Emerson Process Experts

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Adding to the conversation of the Truth about controlling processes... post and response by Emerson's Terry Blevins, is this response from Greg McMillan. Greg is the author of quite a few books on tuning and control which you can see in the DeltaV Bookstore and an author in the monthly Control Talk column in Control magazine.

Greg adds:

The tuning techniques published by David St. Clair are essentially the ultimate oscillation and reaction curve methods developed by Ziegler-Nichols. These models each use two dynamic parameters. An ultimate gain and ultimate period is identified during closed loop tests (controller in automatic) for the ultimate oscillation method. A time delay and ramp rate is identified during open loop tests (controller in manual) for the reaction curve method. Time constants along with the dead time dominant, self-regulating, and integrating responses are also introduced in the publication to help discuss how the ultimate period varies from two to four times the time delay. Laplace transforms are added to explain dynamics and the PID algorithm. The techniques end up with five parameters and three types of processes, which is the same complexity as methods that more explicitly use a model. What seems to be the difference is that the user does not have to identify a process gain. Actually, this process gain is darn easy to find, in that for an integrating process, the integrating process gain is the ramp rate identified in the reaction curve method (same parameter with just an older name). Further, if you multiply this reaction curve ramp rate by the process time constant (largest time constant), you get the process gain for a self-regulating process. Alternately, in an open loop test self-regulating processes, the user could wait for the process to line out and divide the final change in the process variable in percent by the change in controller output in percent. The user just needs to remember to use percent instead of engineering units because the PID algorithm is based on a percent input and output.

The literal use of the reaction curve method assumes the process is lined out. This is rarely the case for tough loops and integrating processes. If a person understands the concept of an integrating process gain, he or she would realize to use the change in ramp rates rather than the lone ramp rate depicted in the reaction curve method.

In fact, all major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve, Lambda tuning, and Internal Model Control) for both self-regulating and integrating process reduce to the same form when the user wants maximum performance. Framing tuning techniques in terms of a simple model (process gain. time delay, and time constant) provides this insight. It also allows you to estimate the effect of plant dynamics on performance. If the user understands the sources of time delay and time constants (e.g. transportation delays, thermowell lags, mixing delays, deadband, sticktion, control communication and execution intervals), the ultimate performance of a control system can be improved. My November Control Talk column will explain the unification of tuning methods and the implications.

If the valve size or the calibration span of an instrument is changed, a user knows how to proportionally change the controller gain based on the change in process gain.

Nonlinearities are prevalent in process control but this is more of a reason to have a simple model so that nonlinearity can be identified and quantified. The user then has the option to schedule the controller tuning based on operating regions or use signal characterization. For example, signal characterization of a pH measurement based on the slope of the titration curve (process gain) has proven to be simple and extremely effective. For nonlinear valve characteristics, the combination of a digital positioner and low friction packing combined with signal characterization of the controller output significantly reduces the nonlinearity from deadband, sticktion, and valve trim. In both cases, the improvement in performance is particularly impressive for operation on the flat portions (low gain) portions of the curve.

The introduction of a fast secondary controller can remove most of the nonlinearity seen by the primary controller in a cascade control system. Also, reducing the time delay and improving the tuning enables a controller to stay closer to set point so that the loop sees less of operating point nonlinearities. This is an important technique for pH, reaction, and distillation column control.

Changes in model parameters provide insight as to what has changed in the plant whether it is an increase fouling of a sensor or heat transfer surface or sticktion in a valve. Models offer plant knowledge and allow you to take the blindfolds off. How you use the models are up to you but ignoring them increases war stories and myths and endless meetings where people go around in circle as to what is wrong and could be better. Some processes have been trying to solve the same old problem for decades because there are no models. A model is even more important if you consider process engineers are taught to think steady state, statisticians analyze snapshots of data, and operators want an instantaneous response. The ability to tune a controller from the same model is a plus.

We should not forget the great contributions from the past but we need to move on and seek greater knowledge and performance of our plants.

You can join the conversation here.

Tags: Ziegler-Nichols | process control | step response model | dynamic response | process model |

August 28, 2006 in Education | Comments (2) | Trackback (0)

A while back I saw a Walt Boyes’ post entitled, The truth about controlling processes… in which he captures the response of Walter Drieger to a post on the Automation List at Control.com.

Walter’s response had some pointed statements like:

All process control loops are nonlinear. That is why the math you learned in school is useless.

I thought I’d run Walt’s post by Emerson Principal Technologist, Terry Blevins, co-author of Advanced Control Unleashed and recognized Automation Hall of Fame honoree for his thoughts on the subject. Here is Terry’s response:

I normally don’t take the time to respond to blogs if I think the topic is not been well framed. However, in this case I am making an exception since I find the comments to be misleading and thus should not be left unchallenged.

For many years I was responsible for the design, implementation and startup of advanced and regulator control strategies for control systems installed in the pulp and paper industry. Commissioning the control was especially challenging on faster processes, such as boiler combustion control, since there was often little time to establish the control tuning. In many cases there was the opportunity to make a small change in the controller output, observe the dynamic response of the process and then set the tuning before placing the control in automatic. What I quickly learned was that control tuning must be based on an understanding of the process. Specifically, to tune PID controller feedback or feedforward strategies in single loop of multi-loop configurations such as cascade or override control, it is necessary to understand the dynamic response of the process to changes in process inputs.

Often times the process response to a step change in the process input may be described in general terms such as the process has low gain i.e. little change in the process output for a change in the process input or there was little delay in the process response or the process was slow to respond. However, to establish control tuning setting, it is necessary to describe the process response in quantifiable terms. For self-regulating processes, the open loop response is often characterized in terms of process gain, deadtime, and time constant. If the control is associated with an integrating process, then the response may be characterized by the integrating gain and deadtime. Such characterization of the process dynamic response is commonly called the step response model. Similarly, the process dynamic response associated with feedback control may be characterized or modeled under closed loop conditions in terms of ultimate gain and ultimate period.

The techniques described by David St. Clair in Controller Tuning and Control Loop Performance and any number of references on this subject of tuning are fundamentally based on a knowledge of process dynamic response, the ability to characterize (model) the response, and to use this understanding in setting control tuning.

Take a read of Walt’s post and Terry’s response and join the conversation.

Tags: process control | step response model | dynamic response |

August 28, 2006 in Education | Comments (1) | Trackback (1)

At the recent conference, Implementing Manufacturing Execution Systems in the Pharmaceutical and Biotech Industry, Emerson Senior Vice President John Gardner presented with DMI International’s Bob Schiros a paper on Integrated Recipe Authoring on the first day of the conference. John is the general manager for the Life Sciences, Food and Beverage, Pulp & Paper, Energy, Metals and Mining industry organizations.

The thrust of their presentation is that manufacturers need to integrate their existing "functional islands". Today people want to get information easier, but that's just the tip of the iceberg. Integrating and simplifying the management of documents, personnel qualifications, equipment and material, work activity, automation, various plant floor systems, etc. is where the real operational benefits occur.

John stresses the place to begin is to analyze the causes of deviations in these areas. Eliminating these deviations provides the potential operational improvements at the heart of your business case for change. These areas of opportunities should be broken into manageable phases.

Gaining executive sponsorship for the changes is critical since people and processes are likely impacted, and organizational inertia tends to resist changes.

For pharmaceutical and biotech manufacturers, the opportunity comes in reducing the cost of goods manufactured. John stresses the place to begin is to analyze your current functional operations and the causes of deviations in these areas. This will lead to better inventory and yield management, lower regulatory compliance costs, and reduced product release times. John stresses the place to begin is to analyze your current functional operations and the causes of deviations in these areas. Focusing on eliminating these deviations is the most immediate potential improvement and that's at the heart of your business case for change. These areas should then be broken into manageable phases.

John believes the key is to start with the low hanging fruit which is to have a structured integrated recipe authoring process. The process starts by disassembling the recipe into its components: personnel, materials, equipment, data, and documents. These components are optimized and a database of reusable objects is created. Now the recipe can be reassembled with the optimized components and made available for execution of the batch with its associated electronic batch record.

The Emerson Life Sciences industry experts use the manufacturing execution system (MES) software product, Compliance Suite, as a platform to help manufacturers achieve this structured approach.

The presentation highlights measurable results which have been achieved including 40-70% reduction in batch record complexity, 30-50% reduction in product release cycle times, 20-40% reduction in documentation authoring and approval cycle times, and up to 40% reduction in errors, omissions and deviations of operational data.

Tags: manufacturing execution system | MES | recipe management | electronic batch record | pharmaceutical | biotech |

August 23, 2006 in Data Management, in Life Sciences, in Process Optimization | Comments (0) | Trackback (0)

Much has been made recently of the need to pass along the expertise of seasoned automation and control professionals to the next generation of automation and control engineers. I have been trying to highlight our role in this process in the Emerson Process Expert’s Education category.

You can also see focus in this area of Education by Control magazine’s Walt Boyes and Automation World magazine’s Gary Mintchell in their respective blogs.

I mentioned the work of John Egnew, a Training Consultant and Instructor in our Educational Services group in an earlier post about the Loop Tips he has been sharing. His stated purpose:

“Loop Tips” is a compilation of years of experience with loop devices and controller tuning necessary for keeping control loops operating at the desired performance levels.

His latest addition, How Fast Do You Want It? explores what to do when your controller tuning is not achieving the desired close loop performance.

Tags: automation education | control loop | loop tuning |

August 21, 2006 in Education | Comments (2) | Trackback (0)

Those involved with safety instrumented systems (SIS) know that the performance of the system to perform on demand requires all elements of the safety loop: logic solver, sensor, and final control element to correctly perform their role.

Data from the Offshore Reliability Database (OREDA) points out that 92% of the failures come from problems in the sensors or final control elements.

The IEC 61508 international safety standard stipulates requirements for equipment to be used in safety applications. It must be suitable for the safety application, that is, for the appropriate safety integrity level (SIL). SIL is defined:

Safety Integrity Levels (SILs) are a safety-measurement standard defined by several bodies including the International Electrotechnical Commission in IEC 61508 to quantify the chance of dangerous failures in electrical or electronic safety devices, that is, the probability of the device to fail in performing its safety function.

Process manufacturers typically seek products certified to this standard by a reputable independent agency like one of the TÜV certification agencies to achieve compliance with the IEC 61511 international safety standard.

Suppliers like Emerson can seek certification either by designing new products to achieve certification based upon the safety requirements or by being “proven in use” as defined by the IEC 61508 standard. The folks at Exida have an excellent write up describing the latter method entitled, What does Proven In Use imply?

I caught up with Al Samson, Director of Product Support for our Micro Motion Coriolis flow meter products. Earlier this year Micro Motion flow meters became the first flow meter to be TÜV-certified to the IEC 61508 safety standards.

The Micro Motion 1700/2700 transmitter family has had more than 5 years in service with high reliability so the team used the proven in use method to achieve certification for use in SIL 1-3 applications. These transmitters are used with the Micro Motion Elite, F, T, and DT sensor families.

The Micro Motion team worked with Exida to develop the Failure Modes, Effects and Diagnostics Analysis (FMEDA) and other essential documentation necessary for the TÜV process audit required to receive the proven in use certification.

Al pointed out that the combination of an inherently redundant Coriolis sensor along with a high level of internal diagnostics provided a safe failure fraction of 92% which is the best among this class of sensors.

Tags: flow sensor | flow transmitter | IEC 61508 | IEC 61511 | safety instrumented system | SIS | TÜV certification |

August 18, 2006 in Safety | Comments (0) | Trackback (0)

Trying to manage energy consumption and steam usage in a manufacturing process can be a tricky undertaking. The need to do it is ever increasing with higher fuel costs. A recent AEI Environment Policy Outlook study shows the gas and oil price trends over the past 25 years.

The variables operations staff typically must juggle include process load requirements, multiple fuel types, boiler/turbine availability and efficiency levels, and electric buy/sell prices to name a few. Of course, steady state operations are rarely possible because product mix and volumes being produced are normally in flux.

You may recall Bob Sabin, a consultant in Emerson's Industrial Energy Solutions organization, from an earlier post on Chemical Recovery Boilers. Bob discussed how the team of energy consultants works with process manufacturers to develop facility specific models and rule sets to continually determine the optimum operating setpoints for all the process units.

They have packaged their approach into a SmartProcess Energy application that is used to reduce the total cost of energy in a mill/plant by automating critical decision-making. The energy optimization process begins with a review of existing Powerhouse operations and recent operating data. The consultants use off-line modeling tools to evaluate improved operating methods and estimate the level of savings that can be achieved. The effort reviews the fuel alternatives, purchased versus produced power options and constraints, and the current decision making process for optimizing energy and steam production and usage.

Bob said that a key to Emerson's energy optimization approach is extensive data validation to help the application tolerate measurement errors and device failures. The decision making rules for optimum operation are implemented using mathematical models running within the automation system controller.

He pointed to two areas of savings. The first is identifying large opportunities for cost improvement such as changes in fuel type usage. Perhaps more important is the second area, which is the constant small adjustments being made to process setpoints in real-time. This helps move the total operation to its absolute best cost point based on current constraints. These are adjustments that could not easily be recognized by the operators.

The Industrial Energy Solutions team has documented typical annual savings of $500K to $2MM USD where the SmartProcess Energy application has been applied.

Tags: energy usage | energy management | steam usage | powerhouse | fuel types |

August 14, 2006 in Boilers, in Process Optimization | Comments (2) | Trackback (0)

One of the real treats for me is to speak with some of the great technologists we have here our Emerson Austin, Texas location. I caught up with Greg McMillan, a Control magazine hall-of-fame winner, and co-writer of the McMillan & Weiner series of humorous yet educational writings.

Greg provides his expertise to the DeltaV embedded advanced process control (APC) developments. He’s also just co-authored a book about to go to press on bioprocess modeling and control which will be available in the October timeframe. Several of Greg’s books are available in the DeltaV Bookstore. I’ll point out when this newest one becomes available in the bookstore or you can find it right now on the ISA website.

Greg has a way of making the complex understandable. I caught a sneak peak at the McMillan & Weiner August Control Talk column where Greg compares control concepts like loop dead time, open-loop and close-loop error, non-self regulating processes, etc. with the impacts of imbibing alcohol. Many of us can immediately grasp these control concepts when they hit so close to home. These analogies really help make difficult control theories easier to learn and retain.

As he has been doing for years, Greg will be presenting at the Emerson Exchange in early October. If you go (the Early Bird conference rate expires August 21^st), make sure to check out one of his sessions. He’ll also be presenting in the next few weeks at Control magazine’s Automation Xchange where he’ll discuss talk about advanced control myths, case histories, and lessons learned. Some of these control myths Greg has shared in one of his columns so you can get a preview of his ideas.

This expertise in control in invaluable to future generations of control engineers and it’s great that Greg and many others continue to share their expertise with others.

Tags: bioprocess | modeling | control | Emerson Exchange | APC | advanced process control |

August 11, 2006 in Education | Comments (0) | Trackback (1)

Many process manufacturers have flow metering stations where ownership of incoming raw materials, intermediates, and/or outgoing products change. This custody transfer process is common with oil and gas producers, refiners, and chemical/petrochemical manufacturers.

Accuracy is critical since these measurements impact the bottom lines for both the seller and buyer. And, with the introduction in the U. S. of the Sarbanes-Oxley (SOX) Act of 2002, companies are required to put the controls in place to prove the accuracy of these measurements. Other countries have similar regulations requiring these documented proof-of-accuracy processes.

Robert Fallwell, a regional manager in Emerson’s Metco Services business, has written an excellent article, Sarbanes-Oxley audits: coming soon in the July issue of Control Engineering magazine.

Robert shares his expertise on how process manufacturers need to prepare for the SOX auditors. He boils it down to:

…they ask for proof that flow measurements are accurate, that you have procedures to ensure measurement accuracy, and that the plant’s operators, engineers, and production accountants have been trained in the correct procedures for the measurement control process.

The article is filled with advice on how to get ready, where to start in your process, and even 9 steps on how to comply with SOX. In addition to the expertise Robert and the METCO team bring to SOX compliance planning, Emerson has well-established flow technology and calibration management software help assure accuracy over time.

If your business is impacted by SOX or similar regulations, you’ll want to incorporate some of the ideas presented in this article.

Tags: Sarbanes-Oxley | custody transfer | regulatory compliance | flow measurement |

August 8, 2006 in Chemical, in Custody Transfer, in Measurement, in Oil & Gas, in Refining, in Regulatory Compliance | Comments (0) | Trackback (0)

Last Friday, I mentioned how RSS is speeding up the process of finding information. Podcasts are the way to receive this information in an audio format, typically via MP3 file or AAC file popularized by the iTunes Music Store.

Unlike reading text, I find it impossible to listen to a podcast on my PC, since it is such a visual medium. Podcasts are perfect for listening on my MP3 player while exercising, playing on the car stereo, or performing mindless chores. Since I'm a bit of technology geek, I enjoy listening to the Gillmor Gang and This Week in Technology.

Even in our conservative space of process automation, podcasts have emerged. You might want to check out:

• AutomationWorld's Gary Mintchell
• Control's Walt Boyes
• Futurist Jim Pinto
• Industrial Automation Insider's Andrew Bond

And I even came across a podcast interview with BMS on the On Pharma blog.

Now your company may not go as far as manufacturer, National Semiconductor, who gave each of their 8,500 employees 30 GB iPods, but you may want to take matters into your own hands, grab your personal iPod or MP3 player, give these pioneers a listen and see if this form of communication is useful to you.

Tags: podcast | audio information |

August 4, 2006 in Miscellaneous | Comments (3) | Trackback (0)

Moore’s Law foretold of computer processing power doubling every eighteen months when the idea was introduced by Intel’s cofounder Gordon Moore in 1965. This law has application in process automation since the microchips that power today’s controllers and I/O have taken advantage of this increasing power.

Advanced process control algorithms, once solely running in the domain of host-level computers running above the automation systems, are now available down in automation system controllers. These algorithms include model predictive control, fuzzy logic, and neural networks to name a few. The benefit is that these controllers are closer to the action of the running process and can use the diagnostic information in smart field devices to make sure they know when to control, and when to leave control in manual with the operators. These controllers are also available in redundant configurations, something that was more difficult and expensive to achieve with host computers.

Another result of this ever increasing processing power is that more applications can take advantage of these advanced process control algorithms. What was once strictly the domain of large applications like refinery optimization due to the cost, complexity and expertise required, can now be applied to smaller applications.

Lime kilns found in pulp and paper mills, cement and steel mills are a great example of a smaller application that is well suited for model predictive control (MPC) technology. I spoke with Gordon Lawther, a consultant in our Pulp and Paper industry center. Gordon explained that lime kilns are highly interactive in that a change to one process variable impacts the others. They are also constrained by excess oxygen, hood draft pressure, and the kiln stack emissions.

Using model predictive control allows the lime kiln to be operated as a unit instead of a collection of loops which all interact with one another. Since it’s an empirical model of the running processes it can predict into the future to help operators see where key variables are heading and help them resist manually intervening and inducing variability into the process.

Gordon noted that this increased variability is reflected in the lime quality and fuel usage which increases operating costs. The team has consistently documented annual energy savings of 10% or more and maximized mud throughput has saved more than $500,000 USD per year in purchased lime.

The team has packaged their expertise into a SmartProcess Lime application. It uses Emerson model predictive control technology and the expertise Gordon and team bring in benchmarking the existing process, creating and commissioning the models, and measuring the performance improvements. The importance of training operations staff cannot be overstated and is also an integral part of all SmartProcess Lime projects.

Tags: lime kiln | model predictive control | MPC | energy savings | APC | optimization |

August 3, 2006 in Lime Kiln, in Process Optimization | Comments (2) | Trackback (0)