A growing literature within the field of chemical engineering describing the use of artificial neural networks (ANN) has evolved for a diverse range of engineering applications such as fault detection, signal processing, process modeling, and control. Because ANN are nets of basis functions, they can provide good empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes certain types of neural networks that have proved to be effective in practical applications, mentions the advantages and disadvantages of using them, and presents four detailed chemical engineering applications. In the competitive field of modeling, ANN have secured a niche that now, after one decade, seems secure.
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Barton, R., “Analysis and Rectification of Data from Dynamic Chemical Processes via Artificial Neural Networks,” Ph.D. Dissertation, Univ. of Texas, Austin, TX (1996).
Billings, S. A. and Voon, W. S. F., “Correlation Based Model Validity Tests for Nonlinear Models,”Intl. J. Control,44, 235 (1986).
Baum, E. B. and Haussler, D., “What Size Net Gives Valid Generalization?,”Neural Comput.,1, 151 (1988).
Chen, S., Billings, S. A., Cowan, C. F. N. and Grant, P.M., “Nonlinear System Identification using Radial Basic Functions,”Intl. J. Systems Sci.,21, 2513 (1990).
Cybenko, G., “Approximation by Superpositions of a Sigmoidal Function,”Math. Control Signal Syst.,2, 303 (1987).
Draper, N., “Straight Line Regression when Both Variables are Subjects to Error,” Proceed. 1991 Kansas State Univ. Conf. Appld. Statistics in Agriculture, 1 (1991).
Elman, J. L., “Finding Structure in Time,”Cognitive Science,14, 179 (1990).
Fiesler, E., “Handbook of Neural Computation,” Oxford Univ. Press, N.Y. (1996).
Franke, R., “Convergence Properties of Radial Basis Functions,”Constr. Approx.,4, 243 (1988).
Fine, T. L., “Feed Forward Neural Network Methodology,” Springer, N.Y. (1999).
Fukuoka, Y., Matsuki, H., Minamitani, H. and Iskida, A., “A Modified Backpropagation Method to Avoid False Local Minima,”Neural Networks,11, 1059 (1998).
Geman, S., Bienonstock, E. and Doursat, R., “Neural Networks and the Bias/Variance Dilema,”Neural Computation 4, 1 (1992).
Hertz, J.A., Krogh, A. S. and Palmer, R.G., “Introduction to the Theory of Neural Computation,” Addison Wesley (1991).
Jang, S. S., Joseph, B. and Mukai, H., “Comparison of Two Approaches to One-line Parameters and State Estimation of Nonlinear Systems,”Ind. Engng. Chem. Process Dev.,25, 809 (1986).
Kamruzzaman, J., Kumagai, Y. and Hikitu, H., “Study on Minimal Net Size, Convergence Behavior and Generalization Ability of Heterogeneous Backpropagation Network,”Artificial Neural Networks,2, 203 (1992).
Karjala, T.W., “Dynamic Data Rectification via Recurrent Neural Networks,” Ph.D. Dissertation, Univ. of Texas, Austin, TX (1995).
Kay, J.W. and Titterington, D.M., “Statistics and Neural Networks,” Oxford Univ. Press, Oxford (2000).
Kim, I.W., Liebman, M. J. and Edgar, T. F., “Robust Error in Variables Estimation using Nonlinear Programming,”AIChE Journal,36, 405 (1990).
Kulawski, G. J. and Brdys, N. A., “Stable Adaptive Control with Recurrent Networks,”Automatica,36, 5 (2000).
Lee, C. C., Chung, P. C., Tsai, J.-R. and Chang, C. I., “Robust Radial Basic Function Networks,”IEEE Trans. SMC-Part B, Cybernetics,29, 674 (1999).
Lee, T. C., “Structure Level Adaptation for Artificial Neural Networks,” Kluwer Acad. Publ. (1991).
Lippmann, R. P., “An Introduction to Computing with Neural Nets,”IEEE ASSP Magn,4, 4 (1987).
Ljung, L., “System Identification, Theory for the User,” Prentice Hall (1987).
Ljung, L. and Sjoberg, J., “A System Identification Perspective on Neural Nets,”Proceed. 1992 IEEE Workshop on Neural Nets for Signal Processing, IEEE (1992).
MacMurray, J. C., “Modeling and Control of a Packed Distillation Column using Artificial Neural Networks,” M.S. Thesis, Univ. of Texas, Austin, TX (1993).
Moody, J., “Prediction Risk and Architecture Selection for Neural Networks,” in From Statistics to Neural Networks, eds. V. Cherkassky, J. H. Friedman, and H. Wechsler, Springer Verlag, Berlin, 147 (1994).
Patwardhan, A. A., “Modeling and Control of a Packed Distillation Column,” Ph.D. Dissertation, Univ. of Texas, Austin, TX (1991).
Psichogious, D. and Unger, L., “A Hybrid Neural Network First Principles Approach to Process Modeling,”AIChE Journal,38, 1499 (1992).
Reed, R., “Pruning Algorithms-A Survey,”IEEE Trans. Neural Net.,4, 740 (1993).
Rumerhart, D. E. and McClelland, J. L., “Parallel Distributed Processing,” MIT Press,1 (1986).
Seinfeld, J. H., “Optimal Stochastic Control of Nonlinear Systems,”AIChE J.,16, 1016 (1970).
Sjoberg, J. and Ljung, L., “Overtraining, Regularization, and Searching for a Minimum in Neural Networks,” in Proceed. IFAC Symp. Adaptive Systems in Control and Signal Processing, IFAC, 669 (1992).
Soderstrom, T., “Identification of Stochastic Linear Systems in Presence of Input Noise,”Automatica,17, 713 (1981).
Suewatanakal, W., “A Comparison of Fault Detection and Classification using ANN with Traditional Methods,” Ph.D. Dissertation, Univ. of Texas, Austin, TX (1993).
van de Laar, P. and Heskes, T., “Pruning Using Parameter and Neuronal Matrices,”Neural Computation,11, 977 (1999).
Werbos, P. J., “Backpropagation through Time: What it Does and How to Do It,”Proceed. IEEE,78, 1550 (1990).
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Himmelblau, D.M. Applications of artificial neural networks in chemical engineering. Korean J. Chem. Eng. 17, 373–392 (2000). https://doi.org/10.1007/BF02706848
- Artificial Neural Networks
- Data Rectification
- Fault Detection