Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation

被引：13

作者：

Li, Hui ^{[1
]}

Chen, Jiajun ^{[1
]}

Li, Xingyan ^{[1
]}

Gan, Jian ^{[1
]}

Liu, Huazong ^{[1
]}

Jian, Zhou ^{[1
]}

Xu, Sheng ^{[1
]}

Zhang, Alei ^{[1
]}

Li, Ganlu ^{[1
]}

Chen, Kequan ^{[1
]}

机构：

[1] Nanjing Tech Univ, Coll Biotechnol & Pharmaceut Engn, State Key Lab Mat Oriented Chem Engn, Nanjing 211816, Peoples R China

来源：

BIORESOURCE TECHNOLOGY | 2024年 / 393卷

基金：

中国国家自然科学基金;

关键词：

L-lysine; Fermentation kinetics; Fermentation control; Artificial neural network; Genetic algorithm; GROWTH-RATE; OPTIMIZATION; PARAMETERS; SYSTEM; LINMAP; ANN;

D O I：

10.1016/j.biortech.2023.130151

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

Fermentation plays a pivotal role in the industrialization of bioproducts, yet there is a substantial lag in the fermentation process regulation. Here, an artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative lysine fermentation control. Firstly, the strategy of coupling GA with ANN was established. Secondly, specific lysine formation rate (qp), specific substrate consumption rate (qs), and specific cell growth rate (mu) were predicted and optimized by ANN-GA. The optimal ANN model adopts a three-layer feed-forward back-propagation structure (4:10:1). The optimal fermentation control parameters are obtained through GA. Finally, when the carbon to nitrogen ratio, residual sugar con-centration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g & sdot;L-1, [1.0, 2.0] g & sdot;L-1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 +/- 5.10 g & sdot;L-1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts.

引用

页数：11

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