Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

doi:10.1016/S1004-9541(13)60453-X

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (2): 155-162.DOI: 10.1016/S1004-9541(13)60453-X

• 过程系统工程与过程安全 • 上一篇下一篇

Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

赵超, 许巧玲, 林思铭, 李学来

College of Chemistry and Chemical Engineering, Fuzhou University, Fuzhou 350108, China

收稿日期:2012-08-03 修回日期:2012-10-22 出版日期:2013-02-28 发布日期:2013-03-13
通讯作者: ZHAO Chao
基金资助:
Supported by the National Natural Science Foundation of China (60804027, 61064003) and Fuzhou University Research Foundation (FZU-02335, 600338 and 600567).

Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

ZHAO Chao, XU Qiaoling, LIN Siming, LI Xuelai

College of Chemistry and Chemical Engineering, Fuzhou University, Fuzhou 350108, China

Received:2012-08-03 Revised:2012-10-22 Online:2013-02-28 Published:2013-03-13

摘要/Abstract

摘要： Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.

关键词: parameter estimation, kinetic model, hybrid differential evolution, Gauss-Newton, feed batch fermentor

Abstract: Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.

Key words: parameter estimation, kinetic model, hybrid differential evolution, Gauss-Newton, feed batch fermentor

赵超, 许巧玲, 林思铭, 李学来. Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems[J]. Chinese Journal of Chemical Engineering, 2013, 21(2): 155-162.

ZHAO Chao, XU Qiaoling, LIN Siming, LI Xuelai. Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems[J]. Chin.J.Chem.Eng., 2013, 21(2): 155-162.

参考文献

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Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems

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