Control Engineering May June 2025

Career, salary survey: Economy, workforce, tariffs | 36, 40, 42

Machine vision | 27

Safety, cybersecurity | 30, 34

Digital twins | 44

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7 | May and June online article sampling of www.controleng.com, with links

8 | Automate 2025: Robots accelerate; new industrial AI tools. System integrators provide automation advice. Benefits, challenges of implementing automation. 10x proven benefits from software. 5 ways cobots, AMRs top humanoid robots. Redefining control with software-defined automation. Emerson Exchange 2025: Modernize, easily integrate industrial automation. And more.

15 | Think Again: Non-AI advice to stay ahead of AI for industrial automation

16 | PID spotlight, part 17: Heuristic tuning of integrating processes

20 | APC 2.0: Base-layer advanced process control

23 | Ways IIoT, AI empower smart manufacturing

27 | Machine vision: New 3D ultrasonic sensor reduces costs, improves mobile robot safety

30 | How IoT, edge computing and digital twins empower process safety

34 | Evolution of automation: AI, cybersecurity, industry-specific needs

36 | Results are in: Control Engineering Career and Salary Survey, 2025Benefits and salaries increased. Artificial intelligence and machine learning top the list of leading automation technologies.

40 | Use 2025 salary survey advice to help your automation career

42 | Ways manufacturers can recruit, train and retain a skilled workforce

44 | How to leverage digital twins for asset lifecycle optimization

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INNOVATIONS

53 | New Products for Engineers, www.controleng.com/products

Control Engineering Product of the Year-Link to the 2025 winners. I/O modules for PLC family; IP69 ultra-premium efficient motor; High power lithium cells for automation; Ultrasonic sensors with IO-Link; Drive system optimization; 64-bit Arm computers with quad-core processor; DCS upgrade; RFID read/write head; High capacity pump station; Control system software for machine control, simulation.

55 | Back to Basics: Single-Pair Ethernet breaks down the limits of connectivity

SPE offers Ethernet connectivity in one set of twisted-pair copper wires, with power. At 10 Mbit/s, SPE run up to 1,000 meters.

NEWSLETTERS ONLINE

Process Instrumentation & Sensors, May 6

• Understanding EPA requirements for CEMS design; Advanced contextualization and visualization adds value to data; Generative AI tool recognized for innovation in industrial operations; and more. System Integration, April 22

• Ask 11 questions to simplify system integration; Six strategic steps to make systems integration seamless; The dark side of the IT/OT integration equation; and more

Go to www.controleng.com/subscribe and select newsletters.

u Global System Integrator Report

We’re preparing for the next edition. Submit articles about system integration; 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

MAY/JUNE

Control Engineering eBook series, now available: Summer Edition

u IIoT Cloud

Featured articles include how new generative AI software helps asset lifecycle intelligence, how to use digital transformation in SCADA master planning, new automation technologies and industrial networking.

Learn more at: www.controleng.com/ebooks

u Digital Transformation

Featured articles include building on industry 4.0, balancing high-output manufacturing and low energy, five technologies for a future-proof edge control platform and flexible stack deployment.

More topics at: www.controleng.com/ebooks

u Control 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

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Online Highlights

INSIGHTS

u Latest automation mergers, March, April 2025: System integrators, SCADA

The Bundy Group reported 15 automation transactions in its March and April 2025 summary reports.

VIDEO

u How Generative AI is Transforming Industry: Insights from Seeq’s Mark Derbecker www.controleng.com/video/how-generative-ai-is-transforming-industry-insights-from-seeqs-mark-derbecker

u SCADA, HMI: How to modernize, upgrade for application benefits www.controleng.com/scada-hmi-how-to-modernize-upgrade-for-application-benefits

WEBCAST ANSWERS

u PID spotlight, part 16: PID spotlight, part 16: Closed loop tuning of an integrating process (A) www.controleng.com/pid-spotlight-part-16-closed-loop-tuning-of-an-integrating-process

u The ultimate guide to configuring multidimensional arrays in SCADA systems (B) www.controleng.com/the-ultimate-guide-to-configuring-multidimensional-arrays-in-scada-systems

u Machines-as-a-service in the time of tariffs? www.controleng.com/machines-as-a-service-in-the-time-of-tariffs

u How to choose or troubleshoot a variable frequency drive (C) www.controleng.com/how-to-choose-or-troubleshoot-a-variable-frequency-drive

u IIoT and OT security: Bridging the gap in industrial environments (D) www.controleng.com/iiot-and-ot-security-bridging-the-gap-in-industrial-environments

u New ways advanced automation is helping steel-industry competitiveness (E) www.controleng.com/new-ways-advanced-automation-is-helping-steel-industry-competitiveness

u How AI-powered video analytics may unify computer vision systems www.controleng.com/how-ai-powered-video-analytics-may-unify-computer-vision-systems

u To drive improved digital transformation, don’t forget about the process www.controleng.com/to-drive-improved-digital-transformation-dont-forget-about-the-process

u Research from Plant Engineering, another WTWH Media publication: How tariffs are disrupting manufacturing, and how to react www.plantengineering.com/how-tariffs-are-disrupting-manufacturing-and-how-to-react

INSIGHTS

Robots accelerate; new industrial AI tools help

uIndustrial software and artificial intelligence are helping robotic applications bring significant savings to industrial manufacturing design and operations, such as 80% reduction in factory planning time and 40% improvement in robot cycle times, according to Deepu Talla, vice president of robotics and edge AI, Nvidia. Talla discussed “Industrial autonomy in the era of physical AI” in a May 13 keynote presentation at Automate 2025, by the Association for Advancing Automation (A3). The show and conference were in Detroit, May 12-16, with more than 875 exhibitors, more than 40,000 registrants, and more than 140 conference sessions on robotics, machine vision, artificial intelligence and other industrial automation topics. Physical artificial intelligence (AI) models that can understand and interact with the physical world will transform today’s rule-based automation, Talla suggested. Nvidia’s ecosystem of industrial software, hardware, AI and robotics partners are helping accelerate AI for industrial transformation. Savings include:

• A Foxconn plant optimized the facility layout and use of robots, saving 50% in planning time and saw 150x faster robot simulation with the Nvidia Omniverse digital twin.

• Mercedes-Benz reduced factory planning time by 80%.

• Schaeffler improved robot cycle times 40% and decreased costs of digital facility planning about 65%.

• Pegatron optimized standard operating procedures on assembly lines to reduce defects 67% and decrease labor costs 7%.

More from Automate 2025

System integrators provide automation advice: A panel of system integrators provided advice about automation implementation and working with system integration firms.

Benefits, challenges of implementing automation: Understand where and why to apply automation as noted by Joseph Gemma, CEO, Wauseon Machine and Manufacturing.

Deepu Talla, vice president of robotics and edge AI, Nvidia, discussed “Industrial autonomy in the era of physical AI” in a keynote presentation at Automate 2025. Courtesy: Mark T. Hoske, Control Engineering, WTWH Media

10x proven benefits from software: Open automation ecosystems help resolve workforce challenges and provides metrics such as a 50% downtime reduction and a 20% increase in overall equipment effectiveness (OEE) over two years, according to Aamir Paul, president, North America Operations, Schneider Electric.

5 ways cobots, AMRs top humanoid robots: Collaborative robots and automated mobile robots (AMRs) are more efficient and cost effective for manufacturing and logistics than humanoid robots, which have large opportunities in other applications. See five ways automation helps industry now. ce

Mark T. Hoske, Control Engineering editor-in-chief, mhoske@wtwhmedia.com. At www.controleng.com, search “Automate 2025” for more from the show.

Redefining control with software-defined automation

AT AUTOMATE 2025, Schneider Electric introduced a prototype of its industrial copilot. Developed in collaboration with Microsoft, this copilot integrates Microsoft Azure AI Foundry — a platform for developers to build GenAI applications — with Schneider Electric’s secure industrial automation systems. Using the copilot as a PLC code generator, an engineer described what is to be controlled, such as “generate the code for a pumping application,” and copilot will automatically generate the code. It includes flow diagrams to explain how the code is put together, including details of every line to help engineers learn how to code. For those engineers writing code, it can test and validate it for them. After coding, “you have to make sure you have the right devices selected, the right memory, and the right communication capability. Once you select them, you can make sure your system

is working,” said Alan Grightmire, Schneider Electric’s digital factory manager, during a demonstration. Time savings for programming using copilot is up to 50% and about 30% for commissioning.

SOFTWARE-DEFINED AUTOMATION is freeing equipment from the closed, proprietary systems of the past. Industrial hardware and software can work together even from different suppliers. EcoStruxure Automation Expert integrates across various hardware and software platforms, enhancing collaboration among copilots, operators and engineers by incorporating real-time data access to provide accurate recommendations, predictive maintenance and immediate troubleshooting. Read more online.

Stephanie Neil is editorial director, engineering, automation and control, WTWH Media

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Modernize, easily integrate industrial automation

uAt Emerson Exchange 2025 the company discussed how it is developing a new software-defined enterprise operations platform, known as Project Beyond, aimed at redefining current methods used in industrial automation.

Emerson’s Project Beyond is a software-defined, OT- compatible digital platform designed to support the integration and coordination of industrial operations. It incorporates technologies such as software-defined control, data management, zero-trust cybersecurity and artificial intelligence to improve automation outcomes while aiming to reduce operational complexity. The ongoing development of industrial artificial intelligence (AI), along with rising computing demands and the

In a image projected above the stage, Lal Karsanbhai, Emerson president and CEO, explained Emerson's Project Beyond during the Emerson Exchange 2025 opening keynote. Courtesy: Mark T. Hoske, Control Engineering, WTWH Media

growing volume and complexity of siloed data from industrial assets, highlights the need for a cost-effective approach to implementing automation across operations without requiring major changes to existing systems.

Project Beyond includes: Modern computing environments: Project Beyond will combine on-premise edge computing with cloud-based resources to support advanced data analytics.

See how simple the SLA solution really is!

Networking and connectivity: The platform includes secure connectors, APIs and software interfaces that enable integration with existing OT systems, devices and legacy I/O, and connectivity with IT environments.

DataOps: A data architecture that connects OT assets, to transform contextual data and documented knowledge into actionable operational insights.

App marketplace: A collection of applications for simplified access and deployment.

AI orchestration: A management system designed to support the deployment and ongoing operation of AI tools alongside human workflows.

Zero-trust security architecture: A security layer that manages access and protects device, application, connection and data within the platform.

More news ONLINE

READ more on each of the following stories at www.controleng.com.

• Enhanced device profiles, security and energy management – ODVA updates EtherNet/ IP, CIP security and energy management.

• From test to tech, computational design platform creates robot design – Researchers at Duke University developed Text2Robot, a platform that uses AI to design function robots from simple commands.

• $6 million seed extension to build synthetic brains for industrial robotics – Xaba will use new capital from Hitachi Ventures to accelerate its “Open AI for Industrial Automation” platform.

• Could this be a solution for engineering's labor shortage – Hargrove Controls & Automation’s co-op program connects students with seasoned engineers to help resolve the engineering labor shortage.

• Study uncovers key challenges and opportunities for US manufacturers - Deloitte’s study covers automation, workforce gaps, cybersecurity risks in smart manufacturing.

• Pittsburgh’s switchgear facility to expand energy manufacturing sector – Mitsubishi Electric Power Products adds at least 200 jobs in an $86 million expansion.

• University teams recognized for robotics design and functionality – Northeastern University team wins MassRobotics Form & Function Robotics Challenge.

• Tackling tariffs with AI-enabled supply chain technology – Fictiv adds automations to tariff documentation and a team of experts to help manufacturers avoid expensive delays.

• New specification supports higher speeds for modular systems – CompactPCI Serial specification, release 3, increases available bandwidth and data transfer rates.

• Outlook for U.S. industrial automation remains uncertain amid new tariffs – February 2024 to January 2025 machinery production improved; momentum may have stalled with tariff uncertainties, said Interact Analysis.

• ABB plans to spin off its robotics division – Second-quarter 2026 independence is planned. (See photo.)

• The Robotics Summit & Expo 2025- Innovations in sensors, code architecture and more.

• Robotics Summit & Expo 2025- Reliving all the action in photos.

• Research whitepaper- digital twin challenges in aerospace and defense – Digital Twin Consortium provides guidance across the product lifecycle.

• ODVA: Leadership elected for 24th term, new initiative announced.

• Global fab equipment investment to hit

$110B in 2025 – SEMI projects a 2% year-over-year increase in global front-end semiconductor fabrication equipment.

• Robotics video-Why new controllers fit all robot sizes, types: Peter Fixell, ABB global robot portfolio global manager, discusses advantages of flexible robot control.

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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

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Amanda Pelliccione, marketing research manager 978-302-3463, APelliccione@WTWHMedia.com

Anna Steingruber, associate editor ASteingruber@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

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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.)

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Non-AI advice to stay ahead of AI for industrial automation

ARC Industry Forum 2025’s theme was “Winning in the industrial AI era.” Think again: Are you smarter than AI? Verify AI answers to be sure.

I’ve heard engineers talk about industrial artificial intelligence (AI) as if it were a younger coworker who can help a lot ... and still needs careful oversight. AI does what control engineers have done since the inception of control engineering profession, creates models that can help guide outcomes. Major automation companies offer AI tools with to help guide operators and engineers shorten time to the finish line for many tasks. Don’t dismiss industrial AI as hype, though there’s some of that, too. Like any other automation tool, it’s important that engineers and others using AI software take time to verify truth. Do not, in haste, think, “Sound feasible” or “I guess” and plow ahead.

to help lower costs, ease networking and system integration efforts and address the skills gap (demographic loss of talent through retirements, with too few young replacements).

Greg Gorbach, ARC Advisory Group, noted that ARC’s research said industrial AI is technology making the most impact in OT, for the third year. Aveva, part of Schneider Electric, is attaching operations control to its Connect industrial intelligence platform, to drive use cases, including an Industrial AI assistant.

Michael Hotaling, ExxonMobil, innovation and strategy digital manager and leader of a digital twin working group suggested that a software-usefulness test is to ask, “Is your cognitive load more or less now?”

The 2025 salary and career research (page 36) asked subscribers, "What technologies will be helping in the coming year?" Among a plentiful list of automation technologies expected to help, artificial intelligence and machine learning (AI/ML) moved solidly into first place in 2025 at 36%, up 8 percentage points from third in 2024 (28%), and eighth in 2023.

Staying ahead of AI progress

Every recent show I have attended discusses AI applications. Control Engineering frequently adds more industrial AI/ML progress at www.controleng.com/ai-and-machine-learning. A few observations on AI from the 2025 ARC Industry Forum follow:

Industrial AI is picking up pace in acceptance and product implementations for the industrial space. Greater use of autonomous operations, cybersecurity, Open Process Automation, digital twins and other open standards efforts and implementations continue

Dr. Mathias Oppelt, head of customer-drive innovation at Siemens Digital Industries, said Siemens is helping customers use IT/OT convergence to transition from general-purpose to industrial-grade AI.

As with other technology innovations, AI gets us to think again about our technology future. I recently noticed how polite a daughter was with a generative AI software application.

"Well, you know," she said. "Just in case." ce

Mark T. Hoske is editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

Mark T. Hoske Control Engineering

ANSWERS

Ed Bullerdiek, process control engineer, retired

PID spotlight, part 17: Heuristic tuning of integrating processes

You

are asked to tune an integrating process that is swinging nearly continuously. You’ve done all the checks to prove that it really is a tuning problem. Open loop testing is out of the question; how do you fix it? Using heuristics can help.

HKEYWORDS: Proportionalintegral-derivative, PID tutorial

LEARNING OBJECTIVES

Understand the relationship between controller performance goals and controller tuning constants and review the visual cues used to identify why a controller is misbehaving. Know the induced disturbance process testing method and the rules to correct PID controller misbehavior (heuristics).

Understand that heuristic tuning may require multiple steps and the limitations of heuristic tuning.

CONSIDER THIS

Open-loop, closed-loop and heuristic tuning methods are complimentary. How will having a working knowledge of the concepts behind all three methods speed and improve your tuning efforts?

euristic (guided trial and error) tuning of integrating processes is complicated. Not because it is difficult, but because we are tuning for a primary purpose and also have to work within secondary constraints. This means that there are not necessarily hard guidelines for what good looks like, and that two identical processes in different services may require very different tuning based on their role in the overall process. Figure 1 is a tuning map for an integrating process with a process gain (Kp) of 0.2%/minute, three lags (T1, T2, T3) of 30 seconds each, and no deadtime (Dt). This will help explain the problem we can have when tuning the most common integrating process, a level.

For this process Figure 1 shows us the window within which we can set the controller gain and integral for this integrating process.

• To the left and above the black line titled “stability limit” this process is unstable.

• The blue line titled “min contain gain” is the minimum combination of controller gain and integral that will contain a 50% change in input (or output) flow when the vessel is 50% full. This is an arbitrary convention; if you have specific process knowledge you can set your own minimum.

• The green line titled “min gain w/ integral” is the minimum controller gain that will suppress integral driven oscillations. In PID spotlight, part 11, we learned that controller gain is required to prevent uncontrolled process swings driven by controller integral action; the faster the integral the larger the controller gain required to prevent instability. In this particular process once the integral gets below 6.0 minutes/repeat oscillations begin regardless of the controller gain setting. At this point oscillations are going to occur and get worse as integral is made faster. The window above the blue and green lines and below and to the right of the black line is the limit of possible tuning for this controller.

Finally, the black dashed line titled “100% gain margin” is one-half the maximum stable controller gain (the black line). Above this line there will be controller gain driven oscillations, therefore normally controller gains should not much exceed this line.

For levels there are three possible performance goals. In order of frequency these are surge control, disturbance rejection and setpoint tracking tuning. We can see that each exists in its own area of the tuning window, and that the window of acceptable tuning for surge control and setpoint tracking tuning can be very wide. Tuning for each performance goal can be summarized as:

• Surge control: Minimum controller gain and slowest integral consistent with keeping the process variable within acceptable limits.

• Disturbance rejection: Maximum controller gain and fastest integral with minimum oscillation.

• Setpoint tracking: Maximum controller gain without oscillation and slowest integral with acceptable recovery from disturbances.

I will add a final caveat: If you are trying to bal-

ance multiple goals the best tuning constants may be somewhere between all three zones.

Heuristic tuning: Too much or ... ?

To recap PID spotlight, part 9, heuristic tuning is nothing more than pattern recognition to answer these questions:

• Is there too much controller gain or too little?

• Is the integral too fast or too slow?

• Is there too much derivative?

As discussed in PID spotlight, part 12, the visual cues used to answer these questions are:

• If a controller has too much controller gain, integral or derivative it swings.

–If it has too much controller gain the process variable (PV) and controller output (OP) peaks line up (or are very close).

–If the integral is too fast the OP peaks trail the PV peaks.

–If it has too much derivative the OP peaks lead the PV peaks, and the amplitude of the OP swings are usually larger than the PV swings.

• Too little controller gain will look like the integral is set too fast; the process will oscillate and the OP peak will trail the PV peak. Slowing down integral will not fix the tuning problem. It will likely make control worse. As a general rule if the controller gain (K) is less than 1.0 raise it to at least 1.0.

• Integral set too slow will not show up in a setpoint step test. Integrating processes will follow setpoint (SP) changes very well on gain only control. To check integral action, use an induced disturbance test.

Heuristic tuning methodology: Two types

There are two heuristic tuning methodologies depending on the purpose of the controller. If the purpose of the controller is setpoint response we will use the same methodology that was used for self-limiting processes:

1. Verify the process is stable and the process variable is on setpoint.

2. Change the controller setpoint (the controller is in auto).

3. Observe the process response; what pattern does it match?

FIGURE 1: Integrating process PI controller tuning map. Kp = 0.2%/minute, T1, T2, T3 = 30 seconds, Dt = 0 seconds. Images courtesy: Ed Bullerdiek, retired control engineer

4. Execute the rules for the identified pattern.

5. Repeat as necessary.

However, if we are tuning for surge control or disturbance reduction we need to induce a disturbance into the process. The methodology to do this is:

1. Verify the process is stable and the process variable is on setpoint.

2. Place the controller in manual.

3. Change the controller output.

4. Immediately place the controller back in auto.

5. Observe the process response; what pattern does it match?

6. Execute the rules for the identified pattern.

7. Repeat as necessary.

This methodology is required because of the very different way integrating processes respond to setpoint changes and disturbances. It is also assumed that the process will respond about the same way to changes in the input and output flows.

Figure 2 shows the response of an integrating process to an induced disturbance at the 5-minute mark, a natural disturbance at the 30-minute mark and a setpoint change (SP) at the 60-minute mark. The process variable (PV) response to the induced and natural response is similar but not identical. This is because the process responds immediately

‘There are not necessarily hard guidelines

for

what

good looks like, and that two identical processes in different services may require very different tuning based on their role in the overall process.

’

ANSWERS

FIGURE 2: Induced disturbance test method, integrating process. Tuning constants are K = 3.5, Ti = 10 minutes/ repeat, Td = 0 minutes.

to an input flow change, but the response to the output flow is delayed by the three 30-second lags. Regardless, the two responses appear to be similar enough that we should be able to draw proper conclusions from the response to an induced disturbance. The response to the setpoint change is considerably different, highlighting the need to induce a disturbance when the controller purpose is specifically intended to manage disturbances.

Three rules for too much controller gain

‘

A bad control valve will warp loop-tuning results. One of the more common problems with bad valves is hysteresis will induce a limit cycle swing.’

It is very unusual to run into a level controller with too much controller gain. Typical levels have very large fill time/deadtime ratios. If you should happen to run into a case where the PV and OP peaks line up you should check for excessive deadtime. The rules are:

• Cut the controller gain by 30% (anywhere between 25-50% will do). –Repeat until swinging is reduced to an acceptable level.

• If the tuning logs show the controller gain was recently raised split the difference.

• If the swing is quarter amplitude dampening or more (each peak is 25% the size of or larger than the previous peak) use closed-loop tuning rules to estimate new tuning constants.

If you end up with a controller gain below 1.0 you should take a closer look at the process. Does the process make sense? The controller may no longer be capable of keeping the process within limits.

Two rules for integral set too fast

This is the most common problem with level controllers. Inexperienced controller tuners (a much younger me) may not understand that integrating processes are different and use self-limiting process tuning methods inappropriately. This may result in tuning that is short on controller gain and with the integral set too fast. When you run across a process where the OP peaks trail the PV peaks the rules are:

• Increase the integral by 50% (anywhere between 25% and 75% will do).

–Repeat until swinging is reduced to an acceptable level.

• If the tuning logs show the integral was recently sped up split the difference.

Depending on the controller purpose you may want to consider raising controller gain at the same time.

Two rules for too little controller gain

This can occur through a misguided attempt at surge control tuning or an attempt to stop an integral induced swing by cutting controller gain (the standard fix for a swinging controller most young control professionals are taught). Should you run across a process with a very slow oscillation and a controller gain less than 1.0 the rules are:

• Set the controller gain using the open loop testing rules for controller gain. Be forewarned that this could go unstable if the process has a low fill time/deadtime ratio.

–If this is a disturbance rejection controller (unlikely if the controller gain is less than 1.0) continue to increase until a slight swing occurs, and then reduce the gain.

–If the purpose is surge control and you can do a disturbance test to estimate the process fill time and deadtime from the response curve. Use open-loop tuning rules to estimate new controller gain and integral tuning constants.

• If the tuning logs show controller gain was recently lowered split the difference.

Two rules for integral set too slow

If the purpose of the controller is setpoint following or surge control integral should be set to restore the PV to SP within some reasonable amount of time. There are no hard and fast rules here, although generally the integral constant should be somewhere between 10 and 50 minutes/repeat. If, however, disturbance rejection tuning is required:

• Decrease the integral by 25% to 50%. –Repeat until a little swinging is detected, then increase if necessary.

• If the tuning logs show the integral was recently slowed down split the difference.

Three rules for too much derivative

Level controllers should not require derivative, therefore derivative should be set to zero. However other integrating processes may benefit from derivative if the process appears to have multiple lags. The general process is:

• Set derivative to zero.

• Verify and fix, if necessary, the controller gain and integral.

• Increase derivative stepwise until desired performance is achieved or swinging starts to occur.

Unlike self-limiting processes setting derivative to one-fourth of the integral may not work. Start with a smaller value.

Heuristic tuning tips

Heuristic tuning is generally safe since, except for the few seconds the controller will be in manual to execute an induced disturbance test (should you use this test method), the controller remains in auto. Regardless, work with the operator to determine the largest setpoint or controller output step the operator is comfortable with. Larger steps are preferred because noise and control valve problems will have less impact on the results. If you suspect that there are valve problems, you should make

multiple steps of different sizes both up and down. If the process response is not the same, you likely have problems tuning cannot solve.

You should keep a loop-tuning log. There are a number of good reasons to keep a log; one is if you are using heuristic methods, you can use the log to guide your tuning efforts. Specifically, if you (for example) recently raised controller gain, and now it appears you went too far you can split the difference between the last setting and the current setting.

Heuristic tuning limits

Most integrating processes that we work with will be slow, and it is necessary for the response to play out fully to make accurate problem determinations. Of course, you can make rapid tuning changes if you walk up to a process that is performing poorly, and you can make an immediate problem diagnosis (this is why I like heuristics). Once you make that first change, exercise patience.

Heuristics can require multiple steps. If a control loop’s tuning is far off the mark it may be better to use open-loop tuning to get a first approximation and then use heuristics to finalize the tuning constants. However, if you are in a reasonably well-tuned facility and you find you must retune a control loop, it is very likely that the existing tuning constants are close to optimum. It will be far quicker to use heuristics, especially if there is a recent SP change in the trends, to estimate new tuning constants.

An induced disturbance test assumes that the process responds the same to input and output changes. If this is not substantially true, then the test results may not be valid.

A bad control valve will warp loop-tuning results. One of the more common problems with bad valves is hysteresis will induce a limit cycle swing. If you are not aware of the unique signature a bad valve creates, you may mistake this for a controller with too much controller gain or integral. Nothing you do will fix the swing, but your tuning efforts could slow the loop down to the point where it does not work. 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.

‘

Level controllers should not require derivative, therefore derivative should be set to zero. However, other integrating processes may benefit from derivative if the process appears to have multiple lags.

’

controleng.com

With part 17 online, see two examples, more graphs and tables:

Example 1: Disturbance rejection

Example 2: Test of a controller mistuned for surge control Link to PID spotlights, parts 1-16 and with this article online, starting with “Three reasons to tune control loops: Safety, profit, energy efficiency.”

https://www.controleng.com/ articles/three-reasons-to-tunecontrol-loops-safety-profitenergy-efficiency

Aug. 1 RCEP webcast is available for one year: How to automate series: The mechanics of loop tuning

https://www.controleng.com/ webcasts/how-to-automatethe-mechanics-of-loop-tuning

ANSWERS

Allan Kern, P.E., Lin & Associates Inc.

APC 2.0 spotlight, part 2: Base-layer advanced process control

Base-layer advanced process control provides new capabilities.

In the old days, the idea of base-layer advanced process control (APC) would have been an oxymoron. In those days, APC usually meant something deployed in the supervisory control layer. But nowadays, APC usually means multivariable control and optimization, and now it can often be deployed right in the base-layer. This brings some important new capabilities and benefits.

Base-layer APC basically refers to running multivariable control and optimization directly in the basic process control system (BPCS) layer, that is in the distributed control system (DCS) control-

FIGURE 1: The APC application gap can be defined as applications having roughly 3-20 variables. This range of applications has been largely overlooked and underserved during APC 1.0, whose economics favor large matrix designs. Experience has shown that this smaller size may actually be the “right size” for most multivariable control and optimization applications. Graphics courtesy: Lin & Associates Inc.

ler or in a programmable logic controller (PLC). These controllers are typically fault tolerant and have effectively 100% uptime. And they have execution frequencies on the order of seconds, not minutes, which can make all the difference in many applications. Speed and reliability are just two of several advantages of base-layer APC. To fit an APC application in a base-layer controller, it needs to have a much smaller footprint and less resource consumption (memory and CPU) than conventional multivariable control. The decades of experience of APC 1.0 have revealed many lessons and shed much light on the essential nature of multivariable control and optimization. From this, modern APC companies are finding novel and more efficient methods to do APC that meet this these more limited resource criteria, while maintaining high performance and enhancing owner-friendliness.

The APC application gap

Of course, there is a limit to how much multivariable control can be accomplished in a DCS or PLC time slice. As an example, one base-layer APC tool on the market today accommodates applications up to 20 variables (number of MVs plus number of CVs). This is smaller than many traditional APC 1.0 applications that often numbered in the dozens or even hundreds of variables.

But this smaller matrix size may actually be more appropriate going forward in APC 2.0. Large matrix designs became the norm under APC 1.0 because the high cost and complexity favored economies of scale and “big envelope” matrix designs. In contrast, when operating teams carry out manual multivariable control, they typically need to consider only a handful of variables, not dozens or hundreds. This can render APC more

‘Advanced process control companies are finding novel and more efficient methods to do APC that meet this these more limited resource criteria, while maintaining high performance and enhancing owner-friendliness.’

compact, reliable and intuitive. A controller that accommodates 5, 10 or 20 variables may actually be the right size for many applications.

This range of applications – less than about 20 variables – has largely been overlooked and underserved by APC 1.0, so it is sometimes referred to as the APC application gap (Figure 1).

Base-layer APC is a tool that fills this gap. Loop intervention analysis often reveals that many multivariable control applications in this range remain unaddressed after decades of APC 1.0. It could be that the best and biggest days of APC and new levels of successful process automation and optimization are yet to come.

Base-layer APC advantages

Base-layer APC brings with it several new capabilities and advantages. Base-layer APC has naturally high availability (essentially 100%), just like all base-layer controls. It also has (relatively speaking) lightning-fast execution speed, such as 2-5 seconds. This overcomes multiple vulnerabilities that have historically undermined APC 1.0 performance stemming from its typical 30- or 60-second execution speed.

Most cybersecurity, operating system update, control system software update and backward compatibility issues naturally go away when deploying APC at the base layer rather than the supervisory level.

FIGURE 2: Fired heaters are ubiquitous in the process industries. Typical heater controls include combustion/temperature control, draft control, burner pressure overrides, and excess oxygen control (or limits). Under APC 2.0, these can all be combined in one high-speed/high-availability base-layer APC application. Pass balancing could also be included, or it could be implemented as a separate controller.

Base-layer APC basically implies a function block deployment within the normal engineering environment of most modern control systems. This means that deployment, support, troubleshooting, graphics and nearly every other aspect of ownership, fall naturally under existing site control system support capabilities and resources, while reducing dependency on off-site resources.

Figure 2 is an example of a fired heater base-layer APC 2.0 application. It combines combustion control, burner pressure overrides, draft control and oxygen control, in one high-speed / high availability base-layer multivariable controller. Under APC 1.0, heater controls typically required a relatively clumsy, complicated and less reliable combination of base-layer controls, overrides and supervisory level APC. Pass balancing could be added into Figure 2, or it could be implemented as a separate base-layer APC application function block. ce

Allan Kern, P.E., is principal APC consultant with Lin & Associates Inc. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

KEYWORDS: Advanced process control, APC 2.0

CONSIDER THIS

If you’ve held back applying APC software, is it time to reconsider?

ONLINE

www.controleng.com/apc2-0-spotlight-part-1-what-isapc-2-0

Manual multivariable control www.controleng.com/ multivariable-control-as-acore-competency

Loop intervention analysis www.controleng.com/ understanding-the-criticalrole-of-metrics-for-advancedprocess-controls

LEARNING OBJECTIVES

Learn the new capabilities and opportunities that come with base-layer APC.

Understand the APC application gap.

See why the best days of APC may be yet to come.

John Clemons, Solutions Consultant, Rockwell Automation, Birmingham, Alabama

Ways IIoT, AI empower smart manufacturing

The Industrial Internet of Things (IIoT) and artificial intelligence (AI) are driving significant bottom-line benefits for manufacturing operations.

Smart manufacturing is transforming the industry back into an economic powerhouse. It’s helping manufacturers increase productivity, reduce costs and deliver exciting new products and capabilities to customers. Smart manufacturing is the intelligent real-time orchestration and optimization of business, physical and digital processes within factories and across the entire supply chain. Smart people are using smart technology to improve manufacturing operations.

A wide mix of new and not-so-new technology is fueling the success of smart manufacturing. Robots, collaborative robots (cobots), advanced identification technologies, cloud technology, 3D printing, manufacturing execution systems (MES), augmented reality, virtual reality, digital twins, digital threads and many more contribute to the success.

Two technologies worth mentioning in more detail are the Industrial Internet of Things (IIoT) and artificial intelligence (AI). These two technologies come in many flavors, but both are becoming ubiquitous in manufacturing operations and are having a significant impact on the bottom line.

IIoT, AI and smart manufacturing

The IIoT has become almost synonymous with smart manufacturing. It serves as the foundation for many global smart manufacturing initiatives and innovations.

The IIoT collects data from virtually every data source in a plant and makes that information available in real time to the people who need it. More importantly, the IIoT adds context to this data so the data is clearly understood and visible across the enterprise. Every key aspect of manufacturing operations relies on data. Understanding what’s happening,

why it’s happening, what will happen next and what actions must be taken is all data-based. The IIoT delivers this information in real time, making sure the right people have access to the data when they need it. In the manufacturing sector, success depends on the data, and the IIoT is critical to delivering it. AI plays a crucial role in managing data with the IIoT. In many manufacturing operations, once the IIoT is fully implemented and operational, the amount of data can be overwhelming. It becomes challenging for people to identify which data is critical and which requires immediate attention for decision making.

Using AI to analyze data

AI is good at analyzing the vast amount of data that manufacturing operations generate to understand what’s happening and why. The amount of data doesn’t matter; AI can analyze the data and quickly identify the root causes of current issues.

And more than that, AI can translate its analyses into natural language, making it easily understandable and actionable. It also provides easy-to-understand recommendations that can be put into action quickly.

The bottom line is the IIoT relies on AI to analyze the vast amounts of data it collects and translate the results into actionable recommendations. But AI needs the IIoT as well, because without the data collected by the IIoT, AI isn’t as effective. Manufacturing is all about the data. When the IIoT and AI work together to collect and analyze data, the impact to the bottom line is significant.

Use of AI agents and copilots

AI agents and copilots are the latest AI tools manufacturers are using. AI agents are standalone autonomous AI applications which perform their

controleng.com

KEYWORDS: Gen AI, IIoT, smart manufacturing CONSIDER THIS

Are you applying IIoT and AI? LEARNING OBJECTIVES

How smart manufacturing enables manufacturers to increase productivity, reduce costs and introduce new products and capabilities.

The IIoT relies on AI to analyze the vast amounts of data it collects and translate the results into actionable recommendations.

Generative AI can generate tailored training materials, customized to individual needs and experiences.

ANSWERS

FIGURE 1:, COVER Smart manufacturing utilizing tools like Industrial Internet of Things, generative AI, digital twins and other technologies can help manufacturers increase productivity and cut costs. Courtesy: WTWH Media

‘When

the IIoT and AI work together to collect and analyze data, the impact to the bottom line is significant.

’

tasks without any collaboration from operators. AI copilots are collaborative AI applications that operators use interactively to perform their tasks.

Uses of AI agents and copilots follow.

Predictive maintenance

With the IIoT, AI agents collect and analyze information about the manufacturing equipment operation. The AI agent constantly analyzes all aspects of the data in real time, looking for potential anomalies before they occur.

AI agents must be trained on existing data to recognize specific patterns, but they can also use open training to identify new or previously undetected patterns. This approach enables the AI agent to predict potential problems before they occur, allowing personnel to perform equipment maintenance when needed. This process minimizes downtime and maintenance costs. With the integration of the IIoT and AI agents, these processes occur in the background, and operators are only alerted when anomalies are detected.

Business systems

Most business systems like enterprise resource planning (ERP) systems require a significant amount of manufacturing data. Business systems usually require data, such as production, consumption, orders, status, quality results and non-conformances from the shop floor.

AI agents, again with the IIoT, collect the data from various shop floor sources. They organize this data according to the business system requirements

and deliver it to those systems when needed. With the IIoT and AI agents working in the background, this process involves minimal operator effort.

Quality inspections

Quality tests and inspections can be complex in that different product variations require different tests and inspections. Ambiguous results or test failures might require different paths forward, such as retesting, additional testing, non-conformances, downgrades and rework. Quality assurance (QA) and quality control (QC) operators use AI copilots to navigate the complexities of various quality tests and inspections. These AI copilots make sure the correct tests are performed, necessary retests are performed, correct actions are taken and proper dispositions are made. The QA/QC operators collaborate closely with the AI copilots, who guide them through each step of the process based on the test results and the requirements for the next steps.

AI computer vision systems are a great example of AI-based solutions being used to improve quality inspections. AI vision systems leverage AI tools to extract meaningful data from images, enabling quality inspection applications to detect and categorize anomalies at high speeds and high levels of accuracy. Feedback from the AI vision systems supports openand closed-loop adjustments to the manufacturing equipment in real time, significantly increasing the first-pass quality output of the manufacturing process.

Error proofing

Manufacturing processes are getting more complex,

and product variations often introduce subtle differences in the processes. Even simple operator errors can impact the final quality of the product to the point where it can’t be sold or must be reworked before it’s saleable, which costs time and money.

Operators use AI copilots to errorproof their processes and minimize errors in the processes. AI copilots will typically ingest the work instructions, operating manuals and other documents relevant to the manufacturing processes. Next, instead of the operators trying to perform the processes from memory or use these large, complicated and often cumbersome manuals, the operators simply work collaboratively with the AI copilots to execute the manufacturing processes. The AI copilots help the operators follow the steps in the manuals and work instructions correctly and completely.

How generative AI is reshaping manufacturing

Generative AI (GenAI) is another aspect of AI that’s starting to have a huge impact on manufacturing operations. GenAI is AI that processes large amounts of information and generates new content based on that information. This new content can take many forms, leading to numerous applications for GenAI in manufacturing. Examples include:

Training

For manufacturing operations, training is essential and often needed on a continual basis. This necessity is from new personnel joining the team and from the need to cross-train people in various aspects of the operation. Operations constantly change and evolve as new technologies and techniques are introduced. GenAI can process large amounts of documents related to operations, equipment, processes, procedures, manuals and work instructions. Using this information, it can develop comprehensive training materials. These materials include not only traditional training documents but also complete training regimens, which include classroom materials, online resources, refresher courses, interactive content and testing materials. GenAI can create all these elements, confirming that the training curriculum is accurate, comprehensive and up to date with the latest operating procedures. GenAI can generate tailored training materials, customized to individual needs and experiences. Leveraging historical data, GenAI can create personalized training scenarios for diverse learners.

GenAI identifies specific knowledge gaps and develops personalized training content to bridge them. It identifies further training needs by analyzing patterns, such as frequently accessed information, recurring questions and testing and evaluation results, again all based on individual learning needs.

Shift handover

Many manufacturing operations run 24/7 or 24/5 with three shifts per day. This means transitioning from one shift to another must be done quickly, typically within 10 to 20 minutes. For an efficient handover, significant effort is often put into collecting data from the shift, filtering and sorting that data to identify what is most relevant and then generating the required reports, charts or other necessary documentation. GenAI makes this process so much easier. GenAI takes the data collected during the shift from the IIoT and uses it to generate the required reports and charts. GenAI sorts the relevant information from the irrelevant, highlighting only what is needed for the next shift to address open issues and keep operations running smoothly.

Control system code generation

Most manufacturing operations have significant levels of automation and controls, which means significant levels of automation and control systems code. GenAI helps controls engineers generate and manage their code, as well as summarize the code and troubleshoot when problems arise. GenAI helps generate new code, when changes need to be made or when new processes or equipment are added. GenAI helps modernize and optimize the code, making the control engineer much more efficient and effective.

Boost productivity, cut costs

Smart manufacturing is enabling manufacturers to increase productivity, reduce costs and introduce new products and capabilities. Expect to see many more applications of the IIoT, AI agents, AI copilots and GenAI as these tools support smart manufacturing and help transform the manufacturing sector back into an economic powerhouse. ce

John Clemons is a solutions consultant for LifecycleIQ Services at Rockwell Automation. Edited by Sheri Kasprzak, managing editor, WTWH Media, skasprzak@wtwhmedia.com.

‘GenAI helps modernize and optimize the code, making the control engineer much more efficient and effective.’

u

Insights

Smart manufacturing insights

uTools like the Industrial Internet of Things (IIoT) and artificial intelligence (AI) are changing the ways in which manufacturers operate.

u AI can be used to enhance data collection and analysis.

u AI agents and copilots are the latest in AI technology.

Teach your HMI some new tricks with multi-touch technology

Stop settling for those old, single-touch plastic panels. It’s time to try something new. We can help you stand out with multi-touch-capable Control Panels and Panel PCs. These displays with aluminum housings are beautifully designed and more than ready for the plant floor. You can even go for bespoke with custom designs that incorporate your own corporate branding. Now that’s some quality screen time. 9 different diagonal screen sizes from 7 to 24 inches classic and contemporary aspect ratios: 4:3, 5:4 and 16:9 select and configure a variety of push button extensions and mechanical extensions available as a passive control panel or panel PC with integrated CPU entry-level control or high end control – for example, with an 11th generation Intel® Core™ i7 install in a panel or on a mounting arm or pole with a range of mounting options

MACHINE VISION

Ole Marius Rindal, Ph.D., Sonair

New 3D ultrasonic sensor reduces costs, improves mobile robot safety

A new 3D ultrasonic sensor empowers developers to achieve 360-degree obstacle detection around mobile robots at a fraction of the cost of the robot sensor packages typically used today. How does it work? What does it mean for autonomous robot designers? What is ADAR?

Acoustic detection and ranging (ADAR) sensing technology uses beamforming with miniaturized ultrasonic transducers to provide 3D object detection, offering a cost-effective and comprehensive alternative to traditional 2D light-detection and ranging (LiDAR) systems for applications like mobile robots.

Two special moments during the development of an innovative 3D ultrasonic sensor brought home the ground-breaking potential of related sensor research. The first came when Frode Tyholdt, co-founder of Sonair, managed to produce high quality miniaturized (<2mm from the current ~15mm) ultrasonic transducers building on decades of research from the SINTEF research institute. (We were both working at SINTEF at the time and through an innovation project found a way to arrange the transducers in the arrays required for beamforming.)

Beamforming is the principle that underlies other ultrasound technologies from 3D medical ultrasound imaging to SONAR (sound navigation and ranging) and has been around for decades. Beamforming allows sensors to transmit and receive sound from different directions.

But beamforming in air requires transducers that are small enough to be spaced half a wavelength (λ/2) apart. Miniaturization was key to unlocking

the new technology, which enables beamforming in the medium of air (as opposed to body tissue or water) for the first time in a commercial product.

Applying algorithms used in medical ultrasound imaging to a sensor confirmed that the new technology could readily perform depth sensing and obstacle detection.

By tweaking algorithms and exploiting the understanding of air as a medium lead to the feasibility of ADAR-based measurements with a new category of sensors that enables 3D ultrasound object detection in air. Ultrasound in air sensors already exist, but the most well-known, the parking sensors on automobiles, are one dimensional, which means they sense in just one direction and only provide distance information. ADAR provides x, y, z coordinates necessary to create a 3D view.

ADAR, new technology, similar to established technologies

ADAR is a newcomer but can be thought of alongside established technologies such as radar (radio detection and ranging) and SONAR. The key difference is that ADAR works acoustically in the air, sonar works underwater and radar uses radio frequencies. ADAR takes well-established beamforming principles and applies them to air.

Tiny, powerful ADAR sensors have progressed

controleng.com

KEYWORDS: Acoustic detection and ranging (ADAR), 3D ultrasonic sensors, machine vision

CONSIDER THIS Does your mobile robot have blind spots? ONLINE www.controleng.com/ vision-and-discrete-sensors www.controleng.com/ mechatronics/robotics

ANSWERS

‘The sensor can be used to create a 3D safety

shield for obstacle detection around an AMR at a lower cost than other options.

’

FIGURE: 2D LiDAR (left) has a limited field of view and can only sense a narrow two-dimensional slice of the environment around a robot. An ADAR-based sensor provides an automated mobile robot (AMR) with 360-degree protection from obstacles at 50% lower cost compared to 2D LiDAR-based alternatives. The new ADAR sensor from Sonair is on schedule to achieve full safety certification (IEC 61508 and SIL2) toward the end of 2025 and is expected to be commercially released before that. After safety certification is confirmed, users can download and install a firmware upgrade to update the ADAR sensor. Courtesy: Sonair

over the past few years through successful pilots and award ceremonies. After full commercial release set for later this year, ADAR sensors will be available for integration and use in mobile robot designs.

Ensuring safe human-robot coexistence

Humans and autonomous mobile robots (AMRs) are increasingly sharing the same spaces, from city streets and factory floors to hospitals, warehouses and even in residential applications. AMRs are seeing increased adoption worldwide due to their effectiveness across multiple material handling applications and spurred by labor shortages across the manufacturing, logistics and warehouse sectors. In fact, the global mobile robot market grew 27% in 2023 to reach $4.5B, according to analyst firm Interact Analysis.

Ensuring safety for humans in these shared spaces is key to supporting automation adoption across multiple environments. Obstacle detection is, of course, a key element of any mobile robot’s safety system.

Sensors, AMRs:

How to reduce mobile robot cost

What is the most expensive part of an AMR? The chassis? No. The motor? No. The rugged outer shell? No. Surprisingly, it’s the sensor package. Today’s AMRs employ various combinations

of sensing and camera technologies, including 2D safety certified LiDAR, to help robots navigate their environment while safely detecting obstacles. The sensor package in a typical AMR can account for 30% or more of the total hardware cost.

LiDAR has known limitations when it comes to effective obstacle detection. Most importantly, 2D LiDARs have limited field of view (FOV) and can only sense a small, two-dimensional slice of the environment around a robot. This means that LiDAR cannot detect objects outside the 2D plane.

LiDAR’s

detection issues

2D safety LiDAR are typically mounted on AMRs at a height of around 20cm (7.9 inches). If there's an object 10 cm above the floor, LiDAR won't detect it. If there’s an object that’s hanging from the roof, LiDAR won't detect it. If there are items sticking out from walls or shelves, 2D LiDAR won’t detect them.

LiDAR has issues beyond its limited FOV and 2D sensing. Different lighting conditions in the warehouse can create challenges for LiDAR. Moreover, LiDAR cannot detect transparent and reflective surfaces, which presents safety risks.

In contrast, the new 3D ultrasonic sensor provides a 360-degree (180 x 180) view of the scene up to 5m distance (16.4 feet). In effect, the new sensor

can be used to create a 3D safety shield for obstacle detection around any AMR. That’s a major safety enhancement - but perhaps the most remarkable aspect of the new technology from a robot designer’s perspective are the cost savings involved.

50% to 80% cost savings while boosting safety

2D safety certified LiDAR sensor package typically costs about $4000, whereas the new 3D ultrasound sensor will cost approximately one-quarter of that when released later this year.

With AMRs typically carrying two 2D safety LiDAR, removing these expensive sensors produces an immediate $8000 saving. Deploying four ADARbased sensors instead, at an approximate cost of $4000, provides the AMR with 360-degree protection from obstacles and at 50% lower cost compared to 2D LiDAR-based alternatives.

2D safety LiDAR are used to support autonomous navigation capabilities. To retain those capabilities, robot designers could use the new sensor with much cheaper, non-safety rated LiDAR, which costs around $500, while the new sensor performs the improved 3D safe obstacle detection function. Depending on the configuration, the new technology reduces the cost of the safety sensor package by between 50% and 80%, while boosting safety.

ADAR

costs less, produces actionable data for robotic, other

applications

AMR designers are used to handling LiDAR-generated point clouds that produce millions of data points. This requires a lot of processing power and speed and it has also created a sense that more is better when it comes to data points. The new technology demonstrates that this expectation is not necessarily correct.

The new 3D ultrasound sensor produces fewer data points than LiDAR and 3D camera systems, but crucially, the points it produces are actionable. Instead of providing millions of data points that require a lot of postprocessing, the new sensor provides the key data points for AMR to detect obstacles.

Confronting preconceived notions

Before participating in the technology’s successful and ongoing, early ADAR sensor access program, some people expressed skepticism about

the concept of using ultrasonic sensors for obstacle detection. However, in most of those cases, they had tested older ultrasonic sensors that did not meet their expectations. Testing the new technology revealed the powerful capabilities of the ADARbased 3D ultrasonic sensor. It enhance robots’ safety view from 2D to 3D and less cost than most sensor packages.

Future is sensor fusion in mobile robot systems

None of that means that the new 3D ultrasonic sensor will replace all the other sensors used in robots today.

The best performing mobile robot systems tend to use a combination of complementary sensors rather than relying on just one. The future is not for one sensor to rule them all, but an integration and fusion of sensor technologies, providing sensor redundancy.

Combining the new 3D ultrasound sensor with cameras, for example, enables low-cost visual simultaneous localization and mapping (vSLAM) navigation, boosting the sensor’s value and usefulness.

More sensor system integration and sensor fusion are expected in the future, with the new ADAR-based sensors being a powerful addition to any robot’s sensor suite.

ADAR for industrial robot arms

The new sensor is a great fit for mobile robots, especially with the rapid robot adoption in that domain and the need to ensure safe human-robot coexistence, but ADAR use cases extend beyond AMRs. Any robot that would benefit from 3D obstacle detection is a good candidate for this innovative sensor.

For example, it could be integrated into a standard industrial robot arm and used to detect when a human enters the workspace, immediately sending a signal to bring the machine to a halt. Similarly, humanoid robots could use the sensor to ensure there are no collisions with humans or property in domestic settings. ce

Ole Marius Rindal, Ph.D., is co-founder and technology manager, Sonair. Edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com.

Insightsu

Acoustic detection and ranging (ADAR) sensing insights

uLearn how ADAR uses beamforming and miniaturized ultrasonic transducers to achieve 3D object detection in air, offering a novel approach that differs from traditional methods like LiDAR.

uUnderstand the limitations of 2D LiDAR systems, including their narrow field of view and challenges with transparent or reflective surfaces, and how ADAR provides a more comprehensive and cost-effective solution for obstacle detection, especially in the context of mobile robots.

uKnow that ADAR can be integrated with other sensors like cameras for enhanced capabilities and that its applications extend beyond mobile robots to include industrial and humanoid robots.

Herbert Post, Tradesafe

How IoT, edge computing and digital twins empower process safety

Edge computing and digital twin technology integration enhance hazard detection, improve safety system resilience and address cybersecurity challenges as safety infrastructure becomes more interconnected.

There was a time when process safety relied almost entirely on the sharp eyes and quick reactions of seasoned workers on the factory floor. But technology is changing that equation. Today, interconnected sensors, real-time analytics, and predictive modeling are transforming how industries detect, prevent, and respond to hazards.

The convergence of IoT (Internet of Things), edge computing, and digital twins is rewriting the rulebook for process safety. These innovations allow safety teams to move from reactive to proactive, using real-time monitoring, automated deci-

sion-making, and virtual simulations to catch risks before they escalate. The industry is taking notice, with 95% of professionals affirming that digital tools are essential in enhancing process safety.

While these technologies offer unprecedented visibility and control, they also introduce cybersecurity vulnerabilities that must be managed.

How to enhance safety

The integration of IoT, edge computing and digital twins has created a new ecosystem where they work together to improve safety and prevent incidents before they happen. Here’s a quick look at these technologies (see Figure 1).

• IoT (Internet of Things) connects sensors, machines, and systems, enabling real-time data collection and communication. Think of IoT as a network of digital eyes and ears that detect pressure fluctuations, temperature changes, gas leaks and mechanical stress before they escalate into major hazards.

• Edge computing keeps the decision-making closer to the source, allowing safety-critical responses to happen instantaneously.

• A digital twin is a virtual replica of a physical asset, process or system designed to simulate real-world conditions. By analyzing sensor data and predicting failures before they occur, digital twins give safety teams a crystal ball view of potential risks, allowing proactive interventions rather than reactive fixes.

These technologies improve efficiency and change how industrial safety is managed. The ability to track safety conditions as they change in real time is critical to preventing disasters before they unfold.

In chemical processing plants, for example, IoT

FIGURE 1: IoT, edge computing and digital twins.Images courtesy: Tradesafe

sensors can detect small leaks before a full-blown gas release. Edge computing processes data locally, triggering an immediate containment response without waiting for cloud-based analysis. A digital twin of the plant can simulate potential leak scenarios, helping engineers identify weak points before they fail.

Use cases: Technologies in action

Industries worldwide are actively using these technologies to prevent hazards, optimize maintenance, and respond to emergencies faster than ever before. Below are compelling examples demonstrating how these innovations are reshaping process safety across different sectors.

Thermal imaging for hazard detection

Thermal imaging, when integrated with IoT and digital twins, has become a powerful tool for early hazard detection in various environments.

A recent study explored the development of a predictive digital twin for condition monitoring using thermal imaging. This system integrates advanced mathematical models with real-time thermal data to detect anomalies in industrial equipment.

By continuously monitoring temperature fluctuations, this technology identifies hotspots that could indicate potential failures, such as overheating machinery or electrical faults, before they turn into catastrophic incidents.

For instance, consider a chemical processing plant where pipelines carry volatile substances. An IoT-enabled thermal camera can detect an abnormal rise in temperature due to friction, corrosion, or leaks. When this data is processed using edge computing, an immediate alert can be triggered to shut down the affected section before a fire or explosion occurs. Meanwhile, the digital twin of the facility can simulate the leak’s potential spread, helping operators preemptively mitigate risks (see Table 1).

Predictive maintenance with digital twins

Unplanned downtime is a major concern for industries reliant on complex machinery. Predictive maintenance, powered by digital twins and AI-driven insights, is changing how companies prevent failures before they happen.

Siemens recently advanced its Senseye Predictive Maintenance solution by incorporating generative artificial intelligence (AI). This enhancement allows maintenance teams to interact with digital twins in a more intuitive, conversational way,

Table 1: Thermal Imaging for Hazard Detection

Aspect

Technology Integration

Core Function

How It Works

Details

Thermal imaging combined with IoT and digital twins

Early hazard detection through temperature monitoring

Continuously monitors temperature fluctuations to identify hotspots indicating potential failures

Key Benefit Shift from reactive firefighting to proactive prevention

making it easier to anticipate equipment failures and schedule necessary interventions. As a result, companies reduce downtime, optimize maintenance schedules and cut costs.

An application of this technology can be seen in rotating equipment like pumps and compressors. In the past, operators relied on manual inspections and scheduled maintenance cycles, often replacing parts too early (leading to unnecessary costs) or too late (resulting in unexpected failures).

With IoT sensors tracking vibration, pressure and temperature, a digital twin can predict when a component is nearing failure. Edge computing ensures this information is processed in real time, sending alerts for maintenance only when truly needed. This extends equipment life and prevents safety hazards caused by mechanical failures (see Table 2).

Edge computing in crisis response

Delays in detecting and responding to a crisis, whether it's a gas leak, equipment failure, or environmental hazard, can lead to severe consequences. This is where edge computing becomes indispensable by enabling instant decision-making at the source of data generation.

A recent study introduced a digital twin-driven anomaly detection framework based on edge intelligence. This system allows industrial environments to detect and respond to anomalies in real-time, significantly enhancing safety and reliability.

Consider handling a volatile chemical. If an IoT sensor detects an abnormal pressure spike in a pipeline, traditional cloud-based systems might take seconds (or longer) to analyze the data and trigger a response. However, with edge computing, the anomaly is processed on-site, allowing for an immediate emergency shutdown before a rupture occurs.

‘An IoT-enabled camera can detect an abnormal rise in temperature due to friction, corrosion, or leaks.’

KEYWORDS: Process safety, digitalization

ONLINE

Search safety www.controleng.com.

CONSIDER THIS

Online Text online text?

LEARNING OBJECTIVES

Understand how IoT, mist and edge computing and digital twins enhance real-time process safety monitoring.

Explore real-world examples of the ways in which digital twins, edge computing and IoT empower process safety, such as thermal imaging systems for hazard detection and predictive maintenance. Identify cybersecurity risks associated with interconnected safety systems and strategies to mitigate them. Online controleng.com

TABLE 1: Thermal imaging for hazard detection

ANSWERS

Table 2: Predictive Maintenance with Digital Twins

Aspect Details

Technology Integration Digital twins with AI-driven insights

Core Function Preventing equipment failures before they occur

How It Works

Key Benefits

IoT sensors track vibration, pressure, and temperature; digital twin predicts component failures

Reduced downtime, optimized maintenance schedules, cost savings, extended equipment life, prevention of safety hazards

As industries embrace IoT, edge computing, and digital twins to enhance safety, they must also confront the cybersecurity risks that come with increased digital connectivity. While digital transformation enables real-time monitoring and predictive insights, it also widens the attack surface for cyber threats (see Figure 2).

1. Insecure IoT devices and endpoints

A digital twin of the refinery can simulate how the pressure buildup will affect surrounding systems, giving engineers insights to adjust operations before an accident happens. This level of responsiveness is invaluable in crises, where every second counts.

One real-world example of edge computing’s impact can be seen in oil and gas drilling operations. When IoT sensors detect early signs of a wellbore collapse or gas influx, edge computing can automatically halt drilling activities, preventing blowouts and ensuring worker safety (see Table 3).

How to combat cyberthreats

The widespread adoption of IoT in industrial environments has significantly increased the number of attack points. More than 50% of IoT devices contain exploitable vulnerabilities, making them prime targets for hackers seeking to gain access to critical infrastructure. These threats can be mitigated by:

• Disabling default credentials and requiring unique, complex passwords on all IoT endpoints.

• Protecting access to critical systems by adding a Multi-Factor Authentication (MFA).

• Conducting routine firmware updates and vulnerability assessments to close security gaps.

2. Unencrypted data transmission

Connected devices may transmit sensitive process safety data without encryption, creating vulnerability to interception. Attackers can alter safety-critical information, cause false alarms or disable real-time hazard detection. These threats can be mitigated by:

• Implementing strong Transport Layer Security (TLS) encryption for all data transmitted between devices.

• Using Virtual Private Networks (VPNs) or encrypted communication channels to prevent unauthorized interception.

• Ensuring only verified devices and personnel can send or receive safety-critical data.

3. Integrating legacy systems

Many industrial facilities still rely on legacy equipment that was never designed with cybersecurity in mind. A report found that 40% of organiza-

FIGURE 2: Cybersecurity risks related to increased digital connectivity.

Courtesy: TRADESAFE

TABLE 2: Predictive maintenance with digital twins

tions have at least one outdated OT asset connected to the internet with known vulnerabilities.

These threats can be mitigated by:

• Isolating legacy systems using firewalls to prevent them from exposing the entire network to attacks.

• Deploying dedicated security gateways that act as a bridge between old and new systems, filtering out potential threats.

• Gradually replacing obsolete equipment with modern, cybersecurity-ready alternatives.

4. Cloud storage and remote access vulnerabilities

The growing reliance on cloud platforms and remote access tools has increased cyber risks. Attackers exploit misconfigured cloud storage or weak remote access controls to steal sensitive operational data. Verizon’s 2024 Data Breach Investigations Report highlighted a surge in breaches initiated by exploited vulnerabilities in remote access tools. These threats can be mitigated by:

• Using VPNs and strict access policies to ontrol who and what can connect to safety systems.

• Requiring MFA for all remote logins, ensuring only authorized personnel can access critical assets.

• Continuously monitoring cloud-based systems for misconfigurations and apply security patches immediately.

5. Inadequate continuous monitoring and patch management

Slow patch cycles leave industrial systems vulnerable for extended periods. According to Verizon’s 2024 Data Breach Investigations Report, it takes organizations an average of 55 days to patch just 50% of their critical vulnerabilities, leaving them exposed to attacks for weeks or months. These threats can be mitigated by:

Table 3: Edge computing in crisis response

Aspect Details

Technology Integration

Core Function

How It Works

Key Benefit

Edge computing with IoT sensors and digital twins

Enabling instant decision-making at the data source during crises

Processes anomalies on-site for immediate emergency response

Improved responsiveness in crisis situations where every second counts

• Using automated update systems to apply security patches as soon as they’re available.

• Deploying real-time monitoring solutions to detect anomalies and potential breaches.

• Simulating cyberattacks to identify weak points before real hackers do.

By shortening patch cycles and actively monitoring systems, companies reduce cyber threat exposure.

Strategizing to get the most results

Technology has already transformed process safety, but its full potential lies in how we choose to apply it. IoT, edge computing and digital twins give us the ability to detect hazards in real time, predict failures before they happen and respond faster than ever before. Yet these tools are only as effective as the strategies behind them.

As industries advance digital transformation, safety professionals must ask themselves: are we truly using these innovations to their fullest potential? The next time you’re on the factory floor, in the control room, or reviewing compliance reports, consider this. Could a sensor, a simulation or a smarter response system prevent the next incident? If the answer is yes, then the next step is clear. It’s time to act.

But with these advancements comes a critical responsibility: making sure that technology enhances safety without introducing new risks. Are we moving fast enough to secure the very systems designed to protect us? In process safety, being proactive is an advantage that can make the difference between preventing an incident and reacting to a disaster. ce

Herbert Post is the vice president of Tradesafe. Edited by

Sheri Kasprzak, managing editor, Automation and Controls, WTWH Media, skasprzak@wtwhmedia.com.

TABLE 3: Edge computing in crisis response

u

Process safety insights

uDigital twin, IoT and edge computing all empower hazard detection, improve safety system resilience and address the cybersecurity challenges that arise as safety infrastructure becomes increasingly interconnected.

u IoT, edge computing and digital twin technologies can only be successful if companies strategize to use them appropriately.

u It’s critical to utilize process safety technologies responsibly to avoid cyberattacks.

ANSWERS

Felipe Costa, Moxa

How automation, AI and cybersecurity must shift to meet industry needs

Automation is reshaping industries, and applications of automation, artificial intelligence (AI) and cybersecurity play distinct roles in oil and gas, energy and factory automation. Learn how.

Automation has become the backbone of industrial operations, reducing human error, improving efficiency and enabling predictive maintenance. Different industries face distinct operational, regulatory and cybersecurity challenges, requiring unique automation strategies. Below compare differences among oil and gas, energy and factory automation and how AI, cybersecurity and networking are shaping their automation landscapes.

Cybersecurity challenges in oil and gas

• Legacy infrastructure: Many oil facilities use outdated systems that are vulnerable to cyberattacks.

• Ransomware threats: Attacks on oil pipelines (such as the Colonial Pipeline cyberattack) highlight the importance of network segmentation, endpoint security, training and cyber hygiene.

• AI in cybersecurity and network visibility: AI-based threat detection systems monitor industrial networks for unusual activity, identifying and mitigating cyber threats before they impact operations. Better network visibility is a powerful tool for responding to and detecting incidents.

2. Energy sector: Smart grids and cyber-resilience

The energy sector is undergoing rapid digital transformation, with automation improving grid reliability, optimizing power generation and integrating renewable energy sources.

KEYWORDS: Industrial cybersecurity, industry Online Text online text

LEARNING OBJECTIVES

Understand how automation and AI differ in their applications across oil and gas, energy and factory automation.

Learn how cybersecurity challenges and solutions impact industrial automation in critical sectors.

Identify key technologies and trends shaping the future of industrial automation.

CONSIDER THIS

How well is my organization aligning its automation strategy with emerging AI capabilities and cybersecurity requirements to remain

1. Oil and gas: Balancing efficiency and safety in harsh environments

The oil and gas industry operates in some of the most hazardous and remote locations. Automation plays a critical role in maintaining safety, ensuring equipment reliability and optimizing production.

Key emerging automation technologies

The industry uses supervisory control and data acquisition (SCADA) and distributed control systems (DCS) to enable remote monitoring and control of pipelines, refineries and offshore rigs. AI-driven predictive maintenance, which applies machine learning models to analyze sensor data and detect anomalies in drilling equipment and pipelines before failures occur, is an emerging use of AI in this field.

Key emerging technologies in energy automation

Energy automation relies on smart grids and AI-driven machine learning models to optimize power distribution and predict outages. Industrial control systems (ICS) automate energy transmission across substations, reducing downtime. An emerging application is AI-enhanced distributed energy resource management systems (DERMS), which balance demand and supply across decentralized energy grids. One of the most interesting — and somewhat paradoxical — elements of this relationship is that AI adoption is significantly increasing energy demand, particularly in data centers. While AI is improving energy efficiency, it is also indirectly driving the need for more power.

Cybersecurity challenges in energy

• Nation-state attacks: Power grids are prime targets for cyber warfare, making a solid industrial cybersecurity plan essential for resilience.

• Regulatory compliance: Companies not covered by NERC CIP regulations still struggle to develop customized security plans. While NIST is the most widely used framework, IEC 62443 provides additional guidance for securing industrial control systems.

• IoT and edge device security: Securing internet of things (IoT) and edge devices within substations and energy distribution networks is crucial for preventing cyber intrusions and minimizing the attack surface.

3. Factory automation: AI-powered smart manufacturing

Factories are adopting Industry 4.0 technologies, where AI, IoT and advanced robotics transform traditional manufacturing into intelligent, autonomous production systems.

Key emerging technologies in factory automation

Factories are implementing edge AI and machine vision to enable real-time defect detection and waste reduction. Collaborative robots (cobots) assist human workers in improving efficiency and accuracy. Industrial IoT (IIoT) sensors continuously monitor machinery health, enabling predictive maintenance and minimizing downtime.

Cybersecurity challenges in factory automation

• IT-OT convergence risks: The integration of information technology (IT) and operational technology (OT) networks requires security adaptations to prevent disruptions in manufacturing operations.

• Supply chain vulnerabilities: AI-powered anomaly detection systems enhance cybersecurity by monitoring and identifying threats within production environments.

• Lack of network segmentation: This remains a significant issue in industrial cybersecurity, increasing the risk of lateral movement attacks.

4. The role of networking in industrial automation

A robust industrial network is the backbone of automation, enabling secure and reliable communication between devices, controllers and cloudbased systems. A well-designed network ensures low latency, high availability and cybersecurity protection in industrial environments.

An effectiveindustrial network

A reliable industrial network must incorporate redundancy and resilience, using architectures such as ring and mesh topologies to minimize downtime. Time-sensitive networking (TSN) enables low-latency data transmission, essential for factory automation. Security measures such as segmentation and advanced switching security protections prevent unauthorized access. Networks should support industrial protocols like Modbus, Profinet and EtherNet/ IP to ensure interoperability. As industrial automation expands, scalability accommodates the increasing number of IIoT devices and growing data volumes.

5. The future of industrial automation

AI adoption is increasing exponentially, with many applications still in the testing phase. However, as industrial control systems have serious consequences when incidents occur, many decisions are still reviewed by human operators. As AI systems become more precise and trustworthy, we may see a future where some operational decisions are fully automated. Cybersecurity remains a top priority. Implementing a secure by design architecture to prevent unauthorized access and reduce exposure to cyber threats is essential. AI-based anomaly detection systems provide real-time threat intelligence, identifying irregular network activity before it escalates into a breach. In many cases, virtual patching can help protect legacy equipment, allowing it to function securely despite the challenges of deploying regular updates and patching SCADA and ICS software.

AI, networking and cybersecurity

Automation is no longer just about efficiency — it’s about intelligent decision making, secure connectivity and resilience against cyber threats. Oil and gas industries must balance efficiency and safety while securing legacy systems. The energy sector requires robust cybersecurity to protect critical infrastructure and meet new energy demands, being perhaps the only sector physically overloaded by AI adoption. Industries that successfully integrate AI, secure networking and strong cybersecurity frameworks will gain a competitive advantage, ensuring operational efficiency, regulatory compliance and business continuity. ce

Felipe Costa, senior project manager – networking and cybersecurity, Moxa. Edited by Gary Cohen, senior editor, Control Engineering, WTWH Media, gcohen@wtwhmedia.com.

‘Automation is about efficiency, intelligent decisions, secure connectivity and resilience against cyber threats.’

Insightsu

AI and cybersecurity insights

uDifferent sectors like oil and gas, energy and manufacturing have unique operational and regulatory challenges.

u AI is enabling predictive maintenance, optimizing operations and enhancing decision-making across all three industries.

uScalable, secure and resilient industrial networks are essential for enabling real-time data flow, device connectivity and interoperability.

Results are in:

Control Engineering Career and Salary Survey, 2025 2025

Benefits and salaries increased. Artificial intelligence and machine learning top the list of leading automation technologies.

The economy, labor shortages and tariffs are among leading challenges while artificial intelligence and machine learning are among leading technologies that will help those responding to the 2025 Control Engineering Career and Salary Survey and Report. Salaries and benefits increased for Control Engineering subscribers responding. Salaries increased to an average $119,682 (Figure 1), a 4.3% increase compared to $114,771 for those taking the survey in 2024, and up from $111,345 the 2023 survey. The average of those receiving bonuses increased to $18,595 (Figures 2, 3), compared to $16,125 reported last year.

Leading threats (Figure 7) to manufacturing businesses (select up to three, the survey said) was the economy and lack of skilled workers, tied at 38%. In 2025, taxes and tariffs was second at 27% as a leading threat, soaring 19 percentage points from 2024 when just 8% said it was a leading threat. Tied for third were government/ political interference and competition at 21%. In 2024, the economy was the top threat at 41%, and lack of available skilled workers was 33%, while competition and government/political interference and regulations, codes and standards were in a three-way statistical dead heat for third, around 19%.

Automation technologies to resolve key manufacturing challenges

What technologies will be helping in the coming year? (Figure 5) Among a plentiful list of automation technologies expected to help, artificial intelligence and machine learning (AI/ML) moved solidly into first place in 2025 at 36%, up 8 percentage points from third in 2024 (28%), and eighth in 2023. Below AI/ML in first, no technology listed was separated from its neighbor by a percentage greater than the margin of error for the research, at +/-5.8% at 95% confidence in 2025.

Second place was a tie between process design, measurements and optimization (grouped together) and process optimization (on its own), both at 26%.

Sharing third place statistically was a group of five technologies, all within 5 percentage points:

FIGURE 3: In 2025, the average salary of respondents is $119,682 up from $114,771 in 2024, up from $111,345 in 2023. Bonuses increased also.

FIGURE 1: 68% of respondents expect a salary increase in 2025.

FIGURE 2: 27% of respondents expect an increase in 2025 bonuses.

23% Automation application/upgrades

22% Power quality and reliability

20% Analytics: Data analytics

20% Remote controls, monitoring

18% Cloud computing

See graphic for 18 other automation technologies likely to help. Added for 2025 were quantum computing applications (6%), 5G/6G integration (4%) and extended reality or augmented reality for industrial applications (3%). Note that wireless networking and industrial communications also are listed separately tied, at 12%.

Those were among the 2025 salary and career survey highlights. Below find more results. Download the full report (and other Control Engineering research) at www.controleng.com/research.

Anticipated increases for automation experts

Automation, controls and instrumentation help manufacturers operate more efficiently and fill the skills gap. Automa-

tion professionals see themselves part of the solution with 68% expecting an increase in base annual salary, similar to the last three years.

Company profitability (57%) and personal performance (53%) remain statistically tied in 2025 as the dominant criteria for bonus compensation. The next five bonus criteria were tied for second (Figure 4).

Pick up to three factors for job satisfaction with automation, controls

Work-life balance (described as “workload, flexible work hours, ability to work from home, etc.”) was added among factors for job satisfaction in 2025 and immediately scored highest at 42%. Financial compensation (last year’s top factor) tied at second at 28% with feeling of accomplishment at 30%, a 10-percentage-point increase over 2024. The next five criteria tied for third, all within the margin of error: relationship with colleagues at 21%, job security 19%, technical

17%

Criteria for 2025 bonus compensation

FIGURE 4: In 2025, company profits and personal performance were statistically tied as the leading criteria for non-salary compensation.

What technologies are most likely to help you in the coming year?

PERCENT TECHNOLOGY

intelligence (AI) and machine learning (ML)

measurements, optimization

Analytics for predictive or prescriptive maintenance

Industrial internet of things (IoT), such as more interconnected sensors, monitoring, data collection

Resilient and redundant designs for critical infrastructure

Industrial communications: faster and easier among devices and systems

Industrial communications: Wireless networking

Motion control optimization with advanced actuators, drives

twins and simulation

HMI and SCADA designs

technologies

Robotics, collaborative robotics, mobile robotics

metrics, measurements and related optimization

integrated industrial safety, fail-safe technologies

process controls (APC) optimization

computing

system optimization

computing applications

reality (XR) or augmented

(AR) for industrial applications

FIGURE 5: Artificial intelligence and machine learning made largest jump in 2025, taking the lead among technologies most likely to help in the coming year. (In 2024, AI/ML made the second largest jump to tie for first with six other technologies.)

2025

and benefits 17%. Figure 6 shows 10 other criteria.

To follow-up with work-life balance, a new question in 2025 asked about percentage of work performed remotely; 70% performed some work remotely, with the largest grouping, 27%, fell in the 1% to 20% range. Interestingly, 16% performed more than 80% of their work remotely. In hindsight, clarification is needed to discern between working at home and working someplace other than the office, such as a client’s site for a control system integrator.

Hours worked nearly equaled respondents from 2024 research.

7% worked fewer than 40 (11% in 2024, 9% in 2023, 11% in 2022)

53% worked 40 to 44 hours (48% in 2024, 42% in 2023, 43% in 2022)

17% worked 45 to 49 hours (19% in 2024, 24% in 2023, 29% in 2022)

13% worked 50 to 54 hours (13% in 2024,

14% in 2023, 9% in 2022)

3% worked 55 to 59 hours (same in 2024, 2023, 2022)

7% worked 60 or more (6% in 2024, 8% in 2023, 5% in 2022).

Help from a stunning diversity of automation-related technologies

Control Engineering subscribers develop, integrate and use a wide diversity of controls, automation and instrumentation. In 2025, subscribers selected from among 26 technologies, up from 23: “What technologies are most likely to help you in the coming year? Check all that apply.” In 2024, seven replies were in a statistical dead heat for the top spot, but AI/ML dominates the top spot in 2025 at 36%, as discussed above (Figure 5). Twenty-three percentage points separate the top from the bottom technology in 2024 and in 2025, except for the breakaway AI/ML in 2025, 10 percentage points

above the others.

AI/ML’s impact was suggested in 2024 Control Engineering and Plant Engineering research saying: “When asked what factors are considered when deciding whether or not to implement AI-based automation solutions in industrial processes, the leading responses were: Operational efficiency gains; cost and return on investment (ROI); and availability and quality of data.” Read more in a Feb. 1, 2024, article, “Research: What are obstacles, advice for integrating AI, industrial automation?”

Survey methods for 2024 career and salary report

Research for the 2025 Control Engineering Career and Salary Report resulted from an emailed survey to subscribers, producing 286 qualified responses from March 21 to April 8, 2025, for a margin of error of +/-5.8% at a 95% confidence level. Survey respondents were invited to anonymously provide their annual compensation information and opinions on the current state of their facilities and industries as well as submitting advice for peers. (See next article.)

Engineering salary, bonus details

In Figure 1, 45% expect a salary increase of up to 3% in 2025 (42% in 2024; 45% in 2023 and 2022; 51% in 2021; 52% in 2020; 63% in 2019; 56% in 2018); 16% expect an increase of 4% to 6% or more (18% in 2024; 19% in 2023; 12% in 2022; 14% in 2021; 18% in 2020; 11% in 2019; 19% in 2018); 7% expect more than 6% increase (6% in 2024; 7% in 2023; 10% in 2022); 30% expect the same (31% in 2024; 28% in 2023; 32% in 2022 and 2021; 30% in 2020; 25% in 2019; 23% in 2018); and 2% expect a salary decrease (3% in 2024; 1% in 2023 and 2022; 3% in 2021; 1% in 2020 and 2019; 2% in 2018).

For base salary compensation, the minimum was $22,080 ($20,000 in 2024; $22,000 in 2023; $20,000 in 2022; $28,000 in 2021), and the maximum was $350,000 ($360,000 in 2024; $300,000 in 2023; $266,700 in 2022

What are the biggest threats to manufacturing today?

Lack of necessary materials, parts

Inadequate management

Lack of investments for equipment, software upgrade/replacement Outsourcing, offshoring

Lack of investments for workflow, manufacturing design upgrades

FIGURE 7: Leading threats to manufacturing businesses (select up to three) was the economy and lack of skilled workers, tied at 38%. Taxes and tariffs threat was second at 27%; just 8% said taxes/tariffs was a leading threat in 2024.

and $250,000 in 2021).

For bonus compensation (Figure 2), 13% expect an 1% to 3% increase (18% in 2024; 15% in 2023); 6% expect an increase of 4% to 6% (same in 2024 and 2023); 8% expect an increase greater than 6% (7% in 2024; 8% in 2023); 62% expect about the same (59% in 2024; 57% in 2023); and 11% expect less (10% in 2024; 14% in 2023).

For those receiving bonus compensation (Figure 3), the average received was $18,595, compared to $16,125 and $15,929 in the two years before). The average across all respondents is $13,765, compared to $11,840 the year before); 25% received no bonus (25% last year).

Engineering bonus criteria

Two leading criteria for non-salary compensation were statistically tied: company profitability at 57% (51% in 2024; 55%

in 2023), and personal performance at 53% (51% in 2024; 43% in 2023); see Figure 4. New business/sales at 18% (17% in 2024 23% in 2023); quality metrics at 15% (14% in 2024; 17% in 2023); product profits at 14% (9% in 2024; 19% in 2023), customer feedback at 13% (15% in 2024; 10% in 2023) and company stock performance at 12% (10%in 2024; 13% in 2023), among other responses.

Engineering job satisfaction, manufacturing threats shift

As mentioned, work-life balance (new among factors listed for job satisfaction) was the most important, followed by feeling of accomplishment and technical challenge (tied), followed by relationship with colleagues, job security, technical challenge, relationship with box and benefits all tied for third.

MORE ANSWERS

For more information, see this article online and download the Control Engineering 2025 Career and Salary Report.

CONSIDER THIS

On the next pages, see related articles on: Advancement skills and advice

KEYWORDS: 2025 salary survey, career advice

LEARNING OBJECTIVES

Learn about trends in salary and benefits from the 2025 Control Engineering Career and Salary Survey.

Examine perceived threats to manufacturing and changes from prior years.

Compare and benchmark your career progress with peers in the online version of this study.

ONLINE

Download the full 2025 Control Engineering Career and Salary Survey and Report to see more details, including benchmarking tables. www.controleng.com/research

Learn more about workforce development. www.controleng.com/workforce-development

See recent Control Engineering magazines: www.controleng.com/magazine

All graphics courtesy: Control Engineering research, WTWH Media

See Figure 6 for other criteria. Among manufacturing threats, far and away the economy and lack of skilled workers tied for the lead at 38%; taxes and tariffs, just 8% in 2024, soared to 27% in 2025; in a three-way tie for third are government/ political interference, competition and regulation codes and standards. See Figure 7.

More online and in report

Control Engineering research provides demographics as context, and extra figures in the report provide more information and benchmarking, examining compensation by years, education, years in industry, hours worked by employees managed, job function and facility size. ce

Mark T. Hoske is editor-in-chief, Control Engineering, WTWH Media, mhoske@wtwhmedia.com. Amanda Pelliccione, marketing research manager, WTWH Media, conducted the research and assembled the related report online.

Use 2025 salary survey advice to help your automation career 2025

Respondents to the 2025 Control Engineering Career and Salary Survey provided advice.

Greater use of artificial intelligence and machine learning (AI/ML) led automation topical advice from the 2025 Control Engineering Career and Salary Survey and Report. Two write-in questions asked survey respondents for advice. Among survey respondents, 103 offered advice on skills; 81 provided technology-related related advice. AI was mentioned 50 times in the two writein questions. Frequency of AI mentions was consistent with subscribers’ naming AI as the technology most useful for those working with automation in the coming year (see “Results are in: Control Engineering Career and Salary Survey, 2025.”)

Summary advice follows, lightly edited for style. (Online see more categories and advice, sorted by first category mentioned.) Thanks to all who contributed.

Skills on technologies, applications

Electronics will help, specifically the advances in building automation and equipment interfaces, all associated with the remote access through client security systems.

Be willing to continue to learn new technologies and learn how to apply them to your responsibilities. Be willing to mentor and be mentored by others no

How to advance skills

Reading industry publications, sites, email newsletters, digital guides, vendor, research and association sites and materials

Networking at conferences, memberships in industry organizations, participation in standards bodies and other industry groups

FIGURE: Best way to advance skills are hands-on experiences at 73%, followed by continuing education at 65%. In a statistical three-way tie for third were being mentored, mentoring others and industry publications/websites. All options scored 24% or more. Courtesy: Control Engineering research, WTWH Media

matter their (or your) level of experience.

Know how to apply your knowledge and how to think outside the box. Ask questions and don’t give up easily. Read and review technical articles and always try to be the very best at what you do.

Trends and new technologies are cool and worth checking into, but some of the old, tried-and-true technologies are still the best today.

Understanding the needs of telecommunication power applications, equipment power draws, and communications with the construction team are vital for project completion.

Technical expertise in your specific engineering discipline remains fundamental. Project management and leadership skills become increasingly valuable as you advance. Communication skills help for explaining complex concepts to non-technical stakeholders. Adaptability and continuous learning will help you to keep pace with evolving technologies. Cross-functional collaboration enables you to work effectively with different departments.

It is important to be informed about the upcoming technologies in the market through relevant courses, certifications and reading articles from reliable sources.

Pilot new ideas before wide implementation while working with suppliers. ce

Mark T. Hoske is content manager, Control Engineering, WTWH Media, mhoske@cfemedia.com. Amanda Pelliccione, marketing research manager, WTWH Media, conducted the research and assembled the related report available online.

50 Years of Robust Reliability

Yokogawa’s CENTUM, the first distributed control system in the world, proudly celebrates its 50th anniversary in June 2025. Since its introduction to the market in 1975, CENTUM has undergone regular updates to adapt to the steady stream of evolving technologies over the past 50 years. As new technologies have emerged, Yokogawa continues to maintain backward compatibility across CENTUM generations, ensuring seamless transition for clients.

www.yokogawa.com/special/centum-50th/

Ways manufacturers can recruit, train and retain a skilled workforce

The engineering industry has a labor shortage, particularly among entry-level engineers. Get advice here.

controleng.com

KEYWORDS: Technology co-ops, Skilled labor gap

CONSIDER THIS

How are you addressing the skill labor shortage? Could these techniques help?

LEARNING OBJECTIVES

Understand the complex reasons behind manufacturing’s labor shortage.

Learn about the ways in which a co-op program can help fill entry-level positions.

Determine how a co-op program can help manufacturers retain and continually hire employees.

Companies involved in all aspects of manufacturing and process control face a shortage of candidates for entry-level and early career jobs. Nearly one-third of engineering jobs will go unfilled annually through 2030, according to Boston Consulting Group. Employees early in their careers change jobs more frequently than older peers traditionally have. Many companies focus primarily on candidates who have recently graduated from a university, community college or trade school with little to no industry experience. This strategy has disadvantages. Two of the biggest challenges employers face are training inexperienced employees to get them up to speed as quickly as possible and ensuring the employer supports continued career development.

How co-op programs

Recruiting and retention aren’t one-sided. From the candidate’s perspective, early career employees aspire to assignments and projects where they can make an impact and put their strengths to use. How does an employer fill its engineering roles with candidates who want to contribute, learn and can integrate into the organization in a relatively quick timeframe and at a lower cost? The answer is simple: create and grow a successful co-op program.

A co-op program is designed to hire students over multiple, semester-long periods with the desired result of full-time employment once they graduate from their respective programs.

Students who begin as co-op participants and later

transition to full-time employees benefit from shorter learning curves and are more cost-effective than other entry-level employees. The retention rate tends to be much higher than that of employees who are hired without an existing relationship. Co-op participants get the benefit of learning about the employer, its products, processes and culture during rotations, which provides a better understanding when moving into the workforce full-time. (The following sections are abbreviated. See more details online.)

Manufacturers can recruit smarter

Recruiting co-op participants requires time, effort and passion to grow the entry-level workforce. Meeting, screening and interviewing potential candidates results in a pipeline of student candidates which provides a pipeline of future talent for full-time positions.

Train and retain, recruiting

A mentorship program gives new hires a confidential channel to ask questions or voice concerns. It also allows employees to get to know one another in an environment outside of work, which may be more comfortable for addressing issues or sharing ideas.

To evaluate a co-op program ask three questions: 1) What percentage of co-ops stay in the program for the full term of typically two to four rotations? 2) What is the conversion rate of co-ops to full-time employees? 3) What is the average length of service for employees who participated in the co-op program? ce

Megan McIntosh is a control engineer, and Jody Poirier is a controls and automation division engineering leader with Hargrove Controls & Automation. Edited by Sheri Kasprzak, managing editor, Automation & Controls, WTWH Media, skasprzak@wtwhmedia.com.

ANSWERS

Zachary Sample, Emerson and AspenTech, Austin, Texas

How to leverage digital twins for asset lifecycle optimization

Forward-thinking organizations extend the value of digital twins by using them to drive collaboration and optimize processes throughout the entire lifecycle of their assets.

Process manufacturers have increased their need for optimal performance to meet shifting sustainability standards and increasing global competition in the marketplace. To achieve these goals, many have begun to pursue new strategies to drive process improvements. Dynamic simulation has gained traction in recent years — particularly when employed as part of a digital twin. More organizations have recognized the ability of a digital twin simulation to bring critical value to project execution, helping teams more easily engineer and test their processes and equipment well before any physical technology must be purchased or implemented.

change management workflow. The teams that do so quickly find that their digital twin not only pays for itself as part of the project, but it also delivers continuous value as a sandbox for collaboration among personnel in process and automation engineering, plant management, training management, operations and maintenance, and more (Figure 1).

Digital twin simulation use cases

Today, digital twin simulation tools are often thought of as discrete assets designed to deliver project success through both automation testing and operator readiness. This understanding makes sense, as three of the core use cases of a digital twin deliver significant value and ROI during project execution.

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KEYWORDS: Digital twin, new applications

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CONSIDER THIS

Are you applying the value of digital twin simulation use cases?

The most successful teams have even discovered they can take their project simulations further by using existing digital twins to create operator training simulations. Using these systems, operators can practice new control strategies via interfaces virtually identical to the ones they will see during actual production. By creating this additional critical use case for simulations, most project teams have discovered they can easily make their digital twin simulation technologies pay for themselves with an extremely rapid return on investment (ROI).

However, the value and ROI delivered in the project phase by simulation technologies is just the beginning. With the right planning and strategy, today’s organizations can move away from a project-centric model for deploying digital twin simulations, instead opting for a more holistic model that incorporates the digital twin into a full lifecycle

1. Engineering design - In the earliest stages of project design, a digital twin simulation can help slash costs and schedule risk when teams use it to evaluate and validate their engineering and automation designs. In addition, teams can use their digital twins in tandem with capital cost estimation simulations to more easily scale project elements up and down to stay within budget.

2. Automation testing - Teams use digital twin simulation to test configurations, develop new process and automation strategies and fine tune operational strategies and results before any equipment needs to be ordered, much less installed onsite. This type of offline startup and commissioning saves weeks to months of time on greenfield projects by having the automation system and operators run through startup procedures repeatedly until they are comfortable with the result, testing the automation in a variety of configurations — potentially hundreds of times — to determine the safest, most effective procedures.

3. Operator training - Immediately after project completion, a digital twin continues to deliver value.

Operator training software used in parallel with the digital twin empowers operators with enhanced training. Users can experience new control strategies on systems that look, feel and respond exactly like the controls they will use in their day-to-day work, without any of the risk associated with practicing on live equipment. Because the digital twin is completed early in project development, the training solution can be built well before the project is finished; operators can be trained and ready on the first day of operation.

New value to digital twin simulation

Often, after project completion, the digital twin is shelved. However, an additional, more powerful use case exists for teams ready to make the most of their investment. Dynamic simulation tools also offer a versatile platform for operations teams to experiment with new equipment, strategies and configurations, enabling them to boost performance and promote sustainable practices — all without disrupting or jeopardizing plant operations — across their plant lifecycle. When equipped with the appropriate simulation software, these tools stay synchronized with the evolving plant environment, ensuring they remain a reliable resource for every functional area of the plant to enhance workflows and support teams in achieving their constantly shifting objectives.

Digital twins as a digital sandbox

When teams embrace all the use cases of a digital twin simulation, they can unlock the benefits of

1: The value of a digital twin does not end when a project is completed but instead increases when teams evolve their simulation into a lifecycle change management workflow tool. Images courtesy: Emerson

a lifecycle change management workflow solution. Successful changes depend on accurately designed solutions, proper configuration in implementation and successful adoption site wide, which requires clear communication and training. Whether it is deployment of a new graphics package, new platform, advanced process control, state-based control or even a change in how the process behaves, every adjustment impacts a complex existing workflow that touches many functional groups in the plant.

To ensure that impact does not cause disruption, teams must understand the areas of improvement from process and automation engineering perspectives, assess the best ways to make changes, test the solutions, update the procedures, notify operations of any changes, train operators on changes, deploy the solution to production and ensure the value of change is recognized through adoption by users. Each of those steps is important and interconnected, and a mistake in any one can result in a complex, costly and time-consuming roll-back.

Digital twins empower teams to simulate the process and automation system to understand how everything will work together. The team simulates any issues so they understand them and can use their understanding to develop potential solutions. They test those potential solutions against the offline digital replica of the plant to gauge results and work out issues before implementing anything on the actual control system. Doing so simplifies change management and helps ensure smoother

‘Digital twins can apply changes to an offline digital replica of the plant to gauge results and work out any issues.’

FIGURE

ANSWERS

transitions for all stakeholders because every modification is examined from a wider lens.

For example, an organization that recently experienced a safety event might need to remedy that event and prevent it in the future. But that job is often not the responsibility of one group but many, including process and automation engineering, plant management, training management, operations and maintenance. If all those groups have access to a central sandbox for collaboration built on a digital twin, they can more easily work together to find a solution that most effectively solves the problem before implementing in production.

Hazard, cost-benefit, other analyses

First, users can employ the digital twin to support process hazard analysis to identify what went wrong. Once everyone understands the root cause, they can come up with solutions. Automation and process engineering can determine if they need to automate or create new processes around operations or even change the process so that safety incident cannot happen again.

Insights

Digital twin simulation use cases

uEngineering design

uAutomation testing

uOperator training

Further employing the digital twin, the team can perform cost-benefit analysis to identify which available solutions are optimal. They can also identify the safest, most accurate and most cost-effective ways to implement those solutions to explore deployment in the field, change operating procedures to reflect the new environment and train the operators effectively. Ultimately, because everyone is using the same comprehensive offline tool, that deployment is seamless when the team is ready to deploy a change to production (Figure 2).

However, it is not just safety incidents where a

collaborative environment based on a digital twin is valuable. The solution can be applied to any change — from debottlenecking to control strategy changes and more — to continuously determine what manufacturers need, identify the solutions, train personnel, implement changes and then start the process over as many times as needed to identify and patch further gaps.

Owning digital twin solutions for success

Turning a digital twin simulation into a lifecycle change management workflow solution typically requires one key element that is often left out of project planning: ownership. If ownership of the digital twin solution by a long-term stakeholder is not established up front, it is often neglected and the simulation becomes increasingly irrelevant, making it a poor tool for change management. In-house teams are typically entirely capable of managing a simulation across the lifecycle, but only if the team has a plan for who owns what and how often changes need to be synchronized to keep the digital twin current. Establishing this ownership upfront eliminates the extra costs necessary for a massive overhaul of the simulation if it drifts far out of sync with the production system. Ideally, identifying a long-term stakeholder and workflow for simulation management over its lifecycle will be part of the initial procurement of the digital twin.

Capturing the advantages

As access to and use of digital twin simulations has become more common, teams around the globe are seeing dramatic benefits in the cost and schedule of their project execution. However, it is not a huge leap to extend the project benefits to lifecycle benefits from a digital twin, and the results will deliver significant value over the lifecycle of the system.

If organizations recognize the lifecycle potential of a digital twin, they can take steps early in project design and lifecycle planning to leverage the technology to create a collaborative sandbox. Doing so can help support the operational flexibility and organizational cohesiveness that drive competitive advantages and improve safety, production and sustainability across a plant or a fleet. ce

Zachary Sample is an enterprise consultant with Emerson and AspenTech.

FIGURE 2: A properly configured and maintained digital twin becomes a collaboration sandbox driving continuous improvement.

No other platform manages redundancy better.

Calling all system integrators...

Entries are due Friday, August 29, 2025

Who should enter?

If you’re a system integrator with demonstrable industry success, Control Engineering and Plant Engineering urge you to enter the 2025 System Integrator of the Year competition. Past System Integrator of the Year winners—Class of 2025, Class of 2024, and Class of 2023—are not eligible to enter the 2026 System Integrator of the Year program.

What’s in it for the winners?

The chosen System Integrator of the Year winners will receive worldwide recognition from Control Engineering and Plant Engineering. The winners also will be featured as the cover story of the Global System Integrator Report, distributed in December 2025.

How will the competition be judged?

Control Engineering and Plant Engineering’s panel of judges will conscientiously evaluate all entries. Three general criteria will be considered for the selection of the System Integrator of the Year:

• Business skills

• Technical competence

• Customer satisfaction

June 2, 2025

The 2026 System Integrator Giants program opens for submissions

In order to be considered for the SI Giants program, your company must have a complete, valid listing within the Global System Integrator Database, and the entry form must be completed truthfully and accurately.

Smart. Modular. Cabinet-free. The MX-System Advantage.

The MX-System from Beckhoff is a flexible, space-saving, and intelligent automation hardware platform that can completely replace conventional control cabinets. The machine mounted system is based on modular baseplates and pluggable function modules that handle everything normally housed in a traditional control cabinet.

This opens up exciting new possibilities to reduce equipment footprint, installation time, and maintenance efforts in today’s manufacturing operations. As a modular control cabinet replacement that can also be decentralized on the machine or line, the IP67-rated MX-System dramatically cuts down on engineering, assembly, installation, and maintenance work for wide-ranging industries and applications. This enables much more efficient processes for machine builders, systems integrators, and end users. These efficiencies extend from planning, setup and installation of the MX-System right through to the maintenance of MX-System-equipped machines deployed in the field.

A complete MX-System can be set up in just one hour, including final testing. For a comparable control cabinet, the total setup time would require at least 24 hours. The MX-System directly counters skilled worker shortages, as tasks are greatly simplified and take far less time to complete. In addition, wiring errors are completely eliminated and users can migrate to cabinet-free machines over time via hybrid systems.

The modular and scalable MX-System offers a range of other benefits, including:

• Reducing overall machine footprint by up to 70%.

• Slashing the number of control components required by a factor of 10.

• Consolidating documentation by as much as 80%.

• Removing numerous points of failure.

• Accelerating time to market through rapid commissioning.

The cabinet-free MX-System is a completely new approach to automation system design and is set to transform how equipment and machines are designed. As a modular control cabinet replacement that can also be decentralized on the machine or line, the IP67-rated MX-System dramatically cuts down on engineering, assembly, installation, and maintenance work for wide-ranging applications.

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.

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.

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.

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.

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.

Cogent DataHub™ Tunnel/Mirroring solves DCOM security patch issues

Cogent DataHub™ technology from Skkynet lets you network OPC DA data using only local OPC connections, and pass the data across the network over TCP, with optional SSL if needed. A DataHub tunnel/mirror avoids DCOM and solves the Microsoft KB5004442 security patch problem.

Microsoft took an important step recently toward keeping industrial systems secure. They made their KB5004442 security patch for DCOM mandatory. This affects all systems that network OPC DA, one of the most widely used industrial protocols in the world. Now all OPC DA systems that use DCOM across a network must use the highest security settings. Any networked connections with lower security settings will fail.

To solve this problem, DataHub tunnel/mirroring makes only local connections to both OPC DA servers and clients. It passes their data across the network over TCP, using SSL if required. Both OPC DA server and client stay connected if the connection goes down, and the client is informed of the break. This approach completely eliminates the need for DCOM.

For moving data beyond the plant network, DataHub tunnel/mirror technology offers a more secure connection than DCOM. You can configure it to make only outbound connections from the OPC server side. This keeps all inbound firewall ports closed, while still allowing the data to flow one way or both ways.

To connect OT to IT for remote access, DataHub tunnel/mirror technology supports network isolation through a DMZ. By installing a third DataHub instance on the DMZ, each side can make outbound connections through firewalls, and still maintain one-way or two-way data flow.

Whatever your application, there’s no need to view Microsoft’s move to secure DCOM as a problem. DataHub tunnel/mirror technology offers solutions at any level that are more flexible and more secure than DCOM.

Find out more.

1-905-702-7851 | sales@skkynet.com https://cogentdatahub.com

Yokogawa Enables the Shift to Autonomous Sustainable Industry

Yokogawa is positioned at the forefront of industrial innovation and environmental stewardship, serving as a trusted partner to customers worldwide. As a global leader in Industrial Automation, Control, and Test & Measurement, we integrate advanced technologies with deep engineering expertise, comprehensive project management, and lifecycle services to deliver measurable improvements in safety, quality, reliability, and operational excellence across the energy, chemicals, power, life sciences, water, and emerging energy transition sectors.

By leveraging our extensive domain knowledge and pioneering automation solutions, Yokogawa empowers industry leaders to drive decarbonization, accelerate digital transformation, and confidently invest in next-generation energy systems—including green hydrogen, battery manufacturing, and CCUS-enabled blue ammonia production. Our vision for Industrial Automation to Industrial Autonomy (IA2IA) empowers customers to achieve higher productivity, enhanced safety and reduced operational risk through AI-driven technologies and real-time connected intelligence while translating these advancements into practical, actionable roadmaps that unlock value today and build resilient, future-ready enterprises for tomorrow.

Our brand promise, “Co-innovating tomorrow,” reflects our commitment to collaborative partnerships with operators, OEMs, energy providers, and research institutions. Together, we expand the boundaries of plant

performance and accelerate the transition toward autonomous operations, AI-enabled decision support, and open, vendor-agnostic ecosystems.

Yokogawa’s dedication to sustainability is embodied in our three overarching goals: achieving net-zero emissions, ensuring well-being across the value chain, and fostering circularity and resource harmony. Through our awardwinning measurement technologies, carbon management solutions, and energy optimization platforms, we help manufacturers significantly reduce greenhouse gas emissions while safeguarding profitability, supporting a more sustainable future for all.

Beyond technology, Yokogawa advances corporate social responsibility initiatives rooted in our core philosophy, supporting STEM education, community resilience, and transparent governance in security and data ethics. Our commitment to sustainability and corporate social responsibility (CSR) reports provides clear, transparent metrics on our progress and ongoing improvement, showing that our goals are aligned with our accountability.

With over a century of experience, a strong track record of reliability, and a commitment to co-innovation, Yokogawa remains dedicated to enabling a sustainable society and delivering long-term value for our customers, stakeholders, and the planet.

Innovations

Product of the Year Winners, Control Engineering, 2025

Winners will be announced June 2, after the printer deadline for this publication, but see who won and access more information about the winning automation products and the grand winner (most votes overall), here. https://www.controleng.com/product-of-the-year

Option slot and stackable I/O modules for the PLC family

AutomationDirect added specialty modules for its Click PLC family. CPUs offer the utmost versatility with up to two option slots for custom I/O configurations. Option slots fit specialty modules, such as C2-DCM serial communication module and the new C2-NRED and C2-OPC UA modules. The C2-NRED module provides an industrial interface to the open-source software tool for developing IIoT applications, Node-RED. With low-code Node-RED programming, you can easily facilitate interactions between the Click Plus CPU and upper-level IT/business systems. OPC UA provides a standard way to share data. AutomationDirect, www.automationdirect.com

High power lithium cells for mobile or remote automation

Tadiran Batteries introduced TLM-1550SPM high-power AA-size lithium oxide batteries for critical applications. The lithium metal oxide battery combines a small footprint, high-power output, high capacity, energy density, on-demand response, extended operating life and extreme reliability in harsh environments. It is miniaturized to deliver high power within a compact form, is ruggedly designed with a wide temperature range for extreme environments and has an ultra-long shelf life (up to 20 years) due to a very low annual self-discharge rate. Industrial applications include IIoT-connected devices, industrial monitoring and actuation and emergency backup power. Tadiran Batteries, www.tadiranbat.com

IP69 ultra-premium efficient motor

ABB launched the Baldor-Reliance Food Safe SP5+ motor, the world’s first IP69 ultra-premium efficient motor in January. The energy-efficient design that leverages cutting-edge rotor technology to reduce energy consumption and optimize performance. Meeting or exceeding IE5 efficiency standards, SP5+ offers significant cost savings and sustainability gains when paired with a required variable speed drive. Built for food and beverage applications, the motor’s IP69-rated stainless steel construction is engineered to endure high-pressure washdowns, steam cleaning and exposure to dust. ABB NEMA Motors, www.abb.com

Ultrasonic sensors integrate IO-Link, fit compact areas

Three new series of ultrasonic sensors integrate IO-Link communication: the Carlo Gavazzi Automation UA12, UA18 and UA30 ASD IO Series. Their compact design, reduced blind zone, improved sensing range and simplified “teach” procedure support efficient performance inww industrial applications. They offer contactless position and distance measurements. Carlo Gavazzi, www.gavazziautomation.com/en-global

Drive system optimization enables energy reductions

Siemens Connection Module IOT collects data from the drive train and sends it to the Drivetrain Analyzer Cloud. Load, energy consumption and power use are displayed in real time. The system uses AI-based analysis to identify deviations from optimal operation, track energy use, CO2 emissions and costs and suggest areas for improvement. It can cut drive system energy consumption by 10% to 20%.

Siemens, www.siemens.com See

INNOVATIONS

See more New Products for Engineers www.controleng.com/products

New 64-bit Arm computers with quad-core processor for IIoT applications

Moxa Inc. has launched new 64-bit Arm-based computers Moxa UC-3400A and Moxa UC-4400A Series featuring dual-wireless, 5G/LTE and Wi-Fi 6 connectivity. Built with an Arm Cortex-A53 quad-core processor, these computers are tested for radio-frequency (RF) performance reliability and designed for industrial IoT applications. System software is designed to streamline development and IEC 62443-4-2 Security Level 2 compliance. Moxa Inc., www.moxa.com

Smarter alarms, a.NET features, and seamless control for DCS upgrade

Yokogawa Electric Corp. released Yokogawa Centum VP R6.12, the latest version in the Centum VP series distributed control system (DCS). The Centum VP architecture includes human machine interfaces, field control stations and a control network. It is compatible with continuous and batch process control, as well as manufacturing operations management. Enhancements include ability for users to add comments to shelving operations to rationalize actions; shelved alarm statuses can be displayed dynamically on graphic panels. Browser bars show the number of active alarms, supporting clearer alarm tracking and operator collaboration and transition status of alarm suppression can be notified to upper OPC Clients through Exaopc. Yokogawa, www.yokogawa.com

New HF RFID read/write head for tracking and material handling

Turck Q130 HF read/write head reads and writes data to RFID tags on moving products and carriers. It can be used in automatic identification and tracking applications that need a read point. It supports applications such as conveyor transport, overhead cranes, autonomous mobile robots (AMRs) tote tracking and material handling in packaging plants and warehouses. Its extended temperature range of -40 to +70 °C works for for cold storage logistics. The read/write head can read and write data carriers while in motion, supporting high conveyor throughput rates. This device integrates the interface block, which can help reduce hardware requirements and simplify installation. It supports communication over Profinet (PI North America), EtherNet/IP (ODVA) and Modbus TCP networks. Turck, www.turck.us/en

Pump station has duplex high-capacity pumps

Smith & Loveless Inc. Everlast Series 5000 Pump Station is an above-ground wastewater pump station providing high efficiency, long service life, operator ease and safety and low operation and maintenance costs in a factory-built and tested system that is fast and simple to install in new installations and replacements of submersible pumps. Larger, premium efficiency motors are designed for reduced energy consumption costs and a smaller carbon footprint. Unlike submersible pump stations, all mechanical equipment is safely located and easily accessible above ground and outside the wet well. Pumping capacity is up to 3,000 gpm (189 lps) and higher heads (TDH) up to 255 ft. (78 m). The EV Series 5000 pump station includes a split, dual-piece enclosure supported by gas shocks, specifically designed to accommodate larger pumps up to 10 in. (250 mm) in size. Smith & Loveless Inc., www.smithandloveless.com

Control system software for machine control, simulation

Emerson releases PAC Machine Edition (PME) 10.6, the integrated development environment software used to configure and manage control system devices, including PACSystems programmable logic controllers (PLCs), QuickPanel human-machine interfaces (HMIs), and more. This update elevates the user experience with simulation, data monitoring enhancements, optimized connectivity and programming efficiency. PACSystems Simulator is a newly incorporated component, empowering users to easily write, test, and troubleshoot control logic on a PLC emulator. This can be done before purchasing new control hardware to speed development and ensure the correct selections are made. Emerson Systems, www.emerson.com

Back to Basics

INDUSTRIAL NETWORKING

Single-Pair Ethernet breaks down the limits of connectivity

Single-Pair Ethernet (SPE) is a networking technology designed to provide Ethernet connectivity over one set of twisted-pair copper wires instead of two pairs or more. SPE can include power. At 10 Mbit/s, SPE run up to 1,000 meters.

Single-Pair Ethernet found its way into the automation and control markets via Ethernet-Advanced Physical Layer (APL), the intrinsically safe version of SPE. A need in the process control industries to adapt SPE for hazardous environments was met with an effort known as 2-Wire Intrinsically Safe Ethernet (2-WISE).

The same companies that created Ethernet-APL want to ensure SPE will function equally well without intrinsic safety. It is projected these harmonization activities will commence in 2025 to standardize its power classes. This will ensure SPE products from all vendors can connect seamlessly.

In November 2024, Profibus & Profinet International (PI) announced a harmonized mating face for SPE connectors. PI Working Groups put forth a concept based on the hybrid IEC 63171-7 plug variant –– but adapted for IP20 and IP65 (M8 and M12) environments. Until then, no single connector had been agreed upon.

Figuring out the future of SPE Ethernet

The Ethernet-APL Group decided 10 Mbit/s SPE (10BASE-T1L), due to its long-distance capabilities, was best suited for process control plants. Such speeds are hundreds, or in some cases, thousands of times faster than current technologies. Even outside the process control industry, 10 Mbit/s satisfies many use-cases. However, SPE can be utilized at 100 Mbit/s (100BASE-T1L) with cable runs of potentially up to 500 meters, or at 1 Gbit/s for up to 40 meters. Therefore, work has begun to prepare Profinet for these standards, when they emerge.

How SPE Ethernet increases transparency

Single-Pair Ethernet allows for even the smallest devices at the field level to use the same protocols as those at higher levels. Transparency and data throughput are greatly increased, making the realization of IIoT/Industry 4.0 use-cases easier than ever before. ce

Michael Bowne is the executive director, PI North America, the organization for Profinet, Profibus, and other networking technologies. Edited by Sheri Kasprzak, managing editor, Automation & Controls, WTWH Media, skasprzak@wtwhmedia.com.

LEARNING OBJECTIVES

Discover how Single-Pair Ethernet can break through connectivity limitations. Learn about how standardized power classes will ensure SPE products from all vendors can connect seamlessly. Find out why SPE is appropriate for process control plants.

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