Neural network inverse model-based controller for the control of a steel pickling process

Daosud, Wachira and Thitiyasook, Piyanuch and Arpornwichanop, Amornchai and Kittisupakorn, Paisan and Hussain, Mohd Azlan (2005) Neural network inverse model-based controller for the control of a steel pickling process. Computers & Chemical Engineering, 29 (10). pp. 2110-2119. ISSN 0098-1354, DOI

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The present work investigates the use of neural network direct inverse model-based control strategy (NNDIC) to control a steel pickling process. The process is challenging due to the fact that the pH of effluent streams must be regulated accurately to protect aquatic and human welfare, and to comply with limits imposed by legislation. At the same time, the concentration of acid solution in the pickling step needs to be maintained at the optimum value in order to obtain the maximum reaction rate. Various changes in the open-loop dynamics are performed before implementation of the inverse neural network modeling technique. The optimal neural network architectures are determined by the mean squared error (MSE) minimization technique. The robustness of the proposed inverse model neural network control strategy is investigated with respect to changes in disturbances, model mismatch and noise effects. Simulation results show the superiority of the NNDIC controller in the cases involving disturbance, model mismatch and noise while the conventional controller gives better results in the nominal case.

Item Type: Article
Additional Information: Cited By (since 1996): 15 Export Date: 5 March 2013 Source: Scopus CODEN: CCEND Language of Original Document: English Correspondence Address: Kittisupakorn, P.; Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand; email: References: Hussain, M.A., Review of the applications of neural networks in chemical process control-simulation and online implementation (1999) Artificial Intelligence in Engineering, 13, p. 55; Hussain, M.A., Adaptive inverse model control of a continuous fermentation process using neural networks (2001) Application of Neural Networks and Other Learning Technologies in Process Engineering, pp. 199-216. , I. M. Mujtaba, & M. A. Hussain (Eds.) London: Imperial College Press; Hussain, M.A., Kershabaem, L.S., Implementation of inverse-model-based control strategy using neural networks on a partially simulated exothermic reactor (2000) Transactions of the Institution of Chemical Engineers (Part A), 78, p. 299; Hussain, M.A., Ng, C.W., Aziz, N., Mujtaba, I.M., Neural network techniques and applications in chemical process control systems (2003) Intelligent Systems Techniques and Applications, 5. , CRC Press NJ; Kittisupakorn, P., Kaewpradit, P., Integrated data reconciliation with generic model control for the steel pickling process (2003) Korean Journal Chemical Engineering, 20, p. 985; Nahas, E.P., Henson, M.A., Seborg, D.E., Non-linear internal model control strategy for neural network models (1992) Computers and Chemical Engineering, 16, p. 1039; Palancar, M.C., Aragon, J.M., Torrecilla, J.S., PH-control system based on artificial neural networks (1998) Industrial and Engineering Chemistry Research, 37, p. 2729; Piovoso, M.J., Williams, J.M., Self-tuning pH control: A difficult problem, an effective solution (1985) Intech, p. 45; Pottmann, M., Seborg, D.E., A non-linear predictive control strategy based on radial basis function models (1997) Computers and Chemical Engineering, 21, p. 965; Pourboghrat, F., Pongpairoj, H., Liu, Z., Farid, F., Aazhang, B., Dynamic neural networks for adaptive of non-linear system (2002) Proceedings of the Fifth Biannual World Automation Congress, p. 97. , June 2002; Psichogios, D.C., Ungar, L.H., Direct and indirect model-based control using artificial neural networks (1991) Industrial and Engineering Chemistry Research, 30, p. 2564; Rhinehart, R.R., Wastewater pH control (1990) Intech, p. 42; Williams, G.L., Rhinehart, R.R., Riggs, J.B., In-line process model-based control of wastewater pH using dual base injection (1990) Industrial and Engineering Chemistry Research, 29, p. 1254
Uncontrolled Keywords: Neural network inverse model-based control strategy; Neural network modeling; Steel pickling process; Acoustic noise; Computer simulation; Concentration (process); Effluents; Error analysis; Laws and legislation; Neural networks; pH effects; Acid solutions; Steel pickling processes; Steel metallography; computer control; control system; model; pickling.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 04:56
Last Modified: 10 Feb 2021 02:58

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