Advanced Process Control with Economic Optimization by Sigurd Skogestad

Advanced process control with focus on selecting economic controlled variables ( self- optimizing control ) Sigurd Skogestad, NTNU 2016

Course information 7 lectures by Sigurd + 2 industrial lectures Time to be determined at the end 6 exercises + help sessions Exercises count 20% of the grade of the module Exercise hours to be determined at the end

Course Summary This course is about how to operate and control complete chemical plants Find active constraints + self-optimizing variables (CV1). (Economic optimal operation) Locate throughput manipulator (TPM) Gas pedal Select stabilizing CV2 + tune regulatory loops SIMC PID rules Design supervisory layer (control CV1) Multi-loop (PID) ++ MPC 1. 2. 3. 4.

Plantwide process control Part 1 : Plantwide control Part 2 : More on self-optimizing control. Part 3 : Consistent inventory control, TPM location, Structure of regulatory control layer Part 4 : PID tuning Part 5 : Advanced control and case studies

Part 1: Plantwide control Introduction to plantwide control (what should we really control?) Introduction. Objective: Put controllers on flow sheet (make P&ID) Two main objectives for control: Longer-term economics (CV1) and shorter-term stability (CV2) Regulatory (basic) and supervisory (advanced) control layer Optimal operation (economics) Define cost J and constraints Active constraints (as a function of disturbances) Selection of economic controlled variables (CV1). Self-optimizing variables.

Part 2: Self-optimizing control theory Ideal CV1 = Gradient (Ju) Nullspace method Exact local method Link to other approaches Examples, exercises

Part 3: Regulatory (stabilizing) control Inventory (level) control structure Location of throughput manipulator Consistency and radiating rule Structure of regulatory control layer (PID) Selection of controlled variables (CV2) and pairing with manipulated variables (MV2) Main rule: Control drifting variables and "pair close" Summary: Sigurd s rules for plantwide control

Part 4: PID tuning PID controller tuning: It pays off to be systematic! Derivation SIMC PID tuning rules Controller gain, Integral time, derivative time Obtaining first-order plus delay models Open-loop step response From detailed model (half rule) From closed-loop setpoint response Special topics Integratingprocesses (level control) Other special processes and examples When do we need derivative action? Near-optimality of SIMC PID tuning rules Non PID-control: Is there an advantage in using Smith Predictor? (No) Examples

Part 5: Advanced control + case studies Advanced control layer Design based on simple elements: Ratio control Cascade control Selectors Input resetting (valve position control) Split range control Decouplers (including phsically based) When should these elements be used? When use MPC instead? Case studies Example: Distillation column control Example: Plantwide control of complete plant Recycle processes: How to avoid snowballing

Course Plan 2016 Week/Date Week 34 / 22.08. Lecture 0. Introduction Exercise Exercise 1 out (2 weeks) 1. Plant-widecontrolprocedure 2. Self-optimizingcontrol 3. Self-optimizingcontrol Week 35 / 29.08. Week 36 / 05.09. Exercise 1 deadline Exercise 2 out (1 week) Exercise 2 deadline Week 37 / 12.09. 4. Self-optimizingcontrol Exercise 3 out (1 week) Exercise 3 deadline Exercise 4 out (2 weeks) Week 38 / 19.09. 5. Regulatory layer, TPM Selection Week 39 / 26.10. Week 40 / 03.10. 6. ControllerTuning 7. Advanced controlstructures Exercise 4 deadline Exercise 5 out (2 weeks) Week 41 / 10.10. Week 42 / 17.10. Exercise 5 deadline Exercise 6 out (2 weeks) Week 43 / 24.10. Week 44 / 31.10. Exercise 6 deadline Lecturer: Exercises: Sigurd Skogestad (skoge@ntnu.no) Julian Straus (julian.straus@ntnuno) Note, that the days of the lecture may change. There will be two guest lectures given by Stig Strand: MPC application in Statoil Krister Forsman: Advanced process control in Perstorp The dates of both guest lectures have to be defined