Advanced control foundation tools and techniques

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A new book on advanced control fundamental will be published by ISA in September, 2012. This book addresses all the advanced control products that are included in the DeltaV control system or are planned for a future DeltaV release. In this session two of the authors present and discuss key areas addressed in the book and demonstrate web site that accompanies the book.

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Advanced control foundation tools and techniques

  1. 1. Advanced Control Foundation - Tools & Techniques Terry Blevins – Principal Technologist Willy Wojsznis – Senior Technologist Mark Nixon – Director, Research
  2. 2. Presenters  Terry Blevins  Willy Wojsznis
  3. 3. Introduction  Over the last 10 years significant improvements made in advanced control tool capabilities and in user interfaces, improvements that make it easier to design and commission advanced control solutions.  Also since then, new advanced control applications have been introduced for batch and continuous processes.  The book Advanced Control Foundation – Tools, Techniques, and Applications provides a fresh look at some of the latest advanced control technologies that are available to the process industry. A web site for the book allows the solutions to the book’s workshops to be viewed using a web browser.
  4. 4. Areas to be Addressed This session will focus on how advanced control technique may be used to improve process operations. Areas that will be addressed are:  Maximizing Return on Control System Investment  Evaluating Control System Performance  On-demand Tuning  Adaptive Tuning  Fuzzy Logic Control  Intelligent PID  Neural Networks for Property Estimation  Batch and Continuous Data Analytics  Simple MPC  MPC Integrated with Optimization  On-line Optimization  Process Simulation, Integrating Advanced Control Into a DCS
  5. 5. Basis for Presentation  Material that will be presented is based on Advanced Control Foundation.  This book was published in Sept 2012 by ISA and addresses the advanced control products in the DeltaV control system (or targeted for a future DeltaV release).  The book is available in the ISA bookstore or may be purchased on-line through ISA - see
  6. 6. Basis for Presentation (Cont) The solution to workshop included in the book may be viewed using your web browser- see http://www.advancedcontrolfoundation.com/
  7. 7. Maximizing Return on ControlSystem Investment  By reduce process variation, production rate or quality parameter targets may be shifted. This simple concept often justifies control upgrade.  When target control performance cannot be achieved using Single-loop PID feedback and multi- loop traditional control techniques then advanced control techniques may be required.
  8. 8. Example
  9. 9. Evaluating Control SystemPerformance  The first step in improving control is to insure all controls are operating as designed.  Performance monitoring tools may be used to quickly identify when control is not being utilized i.e. control is on manual.
  10. 10. Evaluating Control SystemPerformance (Cont)  Report generation is an important feature of performance monitoring tools.  Reports can be used to gain the management support that is required to address low control utilization or to determine the source of excessive process variation that impacts production or product quality.
  11. 11. Evaluating Control SystemPerformance (Cont)  A plant equipped with the latest control systems and field instrumentation may still be found to have low control utilization.  Often the key to getting control loops back on automatic is for plant management to be aware of the low control utilization and its impact on product quality and production rate.
  12. 12. Resolving Problems that ImpactControl Utilization
  13. 13. On-demand Tuning  Where low control utilization is due to PID tuning, then On-demand tuning may be used to commission the loop.  Capturing process dynamics in the field (controller or device) allows better process identification, particularly for the fastest loops.
  14. 14. On-demand Tuning (Cont)  When using an on- demand tuning application, consider that the process gain may change with the operating conditions.  For robust control, tuning should be based on the operating conditions that provide maximum process gain.
  15. 15. Adaptive Tuning  In some cases, the tuning established at one operating point may not provide the best control for the full operating range.  Adaptive tuning allows the process gain and dynamics to be automatically identified and used in control.
  16. 16. Viewing Identified Models
  17. 17. Predefined Fuzzy Logic ControlFunction Block
  18. 18. Fuzzy Logic Control  For some specific process applications, fuzzy logic control enables faster setpoint recovery with less overshoot than PID control for both setpoint and load changes  Fuzzy logic is best suited for controlling processes characterized by large time constants and little or no deadtime
  19. 19. Fuzzy Logic Control WorkshopDemo
  20. 20. Intelligent PIDRecovery From Process Saturation  The PIDPlus provides quicker recover from process saturation.  Also, the PIDPlus allow the non-periodic, slow measurement values provided by a wirelessControl Using WirelessTransmitter device to be used in closed loop control.
  21. 21. Compressor Surge Control
  22. 22. Compressor Surge Control (Cont) Control Response with Preload Applied Control Response with Variable Preload
  23. 23. Bioreactor with WirelessInstrumentation
  24. 24. Neural Networks for PropertyEstimation  When a product quality measurement is available only from the lab, it is often possible to use upstream measurements to calculate an estimated value.  To address the non- linear response of product quality parameters to changes in process inputs, the estimator can be based on a neural network model.
  25. 25. Continuous Digester Example
  26. 26. Batch and Continuous Data Analytics Through the use of on-line data analytics, it is possible to provide:  Product quality predictions which allow quality problems to be identified while there is time to take corrections action.  Detection of abnormal process operation and/or equipment problems and support of root cause analysis
  27. 27. Continuous Example – Static Mixer
  28. 28. Batch Example
  29. 29. Simple MPC MPC has proven advantages over multi-loop PID control techniques in a variety of small applications that are characterized by:  Long process delay and interaction,  Measured disturbances or constraints  Production is limited by process input(s)  Embedded MPC capability in the control system.is an advantage
  30. 30. Replacing PID with MPC One Measured Disturbance Input MPC Constraint Control
  31. 31. MPC Integrated with Optimization  For larger, more complex applications that are used in batch or continuous processing, the plant operation objective(s) may be best met using MPC integrated with optimization.  Such applications are often MEE Process characterized by numerous operating constraints and the need to address broader operating objectives, such as maximizing throughput while minimizing production costs  Installation examples are included in this chapter CTMP Process
  32. 32. CTMP Refiner Process Step Response
  33. 33. On-line Optimization  Examples are provided of on-line LP (linear programming) optimizer to minimize the cost of power generation.  On-line operation was achieved by using MPC with an integrated optimizer; however,the MPC functionality was disabled.  The economic benefits achieved from on-line optimization applications indicate use of online optimization will be expanding in the coming years.
  34. 34. Workshop for On-line Optimization
  35. 35. Process Simulation  Dynamic process simulation can be a useful tool when working with basic as well as advanced control techniques  Such simulations can easily be created using the tools that exist in most modern control systems.  The steps for developing process simulations starting with the P&ID are described in detail.
  36. 36. Integrating Advanced Control Into a DCS When advanced control is embedded in the distributed control system (DCS), the plant operator has a single window interface with consistent system interaction and single log-in and span of control. If the DCS does not support advanced control, then the advanced control applications must be layered onto the DCS. Several approaches may be taken depending on the DCS support for layered applications.
  37. 37. Business Results Achieved The economics of plant operation can be impacted by process variation when production is limited by equipment capacity or when maximum production and operating efficiency are achieved at a specific operating condition.  Through the application of advanced control techniques such as performance monitoring, on-demand and adaptive tuning, fuzzy logic control, intelligent PID, and MPC, it is often possible to reduce variations in process operation and to shift and maintain the plant to a more efficient point of operation.  Data analytics may be used to improve batch and continuous process operation through the on-line prediction of quality parameter and fault detection.
  38. 38. Summary Advanced control techniques and tools should be considered when:  The control objectives cannot be achieved through the improvement of traditional control techniques.  Traditional control strategies are more difficult to maintain at optimal performance because of their complexity. Advanced control products are available as embedded applications within modern process control systems or as layered applications that may be added to older control systems.
  39. 39. Where To Get More Information  Web site for Advanced Control Foundation workshop Solutions - see: http://www.advancedcontrolfoundation.com/  Information on the Book - see: http://modelingandcontrol.com/2012/08/advanced- control-foundation-coming-soon/  ISA Web Site on the Book - see: http://modelingandcontrol.com/2012/09/advanced- control-foundation-isa-web-site/
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