基于PSO算法的微生物间歇发酵动力学参数辨识  

Parameter identification of dynamical system for microorganism in batch fermentation based on PSO algorithm

作　　者：宫召华[1] 

机构地区：[1]山东工商学院数学及信息科学学院,山东烟台264005

出　　处：《计算机工程与应用》2007年第27期193-195,共3页Computer Engineering and Applications

基　　金：国家"十五"科技攻关计划项目(the Key Technologies R&D Program of China under Grant No.2001BA708B0104);国家自然科学基金(the National Natural Science Foundation of China under Grant No.10471014)

摘　　要：主要根据微生物间歇培养过程的特征、动态行为及实验数据,建立了能够更好反映间歇发酵过程的简化的多阶段参数辨识模型,然后证明了该模型中最优参数的存在性;最后结合模型特点构造了一种改进的粒子群优化(PSO)算法求得最优参数,并利用所得的参数进行过程仿真。结果表明该模型和算法大大减少了实验数据和计算数值之间的误差,能够更好地模拟微生物间歇发酵过程。In this paper,a simplified two-stage parameter identification model is constructed in the process of bio-dissimilation of glycerol to 1,3-propanediol （1,3-PD） based on the characteristics and experimental data of the batch fermentation.Subsequently, we briefly discuss the identifiability of the parameters.Finally,in order to find the optimal parameters of the identification model, an improved PSO algorithm is constructed.Numerical results of simulations show that the model and algorithm reduce greatly the error between the experimental data and computational values,and they can simulate the process of batch fermentation better.

关 键 词：动力系统 间歇发酵 参数辨识 粒子群优化算法 1 3-丙二醇 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] O175.14[理学—数学]