微生物批式流加发酵的建模及基于HPSO算法的参数辨识(英文)  

Modeling in fed-batch fermentation and its parameter identification based on HPSO algorithm

作　　者：宫召华[1,2] 刘重阳[1] 冯恩民[2] 

机构地区：[1]山东工商学院数学与信息科学学院,山东烟台264005 [2]大连理工大学数学学院,辽宁大连116024

出　　处：《山东大学学报（理学版）》2009年第7期71-76,共6页Journal of Shandong University(Natural Science)

基　　金：Supported by National Science Foundation of China(10871033,10671126); National Basic Research Program of China(2007AA02Z208)

摘　　要：研究了微生物批式发酵甘油生产1,3-丙二醇过程的建模和参数辨识。由于流加过程中甘油和碱被间断地注入发酵罐,因而本文提出一个非线性多阶段动力系统描述该过程,并讨论了该系统的性质。以实验数据和计算值之间误差平方和最小为性能指标,建立了系统辨识模型,并证明了参数的可辨识性。最后构造了混杂粒子群算法求解该参数辨识模型,数值结果表明实验观测值和计算值之间的误差比已有文献降低了1.76%～41.77%,因而该系统能更好的描述批式流加发酵过程。Modehng and its parameter identification are investigated in glycerol bioconversion to 1,3-propanediol （1,3-PD） by Klebsiella pneumon/ae （ K. pneumoniae） in fed-batch cultures. Considering the discontinuity of adding glycerol and alkali in the process, a nonlinear multistage dynamical system is presented to formulate the process. Taking the minimal errors between the experimental data and calculated values as the performance index, we propose an identification model and prove the existence of optimal kinetic parameters. Finally, a hybrid particle swarm optimization （HPSO） algorithm is constructed to solve the identification model. Numerical results show that the error is reduced by 1.76 %- 41.77 % and the proposed dynamical system can better formulate the fed-batch culture.

关 键 词：建模 参数辨识 粒子群优化 1 3-丙二醇 批式发酵 

分 类 号：O175.14[理学—数学]