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

Abstract

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 (μ) 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 concentration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g·L−1, [1.0, 2.0] g·L−1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 ± 5.10 g·L−1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts.