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

›› 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]. , 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

1 Cheruy, A., “Software sensors in bioprocess engineering”, J. Biotechnol., 52 (3), 193-199 (1997).
2 Komives, C., Parker, R.S., “Bioreactor state estimation and control”, Curr. Opin. Biotech., 14 (5), 468-474 (2003).
3 Sonnleitnert, B., Kappeli, O., “Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity:Formulation and verification of a hypothesis”, Biotechnol. Bioeng., 28 (6), 927-937 (1986).
4 Birol, G., Undey, C., Parulekar, S.J., Cinar, A., “A morphologically structured model for penicillin production”, Biotechnol. Bioeng., 77 (5), 538-552 (2002).
5 Dochain, D., “State and parameter estimation in chemical and biochemical processes:A tutorial”, J. Process Contr., 13 (8), 801-818 (2003).
6 Vapnik, V.N., The Nature of Statistical Learning Theory, Springer Verlag, New York (1995).
7 Desai, K., Badhe, Y., Tambe, S.S., Kulkarni, B.D., “Soft-sensor development for fed-batch bioreactors using support vector regression”, Biochem. Eng. J., 27 (3), 225-239 (2006).
8 Wang, J.L., Yu, T., Jin, C.Y., “On-line estimation of biomass in fermentation process using support vector machine”, Chin. J. Chem. Eng., 14 (3), 383-388 (2006).
9 Patnaik, P.R., “Improvement of the microbial production of streptokinase by controlled filtering of process noise”, Process Biochem., 35, 309-315 (1999).
10 Romanenko, A., Castro J. A., “The unscented filter as an alternative to the EKF for nonlinear state estimation:A simulation case study”, Comput. Chem. Eng., 28 (3), 347-355 (2004).
11 Julier, S.J., Uhlmann, J.K., “A new extension of the Kalman filter to nonlinear systems”, In:Proceedings of AeroSense:The 11th International Symposium on Aerosoace/Defence Sensing, Simulation and Controls, SPIE, Orlando, USA, 182-193 (1997).
12 Kandepu, R., Foss, B., Imsland, L., “Applying the unscented Kalman filter for nonlinear state estimation”, J. Process Contr., 18, 753-768 (2008).
13 Marmol, E.M., Luyben, W.L., Georgakis, C., “Application of an extended Luenberger observer to the control of multicomponent batch distillation”, Ind. Eng. Chem. Res, 30 (8), 1870-1880 (1991).
14 Kennedy, J., Eberhart, R., “Particle swarm optimization”, In:Proceedings of the IEEE International Conference on Neural Networks, IEEE, Perth, Australia, 1942-1948 (1995).
15 Kolas, S., Foss, B.A., Schei, T.S., “Constrained nonlinear state estimation based on the UKF approach”, Comput. Chem. Eng., 33 (8), 1386-1401 (2009).
16 Birol, G., Undey, C., Cinar, A., “A modular simulation package for fed-batch fermentation:Penicillin production”, Comput. Chem. Eng., 26 (11), 1553-1565 (2002).
17 Eberhart, R.C, Shi, Y., “Comparing inertia weights and constriction factors in particle swarm optimization”, In:Proceedings of the IEEE Congress on Evolutionary Computation, IEEE, San Diego, USA, 84-88 (2000). 1
8 Fan, R.E., Chen, P.H., Lin, C.J., “Working set selection using second order information for training support vector machines”, J. Mach. Learn. Res., 6, 1889-1918 (2005).
19 Tahir, S.F., Khan, A., Majid, A., Mirza, A.M., “Support vector machine based intelligent watermark decoding for anticipated attack”, Int. J. Appl. Math. Comput. Sci., 1 (1), 7-12 (2005).

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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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