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Modeling pH Neutralization Process using Fuzzy Dynamic Neural units Approaches

by Lyes Saad Saoud, Faycal Rahmoune, Victor Tourtchine, Kamel Baddari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 4
Year of Publication: 2011
Authors: Lyes Saad Saoud, Faycal Rahmoune, Victor Tourtchine, Kamel Baddari
10.5120/3375-4666

Lyes Saad Saoud, Faycal Rahmoune, Victor Tourtchine, Kamel Baddari . Modeling pH Neutralization Process using Fuzzy Dynamic Neural units Approaches. International Journal of Computer Applications. 28, 4 ( August 2011), 22-29. DOI=10.5120/3375-4666

@article{ 10.5120/3375-4666,
author = { Lyes Saad Saoud, Faycal Rahmoune, Victor Tourtchine, Kamel Baddari },
title = { Modeling pH Neutralization Process using Fuzzy Dynamic Neural units Approaches },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 4 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number4/3375-4666/ },
doi = { 10.5120/3375-4666 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:13:53.066702+05:30
%A Lyes Saad Saoud
%A Faycal Rahmoune
%A Victor Tourtchine
%A Kamel Baddari
%T Modeling pH Neutralization Process using Fuzzy Dynamic Neural units Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 4
%P 22-29
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to its large existence in the real industrial life and it is a realistic dynamic nonlinear system to demonstrate the feasibility and the performance of the founding results using the fuzzy dynamic neural units. A comparison was made between four strategies, the fuzzy modeling, the recurrent neural networks, the dynamic recurrent neural networks and the fuzzy dynamic neural units.

References
  1. G. Lightbody, G. Irwin. 1997. Nonlinear control structures based on embedded neural system models. IEEE Trans. on Neural Networks, 8, pp. 553–567.
  2. Narendra, K. S. and Parthasarathy, K. 1990. Identification and Control Of Dynamical Systems Using Neural Networks. IEEE Trans. on Neural Networks, 1 (1), 4-27.
  3. Patan, K. 2008. Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes. Springer-Verlag Berlin Heidelberg.
  4. Roffel, B. and Betlem, B. 2006. Process Dynamics and Control, Modeling for Control and Prediction. John Wiley & Sons.
  5. Babuska, R. 1998. Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston.
  6. Aoyama A., Doyle III F.J. and Venkatasubramanian V. 1995. A Fuzzy Neural-network Approach for Nonlinear Process Control’, Engineering Applications of Artificial Intelligence, 8(5), 483-498.
  7. Vieira, J., Dias, F.M. and Mota, A. 2004. Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems a comparative study’, Engineering Applications of Artificial Intelligence, 17, 265–273.
  8. Zhang, J. and Morris, A.J. 1995. Fuzzy neural networks for nonlinear systems modeling. IEE Proceedings on Control Theory Applications, 142(6), 51-561.
  9. Jang, JS. R., Sun CT. and Mizutani, E. 1997. Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. Prentice Hall.
  10. Alavala, C.R. 2008. Fuzzy Logic and Neural Networks Basic Concepts and Applications. New Age International Publishers.
  11. Nelles, O. 2001. Nonlinear system identification from classical approaches to neural networks and fuzzy models. Springer-Verlag.
  12. Rutkowski, L. 2004. Flexible Neuro Fuzzy Systems Structures Learning and Performance Evaluation. Kluwer Academic Publishers, Boston.
  13. Gupta, M.M. Jin, L. Homma, N. 2003. Static and dynamic neural networks: from fundamentals to advanced theory. Wiley-IEEE Press, New York.
  14. Ayoubi, M. 1994. Nonlinear dynamic systems identification with dynamic neural networks for fault diagnosis in technical protests. In: IEEE international conference systems man and Cyberntics SMC’94 USA, 2120–2125.
  15. Saad Saoud, L. and Khellaf, A. 2009. Identification and Control of a Nonlinear Chemical process Plant Using Dynamical Neural Units.’ Third International Conference on Electrical Engineering Design and technologies, Tunisia, October 31- November 2.
  16. Yeo, Y. K. and Kwon, T. I. 2004. Control of pH Processes Based on the Genetic Algorithm’, Korean Journal of Chemical Engineering, 21(1), 6-13.
  17. Lazar, C. Pintea, R. and De Keyser R. 2007. Nonlinear predictive control of a pH process’. 17th European Symposium on Computer Aided Process Engineering, 24, 829-834.
  18. N Bhat, N. and McAvoy, T.J. 1990. Use of neural nets for dynamic modeling and control of chemical process systems’. Computers and Chemical Engineering, 14(4/5), 573-583.
  19. Loh, A.P. Looi, K.O. and Fong, K.F. 1995. Neural network modelling and control strategies for a pH process. Journal of Process Control, 5(6), 355-362.
  20. Nie, J. Loh, A.P. and Hang, C.C. 1996. Modeling pH neutralization processes using fuzzy-neural approaches’. Fuzzy Sets and Systems, 78, 5-22.
  21. Li, N. Li, S. and Xi, Y. 2001. Modeling pH Neutralization Process using Fuzzy Satisfactory Clustering. Fuzzy Systems, 308 – 311.
  22. Chen, J. and Huang, T.C. 2004. Applying neural networks to on-line updated PID controllers for nonlinear process control’. Journal of Process Control, 14, 211–230,
  23. Akesson, B.M., Toivonen, H.T. and Waller, J.B. 2005. Neural network approximation of a nonlinear model predictive controller applied to a pH neutralization process. Computers and Chemical Engineering, 29, 323–335.
  24. Duan, S., Shi, Z., Feng, H. Duan, Z. and Mao, Z. 2006. An on-line adaptive control based on DO-pH measurements and ANN pattern recognition model for fed-batch. Biochemical Engineering Journal, 30, 88–96.
  25. Norquay, S.J., Palazoglu, A. and Romagnoli, J.A. 1999. Application of Wiener model predictive control (WMPC) to a pH neutralization experiment. IEEE Transactions On Control Systems Technology, 7(4), 437 – 445.
  26. Gomez, J.C., Jutan, A. and Baeyens, E. 2004. Wiener model identification and predictive control of a pH neutralization process. IEE Proceedings on Control Theory Applications, 151(3), 329 - 338.
  27. Arefi, MM., Montazeri, A., Poshtan J. and Jahed-Motlagh, MR. 2006. Nonlinear Model Predictive Control of Chemical Processes with a Wiener Identification’. IEEE International Conference on Industrial Technology, 15-17 (Dec. 2006), 1735 – 1740, Mumbai.
  28. McAvoy, T., Hsu E. and Lowenthal, S. 1972. Dynamics of pH in CSTRs. Industrial and Engineering Chemistry Process Design and Development, 11, 68-70.
  29. Saad Saoud, L. and Khellaf, A. 2011. A Neural Network Based on an Inexpensive Eight Bit Microcontroller. Neural computing and application, 20(3), 329-334.
  30. Widrow, B. and Holt, M. 1960. Adaptive Switching Circuits’. IRE WESCON Convention Record., New York, 96-104.
  31. Chafaa, K., Ghanai, M. and Benmahammed, K. 2007. Fuzzy modelling using Kalman filter. IET Control Theory Applications,, 1(1), 58-64.
  32. Karaboga, D. and Ozturk, C. 2010. Fuzzy clustering with artificial bee colony algorithm’. Scientific Research and Essays, 5(14), 1899-1902.
  33. Gustafson, D.E. and Kessel, W.C. 1979. Fuzzy clustering with a fuzzy covariance matrix. In Proc. IEEE CDC, 761-766, San Diego, CA, USA,
  34. Babuska, R. Roubos J.A. and Verbruggen, H.B. 1998. .Identification of MIMO systems by input-output TS fuzzy models. The IEEE World Congress on Computational Intelligence, Vol.1, (4-9 May 1998), 657 – 662.
  35. Zhang, H. and Liu, D. 2006. Fuzzy Modeling and Fuzzy Control. Birkhauser, Boston,
  36. Zhu, Y. 2001. Multivariable System Identification for Process Control. Pergamon, An imprint of Elsevier Science.
  37. Doherty, S. K. 1999. Control of pH in chemical processes using artificial neural networks. Ph. D thesis, School of Engineering, Liverpool John Moores University,
  38. Mokhtari, M. and Marie, M. 1998. Applications of Matlab 5 and Simulink 2. Springer-Verlag, France (in French).
Index Terms

Computer Science
Information Sciences

Keywords

pH process Dynamic neural units Nonlinear system identification Fuzzy modeling

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