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

Rongjian Zheng,Feng Pan

doi:10.3844/ajbbsp.2017.90.98

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.

Highlights

Glutamate is commercially one of the most important amino acids produced mainly by fermentation process, its fermentative production amount exceeds 2.2 million tons annually (Xiao et al, 2006; Khan et al, 2005)
Once the phase recognition of fermentation process has been determined, the proposed Multi-Phase SVR (MPSVR) prediction models are built for online product quality prediction of glutamate fermentation base on the phase partition results
A soft sensor prediction model based on Multi-Phase SVR (MPSVR) was proposed for online quality prediction in glutamate fermentation process

Summary

Introduction

Glutamate is commercially one of the most important amino acids produced mainly by fermentation process, its fermentative production amount exceeds 2.2 million tons annually (Xiao et al, 2006; Khan et al, 2005). In addition to run-to-run modifiability, this could result in abnormal situation, in which any deviation from desired operating regimes could lead to product quality change. This provides a strong incentive for automating operation supervision. During manipulation, the product quality and batch performance are desired to be controlled and monitored by way of offline laboratory assays of concentrations of the product, substrates and biomass, which might take up to 2 h These laboratory assays are high investment costs, manpower inputs and time consuming, are obtained at low frequencies and may not necessarily acquire timely information about the fermentation status of the batch.

Methods

Results

Conclusion