The Un-Tunable PID Control Loop Best-Practices and Innovations for Tuning Oscillatory, Noisy and Long Dead-Time Processes - PDF

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The Un-Tunable PID Control Loop Best-Practices and Innovations for Tuning Oscillatory, Noisy and Long Dead-Time Processes Robert Rice Vice President, Engineering March
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The Un-Tunable PID Control Loop Best-Practices and Innovations for Tuning Oscillatory, Noisy and Long Dead-Time Processes Robert Rice Vice President, Engineering March Agenda Economic Drivers Real-World Challenges Tuning Demystified Real-World Successes Closing Thoughts Economic Drivers Process Automation: A State-of-the-State Assessment The Amazing Problem-Free Plant Michael Brown Control Engineering 85% of controllers perform inefficiently when operated in automatic mode 65% of controllers are poorly tuned to mask control-related problems 30% of PID control loops are operated in manual mode 20% of control systems are not properly configured to meet their objectives Economic Drivers Top Line and Bottom Line Benefits Invest in Control Payback in Profits Carbon Trust 2 5% 5 10% 5 15% 25 50% Production Throughput Production Yield Energy Consumption Production Defects Economic Drivers Missed Opportunities for Financial Gain Annual Production & Efficiency Losses Control Station, Inc. $7.6 Million $5.0 Million $1.8 Million $8.0 Million Basic Materials Chemicals Power & Utilities Oil & Gas Agenda Economic Drivers Real-World Challenges Tuning Demystified Real-World Successes Closing Thoughts Real-World Challenges The Black Art of PID Controller Tuning Limited Education Chemical Engineering curriculum Single semester totaling 16 hours Not covered by most trade schools Focus on PLC programming Limited Experience Few staff tasked with PID tuning Methods handed down No formalized approach or methodology Out-of-the-box parameters applied Limited Emphasis Other projects deemed more important Real-World Challenges The Devil is in the Data Noise Wait for it Oscillations Wait for it Dead-Time Real-World Challenges Where to Turn? Economic drivers Clear opportunities for improvement Strong financials: Payback, ROI Training & experience Limited skilled resources Pool of candidates drying up Traditional state-of-the-art software Struggles under real-world conditions Agenda Economic Drivers Real-World Challenges Tuning Demystified Real-World Successes Closing Thoughts PID Controller Tuning Demystifying the Process Find Step Model Tune Test Document Identify the Controller and Specify the DLO and Control Objective Perform a Bump Test and Collect Dynamic Process Data Fit a Model to the Process Data Use Tuning Correlations to Calculate Tunings Based on Model Implement and Test results Document the Tuning Process 11 Tuning Recipe: A Simplified, Repeatable Process How do you identify PID control loops that need to be tuned? Reactive: Respond to the Operator s Needs Proactive: Analyze Process Data to Identify PIDs that Contribute to Increased Process Variability Proactive monitoring should: Identify Mechanical, Process and Controller Tuning Issues Facilitate Root-Cause Detection Recommend Appropriate Corrective Action Track and Report Findings Step 1: Find Controller, Specify Objective Good Control is SIMPLE Step 1: Find Controller, Specify Objective Reflux Drum Level Control Example What is/are the primary Control Objective(s)? Maintain Liquid Level In the Reflux Drum Maintain Column Stability Prevent Environmental Release by Avoiding Drum Hi Limit Step 2: Step or Bump the Process Data should show Cause and Effect A bump test must generate a response that clearly dominates the random (noisy) PV behavior Here the PV moves approximately four (4) times the noise band a good value Step 2: Step or Bump the Process Good bump tests Open loop tests require the Controller Output to be stepped Closed loop tests require a sharp Controller Output change Step 2: Step or Bump the Process Bad bump tests AVOID Disturbance-Driven Data & Slow Ramping CO Changes Step 2: Step or Bump the Process Types of process behavior Self-Regulating If all inputs are held constant, the process will seek a steady-state Example: Heat Exchanger Non Self-Regulating Process will only reach a steadystate at its balancing point Example: Surge Tank Step 2: Step or Bump the Process Simple First Order Models Self-Regulating Non Self-Regulating K P Process Gain [ PV ] CO Ƭ P Time Constant [time] θ P Dead-Time [time] PV K* P Integrator Gain [ time CO ] θ P Dead-Time [time] All models are wrong, some are useful George Box Step 3: Fit a Process Model First Order Plus Dead-Time (Self-Regulating Model) Process Time Constant How Fast How Fast does it take the PV to reach 63% of its total change Process Gain How Far How Far does the PV Move for Change in the Output Process Dead- Time How Much Delay How much delay is there from when the CO is changed until the PV first moves 63% Step 3: Fit a Process Model First Order Plus Dead-Time (Non Self-Regulating Model) Integrating Process Gain How Far and How Fast How Far and How Fast does the PV Move when the CO is moved from its balancing point Process Dead-Time How Much Delay How much delay is there from when the CO is changed until the PV first moves Step 3: Fit a Process Model Tunings are only as good as the model Manual or Auto-Tune Approaches Sufficient for Simplest of Controllers Software Modeling Much More Robust Open Loop and Closed Loop Noisy and Non-Steady State (NSS) Conditions Step 4: Tune the PID Control Loop 1 First compute, Ƭ C, the Closed Loop Time Constant A small Ƭ C provides an aggressive or quick response Choose your performance using these rules: Aggressive: Ƭ C is the larger of 0.1Ƭ p or 0.8θ p Moderate: Ƭ C is the larger of 1Ƭ p or 8θ p Conservative: Ƭ C is the larger of 10Ƭ p or 80θ p PI tuning correlations use this and the FOPDT model values: and Step 4: Tune the Level PID Control Loop IMC tuning correlation: Depending PID, Non Self-Regulating Process 1 The Closed Loop Time Constant,, should be as large as possible but still fast enough to arrest or recover from a major disturbance. PI tuning correlations use this and the FOPDT Integrating model values: 1 2 2 Step 4: Tune the PID Control Loop Closed Loop Time Constant rules of thumb: Flow Loops 3 to 5 times the Open Loop Time Constant, Pressure Loops 2 to 4 times the Open Loop Time Constant, Temperature Loops 1 to 3 times the Open Loop Time Constant, Step 4: Tune the PID Control Loop Expected PI Controller Response: Conservative Moderate Aggressive Set Point tracking (servo) response as changes Copyright 2007 by Control Station, Inc. All Rights Reserved. Step 4: Tune the PID Control Loop Challenges of PI Control: Self-Regulating Processes K c *2 Base Case Performance K c K c /2 2 Copyright 2007 by Control Station, Inc. All Rights Reserved. T i /2 T i T i *2 Step 4: Tune the PID Control Loop Challenges of PI Control: Non Self-Regulating Processes K c *2 K c K c /2 T i /2 T i T i *2 Step 4: Tune the PID Control Loop PI vs. PID Set Point tracking response PID shows decreased oscillations compared to PI performance PID has somewhat: Shorter Rise Time Faster Settling Time Smaller Overshoot Step 5: Implement and Test Results Modified tuning parameters must be tested Testing PID Controllers Typically Involve: Adjust Set-Point to ensure adequate tracking Did the Process Variable overshoot? Did the Controller Output move too much? Introduce a Load Change or Disturbance Did the Process Variable recover quick enough? NOTE: PID controllers work off of controller error (SP-PV). If there is no error, there is nothing for the PID controller to do. You MUST introduce controller error and force the controller to respond before it can be determined if the tuning changes actually improved the system. Step 6: Document, Document, Document Who: Who is accountable for the change(s)? What: Which loop was tuned? What were the As Found and Recommended tuning values? When: When was the loop adjusted? Why: Why was this particular loop tuned? Industrial-Grade Software for Real-World Applications How do you identify PID control loops that need to be tuned? Reactive: Respond to the Operator s Needs Proactive: Analyze Process Data to Identify PIDs that Contribute to Increased Process Variability Proactive monitoring should: Identify Mechanical, Process and Controller Tuning Issues Facilitate Root-Cause Detection Recommend Appropriate Corrective Action Track and Report Findings Agenda Economic Drivers Real-World Challenges Tuning Demystified Real-World Successes Closing Thoughts Case Study: Praxair Continuous Improvement & Process Optimization Praxair, Inc. The largest industrial gases company in North and South America and one of the largest worldwide. Over 400 Cryogenic Plants Worldwide On-stream reliability of 99% Standardized on Rockwell Automation Process Controllers Standardized on LOOP-PRO TUNER PID tuning software across all regions The following 2 PID controllers alone contributed between $75K-$100K USD / year of savings Case Study: Known Underperformers Continuous Improvement & Process Optimization Example #1: LIQUID LEVEL CONTROL Instability occurred at lower levels making PID tuning difficult Control the level at a reasonable value (i.e. lower is better) Before: Highly noisy PV Process safety and efficiency impact Impact Stable control at lower value Savings: ~1% higher process efficiency BEFORE 0:01 1:37 3:13 4:49 6:25 8:01 9:37 11:13 12:49 14:25 16:01 17:37 19:13 20:49 22:25 AFTER 0:01 1:31 3:01 4:31 6:01 7:31 9:01 10:31 12:01 13:31 15:01 16:31 18:01 19:31 21:01 22:31 Case Study: Known Underperformers Continuous Improvement & Process Optimization Example #2: MIXING VALVE CONTROL Mix two flows with different specifications (higher is better) Before: Poor tuning. Once in Auto, nearly tripped the plant. As a result, most of time in Manual, with low PV. Process safety and low product recovery impact Impact Change PID loop from Manual to Auto; Stabilize control at higher SP Savings: 2% product recovery increase :01 0:24 0:47 1:10 1:33 1:56 2:19 2:42 3:05 3:28 3:51 4:14 4:37 5:00 5:23 5:46 SP PV OT 0:01 0:24 0:47 1:10 1:33 1:56 2:19 2:42 3:05 3:28 3:51 4:14 4:37 5:00 5:23 5:46 PlantESP TuneVue Continuously Watches for Suitable Data For Analysis and Recommends Tunings Parameters Including SP Changes, Manual Bump Tests No configuration required for setting noise limits, minimum step size or window length Model Fits are Generated using full Non Steady State (NSS) Modeling Innovation Tuning Parameters Generated for each loop based on the criteria specified by the user (Fast/Slow, Slider Bar) Reports/Alerts Generated based on Deviation from Recommended Tunings Case Study Models and Tuning Range Automatically Determined Level Control of Medium Pressure Steam Separator TuneVue Used Existing Set-Point Changes to Identify A Suitable Tuning Parameter Range Agenda Economic Drivers Real-World Challenges Tuning Demystified Real-World Successes Closing Thoughts Closing Thoughts Demystify PID controller tuning Apply a proven, repeatable recipe Integrate the procedure with existing processes Apply industrial-grade technologies Eliminate the steady state requirement Leverage advanced heuristics Proactively address performance issues Improve plant-wide awareness Identify problems, isolate root-causes Questions Robert Rice, PhD Vice President, Engineering November
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