26 minute read
the future technology of the maritime sector
SESSION 2
Digitalization, Digital Efficiency and the Future Technology of the Maritime Sector
MIA ELG
R&D Manager, Deltamarin
BIOGRAPHY
Mia has 15 years of experience with various ship energy efficiency and machinery design related tasks. Mia Elg’s current role as R&D manager is related to leading the development of Deltamarin’s products and services. Mias areas of expertise include: Thermal engineering, product development and productization of different energy- and environmental services, energy balance calculation, energy flow simulation and environmental impact assessment. In addition to this, she has led the development of zero emission ship machinery in several projects. Mia has a Master’s degree in Thermodynamics. At the side of the work at Deltamarin Mia is also committing doctoral studies at Aalto University in the field of energy efficiency in marine applications. As a “high level task” both at Deltamarin and in the studies, Mia develops an advanced energy efficiency analysis method, suitable for maritime and offshore domain.
Shipping decarbonization and digital thread
Shipping decarbonisation can be technically divided into three main categories: operational optimization, design and equipment improvement and optimization and low-carbon fuel alternatives. In our earlier studies, such as in a joint industry project regarding tanker decarbonisation alternatives together with DNV, Total and Minerva Marine in 2020 we have evaluated the total cost of ownership of the case ships with various fuelling alternatives. In the study we estimated that over 90% decarbonisation of the ships on a well to wake perspective could easily double the total cost of ownership of the vessels, compared to the current fossil fuel operated modern designs. Therefore, focusing on energy saving is nowadays more important than ever. The development is also pushed from legislative perspective in form of ship carbon intensity index CII or energy efficiency index for existing ships, EEXI. Improving ship energy efficiency with single actions, such as optimizing ship hull further in single specified conditions is not alone enough to provide substantial energy savings for ships. Rather, the still untapped potential of energy saving lies in optimizing the designs and selecting the ship equipment and energy saving devices considering the realistic operational patterns. The operational profile includes also estimating the magnitude of operative energy management and optimization, such as weather routing performed on route. This hybrid of operational- design and technology optimization is also at the heart of an ongoing EU-project CHEK, where Deltamarin is the ship design partner in the large consortium consisting of technology providers, research parties, classification society representation and ship charterer and owner. The enabler for the design optimization in the project is digital modelling, which works as a platform for the studied operational improvements and energy saving technologies integrated in the ship design.
Digital twins and Digital thread
Digital twins based on measured system data are often suggested as technical tool for shipping decarbonisation, at least in connection to energy savings and for maintaining good operational efficiency. A digital twin can be formed of a ship utilizing measurement data of the operating vessel. One clear benefit of digital twin is that it can function as a single point of contact to a highly complex system, such as a ship. However, if we want to make the largest possible impact for a ship regarding her overall performance, the key decisions are made long before the ship or her digital twin are born: during the early concept design phase. This is also the time for ensuring a necessary capacity onboard the ship for future sustainable fuels.
Therefore, creating a digital representation of the ship as early as possible during ship life cycle enables focusing on the cost efficient technical ship optimization, such as system dimensioning and machinery, hull and propulsion system optimization when it is still possible. Creating ship digital models in several “generations” is a pragmatic way to combine the ship environmental performance development in the traditional ship design phases form conceptual design to basic and detail design and ship building, followed by the operational phase.
Dimensions of ship digital model
For a ship designer a ship digital model should be rather a flexible combination of scalable tools and processes, which allow a holistic evaluation of the vessel energy and environmental efficiency, considering even the ship entire life cycle. The figure below illustrates Deltamarin’s in-house tools and processes for this.
The first dimension includes the ship hull and volume model including the ship main structures and the cargo carrying capacity. This model including the main equipment is the basis for performing economical evaluations, in addition to the necessary naval architectural analysis.
The second dimension of the digital ship model is the virtual operation profile of the vessel including the external forces caused by wind and waves and other environmental factors. This part of the analysis introduces a realistic propulsion power profile for the vessel and together with the environmental conditions it is the input for the energy model of the ship.
The third dimension, the ship energy simulation “layer” combines the ship energy consumers, such as heat, electricity and mechanical power requirements to the machinery and other equipment onboard. Modelling the energy conversions onboard including the stored energy onboard in form of fuel or batteries and considering also renewable energy sources and shore power, for instance. The result of this dimension is simulation of ship fuel consumption and environmental performance on a typical route.
A digital representation of the ship enables studying a large number of variations regarding any single aspect of shipping decarbonisation. Most of all, the method is valuable in evaluating the impact of a combination of design, operational or technical improvements and the impact of alternative fuels. The digital model can also be utilized for performing various optimization routines with correct set-up. This optimization platform is in Deltamarin’s technical development focus in project CHEK.
Digital thread in action
In the project CHEK we perform the digital modelling in three generations. The first phase, digital prototyping included compiling the preliminary ship models including operational profiles based on historical reference ship operation. First estimations of the project technical development results were obtained. The digital master generation of the models includes creating a digital representation of the ship performance, including hull and propulsion system modelling, in addition to the typical energy consumers and machinery set-up. Various calibration and validation rounds are performed against reference ship data regarding the operational baseline. The final phase is called “digital twin”, where the results of onboard ship equipment testing and also results of various in laboratory tests are fed into the pure digital models of the project case ships.
Also, in our design projects validation and calibration of the ship digital model components is performed as a natural part of the process. The ultimate indicator of a successful newbuilding or retrofit project is the full-scale results with the operating ship data. During the conference we present examples from Deltamarin’s journey in the digital modelling and digital thread forming in the latest development project as well in our recent design projects.
GREGORY PUCKETT
Head of Group Digital, MAN Energy Solutions
BIOGRAPHY
Gregory Puckett is Vice President & Chief Digital Officer for MAN Energy Solutions, leading the company’s global digital strategies and operations. In this role, he is responsible for working with business segments and engineering to identify and deliver digital strategies that drive greater levels of customer satisfaction, produce new revenue sources and make MAN-ES products more efficient. Gregory has many than 20 years of experience in digitalization and information technologies. Gregory Joined MAN-ES in 2016 to support the establishment of the digital team in MAN-ES. Prior to that Gregory has held digital transformation roles in the HI-TECH and Industrial segments.
ABSTRACT
The Digital Wave
In his presentation, Mr. Puckett describes the “digital wave” within MAN-ES. From connectivity, to cloud, from data to artificial intelligence. The digital wave is continuous, it is variable, it can be calm one moment, and stormy the next. Most interesting it will never not turn back.
The Digital Wave
Hand in Hand –Decarbonizaton and Digitalzation
Decarbonization calls for new technologies
5 Digitalization makes entirely new business -Limit global warming to below 2° Celsius models possible -Intelligent software embedded in every device 5 Carbon neutrality by 2030 -Data analytics enable unprecedented insights
5 Intelligent software embedded in every device 5 Data analytics enable unprecedented insights
A disturbance or variation that transfers energy progressively from point to point in a medium and that may take the form of an elastic deformation or of a variation of pressure, electric or magnetic intensity, electric potential, or temperature -© 2022 Merriam-Webster, Incorporated
Continous Movement
Connectivity
Analytics, ML Platform Applications
Digital Partnerships
Connectivity
Rough Surf
In the beginning: “No Silver Bullet”, Required On-Site Visit, Value Proposition was not clear
As the waters calmed: Educate sales team on Cyber Security, “move from metal to digital”. Exploration of Digital Partnerships mainly for 2s retrofits
Swimming: Self Installation, Added Value Proposition, Engaged with Ships Cyber Security, Engine Connection ready at the yard. Continue exploration of Digital Partnerships
Safe to Swim: “Advice on Vessel” Mobile Application, Data Services
Platforms and Applications
Deep Water Wave
In the beginning: “One Ring to Rule them all” As the waters calmed: Advanced “Engineers” platform. Focused on Domain Experts Swimming: Sharp Segment partnership with Engineering and After Sales. Domain Experts and Software Engineers alike. decarbonization becomes essential
Safe to Swim: Aligned applications for vessel based and cloud based use cases. Focus on CII, Performance and Fleet advise. Application Server on board to host MAN-ES and 3rd party applications – Over the air updates for Marine Products.
Analytics and Machine Learning
Deep Water Wave
In the beginning: Focus on “rules based” approaches. Mainly with component health
As the waters calmed: Focus models on variants, ML comes to light with “health score”
Swimming: Data Scientist working with domain experts, focus on feature teams to drive quicker releases Safe to Swim: Leverage models across solutions (on vessel/cloud) quicker releases specifc to fuel and performance scores with fleet view .
Partnerships
Spilling waves
In the beginning: Sign the Memorandum of Agreement and get a press release As the waters calmed: Alignment with partners to help solve connectivity topic(s). Infrastructure as a service. At this point to also leverage cloud synergies with latest technology
Swimming: Partnering with Classification Societies exploring how to use data together for digital commissioning and classification. Safe to Swim: Exploration of unique mixed services to enhance our engine domain expertise, e.g geo spatial, weather, on-board reporting. Hosting of 3rd party applications
What’s Next
Spilling waves
Decarbonization: Continue to support our customers in providing solutions (Metal and Digital) to comply with sweeping global regulations
Partnerships: No one can do it alone! We will continue to partner with all industries to enhance digital capabilities for MAN-ES, our customers, and the Industry
MATTHIAS WINKLER
Managing Director, CM Technologies
BIOGRAPHY
Matthias Winkler, Managing Director of CM Technologies GmbH, formerly known as Kittiwake GmbH, has acquired his experience in ship and engine operation processes via various positions at sea and ashore throughout his career.
After graduating as a technical marine engineer at the University in Rostock with main focus on ships engines he started his career in a German shipping company to be later appointed as Technical Superintendent.
Mid of the nineties he turned his focus on the area of condition monitoring with working in positions with responsibilities for product development, technical services and assistance to key customers. Since 2002 he runs his own business which is concentrating on condition monitoring technologies with the marine industry as main sector. Following that strategy the company, formerly known as Kittiwake GmbH, has recently changed its name to CM Technologies GmbH, in short CMT, to reflect the core business activity.
Digital tool to ensure engine efficiency
Combustion Optimization / Performance Monitoring
What is needed to judge engine efficiency?
5 Information about conversion of energy 5 Combustion Process 5 Can only be done by indicating 5 Most essential part regarding 5 fuel efficiency, 5 NOx emission and 5 engine conditions
How is it done???
By monitoring the complete combustion process
p – alpha diagram
Using additional acoustic emission signal (AE)
Tier III engine
Measurement on a testbed
Performing the analysis
5 Who can do the job? 5 Chief Engineer 5 Superintendent 5 Dedicated Marine Engineer 5 Experienced with DPA 5 Time to do the analysis 5 External / Internal
The common way
5 Report is going into the office 5 Often as PDF or hardcopy with doubtful results 5 C/E takes a measurement 5 Loads the data into a PC and is happy
What is the result out of that?
5 SI gets frustrated and does not look into the data again. 5 C/E is expected to deal with the problem on his own. 5 M/E is relied on online system only. 5 Auxiliaries are neglected.
A good concept for engine performance evaluation:
5 C/E takes a measurement 5 Sends the data into a cloud service 5 An external marine engineer will evaluate the data shortly 5 Problems are discovered and solutions are returned
A good concept for engine performance evaluation:
5 The C/E will act accordingly 5 Than takes another measurement. 5 The SI can concentrate on his normal duties. 5 He has at any point access to all data anytime, anywhere. 5 Fuel consumption is optimized. 5 No engine will be neglected!
What is paramount for successful indication?
5 Simple task with limited areas for mistakes. 5 Enough accuracy. 5 Quick check for data reliability on the device 5 Direct input for additional parameters 5 Easy data transfer
Requirements for a Cloud System
5 Easy data transfer 5 Best direct from device 5 Not limited to manufacturer 5 Results anytime anywhere 5 Remote engine data setting 5 Easy and quick external help 5 History / Comparision
Important is the Measuring Chain
What can we gain?
5 Optimized fuel consumption 5 Good combustion 5 Optimized maintenance
Ecological effect of ignorance: 5 Each degree delayed combustion increases exhaust gas by about 8‐10°C 5 Each degree that ignition is retarded increases SFOC by approximately 2% 5 Each bar increase in average pmax results in about 0.25% reduction of SFOC
What is the conclusion?
Ecological and economic gains through digitalisation of combustion analysis.
5 Just a good diesel indicator is not enough. 5 You can truly optimize emission and efficiency.
5 You can also reduce maintenance and cost!
PASCAL REOLON
BIOGRAPHY
Pascal is the Head of Digital Product Management in the Digital Customer Solutions product group at Accelleron (formerly ABB Turbocharging), where he is responsible for the development of the Tekomar XPERT product range throughout its full lifecycle. Before joining ABB, Pascal was CIO of Tekomar Group Ltd. His professional career started at Wärtsilä Switzerland in Technical Service as General Manager Technical Information. He holds a master’s degree in Information Systems from the University of Zurich.
The significance of data in achieving sustainable performance
ABSTRACT
This paper describes how we created an interactive display and forecasting for ships’ CII (Carbon Intensity Indicator) rating during specific reporting periods. The analytic layer on top of mandatory emission reporting allows ship owners and operators to constantly assess energy efficiency information and to act early on deviations from set targets.
Introduction and context
In past decades, fuel efficiency was mainly the concern of those who paid for the fuel bill. Although advanced ship owners always had an eye on the technical specification and condition of their owned fleet, keeping the engine performance at its maximum level was typically only an objective of companies with strong technical departments. Engine optimization to new built (FAT/shoptest) condition accounts for savings of FOC in the range of 2% and depending on engine type involves either changing user settings in the engine control system, or maintenance work. The latter means investment, substantial modifications on the engine and turbocharger (potentially subject to recertification) to adjust the engine performance to new operation patterns like permanent slow steaming. Although there is, from a technical perspective, no doubt about the effectiveness of such activities, the contribution to lowering FOC remains in the lower single-digit area. From a practical point of view, the absolute fuel oil consumption in terms of metric tons per day is impacted by many factors. Draft, weather, hull condition etc., make assessment of vessel fuel efficiency a challenge. This results in performance baselines in commercial charter contracts (speed/consumption curves) which aim to incorporate the most unfavourable conditions, respectively defined the conditions under which the ship performance can be assessed at all (design draft, calm see, good weather, etc.). This approach allowed ship owners and charterers to run their business without extensive data acquisition and analytical solutions, a basic noon reporting system being the minimum standard also related to charter party compliance assessment.
With the introduction of ISO19030, the industry turned to a more data driven mode. The concept of high-frequency data acquisition, over a longer period, allows to statistically eliminate outliers (due to external factors) in the speed consumption diagram and to derive typical (service) relations by using linear or polynomial approximation. This in fact gives insights into whether a vessel’s hull and propeller performance has decreased over a period, compared to a previous period just after dry docking.
Relevance for the involved parties
Monitoring hull and propeller performance now receives additional attention because of the new CII regulations. The new metrics become relevant for all involved parties because a vessel’s operational energy efficiency is globally benchmarked. We have identified the following challenges for the different players in the shipping industry:
5 Ship owner / Ship manager (chartering out vessel)
o How are our vessels operated related to CII?
o Which CII rating will result at the end of the year (with continued, unchanged operation). o Can the ship operation be changed to remain within the CII boundaries (rating C)?
What impact would this have on the result?
g How to avoid a vessel’s CII rating being reported at a level where amended appropriate measures need to be filed in the SEEMP III g Under pressure from consumers, vessels with sub-standard energy efficiency rating may have difficulties finding new charter contracts g Poseidon principles may move the financing and insurance sector to change the conditions for financing/insuring vessels not meeting requirements.
5 Ship operators / Charterers
o How will the owner’s concern about CII of owned vessels impact ship operation?
o What is the basis for discussion around operational measures related to CII?
It is to be expected that both owners and operators will seek closer cooperation and exchange to agree on appropriate measures during reporting periods.
We are convinced that CO2 emission management will become an additional driver for remote diagnostics. While the FOC, for the above reasons, had previously only moderately driven the analytics around asset condition, the new regulations will bring it to everyone’s table.
Motivation for the development
Accelleron has a long record of accomplishment in the field of turbocharger design, manufacturing, and servicing. This expertise on performance of high-, medium- and low speed engine components is inherent in our organization. With the acquisition of Tekomar, Accelleron broadened its competencies towards engine performance and the related management of operational engine performance data.
The recent shift in the relevance of the fuel efficiency topic in the shipping industry, combined with Accelleron’s capabilities to ingest, store and process timeseries data efficiently (existing architectural building blocks of our engine- and turbocharger remote diagnostics solutions), is our motivation for raising these discussions around emissions forecasting functionality.
We want to provide an analytic solution which addresses the above needs of the shipping industry. The objective is an interactive display of a ship’s CII profile, for each voyage and year to date, and the forecasting of end-of-period CII ratings based on historic data and simulation parameters.
Data requirements
We are excluding from this paper the prerequisite of collecting the relevant operational data. There are numerous solutions available in the market and we are convinced that the amount of instrumentation, data loggers and gateways should be kept at a minimum level. Also, in the digital era, data collection remains costly to be established and maintained. Therefore, we aim
to generate value out of existing data streams, consequentially increasing the ROI for the data acquisition infrastructure and simplifying the management of a less heterogenous information management landscape.
Being compulsory for international shipping operations, the EU MRV or IMO DCS standards can be met with traditional voyage, bunkering and noon reporting, the final submission, however, being nowadays required in electronic format. We have defined the minimum requirements as follows, based on IMO DCS: 5 Event reports for departure o Date/time and location o Remaining on board (respectively bunkered mass) per fuel type o Distance travelled (for voyages) 5 Event reports are required for these events o Port exit
o Port entry
o Bunkering The data acquisition where the described work is based on used additionally noon reports as part of the reporting process.
The challenge of voyage detection
A flow of events with consumption and distance travelled alone does not yet enable us to run the CII calculation (nor the emission reporting). An algorithm is required to calculate the fuel consumption for each voyage or port stay because bunkering may take place at any point in time. The sailed distance reported covers the distance since last event, but the port entry event does not necessarily report the distance travelled during the full voyage if noon reports have been submitted in between.
Thus, the algorithm initially detects the first (departure) and the last (arrival) event in the series. Afterwards it takes the bunkering, respectively the noon reports into consideration.
EX = Port exit event report
BU = Bunkering event report
NO = Noon report
EN = Port entry event report
ROB = Remaining on board [mt] In sequence of EX-NO-NO-BU-EN, the total fuel consumption and distance travelled are: Voyage fuel oil consumption [mt] = ROB(EN) - ROB(EX) + . bunkered amount (BU) Distance travelled [NM] = . distance travelled (NO) + distance travelled (EN)
The calculation for port stay fuel oil consumption is in analogy of the above.
Depending on the data source/reporting system used, there may be an additional challenge: if the crew is allowed to enter events in the system and complete/correct them afterwards, the calculation for both parameters must be repeated whenever changes occur. Without introducing the necessity of a “report changed” flag, which would cause additional negotiation of the data interface, we have solved this in the way that the derived voyages and port stays in our system are constantly compared with the algorithm output which re-evaluates all data in a reasonable look-back period (e.g., 30 days).
Voyage vs. period CII
Having voyages and port stays properly detected, allows to plot each voyage’s duration and CII in the timeline display of CII. Port stay CII cannot be calculated, because the denominator of the CII calculation formula always results in 0 (no distance travelled). At the same time the boundaries for each CII rating are visualized in classical colour coding (similar to consumer goods).
Engineered visualization of the voyage CII and CII ratings. This example shows the 2nd voyage of the year having a CII of 19.96 which is not visible in the chart for scaling reasons.
Based on each voyage and each port stay data, the accumulated CII year-to-date for each event is calculated in the same way as the full year’s CII is calculated. Port stay emissions must be included here but are not visualized. The 2nd voyage of the year has a relatively high CII and enormous impact on the year-to-date CII at that point in time. The more lower CII rated voyages occur, the less impact single voyages have on the aggregated CII. The line smoothens out during the period. In this example, that is perfectly fine because this vessel (a product tanker) sails at moderate speed and achieves an excellent CII rating. The situation would be different if the aggregated CII converged at higher levels and bringing it down would require constantly high efficiency (low CII voyages) for its improvement.
Adding accumulated “to date” CII
In the case where the historic year-to-date operation is representative of the rest of the period, the result already gives an indication where this vessel’s CII will be without changes to the operational pattern or technical factors. The problem is that these are not unchanged.
CII forecasting
To fully understand the forecasting challenge, we have assessed the several factors which impact the CII.
On the operational side:
5 Log speed (higher speed requires more propulsion power per distance travelled) 5 Cargo (increasing propulsion power at same log speed due to higher draft) 5 Port stay duration (auxiliary engine operation without distance travelled)
On the technical side:
The main technical factors affecting CII are: 5 propulsion power per distance travelled) 5 Engine performance (decreased fuel efficiency means more CO2 emissions per kWh) All these parameters can be set by the user when simulating the year-end forecast. For the operational parameters we provide default values from historic data, where available and as average (log speed, cargo, % of time in port). Deriving historic values for the technical parameters from the underlying emission reporting dataset is not possible. Detecting historic hull performance degradation would require additional, continuous data and an extensive analytic layer which is beyond the scope of this paper. The same applies for the propulsion engine condition where signals from the main engine are required. Despite the absence of a historically determined hull performance degradation, we decided to add a default value of 1% per month to the initial forecast view. Starting from the year-to-date CII, the constantly increased emissions due to the need for additional propulsion power to compensate the increased hull resistance must be added to the historic emission level.
Adding forecasting line including 1% of hull resistance increase.
For a complete simulation of the year end CII, all technical and operational factors shall be added to the forecasting in the same way. Log speed is the most important driver for CII, and as easy as it is to be impacted technically, as challenging it is on the operational side. Contractual obligations may not allow longer travelling time, logistic schedules may require arriving at port in time. However, if there are upcoming changes in contracts (spot market
or charter) which imply different operational patterns, the simulation helps to determine the appropriate conditions.
As a last example of our engineered approach, we simulate the addition of log speed increase. This vessel was sailing at an average log speed of 10 kn. Adding 1 knot (10%) additional log speed already has an immense impact on the year end CII, moving the rating from the superior boundary to the lower boundary.
CII forecast including 1% of hull resistance increase per month and 10% of average log speed increase
From our research we conclude that the following influencing factors are both relevant and practical for simulation:
As detailed:
5 Log speed
5 Hull resistance
Not detailed:
5 Port stay duration
5 Hull cleaning
Combining data streams into to a scalable solution
The above procedure to assess CII can be done manually, no doubt. The challenge is, however, to constantly monitor and forecast the energy efficiency rating and act in time. Therefore, we decided to plug in the existing IMO DCS dataset to the system. The configuration of each vessel’s emission model must suffice with a limited number of parameters (vessel type and capacity) which can be easily obtained. This builds the basis for the CII calculation and rating boundaries.
After initial consideration, we moved away from out of the box dashboard solutions and decided to develop our own user interface based on web development toolkits. This required heavy customization to achieve the desired look and feel, incorporating all the elements which are of interest. The result is an appealing CII forecasting chart with interactive simulation capabilities.
Interactive display of voyages and accumulated CII, including CII ratings for consecutive years (emission reduction trajectory)
Conclusion
Having an automated system available which displays current and forecasted CII is expected to be one of the must-have tools for ship owners. Harnessing the compulsory emission reporting data and adding an analytic layer on top provides the insights which enable ship owners and operators to act early on CII ratings. The simulation of different scenarios can serve as basis for both short term operational changes as well as long term investment planning related to technical measures. The increased transparency with permanent assessment of ship energy efficiency boosts awareness for sustainability. In a world with an increasing number of regulatory measures being introduced, these informed decisions are key to leading the way to decarbonization.
