The teachings and writings of Greg Shinskey have had a profound effect on many process automation professionals, including these authors. Greg McMillan’s comments follow; we start with thoughts from Sigifredo Nino.
The first time I met Greg Shinskey was in the winter of 1993, when I attended his “Process Control Systems Seminar” in Atlanta. Since that time, I have had the opportunity and pleasure of implementing many of his ideas throughout my successful career as process control engineer-turned-consultant. Thanks to my professional relationship with Shinskey, I have become closely familiar with his thinking, methods and creative process. His tutelage and experience continue to guide my work today, and this unique experience has given me huge insight into his approach, more so than any of his books and papers ever could have, no matter how many times I read them. Nonetheless, there is a wealth of priceless process control information in his prolific written production, which I recommend you tap into.
If Shinskey’s name and expertise is unknown to you and doesn’t ring any bells, please allow me to give a brief introduction. He wrote his first process control article under the title: “For Gas-Phase Reactors… Design for Control of Temperature,” published in Chemical Engineering on October 5, 1959. His most recent, “Killing Model-Based Control Dead Time” was published by Control in May 2013. If you tally that up, that is over half a century of invaluable contribution to process control and a confirmation of his profound interest, commitment and dedication to developing techniques and practical methods for real-life applications.
The slow disappearance of process control expertise led me to tell him something that I found saddening and disappointing, to say the least: “Future generations of process control professionals will at best have to discover Shinskey from a dusty library book.” Today, there is less and less understanding and appreciation for what process control is and what it can do for chemical facilities in nearly any industry. Furthermore, true experts in process control are an endangered species. The industry, as many others, has fallen in love with tools, software and gadgets that cannot replace a robustly designed control strategy. You cannot write the best software in the world if you do not know how to program. You can buy the latest video card with cutting edge software, have the best processor and have the best 4K display on the market, but poor coding will still result in a lackluster program. Without a deep understanding of the fundamentals, sub-optimal performance in any domain is guaranteed. There will always be the need for in-house expertise in process control, and Shinskey’s books and papers will always be a source of valuable information whenever there is a need for an ingenious and practical solution.
October 1971 marked the beginning of Shinskey’s quest of closing the gap between industry and academia. In his article, “To teach creativity” (Instrumentation Technology, p. 34), he criticized the professors at higher education institutions for their lack of contact with the real world and the consequent continuous proposition of solutions to problems that don’t exist in real life. The theory is great. Theory allows for exploration of alternatives not yet seen in the field. But theory cannot be the be-all-end-all to the success of a process engineer. It must be realistic, and applicable to real life. We don’t need to just think about it. We need to do it.
Shinskey also advocated for integrating process and control design in chemical engineering curricula. I am not sure to what extent this has happened, as it is rather common to find (very) deficient control strategies in the plant floor. There is still a way to go, and is up to us to continue in the direction Shinskey pointed.
So, what makes a good process control engineer? In his writing, “Reflections on CPC-III” (1986), Shinskey saw himself as a practitioner with the right balance between theory and practice. Someone who was able to gather knowledge from academics like E. Bristol and practitioners like C. Ryskamp, and apply the theories of the former in real life while finding the theory to support the practical application of the latter.
Because exceptional understanding of the process is prerequisite for excellent control application, Shinskey was repeatedly requested to make decisions other engineering fields were responsible for, e.g. fossil fuel combustion and compressors. He prudently declined, insisting he is an expert in process control and not an expert in all kinds of processes.
Shinskey’s deep and functional understanding of the engineering of chemical processes is simply brilliant. I have witnessed his proficiency while diagnosing and creating control designs that are well supported by solid applied engineering concepts and fundamentals and that work, despite the occasional skepticism. Shinskey’s firm belief is that the thing that makes an engineer great is his/her understanding of the basic sciences. Great engineering does not originate from the ability to use advanced tools.
Now, a tale from the field. According to Edgar Bristol, Shinskey gave the “more dramatic name” Relative Gain Array (RGA) to his “New Measure of Interaction for Multivariable Process Control.” Shinskey returned from a refinery where he was discussing a control strategy that wouldn’t work. He proceeded to calculate the RGA and came up to the result of 26, effectively saying that it wouldn’t be possible for the strategy to work. From that point on, RGA became a key element in the design of his strategies, notably for distillation control.
Derived from his process control teaching around the world, some of Shinskey’s books were translated into several languages: Process Control Systems into Japanese (1967), Romanian (1969), Italian (1971), Russian (1974), Spanish (1996) and Chinese (2014); Distillation Control into Japanese (1977); pH and pIon Control into Japanese (1977); and Energy Conservation Through Control into Russian (1981) and Japanese (1981).
In addition to Gregory McMillan’s comprehensive summary below of Shinskey’s most important contributions to process control, I must add:
- A successful process control application starts with the deep understanding of the process, followed by identification of the control objective. The lack of those two elements cannot be resolved, neither by tuning the PIDs nor by the use of a given ingenious control law, however clever we may think it is.
- The importance of acknowledging that certain processes are composed of several interacting lags, notably distillation column compositions and heat exchangers, that modeling those processes as first-order-plus-deadtime is not satisfactory, and that the controller tuning should be specific for those cases. I have personally been involved in cases where this approach for tuning distributed processes has given outstanding results: A power boiler main steam temperature control and a high-purity distillation column composition.
- Controller robustness, defined as the minimum change in the internal process parameters that can bring the loop to the stability limit (sustained oscillation), is inversely related to control loop performance, which is proportional to the product of the proportional band, integral time and the change required in the controller output to bring the process variable back to setpoint following a load disturbance. However, an improperly tuned Smith Predictor can give the loop both low robustness and low performance.
- A good load disturbance elimination capability in a PID controller is characterized by the overshot trajectory of the manipulated variable during the rejection of a load disturbance.
- Feedforward is considered a “high performance controller” by Shinskey. Minimization of the error in a control loop can be accomplished by reducing the proportional band and the integral time (subject to the stability limit), and minimization of the amount of effort, namely the change in the controller output. Feedforward reduces the amount of effort the feedback controller needs to perform by as much as 100 times compared to what the controller output would change on feedback alone.
- Valve position controller is probably one of the most valuable concepts he invented, due to its ability as a practical optimization technique.
Shinskey proposed control strategies across many industries, remarkably oil & gas; fossil power generation; pulp & paper; water treatment; heating, ventilation and air conditioning; mineral processing and food. And in doing so, he proposed strategies for several unit operations, including distillation, exothermic reactors, heat exchanging, steam turbines, steam plant management systems, combustion systems, multiple-effect evaporators, reciprocating and centrifugal compressors, refrigeration, evaporation, solids drying and, of course, pH control.
Process control is a well-established engineering field—it’s still needed today, it will be needed in the future and lest we forget, Francis Greg Shinskey has arguably made the most important contribution to our profession.
A perspective by Gregory McMillan
The one person who has done the most by far to advance the practical understanding and performance of PID control at both the basic and advanced levels is Greg Shinskey. What particularly distinguishes Shinskey is that the solutions originate from a deep and pervasive understanding of process principles and dynamics, and the largely overlooked extensive capability of PID control. Each of his books is the greatest source of knowledge on the respective subject. His articles and papers are eye openers that awaken people to what really works best. In this tribute to what I hope you will realize is the greatest mind in process control, I seek to provide recognition and synopsis of the most important knowledge conveyed in his publications, on which I have built my career.
It is impossible to summarize everything I have learned from Shinskey. I will focus on the key points, concentrating on PID with the ISA Standard Form, a parallel form where the proportional mode gain setting affects all three modes. The proportional band that is the proportional mode tuning setting in Shinskey’s works is simply 100% divided by the PID controller gain in the ISA Standard Form.
Insight into loop performance: Shinskey’s simple equation for the integrated error (IE) for a load disturbance shows the importance of the best tuning for rejecting load disturbances and reveals the effect of deadtime. The equation was developed for a load disturbance on the process input, the most common disturbance. The equation shows the IE is proportional to the integral time and is inversely proportional to the controller gain. Shinskey subsequently shows that for lag-dominant processes, the minimum integral time is proportional to the deadtime and the gain is proportional to the time constant-to-deadtime ratio, revealing that the minimum IE is proportional to the deadtime squared. The time constant mentioned here is the largest time constant in the open-loop response, and the deadtime is the total loop deadtime, which includes the deadtime in the valve, measurement and controller response as well as the process response.
Correspondingly, the maximum magnitude of error (peak error) for lag-dominant processes is inversely proportional to controller gain and hence proportional to the deadtime to time constant ratio. For deadtime-dominant processes, there is a negligible reduction in the peak error from PID action and the IE is consequently proportional to deadtime. In actual process applications, it is better to use integrated absolute error (IAE). The IE for a PID tuned for minimum IAE may be about 30% smaller than the IAE, due to some oscillations cancelling out positive and negative errors, but the metric is still very insightful and useful to provide simple but pervasive guidance.
There is significant benefit from a large process time constant that makes the response lag-dominant. Methods that have the disturbance on the process output bypassing the process time constant fail to recognize this potentially incredible effect. For example, deadtime-to-process constant ratios for well mixed vessels can be as low as 0.01, enabling a minimum peak error that is a factor of 100 less than the error predicted if the disturbance was on the process output. Also, derivative action can be used that can reduce the integral time by 50%, reducing the IE by 50%.
Understanding the effect of deadtime on tuning provides the insight that the goals for improving loop performance are to minimize deadtime in the design of the system, and then to tune the controller to minimize the IE and peak errors for the maximum deadtime including any unknowns. Of course, nonlinearities and uncertainties as to the open-loop gain (e.g., product of valve gain, process gain and measurement scale gain) and open-loop time constant (largest time constant) must be considered and the tuning set for the worst case (largest deadtime and open-loop gain, and smallest open-loop time constant). If the PID is not tuned per the actual deadtime, the control loop will do as poorly as a loop with greater deadtime where money has not been invested to reduce loop deadtime.
In fact, you can easily estimate the increase in deadtime from detuning. This fundamental understanding is not apparent in the many publications and presentations that do tests and provide conclusions on the effectiveness of the PID and the consequences of PID tuning and process variable update rate and filtering. By ignoring this equation, you can prove almost any point you want by how you tune the PID and by ignoring load response. There are more than 40 years of examples, including many papers by academics who try to show the value of their special feedback control algorithm, not realizing that Bohl and McAvoy in a 1976 landmark paper (“Linear Feedback vs. Time Optimal Control, II. The Regulator Problem,” Industrial Engineering Chemistry, Process Design and Development, vol. 15, no. 1, 1976, p. 30-33) proved that the PID provided essentially optimal single-loop control for load disturbances. Also, a simple feedforward signal incorporated into the PID output that has timing and measurement errors totalizing less than 1% can improve PID performance for measured disturbances by a factor of 100.