Abstract
The on-line control of glutamate fermentation process is difficult, owing to the typical uncertainties of biochemical process and the lack of suitable on-line sensors for primary process variables. A prediction model based on Gaussian Process Regression (GPR) is presented to predict glutamate concentration online. First, Partial Least Squares (PLS) is applied to extract the features of the input secondary variables to reduce the number of the variables dimension and eliminate the correlation, through variables selection to reduce model complexity and improve model tracking performance. Validation was carried out in a 5 L fermentation tank for 10 batches glutamate fermentation process. Simulation results show that the proposed model outperforms the PLS and Support Vector Machine (SVM) model and the Root Mean Square Error (RMSE) are 1.59, 7.98 and 1.95, respectively. It can provide effective operation guidance for control and optimization of the glutamate fermentation process.
Highlights
The history of the species Corynebacterium as amino acid producer started in the 1950s when Dr Kinoshita was the first to discover that Corynebacterium glutamicum is a superior amino acid producer (Hermann, 2003)
In order to evaluate the performance of the soft sensor modeling based on Gaussian Process Regression (GPR), the conventional Partial Least Squares (PLS) and Support Vector Machine (SVM) models are constructed to predict production concentration with the same fermentation data
Model is selected as 8; the Gaussian kernel is used in SVM, the hyper-parameters of SVM are tuned by grid search, c = 45.255, γ = 0.1768, ε = 0.01, whereas hyper-parameters of GPR are logΘ = [2.5187, 6.2309, 4.7544, 5.9008, 3.4810, 1.0961]T, both of which can get the best prediction performance
Summary
The history of the species Corynebacterium as amino acid producer started in the 1950s when Dr Kinoshita was the first to discover that Corynebacterium glutamicum is a superior amino acid producer (Hermann, 2003). Glutamate is one of the most important amino acids yielded mainly by fermentation using Corynebacterium glutamicum, which occupies about 53% of the world’s amino acids market and its fermentative production amount exceeds 2.2 million tons per year (Khan et al, 2005; Xiao et al, 2006). An accurate model is essential for further control and optimization of fermentation process. Some crucially primary variables, such as biomass, substrate or product concentration which are more complex and important, can not be measured on-line yet in practical fermentation process. This seriously influences the execution of optimization strategy (Gu and Pan, 2015)
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