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

基于Dezert-Smarandache理论的金霉素发酵染菌实时预测（英文）

作　　者：杨建文 陈祥光 金怀平 

机构地区：[1]School of Chemical Engineering and Environment,Beijing Institute of Technology

出　　处：《Chinese Journal of Chemical Engineering》2015年第6期1009-1016,共8页中国化学工程学报（英文版）

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 yield will 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 from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.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 yield will 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 from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.

关 键 词：ChlortetTacycline fermentation Online prediction of contamination Dezert-Smarandache theory Self-recursive wavelet neural network Unscented kalman filter 

分 类 号：TQ927[轻工技术与工程—发酵工程]