| 利用状态空间模型和不敏卡尔曼滤波的分批补料发酵过程在线估计(英文) |
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| 引用本文： | 王建林,赵利强,于涛. 利用状态空间模型和不敏卡尔曼滤波的分批补料发酵过程在线估计(英文)[J]. 中国化学工程学报, 2010, 18(2): 258-264. DOI: 10.1016/S1004-9541(08)60351-1 |
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| 作者姓名： | 王建林 赵利强 于涛 |
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| 作者单位： | School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China(20476007,20676013) |
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| 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.
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| 关 键 词： | on-line estimation simplified mechanistic model support vector machine particle swarm optimization unscented Kalman filter |
| 收稿时间： | 2009-08-18 |
| 修稿时间： | 2009-8-18 |
On-line estimation in fed-batch fermentation process using state space model and unscented Kalman filter |
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| WANG Jianlin,ZHAO Liqiang,YU Tao. On-line estimation in fed-batch fermentation process using state space model and unscented Kalman filter[J]. Chinese Journal of Chemical Engineering, 2010, 18(2): 258-264. DOI: 10.1016/S1004-9541(08)60351-1 |
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| Authors: | WANG Jianlin ZHAO Liqiang YU Tao |
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| Affiliation: | School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| 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 tar geted 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 pro posed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermenta tion process. |
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| Keywords: | on-line estimation simplified mechanistic model support vector machine particle swarm optimization unscented Kalman filter |
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