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By I. M. Mujtaba, Mehboob A. Hussain

This quantity is a follow-up to the IChemE symposium on "Neural Networks and different studying Technologies", held at Imperial university, London, in may possibly 1999. The curiosity proven via the contributors, in particular these from the undefined, has been instrumental in generating the publication. The papers were written through individuals to the symposium and specialists within the box from world wide. They current all of the very important facets of neural community usage in addition to express the flexibility of neural networks in a number of elements of approach engineering difficulties - modelling, estimation, keep an eye on, optimization and business functions.

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In Chemical Process Control V, ed. Kantor, J. C. , AIChE Sympos. Ser. 93, 316, (1997), 280-283. Bomberger, J. D. and Seborg, D. , /. Proc. Cont. 8 (1998), 459-468. Bomberger, J. D. and Seborg, D. , Determination of the width parameter for Gaussian radial basis function networks using approximate gradient norms. Paper presented at AIChE Annual Meeting, Nov. 16-21, Los Angeles (1997). Bomberger, J. D. , in Proc. of the 1998 ACC, June 24-26, Philadelphia, PA (1998). Chen, S. , Int. J. Control. 55, (1992), 1051-1070.

It is promising, however, that the RBFN nonlinear modeling technique is able to accurately predict the nonlinear behavior of the copolymerization simulation in regions for which only limited data exists. An RBFN model may have learned little of the overall behavior of the process, but, trained only on historical data, the model is still able to predict the behavior of the process for similar operating conditions (Bomberger et ah, 1998). Pavilion Technologies, a commercial neural network software and engineering company, uses the ability of neural network models to easily learn nonlinear behavior by identifying steady state process models from historical operating data.

9) could be explained. 18 Neural Networks in Process Engineering VI VICLASS ° med x low + high Figure 8. Distribution of product composition variable (VI) as a function of two explanatory variables x, and x,. 10. Summary As demonstrated by these deliberately simple examples, neural networks are able to represent certain liquid-liquid extraction data with sufficient accuracy for predictive and control purposes. Neural networks are essentially data driven, and their performance depends on the quantity and quality of available data.

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