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Isa saint-louis-advanced-p h-short-course-day-2

  1. 1. ISA Saint Louis Short Course Dec 9-10, 2010 Advanced pH Measurement and Control - Day 2
  2. 2. Welcome <ul><li>Gregory K. McMillan </li></ul><ul><ul><li>Greg is a retired Senior Fellow from Solutia/Monsanto and an ISA Fellow. Presently, Greg contracts as a consultant in DeltaV R&D via CDI Process & Industrial. Greg received the ISA “Kermit Fischer Environmental” Award for pH control in 1991, the Control Magazine “Engineer of the Year” Award for the Process Industry in 1994, was inducted into the Control “Process Automation Hall of Fame” in 2001, was honored by InTech Magazine in 2003 as one of the most influential innovators in automation, and received the ISA Life Achievement Award in 2010. Greg is the author of numerous books on process control, his most recent being Essentials of Modern Measurements and Final Elements for the Process Industry. Greg has been the monthly “Control Talk” columnist for Control magazine since 2002. Greg’s expertise is available on the web site: http://www.modelingandcontrol.com/ </li></ul></ul>
  3. 3. Bioreactor Control - 1
  4. 4. Bioreactor Control - 2 Bioreactor VSD VSD TC 41-7 AT 41-4s2 AT 41-4s1 AT 41-2 AT 41-1 TT 41-7 AT 41-6 LT 41-14 Glucose Glutamine pH DO Product Heater VSD VSD VSD AC 41-4s1 AC 41-4s2 Media Glucose Glutamine VSD Inoculums VSD Bicarbonate AY 41-1 AC 41-1 Splitter AC 41-2 AY 41-2 Splitter CO 2 O 2 Air Level Drain 0.002 g/L 7.0 pH 2.0 g/L 2.0 g/L 37 o C MFC MFC MFC AT 41-5x2 Viable Cells AT 41-5x1 Dead Cells
  5. 5. pH Growth Rate Factor pH Growth Rate Factor
  6. 6. Convenient pH Model Kinetics pH max = maximum pH for viable cells (8 pH) pH min = minimum pH for viable cells (6 pH) pH opt = optimum pH for viable cell growth (6.8 pH)
  7. 7. Feed Reagent Reagent Reagent The period of oscillation (4 x process dead time) and filter time (process residence time) is proportional to volume. To prevent resonance of oscillations, different vessel volumes are used. Small first tank provides a faster response and oscillation that is more effectively filtered by the larger tanks downstream Big footprint and high cost! Traditional System for Minimum Variability
  8. 8. 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!
  9. 9. Consequences of Poor Dynamics and Tuning <ul><li>The peak error is inversely proportional to the controller gain </li></ul><ul><li>The integrated error is inversely proportional to the controller gain but is also proportional to the reset time </li></ul><ul><li>The maximum controller gain is proportional to the process time constant to loop dead time ratio </li></ul><ul><li>The minimum reset time is proportional to the dead time </li></ul><ul><li>The minimum peak error is inversely proportional to the ratio of the process time constant to loop dead time </li></ul><ul><li>The oscillation period is proportional to the loop dead time </li></ul><ul><li>The integrated error is proportional to the loop dead time squared </li></ul><ul><li>Most of the process time constant seen by the loop is lost for excursions on steep slopes of the titration curve </li></ul><ul><li>If you can increase the ratio of process time constant to loop dead time, you can reduce the excursion along the titration curve and hence the change in process gain (slope) seen by the loop. In other words poor control begets poorer control by the introduction of greater nonlinearity </li></ul>
  10. 10. Basic Neutralization System - Before Can you spot the opportunities for process control improvement? Static Mixer AC 1-1 Neutralizer Feed Discharge AT 1-1 FT 1-1 FT 2-1 AC 2-1 AT 2-1 FC 1-2 FT 1-2 2 diameters Reagent Stage 1 Reagent Stage 2
  11. 11. Basic Neutralization System - After Feedforward Summer Static Mixer AC 1-1 Neutralizer Feed Discharge AT 1-1 FT 1-1 FT 2-1 AT 2-1 FC 1-2 FT 1-2 10-20 diameters Reagent Stage 1 Reagent Stage 2 FC 2-1 AC 2-1 10-20 diameters f(x)  RSP Signal Characterizer *1 *1 *1 - Isolation valve closes when control valve closes
  12. 12. 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 FC 1-2 AC 1-1 LC 1-3
  13. 13. Methods of Reducing Reagent Delivery Delays <ul><li>Locate reagent throttle valve at the injection point </li></ul><ul><li>Mount automatic on-off isolation valve at the injection point </li></ul><ul><li>Reduce diameter and length (volume) of injector or dip tube </li></ul><ul><li>Dilute the reagent upstream to increase reagent flow rate </li></ul><ul><li>Inject reagent into vessel side just past baffles </li></ul><ul><li>Inject reagent into recirculation line just before vessel entry </li></ul><ul><li>Inject reagent into feed line just before vessel entry </li></ul><ul><li>Reduce reagent control valve sticktion and deadband </li></ul>The benefits of feedforward are realized only if the correction arrives at about the same time as the disturbance at the point of the pH measurement. Since the disturbance is usually in a high flow influent stream, any reagent delivery delays severely diminish the effectiveness of feedforward besides feedback control because the disturbance hits the pH measurement before the correction.
  14. 14. High Uniformity Reagent Dilution Control Big old tank acts an effective filter of reagent concentration fluctuations Water FC 1-2 FT 1-2 Diluted Reagent DC 1-1 DT 1-1 FT 1-1 Reagent High Recirculation Flow Any Old Tank LT 1-3 LC 1-3 Eductor FC 1-1 Ratio Density RSP RSP
  15. 15. Cascade pH Control to Reduce Downstream Offset AT 1-2 Static Mixer Feed AT 1-1 FT 1-1 FT 1-2 Reagent 10 to 20 pipe diameters Sum Filter Coriolis Mass Flow Meter f(x) PV signal Characterizer RSP f(x) Flow Feedforward SP signal characterizer Trim of Inline Set Point Integral Only Controller Linear Reagent Demand Controller Any Old Tank M FC 1-1 AC 1-1 AC 1-2
  16. 16. Full Throttle Batch pH Control Batch Reactor AT 1-1 10 to 20 pipe diameters Filter Delay Sub Div Sum  t Cutoff Past  pH Rate of Change  pH/  t Mul Total System Dead Time Projected   pH New pH Old pH Batch pH End Point Predicted pH Reagent Section 3-5 in New Directions in Bioprocess Modeling and Control shows how this strategy is used as a head start for a PID controller
  17. 17. Linear Reagent Demand Batch pH Control Batch Reactor AT 1-1 10 to 20 pipe diameters f(x) Master Reagent Demand Adaptive PID Controller Static Mixer AT 1-1 10 to 20 pipe diameters Secondary pH PI Controller Signal Characterizer Uses Online Titration Curve FT 1-1 FT 1-2 Online Curve Identification Influent #1 Reduces injection and mixing delays and enables some cross neutralization of swings between acidic and basic influent. It is suitable for continuous control as well as fed-batch operation. Influent #2 AC 1-1 AC 1-1 FC 1-1 FQ 1-1
  18. 18. Linear Reagent Demand Control <ul><li>Signal characterizer converts PV and SP from pH to % Reagent Demand </li></ul><ul><ul><li>PV is abscissa of the titration curve scaled 0 to 100% reagent demand </li></ul></ul><ul><ul><li>Piecewise segment fit normally used to go from ordinate to abscissa of curve </li></ul></ul><ul><ul><li>Fieldbus block offers 21 custom space X,Y pairs (X is pH and Y is % demand) </li></ul></ul><ul><ul><li>Closer spacing of X,Y pairs in control region provides most needed compensation </li></ul></ul><ul><ul><li>If neural network or polynomial fit used, beware of bumps and wild extrapolation </li></ul></ul><ul><li>Special configuration is needed to provide operations with interface to: </li></ul><ul><ul><li>See loop PV in pH and signal to final element </li></ul></ul><ul><ul><li>Enter loop SP in pH </li></ul></ul><ul><ul><li>Change mode to manual and change manual output </li></ul></ul><ul><li>Set point on steep part of curve shows biggest improvements from: </li></ul><ul><ul><li>Reduction in limit cycle amplitude seen from pH nonlinearity </li></ul></ul><ul><ul><li>Decrease in limit cycle frequency from final element resolution (e.g. stick-slip) </li></ul></ul><ul><ul><li>Decrease in crossing of split range point </li></ul></ul><ul><ul><li>Reduced reaction to measurement noise </li></ul></ul><ul><ul><li>Shorter startup time (loop sees real distance to set point and is not detuned) </li></ul></ul><ul><ul><li>Simplified tuning (process gain no longer depends upon titration curve slope) </li></ul></ul><ul><ul><li>Restored process time constant (slower pH excursion from disturbance) </li></ul></ul>
  19. 19. Pulse Width and Amplitude Modulated Reagent Neutralizer Reagent AT 1-1 10 to 20 diameters PWM Faster cheaper on-off valve is pulse width modulated PD Controller Cycle Time = System Dead Time Throttle valve position sets pulse amplitude Pulse width modulation is linear. The addition of pulse amplitude modulation introduces a severe nonlinearity but greatly increases the sensitivity and rangeability of reagent addition AC 1-1
  20. 20. Case History 1- Existing Control System 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
  21. 21. Case History 1 - New Control System 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
  22. 22. Case History 1 - Opportunities for Reagent Savings pH Reagent to Waste Flow Ratio Reagent Savings 2 12 Old Set Point New Set Point Old Ratio New Ratio
  23. 23. Case History 1 - Online Adaptation and Optimization 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
  24. 24. 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
  25. 25. Case History 2 - Existing Neutralization System Water 93% Acid 50% Caustic Pit Cation Anion To EO Final acid adjustment Final caustic adjustment AT
  26. 26. Case History 2 - Project Objectives <ul><li>Safe </li></ul><ul><li>Responsible </li></ul><ul><li>Reliable </li></ul><ul><ul><li>Mechanically </li></ul></ul><ul><ul><li>Robust controls, Operator friendly </li></ul></ul><ul><ul><li>Ability to have one tank out of service </li></ul></ul><ul><li>Balance initial capital against reagent cost </li></ul><ul><li>Little or no equipment rework </li></ul>
  27. 27. Case History 2 - Cost Data <ul><li>93%H2SO4 spot market price $2.10/Gal </li></ul><ul><li>50% NaOH spot market price $2.30/Gal </li></ul>2k Gal 5k Gal 10k Gal 20k Gal 40k Gal Tank $20k $30k $50k $80k $310k Pump $25k $35k $45k $75k $140k
  28. 28. Case History 2 - Challenges <ul><li>Process gain changes by factor of 1000x </li></ul><ul><li>Final element rangeability needed is 1000:1 </li></ul><ul><li>Final element resolution requirement is 0.1% </li></ul><ul><li>Concentrated reagents (50% caustic and 93% sulfuric) </li></ul><ul><li>Caustic valve’s ¼ inch port may plug at < 10% position </li></ul><ul><li>Must mix 0.05 gal reagent in 5,000 gal < 2 minutes </li></ul><ul><li>Volume between valve and injection must be < 0.05 gal </li></ul><ul><li>0.04 pH sensor error causes 20% flow feedforward error </li></ul><ul><li>Extreme sport - extreme nonlinearity, sensitivity, and rangeability of pH demands extraordinary requirements for mechanical, piping, and automation system design </li></ul>
  29. 29. Really big tank and thousands of mice each with 0.001 gallon of acid or caustic or modeling and control Case History 2 - Choices
  30. 30. Case History 2 - Tuning for Conventional pH Control
  31. 31. Case History 2 - Tuning for Reagent Demand Control Gain 10x larger
  32. 32. Case History 2 - Process Test Results 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)
  33. 33. <ul><li>If Tank pH is within control band, reduce tank level rapidly to minimum. (CL#1a). If Tank pH is out of control band, close valve to downstream system and send effluent to the other tank if it has more room (CL#1b). </li></ul><ul><li>For caustic reagent valve signals of 0-10%, set control valve at 10%, pulse width modulate isolation valve proportional to loop output, and increase loop filter time and reset time to smooth out pulses (CL#2) </li></ul><ul><li>If reagent valves are near the split range point, periodically (e.g. every 5 minutes) shut the reagent valves and divert feed to other tank for 15 seconds to get tank pH reading (CL#3). </li></ul><ul><li>Coordinate opening and closing of reagent isolation valves with the opening and closing of reagent control valves (CL#4) </li></ul><ul><li>If feed is negligible and tank pH is within control band, shut off the recirculation pump (CL#5) </li></ul>Case History 2 - Control Logic
  34. 34. <ul><li>Signal characterizer translates PV and SP from pH to % Reagent Demand </li></ul><ul><ul><li>PV is abscissa of the titration curve scaled 0 to 100% reagent demand </li></ul></ul><ul><ul><li>Piecewise segment fit normally used to go from ordinate to abscissa of curve </li></ul></ul><ul><ul><li>Fieldbus block offers 21 custom space X,Y pairs (X is pH and Y is % demand) </li></ul></ul><ul><ul><li>Closer spacing of X,Y pairs in control region provides needed compensation </li></ul></ul><ul><li>Special configuration is needed to provide operations with pH interface: </li></ul><ul><ul><li>See loop PV in pH and enter loop SP in pH </li></ul></ul><ul><li>Set point on steep part of curve shows biggest improvements from </li></ul><ul><ul><li>Reduction in limit cycle amplitude seen from pH nonlinearity </li></ul></ul><ul><ul><li>Decrease in limit cycle frequency from final element resolution (e.g. stick-slip) </li></ul></ul><ul><ul><li>Decrease in crossing of split range point </li></ul></ul><ul><ul><li>Reduced reaction to measurement noise </li></ul></ul><ul><ul><li>Shorter startup time (loop sees real distance to set point and is not detuned) </li></ul></ul><ul><ul><li>Simplified tuning (process gain no longer depends upon titration curve slope) </li></ul></ul><ul><ul><li>Restored process time constant (slower pH excursion from disturbance) </li></ul></ul>Benefit depends more upon on slopes rather than accuracy of points of titration curve (more robust than feedforward) Case History 2 - Reagent Demand Control
  35. 35. 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
  36. 36. <ul><li>Study shows potential project savings overwhelm reagent savings </li></ul><ul><li>Modeling removes uncertainty from design </li></ul><ul><ul><li>First principle relationships show how well mechanical, piping, and automation system deal with nonlinearity, sensitivity, and rangeability </li></ul></ul><ul><li>Modeling enables prototyping of control improvements </li></ul><ul><ul><li>Linear reagent demand control speeds up response from PV on flat and reduces oscillations from the PV on steep part of titration curve </li></ul></ul><ul><ul><li>Control logic optimizes pH loops to minimize downtime and inventory to maximize availability and minimize energy use </li></ul></ul><ul><ul><ul><li>Pulse width modulation of caustic at low valve positions minimizes plugging </li></ul></ul></ul><ul><ul><ul><li>Recirculation within tank and between tanks offers maximum flexibility and continuous, semi-continuous, and batch modes of operation </li></ul></ul></ul><ul><ul><ul><li>Periodic observation of tank pH to determine best mode of operation </li></ul></ul></ul>Case History 2 - Summary
  37. 37. Adapted Reagent Demand Control Reduces injection and mixing delays and enables some cross neutralization in continuous and batch operations Neutralizer AC 1-1 AT 1-1 10 to 20 diameters f(x) Master Reagent Demand Adaptive PID Controller Static Mixer AC 1-1 AT 1-1 10 to 20 diameters Secondary pH PI Controller Signal Characterizer Uses Online Titration Curve FT 1-1 FC 1-1 FQ 1-1 FT 1-2 Online Curve Identification Influent
  38. 38. Speed of Response Seen by pH Loop <ul><li>Excursion from pH 1 to pH 2 acceleration makes response look like a runaway to loop </li></ul>(2) Excursion from pH 2 to pH 3 deceleration is not enough to show true process time constant Apparent loss of investment in large well mixed volume can be restored by signal characterization of pH to give abscissa as controlled variable! pH Reagent Flow Influent Flow 6 8 10 12 2 4 pH 2 pH 1 pH 3 Fastest process response seen by Loop at inflection point (e.g. 7 pH) Slow Slow
  39. 39. Speed of Response Seen by pH Loop Batch processes have less self-regulation because there is no discharge flow. If there is no consumption of reagent in the batch by a reaction, the pH response is only in one direction for a given reagent. If there is no split ranged acid and base reagent in the batch, PD instead of PID and predictive strategies are used.  d  o 0 1 2 curve 0 = Self-Regulating curve 1 = Integrating curve 2 = Runaway Time (minutes) pH 0  pH Ramp Acceleration Open Loop Time Constant Total Loop Dead Time  CO (% step in Controller Output)
  40. 40. <ul><li>For a first order plus deadtime process, only nine (9) models are evaluated each sub-iteration, first gain is determined, then deadtime, and last time constant. </li></ul><ul><li>After each iteration, the bank of models is re-centered using the new gain, time constant, and deadtime </li></ul>First Order plus Dead Time Model Identification Changes in the process model can be used to diagnose changes in the influent and the reagent delivery and measurement systems First Order Plus Deadtime Process Estimated Gain, time constant, and deadtime Multiple Model parameter Interpolation with re-centering Changing process input Gain Time Constant Dead time 1 2 3
  41. 41. Scheduling of Learned Dynamics and Tuning Model and tuning is scheduled based on pH
  42. 42. Adaptive Control Efficiently Achieves Optimum hourly cost of excess reagent hourly cost of excess reagent total cost of excess reagent total cost of excess reagent pH pH
  43. 43. Adaptive Control Efficiently Rejects Loads hourly cost of excess hourly cost of excess pH total cost of excess reagent total cost of excess reagent pH
  44. 44. Adaptive Control is Stable on Steep Slopes pH pH
  45. 45. Recently Developed Adaptive Control <ul><li>Anticipates nonlinearity by recognizing old territory </li></ul><ul><ul><li>Model and tuning settings are scheduled per operating region </li></ul></ul><ul><ul><li>Remembers what it has learned for preemptive correction </li></ul></ul><ul><li>Demonstrates efficiency improvement during testing </li></ul><ul><ul><li>Steps can be in direction of optimum set point </li></ul></ul><ul><ul><li>Excess reagent useage rate and total cost can be displayed online </li></ul></ul><ul><li>Achieves optimum set point more efficiently </li></ul><ul><ul><li>Rapid approach to set point in new operating region </li></ul></ul><ul><li>Recovers from upsets more effectively </li></ul><ul><ul><li>Faster correction to prevent violation </li></ul></ul><ul><ul><li>More efficient recovery when driven towards constraint </li></ul></ul><ul><li>Returns to old set points with less oscillation </li></ul><ul><ul><li>Faster and smoother return with less overshoot </li></ul></ul>
  46. 46. PID Valve Sensitivity and Rangeability Solution 1 Difficult to tune Neutralizer AC 1-1a AT 1-1 PID Controller Large (Coarse) Small (Fine) AC 1-1b P only Controller Reagent
  47. 47. PID Valve Sensitivity and Rangeability Solution 2 Difficult to tune Neutralizer AC 1-1 AT 1-1 PID Controller Large Small ZC 1-1 I only Controller Reagent
  48. 48. MPC Valve Sensitivity and Rangeability Solution 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
  49. 49. MPC Valve Sensitivity and Rangeability Solution
  50. 50. MPC Valve Sensitivity and Rangeability Solution
  51. 51. MPC Valve Sensitivity and Rangeability Solution
  52. 52. MPC Maximization of Low Cost Reagent
  53. 53. MPC Maximization of Low Cost Reagent
  54. 54. MPC Maximization of Low Cost Reagent 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
  55. 55. MPC Maximization of Low Cost Reagent manipulated variables Supplemental Reagent Flow SP Cheap Reagent Flow PV Neutralizer pH PV Acidic Feed Flow SP Supplemental Reagent Valve Position controlled variable constraint variable MPC disturbance variable Acid Feed Flow SP null optimization variable null Maximize Note that cheap reagent valve is wide open and feed is maximized to keep supplemental reagent valve at minimum throttle position for minimum stick-slip
  56. 56. Review of Key Points <ul><li>More so than for any other loop, it is important to reduce dead time for pH control because it reduces the effect of the nonlinearity </li></ul><ul><li>The effectiveness of feedforward control greatly depends upon the ability to eliminate reagent delivery delays </li></ul><ul><li>If there is a reproducible influent flow measurement use flow feedforward, otherwise use a head start or full throttle logic for startup </li></ul><ul><li>The reliability and error of a pH feedforward is unacceptable if the influent pH measurement is on the extremities of the titration curve </li></ul><ul><li>Except for fast inline systems, use cascade control of pH to reagent flow to compensate for pressure upsets and enable flow feedforward </li></ul><ul><li>Use adaptation of the charge balance model pH or online identification of the titration curve to compensate for a distortion of the curve </li></ul><ul><li>Linear reagent demand can restore the time constant and capture the investment in well mixed vessels, provide a unity gain for the process variable, simply and improve controller tuning, suppress oscillations and noise on the steep part of the curve, and speed up startup and recovery from the flat part of the curve </li></ul>
  57. 57. Review of Key Points <ul><li>Nearly all the previously develop adaptive controllers are playing catch up and do not reveal the process model or the imbedded tuning rules </li></ul><ul><li>New adaptive controllers will remember changes in the process model as a function of operating point and preemptively schedule tuning </li></ul><ul><li>Changes in the process model can be used to predict and analyze changes in the influent, reagent, valve, and sensor </li></ul><ul><li>The use of reagent demand control can free up the adaptive controller to find the changes in titration curve and make a MPC more effective </li></ul><ul><li>Use a wide open reagent valve that is shut based on a predicted online pH measurement to provide the fastest pH batch or startup </li></ul><ul><li>Use pulse width and amplitude modulation of a proportional plus derivative controller output to mimic lab titration for batch pH control </li></ul><ul><li>Use online titration curve identification and linear reagent demand control for extremely variable and sharp titration curvature </li></ul><ul><li>Model predictive control (MPC) can adapt online process models and improve reagent resolution and rangeability and minimize reagent costs </li></ul>
  58. 58. Advanced Application Notes
  59. 59. A Funny Thing Happened (E-Book Online)
  60. 60. Elimination of Lime Delay and Lag Times FC 1-1 FT 1-1 AC 3-1 AT 3-1 LC 1-1 LT 1-1 Liquid Waste Storage Lime Conveyor < HC 2-1 Delay Lag Sum RSP Rotary Valve Speed Conveyor Transportation Delay Lime Dissolution Lag Time Feedforward Summer Low Signal Selector Neutralizer Lime Hopper Manual Loader

Editor's Notes

  • Provide material for this section. At the end of the last section, follow the last section slide with the Review of Key Points. Then use the final Q&amp;A slide to cover questions over the entire presentation, not only specific to the section just covered.
  • Provide material for this section. At the end of the last section, follow the last section slide with the Review of Key Points. Then use the final Q&amp;A slide to cover questions over the entire presentation, not only specific to the section just covered.
  • Provide material for this section. At the end of the last section, follow the last section slide with the Review of Key Points. Then use the final Q&amp;A slide to cover questions over the entire presentation, not only specific to the section just covered.
  • Provide material for this section. At the end of the last section, follow the last section slide with the Review of Key Points. Then use the final Q&amp;A slide to cover questions over the entire presentation, not only specific to the section just covered.

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