Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

doi:10.1016/j.cjche.2015.09.005

Chin.J.Chem.Eng. ›› 2015, Vol. 23 ›› Issue (11): 1801-1810.DOI: 10.1016/j.cjche.2015.09.005

• PROCESS SYSTEMS ENGINEERING AND PROCESS SAFETY • Previous Articles Next Articles

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

Liqiang Zhao, Jianlin Wang, Tao Yu, Kunyun Chen, Tangjiang Liu

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

Received:2015-02-10 Revised:2015-07-22 Online:2015-12-18 Published:2015-11-28
Contact: Jianlin Wang
Supported by:
Supported by the National Natural Science Foundation of China (61503019), the Beijing Natural Science Foundation (4152041) and Beijing Higher Education Young Elite Teacher Project (YETP0504).

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

Liqiang Zhao, Jianlin Wang, Tao Yu, Kunyun Chen, Tangjiang Liu

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

通讯作者: Jianlin Wang
基金资助:
Supported by the National Natural Science Foundation of China (61503019), the Beijing Natural Science Foundation (4152041) and Beijing Higher Education Young Elite Teacher Project (YETP0504).

Abstract

Abstract: State estimation of biological process variables directly influences the performance of on-linemonitoring and optimal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithmis provided and the implementation of the proposed algorithmis constructed. Then a nonlinear state spacemodel for fermentation process is established and the SCKF algorithmincorporating delayedmeasurements based on fermentation processmodel is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposedmethod with higher estimation accuracy and better stability.

Key words: Nonlinear state estimation, Fermentation process, Cubature Kalman filter, Delayed measurements, Sample-state augmentation

摘要： State estimation of biological process variables directly influences the performance of on-linemonitoring and optimal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithmis provided and the implementation of the proposed algorithmis constructed. Then a nonlinear state spacemodel for fermentation process is established and the SCKF algorithmincorporating delayedmeasurements based on fermentation processmodel is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposedmethod with higher estimation accuracy and better stability.

关键词: Nonlinear state estimation, Fermentation process, Cubature Kalman filter, Delayed measurements, Sample-state augmentation

Liqiang Zhao, Jianlin Wang, Tao Yu, Kunyun Chen, Tangjiang Liu. Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements[J]. Chin.J.Chem.Eng., 2015, 23(11): 1801-1810.

References

[1] J.S. Alford, Bioprocess control: Advances and challenges, Comput. Chem. Eng. 30 (10-12) (2006) 1464-1475.

[2] K. Schügerl, Progress in monitoring, modeling and control of bioprocesses during the last 20 years, J. Biotechnol. 85 (2) (2001) 149-173.

[3] H.P. Jin, X.G. Chen, J.W. Yang, L. Wu, Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes, Comput. Chem. Eng. 71 (2014) 77-93.

[4] P. Kadlec, B. Gabrys, S. Strandt, Data-driven soft sensors in the process industry, Comput. Chem. Eng. 33 (4) (2009) 795-814.

[5] A. Jahanmiri, H. Rasooli, On-line states and parameter identification of acetone-butanol-ethanol fermentation process, Biochem. Eng. J. 24 (2) (2005) 115-123.

[6] L. Dewasme, G. Goffaux, A.L. Hantson, A. Vande Wouwer, Experimental validation of an extended Kalman filter estimating acetate concentration in E. coli cultures, J. Process Control 23 (2) (2013) 148-157.

[7] S.J. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation, Proc. IEEE 92 (3) (2004) 401-422.

[8] R. Kandepu, B. Foss, L. Imsland, Applying the unscented Kalman filter for nonlinear state estimation, J. Process Control 18 (7) (2008) 753-768.

[9] J.L. Wang, L.Q. Zhao, T. Yu, On-line estimation in fed-batch fermentation process using state space model and unscented Kalman filter, Chin. J. Chem. Eng. 18 (2) (2010) 258-264.

[10] I. Arasaratnam, S. Haykin, Cubature Kalman filters, IEEE Trans. Autom. Control 54 (6) (2009) 1254-1269.

[11] K.P.B. Chandra, D.W. Gu, I. Postlethwaite, Cubature information filter and its applications, Proceedings of 2011 American Control Conference, San Francisco, CA, USA 2011, pp. 3609-3614.

[12] R. Dondo, D. Marqués, Simulation results for on-line optimization of a batch bioreactor using nonlinear filtering and optimal control, ISA Trans. 42 (2) (2003) 289-303.

[13] R.D. Gudi, S.L. Shah, M.R. Gray, Adaptive multirate state and parameter estimation strategies with application to a bioreactor, AIChE J 41 (11) (1995) 2451-2464.

[14] R.D. Gudi, S.L. Shah,M.R. Gray, P.K. Yegneswaran, Adaptivemultirate estimation and control of nutrient levels in a fed-batch fermentation using off-line and on-line measurements, Can. J. Chem. Eng. 75 (3) (1997) 562-573.

[15] Z.I.T.A. Soons, J. Shi, J.D. Stigter, L.A. Van Der Pol, G. Van Straten, A.J.B. Van Boxtel, Observer design and tuning for biomass growth and kLa using online and offline measurements, J. Process Control 18 (7-8) (2008) 621-631.

[16] M.A. Myers, S. Kang, R.H. Luecke, State estimation and control for systems with delayed off-line measurements, Comput. Chem. Eng. 20 (5) (1996) 585-588.

[17] S. Tatiraju,M. Soroush, R. Mutharasan,Multi-rate nonlinear state and parameter estimation in a bioreactor, Biotechnol. Bioeng. 63 (1) (1999) 22-32.

[18] W. Cao, Y.C. Soh, Nonlinear multi-rate current state estimation: Convergence analysis and application to biological systems, Comput. Chem. Eng. 28 (9) (2004) 1623-1633.

[19] V. Prasad,M. Schley, L.P. Russo, B.W. Bequette, Product property and production rate control of styrene polymerization, J. Process Control 12 (3) (2002) 353-372.

[20] A. Gopalakrishnan, N.S. Kaisare, S. Narasimhan, Incorporating delayed and infrequent measurements in extended Kalman filter based nonlinear state estimation, J. Process Control 21 (1) (2011) 119-129.

[21] R.K. Mutha,W.R. Cluett, On-line nonlinear model-based estimation and control of a polymer reactor, AIChE J 43 (11) (1997) 3042-3058.

[22] R. Amirthalingam, S.W. Sung, J.H. Lee, Two-step procedure for data-based modeling for inferential control applications, AIChE J 46 (10) (2000) 1947-1988.

[23] R. Van DerMerwe, Sigma-point Kalman filters for probabilistic inference in dynamic state-space models(Ph.D. Thesis) University of Stellenbosch, 2004.

[24] S.C. Patwardhan, S. Narasimhan, P. Jagadeesan, B. Gopaluni, S.L. Shah, Nonlinear Bayesian state estimation: A review of recent developments, Control. Eng. Pract. 20 (10) (2012) 933-953.

[25] M. Farza, K. Busawon, H. Hammouri, Simple nonlinear observers for on-line estimation of kinetic rates in bioreactors, Automatica 34 (3) (1998) 301-318.

[26] D.P. Niu,M.X. Jia, F.L.Wang, D.K. He, Optimization of Nosiheptide fed-batch fermentation process based on hybridmodel, Ind. Eng. Chem. Res. 52 (9) (2013) 3373-3380.

[27] G. Birol, C. Ündey, A. Çinar, A modular simulation package for fed-batch fermentation: Penicillin production, Comput. Chem. Eng. 26 (11) (2002) 1553-1565.

[28] U. Yüzgeç, M. Türker, A. Hocalar, On-line evolutionary optimization of an industrial fed-batch yeast fermentation process, ISA Trans. 48 (1) (2009) 79-92.

[29] L.Z. Chen, S.K. Nguang, X.D. Chen, X.M. Li, Modelling and optimization of fed-batch fermentation processes using dynamic neural networks and genetic algorithms, Biochem. Eng. J. 22 (1) (2004) 51-61.

[30] B. Sonnleitner, O. Käppeli, Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: Formulation and verification of a hypothesis, Biotechnol. Bioeng. 28 (6) (1986) 927-937.

[31] C.C. Chang, C.J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (3) (2011) 1-27.

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

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