Presented August 11, 2010 by Greg McMillan as on-line demo/seminar. Video recording available at: http://www.screencast.com/users/JimCahill/folders/Public
Using Grammatical Signals Suitable to Patterns of Idea Development
PID Control of True Integrating Processes - Greg McMillan Deminar
1. Interactive Opportunity Assessment Demo and Seminar (Deminar) Series for Web Labs – PID Control of True Integrating Processes Aug 11, 2010 Sponsored by Emerson, Experitec, and Mynah Created by Greg McMillan and Jack Ahlers www.processcontrollab.com Website - Charlie Schliesser (csdesignco.com)
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7. Integrating Response Time (seconds) o K i = { [ CV 2 t 2 ] CV 1 t 1 ] } CO CO ramp rate is CV 1 t 1 ramp rate is CV 2 t 2 CO CV Integrating process gain (%/sec/%) Response to change in controller output with controller in manual % Controlled Variable (CV) or % Controller Output (CO) observed process deadtime
8. Loop Block Diagram p1 p2 p2 K pv p1 c1 m2 m2 m1 m1 K cv c c2 Valve Process Controller Measurement K mv v v K L L L Load Upset CV CO MV PV PID Delay Lag Delay Delay Delay Delay Delay Delay Lag Lag Lag Lag Lag Lag Lag Gain Gain Gain Gain Local Set Point DV Total Observed Dead Time : o v p1 p2 m1 m2 c v p1 m1 m2 c1 c2 % % % Delay => Dead Time Lag =>Time Constant K i = K mv (K pv / p2 ) K cv 100% / span K c T i T d
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10. PID Structure ER is external reset (e.g. secondary PV) Dynamic Reset Limit SP proportional derivative Gain Rate CO filter filter CV filter Filter Time Rate Time filter Filter Time = Reset Time ER Positive Feedback
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12. Contribution of Each PID Mode (Step Change in the Set Point) CO 2 = CO 1 SP seconds/repeat CO 1 Time (seconds) Signal (%) 0 kick from proportional mode bump from filtered derivative mode repeat from integral mode For fastest setpoint response we want to maximize kick from proportional mode bump from derivative mode, and setpoint weighting factors ( = 1 and = 1)
13. Lambda Tuning for Integrating Processes Integrating Process Gain: Controller Gain: Controller Integral (Reset) Time: Lambda (closed loop arrest time) is defined in terms of a Lambda factor ( f ): Closed loop arrest time for load disturbance Controller Derivative (Rate) Time: To prevent slow rolling oscillations: secondary lag
14. Primary and secondary K p secondary p primary p total o K i K p p Tuning for Today’s Example
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18. Example of Advanced Regulatory Control (reduced batch cycle time by 25%) 08/11/10 feed A feed B coolant makeup CAS ratio CAS reactor vent product maximum production rate condenser CTW PT PC-1 TT TT TC-2 TC-1 FC-1 FT FT FC-2 < TC-3 RC-1 TT ZC-1 ZC-2 CAS CAS CAS ZC-3 ZC-4 < Override Control override control ZC-1, ZC-3, and ZC-4 work to keep their respective control valves at a max throttle position with good sensitivity and room for loop to maneuver. ZC-2 will raise TC-1 SP if FC-1 feed rate is maxed out
22. Model Predictive Control (MPC) of Growth Rate and Product Formation Rate Product Formation Rate Biomass Growth rate Substrate Dissolved Oxygen
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24. Model Predictive Control (MPC) Reduces Batch Cycle Time Batch Basic Fed-Batch APC Fed-Batch Batch Inoculation Inoculation Dissolved Oxygen (AT6-2) pH (AT6-1) Estimated Substrate Concentration (AT6-4) Estimated Biomass Concentration (AT6-5) Estimated Product Concentration (AT6-6) Estimated Net Production Rate (AY6-12) Estimated Biomass Growth Rate (AY6-11) MPC in Auto
25. Model Predictive Control (MPC) Improves Batch Predictions Current Product Yield (AY6-10D) Current Batch Time (AY6-10A) Predicted Batch Cycle Time (AY6-10B) Predicted Cycle Time Improvement (AY6-10C) Predicted Final Product Yield (AY6-10E) Predicted Yield Improvement (AY6-10F) Batch Basic Fed-Batch APC Fed-Batch Batch Inoculation Inoculation MPC in Auto Predicted Final Product Yield (AY6-10E) Predicted Batch Cycle Time (AY6-10B)
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27. Structure 3 Rise Time = 8.5 min Settling Time = 8.5 min Overshoot = 0% Structure 1 Rise Time = 1.6 min Settling Time = 7.5 min Overshoot = 1.7% Structure 1 + SP FF Rise Time = 1.2 min Settling Time = 6.5 min Overshoot = 1.3% Structure 1 + Bang-Bang Rise Time = 0.5 min Settling Time = 0.5 min Overshoot = 0.2% Summary of Demo Results
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