CONTROLENG.COM
PID controller tuning best practices | 26 Next-gen digital manufacturing | 34 Five ways to safer facilities | 38 Motor-drives: Select, configure, tune | 40 CTL2508_MAG1_COVER_V3msFINAL.indd 1
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Vol. 72 • No. 4
Contents JULY/AUGUST 2025
Make your
AI/ML integration roadmap Assessments and planning are crucial steps toward digitalization Courtesy: CDM Smith
INSIGHTS 7 | Online article sampling of www.controleng.com, more headlines with links 8 | Market Update: Latest automation mergers, June 2025: control system integration, flow, motion; Machinery sector trends diverge as tariffs and tech shift demand 10 | Technology Update: How to employ an AI assistant to streamline asset management 12 | NEWS: On the road: AI fuels the future cognitive control room at Honeywell Users Group; Emerson Exchange: How to integrate resiliency into process controls, digital transformation. ONLINE: 9 more automation-related headlines.
14 | Think Again: More of your advice from our research
ANSWERS 16 | COVER: Smart control systems: Make your AI/ML integration roadmap
p.38
22 | How to incorporate AI into process manufacturing 26 | PID spotlight, part 19: PID controller tuning best practices 34 | Why time-sensitive networking (TSN) is the backbone of next-gen digital manufacturing 38 | Interactive hazard and operability assessments: 5 ways to safer facilities
p.40
40 | How to select, configure and tune industrial motor drives
CONTROL ENGINEERING ( Vol. 72, No. 4, ISSN 0010-8049, USPS PUBLICATION #813480 ) is published bimonthly by: WTWH Media, LLC; 1111 Superior Ave., Suite 1120, Cleveland, OH 44114. Periodicals postage paid at Cleveland, OH and additional mailing offices. POSTMASTER: Send address changes to CONTROL ENGINEERING, 1111 Superior Ave., Suite 1120, Cleveland, OH 44114. CONTROL ENGINEERING copyright 2025 by WTWH Media, LLC. All rights reserved. CONTROL ENGINEERING is a registered trademark of WTWH Media, LLC, used under license. Circulation records are maintained at WTWH Media, LLC, 1111 Superior Ave., Suite 1120, Cleveland, OH 44114. Telephone: 630/571-4070. Publications Mail Agreement No. 40685520. Return undeliverable Canadian addresses to: WTWH Media, LLC, 1111 Superior Ave., Suite 1120, Cleveland, OH 44114. 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, WTWH Media, LLC, 1111 Superior Ave., Suite 1120, Cleveland, OH 44114. Printed in the USA. WTWH, 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.
control engineering — www.controleng.com
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Vol. 72 • No. 4
Contents JULY/AUGUST 2025
INNOVATIONS
Control Engineering eBook series, now available: Summer Edition
42 | New Products for Engineers www.controleng.com/products
uPLCs
New ultrasonic sensor supports use in hazardous locations; Environmental impact of ring motor documented; Simplified Modbus monitoring: data collection, analysis and alerts; Lean managed Ethernet switches, economical design; Industrial PC integrates GPU for AI applications; Hybrid scanner combines barcode and RFID in one device; OT/IT software integration for petrochemical applications; Controller-drive provides sensorless, closed-loop control; New box thin clients designed for control room operations; Improved energy efficiency inverters enhance setup, use
Featured articles in this eBook include energy-efficient manufacturing with smart technology; How to enhance facility performance through strategic systems integration and Petahertz-speed phototransistor in ambient conditions. Learn more at: www.controleng.com/ebooks
44 | Back to Basics: Zero trust in OT Why traditional approaches fail, and what manufacturing leaders must do instead.
NEWSLETTERS ONLINE Mechatronics & Motion Control, July 16 Smart manufacturing; machinery; robotics; motors, drives Process Instrumentation & Sensors, July 1 PID help, process safety, salary survey results, asset lifecycle, podcast, sensors Go to www.controleng.com/subscribe and select newsletters. uGlobal System Integrator Report
We’re preparing for the next edition. Encourage your favorite system integrator to apply for System Integrator of the Year and update SI Giants data. Questions? mhoske@wtwhmedia.com or gcohen@wtwhmedia.com. www.controleng.com/global-systemintegrator-report
control engineering — www.controleng.com
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uProcess Instrumentation & Sensors
Featured articles in this eBook include: Understanding EPA requirements for CEMS design; Three ways sensors and smart devices improve OEE; New patent uses AI to help reduce process safety hazards. More topics at: www.controleng.com/ebooks uControl Engineering digital edition
Digital edition advantages: 1. Click to more using live links with more text and often more images and graphics. 2. Download a PDF version. 3. Slide bar at bottoms navigates more quickly. 4. Greater sustainability. www.controleng.com/ magazine
July/August 2025
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Online Highlights controleng.com
INSIGHTS uC ontrol Engineering hot topics- June 2025 (A)
www.controleng.com/control-engineering-hot-topics-june-2025 uT he Downtime podcast - Episode 13- Driving Forward - Control Engineering
www.controleng.com/the-downtime-episode-13-driving-forward
WEBCAST
(A) Courtesy: Kalypso: A Rockwell Automation business
u Motor replacement criteria for industrial systems:
Key metrics and standards www.controleng.com/webcasts
(B)
ANSWERS u PID spotlight, part 18
www.controleng.com/control-systems Courtesy: Patti Engineering
u How virtual commissioning paves the way for digital twins (B)
www.controleng.com/how-virtual-commissioning-paves-the-way-for-digital-twins u Addressing unique challenges in oil and gas with digital twins
www.controleng.com/addressing-unique-challenges-in-oil-and-gas-with-digital-twins u Manage operations, not servers - The SaaS approach to SCADA
www.controleng.com/manage-operations-not-servers-the-saas-approach-to-scada/ u How do you use AI in your daily routine?
www.controleng.com/how-do-you-use-ai-in-your-daily-routine
(C)
Courtesy: APCO
u Advice compendium for controls and
automation programmers (C)
www.controleng.com/advice-compendium-for-controls-and-automation-programmers u The best ways to deploy a digital twin strategy
www.controleng.com/the-best-ways-to-deploy-a-digital-twins-strategy
(D)
u 5 ways to transform manufacturing efficiency
with AI vision inspection
www.controleng.com/5-ways-to-transform-manufacturing-efficiency-with-ai-vision-inspection u A practical approach to industrial IoT and device management
www.controleng.com/a-practical-approach-to-industrial-iot-and-device-management (D)
control engineering — www.controleng.com
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(D)
Courtesy: Moxa
July/August 2025
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INSIGHTS
MARKET UPDATE
Automation mergers, June 2025: control system integration, flow, motion
The Bundy Group reported 15 automation transactions in a February 2025 summary. Acquisitions and reports include Ace Controls, Ati Motors, Motion Industries, RMH Systems and others.
T
he Bundy Group reported nine automation transactions in its June 2025 summary reports, involving ECS Solutions, EnergyDrive Systems, E Tech Group, Flowserve and Gecko Robotics, among others. See more and links with this report online.
Cox Enterprises funded Gecko Robotics, June 12 Gecko Robotics, an AI and robotics company focused on critical infrastructure, has raised a Series D funding round at a $1.25 billion valuation, double its previous valuation. Cox Enterprises lead the $125 million investment, with existing investors USIT, XN, Founders Fund and YCombinator. The capital will support Gecko’s expansion in defense, energy and manufacturing, following recent partnerships with NAES, L3Harris and Abu Dhabi National Oil Co. Pears Partnership Capital funded EnergyDrive Systems, June 2 Energy Drive Systems, an English subsidiary of Apogee Sustainability Ltd. and a provider of energy efficiency for large motors in the mining, metals and utilities industries, has secured a strategic growth investment of $20 million from The Pears family, a financing and real estate group based in London. Chart Industries agrees to merge with Flowserve, June 3 Chart Industries [engineering equipment and services for industrial gas markets] and Flowserve [flow control and fluid motion products] have entered into a definitive agreement to merge in an allstock transaction, forming a $19 billion industrial process technology company.
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With an installed base of more than 5.5 million assets in more than 50 countries, the combined company will address the full customer lifecycle from process design through aftermarket support.
Magnum Systems acquired ECS Solutions, Feb. 28 Magnum Systems acquired ECS Solutions, expanding its family of brands and strengthening its ability to deliver endto-end, customized industrial manufac-
‘
Chart Industries and Flowserve agreed to merge in an all-stock transaction, forming a $19 billion industrial process technology company,
’
said the Bundy Group.
turing implementations. The integration of ECS enhances Magnum’s position as a CSIA-certified provider of batch and process control automation, control systems integration and manufacturing execution systems.
E Tech Group acquired JSat Automation, May 28 E Tech Group acquired JSat Automation, a Pennsylvania-based system integrator specializing in automation, IT/OT convergence and compliance. JSat will operate as “JSat, an E Tech Group Company,” with founder Jeetu Satpute joining E Tech’s leadership team. This is E Tech’s third acquisition since 2023. ce
Search "Bundy" at www.controleng.com for more merger and acquisition news. https://bundygroup.com Clint Bundy is managing director, Bundy Group, which helps with mergers, acquisitions and raising capital. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.
Machinery sector trends diverge When analyzing machinery market performance, two primary factors have influenced recent short-term forecasts from the Interact Analysis Manufacturing Industry Output (MIO) Tracker. Machinery production has been affected by elevated interest rates and increased inventory levels. In 2024 and 2025, destocking efforts have contributed to a slowdown in production. Trends vary by sector. Packaging machinery, mining and quarrying, and semiconductor and electronics machinery production are projected to perform relatively well, supported by ongoing technological developments, energy transition and macroeconomic factors. Among factors influencing the semiconductor industry are increased use of AI, 5G adoption, and growing electric vehicle (EV) demand. Increased EV use, wind turbines, and battery storage has contributed to growth in critical minerals mining and supported expansion in the tunnelling and extraction equipment markets. See more text, graphics online. ce www.controleng.com/machinery-sector-trendsdiverge-as-tariffs-and-tech-shift-demand control engineering — www.controleng.com
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INSIGHTS
TECHNOLOGY UPDATE
How to employ an AI assistant to streamline asset management Artificial intelligence (AI) is reshaping the ways in which many organizations conduct asset management. A new AI assistant aims to offer insights for enterprise asset management.
A
Online
u
controleng.com KEYWORDS: text LEARNING OBJECTIVES Understand the role an AI assistant plays in enterprise asset management. Determine the ways in which AI can optimize asset management workflows. Learn how AI can analyze systems for wear and inefficiencies, so maintenance is only conducted when needed.
s businesses continue to grapple with growing operational complexity, IBM is positioning its Maximo application suite as a next-generation solution for smarter, AI-enhanced asset management. The company recently announced upgrades to Maximo’s AI assistant, aimed at helping organizations shift from reactive to predictive strategies. The improvements promise to boost equipment longevity, reduce unnecessary maintenance and free up technicians to focus on more valuable tasks. Maximo’s AI assistant is designed to optimize asset management workflows — by embedding intelligence directly into how teams work.
Smarter maintenance, real-time insights Traditional calendar-based maintenance schedules often result in over-servicing some equipment while neglecting others. Maximo’s AI aims to change that by enabling condition-based main-
FIGURE 1: IBM’s latest version of its Maximo application suite features an embedded AI assistant that can help organizations manage work orders. Images courtesy: IBM
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tenance strategies. By analyzing live asset data, the system can detect early signs of wear or inefficiency, triggering service only when it's truly necessary. This targeted approach not only helps prevent unexpected breakdowns but also extends the usable life of expensive equipment. The shift from static schedules to real-time, sensor-informed servicing is already being adopted in industries such as energy, transportation and manufacturing, where uptime and precision are critical.
Cleaning up work order management Maintenance logs and work orders are only as good as the data entered into them — a longstanding challenge for operations teams. Maximo’s updated Work Order Intelligence feature addresses this by suggesting accurate problem codes based on user input and historical data. The AI assistant analyzes past reports and failure patterns to recommend the most relevant codes, even for less experienced technicians. IBM said the tool is being expanded to identify deeper patterns across asset classes, usage cycles and maintenance histories — without requiring users to sift through spreadsheets or be data experts. The result is cleaner, more consistent documentation that feeds back into the system, improving future decisions. Faster failure mode analysis Conducting failure mode and effects analysis (FMEA) has been a time-consuming, manual task. Using Maximo’s expansive repository of historical and operational data, the AI assistant can perform this analysis in less time. By rapidly surfacing likely causes and recommending preemptive actions, Maximo helps teams implement proactive strategies that can reduce risk and prevent downtime. This capability is especially valuable in high-risk environments — such as oil and gas or heavy manufacturing — where minutes of outage can create substantial financial loss. control engineering — www.controleng.com
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FIGURE 2: IBM’s new embedded AI assistant, which is part of its Maximo application suite, offers an AI assistant through Watsonx.
FIGURE 4: IBM’s embedded AI assistant, part of its Maximo application suite, allows organizations to take a look at multiple outcomes from a variety of scenarios.
AI-powered visual inspections Inspecting equipment for flaws — such as cracks, leaks or wear — has traditionally required a trained eye and hours of manual review. Maximo’s Visual Prompting feature uses AI to simplify and accelerate this process. With this tool, users can highlight specific parts of an image — such as a valve or circuit board — and train the AI to recognize and monitor those components automatically. This means faster, more accurate detection of potential problems without the need for extensive data labeling or AI training. It’s a practical solution for manufacturers seeking to scale up predictive maintenance across large fleets of machines without overwhelming technical teams. A real-world scenario To illustrate the assistant’s potential, IBM points to a real-world example in offshore energy operations. In this scenario, a supervisor aboard an oil rig receives a high-priority alert about a transformer showing abnormal behavior. Within seconds, Maximo’s AI assistant pulls up the asset’s maintenance history, recent anomalies and likely causes. Based on the information, it recommends immediate repair steps. control engineering — www.controleng.com
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When the technician confirms physical damage during inspection, they update the assistant, which automatically validates the issue, assigns the correct problem codes and triggers a follow-up work order. The process — normally requiring backand-forth between multiple systems — is condensed into a streamlined, AI-assisted workflow. The benefits: faster responses, reduced human error and a continuously learning system. IBM describes the assistant not FIGURE 3: The new embedded AI as an add-on but as an embedded assistant can help organizations track resource designed to evolve alongside down items by simply asking in simple the organization. The AI is built to terms. adapt to natural workflows, eliminating the need for complex commands or disruptive interfaces. With natural-language inputs and conversational feedback, Maximo aims to democratize access to operational insights, enabling everyone to make data-informed decisions. ce Sheri Kasprzak is the managing editor of automation and control brands for WTWH Media. July/August 2025
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INSIGHTS
NEWS
AI fuels the future cognitive control room uWhen artificial intelligence (AI) showed signs of potential in manufacturing, Chevron asked its automation partner, Honeywell Process Solutions, how it could effectively use the technology in its plant operations. This was not a shortsighted request to layer on a query-based AI assistant, but rather a plea to help fix some existing process-related problems that, when not handled correctly, negatively affect operations. The call to action required Honeywell executives to figure out the areas AI would have the most impact. “We went into the personas to understand what a plant operator needs,” said Pramesh Maheshwari, president and CEO of Honeywell Process Solutions, in an interview with Control Engineering. “What are the challenges a plant operator in the control room is facing which we can solve? What are the challenges a field operator is facing which we can solve?” At Chevron, the focus was on managing abnormal situations and understanding what happens in the plant before the alarm comes on, as well as what actions an operator should take. “They provided a lot of insights and we started developing the solution,” Maheshwari said. That solution came in the form of AI-based alarms in the control room, which are currently being tested by Chev-
Honeywell Process Solutions president and CEO Pramesh Maheshwari discussed a new wave of AI-enabled digital technologies at Honeywell Users Group (HUG) in San Antonio, driven by Chevron and other customers. Courtesy: Stephanie Neil, vice president, editorial director, Automation Group, WTWH Media.
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ron. Honeywell recently rolled out its AI-enabled digital suite for connected platforms and applications. In June, during the 49th annual Honeywell Users Group (HUG) in San Antonio, Maheshwari (photo) unveiled key innovations that developed from the Chevron conversation — as well as many other customer discussions. Among the announcements: • A new wave of AI-enabled digital technologies designed to accelerate the shift from automation to autonomy. • Sophisticated AI-enabled operational technologies (OT) cybersecurity offerings to detect, mitigate and thwart attacks. • An updated Honeywell Digital Prime Ecosystem that allows engineering teams to test projects before implementation to reduce plant downtime and increase throughput in production. Another way to look at this is through the lens of “digital cognition” that combines humans and machines to build expertise into the process. “Harness the power of data, analytics and digital technology to provide guidance that helps the least experienced personnel act like the most experience personnel,” said Jason Urso, CTO of Honeywell Industrial Automation. To make this leap, control systems need to transform, Urso said. Controls run effectively during steady state, but outside of that it is dependent on human expertise to make decisions when presented with alarms. What’s needed is a new kind of distributed control system (DCS) with digital cognition capabilities that convert data into knowledge. “What if we could automate decision making in the same way we automated the process control equipment 50 years ago?” Urso asked. Stephanie Neil is vice president, editorial director, engineering, automation and control, WTWH Media, sneil@wtwhmedia.com.
Courtesy: Mark T. Hoske, Control Engineering, WTWH Media
How to integrate resiliency into process controls, digital transformation Vidya Ramnath, Emerson senior vice president and chief marketing officer (left, bottom), and Lisa McEvoy, Merck associate vice president of digital manufacturing, discuss the value of digital transformation at the Emerson Exchange 2025 opening keynote. See full article: www.controleng.com/how-to-integrate-resiliency-into-process-controls-digital-transformation
MORE NEWS ONLINE SEARCH on the headlines below at www.controleng.com for: • New leader to head global control systems group • Exclusive spotlight on 2025’s top system integrators • New engineering design center for AI, technology, digital manufacturing • Manufacturing expansion brings new jobs to Wisconsin • Member-led testbeds explore emerging digital twin technologies • U.S. site selected to begin microreactor experiments • Research center to advance digital twins for manufacturing • 2025 robotics awards celebrate women in the field
control engineering — www.controleng.com
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INSIGHTS THINK AGAIN
®
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Content Specialists/Editorial Mark T. Hoske, editor-in-chief 847-830-3215, MHoske@WTWHMedia.com Gary Cohen, senior editor GCohen@WTWHMedia.com Sheri Kasprzak, managing editor, engineering, automation and control, SKasprzak@WTWHMedia.com Stephanie Neil, vice president, editorial director engineering, automation and control, 508-344-0620 SNeil@WTWHMedia.com Jill Lowe, webinar manager JLowe@WTWHMedia.com Amanda Pelliccione, marketing research manager 978-302-3463, APelliccione@WTWHMedia.com Anna Steingruber, associate editor ASteingruber@WTWHMedia.com Puja Mitra, contributing editor PMitra@WTWHMedia.com
Contributing Content Specialists Suzanne Gill, Control Engineering Europe suzanne.gill@imlgroup.co.uk 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 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 Eric J. Silverman, PE, PMP, CDT, vice president, senior automation engineer, CDM Smith, www.cdmsmith.com Mark Voigtmann, partner, automation practice lead Faegre Baker Daniels, www.FaegreBD.com
WTWH Media Contributor Guidelines Overview Content For Engineers. WTWH Media focuses on 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. * Control Engineering Submissions instructions at https://www.controleng.com/connect/how-to-contribute gives an overview of 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 in nature or that are critical of other products or organizations will be rejected. (Technology discussions and comparative tables may be accepted if nonpromotional and if contributor corroborates information with sources cited.) * If the content meets criteria noted in guidelines, expect to see it first on the website. Content for enewsletters comes from content already available on the website. All content for print also will be online. All content that appears in the print magazine will appear as space permits, and we will indicate in print if more content from that article is available online. * Deadlines for feature articles vary based on where it appears. Print-related content is due at least three months in advance of the publication date. Again, it is best to discuss all feature articles with the content manager prior to submission. Learn more at: https://www.controleng.com/connect/how-to-contribute
More of your advice, links to smarter automation, controls Control Engineering research provides opportunity to learn more about automation planning, interoperability, real-time controls and energy efficiency by design ... and thank you. Control Engineering subscribers
shared advice on automation, control and instrumentation technologies and applications as part of research to create 2025 editorial calendar topics. As we continue planning for 2026, I want to thank you for your automation advice. Excerpts, edited for length and clarity, follow. In the online version of this article, find more on related automation topics.
interchangeability of various control equipment at supervisory layer and on the management layer. Optimize systems for minimizing energy consumption. Put devices and systems into low-energy mode (or turn them off, Mark T. Hoske when feasible) when not in use to Control Engineering improve revenue streams. Consider designing-in energy savings to reduce costs and improve performance.
Automation planning, sourcing Networking, safety, information Extensive planning and testing of new autosharing mation systems to replace obsolete systems is Sharing basic automation knowledge with critical for success. newer co-workers can help. Communications for input/output devicI prefer on-site data storage to cloud-based es and operator interfaces should be validated anything. Knowing about safety controllers offline before taking the existing system down. and levels of safety on a machine are useful. Plans for rewiring should be detailed and A good decision in one of the latest autoeasy for electricians to understand. mation projects was to isolate high-throughControls diagrams must be kept concise and put network devices from the customer’s must closely match the sequence of operations. network through a bridge to minimize conThe supply chain still has troubles. Alternanection and throughput problems. tive [nontraditional] acquisition methods can Automation projects are among areas takbe beneficial. ing most of my time. You are doing a great job Create better plans and specifications with in providing resources. more details for better automation. Teamwork is more important than ever. Keep sharing automation wisdom Technical merit of automation technologies You’re certainly welcome. Please think must not be allowed to be superseded by cost/ again about sharing knowledge with peers. ce economy considerations. Quality cannot be compromised even if it may temporarily seem u to be less cost-effective. controleng.com
Online
Automation efficiency Like industrial-network-certified products, require that control systems have open-protocol certifications for seamless integration and control engineering — www.controleng.com
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Online version of this article includes a mini case study on real-time process monitoring, process controls and links to articles with more advice on many of these topics. www.controleng.com/contribute-to-control-engineering
July/August 2025
| 15 7/28/25 10:33 AM
ANSWERS
COVER STORY: CONTROL SYSTEMS
Karthicraja Vellaichamy Munisamy, Vinoth Upendra Janardhanan, Srisylesh Balaji, CDM Smith
Smart control systems:
Make your AI/ML integration roadmap Smart water utility control systems and digital transformation: Learn how to apply artificial intelligence and machine learning in the water sector to create the ultimate AI/ML integration roadmap. See eight steps to integrating digital technologies and control systems.
FIGURE 1: Water and wastewater utilities can use emerging technologies to resolve long-standing operational difficulties. Figures courtesy: CDM Smith
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ontrol systems integrated with artificial intelligence (AI) and machine learning (ML) provide real-world benefits. Applications include predictive maintenance, energy optimization, improved water quality monitoring and enhanced customer service. Create a detailed roadmap for integrating these tools with existing supervisory control and data acquisition (SCADA) and digital systems. Whether starting with one application or transforming a full network, utilities need to transform responsibly, efficiently and effectively, without disrupting the core mission of delivering safe sustainable water.
Why SCADA, AI/ML integration? Existing urban water and wastewater utility infrastructure is at a critical juncture. Because regulations are becoming more stringent in many developed cities across the United States and Europe, utilities are facing operational challenges to meet these regulatory requirements. Amid aging infrastructure, rising operational costs and unpredictable climate changes, utilities are expected to deliver uninterrupted service, ensure environmental compliance and optimize energy and resource use. This situation is particularly challenging in areas that are rapidly urbanizing, thus forcing utility providers to expand services under significant pressure while overcoming decades of existing issues. In this context, new tools are emerging with the potential to transform the future of the water sector including AI, ML and digital technologies. Water utilities are undergoing a digital transformation that is beyond flashy visualizations and “trendy” innovations (Figure 1). This transformation is about resolving long-standing operational difficulties, such as leaks that waste billions of gallons of water, energy-hungry pumps, treatment control engineering — www.controleng.com
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facilities that are struggling to maintain compliance during adverse weather conditions, and asset failures that result in disruptions to communities. By employing AI and ML, utilities can benefit from their existing plant data—controlled and monitored using remote input/output (I/O) networks, programmable logic controllers (PLCs) and SCADA systems—to attain real-time insights and help inform decision-making. A combination of these technologies offers quantifiable gains in performance and cost-efficiency, from energy optimization to predictive maintenance. Water utilities can use AI and ML to boost operational efficiency, optimize energy consumption, strengthen environmental compliance and promote predictive analytics. To accelerate facility benefits, demystify the process of digital transformation for integrating AI and ML into present-day control systems by presenting a clear roadmap and real-world examples. The straightforward goal is to take one digital step at a time and assist utilities in contributing to a smarter, cleaner and more resilient water service.
AI/ML: practical applications Within the water and wastewater landscape, AI and ML have advanced from theory to measurable and clear outcomes. AI and ML are being integrated into everyday operations to address operational inefficiencies, achieve cost savings, minimize environmental impacts and operate the plant safely. Current utility operations can and will be improved effectively and intelligently, without the need for a complete overhaul. Leak detection is simple but costly; by employing AI models, utilities can examine flow and pressure data in SCADA to detect anomalies and initiate real-time alerts, thereby reducing the frequency of manual checks and complaints from customers. This approach has allowed cities to substantially reduce non-revenue water and to maintain assets. However, efficiency is not the sole advantage provided by AI/ML. By monitoring water quality in real time, these tools can detect potentially hazardous blooms or contaminants before laboratory testing, thereby ensuring compliance with American Water Works Association (AWWA) standards https://store.awwa.org/standards and the Smart Water Networks Forum (SWAN) https:// swan-forum.com/. Digital twin simulations and AI/ ML-based effluent control help treatment plants to control engineering — www.controleng.com
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significantly reduce greenhouse emissions while staying within discharge limits (which constitutes an essential step toward achieving net-zero goals). In addition, AI/ML helps utilities to operate faster and with fewer failures, thus demonstrating that data-driven systems are more dependable. Based on real-world urban applications worldwide, Table 1 outlines the main AI/ML applications and the benefits derived. These applications contend that intelligent operations are not only feasible but are already happening. The following roadmap shows how to incorporate these capabilities into any utility.
Paving the future with AI and ML: A roadmap for water and wastewater utilities Employing AI and ML in water and wastewater utility operations is no longer theoretical. AI and ML have demonstrated their ability to transform operations, improve reliability and promote better decision-making. Yet, the transition from inspiration to implementation requires more than just a desire; it demands an organized and planned approach that recognizes the legacy infrastructure already in place and considers the complexity and criticality of water and wastewater systems. The infrastructure and systems that have powered water and wastewater utilities for decades are still serviceable operational backbones. Rather than replacing these existing systems, the goal is to equip and augment them with supporting digital technologies, which is the key to achieving this transformation. By purposefully integrating digital technologies, such as AI, ML and digital twins, into SCADA sys-
COVER IMAGE, FIGURE 2: Assessments and planning are crucial steps toward digitalization.
Online
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controleng.com KEYWORDS: Control systems, AI/ML integration, water/wastewater CONSIDER THIS Are you assessing benefits of and integrating new technologies, such as AI/ ML applications, into existing control systems? ONLINE Read another article from Vinoth Upendra Janardhanan, CDM Smith Inc., on SCADA arrays. https://www.controleng. com/the-ultimate-guide-toconfiguring-multidimensionalarrays-in-scada-systems See the Control Engineering AI/ML page for more learning. https://www.controleng.com/ ai-and-machine-learning
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ANSWERS
COVER STORY: CONTROL SYSTEMS
1. Every successful engineering project begins with planning and assessment. In the beginning, utilities need to perform a complete analysis. Utilities should consider questions such as: • What do we have? • What is working and what is not working? • What gaps exist in the data?
FIGURE 3: The key to successfully integrating control systems, AI/ML and other digital technologies is cross-functional team collaboration and change management.
Aside from establishing the strategy for digital transformation, this preliminary assessment identifies immediate opportunities for improvement, such as developing predictive maintenance for an aging pump station or building AI models for leak detection. Most importantly, utilities must schedule upgrades in stages, preserving critical plant controls in place while also ensuring operators are involved from the beginning (Figure 2). Modernizing existing SCADA can be compared to “updating flight control system mid-flight.” It’s doable, but only with meticulous preparation.
2. Once the priorities have been determined, the next step is to enhance the data management and integration framework, also known as the data fabric. SCADA infrastructure must be modernized to enable seamless data integration and access; this could involve updating existing PLCs with modern communication modules that can transmit data to either on-premise platforms or cloud platforms. Furthermore, information must not only be sourced from SCADA, but also from geographic information system (GIS) software, computerized maintenance management system (CMMS) maintenance logs and even customer usage data. Such a unified data ecosystem is the backbone of any AI/ML effort.
FIGURE 4: A futureready utility should emphasize data interoperability for incremental digitalization progress.
tems, utilities can evolve intelligently without necessitating costly upgrades or inconvenient service interruptions. This purposeful integration also allows developing cities to adopt and expand modularly in pace with their resources and requirements.
8 steps to integrating digital technologies, control systems To integrate digital technologies, utilities should follow this detailed roadmap:
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3. Raw data is insufficient. Data must be refined, secured, synchronized and organized. Many digital projects falter at this point. In-depth pre-processing is a vital aspect of a well-executed data integration strategy. This includes filling gaps, denoising sensors and tagging historical events (such as storm surges or asset failures). Utilities must establish data standards, enforce access controls and secure their networks. When SCADA and information technology (IT) systems begin to integrate, cyber threats change, making robust governance and segmentation critical. At this point AWWA and SWAN guidance is crucial. control engineering — www.controleng.com
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4. After all the required frameworks are laid, utilities can begin building and assessing AI/ML models. These models should start off small, such as building an AI leak-detection model trained on flow and pressure data or building an AI algorithm for predicting pump failures. In addition to usable data, success also depends on successful collaboration between data scientists and engineers. Domain expertise is essential for building models, detecting significant anomalies and adjusting thresholds to prevent false alarms. The models develop iteratively, thereby learning, improving and eventually generating actionable insights, regardless of whether the platform used to develop these models is simple or complicated. 5. At this point, a significant elevation is achievable by integrating digital twins. Using high-end visualization (HEV), a treatment facility can forecast outcomes in addition to simulating operations (for example, visualization can depict how the effluent quality and energy use is affected by altering the aeration rate). Digital twins combine realtime data with ML and physics-based models to further optimize the prediction outcome, such as the one used in Denmark’s TwiN2Ops project in a wastewater treatment plant (https://blog.dhigroup.com/ when-wastewater-becomes-smart-digital-twins-open-new-doors/). By incorporating this simulation layer, operators can formulate “what if ” scenarios and determine results backed by data (not just conjecture). The digital twin becomes more intelligent over time as new data and deeper learning enhance predictions. 6. Next step would be to integrate the insights from the digital twin and AI/ML models back into operations. When doing so, it must be ensured that the AI does not take full control of operation, but rather that create a robust feedback loop. By using dashboards or key performance indicators, AI/ML models can notify operators with recommended SCADA control setpoint changes via open platform communications (OPC) https://opcfoundation.org/about/what-is-opc/ or an application programming interface (API). Some utilities may enable AI to directly influence control parameters, but only with human oversight. In some cases, systems can be run in advisory mode, gradually improving the recommendations’ credibility. In all likelihood, operational integration will ensure that AI/ML is incorporated into everyday operations rather than merely being used as an external analytics tool. 7. People must be credited for transforming utilities. The frequently overlooked key to success is organizational change management. Operators need to understand how to use dashboards, but they also need to be FIGURE 5: Illustration educated on the purpose and function of of a digital transformathe algorithms. Cross-functional teams tion roadmap provides that combine IT, operations, asset manguidance for water and agement and analytics are vital for supwastewater utilities. porting and growing digital projects control engineering — www.controleng.com
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ANSWERS
COVER STORY: CONTROL SYSTEMS
Table 1: AI/ML applications, their use cases and key benefits AI/ML application
Approach
Leak detection
ML anomaly detection on flow/pressure data
Real-time alerts for leaks, significant reduction in water loss and property damage
Distribution optimization
Reinforcement learning on network controls
More efficient water distribution, minimized pumping energy and water loss, and increased reliability
Pump/energy scheduling
Predictive control and optimization models
Lower energy consumption (often 20% to 30% savings), reduced peak demand charges
Water quality monitoring
ML on sensor/ meteorological data
Early detection of contaminants or blooms, compliance with regulations, and improved public health
Effluent process optimization
Digital twin simulations with AI
Maintained treatment quality while cutting greenhouse emissions by as much as ~30%.
Demand forecasting
Time-series ML (weather + usage data)
Accurate demand forecasts for planning, optimized resource allocation during droughts
Predictive maintenance
ML on historical SCADA/asset data
Significant reduction in unplanned downtime, extended asset life, and lower maintenance costs
Flood and incident prediction
ML on rainfall, topography, soil data
Up to 7-day advance flood warnings, improved emergency response and resilience
TABLE 1: AI/ML applications are shown with their use cases and key benefits with some quantifications. Tables courtesy: CDM Smith
Key benefits
(Figure 3). Confidence is bolstered when a predictive maintenance model prevents an expensive unplanned outage or a leak-detection system conserves millions of liters of water. Each success contributes to building momentum.
8. Interoperability is the final piece of any digital transformation (Figure 4). A future-ready digital utility must avoid proprietary hardware, software and protocols. Systems must be made plug-and-play by using open standards such as Open Geospatial Consortium (OGC) https:// www.ogc.org/ for geospatial metadata, OPC-UA for control data, and evolving technologies such as SWAN Digital Twin architecture. By implementing this approach, future investment is ensured and integration costs are minimized. Utilities can upgrade their platforms by adopting new tools and collaborating with third-party vendors without disrupting existing systems. This open modu-
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lar architecture allows for incremental digitization based on available funding and infrastructure, thus maintaining long-term flexibility (which is particularly important for cities in developed and developing nations). This roadmap can be adapted to the real-life complexities of utility operations, as opposed to a one-size-fits-all blueprint. For cities in North America and Europe, this helps to update aging infrastructure without causing major disruptions. It presents a modular way for developing cities to overcome legacy constraints. Additionally, it provides a clear achievable framework for utility professionals to fully use AI and ML. (See Figure 5, digital transformation roadmap.)
Achieving control system reality Integration of AI and ML into water and wastewater utility control systems signifies a fundamental change in the way critical infrastructure is viewed, managed and developed. Utilities can transform fragmented data and reactive operations into a predictive, adaptive and resilient future with a well-executed roadmap. This digital transformation journey does not require a clean slate. On the contrary, it builds upon the capabilities of conventional SCADA, PLCs and field systems by enhancing them with intelligent layers of insight and automation. Utilities can maintain operational continuity while confidently entering a new era of digital capability that employs non-invasive methods without the need for replacing existing infrastructures. The digital transformation process is not without challenges; cybersecurity risks, data silos and organizational inertia can all hinder progress. However, these challenges can be overcome with effective planning, governance and change management. Achieving minor successes (such as identifying a leak before it escalates into a significant issue, or forecasting pump failure several days in advance) fosters trust and generates momentum. These early victories show that AI augments human expertise rather than replaces it. The roadmap is adaptable. A comprehensive digital twin with thousands of sensors and dedicated analytics teams could be the goal of a large metropolitan utility. In contrast, a mid-sized or emerging city utility may begin with simple anomaly control engineering — www.controleng.com
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detection in a few distant pump stations using an AI/ML model. Both approaches are effective and legitimate. The value lies not in the quickness of digitizing, but in the how deliberately it is done. Ultimately, AI/ML are catalysts for improving customer service, promoting environmental stewardship and boosting operational efficiency. They also serve as catalysts for the creation of smarter cities that can adapt to resource constraints, rising demand and climate change. Digital technology is the future of water management. By adopting a practical and human-focused strategy, utilities of all sizes and stages of development can effectively implement this vision. ce
Table 2: Key components in an integrated digital water system
Karthicraja Vellaichamy Munisamy is mid-level automation engineer; Vinoth Upendra Janardhanan is a senior automation engineer; and Srisylesh Balaji, is a mid-level automation engineer, CDM Smith Inc., Chennai, India; edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.
Role in digital water infrastructure
Component/system SCADA, PLC/RTU
Real-time control and monitoring of assets.
Remote I/O and sensors
Collects field measurements; data serves as input for ML models.
Data historian/database
Central storage for time-series SCADA data; supports queries for ML training and model inference.
GIS, asset systems
Provides spatial and asset metadata (pipe locations, pump capacities, age).
Digital twin/ model platform
Simulation engine that combines process models with real-time data. Allows scenario runs to guide decisions.
AI/ML analytics engine
Hosts machine-learning models. Processes historical and streaming data to produce insights.
Operator dashboards/alerts
Interfaces displaying analytics outputs, forecasts and alarms. Enables human-in-the-loop decisions.
Cloud/on-premises platform
Scalable computing resources for ML model training, data processing and storage.
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ANSWERS
CONTROLS, AI/ML
Claudio Fayad, Emerson, Austin, Texas. Krishnan Kumaran, AspenTech, Raritan, New Jersey.
How to incorporate AI into process manufacturing Not all artificial intelligence (AI) is built for industrial success. Process manufacturers need AI solutions grounded in first principles — ensuring accuracy, safety and efficiency — while reducing complexity in model design and development.
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here have been few technologies in history that have seen the meteoric rise in popularity that artificial intelligence (AI) has experienced in just the last few years. AI is dominating headlines and one of the key messages that companies are hearing is that they must adopt AI or be left behind. Process manufacturing is no exception to this trend. Operational technology (OT) teams are bombarded with a vast array of new AI technologies designed to help them better perform their tasks. However, not all AI technologies are created
FIGURE 1: Continuously adapting model of an industrial process (fluid catalytic cracking shown) using AI-based smart data sampling methods. Images courtesy: Emerson and AspenTech
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equally, especially when it comes to industrial applications. Selecting fit-for-purpose industrial AI technologies is essential for ensuring safety and efficiency of critical operations.
What is industrial AI? Both standard and industrial AI use data and machine learning (ML) algorithms to solve complex problems quickly. However, industrial applications are regulated by physics, chemistry and thermodynamic principles. Therefore, purpose-built industrial AI is differentiated from standard AI in that the former is bound by first principles models that help train the AI model efficiently and guarantee the results are safe for the plant’s operation. Immutable first principles constraints — based in detailed physics and chemical data built into the hybrid models — guide and instruct both the way industrial AI models are trained and the potential results those models can generate. Those guardrails create a safety zone where the AI can operate, ensuring the AI does not simulate scenarios or build models the OT team would not want to run because they are impossible, dangerous, or expensive. The world is too big to explore every possibility, so industrial AI’s exploration space is limited to realistic, safe scenarios. The result of this strategic combination is that industrial AI not only relies upon critical guardrails to limit how the AI can operate, but it also provides clearer guidance for how to most efficiently sample a model, so teams are not required to explore limitless — and often nonsensical — options. Moreover, purpose-built industrial AI enables extrapolation to setpoints where no plant data is available, while eliminating the possibility that the AI tools will generate out-of-bounds recommendations. Ultimately, industrial AI tools help teams more easily and accurately improve their models as they control engineering — www.controleng.com
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FIGURE 2: Example of Generative AI for Optioneering as implemented in the AspenTech Aspen OptiPlant 3D Layout. AI/ML algorithms are used to automatically compute layouts of equipment that minimize piping and infrastructure costs, while respecting distance constraints as required by safety and maintainability regulations.
acquire data to drive faster convergence on accurate setpoints for a quicker path to control optimization.
Industrial AI in action There are a wide variety of use cases for AI tools, but in process engineering, three critical ones have drawn a great deal of attention. Agility Process plant operations are complex. Not only must OT teams manage shifting variables (environmental, asset reliability, schedule, etc.), but many must also manage regular changes in production to meet fluctuating energy supplies from renewables, shifting market demands and other variable operating factors. AI tools simplify and strengthen the process of continually refining models to meet operational needs based on real-time observations. Using contextualized data from control, scheduling, asset performance management software and more, OT teams can bring critical information together to help refine models and their associated processes, ultimately leading to better decisions (Figure 1). Guidance As OT teams continue to lose experienced personnel to retirement, they also sacrifice decades of institutional knowledge. Modern AI tools provide decision support to newer users as they work to better explore options and adjust process control to improve safety, throughput, efficiency, sustainability and more. AI-based virtual advisors not only unlock vast databases of knowledge, but do so through natural-language interactions, increasing the speed and accuracy of decision support. control engineering — www.controleng.com
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Automation The in-depth consideration of various alternaModels can be tives and options to identify the best solution or delivered by fitpreferred approach to a design is often referred to as optioneering. The use of AI to generate the most for-purpose AI effective and efficient engineering options is changagents designed ing the way OT teams approach process and project design. Instead of working toward a single, optito perform exceedmal design, teams can use generative AI optioneeringly efficiently ing tools to add more interactivity to engineering processes, empowering them to generate multiple and effectively designs more quickly and easily. at a given set of Each of the models will have very different characteristics, while remaining within requirements. tasks. Further, optioneering enables designers to consider intangible design criteria that are not captured in the typically used simple numerical/categorical specifications, such as long-term reliability, maintainability, compatibility of the design with the rest of plant or other connected equipment and even u aesthetics (Figure 2). controleng.com
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Optimized AI agents One of the key benefits of building industrial AI models generated around first principles is that those models can be delivered by fit-for-purpose AI agents designed to perform exceedingly efficiently and effectively at a given set of tasks. Today, industrial AI agents use specific and relevant data sources to solve a wide variety of problems. A common example is seen in the reliability space, where enterprise-level reliability solutions are seamlessly integrated with predictive and prescriptive asset health software to create comprehensive asset health solutions. These systems rely
KEYWORDS: AI/ML, controls, process manufacturing CONSIDER THIS How are you integrating AI into processes? LEARNING OBJECTIVES Consider differences in AI offerings connected to process controls. Learn the unique needs of industrial processes frame specific use cases for AI. Discover the value of highquality data to drive AI success in industrial use cases.
July/August 2025
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ANSWERS
CONTROLS, AI/ML
systems to deliver predictive capabilities and vice versa. The future points to a seamlessly interconnected AI workforce, built from individual agents optimized for specific tasks and working in parallel with other AI agents, as well as human operators and technicians.
FIGURE 3: AspenTech’s process monitoring AI agents analyze data from different process sensors and provide a coordinated alert to operators to diagnose root causes of process abnormalities.
Insights
u
AI insights u Not all AI is equal, and
industrial processes require some nuance when it comes to AI.
u High-quality data is
essential for AI success in industrial applications.
u Models can become
AI agents of their own, helping test and refine operations as usecase-specific AI models built for more granular purposes.
on fit-for-purpose agents built on extensive failure mode and effects analysis databases to make advanced analytics more intuitive. AI and machine learning-based reliability agents use pattern recognition algorithms that incorporate multivariate data to predict asset degradation based on embedded domain knowledge. The agents rely on a vast library of existing failure modes and effects — in combination with a wide variety of machinery health and process variables — to identify individual aberrations as part of larger, more serious problems in the plant. Armed with that identification and AI-generated problem-solving steps, reliability teams can quickly intervene to solve the plant’s — or even the enterprise’s — most complex issues. Similarly, in the operations space, industrial AI agents can also be leveraged for process optimization. As teams engineer processes, industrial AI helps them see multiple alternatives and factor in multiple criteria to develop a range of possibilities to select the best design more easily. With the help of industrial AI, hybrid models have become more robust and compatible with existing equipment and plant designs. As those models are further refined, they can become AI agents of their own, helping test and refine operations as use-case-specific AI models built for a more granular purpose, such as those designed for heat exchangers or distillation columns. While today’s typical AI deployments tend to be more independent in nature and geared toward their specific area of the plant, the most advanced solutions are evolving toward something more. AI tools for reliability already use data from operational
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Drive more value from data As the AI workforce, human operators, technicians and executives work together, they will be heavily reliant on high-quality, contextualized data. There is always a difference between simulation and what exists in the plant and industrial AI solutions efficiently use smart data to close that gap. Today’s most effective OT teams are exploring industrial AI solutions that integrate seamlessly with their control systems to provide continual real-time contextualized data so AI agents constantly know the state of the plant and can suggest effective optimization strategies. That type of advanced connectivity simplifies change implementation because any optimization strategy the AI generates will need to be performed via the control system. The more seamless the connectivity between systems, the easier that process will be when implemented. Not just any AI The rise of AI has provided process manufacturers with a wide variety of new solutions for improving performance through a step change in agility, better guidance and improved automation optioneering. However, not every AI solution is appropriate for industrial operations. While unlimited creativity sounds wonderful, truly safe, efficient and effective industrial operations are bounded by the immutable laws of chemistry and physics. Today’s most effective teams opt for industrial AI solutions which are, by definition, grounded in the first principles that keep hybrid models both accurate and efficient. Such solutions save time and effort by drawing on a deep base of existing knowledge to effectively train models, while also streamlining customization by narrowing optionality. ce Claudio Fayad is vice president of technology of Emerson’s Process Systems and Solutions business, and Krishnan Kumaran is a senior director in AspenTech’s AI Technology group. Edited by Sheri Kasprzak, managing editor of automation and control brands for WTWH Media. control engineering — www.controleng.com
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Italian machine builders are bringing innovative and transformative solutions to their industries.
Italian machine builders lead the way in machine and process innovation. Machines Italia’s Fall 2025 issue will focus on how technology-focused innovations makes Italian machinery a standout in the market; how Italian machine builders are overcoming industry challenges through new/ innovative/creative design applications and design solutions on their machinery and lines; how Italian innovation in machinery can help drive down organizations’ operating costs; and how Italian OEMs are working in a collaborative way with end users to bring new innovations and new transformative processes to industries. This issue will seek to place special emphasis on what makes Italian machinery uniquely superior in the market and how Italian machinery and solutions are being used in new, innovative ways to solve old industry challenges. For more details and to read the digital edition, visit machinesitalia.org.
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ANSWERS
ADVANCED PROCESS CONTROL
Ed Bullerdiek, process control engineer, retired
PID spotlight, part 19:
PID controller tuning best practices You’ve been trained to tune PID controllers on self-limiting and integrating processes, but are you ready to tune? Can we trust you to do it safely? Do you know how to work with the operators responsible for the process? Do you know that there are problems that tuning cannot fix? Finally, do you know that after you are done tuning you are not done?
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he formal training of new process control professionals rarely includes guidance on how to work with operators and others to safely and effectively tune a PID controller. This is normally left to mentors to impart the skills necessary to be a truly effective control tuner. The first lesson that a mentor should impart is in a well-tuned facility most loop tuning requests are indicators of a bigger problem. (Yes, I know this is rare, but they do exist). This means that at best your loop tuning efforts will cover up that problem – temporarily. A corollary to this is that your tuning efforts are part of a bigger problem-solving session. There are three possible outcomes: • It really was a tuning problem, and you fixed it. • Tuning worked around the problem, and you have passed the problem along with your thoughts on what the problem might be to someone who can fix the core problem.
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• You didn’t do any tuning because this is not a problem tuning can help. You have passed the problem on to someone who can fix the problem.
The second lesson a mentor should impart is that process control is a team sport. One problem that often occurs is we can get so focused on controller tuning is we lose sight of the fact that the problem may be something other than tuning. This is a real-life example of “if all you have is a hammer everything looks like a nail.” It is expected that you will look beyond tuning the controller and enlist help to solve the underlying problem which may very well be beyond your expertise.
Getting ready to tune You have been asked to tune an existing controller. Where do you begin? Start with these four areas: Tuning logs, talk to people, don’t create surprises and avoid even bigger problems. More on each follows. Look at the loop tuning logs If your facility doesn’t keep loop tuning logs, I highly recommend that you should. If nothing else keep a personal set of logs for your area of responsibility. The logs can be handwritten (I started old school) or any acceptable electronic log. If your system tracks parameter changes and permits adding comments, make it a habit to add a comment whenever any tuning parameter is changed. The purpose of a tuning log is to help you: • Track slowly developing process or instrument problems. • Identify cases where adaptive tuning might be necessary. control engineering — www.controleng.com
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FIGURE 1: An example loop-tuning log. Figures courtesy: Ed Bullerdiek, retired control engineer
• Explain why a controller is tuned a specific way. • Allow restoring old settings or in the case of heuristics allow you to split the difference if you went too far the last time. Figure 1 is an example of a loop tuning log for the Unobtainium Unit feed flow controller. This is done in Microsoft Excel and kept in a location accessible by the entire control group. Entries are chronological with the newest on top. The log should include all commonly used control algorithm parameters for your system. To speed review, omit entries that haven’t changed. A review of the entries tell us: 1/17/2005: This is a new process unit and the flow controller was started up with default flow controller tuning. (There are default tuning constants that work reasonably well for most flow and level controllers. It is not unusual to have a flow controller require no adjustment from the default constants.) 4/12/2010: The unit is expanded. The original tuning became too aggressive; the controller gain was adjusted. The setpoint clamp was raised for the new charge rate. 5/23/2012: The control valve started sticking. Several tuning changes were made to work around the limit cycle that developed starting with reducing the controller gain. A gap was added with an even lower gain. (On this system the gain inside the gap is the controller gain multiplied by the gap gain, or in this case 0.25 * 0.25 = 0.0625). The gap limit will be set outside the limit cycle’s effect on the process variable. A little derivative was added to accelerate the controller output through the control valve backlash limit. Finally, an output rate limit was added to help controller stability. Nearly every control engineering — www.controleng.com
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It can take a few years before the true value of loop-tuning logs
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become obvious.
trick was deployed to keep this controller working (poorly) until the valve could be fixed. The note is short but covers the most important point – a work order to fix the valve was written. 6/12/2012: The control valve has been fixed, and u controleng.com the original tuning restored. Note that three different people worked on this KEYWORDS: Proportionalcontroller. CKR did restore the loop tuning after integral-derivative, PID tutorial the control valve was fixed, but if he weren’t availLEARNING OBJECTIVES Know the steps required to able, anyone could have done this without having prepare for a controller tuning to spend the time to retune the controller. session. While it hasn’t been done in this case, you Understand controller tuning could explain in the notes that a controller (call etiquette. it 00TC0012) was tuned (for example) very slowKnow how to tune controllers safely. ly because it is a slow optimization controller. It is Know the most common tuned this way to avoid interaction with controlproblems to look for while ler 00TC0011, which presumably is tuned quicktuning. ly because it is the economically more important Know how to close out a controller. The note should prevent someone from controller tuning session. speeding up the controller in the future, which CONSIDER THIS How do you safely tune a could happen if someone doesn’t understand why PID controller? Will tuning fix the controller is tuned to be slow. Or restore the what ails the controller, or are original tuning constants after they realize the new there other things you should be looking for while you are faster tuning constants don’t work. tuning? It can take a few years before the true value of ONLINE loop tuning logs become obvious. As the probLink to PID spotlights, parts lem controllers and processes become apparent, 1-18 and with this article online, starting with “Three you can use this information to drive improvement reasons to tune control efforts such as advanced controls or even process loops: Safety, profit, energy improvements. efficiency.”
Online
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ANSWERS
ADVANCED PROCESS CONTROL
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If tuning this control loop will help, by all means tell the operator how you expect this
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will help.
Talk to people about process control problems One of the problems with loop tuning requests is that someone has already decided what the problem is, and that the fix must be tuning the controller. They might be right, but you should check out their thinking. Why do they believe loop tuning will solve the problem? The answers might be: • “We changed the process.” In this case loop tuning is probably required. If you are a member of an operating team, you should have known that this was coming. But it’s not unusual that you were the unlucky soul with the duty phone when the change got completed. A call to the responsible person in your group may be in order. They can brief you on what needs to be tuned. • “We noticed something strange started happening.” Loop tuning requests are a call for help, and, for better or worse, they trust you. This will very likely be bigger than simple loop tuning. Loop tuning may provide a stopgap until the larger problem can be solved. • “This one controller started misbehaving; everything else is fine.” The issue is most likely a valve problem, followed by an instrument problem. While you are talking, walk through the trends. They should be able to point out when the problem started and what they think the problem is. This is your opportunity to clarify what the expectations are and to use your new controller performance problem identification skills to try to narrow down the problem. This is also a chance to save yourself some work. It may quickly become apparent that this is a problem that controller tuning cannot fix or even work around. Regardless, at this point you should be able to form an opinion on what the likely problem is and, if you have questions, get others involved. Loop-tuning etiquette: No surprises In most facilities you can touch the control system from your desk, including tuning controllers. There is an awful temptation to just jump in and start changing numbers. Please don’t. Operators do not like surprises. An operator does not want his job to be any harder than it has to be. An operator needs to know what you are doing and why. The operator also needs to know that he
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can stop you if anything comes up. Remember that the operator is responsible for the process, not you. If anything happens, he will be held accountable. This is a form of professional self-defense. If you ever want to be able to tune a loop again when this operator is working, you need to make him feel comfortable that he has all the information he needs, and that he is a partner in the process, even if only a passive participant. Working with operators is easier if they believe you will make their life better. As a new control professional, you will be under a spotlight. You do not have a track record yet, and they have likely already experienced a new engineer coming in and making their life difficult. These steps should always be part of how you do business, but will be especially important when you are new. • Visit – don’t call. Making the extra effort to show up will impress them that you are serious. If you have many successful interactions with an operator, and you have developed a relationship you may be able to get away with a phone call (although you should never make this a common practice). Of course, if he calls you for help, there is no need to visit. • “Is now a good time?” Be aware that an operator may have a problem on his hands or be in the middle of a procedure that requires all his attention. Furthermore, you do not want to try to tune a controller if the process is upset. (If the process is upset, and you are already in the control room you should sit back and watch the operator work. Focus on things that may be impeding his work with an eye towards fixing these later. Watching will not be time wasted.) • “I want to do this because…” Unless the operator requested that you look at a controller, he will be properly skeptical about your visit. Give him the background; perhaps he will have some insight that will help you out. • “This will help you because…” Operators want their job to be easier. If tuning this control loop will help, by all means say how you expect this will help. • “I expect this to take X minutes/hours.” He may not be busy now, but he knows that in 2 hours he will be. If you expect this will take more than 2 hours, you might not want to get started. And he will want to know when you expect to be out of his hair. control engineering — www.controleng.com
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• “May I change the controller mode?” If you have a lot of credibility the operator will probably let you do what you like. But if you are new, he may want to make the actual moves. This is about building trust. Or you may be in a facility where work rules limit what you are permitted to do. • “May I change the controller output or setpoint?” This is the same as asking about changing controller mode. • “How big of a change may I make?” When tuning, bigger steps give us clearer results. But bigger steps run a bigger risk of upsetting the process. Give the operator the chance to tell you how big a step you can take and what hazards you need to watch out for. Most controllers will not have a huge effect on the process, but making a mistake with some can cause real problems. • Promise to let them know when you are done. If you are working from your office, it is all too easy to forget to call the operator. Please don’t leave them hanging. Here’s a final piece of advice. Make a point of visiting the operators once in a while with nothing on your agenda. Talk about sports, hunting, cars or whatever they are interested in. You should ask if they have any questions about the controls. This doesn’t need to take long, but having some sort of personal connection, a sense of trust, will help you and him when something needs to get done.
Tips to avoid creating a bigger problem Controller tuning requires us to disturb the process during the testing and validation process. Most control loops do not have a huge impact on the overall process. We can afford to make brief mistakes. However, mistakes on some controllers can have wide ranging impacts. The worst one I heard of involved shutting down a utility boiler on low drum level. They were not able to get an immediate restart, and the upset resulted in reduced charge rates through much of the refinery. The report reached the executive vice president’s notice. This is not the kind of attention you want. (My involvement was after the fact and yes, I am trying to focus your attention.) One of our questions for the operator was “How big a step may I take?” This is part of a bigger conversation about the risks involved in pushing the process around while we are testing. Before testcontrol engineering — www.controleng.com
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ing we should know, whether through our own research or by talking to the operators, where we shouldn’t go. Then, depending on the tuning method we should take appropriate steps to avoid going there. This is another aspect of professional self-defense. We will make mistakes during loop tuning. This is inevitable as we are venturing into the great unknown. But there are things we can do to prevent big mistakes depending on the loop tuning method we use. The open-loop tuning method is generally safe. You control the controller output step size and direction, which means as long as you know where the no go zone is you can step away from it. If you must step toward a no-go zone then you should limit step size certainly per the operator’s recommendation and perhaps less if there are any questions. Closed-loop tuning is very risky and generally not recommended. However, should you intend to perform a closed-loop control test you should set controller output clamps before starting testing. The risk is that as you increase controller gain in the search for the ultimate gain you may go too far. If this should happen the controller will “go there,” perhaps faster than you can stop it. Output clamps will limit the process swing to something tolerable assuming you haven’t set the limits too wide. (I almost tripped a gas plant boiler on low fuel gas pressure on my very first distributed control system installation project. That’s when I learned to always set output clamps and to avoid closed loop tuning if at all possible.) Heuristic tuning involves stepping the controller setpoint in auto and looking for the pattern of the controller response. There is no hazard if you are reducing controller gain or slowing the controller integral. If, however, you are increasing controller gain and/or speeding up the controller integral there is a small risk of going unstable. The risk is very small if you follow the rules, but it isn’t zero. Finally, when you do the final tuning test you should consider setting a controller output clamp if there is a hazard. While theoretically whatever tuning constants you end up with should be safe, a bad control valve can considerably affect tuning results, especially with open-loop tests.
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If you are increasing controller gain and/or speeding up the controller integral there is a small risk of going unstable. The risk is very small if you follow the rules, but it isn’t
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zero.
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ANSWERS
ADVANCED PROCESS CONTROL
ture controller will draw a shark fin wave. If manual step tests of the valve results in different process variable responses, then it is likely the valve is sticking. See PID spotlight part 18 for a thorough discussion of control valve performance issues and how you might address them.
Is it controller interaction or ... ? If the controller you’ve been asked to tune is swinging you need to find out why it is swinging. Ask the operator to put the controller in manual. One of two things will happen: • If the controller stops swinging you have a tuning problem, or the valve is bad.
FIGURE 2: Air blower discharge pressure limited by plugged filter.
What problems should I look for while I am tuning? You have looked at the logs and the trends, spoken to whomever requested the tuning check, have the operator’s permission and have set up any required safeguards. There will be clues to what problems need to be solved. But what problems are we likely to run into and how do we identify them? In order of likelihood, they are: • Valve problems. • Control loop interaction. • Unrealistic expectations. • Instrument problems. • Process problems. • Bad control design.
Is it a bad valve? Poor valve performance is the most common cause of control-loop performance problems that do not involve tuning. You should be able to identify the limit cycle that valve stick-slip action causes based on the process trends: • The process variable of a flow controller will draw a square wave. • The process variable of a level controller will draw a saw tooth wave. • The process variable of a pressure or tempera-
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• If the controller continues to swing another controller is driving this loop. You need to find and fix that poorly tuned controller. There is a third possibility. You may tune the controller and, no matter what you do, the controller still swings when you put it back in auto. In this case you likely have another controller interacting with this controller. Control-loop interaction is prone to show up where there are internal recycles in the process. Common places where interaction is found include: • Distillation: Distillation recycles vapor to liquid and back again. • Heat exchanger networks: Particularly bad are feed versus product heat exchange. Distillation feed by bottoms product can be very difficult to manage. • Flow headers: Backpressure caused by the closing of one valve can cause flow to other passes to rise, causing those valves to close. Breaking the cycling that can occur requires special tuning methods. (This is a case where the recycle is contained within the flow line in the form of pressure drop, which affects flow, which affects pressure drop, which… You get the picture.) • Controllers with similar dynamics in series: A common example includes pressure and control engineering — www.controleng.com
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flow controllers in series, for example in balanced draft heaters. Special tuning methods are required here also. Occasionally control loop interaction must be handled by feedforward schemes or adaptive tuning. If you suspect controller interaction you can attempt to break the interaction by changing tuning. Slow control loops do not visibly affect fast process control loops. Fast loops can correct any disturbance caused by a slow loop before it becomes a problem. The rule of thumb is a fast loop will not be affected by slow loops that are three times slower (measured by deadtime plus lag). However, if you choose to go this route the slow controller will not manage disturbances caused by the fast controller, therefore the slow loop needs to be one where being off setpoint for possibly an extended amount of time can be tolerated.
Are there unrealistic tuning expectations? We are often confronted with a request to speed up a controller. Unfortunately, controller speed is limited by the laws of physics. It is a little-known fact that a controller cannot begin to correct a disturbance until one-half the natural period has passed. The natural period is approximately four times the deadtime. This can severely limit what we can do with very slow processes. Of course, talking about the natural period will not likely work with operators or managers. There are a couple of strategies I’ve tried (with varied success): • Remind them of the size of the equipment – or take them on a tour if they are if they are unfamiliar with the equipment. This can work well with young engineers, and it is never a mistake to familiarize yourself with the equipment. control engineering — www.controleng.com
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FIGURE 3: Your completed loop-tuning log entry.
• Tell them that moving this process is like walking an elephant. Once an elephant gets off course, it requires a lot of coaxing to get it back on path. It can be done, but it takes more than a little time. This is somewhat effective with operators who are already familiar with the equipment.
Have you found an instrument problem? Most instrument problems result in immediate failure. You won’t be called about these. However, there are a couple of problems that you will routinely see that occur slowly and are easy to miss:
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Understanding how the proces reacts if a limit is crossed will save you many hours
• Instrument drift. • Partially plugged taps.
trying to fix a “tun-
Instrument drift can result in (for example) process limits violations. Depending on the limit the process can behave very differently. For example, if a controller is pushing distillation tower flooding limits, instrument drift can push the tower into flood, in which case the distillation tower will start to swing wildly. You may be called to tune the loop because of the swing. Understanding how the process reacts if a limit is crossed will save you many hours trying to fix a “tuning” problem that has nothing to do with tuning. This problem usually shows up suddenly when the drift drives the process into a new operating regime. Partially plugged instrument taps will change the apparent process dynamics (the real dynamics haven’t changed). Plugged taps show up as slower process response, which will result in controller swinging. You will be asked to slow the controller down. Be aware of situations where process dynamics shouldn’t change (for example – levels). If significant tuning changes are required to maintain
ing” problem that has nothing to do
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with tuning.
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ADVANCED PROCESS CONTROL
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If, according to the looptuning logs, a controller has run well for years and then suddenly it simply stops working, look for a process
’
problem.
stability, or, in the worst case, you cannot establish stability no matter what tuning you put in, look for plugged taps. This can be a situation that creeps up on you – keeping a tuning log can help. Finally certain types of instruments can have specific types of failures that are not obviously instrument failures. These are too numerous to describe here, but if you see something that doesn’t make sense, ask the old heads what they think.
Have you found a process problem? The problem that young control professionals often have is tunnel vision. This can even affect older professionals. (I am sometimes guilty, too.) To repeat, a controller tuning request is a call for help. In a well-tuned facility, most loop tuning requests are not because you have a loop tuning problem. It is usually something deeper. Therefore, the best thing you can do is keep your eyes open for problems that could affect tuning no matter the source. Refineries prefer to place process engineers in the control group specifically because they expect these engineers to bring their process knowledge to bear. Sudden changes: If according to the loop tuning logs a controller has run well for years and then suddenly it simply stops working, look for a process problem. For example, early in my career I was asked why the composition controls on a preflash tower were working poorly. None of the data looked normal, which led to the question: “Did something happen?” The operator informed me that there was an upset where water blew through the tower from a desalter upset. I called for help, the tower was x-rayed which revealed that 3 of 11 trays had collapsed into the bottom of the tower. The problem was explained, and it was not a tuning problem. (This was more luck than brilliance.) Slow changes: If you find you are making steady changes in controller tuning in one direction, you should suspect a slow developing process problem. Early in my career when I didn’t know better, I was changing the tuning of a temperature controller on a heat exchanger train on a regular basis. This continued for a couple of years until the process engi-
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neer did a temperature survey. This was followed by cleaning the heat exchangers because the loss of heat recovery was costing a lot of money. If I had understood the importance of the steady loop tuning trend, I could have alerted the process engineer to the problem earlier. The data was all there in the loop tuning logs; I didn’t understand the import. Nonlinearities: Most processes are nonlinear. However they tend to get operated in a relatively narrow range, which means, we can get away with one set of controller tuning constants. However, if the process is run at different rates routinely, you may find yourself changing tuning based on rates (or feedstocks, or …). If you find you are frequently changing tuning constants to follow changing feed rates (or other) consider adaptive tuning. Equipment limitations: Figure 2 is an example from a late career project where a blower supplying air to a waste water treatment plant aeration basin. The blower was designed with limited discharge pressure above the minimum necessary to aerate the basin. This was obvious based on a review of the blower curves, which means that if the inlet filters are even a little bit plugged the blower runs out of capacity. Every spring when cottonwood season visits Detroit there would be a call about why the controller stopped working. The response: “Did you check the filters?”
Is it bad control design? It’s very rare to find a controller that simply doesn’t work. These are usually found during initial commissioning. If you should find one (usually by open-loop step testing) please help out your peers. Leave notes in the loop tuning log and in the controller logic (if possible). Lock the controller in manual (if possible) and start a management of change (MOC) process to convert this to an indicator. You may be tempted to avoid this work. Please do not. Someday there will be an effort in your facility to put all the controllers in their “proper” mode. This means that someone (given the short memory span of organizations) will have to waste time rediscovering what you know. control engineering — www.controleng.com
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The induced disturbance test
Closing out a loop-tuning session You found out that the controller really did need tuning to minimize process disturbances, and you have finished tuning. Because this is a unit charge controller, you probably set output (OP) clamps either during tuning if you used heuristics or during the final test if you did an open-loop test. Make sure that they are released. You have told the operator that you are done and that based on your final test things should be better. The induced disturbance test suggests the controller will adequately reject a disturbance. This is not truly definitive, so you tell the operator that you will check back tomorrow to see if any disturbances have come through. Presumably something has happened that is causing upsets. You probably don’t know what that could be, but suspect that it has something to do with the tank farm. Since the tank farm is not your unit you don’t feel comfortable looking into it. Instead, you will refer this to someone who is responsible for the tank farm.
Finally, you will fill out the loop tuning log (Figure 3). The controller gain has been increased to convert the tuning to disturbance rejection from setpoint tracking. The integral has been slowed to compensate for the increased gain (maintain same effective integral). A setpoint rate limit of 100 BPD/minute has been added to smooth transition when the setpoint is changed (this probably should have always been there, but it became necessary to prevent feedrate oscillations and the downstream effects this creates when the setpoint is changed). You are not truly done even now. Tomorrow you will have to check back to see if what you have done has worked. But for today you are done. ce Ed Bullerdiek is a retired control engineer with 37 years of process control experience in petroleum refining and oil production. Send comments and questions to freerangecontrol@ameritech.net. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.
suggests the controller will adequately reject a disturbance. This is not definitive. Check back tomorrow to see if any disturbances have come
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through.
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ANSWERS
INDUSTRIAL NETWORKING
Tom Burke, CC-Link Partner Association
Why time-sensitive networking (TSN) is the backbone of next-gen digital manufacturing How deterministic Ethernet with TSN empowers HMI/SCADA integration, unifies IT and OT networks, and drives real-time performance in smart manufacturing environments.
I
for real-time industrial applications such as motion control, robotics and safety systems. The deterministic nature of TSN hinges on two new (introduced in 2016) IEEE sub-standards: IEEE 802.1AS ("Timing and Synchronization for Time-Sensitive Applications") and IEEE 802.1Qbv ("Enhancements for Scheduled Traffic"). These standards work in tandem to synchronize network devices and prioritize time-critical data, laying the groundwork for robust, high-performance industrial networks.
n the rapidly evolving landscape of industrial engineering, time-sensitive networking (TSN) emerges as a transformative technology poised to redefine industrial communications and factory automation. Based on the IEEE 802.1 standards, TSN enhances traditional Ethernet by introducing deterministic capabilities that ensure precise, reliable and predictable data transfer. For engineers tasked with designing futureproof digital manufacturing systems, TSN offers a compelling solution to meet the demands of Industry 4.0 and beyond. Technology exists today in the form of gigabit Ethernet switches, controllers and devices to implement all new TSN-based architectures.
IEEE 802.1AS: Precision synchronization The IEEE 802.1AS standard, an evolution of the IEEE 1588 Precision Time Protocol (PTP), enables microsecond-level synchronization across all devices in a TSN network. This level of precision eliminates the time drift (or jitter) common in standard Ethernet networks, in which devices rely on independent internal clocks. In traditional setups, cumulative timing discrepancies can disrupt data transfer, leading to delays or packet loss — unacceptable outcomes in time-sensitive applications like closed-loop control systems. By contrast, IEEE 802.1AS ensures that all network elements operate in lockstep, providing a stable foundation for deterministic communication.
TSN: A technical overview TSN operates at Layer 2 (data link layer) of the open system interconnection (OSI) model, augmenting Ethernet with deterministic features that traditional Ethernet lacks. Unlike conventional Ethernet, which prioritizes flexibility and scalability over timing precision, TSN guarantees low-latency, jitter-free communication — a critical requirement
IEEE 802.1Qbv: Scheduled traffic management Building on the synchronized environment established by IEEE 802.1AS, the IEEE 802.1Qbv standard introduces traffic scheduling through time-aware shapers (TASs) embedded in network switches. TASs leverage time division multiple access (TDMA) principles to allocate periodic time
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control engineering — www.controleng.com
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FIGURE: Deterministic Ethernet (time-sensitive networking) can benefit manufacturing systems and information technology systems.
slots for transmitting critical data, such as motion control commands or safety signals. During these reserved windows, only high-priority traffic is permitted, effectively isolating it from non-critical data streams like video feeds or diagnostic logs. This prioritization mechanism ensures that time-sensitive packets are delivered with minimal latency and zero interference from lower-priority traffic. Since all network devices and TASs are synchronized via IEEE 802.1AS, each node knows precisely when to send or expect priority data. Engineers can define these schedules within the traffic profiles of data packets, optimizing network performance for specific use cases. This deterministic approach contrasts sharply with traditional Ethernet’s best-effort delivery model, making TSN ideal for applications requiring guaranteed bandwidth and timing.
A new standard is enabling data convergence and network efficiency TSN’s determinism not only prevents network congestion and data loss but also facilitates the convergence of diverse traffic types onto a single-network infrastructure. For example, engineers can integrate HMI/SCADA with input/output (I/O) data, motion control signals and safety commucontrol engineering — www.controleng.com
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nications without compromising performance. Beyond these core industrial flows, TSN supports additional Ethernet traffic — such as from cameras, barcode scanners or printers — alongside other u protocols, all coexisting seamlessly. controleng.com From a systems engineering perspective, this KEYWORDS: Time-sensitive convergence translates to significant cost savings. networking, industrial Ethernet By reducing the need for separate networks, TSN LEARNING OBJECTIVES lowers capital expenditure (CAPEX) and simpliUnderstand how TSN enables deterministic, real-time fies network architectures. A streamlined topology communication over Ethernet enhances bandwidth utilization and reduces points for industrial applications. of failure, enabling faster troubleshooting and minLearn how TSN supports imizing downtime. Moreover, TSN’s flexibility HMI/SCADA integration and simplifies network allows engineers to reconfigure networks dynamiarchitectures through cally — adding or removing devices and adapting convergence. to evolving operational requirements — without Explore how TSN disrupting performance. facilitates IT/OT unification
Online
Bridging IT and OT for data-driven manufacturing The true power of TSN lies in its ability to unify Information Technology (IT) and Operational Technology (OT), a critical enabler of data-driven manufacturing. By converging IT and OT traffic, TSN provides unprecedented transparency into industrial processes. Engineers can extract realtime data from factory floor devices, feed it into
and enhances digital manufacturing through improved data visibility and control.
CONSIDER THIS: Is your current network infrastructure capable of supporting the real-time, converged, and data-driven demands of next-generation digital manufacturing—and if not, how could TSN help you get there?
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INDUSTRIAL NETWORKING
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Ethernet with TSN and gigabit bandwidth delivers a series of benefits to manufacturing, including lower costs, convergence, process
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transparency and added productivity.
Insights
u
Time-sensitive networking insights u Time-sensitive
networking (TSN), through IEEE 802.1AS and 802.1Qbv standards, enables precise synchronization and scheduled traffic, allowing Ethernet to support timecritical applications like motion control and safety systems.
u TSN allows diverse data
types — including HMI/ SCADA, control signals and IT traffic — to coexist on a single network, streamlining architectures, lowering costs, improving uptime and enhancing data transparency.
u By enabling seamless
communication between factory-floor devices and enterprise systems, TSN is a foundational technology for IIoT and digital transformation, supporting advanced use cases like predictive maintenance and process optimization.
predictive analytics models, and derive actionable insights to optimize performance, efficiency, and product quality. For instance, TSN enables the seamless integration of sensor data with enterprise-level systems, supporting advanced applications like predictive maintenance and process optimization. This IT-OT fusion is a cornerstone of the Industrial Internet of Things (IIoT), particularly in North America, a region that has emerged as a global leader in IIoT adoption. To maintain competitiveness in this innovation hub, engineering teams must leverage TSN to unlock the full potential of IIoT frameworks.
How Ethernet with TSN benefits manufacturing Key to communications management is a converged, high-bandwidth network infrastructure to support digital transformation strategies. Convergence allows everything to communicate on the same network architecture, avoiding multiple networks’ cost and complexity. It’s the foundation of high-speed, real-time deterministic communications between disparate devices and systems, sharing data across the entire enterprise, regardless of source or destination. This provides the transparency required for fully optimized operations by allowing the data to flow from its source to be processed for actionable insights, then fed back. This does not just apply to supervisory systems. Having real-time control and coordination of multiple different shop floor or operational technology (OT) systems is also critical. Ethernet with TSN and gigabit bandwidth delivers a series of benefits to manufacturing in general, such as: • Reduces costs, shortens project timelines and increases uptime by simplifying network architectures and hence system designs.
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• Convergence avoids multiple network types to handle different process traffic. • Delivers greater process transparency and optimized operations. Converged network architectures allow data to flow where it’s needed. This is the key to managing processes in the best way. • Delivers greater productivity, as optimized processes will run in the most productive way. Offers better integration of OT and information technology (IT) systems, as a converged stream of data can be shared between the factory floor and supervisory systems more easily.
What industries use Ethernet with TSN? Today, more than 100 leading global manufacturers are reaping the benefits of Ethernet with TSN in their operations worldwide. These cover industries as diverse as automotive, consumer electronics, consumer packaged goods, food and beverage, lithium batteries, logistics, semiconductors and many more. The leading industrial network leveraging gigabit Ethernet with TSN is CC-Link IE TSN, managed by the CC-Link Partner Association (CLPA). CLPA was established in 2000 to develop and promote the CC-Link family of open industrial automation network technologies. Since then, they have grown into a global organization with a track record of innovation. In a series of firsts, the CLPA introduced open industrial Ethernet technology with gigabit bandwidth in 2007. Building on this success, they were the first to combine gigabit bandwidth with time-sensitive networking in 2018. Today, the leading CLPA offering is CC-Link IE TSN, the world’s first, and so far only, open industrial Ethernet technology that combines gigabit bandwidth with time-sensitive networking. This provides manufacturers an excellent choice for machine builders looking to save cost, improve efficiency and improve connectivity between the OT and IT worlds. ce Tom Burke, Global Strategic Advisor, CC-Link Partner Association, www.cc-link.org. Edited by Gary Cohen, senior editor, Control Engineering, WTWH Media, gcohen@wtwhmedia.com. control engineering — www.controleng.com
7/28/25 10:42 AM
ATLANTA
THE WORLD’S PREMIER INDUSTRIAL AUTOMATION TRADE SHOW BRAND IS COMING TO THE U.S.A. Transforming Industrial Manufacturing Through Automation Explore innovation at SPS Atlanta for smart production solutions
September 16 – 18, 2025 Georgia World Congress Center Atlanta, Georgia USA www.spsamericas.com
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SCAN TO REGISTER
7/24/25 1:53 PM
ANSWERS SAFETY
Michael Pfeifer, Alexander Kurdas, TÜV SÜD Industrie Service GmbH
Interactive hazard and operability assessments:
5 ways to safer facilities
How to improve process control safety and risk management? Hazard and operability studies need to integrate sensor data, digital models and safety criteria to optimize plant health. See five components of interactive hazard and operability assessments.
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controleng.com KEYWORDS: Adaptive safety, interactive hazard and operability assessments LEARNING OBJECTIVES Learn why a digital, interactive hazard and operability assessment is needed for process risks. Examine three key benefits of interactive hazard and operability assessment (iaHAZOP) software and five core components. Examples show why runtime risk assessment matters, as it adapts to make safety part of daily operations.
afety and risk management have never been static tasks. In a world of constant change — whether due to evolving technologies or fluctuations in experienced staff — managing operational risk demands more than periodic inspections and siloed documentation. The TÜV SÜD interactive Hazard and Operability (iaHAZOP) digital approach transfers critical safety data and expert knowledge into one dynamic software, enabling companies to stay ahead of disruptions while maintaining safe and compliant operations.
Why a digital, interactive hazard and operability assessment is needed As production environments evolve, so must the methods used to oversee them. Regulatory requirements stipulate that each facility must have a risk assessment tailored to its current configuration. But many businesses still rely on static documentation, often in paper files or digital silos that can be difficult to locate or interpret, especially during off-hours or amid an incident. iaHAZOP goes beyond simple digital spreadsheets of conventional HAZOP studies. It transforms risk management into an integrated, run-time process that adapts to changing conditions. This solution continuously integrates sensor
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data, digital models and formal safety criteria to provide a comprehensive view of plant health.
Three key benefits of interactive hazard and operability assessments During design or during active operation, iaHAZOP: 1. Offers immediate insights into potential hazards helping teams act before issues escalate. By aligning live process data with predefined safety thresholds and rules, the software generates upto-date evaluations that reflect the actual state of the facility. 2. Supports fast, informed decision-making and facilitates compliance by automating parts of the documentation process. 3. Intentionally leaves key decisions and approvals with qualified personnel, ensuring that human expertise remains central to operational safety. Behind iaHAZOP: Five core components Five core components make interactive hazard and operability assessments a robust and future-oriented tool: 1. Digital twins replicate how equipment and systems behave, thus making it possible to anticipate problems before they occur. These models can be hosted on-site or in the cloud, depending on a company’s infrastructure. 2. Knowledge graphs structure connections between different elements of the facility, drawing from historical studies, incident records and engineering experience to create a web of meaningful relationships. 3. Real-time sensor inputs offer continuous updates from process parameters like pressure, temperature or flow. This enables quick detection of abnormal conditions. control engineering — www.controleng.com
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4. Hazard rules translate physical and chemical safety principles into programmed logic, allowing consistent risk identification even when systems are modified or reconfigured. 5. Regulatory benchmarks are embedded into the tool, ensuring that evolving plant conditions are constantly evaluated against accepted standards. Together, these methods allow the software to maintain a holistic view of safety, not as a snapshot picture, but as a continuously evolving movie. Examples why run-time risk assessment matters Several real-world cases illustrate the impact of a more dynamic approach to safety. In one incident, a valve was thought to be sealed off but was actually leaking due to erosion. This fact was overlooked because the operator did not have this knowledge available, although the engineering and maintenance department was aware of it, which led to an explosion. In another case, pressure remained undetected in the system. This led to injuries of the maintenance staff during boiler work. The software, with its combination of historical maintenance data, live sensor readings and digital twin simulations, could have flagged these risks in advance. Even subtle operational changes, like modifications of the process that are not accompanied by an update of the risk review, can have significant consequences. The software tracks these adjustments during runtime and updates the risk status accordingly, reducing blind spots that could otherwise go unnoticed. Interactive hazard and operability assessment software adapts Whether managing a flexible modular plant with frequent product changes or overseeing a large, complex facility with compartmentalized teams, interactive hazard and operability assessment software supports safer operations by connecting information that is too often isolated. In modular setups, the software helps reassess risks dynamically as modules are swapped in or out. In larger installations, it bridges gaps between departments by merging data sources and aligning them under a shared safety framework. Staff fluctuation adds to the complexity of the scenario. Experienced operators are retiring, taking with them much of the institutional knowledge that traditional risk management approaches control engineering — www.controleng.com
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FIGURE: Integration of digital twins, sensor data, regulatory framework and hazard and safety rules allow interactive hazard and operability assessment (iaHAZOP) software to maintain a holistic view of safety — not as a snapshot picture, but as a continuously evolving movie. Courtesy: TÜV SÜD aption no base lock
were built on. The result: a growing mismatch between actual operational risks and available safety assessments. Importantly, iaHAZOP can be rolled out in phases, making it accessible to companies at various stages of digital transformation. Businesses with extensive data infrastructures can integrate it quickly, while others can adopt it gradually to build a modern risk management approach over time.
Integrate safety into daily operations The core value of interactive hazard and operability assessment software lies in its ability to make safety a living part of daily operations, not a periodic chore or an afterthought. By creating a network of continuously updated data, expert knowledge and predictive models, TÜV SÜD empowers companies to take control of their operational risks before they turn into problems. This is not just about avoiding downtime. It is about fostering a safer, smarter and more resilient way of working. ce
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Even subtle oper
ational changes, like modifications of the process that are not accompanied by an update of the risk review, can have significant
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consequences.
Michael Pfeifer is senior expert adaptive safety and Alexander Kurdas is expert for machinery and electrical safety, TÜV SÜD Industrie Service GmbH. https://www.tuvsud.com/en Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com. July/August 2025
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ANSWERS
MOTORS AND DRIVES
Rich Houtz, Hargrove Controls & Automation
How to select, configure and tune industrial motor drives Proper selection, configuration and tuning of motor drives helps safety, reliability and performance in industrial automation
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controleng.com With this article online find sections on: Acceleration and deceleration ramps Applications for mechanical and dynamic braking Taking environmental considerations into drive selection Exploring modular drives and dc bus sharing How to tune a closed-loop drive system How to program drives Vendor tools simplify the selection process and debug
otor drives have become ubiquitous within industrial manufacturing. By regulating motor speed and torque, they increase energy efficiency, reduce equipment mechanical wear and improve process consistency. They integrate better with modern automation systems and enable advanced safety functionality. Because motor drives serve a wide range of applications that extend from simple fans or pumps to complex servo systems, it is essential to select, configure and tune them where applicable to ensure the system’s overall safety and performance. It’s critical to know how to correctly size a drive, account for load dynamics, consider the system’s safety requirements, choose an appropriate control mode and tune advanced closed-loop control systems for optimal performance.
Selecting, sizing, configuring a drive Drives are selected and configured to match the electrical and mechanical characteristics of the motor they control, the dynamics of the connected load and the functional and safety requirements of the application. While variable frequency drives (VFDs) are used most of the time to control ac induction motors, servo systems require more specialized motors and advanced servo drives for control. Configuring a drive involves setting up its internal parameters to ensure safe and accurate operation with the connected motor and application. Drives must be sized to at least match the motor’s nameplate data, including voltage, full load current, speed, horsepower and application-specific requirements. These values
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are entered during configuration so that the drive regulates power correctly to the motor. In some cases, a drive may be selected with a higher power or current rating than the motor requires, either to accommodate intermittent high-demand conditions or in anticipation of future system upgrades. Application-specific requirements will often affect the options such as whether to use encoder, different safety functions, control system communications or drive input/output (I/O) options.
The importance of load dynamics The physical behavior of the connected load significantly influences drive selection and configuration. Understanding of the system’s load inertia, torque variability, and feedback requirements is critical for choosing the correct drive and configuration to perform safely and reliably. The load inertia is a function of how much mass the motor must turn to get the connected load to the correct speed. A load with a large mass will require significant effort on the drive, whereas a small mass not as much. Another load influence for drive selection is the interaction of the load with other parts of the machine or a drastic change in load. This torque variability could lead to more mechanical stress and higher maintenance requirements. Motor drive safety requirements The functional safety requirements of the application must be considered when selecting a drive to ensure it supports the required safety features. The safety requirements are typically based on the potential hazards associated with the application. Most modern drives come equipped with safe torque off (STO), which ensures that no torque is delivered to the motor when an emergency stop is triggered without fully cutting power to the drive. Some applications may require additional safety features, including safe speed monitoring (SSM) or safe limited speed (SLS) control engineering — www.controleng.com
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to ensure the motor remains below defined thresholds during certain operating modes including those where personnel are nearby.
Exploring control mode The control mode refers to the method a drive uses to regulate the motor’s speed, torque and position. The selected control mode influences drive performance and must be matched to the demands of the application. The primary ac control modes include V/Hz, vector and servo control. • V/Hz control: V/Hz control is the simplest control method for ac induction motors. It is generally provided by a standard VFD which adjusts motor speed by maintaining a fixed voltage to frequency ratio. The drive regulates motor speed by maintaining a fixed ratio between voltage and frequency. For example, if the motor is rated for 60Hz and 480V at full speed, then to operate at half speed, the drive applies 30Hz and 240V to the motor. This control method is commonly used for variable torque applications such as pumps and fans, as well as some constant torque systems where precise speed regulation is not critical. It does not perform well in applications requiring fast response, precise speed control, or stable low-speed operation. • Vector control: This type of control is available on higher level VFDs for controlling ac induction motors in applications where torque must change dynamically or precise speed control is required. Vector control works by mathematically breaking the motor’s current into its two orthogonal (vector) components: the one that creates the stator’s magnetic field, and the other that produces the torque in the rotor. By independently controlling each current vector, the drive can be more responsive to variations in load or speed and performs better at low operating speeds. • Sensorless vector control: Open-loop control relies entirely on the calculated estimates of rotor position, speed, and torque. This method has better dynamic performance than V/Hz control, however its accuracy may degrade at low operating speeds or those involving very dynamic load changes. An example of an application that may be suitable for this type of control is a conveyor system requiring moderate speed changes. • Closed-loop vector control: Closed-loop control uses an encoder to directly measure the rotor’s control engineering — www.controleng.com
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position, significantly improving the precision of the model’s calculations of speed and torque. Closed-loop vector control is excellent for applications requiring high inertia handling, precise start and stop, direction reversal or stable low-speed operation. A suitable application may be precision rollers with reverse motion and positioning requirements for material handling. Additionally, this type of control is well suited for applications that involve a mechanical clutch. Because the drive receives real-time feedback on the motor's position and speed, it can maintain accurate low speed steady torque during clutch engagement or disengagement. This allows the control system to more precisely coordinate the timing of clutch operation, reducing mechanical shock and wear. Servo control: Closed-loop control. Used with motors most suited to high performance including dc brushless motors and ac permanent magnet motors. Servo control uses feedback from high-resolution encoders with far more pulses per revolution than their regular counterparts. This
FIGURE: A phasing drive for a face mask machine. It allows independent timing on the visor and ear loops attachments. Courtesy: Hargrove Controls & Automation
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Closed-loop control uses an encoder to directly measure the rotor’s position,
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significantly improving precision. high-resolution position allows feedback control loops to operate at much higher frequencies and therefore provide very responsive speed and torque corrections compared to closed-loop vector control. Servo systems are costlier and the most complex of all solutions; however, they provide the accuracy and responsiveness needed for high-demand applications such as robotics, computer numerical control (CNC) machinery and highspeed packaging. ce Rich Houtz, PE, is a controls and automation engineer with Hargrove Controls & Automation. Edited by Sheri Kasprzak, managing editor of automation and control brands for WTWH Media.
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Innovations New ultrasonic sensor supports use in hazardous locations For distance measurement and object detection in hazardous or explosive environments, Migatron Corp. has introduced a new sensor, the Migatron RPS-429AA-40P-IS2. It meets intrinsic safety standards, making it suitable for use by engineers and professionals operating in such conditions. This sensor meets ATEX, IECEx and C-UL-US intrinsic safety standards and supports distance and level measurements in industrial environments. It is enclosed in an IP66/IP67 rated housing and uses a 4-20mA two-wire current loop design. The sensors are designed for accurate distance measurement in industrial settings. The sensor is certified for intrinsic safety and is approved for use in hazardous locations including Zone 0–2 and 20–22 and Class I, II and III environments. Migraton, www.migatron.com
Environmental impact of ring motor documented
Simplified Modbus monitoring: data collection, analysis and alerts
ABB obtained Environmental Product Declaration (EPD) status for the ABB Gearless Mill Drive (GMD) ring motor, a widely used technology for powering large grinding mills in the mining industry. The EPD provides standardized information on its environmental impact across its life cycle. The International EPD System (IES) is a global program for EPD, a Type III environmental declaration following the principles of ISO 14025. The 24 MW ring motor is a gearless drive in which torque is transferred from the motor to the mill using the magnetic field in the air gap between the motor stator and rotor.
Altech Corp. released the Altech DO-1 universal monitor for Modbus devices so users can monitor, collect and analyze equipment and process data without subscription fees or licenses. The DO-1 is a compact device that connects to up to 128 Modbus RTU/TCP devices. It supports integration with new and legacy Modbus networks, allowing for data monitoring without extra infrastructure often associated with more complex Industrial internet of things (IIoT) systems. Altech, www.altechcorp.com
ABB, https://global.abb/group/en
Lean managed Ethernet switches, economical design AutomationDirect offers Wago lean managed industrial Ethernet switches that provide a cost-effective managed network solution. These switches integrate principles of lean manufacturing into their design and functionality, offering advantages such as waste reduction, improved efficiency and other features. A web-based interface and easy-to-use diagnostic tools reduce configuration, monitoring and troubleshooting time. Diverse communication protocol choices eliminate the need for specialized switches, useful for IIoT applications. VLAN support allows logical network segmentation, which improves traffic management and performance with efficient bandwidth use. AutomationDirect, www.automationdirect.com/lean-managed-switches
Industrial PC integrates GPU for AI applications The C6043 ultra-compact C60xx industrial PC (IPC) from Beckhoff has an NVIDIA graphics processing unit (GPU) integrated by Beckhoff. This IPC provides the ideal hardware foundation for high-intensity computing applications, particularly in artificial intelligence (AI) applications. The C60xx series has modern Intel Core processor options, measures 132 x 202 x 127 mm and can be equipped with long-term available NVIDIA GPUs for parallel processing via a factory assignable slot. This IPC can serve as a central control unit for highly demanding applications, including with high demands on 3D graphics or with deeply integrated vision and AI programs with minimal cycle times. TwinCAT 3 automation software from Beckhoff can map these functions alongside the main control program, without added software or interfaces. Beckhoff Automation, www.beckhoff.com/C6043
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control engineering — www.controleng.com
7/28/25 10:45 AM
See more New Products for Engineers www.controleng.com/products
Hybrid scanner combines barcode and RFID in one device Datalogic PowerScan 9600 RFID Series is a handheld unit is designed to combine high-performance barcode scanning with embedded UHF RFID tag reading in a durable device. Applications include retail, transportation and logistics (T&L), manufacturing and health care. It combines standard 1D/2D barcode reading with UHF RFID functionality to help improve efficiency and data visibility. It reduces the need to manage multiple devices. It allows workers to capture barcode data and RFID tags with a trigger pull. This dual-technology approach streamlines workflows. Datalogic, www.datalogic.com
Controller-drive provides sensorless, closed-loop control Nanotec introduces the N6 controller/drive for stepper motors (NEMA 14 to 34) and brushless DC (BLDC) motors up to NEMA 23. With field-oriented control (FOC), the N6 ensures smooth operation and high energy efficiency. Sensorless closed-loop control provides precise management of torque and speed. The controller/drive supports Hall sensors, incremental encoders (QEI) and serial synchronous interface (SSI) encoders. An external braking resistor can also be connected. Rated at 6 A continuous and up to 18 A peak, the N6 is ideal for applications up to 300 W. It features six digital and two analog inputs, three feedback channels and a brake output, making it easy to integrate into complex systems. Nanotec Electronic, www.us.nanotec.com
New box thin clients designed for control room operations The Pepperl+Fuchs BTC22 and BTC24 box thin clients provide continuous operation in control rooms and laboratories. These compact devices are built for reliable, long-term use in industrial environments. The BTC24 supports up to four 4K displays, while the BTC22 can connect to two 4K or three full high-definition displays via USB-C ALT mode. Both models come with 8GB DDR4 RAM —with the BTC24 configured with two 4GB dual-channel modules—and include a third LAN port. This additional port enables separate network connections, such as to an external network along a redundantly connected DCS or MES system. These specifications make the devices suitable for use in control room environments requiring multi-display support and flexible network integration. The devices are designed for long-term availability of at least five years, helping organizations plan for consistent hardware support. They have no moving parts. Pepperl+Fuchs, www.pepperl-fuchs.com/en-in
control engineering — www.controleng.com
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NEW PRODUCTS FOR ENGINEERS
OT/IT software integration for petrochemical applications L&T Technology Services (LTTS) offers RefineryNext, an integration of information technology and operational technology (IT/OT), digital governance frameworks and artificial intelligence (AI) tools for predictive maintenance, intelligent asset solutions and demand forecasting. It includes digital twins and carbon capture capabilities for sustainability practices such as energy optimization and net-zero carbon compliance. RefineryGovern enhances cybersecurity to safeguard operations. RefineryConnect provides real-time insights with legacy equipment. Next-generation wireless networking can integrate private 5G wireless and 4G LTE. Other modules include RefineryBuild, RefineryManage, RefineryInsight and RefinerySecure. L&T Technology Services, www.ltts.com
Improved energy efficiency inverters enhance setup, use Mitsubishi Electric FR-D800 series inverters are designed to deliver better performance, easy operation and improved energy efficiency for a wide range of industrial applications. The compact and intuitive inverters have features designed to make selection, installation and operation simpler. With a focus on user-friendliness, the inverters feature a door-style surface cover and integrated wiring to make installation faster and easier. They are up to 37% smaller than the equivalent predecessor, reducing enclosure size requirements, allowing for more flexible mounting and lower installation costs. A USB Type-C interface lets users set parameters from a PC without powering up the inverter, streamlining setup and maintenance. Mitsubishi Electric, https://us.mitsubishielectric.com/fa/en
July/August 2025
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Back to Basics ZERO TRUST IN OT
Why traditional approaches fail; what manufacturing leaders must do instead How manufacturers can rethink and reshape cybersecurity strategies to protect production operations—without compromising uptime or disrupting the plant floor.
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pplying zero-trust architectures and frameworks to operational technology (OT) environments creates significant challenges for manufacturing and critical infrastructure. Forcing IT-developed zero-trust frameworks into industrial environments often leads to operational disruptions and security failures. Manufacturing operations are focused on production. When implementation causes downtime, OT teams inevitably find workarounds that create even greater vulnerabilities.
The zero-trust collision in manufacturing The zero-trust principle of “trust, but verify" fundamentally conflicts with OT environments that prioritize availability and production continuity above everything else. When zero-trust implementation disrupts remote access during equipment failures, manufacturers can face hundreds of thousands, if not millions, of dollars a day in downtime costs. This is one reason IT-OT convergence often becomes an IT-OT collision. Why traditional zero-trust approaches fail in OT Top-down vs. bottom-up implementation IT environments are well defined with consistent applications and clear traffic segmentation. OT systems are a "melting pot" of diverse technologies, protocols and legacy equipment. Zero trust in industrial environments requires a bottom-up approach focused on understanding what's actually in each "bucket" of systems. "Super flat" network realities Many manufacturing facilities operate with "super flat" networks, where critical production equipment shares network space with business systems. Implementing zero-trust segmentation in these environments is technically possible but dangerously impractical without substantial preparation. Identity management conflicts Zero trust demands robust identity verification, but industrial systems often use shared credentials or lack
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authentication capabilities entirely. Plant floor operators wanting to avoid authentication delays will find ways around these controls.
Five recommendations for zero trust in manufacturing 1. Implement an asset-first approach Before pursuing zero-trust initiatives, deploy OT-specific tools that provide continuous visibility into the 20/25-to-1 ratio of OT assets compared to IT assets on your plant floor. 2. Adopt the "bucket approach" to segmentation Instead of comprehensive segmentation, identify targeted "buckets" of critical systems. Move assets incrementally from unsecured buckets to secured ones without disrupting operations. 3. Establish OT-specific change management Standard IT change processes that require a week to get somebody onboarded for remote access are incompatible with production needs. Create OT-specific change processes that maintain security while accommodating manufacturing's rapid response requirements. 4. Build field experience among security teams If you're an IT security professional and either don’t know your OT counterparts or don’t visit the manufacturing plants on a regular basis, you’re at a disadvantage. It will be difficult to develop an all-encompassing, detailed cybersecurity scope for your manufacturing environments without IT-OT collaboration. 5. Create cross-functional funding models Zero-trust initiatives must be funded from both IT and OT budgets, with input from plant managers, engineering teams and operations management executives. Without this shared investment, progress and commitment toward securing the industrial plant environment will be slow going. ce Dino Busalachi, director, BW Design Group; edited by Gary Cohen, senior editor, Control Engineering, WTWH Media, gcohen@wtwhmedia.com. control engineering — www.controleng.com
7/28/25 10:46 AM
Technical resources you need from an automation vendor you can depend on The Beckhoff team works hard to design and deliver the most advanced automation and controls technologies available. Of course, that is only half the battle as offering best-inclass education and training is also crucially important. A wide range of online resources are available 24/7/365 from Beckhoff for engineers:
Free E-Learning Portal Beckhoff USA offers an extensive e-learning portal with a range of presentations on topics related to industrial automation – and you’re invited to join! These useful educational resources are free and open to the readers of Control Engineering magazine. Multiple classes are available, including topics from all Beckhoff product families: automation software, industrial PCs, I/O, drive technology and advanced mechatronics.
Visit www.blog.beckhoffus.com/events/webinar-series to learn more. Don’t forget to visit the on-demand webinar archive to view the complete history of Beckhoff webinars anytime.
Each presentation is followed by a quiz to reinforce the topics covered and gauge what you’ve learned. No Beckhoff hardware is required to participate, but there are modules that permit students to use their own Beckhoff equipment during the training.
FREE TwinCAT Engineering Environment
Register for an account at learn.beckhoffus.com and start learning today!
Visit www.beckhoff.com/twincat3 to quickly download and install TwinCAT 3 on your programming and development PC today!
Programmers and engineers can download the base engineering module of TwinCAT 3, the leading PCbased automation software at no charge from Beckhoff (TE1000).
Webinar Wednesdays Pressed for time? Beckhoff offers many webinars throughout the year on a wide range of interesting topics, particularly automation and controls programming, industrial Ethernet applications, tips for designing world-class motion control architectures and much more.
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www.beckhoff.com
7/24/25 1:55 PM
The Future of Edge Computing – Built by OnLogic The relentless drive to optimize processes, ensure system reliability, and push the boundaries of automation – this is the world of control engineering. You’re at the forefront of building and maintaining the critical infrastructure that powers industries.
These aren’t just box PCs, edge servers or HMIs; they’re the tools that empower you to bring your boldest ideas to life. They’re the dependable infrastructure that transforms concepts into reality, making the seemingly impossible, possible. And as you continue to innovate, so do we. Get a glimpse of what’s next with our upcoming scalable HX520 Series of industrial computers, designed to redefine reliable performance.
At OnLogic, we understand the demands of this vital work, and we engineer our industrial and rugged computing solutions to be the dependable foundation upon which your innovations are built. Picture OnLogic fanless, passively cooled systems operating reliably in harsh industrial settings, keeping out the dust and debris that can choke traditional computing hardware.
Ready to power your innovation at the edge? Explore our diverse portfolio, discover solutions tailored for your industry, and see how innovators like you are making the future of automation possible using OnLogic hardware by visiting our website. Reach out to our team for help choosing and configuring the ideal hardware platform. Envision the flexibility of diverse mounting options and wide operating temperatures, allowing deployment exactly where it’s needed within your control systems and specialized machinery. Leverage our scalable OnLogic server platforms to deliver the processing power essential for complex control algorithms, real-time data acquisition, and advanced AI training and inference, all while our industrial panel PCs provide durable and responsive interfaces for critical monitoring and interaction.
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802-861-2300 | info@onlogic.com www.onlogic.com
7/24/25 1:56 PM
Secure Remote Access to Production Data
Network Security VPN - shared key
Industry 4.0 and AI applications need production data. How can you provide that data to IT and still keep your OT networks and systems secure? The best approach is to share the data, but not the network. Securing the network is like locking doors of a house to prevent unauthorized access, while securing data means protecting the information inside the house. Implementing zerotrust access and VPNs will not fully protect you. Malicious code can still spread from IT to OT networks through compromised devices or phishing attacks.
Data Security Invisible mail slot
Data connections within the network can and must be secure. This can be done by configuring outbound-only connections through firewall ports, like an invisible mail slot in a door, where data is exchanged securely with zero attack surface. Technologies like MQTT brokers, Skkynet’s Cogent DataHub, or data diodes can facilitate this process by establishing secure, outbound connections to support one-way or bidirectional data flow, as needed. To fully enable this approach, the best practice is to implement a DMZ (Demilitarized Zone) between IT and OT networks. This is recommended by the EU’s NIS 2 Directive and NIST SP 800-82, because a DMZ segregates OT and IT networks, keeping both secure. Using these kinds of data security measures alongside
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Watch the video network security ensures comprehensive protection. Skkynet offers a range of solutions to integrate these security layers effectively, ensuring robust protection for OT systems.
www.skkynet.com
7/24/25 1:57 PM
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Automation Direct. . . . . . . . . . . . . . . . . . C2, 1 . . . . . www.automationdirect.com Beckhoff . . . . . . . . . . . . . . . . . . . . . . . . . . 6, 45 . . . . . www.beckhoff.com Bristol Instruments . . . . . . . . . . . . . . . . . . . 9 . . . . . . . www.bristolinstruments.com Digi-Key Electronics. . . . . . . . . . . . . . . . . . . 4 . . . . . . . www.digikey.com/automation Dura-Belt . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 . . . . . . www.durabelt.com Honeywell. . . . . . . . . . . . . . . . . . . . . . . . . . . 2 . . . . . . . www.honeywell.com Machines Italia . . . . . . . . . . . . . . . . . . . . . . 25 . . . . . . www.machinesitalia.org Migatron . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 . . . . . . www.migatron.com Moore Industries . . . . . . . . . . . . . . . . . . . . 33 . . . . . . www.miinet.com Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 . . . . . . www.motion.com On-Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 . . . . . . www.onlogic.com SEW Eurodrive . . . . . . . . . . . . . . . . . . . . . . C4 . . . . . . www.seweurodrive.com Skkynet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 . . . . . . www.skkynet.com SMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 . . . . . . www.smcusa.com SPS—Atlanta . . . . . . . . . . . . . . . . . . . . . . . 37 . . . . . . www.spsamericas.com Trihedral . . . . . . . . . . . . . . . . . . . . . . . . . . . C1 . . . . . . www.vtscada.com/redundancy
Sales Account Manager Richard Groth Sales Account Manager Robert Levinger Sales Account Manager Judy Pinsel
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Publication Services Patrick Lynch, Senior Vice President, Sales & Strategy 847-452-1191, PLynch@WTWHMedia.com McKenzie Burns, Marketing Manager MBurns@WTWHmedia.com Courtney New, Program Manager, Content Studio CNew@WTWHMedia.com Paul Brouch, Operations Manager 708-743-5278, PBrouch@WTWHMedia.com Rick Ellis, Director, Audience Growth 303-246-1250, REllis@WTWHMedia.com
Yaskawa . . . . . . . . . . . . . . . . . . . . . . . . . . . C3 . . . . . . www.yaskawa.com
MEDIA SHOWCASE FOR ENGINEERS
Custom reprints, print/electronic: Matt Claney, 216-860-5253, MClaney@WTWHMedia.com Information: For a Media Kit or Editorial Calendar, go to https://www.controleng.com/advertise-with-us. Letters to the editor : Please e-mail us your opinions to MHoske@WTWHMedia.com. Letters should include name, company, and address, and may be edited.
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WHAT COULD BE SIMPLER?
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EMC Filters
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SIMPLE Less Space Faster Setup Higher Efficiency
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U1000
3 Wires Out
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Innovative Matrix Technology Low Harmonic Input Current Facilitates IEEE 519 Compliance Near Unity Power Factor Energy Saving Power Regeneration Compact All-In-One Solution Fewer Parts = Higher Machine Reliability Cost Savings Through Increased Efficiency Time Saving Installation and Startup
Contact Yaskawa for help today.
Yaskawa America, Inc. 1-800-YASKAWA Email: info@yaskawa.com | yaskawa.com
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What’s the cost of a superior system? A lot less than an inferior one.
At SEW-EURODRIVE, we engineer the highest quality drive automation solutions. What truly sets us apart is our unwavering commitment to customer support, long after the sale. We go above and beyond to ensure your business stays on the move with exceptional service at every turn. Upgrade everything.
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