Design and Implementation of Adaptive Fuzzy Knowledge Based Control of pH for Strong Acid-Strong Base Neutralization Process
Abstract
pH control is a benchmark for control of nonlinear processes because of its importance in number of industrial process applications. Fuzzy Knowledge Based Control (FKBC) of such nonlinear processes incorporates the method for constructing nonlinear controllers using heuristic experience. This paper describes design and implementation of adaptive FKBC for a pH neutralization process consisting of strong acid (Hydrochloric acid, HCl) and strong base (Sodium Hydroxide, NaOH) streams in the multifunctional Process Control Teaching System (PCT40) with Process Vessel accessory (PCT41) and pH Probe accessory (PCT42) of Armfield® Ltd., United Kingdom. The adaptive FKBC modifies fuzzy universe of discourse by using adaptive gain matrix based on error and change in error. The adaptive FKBC has been found to operate over wide range with satisfactory performance. Results of adaptive FKBC for servo and regulatory operations have been compared with optimized fuzzy logic control schemes. The pH neutralization system is interfaced with Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW®) for experimental validation of results.References
Shinskey F.G. 1979. Process-Control Systems: Application/Design/Adjustment. McGraw-Hill, Inc., USA.
Cohen W. & Friedmann P. 1974. Adaptive Feedforward-Feedback Control of the Concentration of a Selected Ion of a Solution. Available - http://www.google.co.in/patents/US3791793.
Åström K.J. & Wittenmark B. 2008. Adaptive Control. Dover Publications, Inc., New York.
Lin J.-Y. & Yu C.-C. 1993. Automatic Tuning and Gain Scheduling for pH Control. Chemical Engineering Science. 34: 3159-3171.
Chan H.-C. & Yu C.-C. 1995. Autotuning of Gain-Scheduled pH Control: An Experimental Study. Ind. Eng. Chem. Res. 34: 1718-1729.
Klatt K.-U. & Engell S. 1996. Nonlinear Control of Neutralization Processes by Gain-scheduling Trajectory Control. Ind. Eng. Chem. Res. 35: 3511-3518.
Palancar M.C., Aragón J.M., Miguéns J.A. & Torrecilla J.S. 1996. Application of a Model Reference Adaptive Control System to pH Control. Effects of Lag and Delay Time. Ind. Eng. Chem. 35: 4100-4110.
Åström K.J., Borisson U., Ljung J. & Wittenmark B. 1977. Theory and Applications of Self-Tuning Regulators. Automatica. 13: 457-476.
Zadeh L.A. 1965. Fuzzy sets. Information and Control. 8: 338-353.
Zadeh L.A. 2008. Is there a need for Fuzzy Logic? Information Sciences An International Journal. 178: 2751-2779.
Mamdani E.H. 1974. Application of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proceedings of the Institution of Electrical Engineers. 121: 1585-1588.
Mamdani E.H. 1977. Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis. IEEE Transactions on Computers. C-26: 1182-1191.
King P.J. & Mamdani E.H. 1977. The Application of Fuzzy Control Systems to Industrial Processes. Automatica. 13: 235-242.
Procyk T.J. & Mamdani E.H. 1979. A Linguistic Self-Organizing Process Controller. Automatica. 15: 15-30.
Takagi T. & Sugeno M. 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics. SMC-15: 116-132.
Palancar M.C., Martin L., Aragón J.M. & Villa J. 2007. PD and PID Fuzzy Logic Controllers. Application to Neutralization Processes. Proceedings of European Congress of Chemical Engineering (ECCE-6). Copenhagen.
Jiayu K. Mengxiao W.,Zhongjun X. & Yan Z. 2009. Fuzzy PID Control of the pH in an Anaerobic Wastewater Treatment Process. International Workshop on Intelligent Systems and Applications, 2009 (ISA 2009). Wuhan. 1-4.
Saji K.S. & Sasi M.K. 2010. Fuzzy Sliding Mode Control for a pH Process. 2010 IEEE International Conference on Communication Control and Computing Technologies (ICCCCT). Ramanathapuram. 276-281.
Karasakal O., Guzelkaya M., Eksin I., Yesil E. & Kumbasar T. 2013. Online Tuning of Fuzzy PID Controllers via Rule Weighing based on Normalized Acceleration. Engineering Applications of Artificial Intelligence. 26: 184-197.
Heredia-Molinero M.C., Sánchez-Prieto J., Briongos J.V. & Palancar M.C. 2014. Feedback PID-like Fuzzy Controller for pH Regulatory Control near the Equivalence Point. Journal of Process Control. 24: 1023-1037.
Jang J.-H.R. & Sun C.-H. 1995. Neuro-Fuzzy Modeling and Control. Proceedings of the IEEE. 83: 378-406.
Eikens B., Karim M.N., Saucedo V. & Morris A.J. 1995. Waste Water Neutralization using a Fuzzy Neural Network Controller. Proceedings of the American Control Conference. Seattle. Vol. 4: 2662-2666.
Chen C.-L. & Chang F.-Y. 1996. Design and Analysis of Neural/Fuzzy Variable Structural PID Control Systems. IEE Proceedings Control Theory and Applications. 143: 200-208.
Adroer M., Alsina A., Aumatell J. & Poch M. 1999. Wastewater Neutralization Control based in Fuzzy Logic: Experimental Results. Ind. Eng. Chem. Res. 38: 2709-2719.
Fuente M.J., Robles C., Casado O. & Tadeo F. 2002. Fuzzy Control of a Neutralization Process. Proceedings of the 2002 International Conference on Control Applications. Glasgow. Vol. 2: 1032-1037.
Fuente M.J., Robles C., Casado O., Syafiie S. & Tadeo F. 2006. Fuzzy Control of a Neutralization Process. Engineering Applications of Artificial Intelligence. 19: 905-914.
Babuska R., Oosterhoff J., Oudshoorn A. & Bruijn P.M. 2002. Fuzzy Self-Tuning PI Control of pH in Fermentation. Engineering Applications of Artificial Intelligence. 15: 3-15.
Venkateswarlu C. & Anuradha R. 2004. Dynamic Fuzzy Adaptive Controller for pH. Chemical Engineering Communications. 191: 1564-1588.
Salehi S., Shahrokhi M. & Nejati A. 2002. Adaptive Nonlinear Control of pH Neutralization Processes using Fuzzy Approximators. Control Engineering Practice. 17: 1329-1337.
Singh P.K., Bhanot S., Mohanta H.K. & Bansal V. 2018. Design and Implementation of Intelligent Control Schemes for a pH Neutralization Process. WSEAS Transactions on Systems and Control. 13: 248-265.
