Successfully reported this slideshow.
Your SlideShare is downloading. ×

Isa saint-louis-exceptional-opportunities-short-course-day-3

Loading in …3

Check these out next

1 of 74 Ad

More Related Content

Slideshows for you (20)


Similar to Isa saint-louis-exceptional-opportunities-short-course-day-3 (20)


Recently uploaded (20)

Isa saint-louis-exceptional-opportunities-short-course-day-3

  1. 1. ISA Saint Louis Short Course Dec 6-8, 2010 Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements - Day 3
  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: </li></ul></ul>
  3. 3. Top Ten Reasons I use a Virtual Plant <ul><li>(10) You can’t freeze, restore, and replay an actual plant batch </li></ul><ul><li>(9) No separate programs to learn, install, interface, and support </li></ul><ul><li>(8) No waiting on lab analysis </li></ul><ul><li>(7) No raw materials </li></ul><ul><li>(6) No environmental waste </li></ul><ul><li>(5) Virtual instead of actual problems </li></ul><ul><li>(4) Bioreactor batches are done in 14 minutes instead of 14 days </li></ul><ul><li>(3) Plant can be operated on a tropical beach </li></ul><ul><li>(2) Last time I checked my wallet I didn’t have $100,000K </li></ul><ul><li>(1) Actual plant doesn’t fit in my suitcase </li></ul>Improving Loops - Part 2
  4. 4. <ul><li>PID on Error Structure </li></ul><ul><ul><li>Maximizes the kick and bump of the controller output for a setpoint change. </li></ul></ul><ul><ul><li>Overdrive (driving of output past resting point) is essential for getting slow loops, such as vessel temperature and pH, to the optimum setpoint as fast as possible. </li></ul></ul><ul><ul><li>The setpoint change must be made with the PID in Auto mode. </li></ul></ul><ul><ul><li>“ SP track PV” will generally maximize the setpoint change and hence the kick and bump (retaining SP from last batch or startup minimizes kick and bump) </li></ul></ul><ul><li>SP Feedforward </li></ul><ul><ul><li>For low controller gains (controller gain less than inverse of process gain), a setpoint feedforward is particularly useful. For this case, the setpoint feedforward gain is the inverse of the dimensionless process gain minus the controller gain. </li></ul></ul><ul><ul><li>For slow self-regulating (e.g. continuous) processes and slow integrating (e.g. batch) processes, even if the controller gain is high, the additional overdrive can be beneficial for small setpoint changes that normally would not cause the PID output to hit a limit. </li></ul></ul><ul><ul><li>If the setpoint and controller output are in engineering units the feedforward gain must be adjusted accordingly. </li></ul></ul><ul><ul><li>The feedforward action is the process action, which is the opposite of the control action, taking into account valve action. In other words for a reverse control action, the feedforward action is direct provided the valve action is inc-open or the analog output block, I/P, or positioner reverses the signal for a inc-close. </li></ul></ul>Fed-Batch and Startup Time Reduction - 1 Improving Loops - Part 2
  5. 5. <ul><li>Full Throttle (Bang-Bang Control) - The controller output is stepped to it output limit to maximize the rate of approach to setpoint and when the projected PV equals the setpoint less a bias, the controller output is repositioned to the final resting value. The output is held at the resting value for one deadtime. For more details, check out the Control magazine article “ Full Throttle Batch and Startup Response. ” </li></ul><ul><ul><li>A deadtime (DT) block must be used to compute the rate of change so that new values of the PV are seen immediately as a change in the rate of approach. </li></ul></ul><ul><ul><li>If the total loop deadtime (  o ) is used in the DT block, the projected PV is simply the current PV minus the output of the DT block (  PV) plus the current PV. </li></ul></ul><ul><ul><ul><li>If the PV rate of change (  PV/  t) is useful for other reasons (e.g. near integrator or true integrating process tuning), then  PV/  t =  PV/  o can be computed. </li></ul></ul></ul><ul><ul><li>If the process changes during the setpoint response (e.g. reaction or evaporation), the resting value can be captured from the last batch or startup </li></ul></ul><ul><ul><li>If the process changes are negligible during the setpoint response, the resting value can be estimated as: </li></ul></ul><ul><ul><ul><li>the PID output just before the setpoint change for an integrating (e.g. batch) process </li></ul></ul></ul><ul><ul><ul><li>the PID output just before the setpoint change plus the setpoint change divided by the process gain for a self-regulating (e.g. continuous) process </li></ul></ul></ul><ul><ul><li>For self-regulating processes such as flow with the loop deadtime (  o ) approaching or less than the largest process time constant (  p ), the logic is revised to step the PID output immediately to the resting value. The PID output is held at the resting value for the T 98 process response time (T 98  o  p ). </li></ul></ul>Fed-Batch and Startup Time Reduction - 2 Improving Loops - Part 2
  6. 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%
  7. 7. <ul><li>Output Lead-Lag </li></ul><ul><ul><li>A lead-lag on the controller output or in the digital positioner can kick the signal though the valve deadband and sticktion, get past split range points, and make faster transitions from heating to cooling and vice versa. </li></ul></ul><ul><ul><li>A lead-lag can potentially provide a faster setpoint response with less overshoot when analyzers are used for closed loop control of integrating processes When combined with the enhanced PID algorithm (PIDPlus) described in: </li></ul></ul><ul><ul><ul><li>Deminar #1 </li></ul></ul></ul><ul><ul><ul><li>White paper </li></ul></ul></ul><ul><li>Deadtime Compensation </li></ul><ul><ul><li>The simple addition of a delay block with the deadtime set equal to the total loop deadtime to the external reset signal for the positive feedback implementation of integral action described in Deminar #3 for the dynamic reset limit option . </li></ul></ul><ul><ul><li>The controller reset time can be significantly reduced and the controller gain increased if the delay block deadtime is equal or slightly less than the process deadtime as studied in Advanced Application Note 3 </li></ul></ul>Fed-Batch and Startup Time Reduction - 3 Improving Loops - Part 2
  8. 8. Deadtime Compensator Configuration Improving Loops - Part 2 Insert deadtime block Must enable dynamic reset limit !
  9. 9. <ul><li>Deadtime is eliminated from the loop. The smith predictor, which created a PV without deadtime, fools the controller into thinking there is no deadtime. However, for an unmeasured disturbance, the loop deadtime still causes a delay in terms of when the loop can see the disturbance and when the loop can enact a correction that arrives in the process at the same point as the disturbance. The ultimate limit to the peak error and integrated error for an unmeasured disturbance are still proportional to the deadtime, and deadtime squared, respectively. </li></ul><ul><li>Control is faster for existing tuning settings. The addition of deadtime compensation actually slows down the response for the existing tuning settings. Setpoint metrics, such as rise time, and load response metrics, such as peak error, will be adversely affected. Assuming the PID was tuned for a smooth stable response, the controller must be retuned for a faster response (see slide 11). For a PID already tuned for maximum disturbance rejection, the gain can be increased by 250%. For deadtime dominant systems where the total loop deadtime is much greater than the largest loop time constant (hopefully the process time constant), the reset time must also be decreased or there will be severe undershoot. If you decrease the reset time to its optimum, undershoot and overshoot are about equal. For the test case where the total loop deadtime to primary process time constant ratio was 10:1, you could decrease the reset time by a factor of 10, smaller than what was noted on slide 11. Further study is needed as to whether the ratio of the old to new reset time is comparable to the ratio of deadtime to time constant and whether the PID module execution time (0.5 sec) is the low limit to the reset time for an accurate deadtime. </li></ul>Deadtime Myths Busted in Deminar 10 Improving Loops - Part 2 For access to Deminar 10 ScreenCast Recording or SlideShare Presentation go to
  10. 10. <ul><li>Compensator works better for loops dominated by a large deadtime. The reduction in rise time is greatest and the sensitivity to per cent deadtime modeling error particularly for an overestimate of deadtime is least for the loop that was dominated by the process time constant. You could have a deadtime estimate that was 100% high before you would see a significant jagged response when the process time constant was much larger than the process deadtime. For a deadtime estimate that was 50% too low, some rounded oscillations developed for this loop. The loop simply degrades to the response that would occur from the high PID gain as the compensator deadtime is decreased to zero. While the magnitude of the error in deadtime seems small, you have to remember that for an industrial temperature control application, the loop deadtime and process time constant would be often at least 100 times larger. For a 400 second deadtime and 10,000 second process time constant, a compensator deadtime 200 seconds smaller or 400 seconds larger than actual would start to cause a problem. In contrast, the deadtime dominant loop developed a jagged response for a deadtime that was high or low by just 10%. I think this requirement is unreasonable in industrial processes. A small filter of 1 second on the input to the deadtime block in the BKCAL path may have helped. </li></ul><ul><li>An underestimate of the deadtime leads to instability. In tuning calculations for a conventional PID, a smaller than actual deadtime can cause an excessively oscillatory response. Contrary to the effect of deadtime on tuning calculations, a compensator deadtime smaller than actual deadtime will only cause instability if the controller is tuned aggressively after the deadtime compensator is added. </li></ul><ul><li>An overestimate of the deadtime leads to sluggish response and greater stability. In tuning calculations for a conventional PID, a larger than actual deadtime can cause an excessively slow response. Contrary to the effect of deadtime on tuning calculations, a compensator deadtime greater than actual deadtime will cause jagged irregular oscillations. </li></ul>Deadtime Myths Busted in Deminar 10 Improving Loops - Part 2
  11. 11. <ul><li>Feed Maximization </li></ul><ul><ul><li>Model Predictive Control described in Application Note 1 </li></ul></ul><ul><ul><li>Override control (next slide) is used to maximize feeds to limits of operating constraints via valve position control (e.g. maximum vent, overhead condenser, or jacket valve position with sufficient sensitivity per installed characteristic). </li></ul></ul><ul><ul><li>Alternatively, the limiting valve can be set wide open and the feeds throttled for temperature or pressure control. For pressure control of gaseous reactants, this strategy can be quite effective. </li></ul></ul><ul><ul><li>For temperature control of liquid reactants, the user needs to confirm that inverse response from the addition of cold reactants to an exothermic reactor and the lag from the concentration response does not cause temperature control problems. </li></ul></ul><ul><ul><li>All of these methods require tuning and may not be particularly adept at dealing with fast disturbances unless some feedforward is added. Fortunately the prevalent disturbance that is a feed concentration change is often slow enough due to raw material storage volume to be corrected by temperature feedback. </li></ul></ul><ul><li>Profile Control </li></ul><ul><ul><li>If you have a have batch measurement that should increase to a maximum at the batch end point (e.g. maximum reaction temperature or product concentration), the slope of the batch profile of this measurement can be maximized to reduce batch cycle time. For application examples checkout “ Direct Temperature Rate of Change Control Improves Reactor Yield ” in a Funny Thing Happened on the Way to the Control Room and the Control magazine article “ Unlocking the Secret Profiles of Batch Reactors ” . </li></ul></ul>Fed-Batch and Startup Time Reduction - 4 Improving Loops - Part 2
  12. 12. Identified Responses for Fed-Batch Profile Model Predictive Control (MPC) Improving Loops - Part 2
  13. 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. 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. 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)
  16. 16. <ul><li>Reduce wait times, operator attention requests, and manual actions by automation. </li></ul><ul><li>Reduce excess hold times (e.g. heat release can confirm reaction start/end). </li></ul><ul><li>Improve charge times and accuracy by better sensor design (e.g. mass flow meters and valve location (e.g. minimize dribble time and holdup). </li></ul><ul><li>Minimize acquire time by improved prioritization of users (e.g. unit operation with biggest effect on production rate gets access to feeds and utilities). </li></ul><ul><li>Reduce failure expression activation by better instruments, redundancy and signal selection, and more realistic expectations of instrument performance. </li></ul><ul><li>Improve failure expression recovery by configuration and displays. </li></ul><ul><li>Eliminate steps by simultaneous actions (e.g. heat-up and pressurization). </li></ul><ul><li>Increase feed and heat transfer rate by an increase in pump impeller size. </li></ul><ul><li>Minimize non constrained processing time by all out run, cutoff, and coast. </li></ul><ul><li>Minimize processing time by better end point detection (inferential measurements by neural networks and online or at-line analyzers). </li></ul><ul><li>Mid batch correction based on adapted online virtual plant model or batch analytics projection to latent structures (PLS) and first principle relationships. </li></ul>Batch Sequence Time Reduction Improving Loops - Part 2
  17. 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. 18. PID Controller Disturbance Response Improving Loops - Part 2
  19. 19. Open Loop Backup Disturbance Response Open Loop Backup Improving Loops - Part 2
  20. 20. Conductivity Kicker for Evaporator Improving Loops - Part 2
  21. 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
  22. 22. Virtual Plant Opportunities Beyond Operator Training Systems (OTS) <ul><li>Dynamic simulations offer the opportunity to explore, quantify, demonstrate, detail, and prototype process control improvements (PCI). </li></ul><ul><li>However </li></ul><ul><ul><li>The investment in software and time to learn and develop simulations typically limits the creation of models to specialists who have significant simulation and DCS expertise. </li></ul></ul><ul><ul><li>Process deadtime, measurement dynamics, and valve response is often not modeled (not understood by traditional process simulation software suppliers) </li></ul></ul><ul><ul><li>The emulation of the basic and advanced control in a DCS by process simulators is unrealistic </li></ul></ul><ul><li>What is needed is a virtual plant that uses the actual DCS with all of its capability and uses dynamics of all parts of the process and automation systems in a friendly control room environment by the use of the DCS operator interface </li></ul><ul><li>The virtual plant should be useable by any one who wants to learn the best of the practical control technologies for the process industry and to find, demonstrate, estimate, and convince people of the benefits of PCI </li></ul><ul><ul><li>Automation Engineers </li></ul></ul><ul><ul><li>Local Business Partners </li></ul></ul><ul><ul><li>Process Engineers </li></ul></ul><ul><ul><li>Students </li></ul></ul><ul><ul><li>System Integrators </li></ul></ul><ul><ul><li>Suppliers </li></ul></ul><ul><li>The virtual plant offers the ability to develop, prototype, and demo the dynamic advantages of solutions, products, and services </li></ul>Improving Loops - Part 2
  23. 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. 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. 25. Virtual Plant Essentials Improving Loops - Part 2 DeltaV Simulate Product Family MiMiC Simulation Software
  26. 26. Smart Bang-Bang Lab <ul><li>Objective – Show how to reduce batch and startup time by a full throttle setpoint response (bang-bang control) </li></ul><ul><li>Activities: </li></ul><ul><ul><li>Go to Main Display and select Single Loop Lab01 </li></ul></ul><ul><ul><li>Click on PID faceplate and click on magnifying glass icon to get Detail display </li></ul></ul><ul><ul><li>Enter tuning settings: Gain = 1.7, Reset = 210 sec, Rate = 2 sec </li></ul></ul><ul><ul><li>Click on any block in block diagram and then on Process tab detail </li></ul></ul><ul><ul><li>Set primary process Delay = 9 sec, Lag 2 Inc & Lag 2 Dec = 100 sec </li></ul></ul><ul><ul><li>Set primary process Type = Integrating </li></ul></ul><ul><ul><li>Enable setpoint metrics </li></ul></ul><ul><ul><li>Make PID setpoint change from 50% to 60% </li></ul></ul><ul><ul><li>Wait for setpoint response to complete and note metrics </li></ul></ul><ul><ul><li>In PID detail, set Bang-Bang Bias = 4% </li></ul></ul><ul><ul><li>Make PID setpoint change from 60% to 50% </li></ul></ul><ul><ul><li>Wait for setpoint response to complete and note metrics </li></ul></ul>Improving Loops - Part 2
  27. 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. 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. 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. 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. 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. 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. 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
  34. 34. Linear Reagent Demand Control <ul><li>Signal characterizer translates loop 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 pH 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>Improving Neutralizer pH Control
  35. 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. 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. 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. 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. 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. 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
  41. 41. 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>Improving Neutralizer pH Control
  42. 42. 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>Improving Neutralizer pH Control 2k Gal 5k Gal 10k Gal 20k Gal 40k Gal Tank $20k $30k $50k $80k $310k Pump $25k $35k $45k $75k $140k
  43. 43. 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>Improving Neutralizer pH Control
  44. 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. 45. Case History 2 - Demineralized pH Titration Curve Slope pH Improving Neutralizer pH Control
  46. 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. 47. Case History 2 - Tuning for Conventional pH Control Improving Neutralizer pH Control
  48. 48. Case History 2 - Tuning for Reagent Demand Control Improving Neutralizer pH Control Gain 10x larger
  49. 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)
  50. 50. <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 Improving Neutralizer pH Control
  51. 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
  52. 52. <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 Improving Neutralizer pH Control
  53. 53. Neutralizer pH Control Lab <ul><li>Objective – See how optimizing setpoint can reduce reagent use </li></ul><ul><li>Activities: </li></ul><ul><ul><li>Go to Main Display, select pH Lab02b </li></ul></ul><ul><ul><li>Set Desired Run time = minimum run time </li></ul></ul><ul><ul><li>Change from Explore to Run Mode </li></ul></ul><ul><ul><li>Note process metrics when done </li></ul></ul><ul><ul><li>Click on AC1-1 PID Faceplate and change pH setpoint from 7 to 4.5 pH </li></ul></ul><ul><ul><li>Change from Explore to Run Mode </li></ul></ul><ul><ul><li>Note process metrics when done </li></ul></ul>
  54. 54.   The Top Ten Signs You are Ready for a Hawaiian Vacation <ul><li>(10) You give your boss the “hang loose” hand gesture </li></ul><ul><li>(9) You day dream about hula dancers in hardhats </li></ul><ul><li>(8) Your cubicle has a mosquito net with tropical sounds </li></ul><ul><li>(7) You bring a kayak to the company’s waste pond </li></ul><ul><li>(6) You ask “where is the company’s pupu stand”? </li></ul><ul><li>(5) You tell your secretary she is wearing a nice muumuu </li></ul><ul><li>(4) You play a ukulele in your office </li></ul><ul><li>(3) You show up to a meeting in a Hawaiian shirt, shorts and sandals </li></ul><ul><li>(2) You start answering your phone saying &quot;Aloha“ </li></ul><ul><li>(1) You wear a snorkeling mask instead of glasses </li></ul>
  55. 55. Improving Reactor Temperature Control Reactor Control Strategies
  56. 56. Improving Reactor Temperature Control Reactor Cascade Control
  57. 57. Improving Reactor Temperature Control Exothermic Reactions
  58. 58. Improving Reactor Temperature Control Reactor Valve Position Control
  59. 59. Improving Reactor Temperature Control Reactor Equilibrium Control
  60. 60. Improving Reactor Temperature Control Reactor Rate of Change Control A low
  61. 61. Improving Reactor Temperature Control Reactor Override Control
  62. 62. Reactor Temperature Control Lab <ul><li>Objective – See how optimizing setpoint can reduce coolant use </li></ul><ul><li>Activities: </li></ul><ul><ul><li>Go to Main Display, select Temperature Lab02a </li></ul></ul><ul><ul><li>Set Desired Run time = minimum run time </li></ul></ul><ul><ul><li>Change from Explore to Run Mode </li></ul></ul><ul><ul><li>Note process metrics when done </li></ul></ul><ul><ul><li>Click on TC1-1 PID Faceplate and change pH setpoint from 35 to 40 deg C </li></ul></ul><ul><ul><li>Change from Explore to Run Mode </li></ul></ul><ul><ul><li>Note process metrics when done </li></ul></ul>Improving Reactor Temperature Control
  63. 63. Improving Unit Op Temperature Control Heat Exchanger Coolant Control
  64. 64. Improving Unit Op Temperature Control Heat Exchanger By-Pass Control
  65. 65. Improving Unit Op Temperature Control Heat Exchanger Feedforward Control
  66. 66. Improving Unit Op Temperature Control Column Control by Manipulation of Distillate
  67. 67. Improving Unit Op Temperature Control Column Control by Manipulation of Reflux
  68. 68. Improving Unit Op Temperature Control Column Control by Manipulation of Steam
  69. 69. Improving Unit Op Temperature Control Column Control by Manipulation of Bottoms
  70. 70. Improving Unit Op Temperature Control Kiln Feedforward and Valve Position Control
  71. 71. Improving Unit Op Temperature Control Kiln Differential Temperature Control
  72. 72. Improving Unit Op Temperature Control Kiln Oxygen Control
  73. 73. Improving Unit Op Temperature Control Crystallizer Control
  74. 74. Improving Unit Op Temperature Control Extruder Specific Energy Control

Editor's Notes

  • The dynamic, off-line simulator is built to provide a virtual control system and plant equivalent to the on-line control system and process in operation and response. In the real plant we have a unit operation, like this distillation column. In order to operate the column safely and profitably we use a control system like DeltaV with transmitters and final control elements. In the virtual control system we use DeltaV Simulate to emulate the operator stations, engineering station, process controllers and higher level system functions. DeltaV Simulate provides an environment where the control system can run in an identical manner as in the actual plant. The transmitters, final control elements, and the process itself are simulated with MiMiC. MiMiC provide complete IO simulation and an environment where the development of complex, dynamic process models is quick and easy.
  • Provide material for this section.
  • Provide material for this section.
  • 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.