Integrated Soft Sensor Modeling of Fermentation Process Based on Transfer Component Analysis

doi:10.16182/j.issn1004731x.joss.21-1165

Abstract

Abstract:

The Penicillin fermentation process is an uncertain and multi-stage process. There are different working conditions among different batch fermentation processes, and the distribution of process data is not necessarily the same, which degrades the performance of the traditional soft sensing model. Combined with the transfer learning strategy and Gaussian mixture model, a multi-model ensemble soft sensor modeling method based on transfer component analysis is proposed. In this method, the transfer component analysis is used to get the shared feature mapping matrix between samples, and adapt the edge probability distribution of labeled dataset and unlabeled dataset; the modeling data are clustered based on Gaussian mixture model, then use partial least squares algorithm to establish an ensemble model to predict dominant variables. The simulation results based on penicillin platform data show that the proposed method can not only improve the accuracy of the soft sensor model of penicillin fermentation process effectively, but also adapt to the prediction of penicillin concentration under variable working conditions.

Key words: transfer component analysis, ensemble, penicillin fermentation, variable working conditions, soft sensor

CLC Number:

TP274

Yuesheng Zhou, Weili Xiong. Integrated Soft Sensor Modeling of Fermentation Process Based on Transfer Component Analysis[J]. Journal of System Simulation, 2023, 35(3): 623-631.

Figures/Tables 9

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算法	RMSE	MAE	R²
LWPLS	0.031 5	0.021 9	0.995 3
GMM-PLS	0.025 4	0.019 6	0.996 9
TCA-GMM-PLS	0.006 1	0.004 1	0.999 8

算法	RMSE	MAE	R²
LWPLS	0.133 6	0.081 2	0.916 2
GMM-PLS	0.061 9	0.045 7	0.982 0
TCA-GMM-PLS	0.048 2	0.026 4	0.989 1

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