Full Text:   <1412>

CLC number: Q815

On-line Access: 

Received: 2004-06-07

Revision Accepted: 2004-09-06

Crosschecked: 0000-00-00

Cited: 3

Clicked: 4009

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.6 P.530~534

10.1631/jzus.2005.B0530


On-line estimation of concentration parameters in fermentation processes


Author(s):  XIONG Zhi-hua, HUANG Guo-hong, SHAO Hui-he

Affiliation(s):  Institute of Automation, Shanghai Jiaotong University, Shanghai 200030, China

Corresponding email(s):   zhxiong@sjtu.edu.cn

Key Words:  Gaussian processes (GP), Expectation maximization (EM), Multiple models, Soft sensor, Yeast concentration, Fermentation processes


XIONG Zhi-hua, HUANG Guo-hong, SHAO Hui-he. On-line estimation of concentration parameters in fermentation processes[J]. Journal of Zhejiang University Science B, 2005, 6(6): 530~534.

@article{title="On-line estimation of concentration parameters in fermentation processes",
author="XIONG Zhi-hua, HUANG Guo-hong, SHAO Hui-he",
journal="Journal of Zhejiang University Science B",
volume="6",
number="6",
pages="530~534",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.B0530"
}

%0 Journal Article
%T On-line estimation of concentration parameters in fermentation processes
%A XIONG Zhi-hua
%A HUANG Guo-hong
%A SHAO Hui-he
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 6
%P 530~534
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0530

TY - JOUR
T1 - On-line estimation of concentration parameters in fermentation processes
A1 - XIONG Zhi-hua
A1 - HUANG Guo-hong
A1 - SHAO Hui-he
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 6
SP - 530
EP - 534
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B0530


Abstract: 
It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those measurements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models’ specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line monitoring and optimization.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Abonyi, J., Babuska, R., Szeifert, F., 2002. Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics, Part B, 32(5):612-621.

[2] Andres-Toro, B., Giron-Sierra, J.M., Fernandez-Blanco, P., Lopez-Orozco, J.A., Besada-Portas, E., 2004. Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms. Journal of Zhejiang University SCIENCE, 5(4):378-389.

[3] Assis, A.J., Filho, R.M., 2000. Soft sensors development for on-line bioreactor state estimation. Computers & Chemical Engineering, 24(2-7):1099-1103.

[4] Babuska, R., 1998. Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston.

[5] Bilmes, J.A., 1998. A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. ICSI TR-97-021, U.C. Berkeley, USA.

[6] Brahim-Belhouari, S., Bermak, A., 2004. Gaussian process for nonstationary time series prediction. Computational Statistics & Data Analysis, 47(4):705-712.

[7] Gath, I., Geva, A.B., 1989. Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:773-781.

[8] Hoppner, F., Klawonn, F., Kruse, R., Runkler, T., 1999. Fuzzy Cluster Analysis-Methods for Classification, Data Analysis and Image Recognition. John Wiley and Sons.

[9] Johansen, T.A., Babuska, R., 2002. On Multi-Objective Identification of Takagi-Sugeno Fuzzy Model Parameters. Proceedings of IFAC World Congress, Barcelona, Spain, p.847-860.

[10] Kim, E., Park, M., Kim, S., Park, M., 1998. A transformed input-domain approach to fuzzy modeling. IEEE Transactions on Fuzzy Systems, 6(4):596-604.

[11] Kocijan, J., Girard, A., Banko, B., Murray-Smith, R., 2003. Dynamic Systems Identification with Gaussian Processes. Proceedings of 4th Mathmod Conference, Vienna, Int. Association for Mathematics and Computers in Simulation, p.776-784.

[12] Li, N., Li, S., Xi, Y., 2001. A multiple model approach to modeling based on LPF algorithm. Journal of Systems Engineering and Electronics, 12(3):64-70.

[13] Martin, G., 1997. Consider soft sensors. Chemical Engineering Progress, 7:66-70

[14] Murray-Smith, R., Johansen, T.A., 1997. Multiple Model Approaches to Nonlinear Modeling and Control. Taylor & Francis, London, UK.

[15] Rasmussen, C.E., Ghahramani, Z., 2002. Infinite Mixtures of Gaussian Process Experts. Advances in Neural Information Processing Systems 14, MIT Press, p.881-888.

[16] Salgado, A.M., Folly, R.O.M, Valdman, B., Valero, F., 2004. Model based soft-sensor for on-line determination of substrate. Applied Biochemistry and Biotechnology, 113(1-3):137-144.

[17] Seeger, M., 2004. Gaussian Processes for Machine Learning. Technical Report, Department of EECS, University of California at Berkeley, USA.

[18] Shi, J.Q., Murray-Smith, R., Titterington, D.M., 2002. Hierarchical Gaussian Process Mixtures for Regression. Technical Report TR-2002-107, Department of Computing Science, University of Glasgow, Scotland, UK.

[19] Shi, J.Q., Murray-Smith, R., Titterington, D.M., 2003. Bayesian regression and classification using mixtures of Gaussian processes. International Journal of Adaptive Control and Signal Processing, 17:1-16.

[20] Tham, M.T., Montague, G.A., Morris, A.J., Lant, P.A., 1991. Soft-sensors for process estimation and inferential control. Journal of Process Control, 1:3-14.

[21] Tresp, V., 2001. Mixtures of Gaussian Processes. Advances in Neural Information Processing Systems 13, MIT Press, p.654-660.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE