PID Control Of Sampled Measurements - Greg McMillan Deminar Series
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This presentation, PID Control of Sampled Measurements, is from the first in Greg McMillan's live seminar / demo (a.k.a. deminar) series. You can watch a recorded version of this presentation at: http://www.screencast.com/t/ODhlOWY4M For future events and background, visit: http://www.emersonprocessxperts.com/archives/2010/04/free_series_of.html
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PID Control Of Sampled Measurements - Greg McMillan Deminar Series
- 1. Interactive Opportunity Assessment Demo and Seminar (Deminar) Series for Web Labs – PID Enhancement for Sampled Measurements April 7, 2010 Sponsored by Emerson, Experitec, and Mynah Created by Greg McMillan and Jack Ahlers
- 3. The Latest on Smart and Wireless Instrumentation Royalties are donated to the University of Texas Research Campus for Energy and Environmental Resources for Development of Wireless Instrumentation and Control
- 5. Self-Learning Web Lab (Web Access Starts May, 2010)
- 11. Self-Learning Web Lab (Web Access Starts May, 2010) Checkout on March 10 – April 2 Entries on Integrated and Peak Errors on the website http://www.modelingandcontrol.com/
- 12. Peak and Integrated Error Check List
- 14. Control Studies of Reset Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time) Improvement in stability is significant for any integrating process with analyzer delay Reset Factor = 0.5 Standard PID Standard PID Standard PID Reset Factor = 1.0 Reset Factor = 2.0 Enhanced PID Enhanced PID Enhanced PID Reset Factor = 0.5 Reset Factor = 1.0 Reset Factor = 2.0
- 15. Control Studies of Lambda Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time) Improvement in stability is significant for any integrating process with analyzer delay Gain Factor = 1/1.5 Standard PID Standard PID Standard PID Gain Factor = 1/2.0 Gain Factor = 1/2.5 Enhanced PID Enhanced PID Enhanced PID Gain Factor = 1/1.5 Gain Factor = 1/2.0 Gain Factor = 1/2.5
- 17. Control Studies of Reset Factor & Wireless PID for Real Time Self-Regulating Process (40 sec analyzer sample time) Improvement in stability and control is dramatic for any self-regulating process with analyzer delay Standard PID Standard PID Standard PID Reset Factor = 0.5 Reset Factor = 1.0 Reset Factor = 2.0 Enhanced PID Enhanced PID Enhanced PID Reset Factor = 0.5 Reset Factor = 1.0 Reset Factor = 2.0
- 18. Control Studies of Lambda Factor & Wireless PID for Real Time Self-Regulating Process (40 sec analyzer sample time) Improvement in stability and control is dramatic for any self-regulating process with analyzer delay Standard PID Standard PID Standard PID Gain Factor = 1/1.5 Gain Factor = 1/2.0 Gain Factor = 1/2.5 Enhanced PID Enhanced PID Enhanced PID Gain Factor = 1/1.5 Gain Factor = 1/2.0 Gain Factor = 1/2.5
- 21. 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
- 22. Virtual Plant Continuity Explore Discover Prototype Deploy The consistent platform offered by the virtual plant can insure maximum flow of knowledge gained at each step in the commercialization process from bench top to pilot plant to industrial plant operation Train
- 25. Virtual Plant Essentials DeltaV Simulate Product Family MiMiC Simulation Software
- 27. Graphics and Simulation Control
- 29. Help Us Improve These Deminars! WouldYouRecommend.Us/105679s21/
- 31. QUESTIONS?
Editor's Notes
- In the PIDPLUS algorithm, the controller responds quickly to change in the set point through proportional action and to change in the feedforward because the controller execution is kept fast. For PI on error and D on PV, the controller only computes integral and derivative contributions when there is a change in the PV reported. The integral contribution approximates a self-regulating process response if Lambda tuning is used where the reset time is set equal to the process time constant. Both the integral and derivative modes use the elapsed time since the last update in their computation instead of the scan time. This provides a longer term view and smoothing action and in particular eliminates spikes from the derivative mode. Both the traditional and wireless PIDPLUS algorithm use a positive feedback network for integral action, which has advantages for cascade and override control and deadtime compensation. Note that the “Dynamic Reset Limit” in FRSPID options must be enabled to use external reset feedback.
- In this test of a generic integrating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 50% decrease in the reset time in the Lambda tuning equation die out sooner with PIDPLUS Wireless PID is PIDPLUS
- In this test of a generic integrating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 25% decrease in the Lambda factor in the Lambda tuning equation die out quickly with PIDPLUS. The 25% decrease in Lambda factor corresponds to about a 25% increase in controller gain Wireless PID is PIDPLUS
- In this test of a generic self-regulating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 50% decrease in the reset time in the Lambda tuning equation don’t exist with PIDPLUS. The response looks as good as the response for the 1.0 reset factor Wireless PID is PIDPLUS
- In this test of a generic self-regulating process, the sample delay was deliberately chosen to put the loop on the verge of instability. Lambda tuning was used. These tests show that the excessive oscillations caused by a 25% decrease in the Lambda factor in the Lambda tuning equation don’t exist with PIDPLUS. The response looks better than the response for the 2.0 Lambda factor. Further tests indicated the controller gain could be increased to the twice the inverse of the process gain without becoming unstable. In fact the tightest control was achieved when the controller gain was the inverse of the process gain. The 25% decrease in Lambda factor corresponds to about a 25% increase in controller gain Wireless PID is PIDPLUS
- John Payne from BP used the term Demographic Time Bomb to describe the upcoming crisis in the process industry in finding experienced operations personnel. The US Department of Labor has publicly stated that the average age of energy industry workers is over 50 and half the current work force (more than 500,000 workers) are expected to retire over the next 5 to 10 years. As these workers leave industry they will also take an irreplaceable amount of knowledge and experience. Even the best knowledge transfer program will only be able to recover a fraction of the intelligence that will walk out the gate. While older operators came from a pre-digital age and operated based upon mechanical skills and experience, younger employees, in general, have less mechanical and more computer skills. The old days of operators that would know if a compressor was performing well due to the sound it makes are coming to an end. We have already seen petrochemical and energy plants that are compromised due their lack of qualified operators. One plant in particular stated that if they lost one more of their experienced operators they might not be able to run the plant. While the global economic crisis eroded peoples retirement accounts for the process industries it temporarily slowed down the pace of retirement as operations workers had to hold off on retirement plans until their portfolios recovered. As the equity markets recover, retirements plans will start again. Because, equity markets recover first many process plants will not see their own financial positions strengthen before they have to deal with this crisis. In summary, operations staffing issues are bad now and they will only get worse.
- 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.
- The next releases of MiMiC Simulation Software will allow users to build accurate dynamic simulations for operator training and control system modernization easier and quicker than any other simulation product available. MYNAH’s Advanced Modeling enhancements include the following: Modeling Objects for complex unit operations like distillation columns, separators, power house equipment that provide complete solutions for implementation of complex simulation solutions for common process applications. Modeling functions like the Flash/Thermodynamic functions that encapsulate complex chemical engineering equations in IEC1131 function blocs. Unit Operations Objects that provide easy-to-use objects for solving complex process plant unit operations. A Pressure / Flow network solver that balances the dynamics of continuous, integrated process automatically. Unlike other simulation offerings, MiMiC’s Advanced Modeling capabilities are built for Software Testing and Operator Training, scalable, and cost-effective.
- The DeltaV Simulate experience allows operator training, control system testing, on a exact replica of the real plant operator station and control system. Unlike emulated systems that may not closely resemble the actual control system, DeltaV Simulate duplicates the control system environment off-line, allowing the operator to fully understand and gain familiarity with the control system without impacting the safe and normal operation.