Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information

The concentration of the substrate during fermentation is often not measured online. In this paper, Gaussian process regression (GPR) is used to establish an estimation model of substrate concentration, and its soft measurement is realized. Different from traditional regression models, the GPR model can not only predict the quality value, but also provide the estimation variance. In order to improve the prediction performance of the model in the nonlinear fermentation process with correlated variables, the input variables of the model are selected by the k-nearest neighbor mutual information (k-MI) method before the model development. The application results of penicillin fermentation process show the ideal prediction performance based on the kMI-GPR model. © All Right Reserved.