The Route to Model Predictive Control Success

McMillan and Weiner Ask Dennis Cima How He Approaches Making MPC Applications as Successful as Possible

By Greg McMillan, Stan Weiner

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Stan: Navigating the ocean of advanced control applications in the continuous process industries requires commitment and expertise. Many companies have reached their targets and some have been extremely successful. However, others have returned to port empty-handed, and some even have been lost at sea with no understanding of what really happened.

Greg: Mark Darby who was the feature interviewee for the three-part series "MPC Past, Present and Future" (Feb., Mar. and Apr. of this year), recommended we talk with Dennis Cima to get the user perspective on the best route to success achieved at a large oil and petrochemical company. Dennis is the manager of Process Control Network and Control Systems for Chevron Downstream & Chemicals. Dennis, how do you approach making MPC applications as successful as possible?

Dennis: Creating a large MPC application to run a production unit is like configuring a cruise control for a battleship. You want the best technology and the best people. Cheaper is often not better. The view that the automation specialist is a commodity is counter-productive. Chevron has built considerable in-house expertise and is self-sufficient in creating, implementing, maintaining and improving MPC. We cover all the essential aspects; development, documentation, training, maintenance and continuous improvement.

Stan: What keeps you on the best route?

Dennis: Continual measurement and monitoring MPC metrics. For MPC benefits, the ISA Transactions paper "Estimating Benefits from Advanced Control" by Latour, Sharpe and Delany ( is the heart of our guidance system, The statistical methods for quantifying the financial gains from MPC in this paper are tried and true and have stood the test of time. The methods are applicable for any level of control. We have put together an internal course on achieving and documenting benefits.

Greg: I noticed that besides the safety margin opportunity, the paper notes the value of gaining the results of the best operator consistently by MPC. While the benefits may be less than the safety margin, the results having been achieved are more realizable. We did this in an analogous way at Monsanto and Solutia by the use of an opportunity sizing, where Glenn Mertz would analyze the cost sheets and find the best period of operation, as noted in the June 2012 column "The Human Factor." The gap between "normal and best-demonstrated" was the basis of the opportunity assessment that lead to process control improvements (PCI) that achieved on the average a 4% reduction in the cost of goods. In our case, further adventures in PCI stopped when we left the plant. What we implemented stayed online, but innovation ceased for the most part and benefits become less recognizable. How do you make MPC benefits self-sustaining?

Dennis: You need infrastructure to make the benefits consistent. Site A and B are on the same basis. We have corporate standards and systems to historize and report MPC key performance indicators (KPIs).

Stan: Do you have online metrics?

Dennis: Yes, however the online metrics need to be screened for outliers and bad inputs. KPIs need to be reviewed before being reported to management.

Greg: Why is MPC so advantageous in refining, petrochemical, chemical and the special nonlinear MPC in polymer plants?

Dennis: These are all high-volume, continuous processes. An increase of just a few percent in production rate is a huge amount of money. Some refining companies have fallen flat in using and sustaining MPC. You cannot just dump it over the fence. If the developer moves on, and maintenance is not able to deal with changes in the process or objectives, the MPC gets turned off. The same is true for at-line analyzers, which create considerable potential. If an analyzer specialist is not involved in the calibration and troubleshooting, the analyzer will fall into disuse.

Greg: I suspect the negative stories I have seen on MPC are indicative of this problem. I also suspect the difference in opinion between Hunter Vegas (a project manager) and I (a technologist) on the value of analyzers is the result of whether in-house expertise is consistently involved on startup and on an on-going basis. Hunter wanted to write a tip recommending the avoidance of at-line analyzers. We compromised by me adding exceptions and watch-outs to my "Tip #63 – Use Field Analyzers to Measure Key Component Concentrations." One of the problems with near-infrared (NIR) analyzers is that they rely on statistical models that are too mathematically complex for the technician to understand as noted in the December 2011 column with Jim Tatera "Process Analyzers. Analyze This!" The concern was echoed by Michael Chaney in the January 2012 column "Gas Chromatographs Rule." For NIR analyzers the supplier is often needed to decipher and rebuild the model. When do you need to change MPC models?

Dennis: Sulfur plants have models that last 10 years. Hydrogen plants are basically the dynamic models as developed over 15 years ago. These processes are not subject to degradation or to a continuous progression of technology and changes in feeds and products. In fluid catalytic cracking units (FCCU), there are changes in feed stocks (e.g., virgin gas and oils, atmospheric residues and tar sand oils) and shifts in products (e.g., olefins versus diesel). For small changes, we can simply modify gains online. For major differences, we retest typically using automated step-testing software. If you are committed to making MPC work, you make MPC proficiency part of operator certification. To deal with changes in catalyst capability, we use a model developed near the middle of catalyst life expectancy. The time at the start and the end of a catalyst life is relatively short where the catalyst activity is extremely higher or lower, respectively.

Stan: At Monsanto and Solutia, our models were focused on a particular unit operation with four to 10 models. What is the scope of your models?

Dennis: In a large production unit like a crude distillation unit, you may have 20 to 30 MVs (manipulated variables) and 60 to 80 CVs (controlled or constraint variables). Over the course of three to 10 years, some of the dynamic models change. Fortunately, most stay the same so we can focus on specific areas for retesting.

Greg: We were worried about washout where inputs at the very beginning of the process would be so attenuated by back-mixed liquid volumes that the dynamics were not recognizable near the end of the process. The responses at the end were less than the sensitivity of the measurements or were overwhelmed by competing effects in intermediate volumes. Batch operations for reaction and crystallization often made modeling effects of inputs at the front end on the back end impossible. The time horizon of the production unit models also was a day or more, whereas the time horizon of unit operation models was on the order of hours. I suspect for plug flow gas unit operations, the time horizon and washout concern is a lot less. What are some of the special skills and tools needed to develop and maintain a large MPC? Do you separate the MPC into plant areas such as reaction and purification?

Dennis: Let me answer the second question first. Where it makes sense to combine reaction with separation into a single large MPC application is when separation section constraints limit unit upstream reactor section constraints. For example, if unit throughput is limited by a downstream fractionation constraint, then there will typically be an economic driver to combine reactor section and fractionation section controllers. However, larger controllers are more difficult to maintain and are more difficult to implement to insure model and gain consistency. Singular value decomposition (SVD) and linear program (LP) cost calculation tools are necessary when attempting to build and implement large MPC applications.

Stan: Charlie Cutler in his key note talk at ISA Automation Week 2011 lamented the demise of real-time optimization (RTO). Do you use RTO to maximize the overall plant benefits?

Dennis: We do not use the design quality, open-equation, steady-state models that Charlie was talking about for RTO. The development and maintenance of these models is difficult off-line for plant design. Doing them on-line for RTO adds a whole other level of expertise and commitment. We do multi-unit optimization with simplified and focused models as a practical approach. The less rigorous models enable us to develop and support a larger scope of optimization.

Greg: We finish up here with my favorite topic of PID versus MPC control. The Control Talk blog "When do I use PID, MPC and FLC for Basic Control," totaled the greatest number of views per month. The other blog that created almost as much interest was "The Basics of PID Control Modes."

When do you see you need to move on from PID to the MPC?

Dennis: The first flag PID may not be best is if the control solution is multivariable with complex dynamics. Dynamic decoupling and dynamic feed-forward for PID control requires considerable heuristic expertise and effort. By the time you do it if you have the resources, you could have had an MPC on-line, opening up the door to more extensive opportunities. The second flag is dynamics that go beyond the first- or second-order, plus dead time, self-regulating or integrating process. Particularly difficult for a PID is inverse response, higher order dynamics and secondary effects from heat integration and recycle streams. A more immediate material balance effect may be followed by a slower energy balance effect. For example, changing one side draw flow in a fractionator may shift the temperature profile in the column, requiring a consequential change in reflux flow. Another example is a three-bed catalytic reactor where a change in temperature to the first bed results in an eventual shift in the temperature to the third bed that comes back to affect the first bed from preheat of the feed by the reactor outlet stream. A third flag is multiple constraints and competing optimums. PID overrides and valve position controllers provide a sequential rather than a simultaneous honoring of constraints. Optimization is typically limited to a simple objective such as maximizing feed or minimizing the use of a particular utility (e.g., minimizing boiler or compressor pressure or maximizing a refrigeration unit temperature).

Stan: How does PID tuning affect the ability of the MPC to do its job?

Dennis: MPC dynamic models have embedded in them PID regulatory control dynamics. If you radically change the tuning or performance of PID regulatory controls, this will affect the accuracy of the MPC dynamic models. A poorly tuned regulatory control system is a problem even when not being manipulated by the MPC. We typically want no oscillations from an overly aggressive PID controller. Controller aggressiveness and oscillations can excite nonlinearities in the process.

Greg: The avoidance of oscillations and PV overshoot are general objectives. Some consultants have mistakenly put eliminating PID output (MV) overshoot as a general criterion for tuning PID controllers. Many people don't realize that for control of integrating processes (e.g., gas pressure, level and batch temperature), overshoot is necessary to correct for a disturbance or to achieve a new setpoint. For composition control of large, well-mixed volumes and recovery from upsets, overshoot is needed to get to setpoint in a reasonable time frame.

Greg: Most of my top ten lists were conceived while jogging. Since running is out of the picture with my worn-out knees, I am relegated to finding the moments of inspiration from a mind free to roam. Good thing my mind is not worn out. Here is one I composed returning from New Jersey.

Top Ten Reasons to Write a Top Ten List on an Airplane

(10) Takes your mind off screaming kid.
(9) Airline food heightens your sense of absurdity.
(8) Low humidity enhances your dry sense of humor.
(7) High altitude gives you a perspective.
(6) Engine hum is hummer for creativity.
(5) Money saved by not reading Sky Mall.
(4) Your Kindle's battery is too low to read great literature.
(3) Your mind's battery is too low to think anything serious.
(2) At least your fingers are getting exercise.
(1) Passengers will want a subscription to Control.

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