Gregory K. McMillan ( http://www.modelingandcontrol.com ) presents the process of assessing opportunities to apply advanced process control (APC), their potential benefits, and exposes some common myths.

1. 06/06/09 Opportunity Assessment and Advanced Control GREGORY K MCMILLAN use pure black and white option for printing copies

3. 06/06/09 See Chapter 2 for more info on setting the foundation Purchase

4. 06/06/09 See Chapters 2-4 for more info on the application of model predictive control Purchase

5. 06/06/09 See Appendix C for background of the unification of tuning methods and loop performance Purchase

6. 06/06/09 See Chapter 1 for the essential aspects of system design for pH applications Purchase

8. 06/06/09 TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program Pyramid of Technologies APC is in any technology that integrates process knowledge Foundation must be large and solid enough to support upper levels. Effort and performance of upper technologies is highly dependent on the integrity and scope of the foundation (type and sensitivity of measurements and valves and tuning of loops) The greatest success has been Achieved when the technology closed the loop (automatically corrected the process without operator intervention) Basic Process Control System Loop Performance Monitoring System Process Performance Monitoring System Abnormal Situation Management System Auto Tuning (On-Demand and On-line Adaptive Loop Tuning) Fuzzy Logic Property Estimators Model Predictive Control Ramper or Pusher LP/QP RTO TS

9. Loops Behaving Badly 06/06/09 1 E i = ------------  T i  E o  K o  K c where: E i = integrated error (% seconds) E o = open loop error from a load disturbance (%) K c = controller gain K o = open loop gain (also known as process gain) (%/%) T i = controller reset time (seconds) (open loop means controller is in manual) A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)! Tune the loops before, during, and after any process control improvements You may not want to minimize the integrated error if the controller output upsets other loops. For surge tank and column distillate receiver level loops you want to minimize and maximize the transfer of variability from level to the manipulated flow, respectively.

10. Unification of Controller Tuning Settings 06/06/09 Where: K c = controller gain K o = open loop gain (also known as process gain) (%/%)  1  self-regulating process time constant (sec)  max  maximum total loop dead time (sec) All of the major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve, Simplified Internal Model Control, and Lambda) reduce to the following form for the maximum useable controller gain

22. When Process Knowledge is Missing in Action 06/06/09 2-Sigma 2-Sigma RCAS Set Point LOCAL Set Point 2-Sigma 2-Sigma Upper Limit PV distribution for original control PV distribution for improved control Extra margin when “ war stories” or mythology rules value Benefits are not realized until the set point is moved! (may get benefits by better set point based on process knowledge even if variability has not been reduced) Good engineers can draw straight lines Great engineers can move straight lines

26. Batch Control 06/06/09 Reagent Optimum pH Optimum Product Feeds Concentrations pH Product Optimum Reactant Reactant Reactant Variability Transfer from Feeds to pH, and Reactant and Product Concentrations Most published cases of multivariate statistical process control (MSPC) use the process outputs and this case of variations in process variables induced by sequenced flows.

27. PID Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Reactant Optimum Reactant Reactant Variability Transfer from pH and Reactant Concentration to Feeds The story is now in the controller outputs (manipulated flows) yet MSPC still focuses on the process variables for analysis

28. Model Predictive Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Optimum Reactant Reactant Reactant Time Time Variability Transfer from Product Concentration to pH, reactant Concentration, and Feeds Model Predictive Control of product concentration batch profile uses slope for CV which makes the integrating response self-regulating and enables negative besides positive corrections in CV

30. Types of Process Responses 06/06/09 The temperature and composition of batch processes tend to have an integrating response since there is no self-regulation from a discharge flow Self-Regulating Process Gain K p =  CV  CO Integrating Process Gain K i =  CV  t  CO  d  o 0 1 2 curve 0 = Self-Regulating curve 1 = Integrating curve 2 = Runaway Time (minutes) CV 0  CV Ramp Acceleration Open Loop Time Constant Total Loop Dead Time  CO (% step in Controller Output)

31. What Does PID and MPC See of Future? (Long Term versus Short Term View) 06/06/09 time controlled variable (CV) set point manipulated variable (MV) PID loop only sees this present time MPC sees whole future trajectory loop dead time compensator sees one dead time ahead response PID is best if high gain or rate action is needed for immediate action to correct frequent fast unmeasured disturbances or a prevent runaway

32. Linear Superposition of MPC 06/06/09 time time time CV 1 = f(  MV 1 ) CV 1 = f(  MV 2 ) CV 1 = f(  MV 1  MV 2 ) set point set point set point Nomenclature: CV is controlled variable (PV) and MV is manipulated variable (IVP)

33. Feedback Correction of Process Vector and Mirror Image Control Vector 06/06/09 time time time set point set point set point control vector process vector process vector process vector shift vector to correct model error actual CV predicted CV compute future moves for a mirror image vector to bring process to set point trajectory Most MPC packages use standard matrix math and methods (e.g. matrix summation and inversion)

35. Automated PRBS Test for Fed-Batch Reactor 06/06/09 Non-stationary Behavior (operating point is not constant) Test Data During Fed-Batch Operation

36. Linear Program (LP) Optimizer 06/06/09 For a minimization of maximization of a MV as a CV, a simple ramper or pusher is sufficient. If the constraint intersections move or the value of type of optimal CV changes, real time Optimization is needed to provide a more optimal solution. MV1 MV2 CV2max CV2min MV2max MV2min MV1max MV1min CV1max CV1min Region of feasible solutions Optimal solution is in one of the vertexes

38. Example of Basic PID Control 06/06/09 feed A feed B coolant makeup CAS ratio control reactor vent product condenser CTW PT PC-1 TT TT TC-2 TC-1 FC-1 FT FT FC-2 TC-3 RC-1 TT CAS cascade control Conventional Control

39. Example of Advanced Regulatory Control 06/06/09 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

40. Example of Model Predictive Control 06/06/09 feed A feed B coolant makeup CAS ratio RCAS reactor vent product condenser CTW PT PC-1 TT TT TC-2 FC-1 FT FT FC-2 RC-1 TT RCAS MPC MPC MPC Maximize feed rate Model Predictive Control (MPC) set point set point

41. Example of MPC (Responses) 06/06/09 manipulated variables (MVs) TC-2 jacket exit temperature SP TV-1 condenser coolant valve IVP FC-1 reactor feed A SP TC-1 reactor temperature PV TC-3 condenser temperature PV FC-1 reactor feed A SP TV-2 reactor coolant valve IVP TV-3 condenser coolant valve IVP PV-1 vacuum system valve IVP FV-1 feed A valve IVP controlled variables (CVs) constraint variables (AVs) null null maximize MPC

43. 06/06/09 Model Predictive Controller (MPC) setup for rapid simultaneous throttling of a fine and coarse control valves that addresses both the rangeability and resolution issues. This MPC can possibly reduce the number of stages of neutralization needed MPC Valve Sensitivity and Rangeability Solution

44. 06/06/09 MPC Valve Sensitivity and Rangeability Solution

45. 06/06/09 MPC Valve Sensitivity and Rangeability Solution

46. 06/06/09 MPC Valve Sensitivity and Rangeability Solution

47. 06/06/09 MPC Maximization of Low Cost Feed Example

48. 06/06/09 MPC Maximization of Low Cost Feed Example

49. 06/06/09 MPC Maximization of Low Cost Feed Example Riding Max SP on Lo Cost MV Riding Min SP on Hi Cost MV Critical CV Lo Cost Slow MV Hi Cost Fast MV Load Upsets Set Point Changes Load Upsets Set Point Changes Low Cost MV Maximum SP Increased Low Cost MV Maximum SP Decreased Critical CV

51. Virtual Plant Setup 06/06/09 Advanced Control Modules Process Models (first principal and experimental) Virtual Plant Laptop or Desktop or Control System Station This is where I hang out

52. Virtual Plant Integration 06/06/09 Dynamic Process Model Online Data Analytics Model Predictive Control Loop Monitoring And Tuning DCS batch and loop configuration, displays, and historian Virtual Plant Laptop or Desktop Personal Computer Or DCS Application Station or Controller Embedded Advanced Control Tools Embedded Modeling Tools Process Knowledge

53. 06/06/09 Actual Plant Optimization Reactant Ratio Correction Temperature Set Point Virtual Plant Online KPI: Yield and Capacity Inferential Measurements: Reaction Rates Adaptation Key Actual Process Variables Key Virtual Process Variables Model Parameters Error between virtual and actual process variables are minimized by correction of model parameters Model Predictive Control and LP For Optimization of Actual Plant Model Predictive Control and Neural Network For Adaptation of Virtual Plant Optimum and Reference Batch Profiles Actual Batch Profiles Multi-way Principal Component Analysis Super Model Based Principal Component Analysis Adaptation and Optimization