Model Predictive Control For Integrating Processes
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The document discusses controlling integrating processes like liquid levels using model predictive control (MPC). It explains that integrating processes require control since they have no natural equilibrium. The key points covered are: - MPC can effectively control integrating processes by considering feedback, model correction, rotation factors, and tuning parameters. - Tuning time to steady state, penalty on move, and model correction factor impact control performance for setpoint changes and load disturbances. - With proper tuning, MPC provides improved control of integrating processes compared to conventional PI control.
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Model Predictive Control For Integrating Processes
- 1. Model Predictive Control for Integrating Processes Lou Heavner – Consultant, APC
- 5. Integrating Process - Open Loop Response Controller Output Process Variable
- 10. Model Predictive Control Learns From The Past To Predict The Future Past Present Future Modeled Relationship
- 11. Multivariable Dynamic Process Models The Model Consists Of Step Responses That Show The Relationship Between Every Process Input And Output
- 16. Effect of TSS SP changes Increasing TSS stabilizes the level control reducing both overshoot and MV moves Case TSS (Configured) "Lambda" Max CV Overshoot Max MV Move Apparent TSS sec min % % min 1 240 9 2.19 5.64 44 2 360 9 0.77 5.17 28 3 600 15 0.38 2.99 33 4 1080 n/a 0 0.36 27 POM = 39.5 MCF = 0.75 ROT = 0.05
- 17. Effect of TSS on load disturbances Setting TSS = 6* Deadtime gives good results approximating 1 st order response Setting TSS = 10 x Deadtime approaches critically damped response Case TSS (Configured) "Lambda" Max CV Overshoot Apparent TSS sec min % min 1 240 5 2.44 31 2 360 4 2.03 30 3 600 6 2.99 27 4 1080 6 2.42 32 POM = 39.5 MCF = 0.75 ROT = 0.05
- 18. Load Response with 2 different TSS
- 19. Effect of POE Reducing POM improves performance TSS = 240 sec MCF = 0.75 ROT = 0.05 Case POM "Lambda" Max CV Overshoot Max MV Move Apparent TSS min % % min 1 22 6 1.17 1.82 21 2 39.5 9 2.19 5.64 44 3 55.5 11 2.76 4.44 44
- 20. Effect of Model Correction Factor TSS = 240 sec POM = 39.5 ROT = 0.05 Case MCF "Lambda" Max CV Overshoot Max MV Move Apparent TSS min % % min 1 0.5 5 2.11 5.63 33 2 0.75 9 2.19 5.64 44 3 0.9 8 2.12 5.45 32.5
- 21. Effect of Rotation Factor Case ROT "Lambda" Max CV Overshoot Max MV Move Apparent TSS min % % min 1 0.01 8 2.22 5.78 44 2 0.05 9 2.19 5.64 44 3 0.1 8 2.18 5.65 32.5 4 0.5 8 2.11 5.47 33 TSS = 240 sec POM = 39.5 MCF = 0.75
Editor's Notes
- Main Points: A basic concept of Model Predictive Control is that the control predicts future process behavior based on past process input changes. MPC predicts future behavior based on step response models.
- Main Points: For multivariable control problems, a matrix of control models is used to calculate the response of each controlled and constraint variable based on changes to manipulated variables or measured disturbance variables. This is where the term Dynamic Matrix Control come from. Transition: Once you have defined your manipulated variables and control variables, its easy to configure DeltaV Predict using standard DeltaV Control Studio.