Use of neural networks in process engineering thermodynamics, diffusion, and process control and simulation applications
Abstract
This article presents the current status of the use of Artificial Neural Networks (ANNs) in process engineering applications where common mathematical methods do not completely represent the behavior shown by experimental observations, results, and plant operating data. Three examples of the use of ANNs in typical process engineering applications such as prediction of activity in solvent-polymer binary systems, prediction of a surfactant self-diffusion coefficient of micellar systems, and process control and simulation are shown. These examples are important for polymerization applications, enhanced-oil recovery, and automatic process control.
References
Alessandro, V., 1987. "A simple modification of the Flory- Huggins theory for polymers in non-polar or slightly polar solvents", Fluid Phase Equilib, 34: 21 - 35. https://doi.org/10.1016/0378-3812(87)85048-3
Basheer, A. I. and Najjar, Y. M., 1996. "Predicting Dynamic Response of Adsorption Columns with Neural Nets", Journal of Computing in Civil Engineering, 10 (1): 31- 39. https://doi.org/10.1061/(ASCE)0887-3801(1996)10:1(31)
Bhat, N. and McAvoy, T. J., 1990. "Use of Neural Nets for Dynamic Modeling and Control of Chemical Process Systems", Computers Chem. Engng., 14 (4/5): 573 - 583. https://doi.org/10.1016/0098-1354(90)87028-N
Brajesh, K. J., Tambe, S. S. and Kulkarni, B. D., 1995. "Estimating Diffusion Coefficients of a Micellar System Using an Artificial Neural Network", J. Colloid & Interface Science, 170: 392 - 398. https://doi.org/10.1006/jcis.1995.1117
Camacho, O., 1996. "A New Approach to Design and Tune Sliding Mode Controllers for Chemical Processes", Ph.D. Dissertation, University of South Florida, Tampa.
Cherré, D., 1998. "Use of Artificial Neural Networks in Process Control", Master of Science Thesis, University of South Florida, Tampa.
Fendler, J. H. and Fendler, E. J., 1982. Membrane and Mimetic Chemistry, Wiley- Interscience, New York.
Fletcher, R., 1987. Practical Methods of Optimization, J. Wiley,
García, C. E., Pretti, D. M. and Morari, M., 1988. "Model Predictive Control: Theory and Practice a Survey", IFAC Workshop on Model Based Control, June.
Hagan, M.T. and Menhaj, M., 1994. "Training Feedforward Networks with the Marquadt Algorithm", IEEE Transactions on Neural Networks, 5 (6): 989 - 993. https://doi.org/10.1109/72.329697
Haykin, S., 1994. Neural Networks. A Comprehensive Foundation, Macmillan College Publishing Company, N.Y.
Martin, G., 1997. "Nonlinear Model Predictive Control with Integrated Steady State Model-Based Optimization", AICHE 1997 Spring National Meeting.
Ramesh, K., Tock, R. W. and Narayan, R. S., 1995. "Prediction of solvent Activity in Polymer Systems with Neural Networks", Ind. Eng. Chem. Res., 34: 3974 - 3980. https://doi.org/10.1021/ie00038a038
Rumelhart, D. E. and McClelland, J. L., 1986. "Parallel Distributed Processing: Explorations in the Microestructure of Cognition", MIT Press, Cambridge, MA, and London, England. https://doi.org/10.7551/mitpress/5236.001.0001
Savkovic-Stevanovic, J., 1994. "Neural Networks for Process Analysis and Optimization: Modeling and Applications", Computers Chem. Engng., 18 (11/12): 1149 - 1155. https://doi.org/10.1016/0098-1354(94)E004H-Z
Smith, C. A., and Corripio, A., 1997. Principles and Practice of Automatic Process Control, John Wiley & Sons, Inc., N. Y.
Te Braake, H. A. B., Van Can, H. J. L., Van Straten, G. and Verbruggen, H. B., 1997. "Two-step Approach in Training of Regulated Activation Weight Networks (RAWN)", Engng. Applic. Artif. Intell., 10 (2): 157-170. https://doi.org/10.1016/S0952-1976(97)00004-3
Thompson, W., 1996. "How Neural Network Modeling Methods Complement Those of Physical Modeling", NPRA Computer Conference.
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