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— Sustainable technology for more efficient fans
We designed ABB’s new Baldor-Reliance® EC Titanium™ integrated motor drive packages to help meet new system efficiency regulations. These drop-in replacements • • • •
maintain IE5 efficiency at part load conditions offer increased options for fan design are made from readily available materials run on standard permanent magnet control drives
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ProSense® Digital Panel Meters... ...great features at a great price!
NEW! Digital Panel Meters The ProSense DPM family of digital panel meters is a simple, low-cost digital display solution for analog process, temperature, potentiometer, and load cell signals. Available in both 1/32 and 1/8 DIN sizes with easily configured, bright LED displays, these versatile panel meters offer outstanding performance at a great price.
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48 x 24mm 1/32 DIN 4-digit (-1999 to 9999) red LED display Loop, AC or DC powered Inputs for NEW models: AC/DC voltage (100/600V), AC/DC amperage (1A/5A)
• Input types available: 4-20mA, +/-20mA, +/-100mV, +/-10V, +/-20V, +/-200V, RTD Pt100 (3-wire), Thermocouple Type J, K, T or N, • Output options include relay and/or 0/4-20 mA on select models
pros ense
DPM2 Series • 96 x 48mm 1/8 DIN • 4-digit (-9999 to 9999) red LED display or 4-digit (-1999 to 9999) red LED display • Inputs for NEW models: AC/DC voltage (20/200/600V), AC/DC amperage (1A/5A/shunt 60mV/shunt 100mV)
• Input types available: +/-10V, +/-200V, +/-20mA, RTD (Pt100, Pt1000), Thermocouple (Type J, K, T, N), Potentiometer (100 Ohm to 100k Ohm), Resistance (999.9 Ohm, 9999 Ohm and 50k Ohm) • Relay outputs on select models
DPM3 Series • 96 x 48mm 1/8 DIN • 5-digit (-19999 to 39999) tri-color (red, green, amber) LED display • Inputs for NEW models: True RMS AC or DC voltage (2/20/600V), True RMS AC or DC amperage (200mA/1A/5A/Shunt)
• Input types available: +/-10V, +/-20mA, RTD Pt100, Thermocouple (Type J, K, T, N), Potentiometer, Load cell (+/-15mV, +/-30mV, +/-150mV) • 4-20 mA output on select models • AC or DC powered
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the #1 value in automation
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Vol. 67 Number 3
®
MARCH 2020
ANSWERS 16 | How to select, apply process sensors 18 | Skills for the edge computing revolution
16, after 35 COVER: Image on the left appears after p. 35, in the Inside Process section (P1) and is from the Control Engineering webcast, “Advanced process control: Past, present and future,” with Allan Kern, P.E., owner, APC Performance LLC. Image on the right was a CFE Media and Technology image at the Endress+Hauser booth at Automation Fair 2019 from Rockwell Automation; it helps illustrate, along with a related image in the p. 16 article, how to select and apply process sensors.
INSIGHTS 6 | Research: Specifying servo, stepper drives 8 | Integrator Update: Six steps toward eliminating obsolete controls equipment NEWS
10 | Most-viewed university articles 12 | Motion prediction algorithms enhance safety features for automated vehicles, Headlines online 13 | Donation made for mechatronics lab 14 | Think Again: How to compete and win with automation
20 | Software readiness for data analytics and Big Data
p18
22 | Cloud-based analytics for manufacturing 26 | Intrinsic value of industry standards for end users, system integrators, suppliers 28 | Standards capture stranded data, help with device design, data integration
RETURN ON INVESTMENT
TIME EFFORT
STANDARDS MAKING RETURN ON INVESTMENT
p26
32 | Automation of auxiliary processes is the key to the smart factory ONLINE EXTRA | Hannover Messe 2020 delayed to July due to coronavirus concerns; Top 5 Control Engineering articles, Laser-induced graphene for flexible electronics, Enhancing safety with mobile devices
INSIDE PROCESS
P1 | Advanced process control: More answers P7 | Understand partial-stroke testing
CONTROL ENGINEERING (ISSN 0010-8049, Vol. 67, No. 3, GST #123397457) is published 12x per year, Monthly by CFE Media, LLC, 3010 Highland Parkway, Suite #325 Downers Grove, IL 60515. Jim Langhenry, Group Publisher/Co-Founder; Steve Rourke CEO/COO/Co-Founder. CONTROL ENGINEERING copyright 2020 by CFE Media, LLC. All rights reserved. CONTROL ENGINEERING is a registered trademark of CFE Media, LLC used under license. Periodicals postage paid at Downers Grove, IL 60515 and additional mailing offices. Circulation records are maintained at 3010 Highland Parkway, Suite #325 Downers Grove, IL 60515. Telephone: 630/571-4070. E-mail: ctle@omeda.com. Postmaster: send address changes to CONTROL ENGINEERING, PO Box 348, Lincolnshire, IL 60069. Publications Mail Agreement No. 40685520. Return undeliverable Canadian addresses to: PO Box 348, Lincolnshire, IL 60069. Email: ctle@omeda.com. Rates for nonqualified subscriptions, including all issues: USA, $165/yr; Canada/Mexico, $200/yr (includes 7% GST, GST#123397457); International air delivery $350/yr. Except for special issues where price changes are indicated, single copies are available for $30 US and $35 foreign. Please address all subscription mail to CONTROL ENGINEERING, PO Box 348, Lincolnshire, IL 60069. Printed in the USA. CFE Media, LLC does not assume and hereby disclaims any liability to any person for any loss or damage caused by errors or omissions in the material contained herein, regardless of whether such errors result from negligence, accident or any other cause whatsoever.
www.controleng.com
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March 2020
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TIME EFFORT
input #4 at www.controleng.com/information
MARCH 2020
®
INNOVATIONS NEW PRODUCTS FOR ENGINEERS
44 | Digital servo drives, Ultrasonic flowmeter
series, Wired IO-Link bridge device, Data intelligence hub for smart manufacturing, Analytics dashboard for streamlined data visualization. See more New Products for Engineers at www.controleng.com/NPE.
BACK TO BASICS
45 | Making process control valve choices Process control valves offer a wider range of features, benefits for industries that require precise control. Also look at cost effectiveness.
NEWSLETTER: Industrial Networking We have upgraded newsletters designs to deliver a better overall experience for subscribers. • Continuous improvements help Alabama cotton gin increase yields • 2020 Engineers’ Choice Awards • Design advice for connecting IT and OT • Hot topics in Control Engineering for 2019 • Wearable power generator for IoT devices developed. Keep up with emerging trends: subscribe. www.controleng.com/newsletters.
CFE EDU: Pre-register for NEW motors and drives course Pre-register for our upcoming course, “Introduction to Motors and Drives,” starting March 16, and learn about topics such as how motor sizing impacts efficiency and maintenance, understand motor repair processes, review repair best practices to maintain and improve efficiency, and more. Learn more at cfeedu.cfemedia.com/courses/motors-and-drives Other featured courses include topics such as machine learning, cybersecurity, and edge, fog, and cloud.
Control Engineering eBook series: IIoT Cloud Spring edition Learn how the Industrial Internet of Things (IIoT) and the cloud are changing manufacturing. This eBook includes articles on high-speed engine monitoring, working with the cloud, and how easier machine connectivity and communications adds value. Learn more and register to download at www.controleng.com/ ebooks/. Interested in Oil & Gas Engineering? Look at these article to help maximize uptime and increase productivity through the use of industry best practices and new innovations, increase efficiency from the wellhead to the refinery by implementing automation and monitoring strategies, and maintain and improve safety for workers and the work environment. www.oilandgaseng.com.
controleng.com provides new, relevant automation, controls, and instrumentation content daily, access to databases for new products and system integrators, and online training.
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Number of employees supervised
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26 to 100
6%
More than 100
1%
6 to 25
2019 MOTOR DRIVES STUDY
Specifying servo, stepper drives
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orty-six percent of respondents to the Control Engineering 2019 Motor Drives Study buy, specify, use or expect to use servo and/ or stepper drives in the next 12 months. Below are five key findings from this study related to servo and/or stepper drives: 1. Applications: Servo and/or stepper drives are usually specified for new applications (87%), as well as retrofit (67%) and replacement (64%). 2. Purchasing: Six in 10 respondents prefer to purchase servo and/or stepper drive controller products as matched units; 12% prefer to buy them as separate units and 28% have no preference. 3. Expenditures: An average expenditure of about $94,000 was estimated to have been spent on servo and/or stepper drive products over the past 12 months;
17% 42%
20% of respondents reported spending more than $100,000. 4. Important factors: When evaluating servo and/or stepper drives, the most important factors, according to respondents, are accurate positioning (97%), accurate speed and torque control (97%) and motor sizing and tuning software (81%). 5. Ordering preference: The majority (87%) of respondents prefer to order a standard off-the-shelf servo and/or stepper drive versus custom-engineered (3%); 10% have no preference. ce
M More RESEARCH
Access more motor drive trends at www.controleng.com/research. Amanda Pelliccione is the research director at CFE Media.
Servo, stepper drive control types in use 89%
Closed loop
Pulse width modulation control
52%
AC (sinusoidal commutation)
51%
Open-loop (for steppers only)
45%
Brush dc
38%
Brushless dc (trapezoidal commutation)
37%
Closed-loop drive controls for servo and/or stepper drives are most popularly used by respondents, followed by pulse-width modulation, ac and open-loop controls. Source: Control Engineering
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34% 1 to 5
None
Fifty-eight percent of automation professionals are in a supervisory position; the average number of employees managed by these respondents is 10. Source: Control Engineering 2019 Career & Salary Study
70%
of end users need or are interested in remote monitoring capabilities for controller software. Source: Control Engineering 2018 Programmable Controllers Study
$139,000:
Average estimated expenditures for variable-speed drives, ac/dc, over the past 12 months. Source: Control Engineering 2019 Motor Drives Study
>60%
of end users buy or specify robots, robot sensing or vision systems, and robot sensors. Source: Control Engineering 2019 Robotics Study
More research Control Engineering covers several research topics each year. All reports are available at www.controleng.com/research.
www.controleng.com
2018 input #5 at www.controleng.com/information
WINNER
INSIGHTS
INTEGRATOR UPDATE: OBSOLESCENCE Robert Herman, Avanceon
Six steps toward eliminating obsolete controls equipment Replacing obsolete controls equipment is inconvenient, but it is a necessary process and can be made easier with an internal audit that shows what needs to be done.
M
ost people have a least one control panel that’s like a recurring nightmare. Each time it’s opened, something breaks, so naturally don’t open it! Don’t look at it or even breathe around it! That tends to be the situation with a lot of manufacturing facilities, but there is a big problem with that mindset: It won’t work forever. People don’t drive cars from 1985 every day and expect them to keep on chugging along forever; the same goes with the equipment vital to manufacturing facilities. Well-planned Well-planned hardhardware obsolescence projects can ultimately prevent unplanned downtime and ware obsolescence spending, as well as conquer the outdated projects can ultimate- equipment plaguing a plant. While it might be inconvenient and ly prevent unplanned costly, and the benefits aren’t realized, it certainly is better than the alternative. If downtime and something does go wrong, there will be a headache much larger than simply spendspending. ing the money to replace the obsolete controls equipment. And it’s a good bet the problems will be far-reaching, leading to a lot of uncomfortable questions about why it wasn’t done sooner.
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Obsolescence: Six questions to ask
Fortunately, manufacturers can avoid the hassle of unscheduled outages and surprise budget burdens. All it takes is following these six simple steps: 1. Know what you have. A simple audit of the current hardware and status of electrical schematics (existing? on paper? electronic copy available?) is critical. 2. Identify future issues. Ask if the equipment runs perfectly and would there be a return on investment (ROI) associated with fixing an issue that could help offset the upgrade cost. Also ask if this piece of equipment should perhaps not live in the hottest/ stickiest/wettest room of your plant. Also consider if there’s future functionality/equipment that should be planned for? 3. Determine if there are corporate/plant programming or electrical standards that could be
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implemented to ensure ease of troubleshooting and commonality. 4. When will new equipment be installed? Operations needs to be involved in the decision! This will ultimately drive the when and how more than any other factor. Aim for outage windows that will allow for testing prior to the system coming back online and generating product. 5. Know your costs. This can be handled by an internal engineering team or by having an assessment done to allow outside help to dig through the collateral. Add additional expense/benefits of hiring outside help. 6. Coordinate, schedule and implement. Now that you know what is changing, why it’s changing, how it’s changing, where it’s going and when it can change. The rest comes down to execution. Following these steps will help manufacturers face the obsolete equipment haunting a facility with minimal pain and help companies look to the future. ce Robert Herman, program manager, Avanceon, a CFE Media content partner. This article originally appeared on Avanceon’s website. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.
M More INSIGHTS KEYWORDS: control systems, system integration Replacing obsolete control systems is a long and expensive process and many companies put it off. Putting off replacement can incur major costs and downtime if it’s put off for too long. Companies looking to replace old equipment need to do an internal audit to see what the return on investment (ROI) is.
ONLINE Read this article at www.controleng.com for additional stories from Avanceon.
CONSIDER THIS When was the last time you replaced a piece of aging equipment, and what were the steps involved? www.controleng.com
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The EZTouch EZ7 HMI/PLC Operator Interface has bifurcated the HMI in two parts, the front end and the back end. With the EZ7, there is no longer a need to have a panel cutout, only a 3/4” hole with a grommet is needed to pull through an HDMI cable to connect the two. The front end and backend are stud-mounted to the control panel. The front end takes less than 2 minutes to replace wihtout disconnecting any cables from the back end, whcih allows a screen size upgrade, from 7” to 12” input #6 at www.controleng.com/information
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INSIGHTS
NEWS
Most-viewed university articles in 2019 Hot topics from university sources published in 2019 at Control Engineering included drag-and-drop analytics, robotic actuators, nanocrystals, and prolonging power plant life with AI. Catch up on what you may have missed using the links online and in the digital edition.
1. Drag-and-drop data analytics using machine learning, Massachusetts Institute of Technology (MIT), June 28: MIT researchers have developed a system that lets nonspecialists use machine-learning (ML) models to make predictions for medical research, sales, and more. 2. Automated system generates robotic actuators, MIT, July 18: MIT researchers have developed an automated system that designs and 3D prints robotic actuators, which are optimized and automatically created, which is almost impossible for a human to do. 3. Diamond-based quantum sensor
fabricated on a silicon chip, MIT, Sept. 26: MIT researchers have fabricated a diamond-based quantum sensor on a silicon chip. The advance, called a nitrogen-vacancy (NV) center, could pave the way toward low-cost, scalable hardware
for quantum computing, sensing, and communication.
4. Nanocrystals improve quantum dot manufacturing for process monitoring applications, North Carolina State University, March 27: North Carolina State University researchers have developed a system for synthesizing perovskite quantum dots to reduce manufacturing costs for real-time process monitoring to help ensure quality control. 5. Prolonging power plant life through artificial intelligence, West Virginia University, Sept. 25: A West Virginia University chemical engineer is tapping into artificial intelligence (AI) to prolong the lives of power plant boilers. 6. Supersonic jet injector accelerates nanoscale additive manufacturing, Georgia Institute of Technology, July 19: Georgia Tech researchers are using a supersonic jet injector to accelerate nanoscale additive manufacturing used for applications such as electronic circuitry and superconducting materials. 7. Low-power hybrid chip makes small robots more capable, Georgia Institute of Technology, March 8: Researchers
A robotic car controlled by an ultra-low power hybrid chip is placed into an arena to demonstrate its ability to learn and collaborate with another robot. Courtesy: Allison Carter, Georgia Tech
from the Georgia Institute of Technology demonstrated robotic cars that use an ultra-low power hybrid chip to give palm-sized robots the ability to collaborate and learn from their experiences like the human brain does. See video.
8. Machine learning used to determine warehouse ergonomics for worker safety, University of Washington, Aug. 23: Researchers at the University of Washington used machine learning to develop a system that monitors factory and warehouse workers and tell them, in real time, how risky their behaviors are. 9. Mobile process sensor technology developed to identify plant diseases, North Carolina State University, July 31: North Carolina State University researchers have developed a mobile process sensor that uses analytics to identify plant diseases in the field by sampling airborne volatile organic compounds (VOCs) plants release through their leaves with a mobile instrumentation device.
For years, researchers from MIT and Brown University have been developing an interactive system that lets users drag-and-drop and manipulate data on any touchscreen, including smartphones and interactive whiteboards. Now, theyâ&#x20AC;&#x2122;ve included a tool that instantly and automatically generates machine-learning models to run prediction tasks on that data. Courtesy: Melanie Gonick, MIT
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10. Control your career with a personal board of directors, Georgia Institute of Technology, Aug. 16: A personal board of directors is a broader version of a mentorship with many people assisting your engineering career journey from a variety of viewpoints. See four ways to begin. ce Chris Vavra, associate editor, CFE Media, cvavra@cfemedia.com. www.controleng.com
Overworked? Tired of working nights and weekends on motion control projects? Itâ&#x20AC;&#x2122;s time to contact an automation specialist at SEW-EURODRIVE to help solve your design challenges. Using the latest innovation, we provide a complete package from start to finish including, project planning, design, software, components, commissioning, and worldwide support. Go home . . . we got this!
seweurodrive.com / 864-439-7537 input #7 at www.controleng.com/information
INSIGHTS
NEWS
Motion prediction algorithms enhance safety features for automated vehicles The Southwest Research Institute (SwRI) has developed a motion prediction system that enhances pedestrian detection for automated vehicles. The computer vision tool uses a deep learning algorithm designed to predict motion by observing real-time biomechanical movements with the pelvic area being a key indicator for changes. “For instance, if a pedestrian is walking west, the system can predict if that person will suddenly turn south,” said SwRI’s Samuel E. Slocum, a senior research analyst who led the project. “As the push for automated vehicles accelerates, this research offers several important safety features to help protect pedestrians.” Recent accidents involving automated vehicles have heightened the call for improved detection of pedestrians and other moving obstacles. Although previous technologies could track and predict movements in a straight line, they were unable to anticipate sudden changes. Motion prediction often uses “optical flow” algorithms to predict direction and speed based on lateral motion. Optical flow, a type of computer vision, pairs algorithms with cameras to track dynamic objects. The accuracy of optical flow diminishes, however, when people move in unexpected directions. To improve accuracy, SwRI compared optical flow to other deep learning methods, including temporal convolutional networks (TCNs) and long short-term memory (LSTM). After testing several configurations, researchers optimized a novel TCN that outperformed competing algorithms, predicting sudden changes in motion within milliseconds with a high level of accuracy. The temporal design uses a convolutional neural network to process video data. SwRI’s novel approach optimizes dilation in network layers to learn and predict trends at a higher level. Dilated convolutions are structures that store and access video data for spatial observations.
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SwRI’s human motion prediction system overlays sparse skeletal features on pedestrians to predict movement. The blue dot in this image predicts the future direction of pedestrian on right. Deep learning algorithms predict motion based on pelvic biomechanical movement. Courtesy: Southwest Research Institute
People draw on experience and inference when driving near pedestrians and cyclists. SwRI’s research is a small step for autonomous systems to react more like human drivers. “If we see a pedestrian, we might prepare to slow down or change lanes in anticipation of someone crossing the street,” said Dr. Douglas Brooks, a manager in SwRI’s applied sensing department. “We take it for granted, but it’s incredibly complex for a computer to process this scene and predict scenarios.” The research team leveraged SwRI’s markerless motion capture system, which automates biomechanical analysis in sports science. Using camera vision and perception algorithms, the system provides deep insights into kinematics and joint movement. Applications for the project include human performance, automated vehicles and manufacturing robotics. ce - Edited from an SwRI press release by CFE Media. See more Control Engineering machine vision stories.
control engineering
Headlines online Top 5 Control Engineering articles February 17-23 Articles about the Engineers’ Choice winners, process control valves, improving cotton gins, robots and drones in process facilities, and the most-viewed university articles. Laser-induced graphene for flexible electronics A tiny visible beam burns microscopic patterns, which could help flexible electronics. Soft robotic arm uses flexible sensors to understand its position MIT researchers have developed flexible sensors and an artificial intelligence model to help robot positioning in a 3D environment. Boosting security of process control chemical systems Wayne State University researchers enhance cybersecurity of controls. Enhancing safety with mobile devices The use of intrinsically safe smart devices can help improve productivity and safety. www.controleng.com
Donation made for mechatronics lab
‘
OMRON FOUNDATION, the charitable arm of global technology and factory automation company Omron, made a monetary gift along with state-of-the-art robotics equipment to California State University, Chico (Chico State) for a new and expanded mechatronics laboratory and classroom space. The planned 2,505-sq-ft learning environment will feature mint-condition industrial equipment of the type being used in next-generation factories worldwide. Chico State seeks to interest more engineering students in robotics studies and provide them with the skills necessary to succeed in a changing portion of the workforce. “Omron’s mission is to improve lives and contribute to a better society, and a large part of this effort involves educating today’s students to help solve tomorrow’s problems,” said Nigel Blakeway, chairman and CEO of Omron Management Center of America and president of Omron Foundation in a press release. “We are honored to be able to help the next generation of robotics talent gain the skills necessary to innovate and thrive in the future.” “We saw a strong opportunity with Chico State to give a bright and hardworking group of students the opportunity to work directly with state-of-the-art automation equipment, the sort that they will likely encounter in their first engineering jobs right out of school,” says Robb Black, president, CEO and COO of Omron Automation Americas. “It has been a great pleasure for us to work with the university and their exceptional engineering department.” Chico State has the first accredited program in mechatronics engineering in the nation. Laboratory construction began in summer 2019 and opened for Increasing interest for engiclasses in the spring neering students in robotics 2020 semester. Chico State presistudies can provide them with dent Gayle Hutchinnecessary skills to succeed in son said, “The Omron a changing workforce. Mechatronics CoLab at Chico State will enable the University to build an exciting leading-edge lab where we can prepare students as the next generation workforce. It will be a collaborative space that will inspire research and have a transformative impact on our students, faculty, region, state and country.” Nick Repanich, a lecturer from Chico State’s Department of mechatronic, engineering and sustainable manufacturing, said, “We need a place where when you’re an engineering student, and you think mechatronics and robotics are cool, there’s a place you can come to be inspired, work on projects, and learn about it. You need a place where mechatronics is all around you. We don’t currently have a place like that.” – Edited from an Omron press release by CFE Media.
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INSIGHTS THINK AGAIN
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How to compete and win with automation With automation at an inflection point, a variety of technologies and interoperable standards efforts are bringing higher levels of flexibility, integration and optimization.
A
utomation, controls, and er, and change agent who gives frequent instrumentation, applied in talks about innovation. We need to adapt innovative ways, can help to changes in real time, as a creative artist implementations compete and of engineering, he suggested. win. Why? The three were among speakers at the Automation is at an interesting point A3 Business Forum in January. in history, explained John Payne, Yaskawa America Corp. and chair of the Asso- Plugging in standards In another significant advance, ciation for Advancing Automation (A3) board of directors. He noted that we’re at interoperability standards for open proan inflection point for hardware and soft- cess automation have been tested in a specialty chemical process plant, ware capabilities, with inteexplained Igor Stolz, director, gration of robots and motion electrical and process control, and imaging. Artificial intelliEvonik Industries AG, at the gence (AI) capabilities also are ARC Advisory Group’s ARC expanding. Forum in February. It’s chalAI, while useful, is a very lenging for such facilities to be unfortunate term because it fast, flexible and cost efficient implies software is creative and with relatively small throughcan do anything a person can Mark T. Hoske, do, said Byron Reese, author, Content Manager puts. Time to market is crucial while maintaining quality. publisher of the Gigaom webModular production and site and several books on hopeful technology applications to solve development concepts offer high flexibility human challenges. Reese said AI is nar- and functionality, Stolz said, which reducrower and can only do a bit of what es investment risk. Evonik used a standards humans can do. What it can help us do is approach from NAMUR, the User Associaget smarter about the future by looking at tion of Automation Technology in Process past data. AI also can increase productiv- Industries. A module type package (MTP) ity, which is always good. AI makes you was applied to equipment, such as humanmachine interface, maintenance, alarm smarter if you use it, Reese said. However, we cannot rely on models management, safety and security, process of the past and expect the same results. control, and other systems. MTP is a stanHuman creativity cannot be outsourced. dard non-proprietary interface description If don’t use creative problem solving, only for process equipment assemblies. Using MTP designs, standard process will get you a C, 70% of the way there, explained Josh Linkner, author, speak- modules were constructed and moved and integrated from plant to plant. Such an interoperable “skid” design offers seven specific benefits applicable for plant owners, module vendors, system integrators, See more on standards starting on p. 26 and automation vendors. Others involved and more with this article online. in the prototype demonstration were www.controleng.com/magazine ABB, OPC Foundation and Wago. www.namur.net/en Think again about automation innowww.a3automate.org www.arcweb.com vations to raise your competitiveness. ce
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Content Specialists/Editorial Mark T. Hoske, Content Manager 630-571-4070, x2227, MHoske@CFEMedia.com Jack Smith, Content Manager 630-571-4070, x2230, JSmith@CFEMedia.com Kevin Parker, Senior Contributing Editor, IIoT, OGE 630-571-4070, x2228, KParker@CFEMedia.com Emily Guenther, Director of Interactive Media 630-571-4070, x2229, eguenther@CFEMedia.com Amanda Pelliccione, Director of Research 978-302-3463, APelliccione@CFEMedia.com Chris Vavra, Associate Editor CVavra@CFEMedia.com
Contributing Content Specialists Suzanne Gill, Control Engineering Europe suzanne.gill@imlgroup.co.uk Ekaterina Kosareva, Control Engineering Russia ekaterina.kosareva@fsmedia.ru Agata Abramczyk, Control Engineering Poland agata.abramczyk@trademedia.pl Lukáš Smelík, Control Engineering Czech Republic lukas.smelik@trademedia.cz Aileen Jin, Control Engineering China aileenjin@cechina.cn
Editorial Advisory Board
www.controleng.com/EAB Doug Bell, president, InterConnecting Automation, www.interconnectingautomation.com David Bishop, president and a founder Matrix Technologies, www.matrixti.com Daniel E. Capano, senior project manager, Gannett Fleming Engineers and Architects, www.gannettfleming.com Frank Lamb, founder and owner Automation Consulting LLC, www.automationllc.com Joe Martin, president and founder Martin Control Systems, www.martincsi.com Rick Pierro, president and co-founder Superior Controls, www.superiorcontrols.com Mark Voigtmann, partner, automation practice lead Faegre Baker Daniels, www.FaegreBD.com
CFE Media Contributor Guidelines Overview Content For Engineers. That’s what CFE Media stands for, and what CFE Media is all about – engineers sharing with their peers. We welcome content submissions for all interested parties in engineering. We will use those materials online, on our website, in print and in newsletters to keep engineers informed about the products, solutions and industry trends. www.controleng.com/contribute explains how to submit press releases, products, images and graphics, bylined feature articles, case studies, white papers, and other media. * Content should focus on helping engineers solve problems. Articles that are commercial or are critical of other products or organizations will be rejected. (Technology discussions and comparative tables may be accepted if non-promotional and if contributor corroborates information with sources cited.) * If the content meets criteria noted in guidelines, expect to see it first on our Websites. Content for our e-newsletters comes from content already available on our Websites. All content for print also will be online. All content that appears in our print magazines will appear as space permits, and we will indicate in print if more content from that article is available online. * Deadlines for feature articles intended for the print magazines are at least two months in advance of the publication date. Again, it is best to discuss all feature articles with the appropriate content manager prior to submission. Learn more at: www.controleng.com/contribute
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COVER STORY: PROCESS CONTROL Daniel E. Capano, Gannett Fleming Engineers and Architects
How to select, apply process sensors When specifying process sensors, several common factors need to be considered such as the operating environment, mounting options and cable connections.
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hen specifying process sensors, several common factors need to be considered to avoid installation and application problems during implementation. These factors include its intended use, the operating environment, mounting options, installation, calibration, start-up and commissioning, operation and maintenance. All of these factors should be considered during design and will have a material effect on the final form and function of the sensor and connected or controlled equipment. Doing a thorough evaluation of these factors will avoid rework or replacing the sensor in the field, which also will introduce additional cost and possible delays.
Process sensor environments
Capacitive proximity sensors allow sensing of metal and non-metal objects through insulating materials such as wood or plastic and are often used to sense fill levels of liquids or powders. Courtesy: New Products for Engineers Database, AutomationDirect
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Operating environments widely vary. For example, in water treatment applications, environments are usually wet, dirty, corrosive and hazardous. Industrial environments offer some of the same environments, but also introduce materials such as metal dust and chips or fibrous flyings that can foul or damage the sensor. The goal is providing a suitable, safe enclosure that will operate in harsh environments without creating a hazard in and of itself. A case in point is installations in corrosive or
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hazardous locations. In the former case, the sensor housing must be designed to withstand the action of corrosive gases or liquids; in the latter, it is matter of preventing the materials from entering and compromising the sensor. Sensor housings are often rated using the NEMA enclosure rating system or the Ingress Protection (IP) rating system. NEMA 4X and NEMA 7-10 are used for corrosion-resistant and explosion-proof housing, respectively. There is some correlation between the two rating systems, there is a wealth of information online regarding both. In considering the environmental factors, using intrinsically-safe sensors and systems in hazardous locations should be considered whenever possible. Intrinsically-safe sensors use low current and voltage to limit the ability of arcing and sparking to ignite flammable materials. Intrinsically safe barriers can also be used to limit current and voltage to safe levels.
Sensor mounting options There are many mounting options, and they will normally conform to standard interface methods to the process. Open tank process monitoring requires no explanation other than the sensor location should be carefully selected to allow optimum monitoring of the required parameter. The sensor also should be mounted in a way to allow regular inspection, maintenance and calibration regardless of the parameter being measured. Non-standard mountings or mountings requiring special or proprietary tools are an invitation to neglect and will affect process measurement and control. Most sensors do offer standard attachment options, which will easily integrate into process piping, tanks or vessels. It is important to ensure the sensor is mounted in an easily accessible location so staff can perform regular maintenance and avoid process upsets from a bad data. www.controleng.com
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With open tank process monitoring, sensor location should allow optimum monitoring of the required parameter. See related cover photo, also from Endress+Hauser booth at the 2019 Automation Fair by Rockwell Automation. Courtesy: Mark T. Hoske, CFE Media and Technology
One often overlooked issue is the method of cable attachment. In harsh or hazardous environments, a potted cable, which is connected permanently into the sensor body either mechanically or with a potting compound such as an epoxy or filler. This prevents the ingress of dirt or hazardous materials, which may damage the sensor or create arcing and sparking. Plugs and jacks also can be used to connect cables to a sensor, but this requires a clean environment, such as in a laboratory. The use of this method allows an easy swap-out of the sensor in case of failure. In the former case, the entire sensor and cable assembly must be replaced, which may require extensive rewiring. ce Daniel E. Capano is senior project manager, Gannett Fleming Engineers and Architects, and on the Control Engineering Editorial Advisory Board. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.
KEYWORDS: process control, process sensors Process sensor operating environments widely vary and need to be considered. A thorough evaluation will help engineers avoid costly delays. Cable attachments are often overlooked with process sensors. CONSIDER THIS
What is your biggest consideration when choosing a process sensor?
ONLINE If reading from the digital edition, click on the headline for more resources. www.controleng.com/ magazine
IN THIS ISSUE Articles on advanced control and testing after p. 35 and a process valves article on p. 45.
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IIoT Inventory Management Troubleshooting techniques for motors
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EDGE COMPUTING Josh Eastburn, Opto 22
Skills for the edge computing revolution Edge computing is becoming more prevalent, and it’s imperative for engineers to get in on the action. Learn four skills engineers can develop to be better prepared.
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he worlds of information technology (IT) and operations technology (OT) are colliding. The industrial sector is seeing serious investment in large- and small-scale operations to make data more prolific, available and usable for accelerating operations and solving complex field problems. A key trend underlying this shift is the move towards edge-oriented, rather than centralized or hierarchical models, of automation system design, generally called edge computing.
Talking the talk: Terms The first thing to understand is edge computing did not evolve in a vacuum and, in practice, does not refer to a single technology. It’s an architectural concept related to a larger paradigm called distributed computing, in which computing resources are spread throughout a system, rather than centralized in a master controller or application. Engineers who have worked with a distributed control system (DCS) have already seen this paradigm in action. Rather than being an evolution of the DCS,
Opto 22’s groov EPIC is an edge-oriented evolution of the traditional industrial controller. Courtesy: Opto 22
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however, edge computing traces its genesis to the globally distributed networks that made up the internet in the early 2000s. Large, centralized networks faced an inherent problem of constrained bandwidth and latency. To improve responsiveness, it made sense to move resources geographically closer to the areas that commonly requested them. At the same time, the proliferation of smart devices and the push towards the Internet of Things (IoT) made computing resources and services more available locally. The same trends are manifesting themselves in the industrial sector, as networks converge to create an Industrial Internet of Things (IIoT). Compared to DCSs, edge-oriented systems are even more distributed for data processing, storage, and connectivity. These functions are being pushed closer to the lowest layers of the automation network, where real-world sensing and control take place. That’s the edge in edge computing. Depending on the scope of the discussion, however, the edge of the network could be considered anything in the last mile of the network and is better thought of as being distinct from resources located in the network core or in the cloud. The term edge devices can refer to intelligent field devices, like transmitters, programmable logic controllers (PLCs), and programmable automation controllers (PACs), or to edge gateways, like protocol converters, routers and industrial PCs (IPCs), which connect various parts of a network or bridge disparate networks. Devices like these are becoming more involved in aggregating and normalizing process data. Data normalization refers to the process of reformatting data, adding metadata, removing outliers and filling in gaps so core systems and databases can work with data in a consistent format. This becomes important as data is produced and transmitted at scale by many more, possibly heterogeneous, devices and protocols: legacy fieldbuses mixed with OPC data, cloud-sourced JSON, www.controleng.com
and new smart wireless formats. Edge devices help by pre-processing data in the field and eliminating extraneous data, reducing the demand for network bandwidth and core processing power. Industrial edge computing has evolved beyond improving resource utilization. With more powerful devices at the edge, we can bring new functionality to the process. An industrial edge device might have the horsepower, for example, to host visualization, database, communication, or applications servers for the local process, reducing dependence on high-maintenance PCs and tightening IT integration.
Four ways to improve edge computing skills
Edge computing architectures, by design, blend previously segregated functions and technologies. For OT specialists, capitalizing on this shift may require developing new skills. Among leading skills for edge computing are system design, networking, database expertise and security. Here’s a little more on what is required to develop these skills. 1. System design — An edge-oriented system needs someone who understands how everything fits together. Edge devices, computer networks, and software systems each have specific resource constraints, and the quality of their interactions will improve or degrade overall system effectiveness. When data is moving across domains, interoperability becomes a bigger concern. It’s helpful to understand common data exchange formats, like JSON, and how to process and organize data efficiently in collaboration between many devices. These factors determine system-wide attributes, like scalability, which are becoming more important as we move towards IIoT integration over the next decade. 2. Networking — Edge computing improves the performance of IT/OT systems in part by using network resources more efficiently. It helps, then, when working with edge computing systems, if engineers understand how to measure and manage the performance of computer networks. Adding several edge devices pumping out reams of data can slow a network to a crawl. If designed correctly, it can improve responsiveness in the field while reducing usage in the core. Implementing efficient communication protocols, like message queuing telemetry transport (MQTT), can further reduce bandwidth demand. Other factors like scalability and fault-tolerance also may play a role in network design or the selection of devices and communication protocols. 3. Database expertise — Databases are important for data storage, distribution and analysis in IT and OT systems. Edge computing makes them more connected and numerous, which requires OT specialists to understand something about how they are designed and how to interact with them.
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Node-RED is a popular IoT connectivity tool that makes it easy to share data at the edge. Courtesy: Opto 22
Database security and efficiency can be negatively impacted by the quality of incoming data and the frequency and type of transactions. Specific software includes an open-source IoT connectivity tool to help share data among edge devices, databases and cloud services. Another software simplifies database connectivity for industrial applications. 4. Security — Physical and cybersecurity cannot be taken for granted when data can be stored, accessed and managed at many points in a control network. Engineers working with this kind of network will have to become familiar with the potential risks and the tools used to mitigate them. Engineers need to understand the function and implementation of network security devices like firewalls, as well as data protection features like authentication, encryption and certification. They also should be able to assess the security profile of new devices and applications before they’re introduced to the network.
Here comes the edge: Get ready to jump
Do not ignore the inevitability of edge computing, or any of the technologies contributing to IT/OT integration and the move towards IIoT. As everyday devices become more powerful, engineers will be asked to do more with them. This is a trend engineers can get in front of, and will see more opportunities to create value for internal and external customers with the knowledge gained. Engineers who choose to explore and invest in developing new knowledge, skills and connections will find doors opening to them for years to come. ce
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KEYWORDS: edge computing, industrial controller Edge computing is becoming more important for engineers as industrial and operations technology (IT/OT) departments become more intertwined. Edge computing architectures blend previously segregated features and functions by design. Engineers need to develop skills such as database expertise, security, and networking. ONLINE Read this article at www.controleng.com for additional stories from the author linked below.
CONSIDER THIS What skills do you already have with edge computing and what do you need to work on the most?
Josh Eastburn, director of technical marketing. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com. control engineering
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DATA ANALYTICS Travis Cox, Inductive Automation
Software readiness for data analytics and Big Data Expand industrial data access and get more out of it with tools such as message queuing telemetry transport (MQTT) on the way to Industry 4.0 benefits.
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major aspect of Industry 4.0 is Big Data acquisition and analysis to turn data into actionable information and enable systems to make decisions on their own. Despite the presence of new technology, most organizations still use clipboards and paper to gather data and information. In many cases up to 90% of this data ends up stranded in the field in silos or islands. That presents challenges when trying to realize the benefits of Industry 4.0. The good news is new technology can help, and there are several simple steps users can take to prepare for digital transformation, including getting access to more data, edge computing, data cleansing, contextualizing data and standarding common data structures. The journey starts with getting access to more data — a vital component of Industry 4.0. The operational world is complex, involving hundreds of different protocols, communication mediums and legacy device knowledge. The reality of digital transformation is it must be implemented from the bottom up, with operations technology (OT) on board first. It requires a new mentality in keeping systems open, interoperable and secure. The first step is getting access to all the data in an efficient way — with the ability to easily tap into the data when needed, from any source. One of the biggest barriers to data access is legacy software licensing models that charge per tag or user. These models don’t scale, prohibiting growth. Furthermore, industrial applications have been closed, proprietary and have limited functionality and connectivity. Today, we require new models that are fundamentally unlimited and open. These new models can unlock new opportunities for expansion and greater scalability. Another challenge is balancing the convergence of new smart sensors and devices along with existing legacy devices. It’s important to have an infrastructure able to support both. It boils down to one, crucial concept: an architecture change. We need to stop connecting legacy devices to applications with protocols and, instead, connect devices to infrastruc-
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ture. We need to provide an OT solution that meets the needs of operators that is plug-and-play, reliable, and scalable.
The new architecture This new architecture uses message queuing telemetry transport (MQTT), a publish/subscribe protocol that enables message-oriented middleware architectures. This is not a new concept in the information technology (IT) space; enterprise service bus (ESB) has long been used for integration of applications over a bus-like infrastructure. With MQTT, device data is published by exception to a MQTT server, either on the premises or in the cloud. Applications subscribe to the MQTT server to get data, which means there’s no need to connect to the end device itself. MQTT provides several benefits: • Open standard/interoperable (OASIS standard and Eclipse open standard (TAHU)) • Decouples devices from applications • Reports by exception • Requires little bandwidth • Transport layer security • Remote-originated connection (outbound only; no inbound firewall rules) • Stateful awareness • Single source of truth • Auto-discovery of tags • Data buffering (store and forward) • Plug-and-play functionality. To get to a new architecture, the answer is edge computing and protocol conversion. Let’s say there are 10 Modbus devices connected to a supervisory control and data acquisition (SCADA) system. Users can deploy one edge gateway with support of Modbus and MQTT to push the polling closer to the programmable logic controller (PLC). Users can poll more information, potentially at faster rates, and publish the values as they change to a central MQTT server. The SCADA also can be changed to connect and subscribe to the MQTT server to get the data instead of connecting to the end devices. This is an important step www.controleng.com
MQTT’s publish/ subscribe capabilities streamline communication and help move polling to the network edge. Courtesy: Inductive Automation
for future-proofing a SCADA system. As users acquire sensors or upgrade equipment that supports MQTT, the SCADA will get data access without having to know about the end device.
Help systems understand the data
Not only do users need to get access to the data, but they also need to ensure the data is valid, has context and is part of a common structure, if applicable. This is an important step before using analytics and machine learning. These systems need to understand the data in order to properly use it. Typically, new sensors and devices already have these facilities. However, that is not true for legacy devices. There are hundreds of different polling protocols that require mapping and scaling. Most PLCs have addressing schemes that are not human-readable. These mappings commonly exist in SCADA, but still lack context or may contain invalid data or are not part of a standard data structure. The best place to handle this step is in the edge gateway that connects to the PLC. It requires software that has features in place to clean data, add context and support data structure. Let’s start with cleaning data. Suppose there’s a sensor connected to the PLC and the signal drops out sometimes. When the signal drops out, the value in the PLC drops to 0. It may be possible for the value to equal 0, however, not when the last value was 50. In this case, it’s important to look at the delta to determine whether or not we should ignore the current value. Setting up a calculated tag with that logic can solve this problem. It’s important to ensure the data is valid closest to the source before using it with other systems. Another crucial step is providing context to the data. For example, a user can have a Modbus PLC with a tag referencing 40001. In SCADA, we would map that to a tag name like “Ambient Temperature.” If that’s the only data we have, we don’t know if the temperature is Celsius or Fahrenheit and what the low and high range is. Analytics and machine learning models will provide incorrect
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data without the proper context. Using edge gateways with the ability to provide name, scaling, engineering units, engineering low and high, documentation and tooltips will provide other systems crucial information to better understand the underlying data.
Increasing enterprise value
Users need to get data access and ensure the data is valid, has context and is part of a common structure... before using analytics.
The last step is standardizing on common data structures across the enterprise. This step often is skipped because data can be different at each site and it can be difficult to find a common data model. Analytic packages and machine learning models require data to be in the same structure for common objects. Users don’t want to have to create different analytics or machine learning models for each site. This goes beyond an individual data point to a collection of data points for a known object. It’s important to survey each site to find a common model and use an edge gateway that supports user defined types (UDTs). This means adapting the data at each site to fit the model, which can include scaling, calKEYWORDS: message queuing culated tags, conversions and more. This is telemetry transport, Big Data, so the data appears as the same structure MQTT on the surface while hiding the complexity Big Data acquisition and behind the scenes. analysis to turn data into The journey begins with operational actionable information is a major aspect of Industry 4.0. infrastructure and solving the problem of Current architectures use getting data into an infrastructure. Users message queuing telemetry can’t get to analytics and machine learning transport (MQTT) to help until they have access to data. This data understand the data. needs to be valid and have context to be Users need constant access to understood. Users can take small steps to the data being generated and it needs to have a valid context. realize the potential benefits of these technologies by adopting this new mentality ONLINE and architecture. ce Read this article at
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Travis Cox is co-director of sales engineering at Inductive Automation. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.
www.controleng.com for more stories from the author.
CONSIDER THIS What are you doing to streamline data acquisition to better prepare for Industry 4.0?
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DATA ANALYTICS, CLOUD COMPUTING Megan Buntain, Seeq
Cloud-based analytics for manufacturing Industrial organizations already are realizing the benefits of cloud computing for IT and business workloads, providing a path to manufacturing deployments.
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loud computing is recognized by industrial organizations as a key enabler for storing and analyzing data in volumes that seemed unfathomable even a few years ago. This is important because many organizations start and end their digital transformation strategy with the idea that better use of vast amounts of financial, customer, supply chain and operational data will improve operational efficiency and create new business models. If cloud-based analytics tools are not in use today by a manufacturer’s process engineers, another department is likely using them. For example, an HR department is using software to analyze employee data, or analysts in sales and marketing are working to review demand for specific products and services. While these are considered productivity or business intelligence applications, they are also the entry point for cloud-based analytics in most companies. To implement digital transformation strategies, there has been a rise of cloud-based analytics — applications, tools, and techniques deployed in the cloud instead of on premises — enabling organizations to quickly gain data insights. The cloud is streamlining this process because it enables rapid insights on
much more data of different types with the near infinite scalability of computing resources. This empowers employees to solve an increasing array of complex business challenges in near real-time.
Defining cloud-based analytics terms Cloud-based analytics is a broad term referring to several layers of computing capabilities. First is the underlying cloud infrastructure, the operating system and hardware layers, required to host data and applications in the cloud. On top of that infrastructure is the data management layer, in a cloud service or a data lake, where various types of data are stored including structured and unstructured text-based data, video data, and streaming IoT data. Applications in the analytics layer make use of this data. Calculations are performed to supply the visualization layer with the information required for trending, reporting, dashboards and other insights. In manufacturing, analytics has traditionally been done on premises using a combination of historian data and spreadsheet analytics for ad hoc diagnostic, predictive, or operational dashboards for the plant, but that is changing as cloud-based analytics’ benefits are being realized.
Expected benefits Applications deployed in the cloud benefit from core cloud capabilities. First is the cloud’s rental model versus the capital cost associated with hardware and infrastructure. A company’s information technology (IT) department no longer has to provision and maintain expensive servers to host these applications, and the result is a pay-as-you-go model where computing resources are spun up and down on demand. One example is a web store that does 90% of its business in the weeks leading up to the holiday season. Before the cloud, that retailer had to purchase enough servers to handle the website traffic for the “burst” of demand at the peak, while remaining largely idle the rest of the year. In an industrial context, as subject matter experts (SMEs) leverage new analytics tools to gain
Figure 1: The cloud can be used to make data available to the machine learning algorithms used in advanced analytics applications. All images courtesy: Seeq
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Figure 2: Employees in different departments can access cloud-based data and analytics worldwide.
more insight into their operational data, more flexibility is required. SMEs may want to analyze new data sources such as operational data and contextual data, and organizations may need to make analytics tools available to more SMEs and other users to enable better collaboration and decision-making. Another driving factor for cloud adoption is the ability to explore new types of analytics such as making historical and near real-time process data available for machine learning (Figure 1). Many manufacturers don’t want to run machine learning models within their real-time control systems, but they do want to take advantage of these and other advanced capabilities for improving product quality, predicting optimal maintenance windows to prevent unplanned downtime and other purposes. Copying operational data to the cloud makes it available for machine learning, enabling new analytics models to be explored without the risk of impacting the source production data or any existing applications relying on that data. Cloud-based analytics makes it easy to break down data silos so users can access and connect to data regardless of its source. Once those silos are connected via the cloud, SMEs and other users (Figure 2) can scale up analytics to sites around the world and create ways of viewing global operational reporting to ensure the best possible business impact is achieved.
Cloud analytics: Getting started
When implementing cloud-based analytics, it’s important to begin with the end in mind. Too often energy and manufacturing companies spend considerable time planning for and migrating data and applications to the cloud, only to ask, “What now?” after the data is moved. Moving data or aggregating data in a cloud data lake doesn’t make it more valuable; it’s a step along the way to implementing a comprehensive data analytics strategy. The surest way to avoid this outcome is ensuring SMEs are involved early in any analytics project. Only people with deep process expertise and a view of the unique impact of individual units within broader operational procedures can ensure this data leads
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Figure 3: SMEs use Seeq to directly interact with data of interest and find insights.
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IT teams must resist the temptation to summarize process data in the cloud or apply business rules before connecting
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cloud analytics application to the data.
to insight and productive action. The more SMES are provided with relevant, easy-to-use, flexible analytics applications, the more rapid return on investment. One note of caution in operational data and cloud computing models is IT teams must resist the temptation to summarize process data in the cloud or apply business rules before connecting cloud analytics application to the data. When data is summarized, someone without direct knowledge of the asset or process is predetermining what SMEs might KEYWORDS: cloud-based be interested in exploring, which can analytics, cloud software diminish its potential impact. The best Cloud-based analytics allow practice here is store all the data in its manufacturers and subject matter native form so SMEs others can make experts (SMEs) to receive data decisions at analytics time about how faster. and what to modify, for example data SMEs can leverage the data and find unique insights to improve cleansing and access to any data set for operational efficiency. investigation and model development.
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Cloud success stories
An energy company with more than 50 operational sites dispersed across a large and geographically challenging environment was able to implement cloud-based analytics successfully by starting with six engineers at one site. These engineers identified three use cases: asset integrity monitoring and performance trending, predictive maintenance and production forecasting.
Cloud-based analytics makes it easy to break down data silos so users can access and connect to data regardless of its source.
ONLINE Read about additional examples of specific success stories on www.controleng.com with the story “Workloads in the cloud for industrial manufacturers.”
CONSIDER THIS What benefits could your plant gain from cloud-based analytics?
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DATA ANALYTICS, CLOUD COMPUTING Within 90 days, the team had scaled up to more than 50 engineers in more than 10 locations working with these use cases to connect new sources of data by leveraging shared cloud deployment and collaboration capabilities. This unlocked the creativity of these engineers to find dozens of additional use cases to finding insights and improve asset availability and production outcomes (Figure 3). By starting small and leveraging the cloud to scale up analytics quickly, the project owners measured the business impact, building their business case to get more users and sites on board. The next step for the organization is to connect additional IoT data from their remote locations, keeping two critical factors in mind, network latency and analytics performance. Users won’t use cumbersome analytics tools with poor performance and excessive delays, so these factors must be addressed. Before cloud implementation, reporting from these remote locations was offline and manual. To solve these issues, the company is using a hybrid
cloud approach with edge analytics, which maintains some computing and analytics resources at the edge or close to the data source with results delivered to the cloud when the network is available. The manufacturing industry is still in the early days of cloud-based analytics, but critical implementation learnings are emerging. Companies must keep SMEs at the center of any analytics effort, and they can leverage the cloud to scale up and down to connect data silos. Raw data must be kept whole to ensure analytics and insights are flexible. Collaboration across teams and sites also must be ensured to realize returns and broaden the potential business impact. Coupling the right advanced analytics software with cloud platforms in use by most organizations will help yield benefits in improved operations. ce Megan Buntain, director of cloud partnerships, Seeq Corp. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, cvavra@cfemedia.com.
Seeq partners with AWS and Microsoft Azure
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o meet demands from end users in industrial plants and facilities, software of all types is moving to the cloud, and advanced analytics is no exception. In response to these demands, Seeq is available in the Amazon Web Services (AWS) Marketplace and the Microsoft Azure Marketplace, enabling end users to rapidly investigate and share insights from process manufacturing data stored on premise or in the AWS or Microsoft clouds.
Seeq achieved the AWS Industrial Software Competency, the highest tier for Amazon Partner Network (APN) Technology Partners. Doing so requires passing a rigorous technical review of availability and deployment guidelines for secure, high-performing, resilient, and efficient cloud applications. Seeq employees certified on AWS technologies and customer references met the requirements. Steve Sliwa, CEO of Seeq said, “By leveraging the agility of AWS, Seeq enables process engineers, managers, teams, and data sci-
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entists to derive more value from data already collected by accelerating analytics, publishing, and decision making.”
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Leveraging expertise of engineers and machine learning accelerates business value.
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As previously announced, Seeq is availabile as a software as a service (SaaS) offering on Microsoft Azure, addressing demand for cloud-based analytics solutions from customers around the globe in the oil and gas, pharmaceutical, chemical, energy, mining, food and beverage, and other process industries. The cooperation provides support for Microsoft Azure Directory Service, Azure data stores, Microsoft Windows Server and AD—with SQL Server and Office 365 integration. Seeq plans to soon announce Azure Time Series Insights (TSI) support in response to request from
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TSI customers and the Azure Marketplace. Seeq hosts user groups at Microsoft Technology Center offices. Data scientists can use Seeq to access time series data that has been cleansed, contextualized, and modeled in Seeq for analysis with machine learning algorithms in AWS SageMaker or Azure Machine Learning. This enables new opportunities for collaboration between data scientists in IT organizations and subject matter experts in manufacturing facilities to improve production outcomes. “Leveraging both the expertise of the engineers and machine learning innovations, leading-edge solutions like Seeq enable organizations to gain intelligence from operations and obtain business value very quickly,” observes Janice Abel, principal analyst at ARC Advisory Group. “Seeq is designed for ease of use by a variety of plant employees, making it simple and quick for them to find and share insights in their data.”
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We understand you need insightful process information to help you run your plant e�ciently.
MEASURED VALUE + ADDED VALUE You make confident decisions backed by process data and a complete portfolio of services and solutions to support you.
Customers around the world trust us when it comes to process automation. Our shared goal is plant safety, availability and e�ciency. We are with you every day, everywhere. People for Process Automation
Do you want to learn more? www.us.endress.com input #10 at www.controleng.com/information
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STANDARDS UPDATE Thomas J. Burke, Mitsubishi Electric Automation Inc., previously with the OPC Foundation
Intrinsic value of standards Analysis: Collaborate to help standards enhance return on investment for technologies used in factory automation and process automation.
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ow can the value proposition of standards be measured for standards organization, end users, and technology suppliers?
1. For standards organizations developing open standards, the real value proposition is measuring success by the level of adoption. Standards organizations help drive technologies and play a very important role in contributing to new and emerging technologies and how they are deployed across industries. The focus of standard organizations in the ever changing world of technology is to deliver standards, technology, process and certification focused at solving real world problems. 2. For end users to prepare for numerous technical innovations beyond the horizon, the most important technology and standards strategy is working closely with suppliers to identify and develop the right standards to achieve success in factory automation and process automation. Benefits derive from collaborating to develop a complete open system architecture or by standardizing on the data and information models for their respective industries. A number of initiatives involve suppliers working under the direction of end-users and end-user organizations.
RETURN ON INVESTMENT
3. For suppliers, integrating useful standards into products helps TIME TIME STANDARDS enhance customer return on investment (ROI) and EFFORT EFFORT MAKING makes suppliers’ products, software and services more useful. Suppliers RETURN ON INVESTMENT know that to be competTime and effort itive they must deliver products and services that that goes into embrace interoperability with their competitors. The standards making end-users have diverse sets of requirements for autoshould create large mation, and typically have hardware, networks and benefits for end software products from a multitude of different comusers and suppliers panies. The days of a single vendor and single netinvolved. Courtesy: work being used by an end-user are gone. End-users Control Engineeralso need integration between IT and OT systems ing, CFE Media and and the value of standards play a very important role Technology in IT and OT convergence.
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Ten years ago, NAMUR was a driving force across process industries to standardize on device configuration and asset management. Every major process automation supplier was involved in this effort, contributing significant amounts of resources in money and people to develop a standard and to make standards jointly owned by the standards organizations. One specific standardization activity was FDI, and the standards organizations involved in this included OPC Foundation and organizations for FDT, Profinet, Foundation Fieldbus, and HART. A multiple-year effort helped suppliers to deliver a standardized device model to end users. The resulting technology is being used by distributed control system (DCS) vendors, and is managed by the FieldComm Group. (See related FieldComm Group article.)
Usable standards
Standards organizations used to be in business to just develop standards and often did not deliver a standard that was worth more than the “paper” it was printed on. Often standards were developed as a means to an end by a single supplier. One supplier would partner with other companies for the primary purpose of adopting the standard they were developing. Over the years, many standards organizations recognized the need to develop standards that suppliers would widely use in products and systems. Success of standards in consumer industries has had a significant impact on standards organizations and in industrial automation.
IT, OT convergence
Other developments feeding more effective standards include: • Popularity of solution strategies • Convergence of information technology (IT) and operations technology (OT) • Ability to integrate data from the OT world (factory floor, for example) into the IT applications. OT and IT worlds differ, but both sides have recognized the need for integrating data and information among and between their systems. Industry Continued on page 34 www.controleng.com
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input #11 at www.controleng.com/information
As easy as that.
ANSWERS
STANDARDS UPDATE Ted Masters, FieldComm Group
Standards capture stranded data, help with device design, data integration Integrated systems and standards help support advanced information technology (IT) and operations technology (OT) collaboration and optimization in three key ways. Also see four advantages of future process automation facilities.
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t’s an exciting time of technology revolution across industries, and integrated standards are helping. While most home devices can be accessed on computers and smart phones and automotive technologies have advanced to self-parking and near self-driving vehicles, process industries are progressing at a more careful pace. There is inherent risk in disrupting good processes and complex systems. However, we are in an era of real change as a new generation of workers move into the leadership positions that drive this evolutionary transformation. The manufacturing industry, once resistant to collaboration between information technology (IT) and operations technology (OT) teams, now realizes collaboration is a critical factor to success. Educating the current workforce on IT, while assuring
the next generation appreciates the challenges of operating a complex, and sometimes hazardous, process automation plant, requires collaboration across multiple functional disciplines. It is projected approximately 75% of the global workforce will be millennials by 2025, according to a Forbes Magazine article, “What The Ideal Workplace Of The Future Looks Like, According To Millennials.”
IT and OT collaboration: 3 benefits FieldComm Group is aware of the culture change of collaboration between IT and OT and is managing standards development to address existing products and the next generation of products based on a new set of technologies. The vision has been clear with the following plans in place: 1. Assist the installed base by making it easier to capture stranded data 2. Design specifications for new devices with technologies for the next generation 3. Drive the adoption of FDI (Field Device Integration) technology to create a bridge to the next generation, with seamless standardized device integration, regardless of the supplier or protocol.
Figure 1: Technology advancements will allow more future workers to more effectively provide benefits from higher-level tools. All images courtesy: FieldComm Group
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FieldComm Group (FCG) is closely aligned with the Namur Open Architecture (NOA) as a means of digitalization since it does not disrupt existing systems and provides a path for migrating users to a digitalized approach. NAMUR is the User Association of Automation Technology in Process Industries. www.controleng.com
Figure 2: EthernetAPL and PA-DIM technologies will increase the data rate and data integration and information availability for process automation.
Process automation open architecture
The NAMUR open architecture proposes future process automation facilities will: 1. Continue to use existing instrumentation protocols 2. Make use of new high-speed and IP-based physical layers 3. Take advantage of simplified integration tools to move plant floor data throughout the global enterprise 4. Use one-way communications out of the core process control domain to reduce cybersecurity threats. Namur Open Architecture provides an approach for using field devices supporting OPC UA (OPC Foundation) as the data exchange technology. Working jointly with OPC Foundation, FCG has developed a new OPC UA based standard to replicate the most common set of field device parameters in process instruments. The technology is called PA-DIM (process automation device information model). PA-DIM adds specificity to the standard OPC UA device information specification and defines core parameters to establish a standard set of data for each class of process automation device. PA-DIM also delivers these parameters in machine-readable format enabling use by applications, independent of the supplier or protocol that delivers it.
www.controleng.com
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A 25-year-old HART-based device using FDI can co-exist with a new Ethernet-APL device of the future and both use the same information model
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to provide data to the cloud. Two-wire Ethernet
In working to develop next generation of field devices based on Ethernet-APL [two-wire-based advanced physical layer for Ethernet], the PA-DIM can be resident local on the device. The benefit to users is a 25-year-old HART-based device using FDI can co-exist with a new Ethernet-APL device of the future and both use the same information model to provide data to the cloud, and the cloud won’t know the difference. Together, new technologies of Ethernet-APL, FDI and PA-DIM provide the highway to digital transformation for existing instrumentation and new system architectures and data models. FDI plays a major role in this change, helping users worry less about a device’s protocol and care more about how they can use data, instead. Simplifying the device’s integration into systems or the cloud is the purpose of the “FDI Device Package,” control engineering
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Figure 3: NAMUR Open Architecture makes room for existing instruments, a more capable industrial Ethernet for process automation in 2010, Ethernet-APL, and a plant floor-to-cloud information model (PA-DIM).
the new standard “container” of all relevant information needed around a device. The FDI Device Package is supported as the new standard by HART Communication Protocol, Foundation Fieldbus, ISA100 Wireless Systems for Automation, FDT (Field Device Tool, FDT Group) and Profibus (PI North America).
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Standards organizations must work together in the age of digitalization to align on standards that are best
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for the industry.
No desire for more standards?
At the end of the day, collaboration is the key. Standards organizations must work together in the age of digitalization to align on standards that are best for the industry. Users and suppliers don’t want more standards; which means we need to agree on which ones work best for the many use cases incurred for digitalization. Collaboration among industry organizations is helping align key industry issues, such as cybersecurity, next-generation field devices, integration and information models.
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Results of this collaboration contributes to emerging technologies that make it easier for industries to use data to run enterprises better, protect the value of investments and future-proof operations. ce Ted Masters is president and CEO of FieldComm Group. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com.
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KEYWORDS: HART, Foundation Fieldbus, FDI, FieldComm Group IT and OT integration Industrial standards organizations collaborate Ethernet and open process automation advances. CONSIDER THIS Are you looking at how standards can add to your competitiveness?
ONLINE If reading from the digital edition, click on the headline for more resources and links. www.controleng.com/magazine www.controleng.com/networking-and-security/ www.fieldcommgroup.org www.namur.net/en/index.html www.controleng.com
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ANSWERS
STANDARDS UPDATE: SMART FACTORY Jerzy Greblicki, AIUT Sp. z o.o
Automation of auxiliary processes is the key to the smart factory The essence of a factory, based on the Industry 4.0 technology, will be to incorporate further areas and production structures into already automated ecosystem of key processes and a digital expansion should be radical.
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arket demand is continuously evolving, which is forcing companies to reorganize their existing production strategies. Based on traditional automation systems, plants have looked for ways to reduce downtime, organize a safe work environment using mobile automated guided vehicle (AGV) robots and collaborative robots, and take advantage of the potential of advanced analysis of data acquired within the implemented Internet of Things (IoT) ecosystem. For the best possible management of real-time manufacturing process, it is crucial to enhance the competence of the IoT ecosystem in the management of auxiliary processes and company resources. According to the concept of Industry 4.0 KEYWORDS: Automated guided all production processes should be intevehicles, AGVs, robotics grated, monitored and analyzed to achieve The rise of robots on the plant floor is changing how the best management based on the actumanufacturers utilize their staff al state of the factory. In this way, techand operations to better realize a nology can inform people of the need Smart Factory. for repairs and anticipate possible breakCompanies need to move out downs, making downtime less likely. of their comfort zone and force themselves to meet customer In an ideal scenario, a production demand, which is becoming line is implemented with the use of virmore customized. tual commissioning technology, which A polymorphic plant floor will allows to test software, simulate mechanincreases flexibility and find ics and process automation, and as a better uses for human and robot result to detect design errors â&#x20AC;&#x201D; in the workers alike. virtual sphere. This shortens the time of ONLINE actual start-up, especially of large prowww.controleng.com/ duction lines. international, www.aiut.com At the same time, system suppliers can CONSIDER THIS integrate the best digital transformation What does your company technologies according to the needs of the see for its Smart Factory and plant. Individual approaches to digitalizawhat steps are they taking to tion of auxiliary processes will depend on implement it?
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available market technologies. A good example is the evolution of the competence of AGV robots. These autonomous vehicles are supported by natural navigation and operate safely with plant staff.
Radical approach to process automation: new optics
The automation of key production processes will soon cease to be sufficient. Manufacturers seek further areas of optimization, but certain conditions and challenges exist. The consumer market is hungry for new products. Consumers expect customization, which is forcing production lines to create shorter product runs. The expected competences and requirements of employees also is growing because of this growing consumer demand. Factories have faced a shortage of qualified workforce in selected areas. The automation and robotization of processes, where a human being can be replaced by a machine, is a business need for manufacturers. It is necessary to extend the scope of automation with additional processes, which is what the radical automation concept says. The approach to the automation process should be holistic and include all business-relevant areas and resources of the manufacturing company, which will be combined into one coherent communication ecosystem. This should be treated in terms of automation of processes, which until now have been overlooked or postponed. It should be emphasized the goal of process improvement is not to exclude people. Intelligent production assumes cooperation between a human and a robot, and optimization of only those processes in which it is justified: where a human is not needed or will not want to be needed in the future. www.controleng.com
Moreover, as Ben Reynolds notes in his book “The Coming Revolution: Capitalism in the 21st Century:” “(…) automation does not always lead to a decrease in demand for [hu]manpower (…)”. History shows there are certain structural causes for radical automation. We may also have to accept we will never have enough skilled production workers available within the workforce to keep pace with expanding processes. A human being is supposed to be an added value thanks to his unique competences. A plant’s staff is irreplaceable in those areas of production and logistics where the process’ complexity makes implementing traditional automation economically unjustified and difficult to accept temporarily or when we talk about supervisory processes. In addition, increasing personnel competencies, with the simultaneous popularization of robotics, allow manufacturers to better utilize the potential of employees in other areas. In this perspective, a holistic, radical approach to automation should be understood in two ways. First of all, it is a radical increase in involvement in process automation, exceeding the company’s expectations. It also is a radically expanded sense of which production and auxiliary processes can be under automation.
It pays to leave a comfort zone
Excellence in traditional automation has led to the development of radical automation technology with the use of the Internet of Things (IoT) solutions. According to the International Data Corp. (IDC), today’s IoT market in Central and Eastern Europe is valued at more than $11 billion. However, as with any new direction, the approach to the digitalization of subsequent processes in plants is considered in terms of profit and risk. Factories need to see their way out of the comfort zone where, based on the automation of key processes, a certain level of efficiency has been achieved that meets current demand, or deficiencies are not yet severe enough to invest in new areas. The development of the IoT ecosystem, based on the automation of auxiliary processes, will determine the long-term development of the organization and the growth of its position on the market.
Autonomous AGV with facility location system
The new quality of service in the area of automation of internal logistics processes is visible in the case of autonomous AGVs, which in the area of navigation are adapted to the needs of the plant. Cognitive analysis enables the systems to make independent decisions in real time and adapt production processes to the existing situation, without a participation of an operator.
www.controleng.com
The automation and robotization of processes, where a human being can be replaced by a machine, is a business need for many manufacturers. Courtesy: AIUT, Control Engineering Poland
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With Industry 4.0 concepts, all production processes should be integrated, monitored and analyzed to achieve the best management
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based on the actual state of the factory. These self-propelled robots can cooperate with the system of internal location in the space of production halls, which provides a full picture of the current status of moving objects, which allows for a very accurate analysis of traffic and further optimizations related to, among others, the deployment of fixed elements and the management of work of people. Systems that increase the scope of competence are the essence of an intelligent factory. The biggest advantage, and at the same time the market expectation, is short implementation time and unprecedented flexibility and versatility of AGV robot cooperation. Moreover, the latest security systems allow for direct cooperation with people, which means there is no need to separate closed zones in production or warehouse areas.
Polymorphic production will make a factory more flexible
Companies striving to improve the production chain with digitalization of new areas, but also with the use of new solutions, can be improved and extended. The consumer market is mature. New expectations and customer habits have pushed faccontrol engineering
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STANDARDS UPDATE: SMART FACTORY
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This modern
approach is
crucial to produce a short product run.
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tories in the direction where not only quality and speed of production are important, but also flexible production process management, as manufacturers strive to keep up with the needs of buyers. New consumer trends are a challenge for traditional models, where the automation of key production processes has once been established, implemented and operated. A rigid supply market today leads to more constraints than development. This means the need for a new look at the implemented ecosystems of production management and the creation of new solutions such as polymorphic production. Its aim is to expand key manufacturing processes by assigning additional positions or entire production lines in virtual and real space. In such a system, new nests can be serviced by collaborative robots and mobile AGV robots, which, in a simplified way, see the production line virtually. This is a modern approach and is crucial in the context of producing, among other things, a short product run.
Technology integrators: what to expect from them? In the latest report “IoT in the Polish economy,” commissioned by The Ministry of Digital Affairs, market experts predict the manufacturing indus-
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KEYWORDS: Industrial networking standards, interoperability, IT/OT collaboration Standards organizations help drive technologies and contribute to new and emerging technologies and how they are deployed. Operations technology (OT) and information technology (IT) users recognize the need to collaborate and harmonize through common standards. Organizations need to develop the right standards to be relevant for end users and manufacturers. CONSIDER THIS What is the biggest challenge for your organization as standards and networking technologies converge?
ONLINE If reading from the digital edition, click on the headline for more resources. www.controleng.com/magazine
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Continued from page 26 standards play a very important role in IT and OT convergence. Examples of end user and supplier collaboration include initiatives and standardization like MDIS, open process automation, Open-SCS, VDMA, OMAC, and MTConnect. The OPC Foundation recognized the importance of collaborating with standards organizations that were involved in developing their own information models for their respective industries. The OPC UA architecture facilitates these standard organizations developing companion specifications to integrate their information models into the OPC UA infrastructure. Standards organizations need to recognize the reason they are in business: To have their technologies adopted, solve realworld problems for end users and suppliers and provide ROI. Standards must not be academic futility, and standards organizations need not compete. Many opportunities exist to pursue standards convergence and harmonization. Successful standardization in con-
CONTROL ENGINEERING
try will become one of the sectors that will use the IoT technologies to the greatest extent. Acquisition and intelligent analysis of data in factories allows automation and synchronization of work, as well as reduction of repair costs by preventing possible failures and increasing security. This increases the efficiency and profitability of the whole institution. It is important for the manufacturer to have a well-defined business or operational goal, which can be shared with an IoT and Big Data partner. From here, the company will create an ecosystem of information exchange between the plant’s systems and its environment. Although there are many ways of organizing such cooperation — from consulting and technical support — it is the model of responsibility for the process responsibility as a service (PRaaS), where the parties implement common key performance indicators (KPIs), which becomes the most business-desired model of cooperation. ce Jerzy Greblicki is a director of Radical Automation branch and an Industry 4.0 Management Adviser in the company AIUT Sp. z o.o., one of the largest European system integrators for the industry. This was posted on Control Engineering Poland Jan. 8 and edited for Control Engineering North American edition.
sumer electronics resulted in standards adopted by suppliers and used in products that end users want. Industrial end users and suppliers are overwhelmed by a multitude of available technical innovations available. It is important harmonization and standardization is achieved. Standards and technologies need to solve real-world problems to be relevant.
Standards, interoperability, value There are many opportunities for standards that allow true plug-and-play interoperability and leverage technologies involved in adaptive learning and machine learning. Older programming models for many devices have changed as technology has advanced and programming environments have standardized and commoditized. Multiple standards organizations compete for space as they recognize the importance of harmonization. If they want to be successful, they must be developing the right standards and technologies that will be adopted into products and deployed by end users. ce Thomas J. Burke is global director of industry standards at Mitsubishi Electric Automation Inc. and past president and executive director of the OPC Foundation. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media, mhoske@cfemedia.com. www.controleng.com
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ANSWERS
INSIDE PROCESS
Allan Kern, APC Performance LLC
Advanced process control: More answers for optimization After a webcast on “Advanced process control: Past, present and future,” more answers to audience questions are provided by the webcast speaker covering advanced process control (APC) to help with process optimization.
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dvanced process control (APC) comprises a number of tools and techniques whose common characteristic is taking process automation beyond the limits of single-loop control and into the realm of multi-loop (or multivariable) control. The Jan. 9 webcast, “Advanced process control: Past, present and future,” aims to help process engineers (also known as production or operation engineers) increase understanding of how modern control systems can improve process performance and optimization. At the end of the webcast, registrants viewing live ask questions. Below are additional questions and answers the webcast didn’t have time to address.
Question: Please explain more about how MPC has invested a lot of unplanned resources. Answer: Model-predictive control (MPC) has invested a lot of resources over the last several decades, users and suppliers. The question is whether that has been done in a planned or unplanned way. The industry should have evolved towards multi-loop control (MLC) — more agile, lower cost, easier to use tools. But instead, most effort has been struggling with model quality, optimizers, and matrix design. The struggle continues and the cost and complexity of ownership has not come down; if anything, cost and complexity have increased. Industry has learned why these issues won’t be solved (in the case of modeling), or why maybe they are not worth solving at this point (in the case of optimization). Industry should move on to more manageable and promising APC 2.0 solutions with the benefit of this experience. Q: How can the “mighty matrix” be more significant in APC than models can?
Industry expected model-predictive control (MPC) to naturally expand into the multi-loop control (MLC) region, but that never happened; MPC costs of ownership remain high and MPC remains primarily a high-end solution. MLC represents an important, but under-developed part of the APC matrix spectrum. Images courtesy: Control Engineering webcast, Advanced process control: Past, present and future.
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A: This is a good question; the matrix is a relatively simple concept, while model-based control is a very advanced control concept, so logically counts for more. But the point is, like a single-loop trend vs. the proportional-integral-derivative (PID) algorithm itself, the matrix is a simple tool that allows better visualization and understanding of multivariable control, and ultimately that can be more impactful than the nitty-gritty of the algorithm, if that makes sense. Also, industry is realizing more ways to do multivariable control beyond MPC. The matrix is a basic underlying tool, regardless of how it’s carried out — manually, MLC, or MPC. Q: What industries other than HPI do you have experience? What is the longest in-service appliwww.controleng.com
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ANSWERS
INSIDE PROCESS
cation you have? How prominent are services in long-term APC support? A: The MLC tools mentioned are available already, with some applications having been online several years. The controllers have run essentially 100% uptime and maintenance free. I have worked in hydrocarbon processing industry (HPI), chemicals, and power. L&A, the system integration firm mentioned above, has experience in virtually all industries: oil, gas, chemicals, power, paper and some commodities. Q: Does an APC guarantee plant running at optimal conditions? Does multi-loop ensure this?
A: The first and often most important benefit is the intrinsic benefit of closing loops. Everyone in process control understands this is the goal. In many applications, this is sufficient justification, especially if cost is low (MPC cost is high, so greater justification has been needed). In MLC, just like MPC, in some applications, benefits can be quite high. I’m working an application now that is estimated to be worth several hundred thousand dollars annually. In APC 1.0, it’s been well known that a large part of the benefits often come from just a few variables or just a few models, not the entire matrix. Performance monitoring is an accidental sideline — one of the unplanned activities, and essentially impossible, as I mentioned under performance criteria — error minimization vs. safe driving habits. Under MLC, I see performance monitoring, under MPC, going away. Q: What about APC creates value? A: The obvious and intrinsic benefits of closing loops provide value and simple, reliable MLC control. That’s important. Q: Regarding adaptive gain, when should nonlinear gain be used?
Using the same definitions are an intuitive way to understand advanced process control (APC), and its role in process operation, something industry has definitely lacked throughout the model-predictive control (MPC) era. Faster progress can happen with shared understanding.
A: APC guarantees the plant is running at optimal conditions, if: 1. The APC application itself is running. 2. It is not wrongly constrained or defeated by pinched limits, clamped manipulated variables (MVs), etc. 3. It has the correct matrix. 4. MPC complexity and fragility have worked against achieving these, as has the large matrix design paradigm. 5. A goal of MLC is to transcend these “fragile” sources of breakage. Q: What about benefit estimation, performance monitoring and maintenance?
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A: I would emphasize using cascade where possible. Cascade and adaptive gain are complementary. Cascade linearizes gain, instead of using adaptive gain. Beyond that, I’m not a big fan of adaptive gain in practice. It is useful as a learning tool, to illustrate how control algorithms work, but is often troublesome in practice. Like unnecessary use of feedforward, adaptive gain requires assuring the model matches the process, and if not, performance may be made worse for all your trouble. Nor am I a big fan of gap gain in level control, something which also gets more attention than it warrants, in my view. See “Top 10 tuning tips for control engineers.” In most cases, I would rather use an average gain (or the minimum gain, as the application dictates) and see if I can live with that. Q: When talking about APC, where do splitrange control, ratio control, and the Smith predictor fit in? A: I would place these in the category of advanced regulatory control (ARC). These are all important tools to have in your box, along with feedforward and override. And maybe also adaptive gain or cascade. Q: What is the operating envelope? A: Slides 20 and 21 in the webcast (and at right) show a pentagon representing the operating envelope. The circle is the minute-to-minute operating region, www.controleng.com
while the pentagon is the overall envelope (or window). The two-step goal of APC is to put the operating region within the envelope, and then move it in a direction of greater optimization to the extent it can. Q: Aren’t models still needed (lag time, process gain and dead time) for controller tuning? A: Yes. As a feedback controller, MLC (or my version, model-less multivariable control called XMC), will have some basic tuning needs, but also latitude in reliable values, and with a conservative or safe direction to err on the side of stable performance. It’s similar to PID in this regard, but is unlike MPC, which depends on model accuracy. Q: How would you know when to go for MPC, and not end up with a bunch of MLC solutions? A: Appropriate MPC applications are well established by now. Use the technique shown in the presentation to identify MLC applications. In most cases, the applications will be operationally problematic, in terms of creating numerous operator interventions and alarms, i.e. most applications will “find you”, once you realize how to spot them.
mating the way the operating team already knows how to manage constraints and optimize variables.
Q: What programming languages are required to design a non-in-built distributed control system (DCS) MPC?
A: I am familiar with the software you mention, but have not used it. This represents the auto-tuning story all over again, which is one of the big past lessons that I did not have time to address in the presentation. Auto-tuning can be looked at as an attempt to create an “adaptive model” for a single loop. For the most part, industry failed at this, despite a lot of trying. So, how likely is it to succeed on a multivariable basis that is geometrically bigger and more complex? This adds yet another layer of cost and complexity to MPC, with, in my view, doubtful prospects. I wrote about this in a related Control Engineering article: “Pros and cons of autotuning—the big story.”
A: A custom reusable function block, now available in most modern DCSs and programmable logic controllers (PLCs), is the target deployment mechanism, although with today’s flexible technology, other options exist. Visual Basic (VB) or C++ is good. Q: What platforms do you expect will support MLC? Embedded controllers or servers, or what? A: DCS control layer and (with more limits) PLCs can support MLC. In some cases, users may prefer a generic network-based solution, but this adds back complexity and other issues for the owner/operator. Q: Should an MPC require continuous data for the step/pre-testing? A: Theoretically not necessary, but in a continuous run, there is more continuity and ability to spot potential plant events that may cause data irregularities. Q: Not enough emphasis was given to the need that engineers KNOW processes inside and out. A: I agree completely. In MLC, the matrix is designed based on existing (or desired) manual multivariable control methods, that is, it is based on autowww.controleng.com
Q: A major process industry software provider... now offers online dynamic model updating in a couple of forms. Are you familiar with this and have you seen any related applications?
With closed loop multi-loop control (MLC), processes operate closer to targets and limits, because MLC can be relied upon to automatically respond to encroaching constraints, due to normal process variations.
Q: Moving towards a more feedback-centric (as opposed to model-based) paradigm, will MLC be able to respect constraints/limits as well as MPC currently does? A: I believe so. For example, a simple override is actually a multivariable constraint control and optimization tool: The normal setpoint is the optimization target; the override setpoint is the constraint limit; the selector is a 1x2 matrix; and below the selector is the MV. We know these overrides are absolutely reliable and do not use models or optimizers. These well-performing ARC configurations exist throughout the industry and nobody ever considers that they may need or benefit from a model (although technically, if you were to do it with MPC, there would be a model between the MV and concontrol engineering
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Wherever possible, use feedback. You will know where a feedforward model is needed, because it will be kicking you in the tail operationally. In most of the limited number of cases where a feedforward model is truly needed, it already exists in the base layer controls. I am especially doubtful about “adaptive modeling.” See my prior answer on this topic and related article reference. Q: What about dynamics, process changes?
The matrix is perhaps the single biggest contribution to advanced process control (APC) technology and process operation to come from the MPC era, possibly even more so than models. Analyzing a process and diagramming the underlying matrix will become a basic core-competency skill for APC engineers and process engineers.
straint variables). But we know that is not necessary. I think the industry will find the feedback MLC will solve many multi-loop problems simply and reliably. Q: Do you have data that indicates the longerterm performance problems and maintenance requirements of MPC? Can you share this data? You made some very solid points, and some data to back it up would be good. A: Nobody has or publicizes such data, but, slowly, over the years, the story has emerged, and is now generally accepted, even if people still don’t talk about it very openly. A high-available MPC with high utilization is rare; low availability and low utilization are more often the rule than the exception. I have been publishing on this topic since 2005. At some point, I discerned the root problems — shortlived models, the mistaken assumption of error-minimization performance criteria, and oversized matrix designs that exacerbate the first two root issues. At that point, I began suggesting changes, rather than fixes, which has led me to MLC. Q: How about using closed-loop model-identification techniques to maintain MLC or MPC? A: I am not a fan of detailed model-identification in general. The majority of models add maintenance and risk while costing money and time.
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A: Many or most of the dynamic questions, such as inverse-response, become less important or troublesome when feedback is used. Inverse-response is mainly a problem that came out of model-based control (you never heard of it before then). Model-based control first high-lighted the ability to handle inverse response, then turned around and provided detuning knobs to ignore fast dynamics! Similarly, model-based control is theoretically good for deadtime, but cannot handle not variable deadtime, which most applications are (based on feed rate changes). Deadtime always needs to be considered, even in feedback. (See the article on Control Engineering “What is rate-predictive control?”) The following questions were verbally answered in the webcast, but the answers provided below offer more detail. Q: Please clarify how MLC would do optimization without optimizers? A: First, as on slide 23, optimization occurs at business level, where all the needed tools and information are available. At the control layer, or APC layer, optimization refers to how excess MV availability, or degrees of freedom, is used. (It’s a two-step objective; refer to slide.) So the question becomes: Where do the APC optimization targets come from — Production Planning & Optimization (PP&O), engineers and operators, and sometimes live calculations. They really can’t come from control-layer optimization, because there is no information available there outside of the controller itself. By the way, most targets rarely change; a limited number may change daily, and that comes down in the PP&O plan. Optimization is a critical aspect that has been poorly understood throughout the APC 1.0 era. Q: I don’t follow how error-minimization is a mistaken assumption? (If not then, what?) A: If you’ve been a control engineer a long time, you can see a pattern… sites embark on tuning/modeling campaigns, followed by periods of detuning or move suppressing. The problem is, error-minimization is an aggreswww.controleng.com
sive criterion leading to fast/large MV movement, and excessive overshoot and oscillation. In industrial process operation, these are the least desirable performance characteristics. It is much more important to get to setpoint at safe speed and with minimum overshoot or oscillation. Unfortunately, this (latter) type of performance does not lend itself to easy or mathematical measurement, but can only be gleaned by overall metrics, such as loops in manual, multivariable loops in manual, number of alarms, etc.
week to week — many variables change, and there seems to be an endless supply of new types of disturbances. Industry saw this in benchmarking; we’ve seen it in APC modeling; and we’ve seen it in neural networks for inferentials. So, my hat is off to the AI crowd, but it’s not too big on my radar at this time. At least not for APC.
Q: There are many questions about artificial intelligence (AI), deep learning, Industrial Internet of Things (IIoT), recovery time objective (RTO), etc. What are the implications for APC?
A: Model-less can basically be thought of as feedback, whereas model-based is basically feedforward. That said, the algorithms used in our MLC tool (XMC) have been patented. They represent the industry’s only “inherently adaptive” control algorithm, and the only “model-less” method of multivariable control. And they are uniquely well suited to multi-loop applications. ce
A: AI is an exciting field, but it’s not really my field, and I don’t see it having a big impact on APC within my (now somewhat limited) time horizon. I see AI as a tool to do things faster, but not necessarily better. If modeling cannot solve the problem with all the effort we have seen, then I don’t think AI is going to work, until we understand the nature of the problem. Somewhat surprisingly, even to those of us in the business, most plants are highly variable, not the same
Q: How does model-less APC work?
Allan Kern, P.E., is owner, APC Performance LLC. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com.
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KEYWORDS: Advanced process control (APC), multiloop control (MLC), matrix More answers after webcast on past present and future of APC Process engineers can use APC to improve operations. A matrix, used with APC, allows better visualization and understanding of multivariable processes. CONSIDER THIS Are your multivariable control applications running open loop? How much optimization are you missing?
ONLINE If reading from the digital edition, click on the headline for more resources including links to five other control tutorials. www.controleng.com/magazine See www.controleng.com/ webcasts/past
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ANSWERS
INSIDE PROCESS
Sunil Doddi, CAP CFSP, Hydro-Chem
Understand partialstroke testing Partial-stroke tests (PSTs) of emergency shutdown (ESD) valves improve safety instrumented system (SIS) performance; monitor these critical valves to ensure the systemâ&#x20AC;&#x2122;s ability to shut a process down in the event of an emergency
A
partial-stroke test (PST) is a procedure/test used to stroke emergency shutdown (ESD) valves partially. It also is referred to as a partial valvestroke test (PVST). The alternative is a full stroke test (FST), where the valve is completely (100%) closed/opened during the test; the typical range of a PST is 10% to 20% of valve movement. The setpoint for the PST depends on the process upset it will create, and thus, the sizing of the valve and manufacturer recommendations.
Benefits of performing a PST A PST is necessary to achieve higher safety integrity level (SIL) (typically SIL3) where probability of failure on demand (PFD) calculations of Figure 1: Typical flowchart for PST workflow. All images courtesy: Hydro-chem
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the safety instrumented function (SIF) loop do not achieve the desired targets by any other means. PST increases the SIL, but because the implementation is expensive, it should be a last resort to achieve the SIL level targets. This means all other means have been tried and are not feasible, or the cost to achieve the desired SIL target is prohibitively high. The PST requirement arises in plants where turnaround time (TAR) is high, and it is not possible to do a full stroke test for an extended time. The primary objective of PST is to conclude the valve actuator will move when the situation demands it, and it is not stuck due to nonmovement for longer periods because of high TAR time. It should also be noted PST does not detect all valve failures, such as seat failure. Hence, at the end of a TAR, an FST is needed. PST and FST are both necessary for the valve at the end.
PST implementation There are various ways to implement PST and they involve hardware and software in varying degrees. The PST implementation methods can be categorized as field initiated, remote initiated, autoinitiated and manual initiated. Some manufactures offer field-based small panels with pushbuttons and lamps to test the function directly in the field. The test also can be performed remotely from a control room through application software through a distributed control system (DCS) or asset management system/product device manager via HART commands if the field device is intelligent enough. Some manufactures offer smart field devices that work with HART commands. The selected implementation concept depends on considering various factors such as cost, field environmental conditions, remote operated plants, etc. Regardless of the implementation methods, the concept involves initiating valve movement, reading its movement and bringing it back to the original position (see Figure 1). www.controleng.com
Figure 2: Typical PST fail report.
As the flowchart in Figure 1 indicates, the procedure is a controlled valve movement to a PST setpoint within a certain time interval. If the valve reaches the setpoint within the defined interval, the test passes. If the valve does not reach the setpoint within the defined PST interval, the test fails. There also should be procedures in place to address situations when a PST fails without compromising plant safety. The PST setpoint is defined as the final open position during the test. During sizing of the valves (which will go through PST testing in the future), a sizing scenario should be considered to ensure how much process upset can be tolerated during the testing for the PST setpoint. Manufacturer’s recommendations can help here. The PST time interval setting depends on the valve reaction time, which can be obtained from manufacturer’s documentation. The PST setpoint and time interval must be tested and fine-tuned before the plant is put back into full service. Certain process conditions may be used as interlocks for the PST test and thus inhibit valve movement. This is easy if the test is realized with the help of the DCS application program. These can be any of the conditions where process upset would be higher and thus plant shutdown or safety could be compromised. In a manual-initiated option, the user chooses when to start the test. In the auto-initiated option, the software/program (from the DCS or AMS/PDM) can be configured for the time interval for each PST test. In an auto configuration, it may be better to configure different test timer setting for each valve www.controleng.com
Figure 3: Typical PST pass report.
‘
There also should be procedures in place to address situations when a PST fails
’
without compromising plant safety.
so not all valves are started at the same time and thus may create unexpected process upsets. It is advised to do each valve PST individually in the auto configuration, so the timer settings need to be appropriate. The software used for PST tests should be based on functional safety standards (IEC 61508/IEC 61511). It’s less expensive to follow manufacture or DCS vendor KEYWORDS: Partial stroke existing validated software than develtesting, PST oping your own. The typical PST reports Partial stroke tests (PSTs) of emergency shutdown look like those shown in Figures 2 and 3. (ESD) valves improve safety A solenoid test (if applicable) on the instrumented system (SIS) valves, which needs PVST, works on simperformance. ilar principles as outlined in Figure 1, The primary objective of PST except instead of initiating valve moveis to conclude the valve actuator will move when the situation ment, the power supply to the solenoid is demands it. momentarily turned off . The timer and There are various ways to solenoid off-on pulse settings need very implement PST and they involve fine-tuning before implementing. ce hardware and software in varying
M More ANSWERS
Sunil Doddi is a senior control systems engineer at Hydro-chem, a division of Linde Engineering North America. Edited by Jack Smith, content manager, Control Engineering, CFE Media, jsmith@cfemedia.com.
degrees.
CONSIDER THIS If called upon in an emergency, will the emergency shutdown valves in your plant operate properly?
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NEW PRODUCTS FOR ENGINEERS
See more New Products for Engineers. www.controleng.com/NPE
PE
Digital servo drives Advanced Motion Controls’ FlexPro servo drive family, the FM series are equipped with an additional interface board that makes it easy to integrate into your system with standard connectors. The FM060-5-EM and FM060-10-EM are connected to the power supply and motor using screw terminals while the FM060-25-EM features pre-soldered connection leads. They are ideal for collaborative robots, AGVs, portable devices, and other integrated designs. Features include an incremental encoder and BISS C-mode feedback and three operating modes. They have configuration and full loop tuning. Advanced Motion Controls (AMC), www.a-m-c.com Input #200 at www.controleng.com/information
Ultrasonic flowmeter series
Endress+Hauser’s Proline Prosonic Flow G 300/500 flowmeter. The Prosonic Flow G can be supplied with either of two different transmitters: as a compact version (Proline 300), or as a remote version (Proline 500) with up to four inputs and outputs. These inputs and outputs provide flexibility with the ability to output not only flow, but also pressure, temperature, and numerous other process variables. They are ideal for demanding applications, measuring dry and wet gases with precision (±0.5%) even when process and ambient conditions fluctuate. The meter operates at process temperatures up to 150 C and pressures up to 100 bar (1,450 psi) and can be ordered with built-in pressure and temperature sensors. Endress+Hauser, www.us.endress.com
Input #201 at www.controleng.com/information
Wired IO-Link bridge device
CoreTigo’s TigoBridge connects a wired IO-Link device via IO-Link wireless to an IO-Link wireless master. CoreTigo provides an embedded version and an enclosed product version of the TigoBridge. The TigoBridge is an IO-Link wireless class A bridge with IP67 enclosure. It converts IO-Link and digital data to IO-Link wireless. Includes antenna and M12 connectors for data and power. It also has a supply voltage range of 18 to 32 V. It is designed for applications such as automotive, machine building, metals and packaging. It also has mounting brackets and an operating temperature range of -25 to 70 C. CoreTigo, www.coretigo.com
Input #202 at www.controleng.com/information
Data intelligence hub for smart manufacturing
HighByte Intelligence Hub version 1.0 is a DataOps solution that is purpose-built for industrial environments. DataOps aims to improve data quality, reduce time spent preparing data for analysis, and encourage cross-functional collaboration within data-driven organizations. HighByte Intelligence Hub enables operations to securely connect, model, and flow valuable industrial data to the users and systems that require this valuable information throughout the extended enterprise. The platform-agnostic software solution runs on-premises at the Edge, securely connects devices and applications via OPC UA and message queuing telemetry transport (MQTT), is built for scale, and offers a codeless user interface. HighByte, www.highbyte.com Input #203 at www.controleng.com/information
Analytics dashboard for streamlined data visualization
Beckhoff ’s TwinCAT Analytics One-Click Dashboard is designed to data-driven services to individual customer requirements, machine builders and system integrators. The automated functionality in TwinCAT Analytics to convert analysis configurations into executable programmable logic controller (PLC) code now also includes dashboard generation. With One-Click Dashboard, all it takes is a simple mouse click to generate an entire HTML5-enabled analytics dashboard based on the PLC code that is then loaded into a selected Analytics Runtime container. When the process completes, users receive a network address that they can use to access the dashboard in a web browser. This ability to generate dashboards without the need to design graphics or write a single line of code is a huge time-saver in engineering processes. Beckhoff Automation, www.beckhoff.com
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www.controleng.com
INNOVATIONS
BACK TO BASICS: PROCESS CONTROL VALVES Damien Moran, Bürkert
Choosing process control valves Today’s process control valves offer an wider range of features and benefits for industries that require precise control over fluids, steam and other gases. Seek a cost-effective design for the application.
C
ontrol valves are used to manage the flow rate of a liquid or a gas and in-turn control the temperature, pressure or liquid level within a process. As such, they are defined by the way in which they operate to control flow and include globe valves, angle seat, diaphragm, quarter-turn, knife and needle valves, to name a few. In most cases the valve bodies are made from metal; either brass, forged steel or in hygienic applications 316 stainless steel. Actuators will use an on-board system to measure the position of the valve with varying degrees of accuracy, depending on the application. A contactless, digital encoder can place the valve in a thousand positions, making it very accurate. More rudimentary measurements can be applied to less sensitive designs. One of the main areas of debate when specifying control valves is determining the size of the valve required. Often process engineers will know the pipe diameter used in an application and it is tempting to take that as the control valve’s defining characteristic. Of greater importance are the flow conditions within the system as these will dictate the size of the orifice within the control valve. The pressure either side of the valve and the expected flow rate are essential pieces of information when deciding on the valve design.
Improving efficiency
Inside the valve body, the actuator design is often either a piston or a diaphragm design. The piston design typically offers a smaller, more compact valve, which is also lighter and easier to handle than the diaphragm designs. Actuators are usually made from stainless steel or polyphenolsulpide (PPS), which is a chemically-resistant plastic. The actuator is topped off by the control head or positioner. Older, pneumatically operated positioners had a flapper/nozzle arrangement and operated on 3-15 psi, so no matter what the state of the valve, open closed or somewhere in between, the system was always expelling some compressed air to the atmosphere. Compressed air is an expensive commodity, requiring considerable energy to generate and when a manufacturing line is equipped with multiple process control valves all venting to the atmosphere, this can equate to a considerable waste of energy. It is important to not only establish the most appropriate valve design, but also a cost-
www.controleng.com
effective solution that takes account of annual running costs. Modern, digital, electro-pneumatic valves that use micro-solenoid valves to control the air in and out of the actuator have introduced significant improvements for operators. This design means that while the valve is fully open, fully closed or in a steady state, it is not consuming any air. This and other engineering improvements have made substantial advances in economy and precision. Valve seats can be interchangeable within a standard valve body, which allows the valve to fit existing pipe- While choosing the most appropriate work and the valve seat to the sized process control valve can be a comto the application more accurately. In plex task, it is often best achieved some cases, this can be achieved after with help from expert knowledge. the valve has been installed, which Courtesy: Chris Vavra, CFE Media and would enable a process change to be Technology at the Endress+Hauser accommodated without replacing the booth, Process Expo 2019 complete valve assembly. Selecting the most appropriate seal materials is an important step to ensure reliable operation; Steam processes would normally use metal-to-metal seals; a process that included a sterilization stage may require chemically resistant seals. Setting up and installing a new valve is easier and much less time-consuming. Inbuilt calibration procedures should be able KEYWORDS: process control, perform the initial setup procedures autocontrol valves, process safety matically, measuring the air required to Control valves manage the flow rate open and close the valve, the resistance of of a liquid or a gas and control the temperature, pressure or liquid level the piston seals on the valve stem and the within a process. response time of the valve itself. Technology advances make setting Working directly with manufacturers up and installing a process control or knowledgeable distributors enables valve easier than before. process control systems to be optimized Expert advice can make choosing a for long-term reliability as well as preciprocess control valve easier. sion and efficiency. ce
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ONLINE
Damien Moran is field segment manager, Hygienic – Pharmaceutical at Bürkert. This article originally appeared on the Control Engineering Europe website. Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and technology, cvavra@cfemedia.com.
See more on safety and reliability with this article online and more from international partners at www.controleng.com/international.
CONSIDER THIS What’s your company’s process for choosing a process control valve and how effective is it?
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