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 well 1999. The curiosity proven by means of the individuals, particularly these from the undefined, has been instrumental in generating the e-book. The papers were written by means of individuals to the symposium and specialists within the box from worldwide. They current the entire vital elements of neural community usage in addition to convey the flexibility of neural networks in a number of features of approach engineering difficulties - modelling, estimation, regulate, optimization and commercial functions.
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Extra info for Application of Neural Networks and Other Learning Technologies in Process Engineering
Sample text
1997) the solvent feed rate Gs and the reactor feed temperature Trf are treated as measured disturbance variables, and are used along with the other simulation inputs for state estimation. The inhibitor flowrate is treated as an unmeasured disturbance, and is not used for estimation purposes. The same tactic can be pursued for RBFN identification: Gs and Trf are considered as possible inputs to the RBFN models, but Gz is not. Therefore, nu - 11 and n = 4, giving a total of 11 possible inputs to the network and four MISO RBFN models.
These models can then be used to optimize process inputs to obtain desirable process outputs, or can be coupled with dynamic models for use in nonlinear model predictive control strategies (Keeler et al, 1997). 45 46 Neural Networks 0 in Process 50 100 150 200 250 300 51 1 1 1 1 1 r ' 0 50 100 150 250 300 Engineering 350 X10 4 200 time (h) 350 Figure 11. Comparison of one-step-ahead prediction for validation data Set 3 for models identified on Set 2 using stepwise regression with /? 0 and k-means clustering with Mo = 30.
IEEE Control Sys. Mag. 11, April, (1991), 31-38. Moody, J. and Darken, C. , Neural Computation. 1 (1989), 281-294. Pottmann, M. and Seborg, D. , J. Process Control. 2 (1992), 189-203. Rhodes, C. , Computers Chem. Engng. 21S (1997), S1149-S1154. Sjoberg, J. , Automatica. 31, (1995), 1691-1724. Zhu, Q. M. and Billings, S. , Int. J. Control. 64 (1996), 871-886. Acknowledgements The authors gratefully acknowledge the financial support of the National Science Foundation (Grant # CTS-9424094) and DuPont.
