Evolutionary Multivariate Dynamic Process Model Induction for a Biological Nutrient Removal Process
Publication: Journal of Environmental Engineering
Volume 133, Issue 12
Abstract
This paper proposes an automatic process model induction system using an evolutionary computational intelligence, called grammar-based genetic programming, that is specially designed to automatically discover multivariate dynamic process models that best fit observed process data. This automatic process model induction system combines an evolutionary self-organizing system of genetic programming paradigm with various mathematical functions for a multivariate nonlinear model evolution using a grammar system via the mechanism of genetics and natural selection. The results demonstrate how the automatic process model induction system based on grammar-based genetic programming can be used to develop accurate and relatively cost-effective multivariate dynamic process models for the full-scale biological nutrient removal process. Multivariate dynamic process models are derived automatically in the form of understandable mathematical formulas that enable engineers to extract important knowledge hidden in the data and develop better operation and control strategies.
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Acknowledgments
This work was undertaken while the second writer was a visiting professor at the Institute of Geological and Nuclear Sciences, New Zealand during his sabbatical year. He was supported financially through research funding by Chonnam National University, Republic of Korea. The writers thank the anonymous reviewers, whose comments greatly improved this manuscript.
References
Banzhaf, W., Nordin, P., Keller, R., and Francone, F. (1998). Genetic programming—An introduction. Morgan Kaufmann, San Francisco.
Barr, A., and Geigenbaum, E. (1982). “Chap. 10: Automatic programming.” Handbook of artificial intelligence, William Kauffman, Los Altos.
Berthouex, P. M., and Box, G. E. (1996). “Time series models for forecasting wastewater treatment plant performance.” Water Res., 30, 1685–875.
Boger, Z. (1992). “Application of neural networks to water and wastewater treatment plant operation.” Instr. Soc. America Trans., 31(1), 25–33.
Capodaglio, A. G., Jones, H. V., Novotny, V., and Feng, X. (1991). “Sludge bulking analysis and forecasting: Application of system identification and artificial neural computing technologies.” Water Res., 25(10), 1217–224.
Chomsky, N. (1986). Knowledge of language: Its nature, origin and use, Preager, New York.
Cramer, N. L. (1985). “A representation for the adaptive generation of simple sequential programs.” Proc., Int. Conf. on Genetic Algorithms and the Applications, J. J. Grefenstette, ed., Carnegie-Mellon University, Pittsburgh, 183–187.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Mass.
Goldberg, D. E., and Deb, K. (1991). “A comparative analysis of selection schemes used in genetic algorithms.” Foundations of genetic algorithms, Morgan Kaufmann, San Francisco, 69–93.
Holland, J. H. (1975). Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, Mich.
Hong, Y.-S. (2001). “Evolutionary self-organising modelling and optimization: Application of a paper coating process.” Proc., 2001 Joint Conf. of Society of Chemical Engineering N.Z., (SCENZ)/Food Engineering Association of N.Z., (FEANZ)/Engineering Material Group(EMG), Auckland, New Zealand, 77–81.
Hong, Y.-S. (2003). “Automatic model induction of a biological wastewater treatment process using context-free grammar genetic programming.” Genetic and Evolutionary Computation Conf. 2003, Grammatical Evolution Workshop, American Association for Artificial Intelligence, Chicago, 146–149.
Hong, Y.-S., and Bhamidimarri, S. M. R. (2003). “Evolutionary self-organizing modeling of a municipal wastewater treatment plant.” Water Res., 7(6), 1199–1212.
Hong, Y.-S., Bhamidimarri, S. M. R., and Charleson, T. (1998). “A genetic adapted neural network analysis of performance of the nutrient removal plant at Rotorua.” Institution of Professional Engineers New Zealand (IPENZ) Conf., Simulation and Control Section, Vol. 2, 213–217.
Hong, Y.-S., White, P. A., and Scott, D. M. (2005). “Automatic rainfall recharge model induction by evolutionary computational intelligence.” Water Resour. Res., 41, W08422 .
Hopcroft, J. E., and Ullman, J. D. (1979). Introduction to automata theory, languages and computation, Addison-Wesley, Reading, Mass.
Koza, J. R. (1989). “Hierarchical genetic algorithms operating on populations of computer programs.” Proc., 11th Int. Joint Conf. on Artificial Intelligence, Vol. 1, Morgan Kaufmann, San Francisco, 768–774.
Koza, J. R. (1992). Genetic programming: On the programming of computers by natural selection, MIT Press, Cambridge Mass.
Koza, J. R. (1994). Genetic programming II: Automatic discovery of reusable programs, MIT Press, Cambridge Mass.
Lavrac, N., and Dzeroski, S. (1994). Inductive logic programming: Techniques and applications, Ellis Horwood, Chichester, U.K.
McKay, B., Willis, M., and Barton, G. W. (1997). “Steady-state modelling of chemical process systems using genetic programming.” Comput. Chem. Eng., 21(9), 981–996.
McKay, R. I., Hoai, N. X., Shan, Y., and Whigham, P. (2005). “Grammars in genetic programming: a brief review.” Proc. Int. Conf. on Computational Intelligence, China University of Geoscience Press, Wuhan, China, 3–8.
Montana, D. J. (1994). “Strongly typed genetic programming.” BBN Technical Rep. No. 7866, Bolt Beranek and New Man Inc., Cambridge, Mass.
Muggleton, S. (1992). Inductive logic programming, Academic, San Diego.
Nordin, P. (1994). “A complied genetic programming system that directly manipulates the machine code.” Advances in genetic programming, K. E. Kinnear, ed., MIT Press, Mass, Cambridge 311–331.
O’Neill, M., and Ryan, C. (2001). “Grammatical evolution.” IEEE Trans. Evol. Comput., 5(4), 349–358.
Powell, M. J. D. (1964). “An efficient method for finding the minimum of a function of several variables without calculating derivatives.” Comput. J., 7, 152–162.
Shan, Y., McKay, R. I., and Paull, D. (2002). “Building ecological models using genetic programming.” 4th Asia-Pacific Conf. on Simulated Evolution and Learning, Singapore.
van Dongen, G., and Geuens, L. (1998). “Multivariate time series analysis for design and operation of a biological wastewater treatment plant.” Water Res., 32, 691–700.
Whigham, P. A. (1995). “Inductive bias and genetic programming.” Proc. first int. conf. on genetic algorithms in engineering systems: Innovation and application, A. M. A. Zalzala, ed., GALESIA, The Institute of Electrical Engineer, Stevenage, U.K., 414, 461–466.
Whigham, P. A., and Keukelaar, J. (2001). “Evolving structure—Optimizing content.” Proc. 2001 Congress on Evolutionary Computation, COEX Seoul, Korea, 1228–1235.
Whigham, P. A., and Recknagel, F. (2001). “An inductive approach to ecological time series modelling by evolutionary computation.” Ecol. Modell., 146, 275–287.
Willis, M., Hiden, H., Hinchliffe, M., McKay, B., and Barton, G. W. (1997). “Systems modelling using genetic programming.” Comput. Chem. Eng., 21, S1161–S1166.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 133 • Issue 12 • December 2007
Pages: 1126 - 1135
Copyright
© 2007 ASCE.
History
Received: Mar 24, 2006
Accepted: Apr 16, 2007
Published online: Dec 1, 2007
Published in print: Dec 2007
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