Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems  

作　　者：ZHAO Chao XU Qiaoling LIN Siming LI Xuelai 

机构地区：[1]College of Chemistry and Chemical Engineering, Fuzhou University, Fuzhou 350108, China

出　　处：《Chinese Journal of Chemical Engineering》2013年第2期155-162,共8页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China （60804027, 61064003） and Fuzhou University Research Foundation （FZU-02335, 600338 and 600567）.

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 con- vergence 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 oarameters for a comolex mathematical model.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 

分 类 号：O242.23[理学—计算数学] TN911.72[理学—数学]