Implementation of neural network inverse-model-based control (NN-IMBC) strategy in batch reactors

Aziz, N. and Hussain, Mohd Azlan and Mujtaba, I.M. (2003) Implementation of neural network inverse-model-based control (NN-IMBC) strategy in batch reactors. Computer Aided Chemical Engineering, 15. pp. 708-713. ISSN 1570-7946,

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Neural Network Inverse-Model-Based Control (NN-IMBC) strategy is used to track the optimal reactor temperature profiles and its performance is evaluated through a few robustness tests. A complex exothermic batch reaction scheme is used as a case study. The optimal reactor temperature profiles are obtained by solving optimal control problems off-line using Control Vector Parameterisation (CVP) and Successive Quadratic Programming (SQP) techniques. The NN-IMBC strategy is evaluated in tracking both the constant and dynamic optimal set points. Neural Network estimator is embedded to the strategy as the on-line estimator to estimate the amount of heat released by the chemical reaction. The NN-IMBC is found to be well performed in tracking both set points and accommodating changes within its range of training. It also promises robust controller if it is trained with a wide range of the reactor temperature covering all possible conditions of the process and is much easier to implement compared to other typical types of controllers because no tuned parameter is needed. Therefore, it can lead to efficient and profitable operation and provide a better business decision making in setting up new plants or improving the existing operations.

Item Type: Article
Additional Information: Export Date: 5 March 2013 Source: Scopus doi: 10.1016/S1570-7946(03)80389-9 Language of Original Document: English Correspondence Address: Aziz, N.; School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia; email: References: Berber, R., (1996) Trans IChemE, p. 3; Cott, B.J., Macchietto, S., (1989) Ind.Eng.Chem.Res, 28, p. 1177; Aziz, N., Mujtaba, I.M., (2002) Chem. Eng. J, 85, p. 313; Luus, R., Okongwu, O.N., (1999) Chem. Eng. J, 75, p. 1; Dirion, J.L., Cabassud, M., Le Lann, M.V., Casamatta, G., (1996) Chem.Eng. J, 63, p. 65; Galvan, I.M., Zaldivar, J.M., (1998) Chem.Eng. and Processing, 37, p. 149; Zaldivar, J.M., Hernandez, H., Panetsos, F., (1992) Chem.Eng. and Processing, 31, p. 173; Aziz, N., Hussain, M.A., Mujtaba, I.M., (2000) European Symposium on Computer Aided Process Engineering, 10, p. 175. , Florence, Italy; Hussain, M.A., (1999) Artificial Intelligence Eng., 13, p. 55; Aziz, N., (2001) PhD thesis, , University of Bradford; Lennox, B., Montague, G.A., Frith, A.M., Gent, C., Bevan, V., (2001) Journal of ProcessControl, 11, p. 497
Uncontrolled Keywords: batch reactors; control; inverse model; neural network.
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 06:54
Last Modified: 10 Feb 2021 03:43

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