Abstract
Parameter estimation, a key step in establishing the kinetic models, can be considered as a numerical optimization problem. Many optimization techniques including evolutionary algorithms have been applied to it, yet their efficiency needs further improvement. This paper proposes a hierarchical differential evolution (HDE) in which individuals are organized in a hierarchy and mutation base is selected based on the hierarchical structure. Additionally, the scaling factor of HDE is adjusted according to both the hierarchy and the search process, elaborately balancing the exploration and exploitation. To demonstrate the performance of HDE, experiments are carried out on kinetic models of two chemical reactions: pyrolysis and dehydrogenation of benzene as well as supercritical water oxidation. The results show that the proposed algorithm is an efficient and robust technique for kinetic parameter estimation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adam, B.S., James, W.T., Paul, I.B., William, H.G.: Global Dynamic Optimization for Parameter Estimation in Chemical Kinetics. J. Phys. Chem. 110, 971–976 (2006)
Bailey, J.E., Ollis, D.E.: Biochemical Engineering Fundamentals. McGraw-Hill, New York (1986)
Denn, M.M.: Optimization by Variational Methods. McGraw-Hill, New York (1969)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Dorigo, M.: Optimization, Learning and Natural Algorithms, Ph.D thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)
Kennedy, K., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. Neural Netw., pp. 1942–1948 (1995)
Storn, R., Price, K.V.: Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Global Opt. 11(4), 341–359 (1997)
Yan, X.F., Chen, D.Z., Hu, S.X., Ding, J.W.: Estimation of Kinetic Parameters Using Chaos Genetic Algorithms. Journal of Chemical industry and Engineering 53(8), 810–814 (2002)
Zhang, B., Chen, D.Z., Rao, J.: Estimation of Kinetic Parameters by Using Eugenic Evolution Programming. Journal of Chemical Engineering of Chinese Universities 18(5), 638–642 (2004)
Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Kennedy, J., Mendes, R.: Population Structure and Particle Swarm Performance. In: Proc. Congr. Evolutionary Computation, pp. 1671–1676 (2002)
Suganthan, P.N.: Particle Swarm Optimizer with Neighborhood Operator. In: Proc. Congr. Evolutionary Computation, pp. 1958–1962 (1999)
Janson, S., Middendorf, M.: A Hierarchical Particle Swarm Optimizer and Its Adaptive Variant. IEEE Transaction on Systems, Man, and Cybernetics—Part B: Cybernetics 35(6) (2005)
Standen, A.: Kirk-Othmer Encyclopedia of Chemical Technology, 2nd edn., vol. 7, p. 191. Interscience Publishers, New York (1972)
Yokata, T., Sakaguchi, S., Ishii, Y.: Aerobic Oxidation of Benzene to Biphenyl Using a Pd(II)/Molybdovanadophosphoric Acid Catalytic System. Adv. Synth. Catal. 344(8) (2002)
Zhu, Z.N., Dai, Y.C.: Chemical Process Data Treatment and Experiment Design, pp. 190–191. Hydrocarbon Processing Press, Beijing (1989)
Pray, H.A., Schweienert, C.E., Minnich, B.H.: Solubility of Hydrogenoxygen Nitrogen and Helium in Water at Elevated Temperature. Ind. Eng. Chem. 44, 1146–1151 (1952)
Li, R.K., Savage, P.E., Szmukler, D.: 2-Chlorophenol Oxidation in Suercritical Water: Gloabal Kinetics and Reaction Products. Journal of AIChE 39(1), 178–187 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, Y., Zhong, X. (2008). Hierarchical Differential Evolution for Parameter Estimation in Chemical Kinetics. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-89197-0_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89196-3
Online ISBN: 978-3-540-89197-0
eBook Packages: Computer ScienceComputer Science (R0)