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

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.

Graphical AbstractTo predict the contamination of culture process of CTC fermentation, using the available state variables, such as the concentration of DO and carbon dioxide in exhaust, pH, temperature, liquid volume, agitator speed, air flow rate and so on, a novel method is proposed based on DSmT concept. The core of this method is to capture the change tendency of state variables by information fusion to give the probability of infection by bacilli or phage.Figure optionsDownload full-size imageDownload as PowerPoint slide