Semi−Supervised Hybrid Modeling of the Yeast Fermentation Process

Abstract

This study focuses on modeling the yeast fermentation process using the hybrid modeling method. To improve the prediction accuracy of the model and reduce the model training time, this paper presents a semi−supervised hybrid modeling method based on an extreme learning machine for the yeast fermentation process. The hybrid model is composed of the mechanism model and the residual model. The residual model is built from the residuals between the real yeast fermentation process and the mechanism model. The residual model is used in parallel with the mechanism model. Considering that the residuals might be related to the inaccurate parameters or structure of the process, the mechanism model output is taken as unlabeled data, and the suitable inputs are selected based on Pearson’s maximum correlation and minimum redundancy criterion (RRPC). Meanwhile, an extreme learning machine is employed to improve the model’s training speed while maintaining the model’s prediction accuracy. Consequently, the proposal proved its efficacy through simulation.