Development of a Multi-Model Strategy Based Soft Sensor Using Gaussian Process Regression and Principal Component Analysis in Fermentation Processes

Abstract

In fermentation processes, single model based soft sensors cannot guarantee prediction performance owing to process characteristics of non-linearity, shifting operating modes, dynamics and uncertainty. In this paper, a novel multi-model based modeling method using Gaussian process regression (GPR) and principal component analysis (PCA) was proposed to construct a soft sensor for biomass concentration estimation in fermentation processes. In the method, principal components (PCs) extracted from original process data are firstly used to build GPR based sub-models. Then, to obtain final predictions, posteriori probabilities of the GPR based sub-models are used to combine outputs of sub-models. The proposed soft sensor was validated on simulation data of a Penicillin fermentation process. For comparisons, several other soft sensor models, e.g. GPR, back-propagation neural network (BP-NN) and least square support vector machine (LSSVM), were also studied. Results show that the proposed soft sensor has better prediction accuracy and smaller confidence intervals.