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

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. 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 Literature Review

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

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

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

10. 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.

11. 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.

12. Fuzzy Logic Control Crisp man Fuzzy man How are you going to park a car ? It’s eeeeassy……! Just move slowly back and avoid any obstacles. 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………..

13. 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.

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

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

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

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

18. Basic Artificial Neural Network

19. Feed forward ANN – a,b Feed back ANN - c

20. 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.

21. 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

22. 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.

23. Future Plans 1.Develop dedicated control systems for each component using neural network or adaptive control system. 2.Develop sensor fusion modules using neural networks to improve the quality of measured signal. 3.Develop intelligent supervisory control system for overall control, monitoring and diagnostics of the process.