The post How to Optimize Industrial Evaporators first appeared on the ISA Interchange blog site.
The following technical discussion is part of an occasional series showcasing the ISA Mentor Program, authored by Greg McMillan, industry consultant, author of numerous process control books, 2010 ISA Life Achievement Award recipient and retired Senior Fellow from Solutia Inc. (now Eastman Chemical). Greg will be posting questions and responses from the ISA Mentor Program, with contributions from program participants.
Luis Navas is an ISA Certified Automation Professional and electronic engineer with more than 11 years of experience in process control systems, industrial instrumentation and safety instrumented systems. Luis’ questions on evaporator control are important to improve evaporator concentration control and minimize steam consumption
Which criteria should I follow to define the final control strategy with model predictive control (MPC) in an existing PID strategy? Only one MPC for all existing PIDs? Or may be 1MPC + 1PID or 1MPC + 2 PIDs? What are the criteria to make the correct decision? What is the step by step procedure to deploy the advanced control in the real process in the safest way? Which are your hints, tips, advice and experiences regarding MPC implementations?
PID control of a double-effect evaporator
In general you try to include all of the controlled variables (CV), manipulated variables (MV), disturbance variables (DC), and constraint variables (QC) in the same MPC unless the equipment are not related, there is a great difference in time horizons or there is a cascade control opportunity like we see with Kiln MPC control where a slower MPC with more important controlled variables send setpoints to a secondary MPC for faster controlled variables. For your evaporator control, this does not appear to be the case.
We first discuss advanced PID control and its common limitations before moving into a MPC.
For optimization, a PID valve position controller could maximize production rate by pushing the steam valve to its furthest effective throttle position. So far as increasing efficiency in terms of minimizing steam use, this would be generally be achieved by tight concentration control that allows you to operate closer to minimum concentration spec. The level and concentration response would be true and near integrating. In both cases, PID integrating process tuning rules should be used. Do not decrease the PID gain computed by these rules without proportionally increasing the PID reset time. The product of the PID gain and reset time must be greater than the inverse of the integrating process gain to prevent slow rolling oscillations, a very common problem. Often the reset time is two or more orders of magnitude too small because user decreased the PID gain due to noise or thinking oscillations are caused by too high a PID gain.
I don’t see constraint control for a simple evaporator but if there were constraints, an override controller would be setup for each. However, only one constraint would be effectively governing operation at a given time via signal selection. Also, the proper tuning of override controllers and valve position controllers is not well known. Furthermore, the identification of dynamics for feedback and particularly feedforward control typically requires the expertise by a specialist. Often comparisons are done showing how much better Model Predictive Control is than PID control without good identification and tuning of feedback and feedforward control parameters.
While optimization limitations and typical errors in identification and tuning push your case toward the use of MPC, here are the best practices for PID control of evaporators.
The use of model predictive control software often does a good job of identifying the dynamics and automatically incorporating them into the controller. Also, it can simultaneously handle multiple constraints with predictive capability as to violation of constraints. Furthermore, a linear program or other optimizer built into MPC can find and achieve the optimum intersection of the minimum and maximum values of controlled, constraint, and manipulated variables plotted on a common axis of the manipulated variables.
I have asked for more detailed advice on MPC by Mark Darby, a great new resource, who wrote the MPC Sections for the McGraw-Hill Handbook Hunter and I just finished.
It is normally best to keep PID controls in place for basic regulatory control if they perform well, which may require re-tuning or reconfiguration of the strategy. Your case is getting into advanced control and optimization where the advantage shifts to MPC. Multiple interactions and measured disturbances are best done by MPC compared to PID decoupling and feedforward control. First principle models should be used to compute smarter disturbance variables such as solids feed flow rather than separate feed flow and feed concentration disturbance variables. Override control and valve position control schemes are better handled by MPC. More general optimization is also better done with an MPC. Remember to include PID outputs to valves as constraint variables if they can saturate in normal operation. If a valve is operated close to a limit (e.g., 5% or 95%), it may be better to have the MPC manipulate the valve signal directly using signal characterization as needed using installed flow characteristic to linearize response.
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Here are some MPC best practices from Process/Industrial Instruments and Controls Handbook Sixth Edition, by Gregory K. McMillan and Hunter Vegas (co-editors), and scheduled to be published in early 2019. This sixth edition is revolutionary in having nearly 50 industry experts provide a focus on the steps needed for all aspects to achieve a successful automation project to maximize the return on investment.
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