The document summarizes Greg McMillan's presentation on improving process control loops. Some key points discussed include using PID on error structure to maximize response to setpoint changes, adding setpoint feedforward for faster response, and using "bang-bang" control to minimize rise time and settling time. Other techniques mentioned are output lead-lag, deadtime compensation, and model predictive control. The presentation provides examples testing the effectiveness of these methods.

1. ISA Saint Louis Short Course Dec 6-8, 2010 Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements - Day 3

6. Structure, SP Feedforward, & Bang-Bang Tests Improving Loops - Part 2 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%

8. Deadtime Compensator Configuration Improving Loops - Part 2 Insert deadtime block Must enable dynamic reset limit !

12. Identified Responses for Fed-Batch Profile Model Predictive Control (MPC) Improving Loops - Part 2

13. Model Predictive Control (MPC) of Growth Rate and Product Formation Rate Improving Loops - Part 2 Product Formation Rate Biomass Growth rate Substrate Dissolved Oxygen

14. Model Predictive Control (MPC) Reduces Fed-Batch Cycle Time Improving Loops - Part 2 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

15. Model Predictive Control (MPC) Improves Batch Predictions Improving Loops - Part 2 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)

17. Open Loop Backup Configuration SP_Rate_DN and SP_RATE_UP used to insure fast getaway and slow approach Open loop backup used for prevention of compressor surge and RCRA pH violation Open Loop Backup Configuration Improving Loops - Part 2

18. PID Controller Disturbance Response Improving Loops - Part 2

19. Open Loop Backup Disturbance Response Open Loop Backup Improving Loops - Part 2

20. Conductivity Kicker for Evaporator Improving Loops - Part 2

21. Mixer Attenuation Tank AY AT middle selector AY splitter AT FT FT AT AY AT AT AT AY AT AT AT Mixer AY FT Stage 2 Stage 1 middle selector Waste middle selector RCAS RCAS splitter AY filter AY ROUT kicker AC-1 AC-2 MPC-2 MPC-1 pH Kicker for Waste Treatment (Pensacola Plant) Improving Loops - Part 2

23. Virtual Plant Synergy 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 PAT Tools Process Knowledge Improving Loops - Part 2

24. PCI and OTS Virtual Plants Improving Loops - Part 2 Dynamic Process Simulators Virtual Process Virtual Sensors Virtual Valves Virtual I/O MiMiC PCI DeltaV SimulatePro Virtual DCS Virtual Process Virtual Sensors Virtual Valves Virtual I/O Module Actual DCS MiMiC OTS DeltaV ProPlus VIM Configuration Graphics Trends

25. Virtual Plant Essentials Improving Loops - Part 2 DeltaV Simulate Product Family MiMiC Simulation Software

27. Nonlinearity - Graphical Deception Reagent  Influent Ratio Reagent  Influent Ratio Despite appearances there are no straight lines in a titration curve (zoom in reveals another curve if there are enough data points - a big “IF” in neutral region) For a strong acid and base the pK a are off-scale and the slope continually changes by a factor of ten for each pH unit deviation from neutrality (7 pH at 25 o C) Yet titration curves are essential for every aspect of pH system design but you must get numerical values and avoid mistakes such as insufficient data points in the area around the set point Improving Neutralizer pH Control 14 12 10 8 6 4 2 0 pH 11 10 9 8 7 6 5 4 3 pH

28. Effect of Acid and Base Type Slope moderated near each pK a ! Improving Neutralizer pH Control Weak Acid and Strong Base pk a = 4 Weak Acid and Weak Base pk a = 4 Strong Acid and Weak Base pk a = 10 Multiple Weak Acids and Weak Bases pk a = 3 pk a = 5 pk a = 9

29. Effect of Mixing Uniformity and Valve Resolution pH Reagent to Feed Flow Ratio 4 10 6 8 pH Set Point Fluctuations or Oscillations In Flows or Concentrations Control valve resolution (stick-slip) and mixing uniformity requirements are extraordinary on the steepest slope Improving Neutralizer pH Control

30. Control Valve Size and Resolution pH Reagent Flow Influent Flow 6 8 Influent pH B A Control Band Set point B E r =  100%  F imax   F rmax F rmax =  A  F imax B E r =  100%   A S s = 0.5  E r A = distance of center of reagent error band on abscissa from origin B = width of allowable reagent error band on abscissa for control band E r = allowable reagent error (%) F rmax = maximum reagent valve capacity (kg per minute) F imax = maximum influent flow (kg per minute) S s = allowable stick-slip (resolution limit) (%) Most reagent control valves are oversized, which increases the limit cycle amplitude from stick-slip (resolution) and deadband (integrating processes and cascade loops) Improving Neutralizer pH Control

31. Feed Reagent Reagent Reagent The period of oscillation (total loop dead time) must differ by more than factor of 5 to prevent resonance (amplification of oscillations) Small first tank provides a faster response and oscillation that is more effectively filtered by the larger tanks downstream per Eq. 5-3j Big footprint and high cost! Traditional System for Minimum Variability Improving Neutralizer pH Control

32. Reagent Reagent Feed Reagent Traditional System for Minimum Reagent Use The period of oscillation (total loop dead time) must differ by more than factor of 5 to prevent resonance (amplification of oscillations) The large first tank offers more cross neutralization of influents Big footprint and high cost! Improving Neutralizer pH Control

33. Tight pH Control with Minimum Capital Investment Influent FT 1-2 Effluent AT 1-1 FT 1-1 10 to 20 pipe diameters f(x) *IL#1 Reagent High Recirculation Flow Any Old Tank Signal Characterizer *IL#2 LT 1-3 IL#1 – Interlock that prevents back fill of reagent piping when control valve closes IL#2 – Interlock that shuts off effluent flow until vessel pH is projected to be within control band Eductor Improving Neutralizer pH Control FC 1-2 AC 1-1 LC 1-3

35. Case History 1- Existing Control System Improving Neutralizer pH Control Mixer Attenuation Tank AY AT middle selector AY splitter AC AT FT FT AT AY AT AT AT AY AT AT AT Mixer AY FT Stage 2 Stage 1 middle selector AC Waste Waste middle selector Fuzzy Logic RCAS RCAS splitter AY filter AY ROUT kicker

36. Case History 1 - New Control System Improving Neutralizer pH Control Mixer Attenuation Tank AY AT middle selector AY splitter AT FT FT AT AY AT AT AT AY AT AT AT Mixer AY FT Stage 2 Stage 1 middle selector Waste Waste middle selector RCAS RCAS splitter AY filter AY ROUT kicker AC-1 AC-2 MPC-2 MPC-1

37. Case History 1 - Opportunities for Reagent Savings Improving Neutralizer pH Control pH Reagent to Waste Flow Ratio Reagent Savings 2 12 Old Set Point New Set Point Old Ratio New Ratio

38. Case History 1 - Online Adaptation and Optimization Improving Neutralizer pH Control Actual Plant Optimization (MPC1 and MPC2 ) Tank pH and 2 nd Stage Valves Stage 1 and 2 Set Points Virtual Plant Inferential Measurement (Waste Concentration) and Diagnostics Adaptation (MPC3) Actual Reagent/Waste Ratio (MPC SP) Model Influent Concentration (MPC MV) Model Predictive Control (MPC) For Optimization of Actual Plant Model Predictive Control (MPC) For Adaptation of Virtual Plant Virtual Reagent/Influent Ratio (MPC CV) Stage 1 and 2 pH Set Points

39. Case History 1 - Online Model Adaptation Results Adapted Influent Concentration (Model Parameter) Actual Plant’s Reagent/Influent Flow Ratio Virtual Plant’s Reagent/Influent Flow Ratio Improving Neutralizer pH Control

40. Case History 2 - Existing Neutralization System Improving Neutralizer pH Control Water 93% Acid 50% Caustic Pit Cation Anion To EO Final acid adjustment Final caustic adjustment AT

44. Really big tank and thousands of mice each with 0.001 gallon of acid or caustic or modeling and control Case History 2 - Choices Improving Neutralizer pH Control

45. Case History 2 - Demineralized pH Titration Curve Slope pH Improving Neutralizer pH Control

46. Case History 2 - Demineralized pH Control System Signal characterizers linearize loop via reagent demand control AY 1-4 AC 1-1 AY 1-3 splitter signal characterizer signal characterizer pH set point Eductors LT 1-5 Tank Static Mixer Feed To other Tank Downstream system LC 1-5 From other Tank To other Tank Improving Neutralizer pH Control AT 1-3 AT 1-2 AT 1-1 AY 1-1 AY 1-2 middle signal selector FT 1-1 FT 1-2 NaOH Acid

47. Case History 2 - Tuning for Conventional pH Control Improving Neutralizer pH Control

48. Case History 2 - Tuning for Reagent Demand Control Improving Neutralizer pH Control Gain 10x larger

49. Case History 2 - Process Test Results Improving Neutralizer pH Control One of many spikes from stick-slip of water valve Tank 1 pH for Reagent Demand Control Tank 1 pH for Conventional pH Control Start of Step 2 (Regeneration) Start of Step 4 (Slow Rinses)

51. Streams, pumps, valves, sensors, tanks, and mixers are modules from DeltaV composite template library. Each wire is a pipe that is a process stream data array (e.g. pressure, flow, temperature, density, heat capacity, and concentrations) First principle conservation of material, energy, components, and ion charges Case History 2 - Dynamic Model in the DCS Improving Neutralizer pH Control

55. Improving Reactor Temperature Control Reactor Control Strategies

56. Improving Reactor Temperature Control Reactor Cascade Control

57. Improving Reactor Temperature Control Exothermic Reactions

58. Improving Reactor Temperature Control Reactor Valve Position Control

59. Improving Reactor Temperature Control Reactor Equilibrium Control

60. Improving Reactor Temperature Control Reactor Rate of Change Control A low

61. Improving Reactor Temperature Control Reactor Override Control

63. Improving Unit Op Temperature Control Heat Exchanger Coolant Control

64. Improving Unit Op Temperature Control Heat Exchanger By-Pass Control

65. Improving Unit Op Temperature Control Heat Exchanger Feedforward Control

66. Improving Unit Op Temperature Control Column Control by Manipulation of Distillate

67. Improving Unit Op Temperature Control Column Control by Manipulation of Reflux

68. Improving Unit Op Temperature Control Column Control by Manipulation of Steam

69. Improving Unit Op Temperature Control Column Control by Manipulation of Bottoms

70. Improving Unit Op Temperature Control Kiln Feedforward and Valve Position Control

71. Improving Unit Op Temperature Control Kiln Differential Temperature Control

72. Improving Unit Op Temperature Control Kiln Oxygen Control

73. Improving Unit Op Temperature Control Crystallizer Control

74. Improving Unit Op Temperature Control Extruder Specific Energy Control