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Sigurd Skogestad, NTNU Johannes Jäschke, NTNU RPI, 20-22 May 2014

Plantwide process control with focus on selecting economic controlled variables (« self-optimizing control »). Sigurd Skogestad, NTNU Johannes Jäschke, NTNU RPI, 20-22 May 2014. Course Summary. Find active constraints + self-optimizing variables (CV1). (Economic optimal operation)

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Sigurd Skogestad, NTNU Johannes Jäschke, NTNU RPI, 20-22 May 2014

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  1. Plantwideprocesscontrolwithfocusonselectingeconomiccontrolled variables («self-optimizingcontrol») Sigurd Skogestad, NTNU Johannes Jäschke, NTNU RPI, 20-22 May 2014

  2. Course Summary • 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

  3. Plantwide process control • Part 1 (Tue AM): Plantwide control • Part 2 (Tue PM): More on self-optimizing control. Exercise • Part 3 (Wed AM): Consistent inventory control, TPM location, Structure of regulatory control layer • Part 4 (Wed PM): PID tuning • Part 5 (Thu AM): “Advanced” control and case studies

  4. Tue AM Part 1: Plantwide control Introductionto plantwidecontrol (whatshouldwereallycontrol?) Introduction. • Objective: Putcontrollersonflowsheet (make P&ID) • Twomainobjectives for control: Longer-term economics (CV1) and shorter-term stability (CV2) • Regulatory(basic) and supervisory (advanced) controllayer Optimal operation (economics) • Definecost J and constraints • Active constraints (as a functionofdisturbances) • Selectionofeconomiccontrolled variables (CV1). Self-optimizing variables.

  5. Tue PM Part 2: Self-optimizing control theory • Ideal CV1 = Gradient (Ju) • Nullspace method • Exactlocalmethod • Link to otherapproaches • Examples, exercises

  6. Wed AM Part 3: Regulatory («stabilizing») control Inventory (level) controlstructure • Location ofthroughput manipulator • Consistency and radiatingrule Structureofregulatorycontrollayer (PID) • Selectionofcontrolled variables (CV2) and pairingwithmanipulated variables (MV2) • Main rule: Control drifting variables and "pair close" Summary: Sigurd’srules for plantwidecontrol

  7. Wed PM Part 4: PID tuning PID controller tuning: It paysoff to be systematic! • DerivationSIMC PID tuning rules • Controller gain, Integral time, derivative time • Obtainingfirst-order plusdelaymodels • Open-loop stepresponse • From detailedmodel (half rule) • From closed-loop setpointresponse • Special topics • Integratingprocesses (levelcontrol) • Otherspecialprocesses and examples • Whendo weneed derivative action? • Near-optimalityof SIMC PID tuning rules • Non PID-control: Is there an advantage in using Smith Predictor? (No) • Examples

  8. Thu AM Part 5: Advanced control + case studies Advanced controllayer (1h) • Design basedon simple elements: • Ratio control • Cascadecontrol • Selectors • Input resetting (valvepositioncontrol) • Split range control • Decouplers(includingphsicallybased) • Whenshouldthese elements be used? • Whenuse MPC instead? Case studies (3h) • Example: Distillationcolumncontrol • Example: Plantwidecontrolofcomplete plant Recycleprocesses: How to avoidsnowballing

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