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TUSTP 2003

TUSTP 2003. Intelligent Control of Compact Separation System. by Vasudevan Sampath. May 20, 2003. Overview. Objectives Literature Review Compact Separation System Review of Control System Development Fuzzy Logic System Artificial Neural Network System

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TUSTP 2003

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  1. TUSTP 2003 Intelligent Control of Compact Separation System by Vasudevan Sampath May 20, 2003

  2. Overview • Objectives • Literature Review • Compact Separation System • Review of Control System Development • Fuzzy Logic System • Artificial Neural Network System • Future Plans

  3. Objectives • Conduct a detailed study on advanced control systems like fuzzy logic, neural network etc. and study their suitability for compact separation system. • Develop an intelligent control strategy for compact separation system and conduct dynamic simulation and experimental investigation on the developed strategy.

  4. Literature Review Control System Studies: • Wang (2000) : Dynamic Simulation, Experimental Investigation and Control System Design of GLCC • Dorf & Bishop (1998): Modern Control Systems • Grimble (1994): Robust Industrial Control • Friedland (1996): Advanced Control System Design

  5. Literature Review Fuzzy Logic and Neural Networks: • McNeill and Thro (1994): Fuzzy Logic • Leondes (1999): Fuzzy Theory Systems – Techniques and Applications • Terano, Asai and Sugeno (1994): Applied Fuzzy Systems • Passino and Yurkovich (1998): Fuzzy Control • Reznik (1997): Fuzzy Controllers

  6. LC Oil Oil PC Clean Gas Clean Gas WCC WCC WCC LC LC LC Pump Pump Pump WCC FC FC FC PRC PRC PDC LLCC LLCC PRC PDC Hydrocyclones Hydrocyclones Compact Separation System 1 LC-Level Control PC-Pressure Control WCC-Water cut Control FC-Feed Control PDC-Press. Diff. Control Oil Rich Oil Rich Clean Clean LC LC Pipe Type Pipe Type PC PC Separator Separator GLCC (Scrubber) GLCC (Scrubber) Manifold Slug Damper WCC WCC Water Rich Water Rich GLLCC (3 GLLCC (3 - - phase) phase) Clean Water

  7. LC LC Oil Oil PC PC WCC WCC PRC Compact Separation System 2 LC-Level Control PC-Pressure Control WCC-Water cut Control FC-Feed Control PDC-Press. Diff. Control Gas Stream Clean Gas Clean LC LC Pipe Type GLCC (Scrubber) Separator Pump Pump Manifold Slug Damper FC WC PRC PDC LLCC LLCC Liquid Stream PDC PRC Hydrocyclones Hydrocyclones Clean Water GLCC

  8. Control System Development Stages • 1st Stage: Frequency –response design methods for scalar systems by Nyquist, Bode • 2nd Stage: The state-space approach to optimal control and filtering theory • 3rd Stage: Multivariable systems by frequency-domain design methods (MIMO) • 4th Stage: Robust design procedures - H design philosophy • 5th Stage: Advanced techniques – Fuzzy Logic, Neural Networks, Artificial Intelligence.

  9. Adaptive Versus Robust Control • Adaptive Control – Estimates parameters and calculates the control accordingly. Involves online design computations, difficult to implement. • Robust Control – This allows for uncertainty in the design of a fixed controller, thus, producing a robust scheme, which is insensitive to parameter variations or disturbances. H robust control philosophy provides optimal approach to improve robustness of a controlled system.

  10. Limitations of Conventional Controllers • Plant non-linearity: Nonlinear models are computationally intensive and have complex stability problems. • Plant uncertainty: A plant does not have accurate models due to uncertainty and lack of perfect knowledge. • Uncertainty in measurements: Uncertain measurements do not necessarily have stochastic noise models. • Temporal behavior: Plants, Controllers, environments and their constraints vary with time. Time delays are difficult to model.

  11. Fuzzy Logic Control How are you going to park a car ? You have to switch to reverse, then push an accelerator for 3 minutes and 46 seconds and keep a speed of 15mph and move 5m back after that try……….. It’s eeeeassy……! Just move slowly back and avoid any obstacles. Crisp man Fuzzy man

  12. Benefits of Fuzzy Logic Controller • Can cover much wider range of operating conditions than PID and can operate with noise and disturbance. • Developing a fuzzy logic controller is cheaper than developing a model-based controller. • Fuzzy controllers are customizable. Since it is easier to understand and modify their rules.

  13. Operation of Conventional Controller Input Output PID Controller PLANT Feedback Signal

  14. Inference mechanism Fuzzification Defuzzification Rule-base Operation of Fuzzy Logic Controller Reference Input r(t) Input u(t) Output PLANT

  15. Choosing Inputs Measuring Inputs Input scaling factors Scaling Inputs Inputs membership functions Fuzzification Fuzzy rules Fuzzy Processing Outputs Membership functions Defuzzification Outputs Scaling factors Scaling Outputs PLANT Fuzzy Controller Operation

  16. Neural Network Process Control Loop Input Output Sensing System Plant Operating System Neural Network Analysis System Neural Network Decision System

  17. Basic Artificial Neural Network

  18. Basic Artificial Neural Network Feed forward ANN – a,b Feed back ANN - c

  19. Advantages of Neural Network • Simultaneous use of large number of relatively simple processors, instead of using very powerful central processor. • Parallel computation enables short response times for tasks that involve real time simultaneous processing of several signals. • Each processor is an adaptable non linear device.

  20. Neuro Fuzzy Systems • Neural Networks are good at recognizing patterns, not good at explaining how they reach that decision • Fuzzy logic are good at explaining their decision but they cannot automatically acquire the rules they use to make those decisions • Central hybrid system which can combine the benefits of both are used for intelligent systems • Complex domain like process control applications require such hybrid systems to perform the required tasks intelligently • In theory neural network and fuzzy systems are equivalent in that they are convertible, yet in practice each has its own advantages and disadvantages

  21. Applications • Fuzzy Logic and Neural Network applications to compact separation system: • Dedicated control system for each component, like GLCC or LLCC • Sensor fusion – improvement in reliability and robustness of sensors • Supervisory control – intelligent control system with diagnostics capabilities.

  22. Future Plans • Develop dedicated control systems for each component using neural network or adaptive control system. • Develop sensor fusion modules using neural networks to improve the quality of measured signal. • Develop intelligent supervisory control system for overall control, monitoring and diagnostics of the process.

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