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

CIESC Journal ›› 2010, Vol. 18 ›› Issue (2): 258-264.

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

王建林, 赵利强, 于涛

School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

收稿日期:2009-08-18 修回日期:2010-03-26 出版日期:2010-04-28 发布日期:2010-04-28
通讯作者: WANG Jianlin,E-mail:wangjl@mail.buct.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China(20476007,20676013)

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

WANG Jianlin, ZHAO Liqiang, YU Tao

School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2009-08-18 Revised:2010-03-26 Online:2010-04-28 Published:2010-04-28
Supported by:
Supported by the National Natural Science Foundation of China(20476007,20676013)

摘要/Abstract

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.

关键词: on-line estimation, simplified mechanistic model, support vector machine, particle swarm optimization, unscented Kalman filter

Abstract: 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.

Key words: on-line estimation, simplified mechanistic model, support vector machine, particle swarm optimization, unscented Kalman filter

王建林, 赵利强, 于涛. On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter[J]. CIESC Journal, 2010, 18(2): 258-264.

WANG Jianlin, ZHAO Liqiang, YU Tao. On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter[J]. , 2010, 18(2): 258-264.

参考文献

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

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

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