Soft Measurement of the Cell Concentration Based on SVM

Hua Meng; Hui Gao; Bo Yang Zhao

doi:10.4028/www.scientific.net/AMM.742.239

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 742Soft Measurement of the Cell Concentration Based...

Soft Measurement of the Cell Concentration Based on SVM

Abstract:

The process of Pichia pastoris fermentation has a long period and less offline data . The cell concentration and some other important variables can not be measured on line.The soft sensor modeling at present is mainly the artificial neural network (ANN).This paper introduces the Support Vector Machine (SVM). Select fewer off line data and establish soft sensor modeling about cell concentration.Compared with the BP neural network prediction model, Simulation experiment proves that the support vector machine has better prediction effect and generalization ability. template.

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Periodical:

Applied Mechanics and Materials (Volume 742)

Pages:

239-243

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.742.239

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Online since:

March 2015

Authors:

Hua Meng, Hui Gao*, Bo Yang Zhao

Keywords:

Artificial Neural Networks, Cell Concentration, Soft Sensor Modeling, SVM

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* - Corresponding Author

References

[1] Haifeng Sang, Fuli Wang, Dakuo He. On-line estimate of biomass concentration in fermentation process[J]. Journal of Northeastern University: Natural Science, 2006, 27(6): pp.602-605. In Chinese.

DOI: 10.1109/wcica.2006.1713262

Google Scholar

[2] Si.L. Daniel: Dynamic hybrid neural network model of an industrial fed-batch fermentation process to produce foreign protein. Computers and Chemical Engineering. Vol. 31(2007), pp.163-170.

DOI: 10.1016/j.compchemeng.2006.05.018

Google Scholar

[3] Mi.H. Qiang: Based on RBF neural network in the fermentation process of bacteria concentrati-on of soft measurement. Automation & Instrumentation. Vol. 160(2012), pp.178-181.

Google Scholar

[4] Xi.D. Yan, We. Yang: Soft sensor for Ammonia concentration at the Ammonia converter outlet based on an improved Group Search Optimization and BP Neural Network. Chinese Journal of Chemical Engineering. Vol. 20(2012), pp.1184-1190.

DOI: 10.1016/s1004-9541(12)60606-5

Google Scholar

[5] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(1): pp.273-297.

DOI: 10.1007/bf00994018

Google Scholar

[6] Vapnik V N. The nature of statistical learning theory[M]. First Edition. New York: Springer-Verlag, 1995. 79-87.

Google Scholar

[7] Liu G H. Zhou D W. Xu H X, Mei C I. Model optimization of a fementation soft sensor[J]. Expert Systems with Application, 2010, 37(4); 2708-2713.

DOI: 10.1016/j.eswa.2009.08.008

Google Scholar