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november 2016

The eXact I/O you need

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(PX-TCP2: 2-port Modbus TCP Coupler)


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(PX-970: AC Power Feed Terminal)

$277.00 (PX-334-K: Thermocouple)


Xpansion I/O has never been so practical Built to be versatile in the field, the Protos X Field I/O system has a slim design with numerous I/O point configurations. The small footprint lets you install Protos X I/O assemblies exactly where you need them, even in tight locations. No need for excess field wiring, no need for unused I/O points and with the already low price, you’ll save money again and again with Protos X.

Distributing I/O for your process saves space, wiring and money!

• Rackless design for easy installation in areas with limited space • Bus Couplers available in both Modbus RTU/ASCII and Modbus TCP protocols to integrate with a wide variety of controllers and SCADA/HMI packages • Discrete terminals available in AC and DC with a variety of point configurations including: 2, 4, 8 or 16 points • 2, 4 and 8-channel analog terminals with 4-20 mA, 0-10 VDC, and +/- 10 VDC capabilities, as well as, RTD and Thermocouple options • Fully expandable up to 255 I/O terminals • FREE, downloadable, easy-to-use configuration software tool • A variety of power supply and power distribution options give you added versatility

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November 2016 • Volume XXIX • Number 11

Cover Story 30 / Harness big data

How to manage new information streams, identify correlations, and achieve gains. by Jim Montague

features Support & protect

measure & manipulate

38 / Cybersecurity in the SIS world

43 / Motors & drives make magic

Find and slay the dragons lurking in typical safety instrumented systems. by William L. Mostia, Jr., P.E.

Innovation, intelligence and networking bring motion to life in unusual applications and environments. by Jim Montague

CONTROL (ISSN 1049-5541) is published monthly by PUTMAN Media COMPANY (also publishers of CONTROL DESIGN, CHEMICAL PROCESSING, FOOD PROCESSING, THE JOURNAL, Pharmaceutical Manufacturing, PLANT SERVICES and SMART INDUSTRY), 1501 E. Woodfield Rd., Ste. 400N, Schaumburg, IL 60173. (Phone 630/467-1300; Fax 630/467-1124.) Address all correspondence to Editorial and Executive Offices, same address. Periodicals Postage Paid at Schaumburg, IL, and at additional mailing offices. Printed in the United States. © Putman Media 2016. All rights reserved. The contents of this publication may not be reproduced in whole or part without consent of the copyright owner. POSTMASTER: Send address changes to CONTROL, P.O. Box 3428, Northbrook, IL 60065-3428. SUBSCRIPTIONS: Qualified-reader subscriptions are accepted from Operating Management in the control industry at no charge. To apply for qualified-reader subscription, fill in subscription form. To non-qualified subscribers in the Unites States and its possessions, subscriptions are $96.00 per year. Single copies are $15. International subscriptions are accepted at $200 (Airmail only.) CONTROL assumes no responsibility for validity of claims in items reported. Canada Post International Publications Mail Product Sales Agreement No. 40028661. Canadian Mail Distributor Information: Frontier/BWI,PO Box 1051,Fort Erie,Ontario, Canada, L2A 5N8.

N o v e m b e r / 2 0 1 6


The Next Level of Innovation.

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November 2016 • Volume XXIX • Number 11

On-site On-line Hands-on or in the Classroom

Departments 11 / Editor’s page

Cubs win The Chicago baseball team isn’t the only group of “loveable losers” ending a drought.

12 / Control online

Our most recent, valuable and popular offerings at

15 / Feedback

You can do intrinsically safe wireless, human factors and Deepwater Horizon

16 / Lessons learned

Controlling the smart cars, part 2 They’ll be safer if engineers draw on the experience of other industries.

20 / On the bus

Control at the edge, revisited A new flame comes into our long love/ hate relationship with distributed control.

22 / Without wires

Redefining determinism Today, even Ethernet and wireless are almost always fast enough.

24 / In process

Emerson, Yokogawa user groups meet; ICS Cybersecurity event gathers experts and solutions; GE Oil & Gas to merge with Baker Hughes; Festo multitasks at new Ohio plant

29 / Resources

Information to do your level best

46 / Develop your potential

FOPDT modeling A good way and a better way to test a first order plus dead time model.

49 / Ask the experts

How to determine open-loop gain? What to do when the process overshoots the mark.

51 / Roundup

Terminal terminus—and I/O The latest choices in I/O and terminal blocks are more flexible than ever.

53 / Exclusive

Easy, reliable, safe level measurement Rosemount 5408 radar level transmitter and 2140 vibrating-fork level detector.

54 / Products

Selections from our editors’ inboxes.

You choose. A blended training approach to help you keep up with today’s challenges Customize your training experience through the unique offerings provided to you through our Process Training University. Whether it be on-site, on-line or in the classroom, choose a training package that is tailored to meet your needs.

55 / Control talk

Success in career and system migration The motivations and rewards often seem to have a lot in common.

58 / Control report

Busy fall Big data, cybersecurity, election results or other chores all need the same response from you.

Find out more about Endress+Hauser’s unique training: Circulation aUdited december 2015 Food & Kindred Products  12,824 Chemicals & Allied Products  10,797 Systems Integrators & Engineering Design Firms  8,103 Pharmaceuticals  4,405 Primary Metal Industries  4,290 Petroleum Refining & Related Industries  4,198 Miscellaneous Manufacturers  3,171

Electric, Gas & Sanitary Services  Rubber & Miscellaneous Plastic Products  Paper & Allied Products  Stone, Clay, Glass & Concrete Products  Textile Mill Products  Tobacco Products  Total Circulation 

3,451 3,380 2,874 1,604 803 120 60,020

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Cubs win


nce upon a time, not so long ago and for about 10 years, I was on a team of “loveable losers.” On hiatus from Control between 2003 and 2012, as editor of Plant Services magazine I did all I could to champion the cause of asset reliability through proactive maintenance. Convinced of the intrinsic rightness of converting critical equipment from catastrophic breakdown and wasteful preventive to the advanced technologies of predictive maintenance (as part of a world-class, risk-based, reliability-centered asset management program, of course), I sat at the feet of crusty gurus as we wondered aloud how any successful company could survive, how any reasonable person could not see the light, and embrace the power of vibration, ultrasound, infrared and oil analysis. But again and again, most plants found they couldn’t get it done. They couldn’t spare the labor, training and equipment dollars long enough to get enough ROI for sufficient time to sustain the effort—to get over the mountain and reach the promised realm of reliability. And of the few that could, many found themselves slipping back when management recognized and rewarded their good work and savings by cutting their budgets and letting go the very people who made it happen. Their handhelds, cameras and sample containers gather dust, and the fresh trainees’ knowledge slips away as their days are again filled with emergency repairs, expediting replacement parts and rigging workarounds. Every now and then, a vendor PR person from my Control days would realize we were talking about asset management, and would want to tell me again how their instruments and valves can monitor their condition and send alerts to operations when maintenance is needed, and I would say, “That’s nice. But let me know when you’re ready to connect that beautiful DCS and its plantwide network to sensors on real equipment that has bearings, gears and windings, and call me when you get it integrated with asset


management systems so it alerts the right people, and maybe even sends them a work order.” Well, as you probably know, on Nov. 2, the Cubs won the World Series, ending the team’s 108-year streak as “loveable losers.” That’s nice. But the week before, at the Emerson Global Users Exchange in Austin, I learned the company is making a major-league push into rest-of-plant asset condition monitoring. As part of Emerson’s Plantweb digital ecosystem, the company’s “pervasive sensing” initiative uses wireless and fieldbus to connect a new generation of low-cost sensors and provides analytics for insights into asset performance. This year, the company added technologies to monitor pipes and vessels for corrosion and erosion; medium-voltage switchgear for hot spots, partial discharges and humidity; toxic gases; and process temperatures with sophisticated surface-mount sensor/transmitters. There also are pressure gauge, steam trap, relief valve and power monitoring applications. A new Asset Health Advisor performs diagnostics and provides alerts for predictive maintenance. It takes in heat exchangers, blowers, compressors, cooling towers and pumps. By building condition monitoring and predictive technologies into the plant, whether by making them part of the automation system or by using a separate Industrial Internet of Things (IIoT) infrastructure, Emerson and other automation, sensor and analytics suppliers are reducing the needs for handhelds, for making rounds, and even for specialized training, as much of the knowledge is being built into software applications or made available via monitoring and analysis by off-site experts. It’s great that the Cubs have ended the Curse of the Billy Goat. I’m even happier that soon, asset management won’t require so much time at the feet of crusty reliability gurus.


Their handhelds, cameras and sample containers gather dust, and the fresh trainees’ knowledge slips away as their days are again filled with emergencies.

N o v e m b e r / 2 0 1 6



It’s high time to upgrade systems



The latest critical trends in I/O systems take advantage of the versatility and communications

capabilities of intelligent, configurable I/O. Being able to install universal I/O based on approximate point count, then configure or reconfigure it later to match the needed process variables

allows construction and installation to proceed independent from engineering, taking I&C off the critical path. Intelligent I/O transmits more than just the measured and manipulated variables, opening the possibilities for integrating capabilities from condition monitoring and predictive

maintenance to all the potential of the Industrial Internet of Things (IIoT). Here’s the latest I/O

system coverage from the annals of Control. For a deeper dive into I/O technology, applications and analysis, download the March 2015 Control State of Technology Report on I/O Systems.


The key to better decisions isn’t just more data; it’s getting the right information delivered the right way at the right time. This downloadable special report curated by the editors of Control provides a concise, single-volume overview of the trends and technology that are enabling SPECIAL REPORT: decision support with HMI/SCADA. Articles Trends in include: HMI/SCADA • How to design a better operator-centered system • Developing a lasting plan for managing alarms • Using visible data for operational excellence • Understanding and minimizing HMI/SCADA system security gaps Our “Trends in HMI/SCADA” report is available at How advanced systems are simplifying operators’ ability to respond to what matters.

A market analysis by ARC Advisory Group estimates that globally, in excess of USD $65 billion in control systems have reached their end of life, with more than 80% of those systems having been in service for over 20 years. This whitepaper explains why the time is now to upgrade to a new control system migration standard.

NERC CIPs continue to expose the grid Cybersecurity expert Joe Weiss discusses NERC CIP in the wake of the Ukranian hack. www.controlglobal. com/blogs/unfettered/the-nerc-cipscontinue-to-expose-the-grid-to-significant-cyber-vulnerabilities-evenafter-the-ukrainian-hack


he latest critical trends in I/O systems take advantage of the versatility and communications capabilities of intelligent, configurable I/O. Being able to install universal I/O based on approximate point count, then configure or reconfigure it later to match the needed process variables allows construction and installation to proceed independent from The rise of configurable I/O engineering, taking I&C off the critical path. Intelligent I/O transmits more than just the measured and manipulated variables, opening the possibilities for integrating capabilities from condition monitoring and predictive maintenance to all the potential of the Industrial Internet of Things (IIoT). Our latest State of Technology eHandbook explores more.


The rise of configurable I/O

Pepperl+Fuchs acquires ecom The move will accelerate new developments in technology and products, according to CEO Gunther Kegel.

Sponsored by

Industrial computers field guide and resources

12 N O V E M B E R / 2 0 1 6

At the 2016 Yokogawa User Conference, Sandy Vasser told how he and his team at ExxonMobil set out to improve their own project execution processes, and catalyzed industry change in the process.

ControlGlobal E-News Multimedia Alerts INDUSTRIAL COMPUTERS


White Paper Alerts

Industrial computers, PCs and many of their counterpart devices can take almost any form these days, and serve in almost any setting thanks to miniaturization, diskless and fanless technologies, more capable software and other advances. This is good news for users and applications in harsh environments, but it can be hard to sort out all the available options. Here is a bounty of instructional materials, products and other resources that can help you do just that.


Go to and follow instructions to register for our free weekly e-newsletters.


Industrial computers, PCs and many of their counterpart devices can take almost any form these days, and serve in almost any setting thanks to miniaturization, diskless and fanless technologies, more capable software and other advances. This is good news for users and applications in harsh environments, but it can be hard to sort out all the available options. This report includes a bounty of instructional materials, products and other resources that can help you do just that.

Industry change doesn’t ‘just happen’


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editorial team Editor in Chief: PAUL STUDEBAKER Executive Editor: JIM MONTAGUE Digital Managing Editor: KYLE SHAMORIAN Contributing Editor: JOHN REZABEK


design & production team VP, Creative & Production: STEVE HERNER Art Director: JENNIFER DAKAS Art Director: CHRIS YU Senior Production Manager: ANETTA GAUTHIER

publishing team VP, Content and Group Publisher: KEITH LARSON VP, Sales & Publishing Director: TONY D’AVINO 630/467-1300 x 408, Midwest/Southeast Regional Sales Manager: GREG ZAMIN, 630/551-2500, Fax: 630/551-2600 Northeast/Mid-Atlantic Regional Sales Manager: DAVE FISHER 508/543-5172, Fax 508/543-3061 Classifieds Manager: LORI GOLDBERG Subscriptions/Circulation: JERRY CLARK, JACK JONES 888/64 4-1803

executive team


foster reprints Corporate Account Executive: RHONDA BROWN 219-878-6094,


You can do intrinsically safe wireless Regarding “Where wireless meets intrinsic safety,” (Control, Oct. ’16. p. 26, www., you can put wireless network access points in unclassified areas (or zone 2, which does not require intrinsic safety), so wireless sensor networks with intrinsically safe (IS) transmitters can be easy, provided you pick the right wireless topology. Only the wireless sensors need to be in the area requiring intrinsic safety. However, this requires a full mesh topology. There are many wireless networks using IEEE 802.15.4 radio, but they’re not the same. By full mesh topology, I mean capable of seven or more “hops” from the sensor to the gateway, allowing the data to be routed around metal obstructions in the plant. WirelessHART already supports seven or more hops. The two to four hops currently supported by other networks are not sufficient. By using a full mesh topology, you only need the gateway at the edge of the plant unit—there is no need for powered backbone routers in the middle of the plant units. If there is any weak spot in the WirelessHART network, simply drop in an additional device as a router—an IS, battery-powered device needs no power wiring and is very simple to deploy. I agree that IS power over Ethernet (PoE) is an issue at the moment. Apart from the Ethernet switches, are there any field devices actually supporting it? Another problem is that there are no standard IS parameters (I, U, P, L and C), so lots of engineering is needed to demonstrate safety. When you replace any part with another model, you have to redo it. So, avoid this problem by placing the gateway at the edge of the unit as unclassified or Zone 2. JONAS BERGE,

Human factors and Deepwater Horizon The worst shame of the movie [“Truth meets fiction,” Control, Sept. ’16, p. 86, deepwater-horizon-film-likely-sacrifices-re-

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al-safety-discussion-for-hollywood-tropes] is that it seeks to demonize “big oil.” In one of its most egregious moments, it makes it look as if the control panel operator knew something was going to happen, but held back the information because of company direction. In the official review, the operator said the information/data flow to view the process was terrible enough that when the crap hit the fan, she was completely befuddled and confused. This is not the fault of an operator. It has been blamed on human error, and now in the Hollywood version, on political complicity. The real fault lies in poor human factors in the control system HMI. Billions of dollars have been invested in researching the interaction of humans with operating equipment. The blowout was potentially avoidable, but engineering mistakes will always be there. The point is, once the engineering failed, the tragedy could have been mitigated, perhaps even avoided. So, while a company may not be complicit in direct actions to prevent the loss of human life, its lack of making access to tools that could have given humans visibility into the incident is disappointing. Like most companies, they’re content with technology that’s decades in arrears because it is “sufficient,” when in actuality it is not. ROI is hard to measure for human factors until the holes in the Swiss cheese line up. STEPHEN APPLE

N O V E M B E R / 2 0 1 6



Controlling the smart cars, part 2 BÉL A LIPTÁK

[This article suggests improvements in controls for self-driving cars based on experience in the process industries. The state of the art, as exemplified by the Tesla accident May 7 in Williston, Fla., is discussed in part 1 (Sept ‘16, p. 52,]


ver the past century, general industry (power, chemical, oil, nuclear, etc.) faced the same safety concerns that the automobile industry is facing today. One camp argued that manual control is safer because all sensors can fail, computers can freeze, etc., while the other camp argued that automatic control is better because operators can be intoxicated, untrained, tired, distracted, etc. Both camps assumed there’s no third option. It took a long time to realize that one must not choose between manual and automatic control, but should benefit from both simultaneously. In other words, one should always consider both, and select the safer one for control. This is called Selective Safety Control (SSC). It would be wise for the transportation industry to learn from the experience accumulated in other industries, and not attempt to “rediscover the wheel.”

People asked whether the autopilot was on or off at the time of the Tesla crash in Florida. This is the wrong question. 16

Where we are today In just the U.S., more than 35,000 people died in car accidents last year. According to most estimates, smart cars could eliminate 95% of these accidents. Yet 71% of the American public believes that smart cars are less safe than regular ones. These numbers contradict each other, and that’s unfortunate because technological advances require public support, but that support won’t evolve until there’s confidence in the safety of such technological advances. This is the reason I’m writing this series of articles. Many smart cars are electric, and the ac- N O V E M B E R / 2 0 1 6

ceptance of electric cars is growing. Tesla sold 50,000 of these cars in 2015, 82,000 in 2016, and has approximately 400,000 customers on its waiting list for next year. In 2017, it hopes to market an electric car (Model 3) that will cost around $35,000 and can travel 240 miles on one charge. Other car manufacturers (GM’s Bolt, Nissan’s Leaf, BMW’s i3, Volkswagen’s e-Golf, etc.) place less emphasis on driving distance per charge because they’re focusing on commuting. The smart car designs on the road today use autopilots, and require drivers to keep their hands on the steering wheel, so they can take over control when needed. As of today, Tesla has some 70,000 such cars on the road, and others like Mercedes-Benz plan to start selling them in 2017. Tesla is also working on driverless cars—it will have all the sensors needed for autonomous driving in its Model 3, but will not enable the system until more testing is performed. Model 3 has some 300,000 preorders, which Tesla will start filling in 2017. Other manufacturers, like GM and Audi, are also taking an incremental approach by developing cars capable of gradually updating from the autopilot mode of operation to the driverless one. Others are starting out with limited application goals— an example is Google (working with Otto), which plans to market trucks that will be self-driving only on highways. Development of completely driverless cars is still in the testing or field trial stage (Uber in Pittsburgh, Google in California, Ford, etc.) and are generally expected to be available by 2021.

Autopilot vs. driverless People asked whether the autopilot was on or off at at the time of the Tesla crash in Florida. This is the wrong question. Just as we don’t need to turn on the seat belt alarm to be reminded the buckle is closed, the autopilot should have been on all the time—in addi-

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2 3






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FIRST-GENERATION SENSORS AND ZONES Figure 1: These are the types of sensors and their observation zones that are being used in the first generation of smart cars. The instruments are: computer (C), lidar (L), radar (R), ultrasonic (U), video camera (VC). On some cars, as many as 12 cameras are used. Observation zones are: (1) Protection zone detected by 360° rotating lidar, (2) rear-collision protection and lane change assistance zone, (3) self-parking function, (4) traffic light, lane departure, collision avoidance and emergency brake assist functions, and (5) cross-traffic and pedestrian-detection function.

tion to the driver always being ready to take over. The important difference between the design that existed in the Florida eccident and the correct one is that neither the driver nor the autopilot should be able to overrule the other (be the primary source of control). However, whenever they disagree, the control system (SSC) should automatically select the safer one to control braking, accelerating or steering. As I noted earlier, smart cars have great lifesaving potential because they can provide safe driving even if the driver is tired, panicked, drunk, under the influence of drugs, inexperienced, distracted or has slow reflexes, decreasing vision and hearing, etc. Yet their lifesaving potential is 18 N O V E M B E R / 2 0 1 6

reduced because all computer systems can fail, and even if they don’t fail, their software can be hacked or be insufficiently sophisticated to recognize complex situations. It’s for this reason that the software packages in operation (Figure 1) can safely handle only simpler tasks such as changing lanes, stopping at red lights, parking or keeping safe distances between vehicles, but they can’t yet distinguish between, say, a pedestrian trying to hitch a ride or a police officer flagging the car down. Such “fuzzy” conditions haven’t yet been effectively enshrined in computer code, while the human driver can usually recognize them. A great advantage of smart cars is that the software of the whole fleet

can be improved over the air whenever new information becomes available. In other words, whenever the causes of an accident are determined and the software is modified to prevent reoccurance of that accident, the revised software package can be immediately transmitted wirelessly to the entire fleet. As a result, the safety of the fleet can be continually improved. Advocates of driverless cars argue that using the autopilot is less safe than autonomous driving because even if the driver’s hands are on the steering wheel, the driver, being passive, can’t be expected to snap back and make split-second decisions when needed. They refer to studies that have found that the time needed to “wake up” the average driver is 17 seconds, and a car moving at 65 mph travels five football fields during that time. Yet as of today, “driver-assisted collision avoidance software” (autopilot) is better developed, and so for some years more, the “hands on the wheel” mode of driving is likely to prevail. It’s also interesting to note that the major process control firms (ABB, Emerson, Honeywell, Schneider Electric, Siemens, Yokogawa, etc.) seem to be doing very little to develop sensors and control software for this new market. Newer companies are starting to fill this gap, such as Nirenberg Nouroscience, Otto or Saips in the fields of machine and computer vision, and Velodyne in the area of miniaturized lidar (laser imaging and ranging), etc. [The next part in this series will discuss the capabilities of today’s sensors, potential for developing additional or better ones, and improvements in control software packages needed to improve smart car safety.] Béla Lipták, PE, control consultant and editor of the Instrument Engineers’ Handbook is seeking new co-authors for the new edition of that multi-volume work. He can be reached at

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On the Bus

Control at the edge, revisited john Rez abek

contributing Editor

Every little skid had a different little PLC. Some used ladder logic and some used weirdly structured text reminiscent of HP calculators’ “reverse Polish.” 20


very compressor in the facility went down at once that day, when a PLC redundancy switchover didn’t transfer in time. The engineers didn’t know that each P453 remote I/O processor had a dip-switch-selectable timeout setting—if it didn’t hear from the logic solver before the timeout, all the associated I/O would go to the zero power state. And so they did, when the startup team decided to invoke a switchover one day, much to the dismay of the commissioning manager for the new unit. Before PLCs, compressor interlocks were all solved in local panels using relay logic. This “natural” distribution of logic solving lent a certain fault tolerance to the process; at least (barring a total power outage) only one critical piece of machinery would go offline at a time. The disadvantage was, interlocks implemented with hardwired relay logic were difficult to configure, costly and labor-intensive to build, difficult to troubleshoot, difficult to modify, and subject to mechanical assaults on reliability in the form of lose wires, vibration, corrosion and unseen jumpers. This was why the early adopters were eager to move logic to the magical PLC. Fortunately for us, it took less than 10 years for PLCs to become powerful and inexpensive enough for each compressor to have its own, individual, local, dedicated PLC. This was a great capability, but it also introduced a new challenge: every little skid that arrived, from truck loading to wastewater filters, had a different little PLC aboard. Some used ladder logic and some used weirdly structured text reminiscent of HP calculators’ “reverse Polish.” One site tried to stem the divergent solutions by specifying, for example, “all logic shall be solved by Modicon 984 PLCs,” only to find that 1) there were several “grades” of that generation of 984s, and 2) systems integrators that favored another PLC wanted to charge a premium for the deviation, but frequently didn’t excel at programming the PLC of choice. Modbus was still developing as a de facto standard, so networking the growing and divergent field of PLCs N o v e m b e r / 2 0 1 6

to the built-for-purpose DCS host was expensive and complex, requiring painstaking mapping of PLC coils and registers for the DCS to display. The DCS, which I’ll emphasize stood for “distributed control system,” was itself more centralized and less distributed than the network of little PLCs out in local panels and skids. But few entrusted the PLC to do much closed-loop control, since most of the analog measurements were wired to the centrally-located DCS I/O, and control could be solved with greater determinism than the master-slave polling network of Modbus over RS-232/485. So critical, closed-loop “control,” indeed nearly all PID control, remained centralized despite the “DCS” moniker. And so it remains. But today, I can go on Amazon and buy a credit-card sized Raspberry Pi, already in its third generation, for less than $50. You can load a stripped-down Windows 10 OS on Raspberry Pi, and I have little doubt such a platform could solve PID or even invert a matrix for model-predictive control. Not that you would, but the point is that astounding computing power and networking capability have become cheap and ubiquitous. “Control at the edge” is becoming part of the IoT vernacular as it pertains to access control and security, but also because microprocessor-based devices at the edge are smart enough to invoke actions—to solve logic or do closed-loop control—without having to “phone home” to a central host or human operator. Process control professionals have had “control at the edge” since the days of local pneumatic controls, and this heritage lives on almost unnoticed in every valve positioner with a servo solving proportional or PID to position the valve stem where it’s directed. While we might not have trusted PID to 1990s-vintage PLCs, why not empower valve positioners and their ilk to execute rudimentary control loops? To the degree networks and standards can provide easy, consistent and seamless access to device-resident controls, the vision of truly distributed control may finally dawn upon us.







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Redefining determinism IAN VERHAPPEN


The control system can easily be made to believe that updates are as regular as necessary to be viewed as deterministic. 22


s automation professionals, one issue we have about control loops is ensuring we’re able to support real-time control. Back when Ethernet was 10 MB/s with multiple drops on one port, collisions were a concern and impediment to its adoption because we couldn’t guarantee delivery of every message, every time, at a repeatable frequency. Ethernet wasn’t “real time” enough, and hence not deterministic, or so we believed. So we waited until we got faster switched networks that almost eliminate the chance of a message not getting where it should be when it should. We still lose packets, but we can recover fast enough to satisfy our definitions of determinism and real time. In fact, what we’re really doing is confirming that the definition of determinism depends on the application. In factory automation or robotics, response times often need to be in milliseconds, while continuous processes, being essentially analog, are scanned at high enough frequency to allow us to model the system, with “high enough” generally accepted as six times (6x) the process frequency/response time (process time constant plus process delay). Many use a ‘rule of thumb’ of 10x, though I suspect it’s to provide a margin of error, and it’s easier to move the decimal point than divide by 6. Another underlying assumption in conventional PID is that control is executed on a periodic basis, which implies a regular scan and update rate. Fortunately, the scan rate for continuous processes, where flow is likely the fastest changing loop, is normally seconds long. Control systems and their networks are complicated enough to design and build without having to calculate the definition of determinism for every loop, and then design hardware to match. So instead, we configure our systems to scan the I/O at one or perhaps a few different scan rates, based on the applications in the facility. This is one reason why the scan rates for PLCs are in milliseconds (as required by factory applications from which they evolved), while a N o v e m b e r / 2 0 1 6

DCS, which scans many more points per cycle, can have scan rates of seconds. A continuous process doesn’t change that much that quickly, and if it does, a different system such as an SIS, provides the necessary extra protection. Wireless sensor networks (WSN), on the other hand, have update rates of 15 seconds or longer (updating only when the process has changed outside the prescribed “window,” resulting in a non-periodic basis to preserve battery life). And since they’re mesh systems, the signal itself is retransmitted multiple times, increasing the risk that an update can be lost, so the control system and algorithm must also be able to handle a loss of communications. If this sounds similar to some of the challenges associated with legacy 10 MB/s Ethernet, where updates can be affected by a collision or a node malfunction, perhaps our systems aren’t, nor need to be, as deterministic as we think. As long as we have reliable communications with the WSN access point, the control system can easily be made to believe that updates are as regular as necessary to be viewed as deterministic. Terry Blevins, Mark Nixon and Marty Zielinski published an interesting paper “Using Wireless Measurement in Control Applications” ( addressing-control-applications-using-wireless-device/) describing one approach to modifying the PID algorithm, and in particular the reset (integral) component, for irregular signal updates. Other manufacturers are taking different approaches, and if your system does not have a specific solution, with the processing ability of today’s control systems, they’re able to create simple process models to fill in the gaps between the updates, much like we’ve done with manually analyzed samples for many years. In the end, as demonstrated above, everyone’s definition of real time and hence determinism depends on the application. Or perhaps we can argue that determinism no longer has the same clout as it did when things were slower.

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In Process

Emerson users interact and enlighten The 2016 edition of Emerson Global Users Exchange in Austin, Texas, accelerated learning and innovation, and gave its customers the tools and know-how to do it.


everal thousand users, integrators, technical experts, managers, leaders and friends gathered for Emerson Global Users Exchange 2016, Oct. 24-28 in Austin, Tex., and feasted on their usual cornucopia of technical innovations, end user experiences, conference sessions, workshops, training sessions, exhibits and networking. “Time here is well spent getting fresh ideas, making good connections, finding the energy and urgency to solve tough problems, and work with rapid technology change,” says Steve Sonnenberg, chairman, Emerson Automation Solutions (, which recently transformed its internal structure to focus on two business platforms, combining its former Emerson Process Management and Industrial Automation businesses into one organization. “We’re continuing the same sales, support, engineering and services as in the past, and now we can better address life sciences, food and beverage and packaging industries. We’re working harder to be a trusted partner.” Following his appointment as chairman, Sonnenberg added his former role is being filled by Michael Train, executive president, Emerson Automation Solutions, who’s served as president of Emerson’s global sales, analytics group and Asia-Pacific divisions.

Operational Certainty Train stressed the continuing advantages of Emerson’s Top Quartile and Project Certainty programs for improving their engineering, products and services. Plus, he announced that Emerson is introducing its Operational Certainty consulting practice with expanded project execution methods, workshops and services. “By helping customers leverage the best practices of Top Quartile perform24 N o v e m b e r / 2 0 1 6

• Releasing AMS ARES platform to deliver asset and device health data, enabling maintenance decisions that increase availability; and • Detailing recent acquisitions, including Pentair Valves and Controls for pressure management, isolation valves and controls; Permasense for wireless corrosion monitoring; and FMC for blending and transfer technologies.

Supreme sessions

Earnings assist Michael Train, executive president, Emerson Automartion Solutions, reports Top-Quartile practices can help customers improve their earnings by 15%.

ers, Emerson can help improve their earnings as much as 15%,” says Train.

Innovations aplenty To provide users with even better tools and services, Emerson debuted a host of other solutions, services and initiatives at the event. They included: • Launching its expanded Plantweb digital ecosystem, a scalable portfolio of standards-based hardware, software, intelligent devices and services for securely implementing the Industrial Internet of Things (IoT) with measurable business performance improvement; • Collaborating with Microsoft to help manufacturers realize business impact and value of the Industrial Internet of Things (IoT) with help from Emerson’s revamped Plantweb digital ecosystem and Connected Services, powered by Microsoft Azure IoT Suite.

Following yet another tradition, five Best-in-Conference Award winners were chosen from among the 300 sessions delivered at Emerson Exchange. The five tracks and winners were: • Solve and support: “Fighting Irish tackle alarm management—implementing an alarm management program @ UND power plant” by Thomas Cole, UND Power Plant, Bill Farmer, Novaspect, and Todd Stauffer, exida • Measure and analyze: “A wireless odyssey—from resistance to enthusiasm” by Alan Weldon, Hunt Refining, and Donna McClung and Steve Moore, both of Emerson • Operate and manage: “Intelligent solvent tank farm management” by Matt Rauschke of 3M and Kyle Nystrom and Colin Singer, Novaspect • Final control and regulate: “Natural gas pipeline integrity improvements— reducing risks with pressure control station reinforcement” by Niko Boskovic and Andrew Loge, FortisBC, and Reese Dawes, Spartan Controls • Business management and career development: “Better listening, better life—listen like a pro” by Nikki Bishop and Bruce Smith, both Emerson For more coverage, visit

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In Process

Yokogawa and users partner for a brighter future Just before Hurricane Matthew charged up the Florida coast, Yokogawa Users Conference and Exhibition 2016, Oct. 3-6, in Orlando, delivered an equally strong but positive surge of technical presentations, exhibits, corporate initiatives and networking opportunities to help hundreds of users, system integrators, suppliers and other attendees weather today’s economic storms. “The market is facing challenges, and innovation is a key driver to sustaining growth,” says Takashi Nishijima, CEO, Yokogawa Electric Corp., who spoke via video. “We must re-create ourselves to create wealth and value to society. Yokogawa is moving into the future one step at a time. We’ll be working with you to build greater bonds of trust and to stimulate growth.” Opportunities for co-innovation were described by Dr. Tsuyoshi “Ted” Abe, vice president and CMO, Yokogawa Electric Corp. “Analysts are always looking at the short range,” says Abe. “Let me focus on the long range. Our big, hairy, audacious goal (BHAG) is sustainable processes. While Yokogawa today has many industrial customers, our future customers are today’s children. We must all work together, to contribute to save the earth for our children. Leadership does matter, but not only at Yokogawa. Let’s work together for a co-innovative tomorrow for our bright future.” To help its customers overcome today’s challenges, Centum Vigilant Plant (VP) DCS and its supporting sensors, I/O, networks, ProSafe-RS SIS and other components continue to offer a rocksolid foundation backed by Yokogawa’s deep knowledge of process control and optimization. “Yokogawa launched the first DCS on the market in 1975, and it’s continued to offer progressive compatibility for effectively aging in place,” ex26 N o v e m b e r / 2 0 1 6

Big-time sustainability Dr. Tsuyoshi “Ted” Abe, vice president and CMO, Yokogawa Electric Corp., reported that its goal is sustainable processes.

plains Gene Chen, product manager for DCS and safety instrumented systems (SIS) at Yokogawa. “Yokogawa delivers easy upgradeability that’s simple and fast; low complexity and easy expandability; highest proven field reliability with a bulletproof foundation; applications that migrate forward for continued value and reduced lifecycle costs; and knowledgeable engineers to execute, solve your problems, and ensure benefits throughout the solution lifecycle.” For more coverage, including Control’s show daily on technical and other sessions, visit articles/2016/live-from-yokogawa-2016.

ICS Cybersecurity event gathers experts and solutions Security experts from industry, government, academia and elsewhere presented and exchanged their experiences at ICS Cybersecurity Conference 2016, Oct. 24-27 at Georgia Tech University in Atlanta. They represented multiple worldwide industries, government and military defense departments, industrial control system (ICS) suppliers, cybersecurity researchers, consultants and educators. The keynote address was delivered by Adm. Michael Rogers, director of the

U.S. National Security Agency (NSA) and CyberCommand. He addressed many security issues related to control systems, such as tradeoffs of remote access, value of air-gapped systems and the need for educating management. Rogers also asked private industry to work with NSA to help identify precursors to cyber attacks; addressed the recent distributed denial of service (DDoS) attack using IoT botnets; stated that secure control systems need to be designed from the beginning and not use “bolt-on” security. Joe Weiss, control system cybersecurity expert and’s Unfettered blogger, reports this year’s event had several main themes: • General lack of understanding about Level 0,1 devices; • Issue of control system incidents not involving network malware (physics issues); • Continuing cultural and knowledge gaps between ICS and IT security, IT forensics, safety and senior management • Continuing lack of universally-accepted definitions, particularly “OT” and cyber incidents; • Ongoing scarcity of ICS cybersecurity information and incident sharing, including no adequate guidance on preventing ICS cyber incidents from recurring; and • Ability of skilled hackers to compromise ICSs, as well as efforts to identify new ICS zero-day attacks. For more coverage, visit www.control

GE oil and gas unit merging with Baker Hughes GE ( and Baker Hughes ( announced Oct. 31 that they’ve agreed to combine GE’s oil and gas business and Baker


EXPANSIONS AND CONTRACTIONS • Emerson Automation Solutions ( and Flexim ( are combining their flow portfolios. They report their collaboration will enhance flow consulting and selection; reduce piping, design and installation costs; and help their customers execute more effective capital projects. Emerson project teams, using Flexim’s clamp-on, ultrasonic flow-metering tools with Emerson’s broad, in-line flow metering, can consult early and throughout project cycles to reduce engineering, piping, and installation costs, and schedule risk. • GE Digital ( and Gerdau ( agreed Sept. 19 to work together to transform the steel producer’s industrial operations by implementing GE Digital’s Asset Performance Management (APM) solution, GE’s SmartSignal and Historian software as well as services, remote monitoring and analytics expertise at 600 assets in 11 Gerdau plants across Brazil. • Pepperl+Fuchs ( has bought ecom instruments GmbH (, a leading provider of mobile industrial devices for hazardous areas, and developer of

explosion-proof cell phones, 4G smart phones and tablet PCs. Pepperl+Fuchs reports its acquisition complements its portfolio and know-how in explosion protection with mobile solutions. • Advantech ( has launched its WebAccess+IoT Solution Alliance program, which is based on its WebAccess IoT software suite. The program is a market-oriented cooperation model aimed at building win-win partnerships by using WebAccess to link solutions, partner strengths and strategic co-marketing to penetrate focused vertical markets and applications in the IoT industries. Joining WebAccess + IoT lets members buy non-expiring, virtual points that become digital currency. • Schneider Electric ( has acquired software supplier MaxEAM ( to strengthen its asset management portfolio, including enhancing Schneider’s Avantis.Pro software. Together, they’ll give customers a single point of contact for support and delivery services, and more closely align future product development, according to Schneider Electric.

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Level measures up Control’s Monthly Resource Guide BOILER DRUM INSPECTION GUIDE The 2016 edition of Clark Reliance’s Boiler Inspection Guidelines for Drum Level Instrumentation is easy to understand and concisely presents ASME Section I water gauge inspection requirements for handy, on-the-job reference by boiler operators. It includes code requirements for water columns, water gauge valves, gauge glass, remote level indicators, magnetic water level gauges and water column isolation shutoff valves, as well as 2015 Code changes and CSD-1 requirements and recommendations from Section 7. The guide also lists the most common non-compliant, drum-level arrangements and solutions. Copies are available at www.boilerinspectionguide. com, and free to qualified recipients. Cl ark-Reliance Corp. 4 40-572-1500; w w

LEVEL SENSING INTRODUCTION The “Level Sensor” article at Omega Engineering’s website covers non-contact ultrasonic, contact ultrasonic and capacitance technologies. It also provides questions to help choose level measurement sensors, and answers frequently asked questions. It’s at www. Omega Engineering w w

DP LEVEL FOR ROOKIES This online feature article, “Beginner’s Guide to Diffential Pressure Level Transmtters,” by David Spitzer presents “the not-so-straightforward basics of this measurment technique.” To help users avoid costly mistakes, it shows readers how to understand DP

level measurement, and its techniquea and limitations. The guide also covers three different techniques used to calibrate pressure level transmitters. It’s located at differential-pressure-lp CONTROL w w

BASICS OF LEVEL—AND HISTORY This classic, 10-minute video hosted by former Control editor Walt Boytes examines all the essential concepts of measurement, including some of its earliest origins in, where else, Egypt, where floods from the Nile made early level measurement a necessity. The video also demonstrates methods for selecting the correct level technology for different types of process applications. It’s located at watch?v=-MQU0xgh6bA. CONTROL w w

ULTRASONIC VS. GUIDED WAVE This 13-minute video is presented by Jason Beck of Flo-Corp., who explains the some of the basic physics and characteristics of ultrasonic and guided-wave radar technologies, shows how their capabilities work in process applications, demonstrates potential issues with each method, and shows how viewers can find the most useful solution for their requirements. It’s located at

strates how to perform open-tank level measurement with a Rosemount 1151 DP transmitter, though the methods and concepts presented are useful for many technologies. It also covers set up, calibration, system layout, output wiring, input calibration and bench calibration hook-up, and other tasks. It’s located at watch?v=xfo9n_ly8sA. Northern Alberta Institute of Technology

ADVANTAGES AND DISADVANTAGES The level measurement entry in the Encyclopedia of Chemical Engineering Equipment by the Chemical Engineering Dept. at the University of Michigan’s College of Engineering covers the many of the main types of level measurement technologies with descriptions and photos. However, it also presents the advantages and disadvantages of each level measurement method. It’s at http://encyclopedia. Universit y of Michigan w w



This 4.5-minute, blackboard-style video by U.K.-based Gill Sensors & Controls provides a quick summary of the primary aspects of capacitive level sensing, including behavior of the dielectric, and shows how different probe materials can serve the needs of different applications. It’s located at

Initially intended for students at NAIT, this 17-minute tutorial video demon-

Gill Sensors & Controls w w

Flo-Corp. w w

If you know of any tools and resources we didn’t include, send them to with “Resource” in the subject line, and we’ll add them to the website. N o v e m b e r / 2 0 1 6



hat’s so big about big data? Isn’t it just more data—more of the same old information from the same places, which many users are finally waking up to and using? Well, yes, many of the usual data handling methods, software and devices are being trotted out again under the “Big Data” buzzword, and so they must have a new banner and be called something new even if they’re not. However, despite the hype and distractions, there are persistent differences between traditional data and big data that can’t be ignored. Some of these differences include information sources not accessed before, data types not analyzed previously, and new management and storage technologies. These distinguishing features are summarized in big data’s oft-cited “four Vs:” volume, variety, velocity and value. The challenges presented by big data are being met by augmented and new analysis tools, networking pathways

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and flexible cloud computing services. Most come from IT, and thanks to ever-lower microprocessor, software and computing costs, they’re now arriving in force in process control applications, on plant floors and in the field.

Dig deep, find treasure Consequently, though cautious end users remain reluctant to migrate, others are finding ways to cope with all the new data streams coming from newly connected, Internet-enabled devices, identify previously unseen correlations and trends, and achieve unprecedented operating gains. For example, Avangrid Renewables ( in Portland, Ore., collects lots of time series and other information from its 3,000 U.S. wind turbines and other generating assets, and seeks to coordinate it with related operations, independent system operator (ISO),

B I G D ATA weather, market and pricing data (Figure 1). Main sources include OSIsoft PI, SCADA, SQL databases and SAP. Avangrid wanted to examine and better visualize its existing OSIsoft content, so it could understand operations better and improve decisions. Avangrid especially wanted to more accurately report and get paid by the ISO for lost generating capacity during required curtailment periods, but it needed deeper turbine ramp-down cost data to prove its economic losses. “We knew we were losing money, but determining the actual impact required investigating years of turbine data,” says Brandon Lake, senior business systems analyst at Avangrid Renewables. To that end, Avangrid enlisted Seeq Corp. (www.seeq. com) and its data investigation and discovery software, which integrates information from historians, databases and analyzers without altering existing systems. Its software uses a property-graph database geared toward querying relationships across nodes to work with data and relationships between data in objects called “capsules,” which store “time periods of interest” and related data used to compare machine and process states, save data annotations, enable calculations, and perform other tasks. Lake reports that Avangrid tried to compile ramp-down data before using Excel, but it took too much time and labor. “With Seeq’s software, we were able to isolate shutdown events, add analytics and determine what was happening in just hours,” says Lake. “In the past, this would have taken days or weeks.” Once its participating wind farms isolated shutdowns and ramp-down events, determined curtailment times, added pricing and other setpoints, and determined differential power-generation scenarios to determine losses, Seeq could export the data to Excel and identify revenue the wind farms could claim. Depending on its ISO contracts and wind availability or curtailment, Lake reports that Avangrid saves $30,000 to $100,000 per year.

What do you have? What do you want? Despite its obvious advantages, big data is still a hard sell for many users because they must shift their data-gathering gears not just to new tools, but to new ways of thinking— mostly to understanding what big data is and how it can serve their applications and goals. While traditional data architectures move structured information through an integration process before warehousing and analyzing it, Oracle Corp. ( reports in its “Enterprise Architect’s Guide to Big Data” that big data uses distributed, multi-mode, parallel data processing to handle its larger, unstructured data sets, and employs different strategies, such as index-based retrieval for real-time storage needs and map-reduce filtering for batch processing storage. Once filtered data is discovered, it can be analyzed directly, loaded onto other unstructured or semi-structured databases, sent to mobile devices, or merged with regular data warehousing (Figure 2).

Business systems

IoT sensors

Cloud IoT platform Context


Control network


Manufacturing systems

Business systems

CASH BLOWS IN Figure 1: The 400-MW Klondike Wind Power Projects in Sherman County, Ore. (top) is one of several Avangrid Renewables wind farms in the U.S. using Seeq data investigation and discovery software (bottom) to integrate information from historians, databases and analyzers, and recover lost-generation revenue from the local grid. Source: Avangrid

“We’ve always handled many forms of information, and to us, big data begins with multiple streams and events produced by people, machines and processes. However, big data ties these streams together with heuristics and analysis, so users can relate what couldn’t be related before, and find new links and efficiencies,” says Ian Tooke, consulting director at Grantek Systems Integration ( in Oak Brook, Ill. “We do environmental controls for pharmaceutical and warehouse applications, and we can use big data to tie changes in environmental factors to the state of drugs in storage. For example, humidity can affect glue viscosity when we’re making corrugated substrates, but now we can use this new data to make adjustments, which can help improve the shelf life and effectiveness of some drugs. Similarly, there are many regulations for manufacturers about keeping pharmaceuticals cool in storage, but fewer rules for trucks and distribution warehouses, so we provide environmental monitoring in warehouses and on trucks.” Jim Toman, lead consultant for manufacturing IT at Grantek, adds that food manufacturers are also looking at their control and support systems for more traceability by gathering environmental measurements, and applying statistics to help meet production line setpoints. “Food manufacturers are shiftN O V E M B E R / 2 0 1 6


B I G D ATA cause predictive analytics and machine learning use the same mathematics as APC, such as neural networks to model relations between data parameters.”

tions, shipping, maintenance and end of life. It’s a powerful idea that these digital threads can connect, and that any part of the lifecycle can reach the other stages to do useful things. If we can design knowing more about manufacturing—and Wide net, big haul No doubt the best-known aspect of big get data from users to flow back to the data is the namesake amounts of infor- fabricators—then we can make different mation its servers take in, though the less decisions in digital manufacturing and glamorous chore is making sense of it all, achieve greater value, but it’s still hard to get those stages to work together.” and putting that intelligence to use. Scott Howard, regional sales man“Manufacturing generates more data than any other sector of the economy, ager, Statseeker (, but only a little bit of it is used, and so adds that, “Big data begins by collecting there are huge opportunities to create whole bunches of information because value by using that information,” says at first its users don’t know what matters, Bill King, CTO of the Digital Manu- so they gather everything, and then try to facturing and Design Innovation Insti- find correlations and statistical threads, tute ( at Univer- such as more closely matching equipsity of Illinois Labs ( ment performance to effects on quality in Chicago. “This data is produced at and end products.” Statseeker makes a every stage of the manufacturing lifecy- networking monitoring tool that checks Black participating ports every 60 seconds. cle, including Black design, assembly, operaBlack Pantone 185 Black “We were dealing with big data bePantone 185 Black Pantone 185 Pantone Black gray 80 185 Pantone 185 fore it was called big data. We created a gray 80 Black80 gray Pantone 185 gray gray 80 10 Pantone 185 big repository with tons of data in it, but gray 80 gray 10 10 gray gray 10 80 gray the challenge was now that we’ve got it, 80 gray 10 gray 10 what do we do with it? There’s no value gray 10 in reams of data if it doesn’t help your opStacked version option 2 Stacked version Stacked version version option option 2 2 Stacked version version Stacked Stacked Stacked version option 2 erations or business,” adds Chris HemStacked version Stacked version option 2 Stacked version Stacked version option 2 ric, P.E., technical services director, R.J. Stacked version Stacked version option 2 Stacked version Reynolds Tobacco Co. (, favicon favicon Winston-Salem, N.C., who spoke during favicon favicon favicon a panel discussion at Inductive Automafavicon favicon Horizontal version tion’s Ignition Community Conference Horizontal Horizontal version version Horizontal version 2016 in mid-September. “The lesson we Horizontal version Horizontal version learned is that you don’t want to save evHorizontal version ery point. You have to decide what’s the purpose in life for the points you want to North America’s Leading Instrumentation Hardware Manufacturer save, so you don’t create clutter and inNorth America’s America’s Leading Leading Instrumentation Instrumentation Hardware Hardware Manufacturer Manufacturer North version on black North America’s Leading Instrumentation Hardware Manufacturer version on black formation that’s too complex. Collecting background version on black North America’s Leading Instrumentation Hardware Manufacturer background version on black background North America’s Leading Instrumentation Hardware Manufacturer version on black background absolutely everything is too hard for plant North America’s Leading Instrumentation Hardware Manufacturer background version on black background version on black staff, so you need to decide, do I need For Brand standardsBLEED and questions UsCHAMBERS Communications •SEAL 416 255-3918 THERMOWELLS ORIFICE PLATES ANDbackground FLUSHplease RINGS contact LEVEL POTS MANIFOLD For standards and please contact Us Communications 416 THERMOWELLS ORIFICE PLATES AND RINGS LEVEL POTS MANIFOLD For Brand Brand standardsBLEED and questions questions UsCHAMBERS Communications • •SEAL 416 255-3918 255-3918 THERMOWELLS ORIFICE PLATES BLEED AND FLUSH FLUSHplease RINGS contact LEVEL CHAMBERS SEAL POTS MANIFOLD that point or not?” For Brand standardsBLEED and questions UsCHAMBERS Communications •SEAL 416 255-3918 THERMOWELLS ORIFICE PLATES AND FLUSHplease RINGS contact LEVEL POTS MANIFOLD For Brand standardsBLEED and questions UsCHAMBERS Communications •SEAL 416 255-3918 THERMOWELLS ORIFICE PLATES AND FLUSHplease RINGS contact LEVEL POTS MANIFOLD Hermic adds that an overall engiFor Brand standardsBLEED and questions UsCHAMBERS Communications •SEAL 416 255-3918 THERMOWELLS ORIFICE PLATES AND FLUSHplease RINGS contact LEVEL POTS MANIFOLD CANADA USA For Brand standardsBLEED and questions please contact UsUSA Communications •SEAL 416 255-3918 THERMOWELLS ORIFICE PLATES AND FLUSH RINGS LEVEL CHAMBERS POTS MANIFOLD CANADA CANADAMachining Ltd. USA Mac-Weld Mac-Weld USA Inc. neering and management team can talk CANADA USA Mac-Weld Machining Ltd. Mac-Weld USA Mac-Weld Machining Ltd.Ontario Canada N7S 5N7 Mac-Weld USA Inc. Inc. CANADA USA 1324 Lougar Ave, Sarnia, 1465 East Sam Houston Parkway S Unit #190 Mac-Weld Machining Ltd.Ontario Canada N7S 5N7 Mac-Weld USA Inc. 1324 Lougar Ave, Sarnia, 1465 East Sam Houston Parkway S Unit #190 about how to rationalize data, and deCANADA USA East Sam 1324 Lougar Ave, Sarnia, Canada N7S 5N7 1465 Houston Parkway S Unit #190 Mac-Weld Machining Ltd.Ontario Mac-Weld USA Inc. E-mail: Pasadena, TX 77503 1324 Lougar Ave, Sarnia, Ontario Canada N7S 5N7 1465 East Sam Houston Parkway S Unit #190 CANADA USA E-mail: Pasadena, TX Mac-Weld Machining Ltd.Ontario Canada N7S 5N7 Mac-Weld USA Inc. E-mail: Pasadena, TX 77503 77503 1324 Lougar Ave, Sarnia, 1465 East Sam Houston Parkway S Unit #190 E-mail: cide what they need compared to what E-mail: Pasadena, TX 77503 Mac-Weld Machining Ltd.Ontario Canada N7S 5N7 Mac-Weld USA Inc. E-mail: 1324 Lougar Ave, Sarnia, 1465 East Sam Houston Parkway S Unit #190 E-mail: E-mail: Pasadena, TX 77503 Phone: 1 519-332-1388 E-mail: Phone: 1 832-429-3400 1324 Lougar Ave, Sarnia, Ontario Canada N7S 5N7 1465 East Sam Houston Parkway S Unit #190 they’ve got. “We’re drowning in teraE-mail: Pasadena, 77503 Phone: 519-332-1388 E-mail: Phone: 832-429-3400 Phone: 11 519-332-1388 Phone: 11TX E-mail: Pasadena, TX832-429-3400 77503 Phone: 1 519-332-1388 E-mail: Phone: 1 832-429-3400 bytes of data,” he explains. “However, Mac-Weld QMS is certified to ISO 9001:2008 Phone: 1 519-332-1388 Phone: 1 832-429-3400 E-mail: Mac-Weld QMS is is certified certified to to ISO ISO 9001:2008 9001:2008 Mac-Weld QMS Phone: 1 519-332-1388 Phone: 1 832-429-3400 Mac-Weld QMS is certified to ISO 9001:2008 ‘big data’ really is just more data, and it Phone: 1 519-332-1388 Phone: 1 832-429-3400 Mac-Weld QMS is certified to ISO 9001:2008 Mac-Weld QMS is certified to ISO 9001:2008 doesn’t necessarily add value. So, our Mac-Weld QMS is certified to ISO 9001:2008 process controls engineering group, control engineering group and manufacturing from testing for poor quality to preventing it by ensuring that their process controls and documentation give them better traceability, and then maintaining that genealogy through manufacturing and distribution,” he adds. “While traditional process data is stored in historians and crunched later for standard deviations, big data can also participate in more advanced statistical analyses, such as clustering, regression and multivariate modeling, and consider correlations among many more variables,” says Mike Boudreaux, connected services director, Emerson Automation Solutions ( “The process control industries already do process optimization and predictive maintenance, and big data can enable predictive analytics and machine learnMac-Weld logoadvanced Brand mark andprocess icon April 7, con2016 ing as open-loop Mac-Weld Mac-Weld logo logo Brand Brand mark mark and and icon icon April April 7, 7, 2016 2016 Mac-Weld logo Brand mark and icon April 7, 2016 trol (APC). This is a gross generalization, Mac-Weld logo Brand mark and icon April 7, 2016 Mac-Weld logo Brand mark and icon April 7, 2016 but it puts these concepts in context beMac-Weld logo Brand mark and icon April 7, 2016

The promise of The promise of The promise of The promise of precision every time. The promise of precision every time. The promise of precision every time. precision every time. precision every precision every time. time.

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Reference and master data

Enterprise integration

Transaction data

Data warehouse

Analytic capabilities

Your Global Automation Partner


Management, security, governance

Machine generated

Distributed file system

Text, image, audio, video

Key value data store

Map reduce

Discovery lab Data warehouse

Analytic capabilities

Management, security, governance

More data, better paths Figure 2: Traditional architectures (top) send structured data through integration to warehousing and analysis, but Oracle Corp. reports that big data (bottom) uses distributed, multi-mode, parallel data processing to handle larger, unstructured data sets, and employs different strategies for real-time and batch processing. Source: Oracle Corp.

ing managers are working to decide. The control engineering group does factory automation and integration for the upper levels, while the process controls engineering group examines operating trends, OEE issues and other details. As a result, these three groups came up with five points for deciding what data is useful. These include: total product produced by the work cell, good product quality, rejects, work in process, and amount of work in intermediate work in process. “We stumbled onto Ignition SCADA software, and added it to our new processes, including our largest manufacturing process with 70,000 tags, and we’re now connecting it to all our manufacturing, which includes 40 acres under roof. What’s really challenging is the pace of change, and how all the pieces of our SCADA and other systems are evolving in relation to each other. This is another way that Ignition helps because now we can put in standards for HMI and screen development, which reduces development time and cost and ultimately improves our product and quality.”

Big fish handling and filleting Beyond its larger and more varied sources and the amounts of information it takes in, big data is also distinguished

by how its information is handled and stored. While traditional data management involves gathering, compressing, simplifying and asking questions, and then slicing and dicing big chunks of information for analysis, big data is about shuffling many more small pieces of data through as fast as possible with database strategies like online transactional processing (OLTP) or online analytical processing (OLAP). “Where we traditionally used RTUs to manage our wells and drilling pads, our construction schedules are so aggressive now, and bringing so many wells, controls and I/O into our central control pads, that it’s no longer efficient to use RTUs only,” says C. Kisha Herbert, PE, staff electrical engineer at QEP Resources (www.qepres. com), an independent crude oil and natural gas exploration and production firm in Denver. In addition, Herbert reports that QEP has built 32 drilling and production facilities since July 2012, and each has 160-220 I/O points. It also employs a variety of automated valves, manifolds and skid equipment. To help automatically and quickly manage all the new data coming in from its new wells, pads, sensors, controls and other components, QEP recently adopted ControlLogix PLCs from Rockwell Auto-


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Sifting through suds Figure 3: Sierra Nevada Brewing is using Ignition software and Dynamic SQL programming to visualize data streams and find failing RTDs in two cellars with 10-14 beer fermentation tanks each, which have layered, glycol-jacketed zones that generate two different data flows and about 2 million software database rows per year. Source: Sierra Nevada Brewing

mation ( “On a typical QEP pad and production facility, well locations are protected through constant monitoring of protective shutdown devices; alarm and event logs are used to review and track specific information and recent events; and standardized ControlLogix PLCs and RSLogix software are helping us meet our aggressive schedules and maintain safe, standard process controls,” explains Herbert. “Understanding local regulations and requirements upfront and having good controls is a big help, but ControlLogix enables the remote I/O points at our remote pads to provide useful information to our central con34 N o v e m b e r / 2 0 1 6

trol pad. This is easier than using the former RTUs because they require a lot of linear programming to run their loops, routines and subroutines.” Chirayu Shah, marketing manager, visualization and information software, Rockwell Automation, adds that, “Because information is coming in from so many more places, such as unstructured sources, video feeds and social media, users that can leverage this data can make more educated decisions. Process data is no longer isolated at sites where it’s generated, so it can also join with input from outside facilities such as business intelligence, gain a wider con-

text, and function at higher levels in both large and small organizations.” Though it also uses longstanding statistical tools, Emerson’s Boudreaux adds, big data’s innovation is that it applies them using web-based and cloud-computing services carried out by distributed computing and storage infrastructures, such as Hadoop, Cassandra, Mongo DB and others. “These services are based on data storage models that use map-reduction to reduce information into usable forms,” he said. “They also build server clusters that share data across many servers, and process data in parallel to return results quickly—much like searching, clicking and getting immediate results via the web and Internet.” For process control users, Boudreaux adds that big data is an opportunity to get more insights, actionable intelligence and value from information they’ve been collecting for decades. For example, Batch Analytics software embedded in Emerson’s DeltaV DCS has protected services for taking process equipment information from valves and gas chromatographs, and uses big data methods to visualize them; take transactional snapshots to collect their histories and identify trends; and employ machine learning to predict equipment failures and model complex fault scenarios. This is why Emerson recently announced that its Plantweb digital ecosystem and Connected Services will be powered by Microsoft’s Azure IoT Suite. “Because we didn’t have access to big data sets before, the traditional approach was theoretical and used physics models and equations, which were time-limited and had to generalize among many applications,” explains Boudreaux. “Now, we’re using big data to develop empirical equations and models describing actual behaviors, which are more effective because they’re individualized. As computing gets faster and software costs go down, we get closer to continuous monitoring and management that’s more tailored to each user.”

Plan for big data success Similar to any new technology emerging on the process control front, big data can only help users make better decisions if they understand what it is, how it can affect their controls and processes, and how they can use it to optimize operations. Some of the primary tasks include: • Investigate business areas, facilities and applications that could benefit from big data; • Inventory existing processes, data sources and analysis devices and software for information gaps; • Evaluate possible big data tools, including software and statistical packages, server strategies for data storage, and virtualized and cloud-based computing services; • Examine how to coordinate big data devices and software with existing data acquisition systems, historians, and diagnostic and analytics functions; • Determine and enable cybersecurity capabilities for planned big data implementation, including software, servers, networking and cloud devices; and • Apply chosen big data solution, but schedule and periodically carry out future reexaminations, and make adjustments as needed.

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Organize and analyze Of course, big data only delivers on its even bigger promises when users can get useful nuggets they can turn into better decisions, efficiencies and profits. In process control applications, this often means better predictive analytics and maintenance, and/or improved remote monitoring and optimization. For instance, Sierra Nevada Brewing Co. ( in Chico, Calif., recently enhanced critical temperature controls on its fermentation tanks by improving its data visualization tools with Ignition SCADA software, and efficiently sorting through reams of batch data to proactively identify failing resistance temperature devices (RTD) and other issues. The brewery has about 100 tanks at its plant, including two main brewing cellars, each with 10 to 14 beer fermentation tanks (Figure 3). Each of the 15-foot-wide tanks has layered zones and two or three RTDs that can generate two different data flows for the same three- or four-week batch. Temperature is controlled by the solenoids and glycol flowing to jackets on the tanks and by an overall chiller plant. “We wanted, at a glance, to show there was either no problem or there was an item we needed to look at,” says David Lewis, technical services manager at Sierra Nevada. “We also wanted bar graphs show-

ing number of batches per year per tank, as well as number out-of-spec incidents. We also wanted to drill into data about the last several batches, so we could check the details of several out-of-spec incidents.” Ignition let Sierra Nevada moved batch data into tables from 200-300 tanks, brew kettles and supporting devices. “We capture data every five minutes, and we already had about 10 years worth of information in our database,” says Lewis. “In early alert attempts, we had to determine which RTD was indicating it was beginning to fail, for example, by causing the chiller to run on. We also needed to figure out which data tails to exclude, though this might mean missing some stuck solenoids, and we had to avoid email overload. So, we stepped back, prioritized our data, and tried to make it more proactive. We also required an appropriate context in which we could see everything together, so we’d know when operations were happening that were supposed to be happening, instead of looking tank by tank and batch by batch. This meant bringing in much more data, but then separating useful signals from noise.” Because data is captured in five-minute intervals for the brewery’s more than 100 tanks, Lewis reports this generates about five software database rows of data per tank per batch. At about 2,000 batches annually, Sierra’s brewing operations produce a

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B i g d ata Major big data tools To bring in and use big and non-traditional information streams, there are many software packages, data management and storage methods, new communication protocols, programming tools, and cloud-computing services, coming mostly from the IT side. Here’s an incomplete list and glossary of the primary players: • Cassandra (, or Apache Cassandra, is a free and open-source distributed database management system designed to handle lots of data across servers with high availability and no single point of failure • Cloudera ( provides Apache Hadoop-based software, support, services and training • Dynamic SQL is a programming method that lets users build SQL statements dynamically at runtime • Hadoop ( is an opensource, Java-based programming framework that supports processing and storage of large data sets in a distributed computing setting. It’s part of the Apache project sponsored by Apache Software Foundation • Mongo DB ( a free, open-source, cross-platform, document-oriented database program

total of 2 million software database rows per year. To access this data and begin to improve decisions, he adds that he and his colleagues are using Dynamic SQL programming to build queries for their database. Sierra also uses Tableau ( data visualization software to view database results, which are displayed in conjunction with Ignition software. “Our MES is located on one server and batch data is on the DAQ server. This means critical data was on different servers, but our initial cross-server queries weren’t working, and replicating data from one server to another was too cumbersome,” says Lewis. “We needed data from 2,000 batches with their own start and stop times, so we ran a query string using Dynamic SQL, joined one with another, did it 2,000 times, and it crashed. So, we threw a Hail Mary, and cut and pasted the 2,000 queries into Tableau, and five minutes later, we got the 2 million rows we needed.” Lewis adds that all displays for its newly enabled database were built with Ignition, which allows users to click on each batch and see a profile for it. “Then we can use known and previous failure patterns to better determine when the next RTD is going to fail,” adds Lewis. “We can see drift and behavioral changes in the graph for a batch, and fix problems before they become failures.” Similarly, Nick Moceri, president of SCADA Solutions ( in Irvine, Calif., showed how his company is using IoT-based remote monitoring and control to let its client’s legacy wind turbines ramp electricity production up or down more quickly in response to fluctuat36 N o v e m b e r / 2 0 1 6

• •

• •

classified as a NoSQL database program. It uses JSON-like documents with schemas MySQL ( is an open-source database and Oracle’s big data intelligence platform and application Power BI 9 ( is Microsoft’s data visualization/business analytics tool SAP Hana ( is an in-memory, column-oriented, relational database management system developed and marketed by SAP SE Spark Streaming ( is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data st reams Splunk ( produces software for searching, monitoring and analyzing machine-generated big data using a web-based interface Tableau ( is data visualization software that joins databases and graphics TrendMiner ( is a predictive analytics tool for the process industry

ing grid demand and to avoid negative pricing, which may require 200 turbines to be adjusted in 10 minutes or less. SCADA Solutions added its in-house software to Opto 22 ( controllers and other components. “Many older wind farms weren’t built with the Internet in mind, but now they need to add Internet protocol (IP) switches that are addressable to a server, so they can be brought into a central location,” says Moceri. “IP-addressable switches, controls and servers allow us to get ahead of the game, optimize production, and perform predictive maintenance that extends turbine life. In fact, one wind farm in Palm Springs, Calif., went from flat results to a 16% production improvement and complete return on investment in just three months.” To begin to implement a big data strategy, Grantek’s Toman suggests it can’t be done merely from the ground up, and instead requires an organization-wide strategic plan. “You can’t just look at the needs of individual silos. You have to reach out to the rest of the company, evaluate systems in place, and identify other data silos that can be leveraged,” says Toman. “Many times, organizations have blinders on, so they need to bring in someone who isn’t in the existing culture to poke around, and mediate between the engineering and IT sides on how they can adopt some best practices and standards for big data. This isn’t just converging data technologies; it’s about convincing process people to practice and benefit from them.” Jim Montague is Control’s executive editor


Cybersecurity in the SIS world Find and slay the dragons lurking in

the typical safety instrumented system.

by William L. Mostia, Jr., P.E.


ybersecurity is a growing concern in the process industries, and a number of good articles have been written about it for industrial control systems (ICS)—many full of doom and gloom. Here, we will divide the ICS into two parts: safety instrumented systems (SIS) and all other ICS components, which we lump into the basic process control system (BPCS). There are distinct differences between the SIS and BPCS in function, design and cybersecurity. The SIS and BPCS differ in regard to cybersecurity from a process safety perspective, how traditional SIS design practices can help provide cybersecurity, and how cybersecurity concerns can affect the design of the SIS. This article examines some of the differences between the BPCS and the SIS, SIS vulnerabilities to cyberattack and other security concerns unique to the SIS. It also covers how traditional SIS design can help with cybersecurity, and how traditional design practices of the SIS are affected by cybersecurity. Due to its size limits, one article can’t cover all aspects of designing or securing a SIS in the presence of cybersecurity threats, but it’s instead intended to provide food for thought on this topic.

When a cyberattack gets physical

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Governmentand statesupported



Public tor vec eat Thr

It’s important to note that operating a chemical plant or refinery is complex, with many checks and balances as well as human beings to provide 24/7 oversight and some level of resilience. A cyberattack is really a cyber-physical attack because it involves a system with direct connections to the real world, as opposed to attacking a computer and data. A process plant is also a system designed to work in the presence of failures (even multiple ones) and uncertainty, even if the failure mode is unknown, whether it be a cyberattack, control valve failure, pump failure, etc. For example, if a tower is over-pressurized, chances are you’ll have an independent, high-pressure alarm, possibly a high pressure override of the tower reboiler, an SIS and a relief valve protecting it, plus operator observations. This illustrates how defense-in-depth achieves process safety, which also provides protection against a cyberattack as an initiating

cause. This is not to say that cybersecurity is not important for process safety, but rather that it must be considered in the mix of potential failures and safeguarding against those failures. Figure 1 illustrates the overall cyber-domain including the SIS. Generally speaking, only digital systems are a concern for a direct cyberattack, however, even analog or mechanical systems aren’t as completely immune as one might think. For example, the safe operating limit database (alarm and trip setpoints), asset management (changes in device parameters), SIS field instrument calibration databases (incorrect calibrations), and even the relief valve database (incorrect trip setpoints and test intervals) can potentially be corrupted by a cyberattack, leading to failure in the SIS or other process safety systems under the right circumstances.

Threat vector


Unknown unknowns

THE CYBER DOMAIN Figure 1: Along with the basic process control system (BPCS) and safety controls, alarms and interlocks (SCAI), the safety instrumented system (SIS) is a critical component of a process facility’s defense against myriad cybersecurity threats that might lead to loss of life and destruction of property.

SAFETY SYSTEMS The role of the SIS in safety It’s important to understand how process safety is achieved through functional safety, and how the SIS fits into the overall picture. Achieving process safety using functional safety typically involves a defense-in-depth protective scheme consisting of independent protection layers (IPLs). In Figure 2, we can see the SIS is not the only IPL in the layer of protection scheme. Some IPLs are subject to direct cyberattack and some are not. Modern design of functional safety protection systems (FSPS) for hazardous processes is all about preventing a hazardous condition, even in the presence of failures of some of the IPLs. The cyberattack threat does not change that paradigm, but rather adds additional potential failure modes of the BPCS and process equipment that may lead to potential safety demands of unknown frequency (an important risk consideration). A fundamental SIS design principle is that failure of the BPCS to control the process for any reason should not cause a simultaneous failure of the SIS protecting the process. This does not change with the introduction of the cyberattack threat; if a cyberattack has compromised the BPCS, it should be substantially more difficult for the same attack to compromise the SIS either synchronously or asynchronously. Defense-in-depth and the related principle of requiring multiple failures or difficulties—a “tortuous path” before you have a successful cyberattack—are important protective concepts. This also applies to the BPCS, where safety controls, alarms and interlocks (SCAI) and other protective safeguards should present a difficult path to defeat them all to cause a loss of process safety protection and situational awareness of the operator.

How a SIS differs from a BPCS Her are some the primary differences between the SIS and BPCS. The primary purpose of the BPCS is as an active, continuous system that controls level, pressure, temperature and other process variables designed to keep the hazardous materials in the process under control within the safe operating envelope, while efficiently and cost-effectively making on-spec product. The vast majority of SISs, on the other hand, operate as passive systems that sit there doing nothing until a safety demand occurs. When the process exceeds its safe operating limits, the SIS acts to maintain or bring the process to a safe state. This passiveness also makes it difficult for an intruder to analyze the system and its relationship to the BPCS by observation alone. Failure of the BPCS can be an initiating cause for a hazardous scenario, whereas a properly designed, low-demand SIS can’t typically be the initiating cause of a hazard—even during a cyberattack. The BPCS will have tens of thousands of data points (reads and writes) and other parameters transferred digitally between BPCS boxes via multiple paths, where the SIS may

Acceptable risk level

Layers of protection

Risk inherent in the process


Other (safety valves, etc.)






RISK REDUCTION Figure 2: Reducing risk typically involves a defense-in-depth scheme of independent layers of protection including the basic process control system (BPCS); safety controls, alarms and interlocks (SCAI); and the safety instrumented system (SIS).

have a few hundred data points, mostly reads with a limited number of writes. The BPCS will typically talk to the SIS through only one communication path per SIS. The SIS will also have its own internal communication structure. In most cases, the SIS is implemented on different hardware, in some cases by a different manufacturer than the BPCS equipment. The SIS is periodically proof-tested, while the BPCS is many times operated to failure. This provides a mechanism for detecting unauthorized changes.

Cybersecurity standards for SIS There are several standards pertinent to cybersecurity and the SIS. The second edition of IEC 61511-1 will require that a security risk assessment be carried out to identify the security vulnerabilities of the SIS, including both physical and cybersecurity vulnerabilities. The standard also will require that the design of the SIS provide the necessary resilience against the identified security threats. This is a new, substantial requirement. The ISA 99 committee has generated a series of pertinent standards, one of which is IEC 61511-1, ANSI/ISA/ IEC-62443-1-1, “Security for Industrial Automation and Control Systems Part 1-1: Terminology, Concepts and Models.” The ISA 84 committee also has a subcommittee looking at cybersecurity for technical reports (TR). They’re in the draft stages of dTR84.00.09, “Cyber Security Related to the Functional Safety Lifecycle,” which is attempting to bring the principles of ANSI/ISA/IEC62443-1-1 to functional safety and the safety lifecycle. Hopefully, they will do this in a practical manner without too much computer-speak.

Protecting SIS assets Protecting the SIS against cyberattacks is a simple matter of N o v e m b e r / 2 0 1 6


SAFETY SYSTEMS include those in this assessment. This should be coordinated with the cybersecurity efforts on the BPCS. The identified vulnerabilities should be eliminated or their risk minimized. Potential vulnerabilities include remote access, uncontrolled writes, ability to program remotely, configuration database indirect attacks and cyberattacks via manufacturer or third-party software. Red flags include any computer equipment that is Windows-based, commercial off-the-shelf (COTS) technology, “open” systems, connections to the enterprise or Internet, the ability to write to the SIS, SIS equipment under lock and key, and/or portable media (USB ports, memory sticks, CDs, etc.). Ethernet and Ethernet switches (too vulnerable) are a no-no in a SIS zone or to cross the boundary. Wireless may be an open invitation and should be avoided in a SIS. Don’t connect to what you don’t have to connect to. Risk vs. benefit has to be factored in, particularly when convenience is considered. Implement intrusion detection, including monitoring for changes in software and safety-critical parameters. Fortunately,

preventing unauthorized changes that can compromise its safety functionality. Easy as pie, right? To get a high-level view of your SIS and its potential vulnerabilities, draw a boundary around all the SIS assets (typically your SIS zone). Then, identify all of the communications paths and any other data, remote or physical access paths that cross that boundary. This is illustrated in Figure 3 for a generic SIS, but your system may have more or different vulnerabilities. This conceptual boundary can help you visualize your potential cyberattack vulnerabilities and systematically address them. To evaluate your cybersecurity vulnerabilities and current protections, one of the first things to do is an inventory of all SIS equipment, software (with version numbers), and critical operating parameters. This should be followed by a security assessment of the SIS as required by the IEC 61511-1 2nd Ed. This inventory will provide a baseline for monitoring changes in your system. Contact your equipment vendors and ask them to provide an analysis of their equipment’s known cyber vulnerabilities, and

BPCS communication Digital Analog/discrete Conduits (typical)

Remote access

SIS zone Remote access

SIS controller Run/off/ program/ remote switch

Windows vulnerabilities Original, patch and update software Removable media (memory sticks, CDs, etc.)

Safe operating parameters database Original, patch and update software

Engineering station Unknown Unknownthreat threatvector vector Unauthorized access

SIS Field

Physical access

Unknown gap

Windows vulnerabilities Original, patch and update software Remote access

Windows vulnerabilities



Unauthorized access

Unauthorized access

Original, patch and update software Remote access

DETERMINE THE BOUNDARY Figure 3: Draw a conceptual boundary around the safety instrumented system (SIS) assets (typically your SIS Zone). Then, identify all of the communications paths and any other data, remote or physical access paths to help visualize potential cyberattack vulnerabilities, and systematically address them.

40 N o v e m b e r / 2 0 1 6

SAFETY SYSTEMS ICSs typically have extensive logging, and the SIS should log to them all changes in parameters and SIS accesses for programming, maintenance, etc. A cyberattack response plan should be put into place, including operator procedures and a recovery plan. Failure to plan is planning on failure.

SIS design for cybersecurity Most articles on cybersecurity for ICS revolve around the conceptual approach of dividing the control systems up into zones and conduits (essentially, protection by controlled isolation), doing a cybersecurity assessment, and placing a bunch of firewalls or security appliances in your networks. These are important aspects of cybersecurity, but they are not the only things to do in designing the SIS system for protection against cyber attacks. Many of the traditional design principles for SIS provide some level of cybersecurity protection (e.g. independence, separation, diversity, limited digital connectivity, controlled writes, distributed architecture, 4-20 mA signals, etc.). So legacy SIS have some cyber vulnerabilities, but they are not as exposed to cyber attacks as many people seem to assume. Unfortunately, in recent times, the use of some of these principles have declined due to cost considerations, competitive differentiation and changing demographics. Independence, separation and diversity are philosophies that have been cornerstones of SIS design. Independence keeps the safety functionality separate from the control functionality. Keeping the SIS hardware physically separate eases physical security and isolating the SIS into zones. Having diversity in hardware can mean different hardware from the BPCS but the same manufacturer, or it can mean different manufacturers for the SIS hardware. Both are good practices, but having different manufacturers reduces common cause failures, and increases the knowledge required to hack both systems. The safety PLC is commonly used as a logic solver for a SIS, and is typically the focal point of most cyberattacks on the SIS because it communicates with the outside world and is extensively software-based (e.g. requires programming software, software updates and patches, commonly networked, etc.). Safety PLCs are different from general-purpose, industrial PLCs and DCS controllers, and are much less open to unauthorized changes. SIS logic solvers that are more tightly integrated with the BPCS or use the same hardware as the control system can be more exposed to a cyberattack. Most safety PLCs have a hardware watchdog timer that monitors their logic cycle. A separate hardware watchdog timer may also be a good idea. Communication watchdogs can also be created in logic to detect communication problems between the SIS and the BPCS independent of the communication channel, and can detect problems with the communication channel. Run/stop/program/remote switch: Almost all PLCs and certainly safety PLCs have some form of this hardware key-lock switch that can control programming access and in some cases, control “writes” to the PLC. No programming of SIS equipment should be allowed across a network connected to the

BPCS, enterprise network or outside world, even through firewalls. It should be verified with the SIS logic solver manufacturer that their key-lock can’t be overridden externally through a communication link of any sort “Read” requests from the BPCS are common to transfer the SIS status to the BPCS. These should be limited in scope. It should be verified that problems with the PLC communication processor/port (e.g. denial of service attacks, incorrect or garbled “read” requests, etc.) can’t affect the PLC’s safety logic cycle or its safety functionality. Writes: the safest approach is to not allow any writes to the SIS logic solver from the BPCS. Most SIS logic solvers can limit writes to specific memory locations (e.g. will not accept writes to other locations). DCS, PLC or foreign device gateways may also allow only certain tags be read or written to the SIS. These features should be implemented. If you must write to a SIS logic solver, you might consider an analog or digital input to transfer the data. Deep-packet inspection (DPI) security appliances and data diodes can stop all writes, and in some cases can whitelist read and write tags or memory locations. These security appliances must be able to get down to the write command and the write tag or memory location to be effective. The safety PLC should also ensure that write data values are within an acceptable range. Non-digital SIS logic solvers such as relay logic and tripamps are directly immune to a cyber attack. Indirectly, they may have a small cyber vulnerability if the database for their trip and alarm points is corrupted by a cyber attack. These systems can be used as a back-up safety PLC’s safety instrumented functions (SIF). In small applications or localized systems, they can provide a cyber-immune solution. SIS field devices are less prone to cyberattack because the vast majority of their outputs are 4-20 mA or on/off 24 VDC/120 VAC, which are notoriously hard to hack. Safety protocols have been developed for digital fieldbus communications between field devices, but are not very common for SIS. When these are used, they may be more exposed than a 4-20 mA loop to a cyberattack. When a fieldbus safety protocol is used, the transmitters should be connected point-to-point, and high-speed Ethernet (HSE) should be avoided for SIS service. A hardware security jumper blocks changing any of the parameters of a field transmitter, including changes via HART or fieldbus. SIS field sensors and other applicable SIS field devices should always have their security hardware jumper engaged in normal operating service. Software lockouts should not be used unless they’re the only security feature available. If an AMS system is present via HART, it should have read-only access for SIS field devices, even if the security jumper is not engaged. All SIS transmitters should have a deviation alarm where feasible. 4-20 mA smart transmitters typically communicate via a HART communicator during calibration and maintenance. There is a cybersecurity exposure due to the software in the communicator, but it must come indirectly through corruption N o v e m b e r / 2 0 1 6


SAFETY SYSTEMS of the software from the communicator’s manufacturer. Calibration tools are another potential cybersecurity vulnerability for SIS field devices because modern ones are digitally based, and may communicate with a database or AMS system that would typically be on a Windows machine connected to the site enterprise network. Corruption of the calibration database could lead to miscalibration of safety transmitters. Keeping a computer backup of the calibration data, trip and alarm points, transmitter parameters and programs is a good practice, and historical copies should be kept in case the current one gets corrupted. This will help you recover from a cyber or internal security attack. Remember, plan ahead. Final elements, such as solenoids, valves, motor starters, etc. are typically immune to cyber attack. If your valve has a digital valve controller or a smart positioner, it may be possible for a cyberattack to spuriously trip or cycle the SIS valve. These devices may be communicated with by a portable Windows-based computer, and may be subject to a cyber attack. Bypasses are points in the SIS logic solver that are commonly a write from the BPCS to the SIS to bypass a particular sensor to allow maintenance. Erroneous activation by the BPCS or SIS would defeat a SIF or part of a SIF. The common practice of having a manual, bypass-enable switch that must

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be activated, with a short timeframe to enable a bypass and bypasses that time-out are good practices. Also, having a bypass alarm generated from the SIS that restrikes periodically when in bypass, and remotely monitoring the bypass state, are also good practices, again, making it more difficult for a cyber intruder to enable the bypasses undetected. Manual shutdown: IEC 61511-1 states that a “manual means (for example, emergency stop pushbutton), independent of the logic solver, shall be provided to actuate the SIS final elements unless otherwise directed by the safety requirement specifications.” This was put in place because there was a fear that the PLC logic solver would not operate when required, the PLC might go into a loop, or it might begin operating erratically (sounds applicable to a cyberattack). However, primarily for convenience and cost reduction, it’s been the practice of many people, to use the “…otherwise directed by the safety requirement specifications” to route the manual shutdown to the SIS logic solver because they rationalize that the logic solver is highly reliable. From a cybersecurity perspective, this is a bad practice because if the SIS logic solver is compromised, so may be the manual shutdowns in this logic solver. This takes away the operator’s ability to quickly implement a manual shutdown to bring the plant to a safe state. This is particularly worrisome for SIF where there are no other IPLs associated with the hazardous scenario. Also, companies often have procedures that are “gun-drilled” (exact and ingrained) for shutting down the plant due to power loss, cooling water loss, etc. It makes sense that you should have a gun-drilled procedures to shut down your plant if you suffer a cyber attack that compromises process safety. Reset function: Software resets may provide some protection against a cyberattack that cycles the safety PLC outputs, but may be compromised by a knowledgeable attacker. Field manual resets on the solenoids physically prevent the shutdown valve from cycling, and are immune to cyber attacks.

Cybersecurity is never done Cybersecurity is a complex, important topic that is ever evolving. Many practical things can be done based on engineering analysis of the SIS’s vulnerabilities and data flows. Use of the zones and conduit concept, defense in depth, and the torturous path concepts are steps in the right direction. The standards in this area have a steep learning curve, and with the ever-changing cyber threat environment, may require a specialist to keep up. I will leave you with an interesting question: are identified hardware (and related software) vulnerabilities in your ICS and SIS covered by your hardware warrantees and afterward? [I want to thank Mark Carrigan of PAS Inc. ( for the excellent discussion on cybersecurity when I was researching this article.] W.L. Mostia, Jr., P.E., is a frequent contributor to Control.

42 N O V E M B E R / 2 0 1 6


Ongoing innovations, added intelligence and networking let motors and drives serve in unusual applications and replace obsolete equipment. by Jim Montague


ust when it seems like today’s sophisticated motors and drives can’t possibly add more efficiencies and capabilities, engineers conjure up new tricks and refinements, followed by end users and system integrators who materialize new settings and challenges where they can make big gains. As usual, the rest of us are left to wonder, “How did they do that?” This is because, while long-established drives and motor solutions might not appear unusual to most technical professionals and other onlookers, they obviously manifest as lifesavers to those most in need of their capabilities. Necessity doesn’t just lead to invention, it grants new vision along the way. One company with this mindset is Acadian Seaplants (www. in Dartmouth, Nova Scotia, Canada, which grows and processes agricultural biostimulants in liquid and powdered forms at its plant in nearby Cornwallis. The company starts with local, sustainably harvested ascophyllum nodosum seaweed, from which various bioactive compounds are extracted, clarified, filtered and concentrated in a complex production process that requires careful process control. The final products are shipped to more than 80 countries to improve the health and growth of plants worldwide.

CUSTOMIZE, EXPAND SEAWEED CROPS Figure 1: Acadian Seaplants employs Allen-Bradley Centerline motor control centers (MCCs) with IntelliCenter software from Rockwell Automation to more easily reconfigure for different crop biostimulant products, and increased capacity 40% at its plant in Nova Scotia. Source: Acadian Seaplants and Rockwell Automation

Growing gracefully “Our motor controls and facility communications were hardwired before 2006, so if we wanted to change or add a step in the production process, we’d often to rewire entire areas of the facility,” says Wade Hazel, engineering manager at Acadian. “We began automating the Cornwallis facility during 20062008, but this modernization wasn’t enough to meet growing demand, so we decided to build onto the existing plant to add capacity, and automate the new equipment to increase process control and efficiency.” Hazel adds that Acadian supplier Graybar (www.graybar. com) knew it was already using Allen-Bradley CompactLogix programmable automation controllers (PACs) from Rockwell Automation (, so it proposed adding Allen-Bradley Centerline motor control centers (MCCs) with IntelliCenter software and EtherNet/IP networking. This meant Acadian could expand its capacity by integrating with existing controllers, and avoid adding hardwiring for motor controls (Figure 1). Consisting of variable-speed drives (VSD) and full-voltage starters, the MCCs and supporting automation let Acadian increase seaweed processing by 50% during 2008-09. However, demand continued to swell, so Acadian’s management decided to build the new Deveau Center plant across the street with three times the space. This included underground piping for moving product between the plants, three more ControlLogix PACs, expanded Centerline and Intellicenter applications, plantwide EtherNet/IP for monitoring and motion control, and Rockwell Software Studio 5000 to set up and configure the PACs and MCCs. The new plant was finished in 2014, and it’s already running at 40% more capacity than its earlier version, and can expand capacity another 250%. “This facility isn’t static,” added Hazel. “We may need to change functions one day, do improvements the next, or add new processes. With all controls connected via EtherNet/IP, many former hardwires became virtual wires, so we can make changes faster at a lower cost. Integrated MCCs and controllers N O V E M B E R / 2 0 1 6



LONG CONVEYOR FOR COPPER HAULING Figure 2: Codelco is deploying ABB’s 800xA control system and

Figure 3: Tata Steel’s Hartlepool SAW pipe mill in northeastern

Mining Conveyor Control Program (MCCP) to automate the 13-

England is replacing the 40-year-old DC motor on its pipe ex-

km conveyor and gearless conveyor drive system between its

pander’s gripper car with a 150-kW, 300-Amp, 1,070-rpm motor

new underground mine and concentrator plant at the Chuquica-

from Vascat S.A. and a 250-kW Powerflex 755 inverter drive from

mata copper mine near Santiago in Chile. Source: Codelco and ABB

Rockwell Automation. Source: Tata Steel and CP Automation

give us a more connected, reliable and continuous facility with data shared more seamlessly between processes and operators. Engineers can also access the plant’s computer remotely to reduce downtime compared to former onsite visits. “Based on the plant science division’s successful expansion, our engineering team has started using CompactLogix PACs to automate processes in the food science division’s land-based cultivation facility, and we’d like to migrate to EtherNet/IP-enabled MCCs in the animal science division in the next few years,” adds Hazel.

then it’s only 40-50% efficient at half speed,” he explains. “Synchronous reluctance gets that efficiency back up to 70% at half speed, which is why we’re rolling them out.”

Rotating evolution Integrating microprocessors, intelligence, networking and other functions into motors and drives enable them to achieve remarkable gains, but these advances have also altered their basic nature. “Process and discrete controls are more the same now, and their technologies are adapting to fit,” says Robert Soré, product marketing manager for general-purpose, Sinamic and VG drives, Siemens ( “PLCs and other process controllers aren’t much different now, and many drives are much the same, even though their AC motors may run at different speeds. In fact, increasing pressure for efficiency has pushed drives to evolve until they can now do what servo drives did just a few years ago.” Because they’re easier and less costly to deploy, drives are spreading to rotating equipment that hasn’t used them before, according to Soré. “Over the past five to seven years, drives are appearing on more pump jacks, fans, pumps and positive-displacement pumps. These used to have contactors and starters because there were less concerns about saving power. However, with the oil and gas industry down, people want to save money, and they’re doing it with smaller, smarter and less costly drives, and more precise variable-frequency drives (VFD).” Soré adds that similar efforts to save are spurring adoption of more efficient, relatively higher-tech motors, such as synchronous reluctance motors. “A manufacturer may add variable speed to a motor that usually runs at full speed and 60 Hz, but 44


Tough, old environment? No problem Thanks to these and other enhanced capabilities, size reductions and lower costs, users are increasingly willing to apply new motors and drives in difficult environments, and replace aging equipment they couldn’t remove before. At the Chuquicamata open-pit copper mine in Chile, stateowned Codelco ( recently began developing an underground mine to access an ore body beneath the pit. The new mine is scheduled to begin operations in 2019 and expand “Chuqui’s” capacity, but one major hurdle is it will need one of the world’s largest, most complex and most powerful conveyor systems to get copper ore up steep gradients and over long distances to its concentrator plant 13 km away (Figure 2). As a result, Codelco recently contracted with Tenova Takraf GmbH ( to build the conveyor system, and Takraf enlisted ABB ( to automate it. So far, it’s designed to have conveyor flights powered by up to 20 MW and a total requirement of 55 MW, which will allow it to transport more than 11,000 tons of material per hour. ABB will implement a complete power and automation solution at Chuqui, including gearless drives, motors, instrumentation and power components, which will be custom engineered to meet onsite requirements to optimize power, control, measure and actuate the conveyor. This design will integrate the belt’s power and automation through ABB’s 800xA control system, and combine with its Mining Conveyor Control Program (MCCP) to ensure optimum power quality and control. “The gearless conveyor drive system is a key feature of the solution because it meets the conveyor’s extremely high load requirements and necessary power availability, which wouldn’t have been achievable with a conventional drive solution,” says Roger Bailey, head of ABB’s Process Industries division. “The gearless drive system eliminates the gearbox from the motor,

Motors & drives which reduces part wear and required maintenance. Another advantage is the reduction in the drive system footprint and instrumentation required. Less parts increases reliability and efficiency of the overall conveyor system by several percentage points.” Similarly, Tech Folien Ltd. ( in Liverpool, U.K., recently needed to replace the 15-year-old motor/drive system on its extruder due to lacking spare parts and excessive energy consumption. The firm produces polycarbonate, polypropylene and other co-extruded, blown films. As a result, Emerson Automation Solutions ( and Rewinds & J. Windsor (RJW, combined a Unidrive M701 VSD from Control Techniques and an energy-efficient IE4 LSRPM motor from Leroy-Somer, both Emerson Industrial Automation firms. Installed in a cabinet away from the line, the M701 sets the speed control parameters of the motor, which is installed close to the line. Control parameters are programmed via onboard drive macros. Emerson and RJW’s solution is expected to save the extruder and Tech Folien about 75% on its energy costs. “Energy efficiency plays a vital role in ensuring our profitability, and this new drive and motor combination ensures that we have complete confidence that we can control our energy costs and maintain 24/7 operation,” says David Churm, maintenance manager at Tech Folien. Finally, located in the heart of Tata Steel Europe’s ( Hartlepool SAW pipe mill in northeastern England is an expander machine that shapes, sizes and strengthens its pipe products with help from a large gripper car and 40-year-old DC motor, which moves 12.5-meter pipes over the expander’s head. Used only for pipe making, this machine is unique in the U.K. However, the old motor also required frequent maintenance (and DC drives and spare parts were scarce), consumed too much energy, and was hobbled by an obsolete, inaccurate and slow control system that often caused production bottlenecks (Figure 3). Consequently, Tata sought help from Rockwell Automation and CP Automation ( to find a replacement. “Requirements for better energy efficiency and accuracy meant an off-theshelf motor and drive wouldn’t suffice, so we worked with Vascat ( to produce a bespoke, 150-kW motor at 300 A and 1,070 rpm, and Rockwell supplied its 250-kW Powerflex 755 inverter drive with CIP Motion function and PLC,” says John Mitchell, global business development manager at CP. To boost the new motor’s start/stop speed and prevent drive trips, CP introduced a Revcon regenerative braking unit to make start/ stops more seamless, accelerate the gripper car, and move the pipes faster. The old motor was replaced in August 2016, and Tata had also planned to replace the expander’s auxiliary drive later in 2016. “The regenerative unit is where we reaped the benefits of the new equipment, both in increased speed and energy efficiency,” says Tony Brown, electronics engineer at Tata Steel. “Also, the new servo drive’s accuracy allows us to position pipe with 1-mm accuracy, whereas the old DC system’s accuracy was closer to 50 mm. This improved accuracy coupled with a 10% increase in speed gives us productivity improvements throughout the expander.” Jim Montague is Control’s executive editor.

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first-order plus deadtime (FOPDT) model is a simple approximation of the dynamic response (the transient or time-response) of a process variable to an influence. It’s also called first-order lag plus deadtime (FOLPDT), or “deadtime” may be replaced with “delay,” changing the acronym to FOLPD. The FOPDT model is often a reasonable approximation to process behavior, and has demonstrated utility for controller tuning rules, for structuring decouplers and feedforward control algorithms, in communicating essential process attributes, and as a computationally simple surrogate model in simulations for training and optimization. There is no claim that the FOPDT model is a true representation. The process is likely higher order and nonlinear. However, a FOPDT model is a practicable representation, balancing multiple aspects of utility. In FOPDT modeling, typically, we consider that the influence has remained constant in the recent past, and that the process variable (PV) had achieved a steady value. Then, we consider that the influence makes a step-and-hold, and holds that new value until the PV reaches its new steady state. The

75 70 65 60 55 50 45 40 35 30 150





Time (step-and-hold happened at 155)

TYPICAL COMPARISON OF FOPDT MODEL TO DATA Figure 1: Compared to the data (dots), the FOPDT model (solid curve) has a longer delay, changes rapidly to catch up, then rises above the process before relaxing more slowly to the final steady state.

46 N o v e m b e r / 2 0 1 6



deadtime represents the time duration after the influence changes during which the PV does not change. It’s like a transport delay in a pipeline with plug flow or a laboratory analysis time. Figure 1 compares the model to data. Here, for clarity, the data is ideally noiseless, s-shaped, and indicated by the dots. The stepand-hold in the influence happened at a time of 155, but the high-order process doesn’t begin to reveal a response until about 156 when it starts to rise slowly, then achieves its fastest rate of change at about 159. For a best fit to the data, the FOPDT model (solid curve) has a longer delay, and does not begin to make a change until a time of about 157. Because it is a single lag, its fastest rate of change is when it starts to respond. The model has a delay longer than the process, then must change rapidly to catch up to the process. The model rises above the process in the 158-162 time period, then relaxes to the final steady state value a bit slower. This model best balances the “+” and “-“ deviations from the data. Mathematically, the model could be stated as an ordinary differential equation.

The model gain, K m, is the multiplier for the influence change that determines the new steady-state value for the PV. The FOPDT model pretends that once the delay duration, m , has passed, the PV follows a first-order exponential trajectory to the final steady-state value. The FOPDT time-constant, m, is an indicator of how fast the PV moves toward the new value. In contrast to some conventions, I used the subscript “m” for “model,” not the subscript “p” for “process,” to acknowledge that the model is not the process. I explicitly placed a squiggle hat over the model re-

develop your potential




60 40


50 30


40 20




30 10


20 0 0





60 Time

60 Time




0 .04 120 .02


0 120

Figure 2: Nonlinear least squares regression is simple to implement and a skyline input function has advantages in duration, upset size and 42

number of excitations. 41 20 39 42 38 41 37 20 36 39 35 38 34 370

Response Response

lieve reaction curve techniques don’t express best practices in the computer era. However, a crude approximation FOPDT model is oftern all that’s needed. In such cases, a reaction curve technique can be a simple and fast method to get a good-enough model. The reaction curve technique asks you to make a step-and-hold change in the process input, from an initial steady state, and hold the input until the response variable levels to an ending steady state. Unfortunately, noise and drifting alternate influences confound the response. And, a single step pushes the process away from a desired setpoint. Further, a push to one side of a nominal value will misrepresent nonlinear aspects. So, for effective reaction curve tests, we often use an up-down-down-up pattern in the influence step-and-hold values. This generates four reaction curves, and their average can temper the influence of noise and disturbances. Further, the pattern explores both sides of the original manipulated variable (MV) value, making compensating upsets and, ideally, returning the process to the original value. These steps must be large enough to make a noticeable change in the response. If the change is small relative to normal noise and drifts, then the FOPDT model coefficients will have a large uncertainty. Once the response curves are completed, the model coefficients are calculated from a few points on the response curve. There are multi-




sponse variable, (t’), to indicate that it’s the model, not ple twists on the method. Howver, this approach requires operator attention for the process. And, I used the prime mark to indicate that an extended time to wait for four steady-state periods; the model influence, response and time are each a deviamay create process deviations that impact downstream tion from the initial steady conditions as well as the time 75 quality; requires the human to interpret the signal to pro70 for the step-and-hold influence. In figure 1, the change 65 vide data for the mathematical analysis; only uses a small happens at a time of 155. Although t = 155, at that instant 60 part of the data generated; and can be substantially con75 t’ = 0. Similarly, the initial process value is y = 37, but the 55 70 founded by uncontrolled disturbances. 50 deviation value is y’ = 0. 65 45 In the computer era, by contrast, nonlinear least squares Although the concept for the model is a response to 4060 a regression is simple to implement, and a skyline input 55 step-and-hold influence from an initial steady state, and 35 50 function has advantages in operational duration, magnithough this makes for convenient analytical solutions, 30it 45150 155 160 165 170 tude of upsets, and number of excitations over classical is a generic model, and not so restricted when solved with 40 Time (step-and-hold happened at 155) methods. A skyline pattern in the controller output could 35 numerical methods. And, though the model can be equiv2. alently stated in Laplace or z-transform notation, I won’t!30 150look like Figure 155 160 165 170 The nonlinear regression method seeks to fit the model The classic textbook method to generate FOPDT modTime (step-and-hold happened at 155) els is the reaction curve technique, a pre-computer era to all data points, not just the selected several points in method. It’s simple to understand and implement, and it a classic reaction curve fit. So, it better reject noise and can be derived from the analytical solution of the ODE, disturbances. The skyline and regression method does not require so it serves the current content of undergraduate engioperator attention or judgment, which lessens the posneering education appropriately. However, I be-




60 Time




35 34




60 Time




SKYLINE RESPONSE Figure 3: The data (dots) and best FOPDT model (solid curve) from the input sequence in Figure 2 for a pilot-scale process flow rate shows that the model is not perfect, but is a very good representation of the process dynamics. N o v e m b e r / 2 0 1 6


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Though the model can be equivalently stated in Laplace or z-transform notation, I won’t! sibility for operator error or bias. And the skyline input has many ups and downs, which tempers the influence that environmental drifts have on confounding the CV response to the MV. The skyline and regression method does not extend the period of off-nominal production as each “+” or “-“ period is shorter, creating less objection by a quality manager. The skyline and regression method does not require an initial steady state, and the entire test period takes less time. For a nonlinear process, gains, delays and time-constants change with MV and CV. The FOPDT model is linear, and may not provide a great match to the process over a wide operating range. Just because the optimizer converges on a best model, doesn’t mean that the model actually fits the data. So, after the regression, see if the fit is satisfactory for your model-use purposes. Figure 3 reveals the data and best FOPDT model from the input sequence in Figure 2 for a pilot-scale process flow rate response to controller output. The model is the solid line, the data are the dots. It should be noted that early-time data is usually below the model, but late-time data is usually above. Perhaps some drifting influence was affecting the data. In addition, note the kinks in the model at times a bit after 80 and 100. These are expected responses to small changes in the MV at those times, but these are not expressed in the data. Perhaps valve stiction prevented the valve from moving, even though the MV made changes. Finally, this data is not expected to be a linear response. Although the linear FOPDT model does not perfectly match the data, it’s also a very good representation of the process dynamics. My Excel/VBA file for generating FOPDT models is posted at It also offers a user guide to generate skyline data, use the software, and explain the nonlinear regression procedure. The VBA code is open. My entire academic career has been devoted to enabling students with best practices in engineering. I’m using the web site to extend beyond the classroom. I hope you find it useful. Russ Rhinehar t, principal, the R3 Co., was head of the School of Chemical Engineering at Oklahoma State University, president of the American Automatic Control Council, and editor-in-chief of ISA Transactions. For more information, visit


How to determine open-loop gain?



This is for tuning a control valve on a pipeline. In an open-loop gain test, with Pump curve (P1) a step increase applied to valve opening, the controlled parameter increases, then settles at a number. In this case, flow increased and then decreased to settle at x number. To ∆P1@35% ∆P2@48% calculate my Tc, should I use the peak value that PV (flow) reached or the final value that System curve (P2) it settled at? If I use x, then 63% of PV happens much sooner. Flow (%) Our control valve impacts all three: flow, 20 25 30 35 40 valve inlet (suction) and discharge pressure. The flow and discharge pressure controllers PIPELINE SYSTEM are reverse acting, and the suction pressure signal controller is direct acting. After a step change Input Figure 1: When flow is stepped from 35% to 40%, P1 and ΔP to valve (%) from 30% to 35%, flow increases and then de- drop, and P2 rises. This is because at a higher flow, the pipe 40 creases to settle at a higher value than what friction rises, leaving less ΔP for the control valve. A it was before the change was made. My ques- 35 tion is: which number should I use to calcu- 30 In my answer, I’m assuming that your late K, Tp and Tc? If I use first peak for the setprocess fluid is being pumped through 25 Time your pipeline by constant speed pump(s) tled value, then my K and Tc will be higher. 0 Since after this peak the flow decreases, if I and that your goal is to keep the flow and the use the final number, my K will be lower and up and downstream pressures at the control Process valve within the desired safe limits. I’m also hence Tc will also be lower. (PV) that you want to tune the three assuming With everything descaled, my K using final variable Flow (%) so the one that will be in control number for flow is 0.3425. Tp and Tc are near controllers, the low2 sec. If I use the first peak, my K is 0.6225. will always be the one that requires Overshoot My Tp, of course, remains the same at 2 sec, est valve opening, and you want to use the but Tc is 4 sec. This is liquid service. I will use open-loop method of tuning, which requires lower numbers, so the loop is slower since our the knowledge of the process gain. In my answer, I will ∆t briefly discuss both the process is not that fast. B In this case, I could only do two step tests. process and the process gain determination. B1=63%B a pipeline Usually, I do the step test by starting from fully Figure 1 shows the system curve of open, so I also get a full valve profile. These and the pump curve of a constant-speed pump. Time have been good starting point numbers based When you step up the flow from 35% to 40%, P1 0 and P2 td rises. This t63% is because at a on Ziegler-Nichols or GE’s ideal tuning ap- and ΔP drop, This column is moderated proach. GE’s tuning approach formulas are: higher flow, the pipe friction rises, leaving less by Béla Lipták Kp gain = 2•Tc / (3•K•Tp) %/%. Ki = Tc rep/sec ΔP for the control valve. By applying the low-se(, automation and safety and Kd = Ki/4. I did not use D since it is liquid lecting envelope on the three variables (F, P1 consultant and editor service. Of course, I adjust them in closed loop and P2), you’re maximizing flow while protectof the Instrument and with observation during setpoint changes. Units ing the pipe from excess pressure, which might Process gain: K = B/A cause leakage. In my view, this goal could be used in GE block were also important. Automation Engineers’ The second step is from 35% to 40%. 0.9A I more elegantly accomplished if you used a variHandbook (IAEH). If you Proportional Gain P = have your handbook, and have referred∆t it able-speed (instead of constant-speed) pump, have an automationalready. By the way, we use low select at the and just throttled the speed, thereby eliminatrelated question for IntegralsoTime I = saf3.33tding both the control valve and the associated three separate PID outputs, the lowest, this column, write to waste of energy represented by the pressure est output controls the valve. drop through it. HITEN A. DALAL,


N o v e m b e r / 2 0 1 6


P System curve (P2) Pressure

Ask the Experts 20


∆P1@35% 30



Flow (%) ∆P2@48%

System curve (P2) Input signal to valve (%) 20 40

Please note that I’m using the standard terminology for the various parameters in the 25 30 35 40 bump test: A A: Test input 35 B: Test output Input30 signal I: Integral gain setting of controller to valve 25 (%) Time K: Process gain 0 40 P: Proportional gain setting of controller A 35 R r: Reaction rate DEFINITION OF A 30Process td: Dead time variable (PV) τ: Time constant (defined as Δt or as K/R r) Figure input signal 25 2: For an open-loop test, “A” is the change ofTime Flow (%) For tuning the controllers, you can use to the valve. 0 Chapter 2.35 in the 4th edition of my handOvershoot book, where some of the most common controlProcess ler tuning constant and control mode setting variable (PV) recommendations are presented. On flow and Flow (%) ∆t pressure applications, we usually end up with B Overshoot control mode values in the ranges listed below: B1=63%B %PB = 50 to 500 I = 20 to 200 repeat/min. Time D = None ∆t Flow (%)





B B1=63%B Time 0 Process gain:



K = B/A


0.9A Proportional Gain P =∆t

Figure 3: Where A is the valve signal change (Figure 2) and B is

Integral Time Process gain:

the flow change, process gain K = B/A, proportional gain P =

I = 3.33td K = B/A0.9A/B•td•Δt, and integral time I = 3.33td.

0.9A Proportional Gain P =∆t

If you experience an

Integral Timeovershoot I = 3.33td (which

you do), it can be caused by wrong valve characteristics, sticking valve or other causes.


Now, coming to the bump test (Figures 2 and 3), if you’re interested in the open-loop gain (K) of a linear process (such as a linear flow control valve on a pipeline), then you calculate it by the ratio B/A: K = B/A = (% change in flow after reaching steady state) / (bump size in % of full stroke). In other words, the process gain is based on the final PV value. If you experience an overshoot (which you do), it can be caused by wrong valve characteristics, sticking valve or other causes. In any case, we don’t consider the overshoot in the determination of K, but try to eliminate it. When bump testing, I would make the step change in both directions, to make sure that K also remains unaffected when you apply the step to reduce the valve opening and therefore the flow. N o v e mb e r / 2 0 1 6


In addition to the various instrumentation reasons mentioned by Mr. Lipták, you have to consider that your flow is coming from a pipeline. This effect would be more pronounced if it was a gas service. This means that the line resistance is developed over a length of pipe. Initially, if the pipe carries gas, the pressure in the pipe section nearest to the gas source will be high, but as the flow establishes over the whole pipe work, as the flow (and pressure drop) rises, the pressure will drop at the inlet of the valve (compared to the pressure before making the step change by step-opening the valve). If you check the pressure just upstream of the valve, you should see the pressure drop (corresponding to the lower flow). I’m not sure if in your case this is significant enough to be considered for the tuning. Generally, flow loops are fairly straight forward and default parameters are adequate. The only issues I’ve found for flow tuning concerned the characteristics of the valve: linear or equal percentage (EQ%), i.e., if the valve characteristics don’t match the flow range of interest. However, I have not done a lot with pipelines and there may be additional issues.


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The fasis WST Series shielding terminals provide simple, reliable, convenient grounding and interference immunity for cables requiring grounded shields. Accommodating 8-32-mm cable diameters, they provide vibration-proof grounding of shielded cables. Made of hardened steel with high corrosion resistance, they can mount on busbars, TS35 DIN rails or C-profile rails, or directly with screws on flat surfaces. Wieland Electric 800-WIELAND (943-5263);



E3 I/O modules include 17 high-density I/O modules with hardened metal enclosures and powerful communications. They’re configurable with Crimson 3.0 software. With one RS-485 terminal block and dual Ethernet ports including user-selectable Ethernet modes for ring, pass through and two networks, E3 I/O eliminates the need for added switches. They also offer up to 34 mixed I/O points. Red Lion Controls 877-432-9908;

EtherCAT P system and I/O is IP 67-rated, combines ultra-fast EtherCAT communication and power in a standard four-wire Ethernet cable, and reduces wiring requirements by enabling direct power supply for EtherCAT P slaves and connected sensors and actuators, so separate power lines can be eliminated. A full range of components in IP 67 and fourwire Ethernet cables are available for 24-V I/O. Beckhoff Automation 877-TwinCAT;



Wise-4051 2.4-G IoT wireless Ethernet I/O combines the three functions of data acquisition, processing and publishing in one I/O module. Wise-4051 can automatically push data to the cloud, and its Wise data logger can send timestamped data to a Dropbox account. A private-server function lets Wise modules push data to a web server via the RESTful web service and MQTT protocol with WebAccess. Advantech Corp. 888-576-9668;

The reActionOn module for ultra-fast safety applications achieves safety response times down to 100 µs. This makes it possible for time-critical subprocesses to be executed directly in I/O modules, which reduces response times by 100 times or more. No expensive special hardware is needed to use reAction, and programming is as easy as for conventional controls. B&R N O V E M B E R / 2 0 1 6


Easy, reliable, safe level measurement


ust as you can’t have too many Vibrating-fork detector friends, level measurement can’t Hessel reports Rosemount 2140 get enough ease of use, reliabilcombines proven-in-use Rosemount ity and safety. These three priorities short-fork technology and the HART drove development of Emerson Autofunctionality of the wireless Rosemation Solutions’ new SIL 3-capable mount 2160 to create a new wired Rosemount 5408 Series non-contactsolution. It complements the coming, radar level transmitters for continplete 2100 portfolio, but 2140 is a new uous measurement and its world’s first product designed for SIS and control Rosemount 2140 vibrating-fork, liqapplications where HART is used. uid-level detector with wired HART Unlike 2160, 2140 includes proof testfor point-level measurement, as well ing and smart diagnostics. as its dedicated Rosemount 5408:SIS Rosemount 2140 performs in and Rosemount 2140:SIS models for high temperatures and harsh condisafety instrumented systems (SIS). tions unsuitable for other level deRosemount 5408 incorporates tectors, and it’s easy to install and human-centered design to simplify maintain because there are no movLEVEL-HEADED LEVEL MEASUREMENT operator tasks. Pictorial instructions ing parts. It’s virtually unaffected and an intuitive software interface Rosemount 5408 Series non-contacting, radar by flow, bubbles, turbulence, foam, guide users, while on-board diagnos- level transmitter for continuous measurement and liquid properties and product variatics support preventive maintenance. Rosemount 2140 vibrating-fork level detector with tions. It can be used to monitor not Rosemount 5408 can also perform wired HART for point-level measurement. only liquids, but also liquid-to-sand proof-testing and site acceptance tests interface, which can be used to deremotely. In a departure from traditional pulsed-radar devices, tect build-up of sand or sludge deposits in vessels such as Rosemount 5408 employs two-wire, 12-Vdc, loop-powered, fre- separators. An optional LCD shows switch output states and quency-modulated, continuous-wave (FMCW) technology. diagnostics, allowing local inspection, or data can be viewed “Running two-wire FMCW with just normal 12-Vdc power remotely from a host system such as a DCS. is a big game changer because we can now do level measureCompatible with HART 5 and HART 7 hosts, Rosemount ments in places where they couldn’t be done before, including 2140 enables continuous monitoring of electronics and mepotentially explosive environments,” explains Andreas Hessel, chanical health with smart diagnostics. For example, the frestrategic product manager, process radar level measurement, quency-profiling function continuously monitors fork vibraEmerson. “We built the microprocessor and other pieces for tion, and alerts if the instrument sees an unusual frequency Rosemount 5408 from scratch, which really gives us ‘radar-on- or detects a trend change in the frequency. a-chip.’ This means we can do two-wire FMCW for more ac“Rosemount 2140 is clever because it’s a wired HART decurate and reliable measurements because, instead of pulsed tector and not a switch, so it uses changes in frequency and radar, it provides a constant stream of microwaves. With some vibration over time to determine if its forks are coated or corlimits due to individual settings, we estimate Rosemount 5408 roded, and alerts operators when it needs maintenance or is 100 times more sensitive than pulsed, which means much before,” adds Hessel. “What’s fun about Rosemount 2140 is more accurate and reliable measurements, even with interfer- it can still be used as a simple switch when needed.” ence from foams, turbulence and condensation in tanks.” For safety applications, the detector’s dedicated safety verThe transmitter’s safety version, Rosemount 5408:SIS, is cer- sion, Rosemount 2140:SIS, is certified to IEC 61508, with tified to IEC 61508 for use in a SIL 2 loop with redundant in- a 97% safe failure fraction and 96% diagnostics coverage, strumentation required. “This is the only level transmitter rec- making it one of the safest SIL 2 devices available. Roseognizing that overfill prevention and SIS integration is more mount 2140:SIS also has fully-integrated, remote proof-testthan a certificate,” explains Hessel. “Rosemount 5408:SIS can ing, which eliminates the need to access the top of a vessel be used in any common-practice, SIL 2 safety loop, and its to extract the detector from the process. proof-testing interval can accommodate one-, three- or even For more information, visit measurement-instrumentation/lev el five-year turnaround times.” N o v e m b e r / 2 0 1 6






Series MP7000 mechanical diaphragm metering pumps are as rugged as hydraulic pumps without the potential for oil contamination. Oversized check valves, straight-through flow design and elimination of the contour plate improve flow characteristics for viscous, shear-sensitive and fluids with suspended solids. Capacities range to 27 gph and pressures to 235 psi with 10:1 turndown. An optional automatic speed control uses variable-frequency or SCR drive. Neptune

Model 1100 sanitary vent enhances the VCI Model 1088 sanitary blanketing valve, operating at multiple setpoints as a breather valve to avoid vacuum or overpressurization. It has a true sanitary blanketing connection and is available in 2-, 3-, 4- and 6-in. sizes, in 316 or 304 stainless steel with a stainless steel weather screen to prevent debris from entering the tank. Setpoint range is 0.43 psi to 3.42 psi; vacuum is 0.069 psi to 0.385 psi, depending on the size. Cashco 785-472-4461;



CBI uninterruptible power supply (UPS) solutions combine supply, charger, battery care and backup module functions in a single device. Real-time diagnostics continuously monitor battery status, charging levels, and emerging battery faults. Available in 12, 24 and 48 VDC for multiple battery types, they feature three charging levels (recovery, boost or trickle), allow adjustment of charging current, and automatically distribute power between load and battery. Altech

TD-120 oil in water monitor is compact, responds in less than 1 sec., uses a remote flowcell for easy switching of optical configuration, and requires no reagents or chemicals. It features extended dynamic range (up to 6,000 ppm) with 1% accuracy, a touchscreen interface and 4-20 mA or optional HART. A NEMA 4X/IP 66-rated 316 stainless steel enclosure and 316 wetted metallic parts are standard; other materials are available for marine applications. Turner Designs Hydrocarbon Instruments Inc. 559-253-1414;



Memosens CLS82D four-electrode sensor measures conductivity from 1 µS/cm to 500 mS/cm and temperature from 23 to 248 °F (-5 to 120 °C) with 4% accuracy 0.2% repeatability. With 316L stainless steel construction, electropolished surfaces, hygienic process connections and IP68 protection, it can be sterilized in place (SIP) at up to 284 °F (140 °C) for clean-in-place (CIP) operations, and is certified for EHEDG, FDA, 3-A and pharmaceutical applications. Endress+Hauser

IPL Assurance provides real-time predictive analytics on health and availability of safety instrumented systems (SIS), alarm management systems, and other independent protection layers (IPL). It reports SIS performance during demand; manages safety instrumented function (SIF) performance, testing and maintenance; tracks status of safety-related alarms; manages safety system bypass; provides a safety and operational risk dashboard, and more. PAS Inc. N O V E M B E R / 2 0 1 6


How to succeed in career and system migration Greg: Here we take advantage of the chance to talk to Bill Thomas, who provides a great lesson on how to succeed in advancing capabilities and opportunities in his career and the control systems for which he was responsible. His career and the systems he improved are analogous examples of the power of migration and the results gained from extra effort and dedication to improvement. Bill is one of the early members of the ISA Mentor Program. He is a very upbeat and positive guy, who I have had pleasure of getting to know at ISA and user group meetings.

Stan: Bill, what has been your career path? Bill: I started out as an electronics technician in the Navy. When my enlistment was up, I worked at a shipyard doing electronic repair and retrofits on Coast Guard cutters. I started taking classes at night working toward a B.S. degree in electrical engineering. After a while, I decided I wanted my degree a little quicker, even though I’d be poorer during the process. I enrolled at Auburn University in EE, and focused on power and controls. The first year was pretty tough because, in addition to the full course load, I also worked third shift at a local saw mill, hoping to make ends meet. This became my motivational factor in finishing the degree, even though I was struggling financially—I felt that this type of job might be in my future if I didn’t get a degree. Fortunately, after a year or so, I got a position as a co-op at a paper mill. The co-op experience was great for me as I got to work in maintenance, in capital projects and with the automation group. With that experience, I knew that I wanted to go into automation. Upon graduation, I went to work for a large consumer products company as a controls engineer. I worked at their headquarters for eight years and at one of their plants in Texas for another two years. In 2005, I went to work for 3M as a corporate engineer in the Process Information and Control

Solutions (PI&CS) group. I’m presently living in Alabama, and workng at the Decatur Plant.

Greg: What type of equipment? Bill: I worked on automating web lines most of my career, first doing narrow web lines for the consumer products company and then a variety of wider lines for 3M. Some of these lines travel at incredibly fast speeds. They were making around 1,000 products per minute. In the past six years, I’ve focused on chemical reactors and the process industry. When I first came to the chemical side of the site, there was only me, a maintenance controls engineer and the project manager responsible for upgrading controls and automation on all the chemical production units. I was lucky to work with those guys. I don’t think there is a control system that the controls guy hasn’t worked on or a problem that he hasn’t seen. We learned early that we were


Greg McMillan and Stan Weiner bring their wits and more than 66 years of process control experience to bear on your questions, comments and problems. Write to them at

Creatively invest your work ethic into new horizons and opportunities. For the top 10 reasons to migrate, see the online version at how-to-succeed-in-career-and-system-migration. N o v e m b e r / 2 0 1 6


C o n t r o l Ta l k

limited somewhat on the instrumentation side, so we convinced a retired 3M technician to give us a hand. He has been with us ever since, and been instrumental in the success of our projects. We’re now fortunate to have another PI&CS resident engineer, an additional maintenance controls engineer, and some technicians working on these systems. We’re responsible for the migration of about 20 reactors and the tank farm. We decided on one controller per reactor to maximize independent maintainability, since these reactors are going up and down. We’ve completed over a dozen of the reactors, and have about a half dozen to go. Each system can have 300-1,200 inputs and outputs.

Stan: What are some things you learned? Bill: I was a programmable logic controller (PLC) guy, so I had to quickly become proficient in the configuration and implementation of a distributed control system (DCS). I learned to standardize on a preferred, class-based library of modules. I developed the ability to communicate better with manufacturing engineers to make sure we get the right functionality. I also developed a more structured methodology and better documentation in the front-end definition for each reactor system.

Greg: What are some examples of communication needed? Bill: In a vacuum system, we were asked to ramp down from point A to B as fast as you can go. We did this, but the manufacturing engineer wasn’t satisfied because the process variable could not keep up with setpoint, so we worked out a compromise between speed and tightness of control. In another system, the speed of a heat-up cycle was specified, but it was later determined that the cool-down cycle was also critical. We needed to get creative.

Greg: I’ve had process engineers ask to keep the process vari-

Greg: How did you use dynamic models?

able close to setpoint, but not move the manipulated variable. The realization is lacking that you you can’t make variability in a loop completely disappear, and that you can’t predetermine flows as a function of time. The flows and compositions on a process flow diagram (PFD) are a guide and aren’t to be taken literally. Feedback control is designed to automatically correct for the unknowns and disturbances, and can be tuned to provide the desired degree of transfer of variability.

Bill: We started out using simplistic tiebacks with VMware

Stan: What’s been achieved besides the elimination of ob-

on a laptop, but graduated to a workstation with Mimic. This enabled us to get operators involved in the functional testing of the control system. Four to six weeks before the configuration needed to be finalized, we would do this testing to make improvements based on operator input.


Stan: How did you contract out the work? Bill: We split up the work between the local business partner of our DCS supplier and some contract engineering firms, keeping the most proprietary functionality in-house. If we develop the ability to use source protection on the more sensitive details, we can send more outside.

Greg: How did you deal with skids? Bill: We learned that we had to give model numbers and performance specifications as well as manufacturer names of the instruments we wanted. Otherwise, we would end up with less reliable types and poor performance.

Stan: What is your general approach? Bill: We never have time or money to do everything. We do 56

the best with what we have to get it to the customer on time. New projects are more of a challenge in terms of a missing starting point. We help the customer understand automation is their friend and we can keep doing more in that friendship. We make it an ongoing conversation. N o v e m b e r / 2 0 1 6

Bill: Modernizing the control systems has led to better performance, principally by using signal selection and split range to increase versatility, particularly in jacket loops. We also have much more functionality and support available for the new DCS. I really appreciate being able to focus on learning how to get the most out of a new DCS rather than investing my time getting by with an old DCS with no future.

Greg: What about your overall experience as an automation engineer?

Bill: I feel lucky to having gotten into automation. It’s like I’m building multimillion-dollar erector sets. I get to see stuff working in action. The control system is the window into the process and means of affecting the process. The dynamic and visual impact is impressive. I look forward to seeing in 15-20 years how much will change. I’ve gone from ladder logic, analog devices and 4-20 mA to function blocks, digital devices and fieldbus in my career so far. At the end of the day, what we do in automation is solve problems, and we get to see the effect on the plant efficiency and capacity. Automation is a challenging and rewarding profession.


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Control Report

Busy fall Jim Montague

e xecutive Editor

Ignoring change is actually a response, of course, but it’s not a very good one, even though it doesn’t require much effort.. 58


s usual, and I’m sure this is true for many of you, this year’s autumn season has been a frantic dash of work, deadlines, sleep deprivation, indigestion and suspiciously arthritic stiffness. In my case, this means covering all sides of the takeover by digital computing and software of the process control industries and apparently everything else. It’s no stretch to say that big data, cybersecurity, the Industrial Internet of Things (IIoT) and their permutations are all part of the same upheaval. I mostly write about success stories because that’s what people are willing to talk about, even though problems and disasters make better and more instructive stories. However, with an earthquake like the digital emergence, there are plenty of snags, failures, restarts and other wrenching changes. With shifts this big, there’s always some collateral damage. That’s because we aren’t talking about switching out a flowmeter, repairing an RTU or even migrating a DCS. As challenging as those tasks can be, depending on circumstances, they still involve replacing known technologies with other technically familiar solutions. No, the rush to digital is the latest chapter in at least two overarching evolutions from pneumatics to relays to PLCs to PC-based control, and the parallel progression from point-to-point hardwiring to fieldbuses to Ethernet and wireless. The difference here is moving from known technologies to unfamiliar if not unknown ones—and digital’s faster, cheaper and more powerful software and microprocessors are only accelerating its rise and march to critical mass in process control and automation. Of course, even though they’re glued to the same smart phones and tablet PCs as their neighbors, few process engineers can take comfort in digital’s wide acceptance in mainstream, consumer and business settings. In fact, digital’s freewheeling nature makes most process professionals more nervous because of the safety and critical operating responsibilities they bear. N o v e m b e r / 2 0 1 6

So, what’s to be done? Because so many of these shifts are pretty much inevitable, the only solution is to make the best of it. This means learning as much as possible to make digital technologies safe, secure and successful in process settings. It means giving up prejudice and denial, adopting the most useful parts of digital technologies, and mitigating the irrelevant, unhelpful, wasteful or dangerous parts. Cybersecurity? Yes, please. Big data? We’ll see. Ignoring change is a actually a response, of course, but it’s not a good one, even though it doesn’t require much effort. Accepting change, if not embracing it, and trying to work with its details provides more options and possible opportunities. Plus, the comfort in this situation comes from realizing this is how people cope with every new technology and disruptive change through history and in their lives. Which brings me to elections. I’m writing this before 2016’s contests resolve on Nov. 8. I’m not sure how any are going to go, and as a former newspaper person, I take polls with a huge grain of salt. Like most folks, I prefer some candidates over others, and I’ll be glad if the ones I vote for win, and sad if they lose. But what does anyone do after the smoke clears and its time to pick up the pieces? Whether it was a great wedding, holiday or celebration—and especially if it was a bad or even tragic event—someone still needs to wash the dishes, change the diapers and pay the bills. Those chores are always waiting for mom, dad or some other responsible person to get them done. Likewise, I also remember that once election hoopla is over, the routine budget meetings will begin again, and few if any taxpayers will show up to help the elected officials shop smart for the necessities we buy as a community. As usual, our responses to DCS migrations, digital technologies, big data, cybersecurity, IIoT, elections, dishes and diapers are pretty much the same. Win or lose, it’s time to do some chores.

My unit is down again. Perfect. These cheap valves just don’t hold up. I need reliable technology to keep running 24/7/365—no surprises

YOU CAN DO THAT Emerson’s industry leading Fisher™ easy-e™ control valve—available in NPS 1 thru 36—provides users with high performance and proven reliability. The easy-e control valve continues to evolve, bringing you innovative and reliable technology to help solve your toughest challenges. All valve designs undergo rigorous lifecycle testing, so you don’t have to worry about your unit going down unexpectedly. Don’t trust your process to replicated valves. Use the Fisher easy-e control valve to keep your operation running. Day and night. To learn more, visit

The Emerson logo is a trademark and service mark of Emerson Electric Co. ©2016 Fisher Controls International LLC. D352205X012 MBB104

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