Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

Abstract

Glutamate fermentation is inherently nonlinear, multi-phase and an aerobic fermentation process. As long measurement delays and expensive apparatus cost, on-line measurement of the product concentration is not necessarily available. The present fermentation process monitoring and quality prediction involve manual interpretation of highly informative, however, the concentrations of substrates, biomass and products are only low frequency off-line measurements. In this paper, we propose a novel Multi-Phase Support Vector Regression (MPSVR) based soft sensor model for online quality prediction of glutamate concentration. The glutamate fermentation process can be divided into a sequence of five phases by detecting the trend variation events (also termed as singular points or inflection point) of online measured O2 in the exhaust gas, the Inflection Point (IP) are easily identified through combining Moving Window (MW) with Pearson Correlation Coefficient (PCC). For each estimation phase, SVR soft sensor model are constructed and their performance is evaluated against fermentation data in a 5 L fermenter. The efficiency of the proposed soft sensor model for online product quality prediction has been demonstrated to be superior compared to that of reported techniques in a 5 L glutamate fermentation process.