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Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

by Parikshit Kishor Singh, Surekha Bhanot, Harekrishna Mohanta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 9
Year of Publication: 2013
Authors: Parikshit Kishor Singh, Surekha Bhanot, Harekrishna Mohanta
10.5120/9955-4600

Parikshit Kishor Singh, Surekha Bhanot, Harekrishna Mohanta . Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table. International Journal of Computer Applications. 61, 9 ( January 2013), 16-22. DOI=10.5120/9955-4600

@article{ 10.5120/9955-4600,
author = { Parikshit Kishor Singh, Surekha Bhanot, Harekrishna Mohanta },
title = { Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 9 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number9/9955-4600/ },
doi = { 10.5120/9955-4600 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:39.317252+05:30
%A Parikshit Kishor Singh
%A Surekha Bhanot
%A Harekrishna Mohanta
%T Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 9
%P 16-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control.

References
  1. McAvoy, T. J. , Hsu, E. , and Lowenthals, S. Dynamics of pH in controlled stirred tank reactor. Ind. Eng. Chem. Process Des. Develop. 11 (Jan. 1972), 68-70.
  2. Gustafsson, T. K. and Waller, K. V. Dynamic modeling and reaction invariant control of pH. Chemical Engineering Science 38 (Mar. 1983), 389-398.
  3. Wright, R. A. and Kravaris, C. Nonlinear control of pH processes using strong acid equivalent. Ind. Eng. Chem. Process Des. Develop. 30 (Jul. 1991), 1561-1572.
  4. Corriou, J. -P. 2008 Process Control: Theory and Applications. Springer (India) Pvt. Ltd.
  5. Shinskey, F. G. 1979 Process-Control Systems: Application / Design / Adjustment. McGraw-Hill Book Company.
  6. Liptak, B. G. 2006 Instruments Engineers' Handbook: Process Control and Optimization. CRC press.
  7. Seborg, D. E. , Edgar, T. F. , and Mellichamp, D. A. 2007 Process Dynamics and Control. Wiley India (P. ) Ltd.
  8. Zadeh, L. A. Is there a need for fuzzy logic? Information Sciences 178 (Jul. 2008), 2751-2779.
  9. Zadeh, L. A. Fuzzy sets. Information and Control 8 (Jun. 1965), 338-353.
  10. Mamdani, E. H. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Computers C-26 (Dec. 1977), 1182-1191.
  11. Takagi, T. and Sugeno, M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Systems, Man, and Cybernetics SMC-15, (Jan. -Feb. 1985), 116-132.
  12. Erenoglu, I. , Eksin, I. , Yesil, E. , and Guzelkaya, M. 2006. An intelligent hybrid fuzzy PID controller. In Proceedings of the 20th European Conference on Modeling and Simulation.
  13. Saji, K. S. and Kumar, M. S. 2010. Fuzzy sliding mode control for a pH process. In Proceedings of the IEEE International Conference on Communication Control and Computing Technologies.
  14. Daroogheh, N. 2009. High gain adaptive control of a neutralization process pH. In Proceedings of the Chinese Control and Decision Conference.
  15. Bhat, N. and McAvoy, T. J. 1989. Use of neural nets for dynamic modeling and control of chemical process system. In Proceedings of the American Control Conference.
  16. Jang, J. -S. R. and Sun, C. -T. Neuro-fuzzy modeling and control. Proc. of the IEEE 83 (Mar. 1995), 378-406.
  17. Mamdani, E. H. and Assilian, S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Studies 7 (Jan. 1975), 1-13.
  18. Fuzzy Logic Toolbox User's Guide. The MathWorks Inc.
  19. Beale, M. H. , Hagan, M. T. , and Demuth, H. B. Neural Network Toolbox User's Guide. The MathWorks Inc.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy logic lookup table neural network neutralization process pH control

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