Soft-sensing modeling method based on Continuous Hidden Markov Model for microbial fermentation process

Abstract

Considering the temporality of microbial fermentation process, a soft-sensing modeling method based on Continuous Hidden Markov Model (CHMM) for microbial fermentation process is proposed. Firstly, in order to improve the robustness of CHMM, multi-observation training sample sequences are used to train the CHMM. And the modified Baum-Welch parameters re-estimation formula is used to optimize the parameters of CHMM. Then, the new observation vector is inputed to the CHMM model library and the emission probability of each CHMM in the model library is calculated using the Viterbi Algorithm. Finally, the soft-sensing result can be obtained by computing the weighted average. The model is applied to an erythromycin fermentation process, and case studies show that the new approach has better performance compared to the conventional method based on ANN.