Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory

doi:10.1016/j.cjche.2014.06.043

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (6): 1009-1016.DOI: 10.1016/j.cjche.2014.06.043

Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory

Jianwen Yang, Xiangguang Chen, Huaiping Jin

School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China

收稿日期:2014-05-04 修回日期:2014-06-30 出版日期:2015-06-28 发布日期:2015-07-09
通讯作者: Xiangguang Chen

Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory

Jianwen Yang, Xiangguang Chen, Huaiping Jin

School of Chemical Engineering and Environment, Beijing Institute of Technology, Beijing 100081, China

Received:2014-05-04 Revised:2014-06-30 Online:2015-06-28 Published:2015-07-09
Contact: Xiangguang Chen

摘要/Abstract

摘要： Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yieldwill be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information fromthe changing trend of each variable, integral and accurate prediction results for contamination can be achieved. Thismakes preventive and correctivemeasures be taken promptly. The field experimental results showthat themethod can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.

关键词: Chlortetracycline fermentation, Online prediction of contamination, Dezert&ndash, Smarandache theory, Self-recursive wavelet neural network, Unscented kalman filter

Abstract: Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yieldwill be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer-Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information fromthe changing trend of each variable, integral and accurate prediction results for contamination can be achieved. Thismakes preventive and correctivemeasures be taken promptly. The field experimental results showthat themethod can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.

Key words: Chlortetracycline fermentation, Online prediction of contamination, Dezert&ndash, Smarandache theory, Self-recursive wavelet neural network, Unscented kalman filter

Jianwen Yang, Xiangguang Chen, Huaiping Jin. Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory[J]. Chinese Journal of Chemical Engineering, 2015, 23(6): 1009-1016.

Jianwen Yang, Xiangguang Chen, Huaiping Jin. Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory[J]. Chin.J.Chem.Eng., 2015, 23(6): 1009-1016.

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Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory

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[2]	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.