A novel metabolic model incorporating directed signal flow diagram with enzymatic activities data for evaluating glutamate yield in glutamate fermentation

Cao, Yan; Mpofu, Enock; Shi, Zhongping

doi:10.1016/j.bej.2013.05.015

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…The concentrations of glutamate and cells were measured as previously described [17]. The O 2 and CO 2 concentrations in the exhaust gas were measured with a gas analyser (LKM2000A, Lokas Co., Korea); DO was continuously monitored by a sterilizable polarographic electrode (Mettler-Toledo), the DO electrode was calibrated by a zero-point fixed technology; the CO 2 evolution rate (CER) and O 2 uptake rate (OUR) were calculated accordingly.…”

Section: Methodsmentioning

confidence: 99%

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On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations

Zheng

Pan

2019

Applied Sciences

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In this paper, an on-line control strategy that aims to guarantee the maintenance of a minimum dissolved oxygen (DO) concentration during aerobic fed-batch fermentations is proposed. It is a difficult task to maintain the DO concentration, especially during fed-batch fermentation, due to strongly nonlinear, variable conditions and probe dynamics. The algorithm uses information contained in the slope of the profile of the DO, as this evolves in a timely way to adapt to process variations. Moving window technology was used to track the DO tendency variation. This method was tested in Corynebacterium glutamicum and Pichia pastoris fermentations. The performance of tendency control was compared with that of manual control. The experimental results clearly show that the on-line tendency control of DO is effective and can also reduce the frequency of activity of the controller as well as the manpower burden.

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Section: Methodsmentioning

confidence: 99%

“…The composition of the media and the fermentation conditions were identical to those previously described [15][16][17]. C. glutamicum S9114 was cultured for glutamate production at 32 • C in a 5 L fermentor (BIOTECH-5BG, Baoxing Biotech Equipment Co., Shanghai, China).…”

Section: Fermentation Conditionsmentioning

confidence: 99%

On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations

Zheng

Pan

2019

Applied Sciences

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“…glutamicum S9114 was used in the present study, it was kept by the laboratory of industrial biotechnology, Jiangnan University. The fermentation conditions and the compositions of medium were the same as those ahead reported (Zhang et al, 2005;Xiao et al, 2006;Ding et al, 2012;Cao et al, 2013;Zheng and Pan, 2016).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…Temperature was controlled at about 32°C. Electronic balances, which was connected to a PC via RS232, was used to compute the glucose and ammonia consumption rates (Zhang et al, 2005;Xiao et al, 2006;Ding et al, 2012;Cao et al, 2013;Zheng and Pan, 2016).…”

Section: Experimental Methodsmentioning

confidence: 99%

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

Zheng¹,

Pan²

2017

American Journal of Biochemistry and Biotechnology

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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 O 2 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.

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“…Glutamate is widely used in food, pharmaceutical and other industries, especially used as a flavor enhancer with huge market requirement in oriental countries (China, Japan, Korea, etc.) (Cao et al, 2013;Hasegawa et al, 2008). …”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

Zheng¹,

Pan²

2016

American Journal of Biochemistry and Biotechnology

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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.

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A novel metabolic model incorporating directed signal flow diagram with enzymatic activities data for evaluating glutamate yield in glutamate fermentation

Cited by 7 publications

References 12 publications

On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations

On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations

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

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

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