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

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