Based on Principal Component Analysis(PCA) and fuzzy model, a simple and efficient approach to monitor the fed batch streptomycin fermentation is presented.

Principal Component Analysis,a method of multivariate statistical analysis,is used for analyzing the datum of actual industrial streptomycin fermentation.

The kinetics of erythromycin fermentationin an air-lift bioreactor;

The application of dynamic BP network and RBF network to the state estimation of process of erythromycin fermentation was studied.

To deal with the low precise measurement of the“Assumed Inherent Sensor”based ANN(artificial neural network) inverse soft-sensing method in the erythromycin fermentation process, several methods of improvements are presented to increase the precision of soft-sensor and applied to the erythromycin fermentation process in a PHysic Co.

Modeling of penicillin fermentation process based on the combination of nondominated sorting genetic algorithm Ⅱ and error back propagation network;

Type-2 fuzzy time series forecasting penicillin fermentation

In this meth- od, normally model-based-optimal algorithm and fermentation technology is combined to make the optimal result more fit for the real industrial penicillin fermentation process.