.americanpan.com

A M E R I C A N PA N

systems. From trays that connect to conveyors, to tin sets with special strap designs, making sure a pan can run smoothly on an automated system is essential. “Automated baking systems have unique pan requirements to ensure smooth operation,” states Michael Cornelis, Vice President of International Sales and Development for American Pan. “Features like rounded pan corners and precise manufacturing to adhere to tolerances help ensure the pans will work efficiently with the system.” Explaining this further, he states, “Along with running smoothly on conveyors, pans and lids are often stored in specifically engineered spaces and must meet exacting size specifications.” Mr. Cornelis also discusses the new design for completely sealed pan frames which prevents contaminants from entering the pan frame during baking, washing or even the pan refurbishment process. “This design helps prevent contamination of baked products by eliminating particles or liquid from getting into the frame and subsequently damaging the product.” Handling larger pans Trays and tin sets for automated systems are also typically larger than standard pans. This could require additional supports or strengthening elements to be added to the pan design. Cross braces, structural ribs and other metal forming techniques are used to improve durability.

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These systems are used to remove lids off bread tin sets, move pans on and off the conveyors, and even transport pans to and from pan storage areas. To work with automated equipment for these functions, pans and lids must be designed properly to work within machine parameters and prevent damage to the pans or coatings. “It’s about being able to get the pans on and off the production line safely and efficiently,” according to Jesper Albertsen, European Sales Director

I N NOVAT ION ePAN® material and designs

ePAN ® baking trays and tins use a hightensile strength aluminized steel to create pans that are up to 20-50% lighter and substantially stronger than traditional designs. In addition to being lighter and stronger, ePANs ® offer:

+ Extended Pan Life: The use of high-tensile strength aluminized steel creates a

stronger pan and reduces the potential for pan damage.

+ Energy Efficiency: These pans heat and cool up to 25% faster than traditional

pans, decreasing oven energy requirements and space for cooling.

+ Easier on Your Bakery: ePANs® remove thousands of pounds from the bakery cycle and reduce wear on conveyors, stackers and other pan handling equipment.

C O M PA N Y R E P O R T S

Increasing the size of baking trays and tin sets increases throughput but creates a challenge for employees that are required to work with or handle the pans. Even when using proprietary ePAN® material, the weight of the bakeware can be increased significantly. This requires the use of robotics or other mechanisms to assist with moving the trays and tin sets on and off the line.

Due to larger pan designs and additional supports or straps for automated systems, pans inevitably become heavier. The size of the pans, coupled with the weight, often makes the pans too large for bakery employees to handle.

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A M E R I C A N PA N

I N N OVAT ION Stacking designs Stacking baguette trays with interlocking ribs in frame

for American Pan. “Once off the line it is also important that they stack together securely to decrease the amount of space needed to store them and ensure that they nest safely to prevent any hazards.” Safe stacking Larger pan designs present storage challenges – the larger the pans, the more space required for storing them. New stacking designs, engineered over the last several years, have increased in demand especially for industrial systems. Stacking features allow bakeries to stack multiple identical units together. These features depend entirely upon specific applications. But all provide enough room without taking up too much precious space, thus leaving room for other day-to-day bakery activities. Trays that use a screen or other type of insert placed inside a frame can be equipped with a stacking rib. Bread tins can be manufactured with notched designs on the strap, allowing for safe and efficient stacking of these large and heavy sets.

Stacking bread tin set with special straps to nest and protect tin coating

C O M PA N Y R E P O R T S

Bun trays for automated system with stacking design

Protecting the coating One of the biggest issues when it comes to automated handling and stacking of pans has always been damage to the bakeware coating. The tools on robotic equipment or contact with other metal baking pans can scratch the coating and cause premature failure. New pan designs and coating techniques allow the coating to be applied in such a way that it will not come in contact with other metal surfaces that could scratch the coating. This could include applying coating below the contact line or creating a special stacking design that prevents the metal surface of one pan from contacting the non-stick coating of another. Maximizing pan life Over time, pans will inevitably experience wear and damage. Metal may get dented or warped due to bakery conditions and can cause automated line jams, resulting in bakery downtime and an

A M E R I C A N PA N

increased need for maintenance. In addition, all coatings eventually begin to break down, and non-stick properties are diminished; however, pan life can be extended through service options including refurbishment and screen replacement.

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I N NOVAT ION Stacking designs Automated cake tray design stacked on special assembly for safe travel and storage

Pan refurbishment Bakeware performs best when it is clean, straight, and maintains its natural coating properties; thus, bakery pan refurbishment and recoating should occur regularly. Bakeware that is refurbished on a regular schedule has been proven to save bakeries time and money. Pans that have lost the easy release properties of their non-stick coatings will result in baked products sticking to the pan. Products that stick to the pan are damaged when pulled off and often scrapped. Poor performing coatings also require more oil or spray when these release agents may otherwise not be needed.

Baking screen replacement If your tray is designed with a baking screen that is attached to a frame, then screen replacement is an option to extend the life of the tray. Many designs use rivets to attach the screens to the frame; however, the TabLock Baking Screens from American Pan are constructed using a patented attachment system for easy and economical replacement of baking screens. The trays come with custom tools that allow old screens to be removed and new screens to be inserted while remaining in the bakery. There is no need to ship them to a service center. In addition, because there are no rivets or other fastening devices that require drilling, the threat of metal shavings contaminating baked products is eliminated. +++

TabLock baguette tray with stacking rib and screen removal tool

C O M PA N Y R E P O R T S

Pan refurbishment services include cleaning pans to remove the old coating and other residue and then applying a new coating to achieve the same release characteristics as new pans. In addition, hydraulic presses can be used for some pans to reduce warping and remove dents. This extra step helps improve overall pan performance and prevents line jamming and other issues.

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AMF BAKERY SYSTEMS

Future-smart technology arrives

One demand that has not changed in large-scale manufacturing is the need for production efficiency while maintaining the best possible product quality.

C O M PA N Y R E P O R T S

Requests shape solutions Solutions are, in their specific applications, as diverse as a baker’s (present and future) product portfolio. As our customers work to grow their businesses, enhance existing products or create new ones, they also want to be sustainable and implement digitalization at the same time, as frequent requests show. As a professional partner for bakeries and food producers, AMF has comprehensive knowledge of customers’ business drivers, which helps us offer products and solutions that best fit their unique strategies. Auto-

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mation in bakery and food manufacturing, in general, is at the core of business growth. And this is what we do best, the support we provide – it’s what we have always done. C

Higher volumes made faster and safer are increasingly being requested, with processes aimed at minimizing waste and optimizing energy use. This requires technology that traditionally covers manual tasks to gain speed, accuracy and consistency in the various process steps. In addition, there are growing sustainability needs to be met, as bakers recognize their entire production process needs to be green and tools that will help them reach this goal must be prioritized.

AMF_Ad_2_TunnelOven_EuroPub_China.pdf

Recent requests for sustainable and digital solutions led us to develop and introduce smart solutions. In the Bakery Intelligence range of solutions, we are currently introducing the Smart Cheese Applicator, for example, which is cloudbased software that uses artificial intelligence to automate quality control for pizza toppings and ensure consistent application, while minimizing (often expensive) ingredient waste. It greatly improves the application/strewing process, which is more accurate and efficient as it minimizes MY

CMY

AMF Bakery Systems (Sales) Phone:

++31 183 626 252

E-mail: sales@amfbakery.com

AMF Bakery Systems (Service & Parts) Phone:

++31 183 626 252

E-mail: service@amfbakery.com

Fully in supp

We know y operations g days a year with this ma Boer Tunne optimal bak

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AMF BAKERY SYSTEMS

CMY

Another new launch that meets both types of needs is our latest Smart Oven control app, which provides tools to control energy-saving, monitor tunnel ovens in real-time, and optimize all oven processes for bakeries. The app helps to control the oven’s temperature in real-time and contributes to its overall operating sustainability as it ensures optimal use of energy and helps to minimize energy loss. When a tunnel oven is not optimally loaded, for instance, it is needlessly using too much energy. To help prevent this, the app uses warning systems and a self-learning mechanism. We are developing an entire family of digital tools and bakery intelligence solutions which will soon join the Smart Applicator, to step into the smart factory era. More tools, features and solutions that are going to be part of our Smart Suite of products will help manage equipment fatigue and handle the wearing out of parts, engines, motors, as well as predictive maintenance, to ensure our customers can run their lines 24/7 without problems or worries. The automation of bakery processes and food production, plus digitalization, will help AMF’s customers do preventive maintenance, which results in less downtime, minimizing serious issues or malfunctions and thus increases the efficiency of your production flow. AMF Tromp is also working on hygienic improvements such as the NANO technology to be able to use less water in cleaning and energy saving solutions. Our lines are already designed in such a way that cleaning can be done quickly and efficiently without tooling, with this being improved even more.

Moreover, such solutions contribute greatly to ensuring food safety and sustainablility. This has been of particular interest in light of increased concerns post-COVID-19, and therefore improveFully integrated lines and lifetime ments in product safety will continue be a good. support for everytobaked priority. Digital tools such as real-time quality We know your bakery system needs flexibility as your operations and accessible 24 hours a day, 365 checks performed with thegrow AMF Trompsupport Smart days a year. At AMF, we’re bakers too; we design solutions Applicator measure with andthisimage each product in AMF Den master baker mindset, like our modular Boer Tunnel Ovens, which allow us to engineer the most production, checking for weight, specifications, optimal baking solutions for today while preparing your for future growth. ingredients and quality. Thisbakery means that no Get in touch with thehands industry’sanymore only truly global complete product passes through human system supplier for the best unit equipment and integrated and nothing needs to be touched. Line managers solutions for your operation. and operators can review images so they can learn and see, but machines should be able to steer the process themselves.

commercial equipment for an ever-changing

market. AMF’s systems set the standard in the baking industry providing complete solutions for soft bread, buns and rolls, tortillas and English muffins, artisan breads, pizzas and flatbreads, cakes and pies, and pastries and croissants. Along with the latest bakery innovation, AMF provides expert design and applications engineering, production expertise, full-system integration, project management, installation, training and lifetime parts and service support. AMF Tromp – technically leading unit equipment as well as fully integrated bakery lines for the worldwide baking industry. It specializes in sheeting, laminating, make-up, decorating, depositing, injecting, spraying, glazing, seeding, and many more. AMF Den Boer – a leading brand, with over 100 years of experience in tunnel ovens, proofing systems and handling systems. It provides modular systems for every kind of baking requirement, with sustainable solutions like Hydrogen (Ready), Electric and Hybrid ovens with all turnkey solutions delivered anywhere in the world. Together, AMF, AMF Tromp, and AMF Den Boer help bakers grow their product portfolio with confidence to produce soft bread and buns, sheeted artisan breads, pizzas and flatbreads, cakes and pies, and pastries and croissants.

C O M PA N Y R E P O R T S

waste and giveaway, as well as quality control with every single product being weighed and digitally inspected. By doing so, the applicator indirectly helps save the energy behind producing and then eliminating ingredients that are now used to the fullest – and contributes towards sustainability goals. The Applicator can help save up to 3% of otherwise wasted ingredients, meaning up to several hundred thousand euros per year, in some cases.

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Process improvements shape the future: ‘lights-out’ bakery While certain baking processes have traditionally been related to manual labor and craft, there are immediate benefits to streamlining them. Technology comes in to simplify operations and ensure consistency, especially where many people are assigned. The most opportunities where production stands to benefit from employing automation tools are in the process steps involving several operators and where quality control is

The AMF family of digital solutions Smart Applicator This is an intelligent quality and weight control tool, for which AMF Tromp uses real-time digital imaging, with an algorithm that checks every pizza or product on the line. The cloud-based software uses Artificial Intelligence to automate quality control for pizza toppings. It immediately changes the speed, amount and placement of the cheese, to optimize the result. Machine Learning makes sure the algorithm and the Applicator get smarter over time. It can even be incorporated to non-AMF Tromp units! Smart Oven app This software has the benefit of using real-time oven data, to generate important information regarding energy use and energy consumption. It can also predict failure and maintenance needs, propose process corrections while baking, detect parameters that are outside of preset specifications, and optimizes the oven’s overall performance. It uses not only the data measured by local sensors to do so, but also statistics from other ovens worldwide (protected data). In this way, the Smart Oven app not only contributes to individual sustainability goals and records progress, but also effectively builds an up-to-date roadmap of bakeries worldwide, opening the door to entirely new possibilities and benefits. AMFConnect™ This is a single system that provides critical information and control of multiple processes throughout your bakery. Connect-

C O M PA N Y R E P O R T S

ing bakery equipment, baking systems, and bakery processes, AMFConnect™ consolidates data to improve communication and the coordination of plant operations. With secure, reliable, and accurate data collection from an entire production line, this tool gives you insights and better control of upstream and downstream operations. The entire bakery’s data is available across multiple platforms with operator stations in your facility and remote access via smart devices.

concerned. This can be anywhere along the production line, or over the complete production facility. AMF Connect is a tool we have been offering for some years, where the complete production line is under control and managed by factory managers via one platform for the line. This enables bakers to have control over the processes and OEE data. But more can be achieved, and more opportunities are becoming available, as we are now entering an age where machines become smart and have self-learning abilities. For instance, the Smart Applicator is learning to create the perfect setting for Cheese strewing. With these new tools, it is possible to have quality control carried out in real-time, continuously, whereas an operator would perform quality checks at various intervals, and sometimes with varying results, for instance. Our dot on the horizon is the ‘lights out’ bakery, where machines run autonomously without operator intervention. They will be self-sufficient smart to solve issues by themselves; however, we are not there yet. In most bakeries, the operator is still in control of the machine and process. We are not taking steps out of that process, but instead digitalizing them, using data and creating value. The first- and second-line services are available remotely. Through the platform, operators can monitor and input actions to support where needed. This is the role of specialized staff, who can solve problems the machine cannot handle by itself, yet. While machines are not entirely able to run processes without any intervention yet, they will be able to do more in the near future. Smart becomes smarter IoT supports the integration of these tools/services into your processing lines, as devices are connected to the platforms we offer and host, through PLC and Internet connections. Individual units and machines use Artificial Intelligence and Machine Learning features to process data, become Smart and then use that data to create value.

AMF BAKERY SYSTEMS

The Smart Applicator, the AMF Connect and the Smart Oven control app will always be installed to the specifications of each facility. Customizations are made 100% of the time, because the unique data of your specific line is used to optimize the process and results. Of course, the hardware is standard, but the intelligence of it all 1 3/26/19 1:31 PM isAMF_Ad_2_TunnelOven_EuroPub_China.pdf the data we use to create value. To get the best results and make use of the data build-up over time, a connection to our cloudbased platform is needed. This is done according to market standards, using powerful platforms such as Microsoft Azure. We can guarantee the safety of the data, privacy and even benchmarking of machine data worldwide, without sharing any sensitive information. In this way, Smart Solutions become smarter over time and lift the Overall Equipment Effectiveness (OEE) and the quality of the products, respectively. Learning events include deviations from preset parameters, for which built-in responses are also in place to be automatically deployed and quickly bring the process within standard configurations.

CMY

check every product as of now, with the Smart Applicator. It detects every single anomaly, and the system and algorithms will immediately change settings, to go back to the ‘base’ value. This not only applies to the weight of the product but also the distribution of the toppings on each product. Also, when looking at improving baking efficiency, it is the oven that determines the quality of the baked goods. Real-time monitoring of baking temperatures in tunnel ovens makes the process better and thus the end product better – a task for which the Smart Oven app has been developed. Smart and smarter solutions do not only make a manufacturing facility more effective but will also bring added value when operating several production sites. This is where the use of data makes the difference, connecting all units/devices or lines on the cloud platform, to pool together and share data, to really help smart solutions become smarter. Slight mistakes, faults, errors or often recurring problems will be shared and the machines will all learn from it to optimize their performance. The big picture will have synchronization being done over multiple facilities, even internationally. +++

Typical malfunctions we see in the pizza industry, for example, is when pizzas have too much or too little cheese deposited on each piece. Fully integrated Weighing checks, when manually done by lines opera- and lifetime support for every baked good. tors, will only inspect a few pieces every 15 minWe know your bakery system needs utes, on average. We automated that process and flexibility as your operations grow and accessible support 24 hours a day, 365 days a year. At AMF, we’re bakers too; we design solutions with this master baker mindset, like our modular AMF Den Boer Tunnel Ovens, which allow us to engineer the most optimal baking solutions for today while preparing your bakery for future growth.

Get in touch with the industry’s only truly global complete system supplier for the best unit equipment and integrated solutions for your operation. F © AM

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The Multibake® HT Tunnel Oven (Den Boer) is a high temperature oven for pre-baked products like pizza, flatbreads, naan

C O M PA N Y R E P O R T S

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BAKON

C O M PA N Y R E P O R T S

When manufacturing pastries, every aspect that contributes to the final outcome is important for product differentiation and ultimately plays a role in consumer choice. A good understanding of the production steps and a continuous assessment of new innovations and opportunities will always be the foundation to perfecting the process to the exact requirements of present and future pastry specialties. Step by step In the automated production of pastries, the output is consistent in terms of quality, volume and composition. This gives the manufacturer a clear image of the total cost per product and consequently the cost of ownership. Daily quotas will always be equal since the machine(s) is working at the same pace for the set amount of time. An additional advantage is that the customer can use their skilled craftsmen for product development. The science and technology in spraying BAKON’s trademark knowledge, built since its founding in 1986, revolves around understanding the behavior of products such as release agents, egg wash, and glaze but also fat coatings like

The key is knowledge!

chocolate and fondant with the objective to accurately control them to perfect the automated spraying process. Developing glaze spraying machines was the first step. Building on this expertise, BAKON then developed solutions for all types of products, with the in-depth understanding that each type of liquid requires a different way of handling, to best suit its properties and desired utilization. The demand from the market and the cooperation with raw materials suppliers provided BAKON with the knowledge to translate specific needs into the right solution. BAKON has developed different solutions, not only for the industrial production of pastries but also for operations. Moreover, these solutions can handle any type of products, from release agents

Bakon Stanleyweg 1 4462 GN Goes, Netherlands Phone:

++31 113 244 330

E-mail: info@bakon.com Website: www.bakon.com

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to fat coatings including chocolate or fondant. BAKON will find the best solution for your product and help increase your production efficiency, not only in the spraying process but also in the depositing and cutting processes.

can be saved and retrieved via a touch screen, including triangle or diamond shapes, and round products – a must-have for cakes. Recipes can easily be selected and swapped as needed, with several patterns and pre-set dimensions to choose from.

Ultrasonic cutting makes perfect Manually cutting delicate products such as cakes will often negatively impact the surface of the slices. This is the result of an unsteady hand and cutting technique, which is even more visible when cutting with a serrated knife or saw. The resulting product will most likely end up crooked. BAKON acknowledges the value of having the perfect products – and this is not only reflected in, but even highlighted by a perfect cut. To achieve this, BAKON uses ultrasonic cutting technology that cuts the product layer by layer, driven by 20,000 Hz. The results are clean-cut products, each identical in size.

Safety is inherently considered in the cutting process. One of the unique features of BAKON’s Ultrasonic cutting machines is the Safety Light Curtain System. A safety light curtain is a reliable protection system that prevents access to the cutting area when the machine is in operation. It enables you to work fast and safely without having to open security doors. As soon as movement is detected in the ‘curtain’, the machine(s) stop working. Furthermore, the crash detection feature prevents the titanium blade from cutting into the tray or plate. Metal residues will not be created to contaminate the product and the expensive titanium blade will not be damaged either. The blade cleaning system is another feature included in the cutting line that is designed with hygiene and ease of maintenance in mind. It works with a stainless-steel container with spray nozzles inside. As the cleaning cycle is activated, the blade will move in the container where the spray nozzles will be opened. At the end of the wash cycle, an air pulse blows of the water drops. The timing, duration and sequence of the cleaning cycle are free programmable per product cutting recipe.

A dedicated cutting line translates into output in the same quality, volume and consistency with immediate, visible benefits. Moreover, the manufacturer can use their skilled craftsmen and bakers on matters that require their expertise such as quality controls and creating recipes, instead of repetitive tasks such as cutting. It is simply more rewarding to leave the cutting to the BAKON machine since cutting by hand takes a lot of time, effort and burdens manpower. Moreover, by learning and trying new techniques, BAKON is able to shorten the cutting process to approximately one second per cut. In addition to rectangular cuts, several patterns can be programmed in the machine and the dimensions

It takes a thorough comprehension of the process, the ingredients, the mechanisms and the sequence of reactions involved to reach the right solution that will bring measurable benefits efficiency-wise and a fast return of investment. +++

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BAKON

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CETRAVAC

Fast, flexible and sustainable

C O M PA N Y R E P O R T S

The Swiss company Cetravac AG is regarded as the technology leader when it comes to the vacuum conditioning of baked goods. It is now revolutionizing in-store baking with the UDO vacuum oven. ‘Out of stock’ has implications in terms of sales revenue, and too many discarded goods harm the reputation of a business that is operating sustainably. That’s why chain stores and retail baking stations alike are faced with the dilemma of how supply and demand can be matched as closely as possible. Two problems require solutions: the baking process has to start before the actual demand is known, and both chain stores and the retail have a drastic shortage of competent staff. The magic words to solve these problems are digitization, automation and an oven like the UDO. Cetravac’s UDO vacuum oven brings several decisive advantages at the same time for in-store baking and is ideally suited to be included in an automation solution. UDO supplies the plus for end-product quality. UDO delivers fully baked goods, regardless of whether they reach the oven frozen, chilled or at room temperature. It then cools the products down in seconds until their shape is stable. It’s well known that fresh products from standard ovens initially drop into a chute and are subsequently

Cetravac AG Kesselbachstrasse 40 CH 9450 Altstätten, Switzerland Phone: + 41 71 520 75 50 E-mail: contact@cetravac.ch Website: www.cetravac.ch

CETRAVAC

standard

vacuum

so with UDO, because its principle operates just as well and as quickly for bread and any other baked product. Air is taken out of the pores, steam is put in, with heat for the crust – and voilà, a finished loaf. Since the remoistening process eliminates starch retrogradation, consumers cannot distinguish the products from bread baked for the very first time. UDO cools the bread slightly, so it can be picked up and sliced. It stabilizes the structure internally and externally at the same time, so the slices neither smear nor stick together when cut. UDO can also bake off prepacked goods. The UDO’s ability to bake off prepacked goods is likely to be especially important for food retail. This allows a significant increase in freshness on the bread shelf while reducing dependence on daily fresh delivery service from bread factories. Baked goods can go to the outlets via a central warehouse without sacrificing any freshness. UDO, a heavyweight in the foodservice. Bakeries and baking stations can no longer survive without a snack offering. UDO heralds a new era here as well. Even with a heavy topping, its vacuum technology leavens the dough structure again and makes it crisp, which neither a circulating air oven nor a microwave can achieve. In addition to that, it is incomparably fast, and delivers baked goods cooled down to an ‘enjoyable eating temperature’. Cetravac offers the UDO vacuum oven in different variants and sizes to suit the profile requirements. With a view to digitizing and automating the entire bakery process from warehouse to presentation for sale, Cetravac has ensured the oven integration directly into the control of the overall process. This increases efficiency and saves users any possible problems with interfaces. +++

C O M PA N Y R E P O R T S

squashed by the following items. The UDO prevents this. At the same time, UDO ensures that products retain their shape for several hours, and bread rolls don’t acquire a rubber-like consistency. UDO can also handle part loads. Baking is often required several times a day for restocking to ensure customers are not faced with empty shelves, and sometimes fully loading the oven doesn’t make sense. The more accurate the data about actual and expected sales, the more precisely the UDO can be controlled. The UDO also bakes single and even partly loaded trays in perfect quality either automatically or manually fed. There is no crossover of heat from unoccupied areas to the existing products. This is also true for mixed loads. UDO is faster. That is due to the process principle. UDO uses vacuum to remove air from the pores of the backed product and fills them with hot steam. This penetrates the dough’s structure, making it soft and succulent. Infrared and radiant heat simultaneously provide heating and browning. Both vary depending on the product, but always in no time at all. That’s how UDO delivers more finished products than any other oven with comparable goods. UDO loses no product weight. In every conventional baking process, the end product loses moisture, so consequently, its final weight is lower than the dough piece that was fed in. Not so with UDO. This is because, in contrast to other ovens, UDO not only contributes moisture brought in from outside to the product’s exterior but also forces it into each individual pore of the baked goods, thus imparting long-lasting succulence to the baked product. UDO regenerates products stored at ambient temperature, which have already undergone an aging process during storage, and it restores lost freshness by introducing steam directly into every pore of the crumb and crust. Starch retrogradation is largely reversed, and the original crispness of the crust texture and softness of the crumb are restored. UDO delivers freshly baked bread with slicing firmness. As a rule, baking-off partly baked bread in an in-store oven takes up so much time that capacity for all other products is limited, and consequently, another oven is needed. Not

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DIOSNA

C O M PA N Y R E P O R T S

Linear transport system

It is almost impossible to find two dough fermentation processes that are exactly alike. This was something that both the bakery product maker and DIOSNA as the equipment manufacturer and technology specialist experienced when it came to realizing a large-scale system for the preparation of Biga predoughs. The long resting periods for the predough in particular were a real challenge for the planning engineers, as were the large production volumes with up to three tons of dough per hour. In a production line for predough fermentation starters, not only the individual pieces of equipment need to work perfectly, but the way they interact and work together has to be perfectly optimized as well. DIOSNA delivered an overall concept for developing this type of process – bringing everything together. One advantage here is that the dough experts at DIOSNA are able to offer the key processes in dough production from a single source. From measuring out the raw ingredients to preparation of the fermentation starter,

Everything from a single source

along with internal logistics, final quality control and cleaning concepts – everything is covered. Step by step, DIOSNA worked with silo and automation specialist Shick Esteve, its sister company from the Linxis Group, to develop a plant concept that set new standards both in terms of complexity and automation. Intelligent space concept The system places incredibly tough demands on the space concept in terms of the range of different transfer stations, the mixing process, and the cleaning units. These include a dough resting

DIOSNA Dierks & Söhne GmbH Am Tie 23 49086 Osnabrück, Germany Phone: +49 541 33104 0 E-mail: technology@diosna.com Website: www.diosna.com

DIOSNA

At this part of the installation the predough, namely the Biga, is prepared, the heart of the final dough, expecting long and gentle ripening.

Biga Dough

Temperature control was one of the most important aspects in the development of the overall process. The Wendel Mixers from DIOSNA have always stood out with low heat transfer into the process. Higher degree of automation for reliable production It is thanks in part to a very sophisticated automation concept that the line runs like clockwork. To do this, Shick Esteve, a sister company of DIOSNA, developed a bespoke software solution especially for this plant. Cleaning is supported by the hygienic design of the machines.

Over two storeys, more than 100 containers rest in a huge dough resting room, each holding over 120 kg of dough. However, smaller batch sizes also need to be integrated on a regular basis. After the resting time, the containers are automatically moved out from the resting room and are then transported following the FIFO principle to a transfer station. Here the containers are transported upwards and emptied into the stainless steel vats of a kneading system. The empty containers then move automatically to a cleaning station.

Effective cleaning concept In terms of cleaning, the CIP (Clean in Place) and WIP (Wash in Place) systems offer resourcesaving, simple, and efficient cleaning for all line components. For example, the containers of the predough resting system are automatically cleaned at 50°C by spray nozzles, with no need for chemicals. The stainless steel vats are scraped out during the emptying process into the hopper (wall scraper). Scrap dough can accrue in the process steps for dough preparation.

The transferred predough is initially processed by two further batch mixers using a separate silo connection, during which further ingredients are added by dosing devices. Thus, the final dough is prepared. Dough Mixing is followed by a second resting period, which is controlled via a fully automated linear transport system. Only after this second, significantly shorter maturing period, the dough is transferred to the make-up line.

Conclusions and outlook This article looks at just a few of the aspects that are particularly important in automated dough production processes. The key issue is the transfer points. This is the only way to ensure that three tons of dough per hour can be continuously processed without any problems – and with full compliance with all quality and hygiene requirements. +++

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At the heart of the process is the automated mixing concept. All steps are monitored and documented here so that subsequent approvals are made easier. Two Wendel Mixers from the ‘Hygienic Design’ line (WH series; WHA240) ensure gentle dough development with minimal rise in temperature. As well as the dosing device, the Wendel Mixers feature rounded stainless steel profiles to enable faster and more thorough low-pressure cleaning later on.

system, ‘Hygienic Design’ Wendel Mixers, which work in tandem to deposit dough in the resting container via the under bowl discharge principle, a linear transport system with tilting lifter, highperformance Wendel Mixers, and hoppers with dough portioning devices. Because space was limited in the plant – particularly in terms of the large amounts of dough that were to be produced – a two-level storage concept with automated logistics was developed.

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Automation – everyone has heard of it and many have already partially or fully automated their production. Reasons for automation and conversions in operation are the many advantages that automated processes bring. They can have a cost-saving effect, achieve higher throughput while reducing scrap rates, and ensure safety and stability in the quality of the product. Nevertheless, there are also some issues to consider that can prove challenging. Limiting factors in automation include the feasibility of technical requirements and the smooth running of linear processes, which depend on non-linear processes. Technical challenges Regarding the technical requirements of automated processes, a lot has happened in the last years. Especially due to the development of new

Why sourdough plays a decisive role

equipment, much more is possible today in terms of automation than was the case a few years ago. In the past, doughs with a maximum dough yield of 170 were considered suitable for machines because they were otherwise too sticky for processing. Nowadays, with the appropriate equipment, it is possible to process a dough yield

Ernst Böcker GmbH & Co. KG Ringstrasse 55-57 32427 Minden, Germany Phone: +49 571 83799 0 E-mail: info@sauerteig.de Website: www.sauerteig.de www.sauerteig.shop www.boecker-showroom.de www.boecker-bpure.de

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Sourdough fermentation and the cold trick Many companies ferment their sourdoughs themselves with the use of appropriate starter cultures and thus achieve good quality. Now here comes into play the second limiting factor for which a solution is needed. A major problem in the automation of sourdough fermentation is the dependence of the linear process of bakery production on the logarithm of sourdough fermentation, i.e. the ever faster increasing amount of acid in the fermentation. Let's imagine the processes of dough preparation to the finished pastry: First, the ingredients must be weighed, then the dough must be made, divided and formed into the appropriate pastry. Not to forget the resting periods in a defined atmosphere. This is followed by baking and then either cooling or freezing, and finally packaging. This process contains a large number of steps, each of which can lead to delays in production due to a wide variety of causes. If the sourdough is now fermented

automatically with a precisely defined time frame and such a time delay occurs, this is extremely unfavorable for the sourdough. It is essential that the fermentation time is adhered to under constant conditions in order to maintain and ensure good quality. The sourdough would have to be discarded and restarted, and the entire production would have to wait for a new sourdough batch. The delays that could occur would be economically unsustainable. Bakeries have faced exactly this problem in the past. The solution that was then found seems so simple: cooling! By cooling down the finished sourdough, fermentation is slowed down and thus the dependence of bakery production on sourdough production. In this way, the entire process has become more flexible and the risk of a production standstill has been minimized. The attached figure shows the separation of the processes by cooling. When looking at the figure, however, it is important to note that such a process depends on many factors and can only be shown here in a very condensed form. Nevertheless, it clearly shows that cooling is the solution for more time buffer. Sourdough = flexibility Another way to react flexibly in baked goods production with regard to sourdough is to expand the use of sourdough in baked goods. As already mentioned, in the wheat sector the quality of baked goods can be significantly improved by

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BÖCKER – the sourdough specialist recommends using mild sourdough in wheat baked goods. This improves the quality of baked goods by guaranteeing, depending on the choice of sourdough, a mildly sour, fruity flavor and, in any case, improved crumb elasticity and sliceability of the breads, as well as longer freshness. [1] In the case of an already (fully) automated plant, the addition of sourdough to the recipe then turns the process planning upside down from scratch – if the sourdough itself is fermented! As a solution, BÖCKER offers inactive ready-to-use sourdoughs. These products are available in liquid and powder form and can be added directly to the dough. Especially in the case of long-life baked goods, the use of ready-to-use products is a safe method to improve the quality of the baked goods. By inactivating the sourdough, post-souring of the dough is prevented.

of over 180, as we know it, for example, from “handmade” doughs such as ciabatta or baguette. But how does the subject of sourdough fit into automated processes?

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using sourdough, whether cakes, pastries or breads. The more products in which sourdough is used, the more fallback options and further utilization options for the sourdough arise in the event of a delay, and the more flexibly it can be reacted to. To ensure that the end result is a product of consistent quality, however, a number of things must be taken into account right at the start of automation. Especially when it comes to maintaining the fermentation parameters, automated production can simplify fermentation and its monitoring by means of appropriate measurement and control technology. The basic prerequisite for this is that, when introducing an automated sourdough fermentation system, all important parameters, such as fermentation time, temperature or use of the starter quantity, are taken into account and set accordingly. After all, not all sourdough is the same. Depending on the use of the starter products, different management conditions apply, and depending on the function the sourdough is to assume in the product, different conditions apply again. For example, a sourdough that is to be used for yeast-free baking cannot be managed in the same way as one that is only to lower the pH value and bring aroma into the bread. We at BÖCKER are happy to help with the implementation of the projects described and, as sourdough specialists, are available to provide advice. Ultimately, the more baked goods are produced with sourdough, the easier the automation. Our plea is anyway: something fermented belongs in every dough!

About Ernst BÖCKER GmbH & Co. KG At BÖCKER, the specialist for sourdough, everything has revolved around sourdough since the company was founded in 1910. In the fourth generation owner-managed family business, high-quality sourdough products are fermented and shipped worldwide. BÖCKER has been shaping industrial sourdough production for more than 110 years with continuous innovation as an opinion leader and sound knowledge carrier. BÖCKER stands for sourdough in all facets and variations. Thus, the wide range of products includes starters for industrial sourdoughs, dried sourdoughs in concentrated form, liquid and ready-to-use sourdoughs for direct use, sourdough pastes with embedded ingredients (e.g. fresh sprouts or grains) active sourdoughs as well as gluten-free sourdoughs and baking mixes. The company developed these products at an early stage, thus giving birth to the generic term sourdough products, which the baking industry uses as a matter of course. The innovative strength of the company's founder has been preserved to this day and is the driving force behind the company's entrepreneurial activities; thus, many patents line the path of success of the familyowned company. In the course of the company's history, BÖCKER has launched more than 160 sourdough products on the market – from special sourdoughs and customized product solutions to ready-baked gluten-free baked goods. With its gluten-free ready-baked baked goods, BÖCKER offers artisan products for all those who want to eat without gluten, other allergens and vegan.

+++

References 1] Arendt et.al., 2007, Galle et.al., 2013

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Topic 1: new compact bread line ‘PROGRESSA bread’ In 2022, FRITSCH will be bringing out the PROGRESSA bread, a compact and innovative bread line for retail bakers. The Little Machine That Could The trend towards breads and pastries made from soft doughs with long pre-proofing times of up to 24 hours, and often a high rye content, poses major production challenges, especially for medium-sized retail bakers. To be able to produce as many different products as possible at consistently high quality, a production line simply has to be uncomplicated to retool, easy to clean, and ready to expand if necessary. “For many lines, processing soft doughs requires adding greater amounts of separating agents like oil, for example, to be able to make the dough sheet as true to weight and shape as possible,” says Michael Gier, Manager of the FRITSCH World of Bakery. “But increasing the amount of oil has a negative impact on the baking results of the end product. Also, the effort of cleaning increases, and so do

Progress in the world of bakery

the downtimes when changing products,” explains the experienced dough technologist. With the PROGRESSA bread, FRITSCH will be introducing a new solution in the spring of 2022 that is as space-saving as it is efficient. “With this very compact machine, we close a gap in our portfolio and cater above all to artisanal and mid-sized bakers,” says engineer Fred Dorner, Head of Research & Development at FRITSCH. Depending on product size, the machine can process between 800 and 2,000 kilograms of dough per hour. The advantages of this new and

FRITSCH Bakery Technologies GmbH & Co. KG Bahnhofstr. 27-31 97348 Markt Einersheim, Germany Phone: +49 93 26 83 0 Fax:

+49 93 26 83 100

E-mail: mail@fritsch-group.com Website: www.fritsch-group.com Blog: www.passionfordough.com

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innovative solution had only ever been seen in industrial lines before. “With our new PROGRESSA bread, even small-scale bakers can produce a wide range of products with extreme precision when it comes to weight and gentleness in the case of the dough, all while minimizing the use of separating agents and the effort of cleaning,” Dorner explains the results of the development.

belts,” Michael Gier describes the workflow. This flour-dusting on both sides means there is no need to use oil at all. This allows not only greater precision in producing the dough sheet, but also reduces the cleaning effort. Great collaboration with the colleagues from MULTIVAC The R&D team presented the first ideas for the Developed in close coordination with compact bread line in October 2020, and discustomers cussed them with many different customers in The developers and dough technologists of the early phase. They were keen to cover as many FRITSCH are constantly in close contact with of the bakers’ needs as possible with the new their customers, in two ways. They visit the cussolution. Above all, the precision in the dough tomers and learn through talking on-site about sheet form in terms of thickness, length and their individual needs in producing weight is of great importance for downbreads of all different kinds. Then, stream processing. A specially asthere are the many different masembled interdisciplinary team chines and lines at the of experts worked almost excluFRITSCH World of Bakery sively on the new development for testing and experimenting of the bread line for a year. with new recipes. Here, the “The specialists represented FRITSCH experts transform our entire range of expertise, their customers’ ideas and and so we were able to carry needs into real solutions, and out the first test before the end h t s help in the continuing develop- e e t of 2020,” reports Fred Dorner. nk in ha p t e e rf e c t s h a p ment of the lines. “For the new PROThe fact that FRITSCH had been part GRESSA bread, we looked at comparable of the MULTIVAC Group for two years systems on the market. From them, we were able benefited the developers at a crucial point. As a to figure out where there is need for improvement packaging manufacturer, MULTIVAC has a so that we can, even better, meet the customers’ great deal of experience with precise weighing needs,” Fred Dorner describes the approach of the systems. “We were able to benefit from this knowR&D team. how as we cooperated with our colleagues on FRITSCH’s own proven industrial line IMPRESSA further developing our system,” says Fred bread, fitted with the FRITSCH Soft Dough Dorner. The weighing system of the new bread Sheeter (SDS) developed specifically for soft line is equipped with two weighing units. The doughs, also served as the inspiration for the dough sheet is first weighed immediately before PROGRESSA bread. “We adapted a forming the guillotine. Once the desired weight is reached, system from the industrial line to the new line the guillotine is released and the dough sheet is on its smaller scale, and the result is the SDS cut into individual pieces for further processing. nano. Then, combining this with the SDR (Soft The second weighing is done immediately behind Dough Roller) downstream allows the dough sheet the guillotine. If the weight of the dough pieces to be conveyed scrap-free to the guillotine,” says deviates from the target weight, the cutting Michael Gier. The big advantage of the SDS nano process is corrected accordingly. is that it also flours the dough sheet from all sides. “From above and below, all that means is Oil-saving and quick to clean dusting the dough sheet and the work surface. To This extreme precision in dough sheeting, howflour the sides of the dough sheet, we use fold-up ever, is only one of many advantages that bakers IT

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eS DR

FRITSCH

can achieve with the new technology of the shearing forces and separating agents. Once the compact PROGRESSA bread. As a trained baker dough pieces have been cut to precision by the with a passion for dough, Michael Gier looks guillotine, with the help of the double weighing closely at how customers work with their masystem, they are ready for further processing. chines. “The design of a system decides how Manual processing is just as possible, for example, quickly and easily employees can remove or as using a belt rounding device for breads or a clean individual elements or parts,” Michael Gier baguette wrapping table. “There is even the option says. The principle is to achieve as many funcof using the weighing unit as a conveyor table tions as possible with as few parts as only, and transferring the dough sheet to possible. During the development, another FRITSCH processing line, great importance was therefore for example, using a spreading given to ensuring that all parts belt with a longitudinal cutting are easily accessible and simple device to produce multi-cut to remove. “There are no morolls,” says Fred Dorner. tors or other electrical parts, “With the PROGRESSA bread, such as sensors, in the drive bakers can process pre-proofed compartment of the line,” exdoughs as well as doughs with plains Fred Dorner. “This a high rye or water content in Pe means the machine can be disthe same quality as on a larges rf ce ec ie mantled quickly and, for the most scale line,” emphasizes Michael p t ly gh weigh ed dou part, cleaned quickly and thoroughly Gier. But the new bread line can also with the steam jet.” be a useful addition to the existing machine Another big plus of the PROGRESSA bread is parks of industrial customers, thanks to its that the amount of oil needed for processing the performance profile: being only five metres in dough sheet has been reduced to a minimum. length, it can be integrated effortlessly into almost The only oiling is the on-demand spraying of any existing production hall. the hopper on the SDS nano to ensure a uniform flow of dough. This prevents oil inclusions in Topic 2: Smart Production Insights the dough, which in turn guarantees excellent 24/7 LIVE in Production baking results of the finished breads. On top of Until now, it was just the dream of every prothat, it lowers the cost of oil consumption and duction manager or shift supervisor in mediumreduces the effort of cleaning. The hopper is sized to industrial-scale baking companies to filled according to the FIFO principle (‘first in, have an overview of the most important perfirst out’) to ensure consistent dwell times of formance data of the equipment in as close to the dough in the hopper and thus consistent real-time as possible. Not to mention the pre-proofing of the dough sheet. Continuous fill downtimes caused by minor malfunctions, or level monitoring ensures that the lifting tipper even the maintenance status of the equipment. automatically feeds in new dough as needed, Now, all this is possible with the Smart Producwhen the dough in the hopper falls below a tion Insights (SPI) from FRITSCH. “We always defined fill level. wanted to give our customers a live insight into their production and thus ensure the High product flexibility for downstream highest degree of transparency,” says Wolfang processing of the dough sheet Stegmaier, Project Manager for Digital DevelopFloured on all sides, the dough sheet is transferred ment at FRITSCH. “Our customers were already from the SDS nano to the FRITSCH Soft Dough doing a lot on and around their equipment Roller (SDR), which rolls it out extremely gently. and processes in the past to get their production Thanks to proven satellite head technology, a running as efficiently as possible,” Stegmaier highly uniform dough sheet is produced, free of explains. Now, with the continuous capture of IT

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SPI requires very little hardware and software Digitalization doesn’t happen without the communication of data. It is no different for using the Smart Production Insights. “Customers require constant Internet access to ensure data flows from the equipment to the cloud. Internet access is also required for the remote maintenance that FRITSCH offers for its lines. Remote maintenance allows the FRITSCH experts to inspect the performance, fault and maintenance data of the lines via the Internet and to offer the necessary support. SPI is not only available for new lines, but can also be retrofitted to many existing lines. Greatest possible transparency allows targeted improvements The overall equipment effectiveness can be called up on the dashboard, for example. This is represented by a bar and a percentage figure. Additional bars also indicate the availability of the equipment, performance in terms of the number of pieces produced and even their quality. “Also, from a simple tabular overview, the production or shift manager can immediately

recognize how the equipment has been operating in the last 24 hours,” says Wolfgang Stegmaier. “They can not only compare the results between different shifts, but can also follow the current production live,” Stegmaier reports. Realistic production results allow manufacturers not only to plan everything more precisely, but also to be more reliable in delivery times for their own customers. Equipment effectiveness can be improved further still by targeted analysis and troubleshooting, combined with the overview of upcoming maintenance times. Started at FRITSCH, continued with MULTIVAC The first steps towards digitalization had already been taken at FRITSCH in 2018. The structures for data collection from the controllers were defined and a dashboard was developed. The system was revealed to customers for the first time at IBA 2018. “After the takeover of FRITSCH, the system was developed further by the digitalization specialists of MULTIVAC,” says Stegmaier. “For this, they used a platform they had developed, and continued developing the system under the name Smart Production Insights, up to the point where now it is available to customers as a fully matured product,” the project manager explains. In the course of digitalization, Wolfgang Stegmaier, Stefan Eydel and the entire team at FRITSCH will continue working on further digital products. “An important source of information for us is of course the reviews of our customers, who day after day produce millions of rolls, pretzels, croissants, breads and fine pastries on our lines,” says Stefan Eydel. As they did during the development of SPI, the FRITSCH team can continue to rely on the proven expertise and cooperation with the colleagues at MULTIVAC, since they are all working towards the same goal: to continually improve the service for customers, so that they can achieve the highest possible efficiency on their lines. In the years to come, with the insights gained through data analysis, unplanned equipment downtimes will become a thing of the past. +++

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data and visualisation of key performance indicators in real-time, it will be possible to achieve further improvements still in overall equipment effectiveness. An important part of what the Smart Production Insights do is catch, record and report even minor disruptions that occur repeatedly during production. So far, it has been impossible for production or shift managers to respond to these brief disruptions, because they are not even detected. “Often, these are only fleeting problems that interrupt production for a maximum of two to five minutes. But if that occurs multiple times throughout the entire shift, then it all adds up to unplanned production downtime in the end,” says Stefan Eydel, Data Analyst at FRITSCH, outlining the problem. “From the data now presented by SPI, customers can now determine exactly what causes such disruptions, and take suitable actions to prevent these unplanned equipment downtimes,” the data specialist says.

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Thermal oil provides unique benefits when used as a liquid heat transfer medium in baking. Heuft tunnel ovens are the reflection of how the technology encapsulates these advantages to provide constant temperatures throughout the oven, in a gentle and even heat transfer to the products, while also achieving significant energy savings. This is the result of Heuft’s 50-year expertise in the field of thermal oil, and a background in oven manufacturing dating back 300 years. Unique advantages of thermal oil Thermal oil has very high heat conduction properties, minimizing the temperature difference between the transfer medium and the oven chamber. This helps thermal oil ovens to reach the baking temperature very fast; during the baking process, the thermal oil circulates at a constant temperature through the oven system.

Energy savings at the end of the tunnel oven

Depending on the number of products and pans per square meter in the baking area, a certain heating capacity is required, and such volume changes can result in unwanted heat variations. With its huge capacity for heat transfer, thermal oil always has enough power to keep the temperature constant during the baking process. The thermal oil technology perfected by Heuft ensures minimal temperature fluctuations in the baking process and leads to uniform, reproducible quality

www.heuft-industry.com Wehrer Str. 21 56745 Bell Germany

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consistently. Thermal oil not only conducts a lot of heat but also transfers this heat evenly to the products through the radiator plates inside the baking chamber. A constant temperature is maintained thanks to the three-way valves that are continuously regulating the amount of hot oil going into the oven to preserve the requested temperature. The small temperature difference between the heat transfer medium and the baking chamber is made possible due to the high heat storage capacity of the thermal oil, which is approximately 2,700 times higher than some of the other heat transfer mediums such as hot air. With this low Delta-T, there is no flash heat in the oven and, in case of an empty spot, the products are not burned; instead, this powerful heat is gently transferred to the baked goods. The result is a bigger volume, a better crust and a longer shelf life with a moist crumb, as the product is not dried as much as with other baking methods. The amount of thermal oil required varies depending on the size of the oven, and it can sustain baking cycles for years. With our new oil ‘HE30green’ in combination with our upgraded Nitrogen blanketing system, the lifetime of the thermal oil is assured for many years.

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Premium quality in an industrial dimension – tunnel ovens Our latest tunnel ovens running with thermal oil technology can have up to three decks that add up to a useful baking surface of up to 4m in width and 60m in length. We have perfected thermal-oil baking technology for half a century. While the basics of this technology have remained unchanged since then, our breakthrough developments have come with new oven models. The main one was launched in 1988, when we started producing static rack ovens heated with thermal oil. In the following years, we have been inventing different new oven solutions, most of which are custom-made. Over the last 10 years, we have focused on bringing serious improvements to and developing our industrial ovens, which is reflected in our newest equipment. With either a two- or three-deck setup, our tunnel ovens save considerable production space and are much more flexible than tunnel ovens with a single deck. Product-specific oven parameters allow us to reach the required baking result precisely; they include steam quantity, top and bottom heat, different temperature zones, convection zones, steam extraction and fresh air inlet. Our continuous product development always

Our way to thermal oil

+ With a history dating back over 300

years, Heuft is the oldest oven manufacturer in the world.

+ The Heuft group of companies is a

family-run business, now in its 8th generation, with Thomas Heuft as owner and managing director.

+ Bakeries in over 24 countries rely on the Heuft Thermal-Oil ovens.

+ Heuft can look back on more than

50 years of thermal oil technology. The Heuft philosophy of specializing in ‘100% thermal oil’ has been successfully established on the market. Today Heuft is the market leader for ovens with Thermal Oil technology.

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keeps our technology one step ahead. Production capacities can reach tremendous volumes. Each deck can be continuously fed with products via an ingenious but simple table-lift that provides products alternately to each deck. In a different setup, each deck could also receive products from two different production lines and bake just like two independent production lines. Further expansion is being considered. While adding decks at a later time may not always be a viable option because it means stopping the line, the oven can feature built-in additional decks for the future, which can be easily enabled at the appropriate time. Production capacities can vary broadly: our Euroback multi-deck tunnel ovens, for example, can have up to 12 decks ranging from 3 to 15m, with a step loading system. The total baking surface of this setup can reach 540m². The products are loaded continuously, as they are predominantly part of fully automated lines: the loading system can load the products step by step in each deck or it can be set to load each deck batch-wise. Fewer decks can be used in the case of big product varieties.

Different belts can be used, depending on the products being baked: + Hinge plate belt (baking on steel with a baking result that is very close to that achieved with stone baking);

Thermal oil at a glance

+ Thermal Oil technology ensures minimal temperature fluctuations in the baking

process and leads to absolutely uniform, reproducible quality 24/7, batch after batch.

+ The small temperature difference

between the heat transfer medium and the baking chamber is due to the high heat storage capacity of the thermal oil, which is approximately 2,700 times higher than some other heat transfer mediums such as hot air. With this low Delta-T, the powerful heat is gently transferred to the baked goods. The result is larger volume, better crust and longer shelf life due to reduced drying of the product.

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+ Stone plate belt made from natural granite stone; + Mesh belt with open structure for tins or trays; + Mesh belt with a closed structure for free

Significant savings The physical properties of thermal oil are the reason for the highest energy efficiency that can be achieved on the market. This results in enormously high energy savings, measured by comparing the heating capacity per square meter of baking surface, which is much lower with a thermal oil oven. Measurable energy savings are at least 25% and can easily go up to more than 40%, depending on the system it is compared with. Additionally, our Energy Management software makes it possible to save even more energy, and with our Bakery Information Center (BIC) the efficiency in the processes can be optimized. At the BIC, we can centralize all baking parameters to help you to reach the highest possible production efficiency. Our energy management system (EMM) is designed for larger installations, where we use more than one heat exchanger. This system can determine the required energy during the production process and can shut down one heat exchanger if its capacity is not needed. In addition to the savings achieved with the thermal oil technology, we can also recover the heat from the combustion gases and the dampers on the oven, which can be used to heat up water.

This hot water is stored in water tanks and can be used for many different purposes such as providing water for your crate washing machine, heating the offices, for cleaning purposes, or for sanitary use. To assist bakeries to maximize their savings, our Heuft-Energy department will make a projectrelated study and advise how and where energy can be saved. Baking green This level of energy savings contributes to significant drops in CO2 emissions and supports process sustainability efforts. With our central heating system, only one burner need to be installed, which further (and significantly) lowers CO2 emissions. A thermal oil oven with a baking surface of around 150 m², working three shifts, will emit more or less 6,000 tons of CO 2 less each year. Be prepared for future energy changes With our thermal oil system, it is possible to heat the oil with electricity, gas or oil. If in the future it seems that renewable electricity will be much cheaper, you won’t have to change your oven, but just adapt the central heating system!

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ENERGY SAVINGS + Around 6,000 tons of CO2 less emissions per year + Measurable energy savings can range from 25% to more than 40% + The Energy Management Software can contribute to even additionals savings + Heat recovery is also an option

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Custom-made ovens are no problem at Heuft. We build an oven for your products and not the other way around! Customizations can include: + Stone belts, hinge plate belts or different kinds of mesh belts are possible choices, depending on the products to be baked. + Separate top and bottom heat; temperature, steam and turbulence zones can be configured individually, to match product requirements. + The very high vertical range of manufacturing – all our ovens are completely built in Germany directly at the Bell plant, including associated components such as central heat exchangers.

baked products.

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Fresh products, natural ingredients, hygiene and sustainability are increasingly important for the bakery world at this time, bringing us on the cusp of big developments in the bread market. These developments are not only generated by trends such as bringing more time and rest back into the process but also by the world around us. Now that we slowly seem to be gaining the upper hand in the fight against Covid-19related, the next challenge is already on our doorstep. Again, it is no small matter: climate change! Unusually heavy rains recently caused flooding in Europe and China indicate that this change is indeed imminent and together we should also look within our individual industries to see what we can do to turn the tide. What can we do to ensure that future generations can also fully enjoy a nice slice of bread? Perhaps that is the reason why we should take a closer look at the Sustainable Development Goals of the United Nations (UN). We are already working very hard as an industry on SDG2: zero hunger. The goal is to provide good bread and natural ingredients at affordable prices.

Bring time on your side

In many countries, there is a growing demand for fresh, fresher, freshest delivery, transported to the consumer as soon as possible after baking and cooling. This means, on the one hand, that there is less time for production, which makes it vital to accurately handle ingredients in quantity and quality, consistency and precision. The speed and precision of 3D vision-guided robots, for example, ensure high and consistent throughput of scored dough products, with an incision in the right spot every time, while reducing labor costs. However, on the other hand, there is also the demand for natural ingredients. When working only with natural, basic ingredients of water,

Kaak Varsseveldseweg 20a 7061 GA Terborg, The Netherlands Phone:

+31 315 339 111

Fax:

+31 315 339 355

E-mail: info@kaak.com Website: www.kaak.com

KAAK

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luckily bread is taking its time…

Everything in life is going faster, luckily bread is taking its time… More time to rest and shorter production times go hand in hand. If, for example, bowl rest is added to the process the fresh, delicious products need to get to the stores fast. This means shorter processing times after bowl rest, saving time on final proofing, baking and/or cooling. Automation for sustainability However, adjusting the ingredients alone is not enough. As a company, we are also working hard

on solutions to make production more sustainable. Reducing our carbon footprint is an important starting point here. For example, our different types of electric ovens not only literally have a smaller footprint, but they also cause no CO2 emissions, whilst at the same time not compromising on the flexibility, control and quality of baking. We can definitely say that electric ovens are in demand: it is now relatively the fastestgrowing segment. This is a positive step, because electric baking ensures that our industry can move with the times, reducing its footprint, without compromising on taste and quality. Over the past year, circumstances have also helped to accelerate some of the steps towards a more sustainable world. We have all been communicating remotely a lot more; we have even remotely assembled and commissioned complete production lines for bakeries. Traveling less and partly moving meetings online not only benefits the world of tomorrow, but also saves time. We have learned lessons from installing our lines remotely, such as a beautiful pizza line in America and a toast bread line in Africa, as well as from installations closer to home. We have gained insights into how we can do things more efficiently, how we can improve our machines (hygiene, assembly speed, software logic, wiring simplicity, etc.) so that they are even easier to install and use; all with the aim of making it as easy as possible for our customers, while also

C O M PA N Y R E P O R T S

flour and yeast, it means extra TIME also has to be added to reach the desired end product! A long bowl resting period (often 2-3 hours), predough and long proofing times, independent of the quality of the flour, set the dough in motion. The result is the right aroma, the right volume and, certainly, the right taste. Additional benefits, not unimportant, are a natural extension of freshness. This philosophy is strongly reflected in the production of all kinds of baguettes, barras, ‘boules’, freestanding breads and ciabattas: long pre-dough development, longer bowl rest and after dough preparation a long post-rise (6-15 hours). Traditional processes, traditional breads that are then often baked on stone and today more and more MAP (Modified Atmosphere Packed) packaged find their way to the end customer directly or via retail. Process times are exceeding 24 hours in several examples at bakeries in Western Europe, the USA and Australia.

Everything in life is going faster,

KAAK

making our contribution to the SDGs. It's for a very good reason that we say, “You bake, we care!”. Managing waste (materials, ingredients, energy and time) influences how our company works in several ways. On the one hand, 3D printing and circular production (recovering raw materials) gives enormous savings in raw materials, whereas, on the other hand, we are also helping the industry to reduce waste with our dough recovery systems, transporting the dough back to the start of the line, where it is reworked and added to a new batch of dough. We also are always ensuring that the quality of the dough is consistent and of the right quality. We enjoy working with the bakery industry on building the world of tomorrow, a world where trips to our customers will be fewer

Kaak

Kaak has knowledge and experience in the field of bakery

longer needed. The mix of ingredients for the bread can

technology and equipment since 1846, ranging from

be determined independently. The pre-dough forms the

stand-alone machines to an integrated approach for

basis, the rest is up to you.

complex and automated bakery lines. Every day, we use the expertise we have built up over the years to ensure

Mixing & Dough make up

that your expectations are met and exceeded – from silo

Ingredients can easily be dosed from the silos into the

to truck.

MDD mixer (Mechanical Dough Developer).

C O M PA N Y R E P O R T S

The dough make-up can be done with a classic dough Silos & dosing

make-up with a dough divider, rounder, proofer, molder

It all starts with the right raw materials. Our silos and

or with a sheeting line. The right technology is available

dosage systems give you control over the ingredients and

for you, depending on the products and the required

the automated mixing process. With our Smart Bakery

capacity. Our lines can handle many different types of

Solution, recipes, stocks, raw material consumption can

dough, with large amounts of liquid ingredients or with

all be exchanged automatically. This means that you no

additives such as fruit and seeds. Whichever dough

longer have to enter anything manually, there are no

make-up is selected, high accuracy, great flexibility in

duplications and you always have the most current data

products and processes and ease of maintenance and

on hand. In addition, good raw material traceability is

cleaning are always guaranteed.

guaranteed. We offer a solution for all pre-doughs. This has advantages because as soon as the producer uses

Proofing

pre-dough, fewer additives are needed. The basic raw

We have several machines available for final proofing.

materials are available and they are known to be good.

Do you want the final proofing to be done on a flat

Another advantage of pre-dough is that pre-mixing is no

surface or in baskets? In the first case, the 'peelboard

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in number, thanks to new techniques, and a world where our footprint is also becoming a lot smaller due to new technologies, but with even more and more different types of bread.

+ Affordable and clean energy + Industry, innovation and infrastructure + Responsible consumption and production + Partnership for the goals

Sustainable Development Goals Kaak focuses on sustainable entrepreneurship, both towards our employees and from a social point of view. Our goals are derived from the Sustainable Development Goals (SDGs) of the United Nations (UN), with six of the UN goals, in particular, being embraced by our organization, namely: + Zero hunger + Good health and well-being

The activities resulting from this train of thought have a positive influence within the organization. There is plenty to choose from in the world and by letting SDGs guide our decision-making and working methods, we hope that young professionals and companies will continue to choose us and commit to us.

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Our key to innovation is the people that work for us.

+++

line' or 'belt proofer' are good concept choices. If you

Pan handling

prefer baskets, the shape of the bread will determine

After baking, the line transports the pans to the depanner,

the choice of concept. We have various baskets available.

where the products are removed from the straps. Since

The swing tray proofer is suitable for one or two baskets.

we produce and coat the pans and straps for this line

Do you prefer to proof in up to 4 different baskets? Then

in-house, we can guarantee a perfect release. In a toast

the 'multi-click line' is the logical choice.

line, an automatic delidder is placed in the production line between the oven and the depanner.

Decorating

Do you want several different products to be produced

A baker scores the dough products with a knife before

on the line that require different types of straps? Then

they go into the oven. This is done not only to ensure

our straps storage can provide automatic changeover as

that the product can be opened in a controlled manner

soon as other types of bread are to be produced.

during baking, but also to put his/her own signature on the product. A good or bad cut can make or break the

Cooling & Freezing

bread. These operations have now been automated

Baked products can be cooled down in our spiral cooling

because of the ever-increasing demand for a huge variety

system, our multi-step or multi-deck cooler. The empty

of freestanding bread in all sizes, shapes and flavors with

pans return through the strap cooling tunnel to the

their own 'signature'. Our cutting robots always make

starting location where new dough is placed in the pans.

the perfect cut in the dough. Cooled products are transported through our production

Baking or pre-baking, on an oven belt or a stone. There are

line to the packing department where the handling

plenty of choices available when it comes to Kaak ovens.

equipment plays a key role. This ensures that the right

Kaak has all the various baking technologies available

product is placed in the right crate for distribution to the

in-house and can help you make the right choice for

right shop.

your products. This line concept can be complemented, for example, by a cyclothermic oven or a thermal oil oven, both are also available in an electric version.

C O M PA N Y R E P O R T S

Crate handling Baking

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KOENIG GROUP BAKING EQUIPMENT

In little more than 50 years, Koenig has grown from a one-man business to a worldwide organization and is a market-leading specialist through the entire production chain of baked goods. The baking machine manufacturer is continuously cooperating with customers to create new solutions to support bakeries worldwide in their daily challenges and find answers to requirements that have significantly changed over time. This requires flexibility and automation – for bakeries and their partners.

C O M PA N Y R E P O R T S

Koenig leverages its experience to support its worldwide customers in the production of baked goods. This also means providing knowledge on dough technology, dough processing and consumer demands. Only with this know-how, is it possible to fit bakeries with the optimum technical equipment for their unique requirements. Until a few years ago, bread and pastries were produced traditionally by means of dough dividers or commercially. Due to the increasing production volumes and the variety of types, bakers had to massively expand in the area of automation. Other factors included steadily increasing weight accuracy and also the possibility of easier cleaning.

The future of the baking industry is automation

KGV COMBI 2000 – the benchmark for efficient roll plants

Nowadays, automation is indispensable. Automation promises efficient and innovative work and thus offers you a clear advantage. In order to follow this trend of automation processes, Koenig has steadily expanded in this area over recent years. Conveyor technology with the highest demands on hygiene (‘easy clean design’ or ‘hygienic design’), as well as fully automatic insertion systems round off the possibilities offered in special machine construction – thus the company develops the best solutions for individual special requests.

Koenig Group Baking Equipment Statteggerstraße 80 8045 Graz, Austria Phone:

+43 316 6901 0

E-mail: info@koenig-rex.com Website: www.koenig-rex.com

High standards with automation features Innovative design for optimized cleaning and maintenance: It offers easier cleaning, maintenance and access to all modules. + Maximum line availability by shortened cleaning periods and downtime of the line + New frame construction for optimized hygiene and accessibility Sloping surfaces at a 45° angle where neither + flour nor dough residues can deposit + Large service doors for easy accessibility for cleaning and maintenance All transfer belts can be released for easier + cleaning + Forming tools e.g. pressure boards, stamping head, centering unit are all removable for cleaning + All cover plates in the proofer can be removed without tools Open design of the stamping station with easily + and quickly exchangeable stamping + Tools that allow the best accessibility for cleaning and maintenance

+ Forming station, seeding unit and setting unit

in open design allow accessibility from both sides for cleaning and maintenance + Critical areas for cleaning and maintenance easily visible and accessible The right equipment adapts to the recipe – not the other way round “We have European customers operating with completely automated bread lines from mixing to freezing, but still forming the breads by hand in-between for the special hand-made character and authentic shape”, says Wolfgang Staufer, CEO. Koenig is very concerned about the climateneutral, sustainable production of its machines and also with regard to the further processing of the end products. Resource-saving production methods in combination with flexible logistics concepts and the digitalization of business processes are creating exciting opportunities. Koenig provides machines and lines for the entire production process, ranging from mixing and conveying, dough processing, proofing, baking, cooling and freezing. Thus, the manufacturer can find solutions for every bakery. Products can be manufactured automatically which have been made by hand before – while still retaining the artisan character. Wolfgang Staufer states: “There is hardly any product that cannot be produced on a Koenig dough sheeting line”. +++

C O M PA N Y R E P O R T S

Hence, Koenig aims to pick up every customer – from the small one-man bakery to the large industrial company – and bring them up to speed with the help of semi- or fully automated solutions, while at the same time increasing efficiency. However, these automation processes in no way downgrade production. Product quality is maintained in any case, if not improved. To keep the product quality combined with high flexibility in operations and maintenance as high as possible, continued research and innovations are essential. Koenig spends almost 10% in R&D to constantly improve technical standards which leads to easy handling and best quality. The company prioritizes increasing efficiency. With new innovations such as ‘Save Energy’ ovens, and the ‘Easy Clean’ and ‘Hygienic’ design of the modular roll line (e.g. Combiline plus EC) KGV Combi 2000 or raw material saving production, the equipment keeps its ecological footprint as low as possible while constantly increasing quality and process optimization. This makes Koenig more flexible and it facilitates the operation of its lines and machines for customers.

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M E C AT H E R M

C O M PA N Y R E P O R T S

PRODUCT QUALITY The M-NS divider allows the division of high-quality dough into dough pieces to be shaped into baguettes, balls, rolls

The industry has always had to deal with two seemingly contradictory requirements: produce quality and keep costs down. All the ingenuity of the manufacturer consists and is aimed precisely at satisfying these two market demands by leveraging the massification of production. But this lever is now being challenged by two new issues: first, the need for agility due to the markets’ versatility, as well as a requirement for increased sustainability due to the stakes becoming worldwide in terms of environmental preservation and social progress. For Mecatherm, automation is a solution only if the human factor remains central. It is as much about the efficiency of these new technologies as it is about the viability of companies that still need to value the work of

The human must remain the pilot

their operators. Albert Einstein once said rather pessimistically: “It has become appallingly obvious that our technology has exceeded our humanity.” But is this alarming fact inevitable? On the contrary, can't we consider that technology finds its meaning when it is at the service of the human being in our companies? This is how Mecatherm, a

MECATHERM Route du Maréchal de Lattre de Tassigny 67130 Barembach, France Phone: +33 388 47 43 00 E-mail: info@mecatherm.fr Website: www.mecatherm.fr

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M E C AT H E R M

New challenges ahead According to Robert Broh, author of Managing Quality for Higher Profits, “Quality is the degree of excellence at an acceptable price and the control of variability at an acceptable cost.” This seemingly complex definition of quality is actually a perfect summary of the manufacturer's craft itself. For the industrialist, a product is ‘of quality’ if it meets demand at a reasonable price. Indeed, since perfection does not exist, quality corresponds to the satisfaction of a precise expectation that carries a value that consumers are ready to give it: its price. At the same time, the manufacturer must face a large number of variables while remaining within their technical and financial capacities to face them, if not, they run the risk of making the product fall out of its value, of its fair price. This is obvious to any industrialist who has experience. But what can you do when variability increases too much on the demand side? How to do this when the market’s versatility undermines

the very principle of industry, which is the massification of production to achieve an optimized degree of quality and cost? “A fashion that appears in a suburb of Los Angeles can, via the Internet and social networks, be a trend the next day in a European or Asian country”, notes Olivier Sergent, Mecatherm President. This creates the tension that too many industrial companies experience between marketing on the one hand and production on the other. The manufacturer, bound by their industrial logic, on the one hand, will ask for time and quantity to reach quality at the right price, as mentioned above. The marketing department, on the other hand, will recall that the market requires agility at the risk of losing market share, in terms of variety and versatility, in the context of global competition. Added to this are the demands of CSR and compliance. Not that those serious industrialists were devoid of morality, which, as we know, was not invented by the Y generation. But the requirements have increased. The environmental stakes are such that energy waste is no longer just an accounting requirement but a matter of life and death. Also, the globalization of the media and means of communication has made inhabitants of very distant countries feel compelled to participate, or at least not to slow down, the social progress of each other.

FLEXIBILITY The IBA and IBIE award-winning MTA oven is a key element in the flexibility or scalability of the lines

C O M PA N Y R E P O R T S

French company known worldwide for its automatic production lines for bakery and pastry products, Mecatherm considers its mission and implements it with its customers with concrete and reliable solutions at the cutting edge of technology, especially digital.

M E C AT H E R M

C O M PA N Y R E P O R T S

COST OPTIMIZATION The new M-UB mechanizations eliminate 50% of the causes of defects while guaranteeing flexibility and modularity

What can we do about it? Faced with these challenges, the manufacturer has two main areas for action: its staff and its suppliers. For Mecatherm, these two levers can and must work together and enrich each other. Industrial bakers are permanently facing great difficulty to find and keep a qualified workforce. However, according to Olivier Sergent, “as an equipment supplier, Mecatherm is an automation supplier” and automation consists precisely in replacing manual activity. However, automation, far from diminishing the role of the worker and the human element, frees it and improves it. Indeed, it is about automating labor, not thinking or creativity. The machine does not make decisions for the operator, it extends their human capabilities and allows them to accomplish what they have in mind. Such a vision of automation at the service of human skills makes it possible to respond to Einstein's concern that we mentioned above and to value the work of men and women

in the field. In concrete terms, it is digital technology that will enable this fruitful interface between human work and machine automation. Reliable technological solutions When an operator takes up his or her shift on Monday morning, he or she asks three questions: Are my machines in working order with no upcoming failures? Is each element of my production line performing its role satisfactorily? Will I be able to produce everything I need this week? As a supplier, Mecatherm wants to be able to answer positively to each of these questions. But when lines are installed all over the world, it is difficult to accompany the operators to ensure that our promise is kept. This is why Mecatherm has set up a digital interface with which a Mecatherm technician is linked, and which allows them to share solutions with the operator and thus to accompany, train and inform them on the status of the line and its operational performance. From this point of view, digital tools do not automate processes, but they accelerate and facilitate them. And above all, digital solutions allow precious proximity between the industrialist and the supplier. The importance of this proximity was especially measured by Mecatherm during the period of the health crisis. Indeed, many manufacturers have praised Mecatherm's continuity of service during this period that prevented the majority of travel and physical meetings. The digital solutions that Mecatherm had already put in place to solve the equation of geographical distance proved to be indispensable during the pandemic.

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The whole spectrum of equipment upgraded By the end of 2021, Mecatherm will have completed its ambition to upgrade its entire range of equipment, making them all the more reliable. For example, the new M-NS ‘No stretch’ divider, which preserves the quality of a hydrated dough while guaranteeing an ideal weight and shape, is an important improvement to bring to a production line. Ovens and coolers have lost 15% of the possibility of failure and they now have much better agility. For instance, the M-TA oven, which

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M E C AT H E R M

The digital solutions developed by Mecatherm are centered on the needs of the industrialist, the operator on the field. This is why they intervene at all stages, from the design of the production line to its maintenance, via operator training, production planning, line operation, control, etc. If we consider the planning stage, for instance, Mecatherm wanted to bring a concrete solution to this new need of agility and flexibility that was mentioned above. The era of production lines that manufacture the same item without change for long periods of time is over. In order to reduce the high costs associated with changing products and setting up a new manufacturing process, the M-Plan tool allows sequences to be run virtually and thus optimize performance. Similarly, to make operators swiftly functional, the M-Twin tool allows technicians to acquire skills through a realistic simulator. Also, by joining forces with ABI Ltd, Mecatherm benefits from a strong ally in North America.

From this marriage was born, for example, a special production line for bagels; the fruit of Mecatherm’s experience in manufacturing pastries of all kinds, and the specialization of ABI in the field of automation and robotization. A carefully conceived and orderly automation of processes Mecather’s hindsight and experience give it a unique perspective on the market. It is thanks to this view that the French company has been able to integrate its technological progress in the field of automation into a broader way of thinking. This one takes into account a vision of the human being and his place in the organization, who must remain central, and an evolution of the market which does not leave any more the leisure to the industrialists to rely on their established assets by producing the same thing in mass, without paying attention to the versatility of the demand. Mecatherm, under the leadership of its President Olivier Sergent, insists that the key to harmonizing people and process automation is the digital interface. Flexible by nature, it is the link between man and machine and it ensures that the machine serves man and not man the machine. +++

The customer “service” approach starts at the pre-project stage and only ends 20-30 years after the line is commissioned. All Mecatherm digital services are EASY-TO-USE and ACCELERATED by digital applications

C O M PA N Y R E P O R T S

received the IBA Innovation Award in Munich, is specifically designed to satisfy the need for flexibility while maintaining quality requirements and baking homogeneity.

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RADEMAKER

Rademaker Academy: quality in bakery knowledge

C O M PA N Y R E P O R T S

With over 40 years of experience in the development and global supply of industrial food processing equipment and bakery production systems, Rademaker has accumulated extensive knowledge of food products and production technology. Maintaining and sharing this knowledge is the goal of the Rademaker Academy in Culemborg. Kasper Rozeboom, Aad den Braven and Jan Willem Jansen are the driving forces behind the Academy. Rademaker started its Rademaker Academy in 2015 as an additional service. The aim was to familiarize operators and maintenance engineers with a new production line and to retain and share knowledge both internally and externally via high-level courses and training. This made Rademaker the first producer of food processing equipment to have its own Academy. Now there’s a Rademaker Technology Centre (RTC) in Culemborg, a fully-fledged teaching and training location complete with a teaching room, a practice room

Training is money well spent

with a fully operational two-section laminator and universal line, and a virtual reality lab (VR lab). On-site training is also an option using a mobile VR setup. Rademaker offers basic and advanced training (see box) in both operating as well as maintaining Rademaker machines. The basic training begins with a broad explanation of the machine and focuses on building the basic knowledge of dough technology and production technology required for optimal machinery operation. The advanced training focuses on more specific and in-depth content. On a technical level, the training could address fat pump or dosing

Rademaker B.V. Plantijnweg 25 4104 BC Culemborg, The Netherlands Phone: +31 345 543 543 E-mail: office@rademaker.nl Website: www.rademaker.com

RADEMAKER

Sharing knowledge Kasper Rozeboom was involved in the Rademaker Academy from the start. He first worked as a technical trainer to train Rademaker production line users. These training courses were a success and it quickly became clear from the growing number of requests that more people were needed for this. As a general trainer, Kasper now develops and delivers the various Academy training courses, together with Aad den Braven, technical trainer, and Jan Willem Jansen, technological trainer. Rozeboom: “With the new, well-equipped location in Culemborg, we have taken a huge step in offering an even better service to our customers. When knowledge is passed on by people who understand the practice, this offers true added value.” Den Braven added: “I sometimes compare it with obtaining a driving license. You can only drive a car as it should be driven if you have a driving license. It’s the same with our machinery. The users obtain their ‘driving license’ at the Rademaker Academy, which ensures that they can operate the machinery optimally in their own environment.” Jansen: “If a customer’s employee leaves, their knowledge disappears too. New operators don’t always know what they should do if the products that are rolling from the production line no longer meet the required specifications, for example with respect to weight or shape. We help bring their knowledge to the right level. We also use the training to demonstrate to our customers exactly what they may expect from a production line, enabling them to estimate accurately whether they can meet the requirements of their own customers.” Retaining knowledge As well as sharing knowledge there’s another reason for the establishment of the Rademaker Academy. “There are many people working within Rademaker who jointly have huge amounts of knowledge and if any colleagues leave, we don’t

Training on offer A team of technologists and technical trainers developed two training themes that focus on the efficient operation of the Rademaker production lines. Training is given in Dutch, English or German. An interpreter is engaged for any other language. Basic training 80% of the basic training focuses on production line information and 20% on dough technology. The purpose of this training is to familiarize the team with the production line to ensure a smooth start-up that results in efficient production performance. Up to eight participants per trainer. Basic operator training For operators, managers and line managers. Theory: unit descriptions, cascade, synchronization (if applicable), virtual reality training and touch panel explanation. Practical: practice using dough on the line, problem-solving and cleaning training. Basic maintenance training For maintenance engineers and managers. Theory: unit descriptions, cascade, synchronization (if applicable), virtual reality training and touch panel explanation. Practical: training in preventive maintenance, controlling transport lines, lubrication, problem-solving and cleaning. Advanced training The advanced training covers machinery settings and process interactions. As well as the basic principles of technology and working on product refinement, this also includes production line operation, coordination, maintenance and cleaning. Up to five participants per trainer. Advanced operator training For product developers, managers, technologists and line managers. This training focuses on specific units, ingredient information and product and process knowledge and is tailored to the customer. Teams can resolve production line problems after training. Advanced maintenance training For maintenance engineers and managers. Customized training that focuses on specific units, electrical systems, the process and advanced mechanical knowledge. After training, teams can resolve the bigger problems and maintain the production line correctly.

C O M PA N Y R E P O R T S

machine maintenance and in a technological area, training can relate to specific actions in the production process.

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want their knowledge to be lost. By collecting knowledge we can retain this and incorporate this in training that we can then offer at the right time”, explained Den Braven, “and that doesn’t necessarily need to be straight after installing a production line. Our customers can decide to first gain experience with the machinery and study it in more depth later at their own site or our RTC, or they can have their employees trained or retrained. When the engineer installs the machinery, he will give a brief explanation of its operation. During the delivery of a production line, there’s not much time for a detailed explanation and it’s often not the right time as it’s too much for people in one go.” It’s fine to make mistakes There’s a practice space in the Academy that can be used specifically for training purposes, for instance, to demonstrate the most important facets of maintenance and production. Rozeboom: “The course participants come to Culemborg, so that they can focus entirely on the training, without the usual distractions present in their own workplace environment. We can explain the

essence of a production line perfectly in small groups.” Jansen: “It’s also a safe environment. This training line’s first production steps match the large production lines that participants use in their workplace. The big difference is that it’s OK to make mistakes. After all, that’s how you learn. Moreover, the small group size means that everyone receives the attention they need. As trainers, we adapt the training to the group’s needs.” Den Braven added: “Our experience helps us in this. All three of us have tremendous practical experience and have rolled up our sleeves and learned on the job. We know exactly who to turn to within Rademaker if we need specific knowledge, which means we always have up-to-date information within the Academy.” This enables us to continue to expand the Academy’s knowledge base.” VR lab “We’ve been offering part of the operator training in the form of virtual reality training for the past year,” explained Rozeboom, “a fantastic way

RADEMAKER

“The VR training is also a great interim step to clarify the theory. Participants start with the theory that they can then use ‘in practice’ in virtual reality. After that, they use the knowledge they have gained on the training line in the practice area and then they are ready for the real thing,” stated Jansen. Den Braven added: “This training also has a didactic advantage. Sometimes during production line training in practice, a participant may need to be ‘skipped’ due to time pressure, but with the VR version everyone gets a turn.” Combining knowledge Baking is a traditional craft but, following technical developments, it has changed a lot over the years. Jansen: “The range of products across the world is huge and our production lines can do a lot, but basic baking knowledge continues to be important. This knowledge of the craft is increasingly disappearing, but knowing what happens to dough under certain conditions is still needed when making that switch to machinery. This requires knowledge of ingredients and production processes, which is also something we cover in our training. Just like the technical machine aspects for that matter.” The Rademaker Academy trainers have sometimes become aware of friction in this. Den Braven added: “You do sometimes see two camps: operators versus technicians. To optimize the customer’s use of our machinery, it is vital that our training courses are well-aligned. Then operators and technicians strengthen each other and resolve any problems more quickly to keep the machinery running.” Continued development The teaching location in Culemborg and the on-site training, with or without virtual reality,

provide a firm foundation for the continued development of the Rademaker Academy. Rozeboom: “New production lines are arriving all the time and existing lines continue to be developed. This means that we will need to develop new training and will have to keep adjusting the existing training. The operation screens used in the lessons must always match the screens that customers use in their production lines. Virtual reality will replace some of the theory and practice, and the online training that we developed in connection with Covid-19 is also here to stay. We see these types of training as a fantastic addition to our range.” Jansen and Den Braven agree that the Academy will increasingly meet demand: “More and more requirements are set for products and, with the right training, we can help our customers meet those requirements,” stated Jansen, “in addition, good training prevents unnecessary production cost increases and retains or even improves production quality. This means that investment in training is money well spent.” Aad den Braven is a technical trainer and is in charge of developing and delivering the maintenance part of the training. With his extensive experience as a service engineer, he trains colleagues across Rademaker and, together with Jan Willem Jansen, also supports customers on-site, as well as increasingly delivering online training. Jan Willem Jansen knows everything about the dough processing on the production lines and how to operate these. The teacher training he followed ensures that he is able to optimally ‘translate’ his knowledge of manual food production processes into machine operations. He also thinks it’s important to remove people’s fear of the scale of the equipment. Kasper Rozeboom is a general trainer, develops new training and coordinates the teaching program. He was the first technical trainer and has also helped to develop the Rademaker Academy in recent years with the teaching and practice areas and the VR lab. +++

C O M PA N Y R E P O R T S

to practice operating a production line reliably without waste or financial consequences. We do this at our Academy in Culemborg, but with a mobile setup, we can offer it anywhere in the world. Initially, it takes a while for people to get used to walking around with a VR headset. Once they are used to it, the participants are all very enthusiastic. We’ll certainly be developing this further.”

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SUGDEN

Baking for joy

C O M PA N Y R E P O R T S

At Sugden, we are fully committed to meeting the needs of our customers. Our highly skilled design team and engineers have, through years of experience, accumulated comprehensive knowledge to produce worldleading industrial baking equipment. We work closely with our customers to implement efficient and effective solutions tailored to their requirements. We also monitor the market situation and the needs of bakeries of all sizes. With that in mind, we used our tried and tested technology to develop our mini hotplate, a semi-automated production line designed to meet the need of craft bakeries. This line produces quality products on a smaller scale, around 800 crumpets or 1,200 pancakes per hour. This now sits proudly alongside our larger-scale production lines as part of the Sugden portfolio. Products The core business of Sugden Ltd is in designing, manufacturing, installing, and commissioning bespoke industrial baking equipment for English muffins, crumpets and pancakes. We also manufacture a multiplant that can produce a range of batter products and can also incorporate sheeted products. In 2021, we acquired the complete VanderPol brand and range of equipment from AMF. This range of lines and equipment fits perfectly into Sugden’s portfolio: Stroop Waffle lines, Funcake lines, Belgian Waffle lines and Soft Waffle lines. Core competences At Sugden, we work as a team on all fronts worldwide. We remain in contact with the customer from the beginning of each project until well after the delivery of a product, providing support and guidance. Our highly skilled and vastly experi-

enced teams work with the client to ensure each project achieves a successful conclusion. Trust, innovation, listening, executing, and working with the client all combine to ensure a satisfied customer and the successful delivery of products: that is our mission and that has brought us to where we are today, a producer of world-leading baking lines. +++

Sugden Ltd Pendle Court Nelson Lancashire BB9 7BT, United Kingdom Phone: +44 128 261 11 99 E-mail: sales@sugden.ltd.uk Website: www.sugden.ltd.uk Sugden Ltd was formed in 1973 and we currently has 38 employees working with enthusiasm on daily base. They work in a high-tech, 35,000sqft production area to build all equipment and spare parts.

SUGDEN

English Muffin Production Lines (up to 26,000 pcs/hr.)

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Crumpet Production Lines (up to 14,400 pcs/hr.) Multiplant (up to 7,000 pcs/hr.) Mini-Hotplate (for Craft Bakers) (up to 800 pcs/hr.)

Pancake Production Lines (up to 18,900 pcs/hr.) Multiplant (up to 8,400 pcs/hr.) Mini-Hotplate for Craft Bakers (up to 1,200 pcs/hr.)

Stroop Waffle Production Lines (45 to 90 mm, up to 36,000 pcs/hr.)

Funcake Production Lines (from 4,500 up to 20,000 pcs/hr.)

Belgian (Sugar) Waffle Production Lines (from 4,500 up to 20,000 pcs/hr.)

C O M PA N Y R E P O R T S

Soft Waffle Production Lines (from 4,500 up to 40,000 pcs/hr.)

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TECNOPOOL

Complete spiral system control

Tecnopool has always provided complete services for total processing, which comprehensively cover the entire spectrum of required temperatures, from deep-freezing, cooling, baking, pasteurizing, proofing, to product handling and frying. They are all customized to specific needs, thanks to the flexibility of the spiral design. Tailor-made configurations are essential when referring to a system’s integration/overhauling, as the widths of the belts can vary significantly, with values anywhere from 250mm to 1,500mm, for example.

C O M PA N Y R E P O R T S

The spiral is at the core of solutions from Tecnopool, and is built as a flexible and modular system that can support all processes in automated bakery production, from conveying to baking, through to cooling and freezing. Spiral technology can be universally applied and optimized throughout bakery manufacturing processes thanks to its structural flexibility and innovation. Dedicated materials and technical solutions help adapt spirals to perform in all conditions in the production chain. Comprehensive system diagnostics: the next step As Tecnopool researches solutions based on real-live production needs that are shared by customers, what defines a ‘complete’ system has been changing along with technology innovation. Built on patented spiral technology, solutions

continuously incorl oo porate various new deop ecn ©T velopments to meet the manufacturers’ production requirements. Efficiency is the first priority. To increase production efficiency, “Over the years, the customer has requested an increasingly detailed and complete system diagnostic, so we have implemented the mechanical and electrical control of plants and lines, as well as the related warning and alarm signals,” explains Massimo Petranzan, automatic and electric engineering project manager of Tecnopool. Moreover, energy efficiency is one of the priorities for bakeries and for Tecnopool. The company’s solutions aim to lower production costs while being mindful of the impact on the environment: innovations provided help save up to 20% in

TECNOPOOL S.p.A. Via Buonarroti, 81 35010 – San Giorgio in Bosco – PD, Italy Phone: +39 049 94 53 111 Fax:

+39 049 94 53 100

E-mail: info@tecnopool.it Website: www.tecnopool.it

TECNOPOOL

Under development, always Tecnopool creates bespoke designs with innovations and features uniquely applied to the customer’s production needs and plans. The core spiral technology and all auxiliary components have been continuously developed over time. Research and development receive 10% of the company’s turnover to achieve continuous innovation. Some of the newest updates the company provides include the addition of checks on the mechanics of the machine to detect any malfunctions, effectively preventing potential breakdowns and downtimes that might occur if repairs were needed. Another tool, particularly useful in present circumstances caused by COVID-19 measures, and a necessary step up going forward, is remote assistance. “We have inserted a remote assistance system that allows us to assess the status of the machine remotely. This system also allows us to record the variations in the machine’s parameters at a distance. Recorded alert events provide valuable information to help us understand how the machine is working and to intervene where necessary.”

+++

Tecnopool TP FOOD GROUP: World leader in the design, manufacturing and installation of equipment for complete production lines, covering food industry heat treatments during all stages of processing: proofing, baking, cooling, freezing and pasteurization, from -40°C to +300°C, with infinite fully customizable layout configurations. Tecnopool TP Food Group is positive and determinate about its future, driven by the strength of six leading worldwide recognized companies. Constant commitment and ambition will be the leitmotif for continuous growth, to remain on the leading edge of a constantly evolving market.

C O M PA N Y R E P O R T S

Accurate fine-tuning of all processes + Proofing Tecnopool offers a choice of two different Air Treatment Logic solutions: a Centralized Air Treatment Unit or a precise Zone Management System, to ensure precise climate conditions throughout the proofing room. 3D humidity and temperature controls grant an optimal climate inside the cabinet and help the customer to handle even the most sensitive product. + Cooling and freezing A dedicated internal software monitors, calculates and optimizes the temperature of each product. Deep freezing can be particularly challenging to maintain within constant settings, in order to minimize the formation of frost. We offer solutions that implement sequential defrosting of evaporators, where each evaporator defrosts autonomously, while the others keep running to allow customers to operate 24/7. + Baking The temperature regulation in individual temperature zones is very precise, with a +/-2°C

variation; very accurate and flexible baking temperature regulation is possible, even down to the level of a single baking deck, both by deck length and height.

energy, while still supporting the manufacturing of high-quality products consistently. Similarly, Tecnopool prioritizes sustainability not only for customers, to meet multiplying requests, but also adheres to it for its own operations.

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W P BA K E RYG RO U P

processes

Connected

C O M PA N Y R E P O R T S

Bakeries are on the cusp of significant improvements in the efficiency of all manufacturing processes, thanks to innovation in digitization and technology enabling a higherthan-ever degree of accuracy. This is particularly visible in high-volume manufacturing plants, where such solutions contribute to baking efficiency, as well as to product and process safety. As an important added benefit, new technologies contribute to the overall sustainability of the operation. Digitalization can offer a wide range of opportunities, from monitoring production to optimizing processes and analyzing resource availability in real-time. To take advantage of improvements that digitalization has to offer, smart equipment is needed. This doesn’t necessarily have to mean existing technology has to be replaced; software can help equipment become intelligent. Some bakery machinery already comes equipped with an interface for remote control, analysis, or production monitoring – such as ovens from Werner & Pfleiderer, the mixers from WP Kemper, or other WP Bakerygroup products.

WP Bakery Control Software WP Bakery Group developed the WP Bakery Control Software to help make equipment smart. This tool records all process parameters and analyzes the resulting data to control production in real-time. Installation is as easy as plug & play, after which it can run on mobile devices for remote access of current and historical data. Working with the WP Bakery Control application begins with oven integration. The app can store, display and export data regarding the oven, the recipe and the production and contributes to optimizing the baking capacity and energy utilization. By improving the control of the baking sequence according to the capacity used, bakeries have also achieved noticeable gains in the freshness of the products sold. WP Oven-Control In addition, the WP Oven-Control connects all production ovens and in-store baking ovens to a single computer. From there, recipes can be adjusted and added to all of the selected ovens. It also features functions for remote diagnosis

W P BA K E RYG RO U P

Robotics Technology and applications are boosting the use of robotic solutions with numerous opportunities in bakeries, especially in the field of collaborative robots. The possibilites are virtually limitless to match requirements, thanks to their flexibility, making them suitable for any applications from loading and unloading machines, to cutting or tray handling. Possible applications are shown below.

Von-Raumer-Str. 8–18 91550 Dinkelsbühl, Germany Phone: +49 9851 905-0 Fax:

+49 9851 905-8346

E-mail: info@wpbakerygroup.com Website: www.wpbakerygroup.com

WP Kemper GmbH Lange Str. 8-10 33397 Rietberg, Germany Phone: +49 52 44 40 20 E-mail: info@wp-kemper.de Website: www.wp-kemper.de

Werner & Pfleiderer Haton B.V. Industrieterrein 13

5981 NK Panningen, The Netherlands Phone: +31 77 307 18 60 E-mail: sales@wp-haton.com Website: www.wp-haton.com

WP Lebensmitteltechnik Riehle GmbH Heinrich-Rieger-Str. 5

73430 Aalen, Germany Phone: +49 73 61 55 800 E-mail: info@riehle.de Website: www.wp-riehle.de

Werner & Pfleiderer Lebensmitteltechnik GmbH Von-Raumer-Str. 8-18

91550 Dinkelsbühl, Germany Phone: +49 98 51 90 50 E-mail: info@wp-l.de Website: www.wp-l.de

Werner & Pfleiderer Industrielle Backtechnik GmbH Frankfurter Str. 17

71732 Tamm, Germany Phone: +49 71 41 20 20 E-mail: info@wpib.de Website: www.wpib.de The WP BAKERYGROUP is the world’s largest manufacturer of machinery and equipment for bakery business of all sizes.

C O M PA N Y R E P O R T S

WP Connect This digital monitoring system uses hundreds of data points to continuously diagnose the condition and assess the functionality of the machine. In this way, potential malfunctions are identified before they occur, to avoid production downtimes. The comprehensive technical information that is continuously processed and mapped describes in detail the production reliability of the machine. For example, the currents acting on the drives are maintained within the specified range at all times, which is of utmost importance for the machine’s operation and long-term upkeep. WP Connect associates all process data with the identity of the equipment, recording details about the type and the serial number of the machine, the software version used, the number of operating hours and units produced, the maintenance operations carried out, the next maintenance service, and malfunctions, should they occur. It diagnoses all relevant information related to the dough to ensure it meets all desired parameters: the flow of the dough, the filling levels on the roller frame, the dimensions of the dough pieces, their weight, and the oiling temperature. It also includes information about the current operator and the recipe in use. All this information helps to expedite service work significantly.

WP BAKERYGROUP

and preventive maintenance. The app collects production data in real-time, associated with running the baking programs. Depending on the baking program, temperature curves are recorded as well. This information helps bakeries achieve a continuously consistent high quality in all their branch stores, for example. WP’s service department can also connect to the app, if requested, to help sort out any errors that might occur.

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W P BA K E RYG RO U P

1. Articulated robots The main application for which the WP Bakery Group supplies robotic solutions is tray handling. The robots can perform a variety of movements that are required for different tasks and are particularly well suited for simple assignments such as moving trays independently. Some of the actions these versatile robots can perform include: Tray loading/unloading In combination with a machine, robots can position and place trays or lift them, as needed. For example, they can be assigned to remove empty trays from tray trolleys or stacks from unloading stations and place them on conveyor belts, which then transport the empty trays underneath the deposit belt of a running machine, and deliver a full tray to the loading station. The second robot in this scenario takes the full baking trays from the conveyor belt and places them into an empty proofing trolley. Further automation steps can be added along the process such as supplying and removing the proofing trolley. Emptying trays One robot or two can be installed to empty the baking trays, each with an output of four trays per minute. As the trolleys are conveyed to the unloading station, the robot removes the trays

and slides the products onto a conveyor belt. Before placing the trays back into the trolley, they are cleaned at a cleaning station. When two robots are used for this process, they remove trays from trollies positioned in four unloading stations. Articulated robots can perform all the movements of a human arm. However, the robots do not share the human limitations in their range of motions. Thanks to their 360-degree joints, more freedom of movement can be programmed. The safety of the team in the workspace is taken into consideration as well, so that the robots can be set up without the use of a space-demanding protective cage. An environment scanner is placed to monitor when people enter the work area, to prevent accidents that might otherwise occur.

C O M PA N Y R E P O R T S

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W P BA K E RYG RO U P

Cutting Delta robots can be programmed to cut different patterns into dough. Applications can range from pretzels and bread rolls to baguettes. A 3D camera system recognizes the dough pieces and applies the selected cutting shapes. Ultrasonic, rotating knives or water jets can be used to perform the cuts. The choice of the robot that is the best fit for the operation at hand is determined by several factors: the product itself and its characteristics will be the first aspects to be looked at, as well as its weight, the type of cut and the desired cutting angle. It is important to match the products to the cut, for best results. Smaller products, in particular, can move when the knives are rotating, which can result in inconsistent or substandard cuts. When cutting is carried out with water jets, water streaks can appear along the cutting marks of lye products, for example. Monitoring is continuous and it helps to improve process performance on the spot. The products are measured so that when they are not correctly positioned, the robot immediately reacts and adjusts its action to the position it has scanned and identified. Live process monitoring is also an option. As they operate, the robots will continuously specialize in their actions based on the data they gather. The cutting parameters are automatically determined. WP Robot at work WP Riehle has connected a robotic module to the COMJET automatic lye application unit for increased efficiency when manufacturing lye products. The robot makes accurate cuts into the dough pieces, after the lye application: lyedipped dough pieces move automatically into the robot cell, which is equipped with camera technology that uses a laser-3D triangulation method with up to 78 cameras to visually record

the dough pieces. Robots then make precise cuts exactly as specified. Different cutting techniques are available: ultrasound, water jet, rotating knife and fixed blades. The ultrasound knives are available in titanium alloys with various cutting widths. The mechanical knives are available with regular cutting blades, rotating circular knives or serrated blades. The water cutting technique generates a fine jet of water that can cut into and through the individual products. As the lye application process is fully automated, only one operator is needed to run the plant to apply lye to up to 300 trays/hour. Feeding proofed dough can be done with a roll-off device. Peelboard feeding is now also available. The loading trolley is automatically docked onto the loading system, and all subsequent process steps run fully automatically. The dough pieces are received at the loading station and fed into the lye application machine. In addition, the belt speed can be adjusted according to individual products. After this process, the dough pieces are deposited directly onto a baking tray or transferred to a cutting plant. The COMJET has a 180-liter lye tank, which can also be equipped with lye tank heating. Special models can be built to link directly to downstream processing machines – e.g. tunnel ovens or shock-freezers. Handlling: Doing the heavy lifting For safe handling of the dough, automated lifters ensure the bowl or box is lifted/lowered quickly and gently, to the required heights, and tipped to feed lines such as the PANE or PANE PUR dough strip lines. Throughout this process, the characteristics of the rested dough are preserved. This process is especially handy for the trend for products with soft dough with a long resting time. The lifters are designed with a compact layout to minimize the space they need in the production area. The large-capacity lifters incorporate safety technology and the control of the filling level is also automated. Uniform lifting is ensured in day-to-day operations, regardless of the amount of dough in the bowl. They can be customized to specific mounting positions and tipping directions. +++

C O M PA N Y R E P O R T S

2. Delta robots Delta robots such as cutting systems are an important solution in the production of baked goods, and are often used together with articulated robots.

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Other books published in the Yearbook series: + The European Bakery Market, 2019 + The Future of Baking, 2018 + The European Bakery Market, 2017 German edition + The European Bakery Market, 2016 + Bäckereitechnologie: Forschung und Innovationen, 2015, German edition + Innovations, 2014 + The European Bakery Market, 2012 + Bread, 2010

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+ Dictionary of Bakery Engineering and Technology

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Articles inside

WP BAKERYGROUP: Connected processes

TECNOPOOL S.p.A.: Complete spiral system control

Rademaker B.V.: Training is money well spent

Sugden: Baking for joy

MECATHERM: The human must remain the pilot

Koenig Group Baking Equipment: The future of the baking industry is automation

Kaak: Bring time on your side

Heuft Industry: Energy savings at the end of the tunnel oven

FRITSCH Group: Progress in the world of bakery

Diosna: Everything from a single source

Ernst Böcker: Why sourdough plays a decisive role

Cetravac: Fast, flexible and sustainable

AMF Bakery Systems: Future-smart technology arrives

Bakon: The key is knowledge

American Pan: Pan design and handling for automated bakery systems

Cybersecurity: Safe and smart bakery production

3D printing: Will we 3D print the bread of the future?

Artifical intelligence: The role of artificial intelligence in designing baking ovens

Image processing: Image processing applications for baking process monitoring

Design thinking: Using design thinking to facilitate automation

Digitization: Digitizing food supply chains

Smart stores: The search for answers is on

Rheology: Bread dough rheology

Mixing: Dough mixing supervision: an overview

Baking line audit: Metrology on baking and freezing lines

Robotics: Autonomous performance

Software: Manufacturing Execution Systems in bakeries

Digital twins: Digital twins in baking process automation