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

• Main Authors: C. Mei, Y. Chen, H. Zhang, X. Chen, G. Liu
• Format: Article
• Language: English
• Published: AIDIC Servizi S.r.l. 2017-10-01
• Series: Chemical Engineering Transactions
• Online Access: https://www.cetjournal.it/index.php/cet/article/view/115

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.