Soft-sensing modeling based on multi-phases for fermentation process

For the process of Nosiheptied fermentation, a new method for soft-sensing modeling by phases is presented. By using the state equations established for the Nosiheptied fermentation process, the secondary variables are determined according to the inverse system theory. Then, fuzzy c-means clustering algorithm and neural network are used for phase identification, and for each phase, a local neural network model for soft sensing is developed Finally, the estimation is implemented by computing the sum of outputs of the developed local models weighted by the cot-responding degrees of membership from the phase identification. The testing result shows the effectiveness of the approach to the development of the soft-sensing model.