On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter  

作　　者：王建林 赵利强 于涛 

机构地区：[1]School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

出　　处：《Chinese Journal of Chemical Engineering》2010年第2期258-264,共7页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China（20476007 20676013）

摘　　要：On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO) ,is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM) method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization（SECPSO） ,is presented for the parameter identification in the mechanistic model,and the support vector machines（SVM） method is adopted to establish the nonlinear measurement model.The unscented Kalman filter（UKF） is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

关 键 词：on-line estimation simplified mechanistic model support vector machine particle swarm optimization unscented Kalman filter 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TQ920.5[自动化与计算机技术—控制科学与工程]