The real time values of these parameters will be fed to a central control system. These values are compared with the preset set-points through feedback systems and the necessary alerts are output on the display system, so that corrective action can be taken. It is then converted in digital form and picked by the processor. The cost-performance trade off is to be balanced to implement application-specific peripherals for achieving the desired core performance. Once the core is optimized for working with the desired peripherals, and speed, the rest of the system may be designed as per budget considerations.
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McMillan and Sigifredo Nino Oct 26, The teachings and writings of Greg Shinskey have had a profound effect on many process automation professionals, including these authors. 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 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. 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. 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 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? 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. He prudently declined, insisting he is an expert in process control and not an expert in all kinds of processes. 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.
Great engineering does not originate from the ability to use advanced tools. Now, a tale from the field. From that point on, RGA became a key element in the design of his strategies, notably for distillation control. 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.
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 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. 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.
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 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. 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. Of course, nonlinearities and uncertainties as to the open-loop gain e. 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. Time Optimal Control, II. Not revealed is that the most aggressive but non-oscillatory settings, when the dynamics are well known and fixed, are similar to the Ziegler Nichols Reaction Curve method settings with the PID gain simply cut in half. Most studies to showcase a proposed innovation compare test results for an unmodified Ziegler Nichols Ultimate Oscillation method that is inherently oscillatory, typically showing quarter-amplitude damping.
The minimum IAE has one-seventh amplitude damping. It is important to realize almost every aggressive tuning method will have an oscillatory response and insufficient robustness. An oscillatory response is not appreciated in most processes because it increases variability indices and can cause resonance.
Also, nearly all loops have gains, deadtimes and time constants that are not constant and often not well known. What you need to do is get beyond ego and be practical as to objectives of IE and peak error.
Also, the load response should first be tested in tuning the PID. This can simply be done by momentarily putting the PID in manual, making a step change in the output and immediately returning the controller to automatic before the process starts to respond. Most tuning methods are tested by making a setpoint change, and proposed improvements in tuning are recommended to reduce overshoot. Not realized is that the best load response tuning settings could be used and a lead-lag applied to the setpoint to minimize overshoot.
The lag time is simply set equal to the integral time and the lead time equal to the half of the lag time to reduce the time to reach setpoint. The lead time can be decreased to minimize overshoot. A lead time of zero and a lag time equal to the reset time would correspond to PID structure of integral on error and proportional and derivative on process variable I on E, PD on PV.
A two degrees of freedom 2DOF structure can be used instead of a setpoint lead-lag to accomplish the same objectives. Note that for secondary loops, a lag time or setpoint filter should generally not be used because it seriously slows down the ability of the primary loop to make corrections. For production rate changes where reactant flows are ratioed, setpoint filters are used to prevent the short-term upsets in mass balances from different flow loop response times.
This is preferable to the practice sometimes cited of detuning the reactant flow controllers to match the slowest flow loop response, reducing the ability of these flow controllers to deal with flow disturbances such as pressure changes.
To summarize, the PID should generally be tuned for best load disturbance rejection, which incidentally was the goal of Zeigler Nichols methods.
An exception is a mammalian bioreactor temperature and pH response, where the cells are extremely sensitive to any overshoot, the load disturbances originating from the cells are extremely slow, and the time to reach setpoint is of no concern since the batch cycle time is a week or longer. Important points were made by Shinskey that the Lambda tuning method as documented in the literature did not give a good IE for lag-dominant processes. Publications need to emphasize that lambda is set relative to deadtime with a minimum setting of one deadtime for a non-oscillatory minimum IE if system dynamics are well known and constant, and set as three deadtimes for much more robustness.
Of greater importance is to realize that when the time constant is more than four times the deadtime, the process is classified as near-integrating, and integrating process rules are used where lambda is now an arrest time for load disturbance rejection. Effect of digital and analyzer sample time: There is an incredible amount of misinformation about the effect of sample times, whether we are talking about transmitter digital or wireless update rate time between updates in transmitter output signal , analyzer cycle time, or PID execution rate time between PID executions.
If the disturbance arrives immediately before the sample, what the PID sees is only delayed by latency. Shinskey clearly details the effective deadtime as one-half the sample time for zero latency. This is seen in the phase lag and by the consideration that the average time that a disturbance arrives is in the middle of the sample time. Additional deadtime from latency is the time required for calculations or analysis before a result can be sent.
For analyzers where the result is available at the end of the cycle time, the latency is the cycle time, giving a total deadtime that is 1. Additional deadtime comes from the sample transportation delay, equal to sample system volume between sample point and analyzer divided by sample flow.
Hence, remotely located analyzers in particular need a high sample flow rate achieved by a high sample recycle flow. The increase in deadtime will decrease the maximum controller gain and increase the minimum integral time, and thus result in a two-fold increase in IE.
There are additional effects in terms of PID execution rate. The effectiveness of derivative action starts to significantly decrease if the PID execution rate is greater than one-tenth the rate time, which subsequently requires a larger integral time.
Also, the effective integral time must be incremented by the PID execution rate. A small signal filter has a similar effect. Consequently, the numerator of the IE equation is the integral time plus the PID execution rate and signal filter time.
McGraw Hill - Process Control Systems (Shinskey)
Gregg Shinskey The best-selling guide to the design of control systems for the fluid process industries is now updated and expanded. Emphasizing performance-based design and tuning, the new edition of the best-selling guide to process control provides engineers with reliable coverage of control technology principles for industrial fluid processes - from basic theory to advanced control applications. Written by the foremost authority on process control, this book serves as a complete reference to controller selection and tuning, controller performance evaluation, and as a design guide for configuring optimum systems. Using time-domain and relative-gain analysis throughout, Greg Shinskey shows you how to solve common control problems and apply proven system solutions - with a minimum of effort and mathematical skill. Thoroughly revised and updated, the fourth edition includes new information on inventory control, internal model and model predictive control, minimizing deviation and integrated error following load changes, closed-loop responses for distributed-lag processes, tuning rules for the Smith predictor and PID controller wdead timetime compensation, the dynamics of the static mixer with and without recirculation, process design guidelines for pH control, set-point filtering, sampling effects, and more.
The greatest source of process control knowledge
I Preface are not communicated to the people who must apply them. Control problems arise in the plant and must be solved in the plant. Until plant engineers and control designers are able to communicate with each other, their mutual problems await solution. I do not mean to imply that abstract mathematics is not capable of solving control problems, but it is striking how often the same solution can be reached by using good common sense. High-order equations and high-speed computers can be manipulated to the point where common sense is dulled.
Process control systems