RESUMEN
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.
Asunto(s)
Algoritmos , Lisina , Fermentación , Redes Neurales de la Computación , NitrógenoRESUMEN
Packed tower reactors, mechanically stirred reactors, airlift reactors, and gas-self-inducing reactors are frequently utilized among the various types of reactors. Self-inducing reactors exhibit notable advantages owing to their simple structure, effective gas-liquid intermixing, and low energy requirements, rendering them highly suitable for bioengineering endeavors. The purpose of this analysis is to shed light on the use of self-inducing reactors in bioengineering by examining the following five parameters: critical speed, suction rate, volumetric mass transfer coefficient, power characteristics, and gas hold-up. Through a comprehensive analysis of the advancements achieved in these domains, it is possible to determine the challenges and opportunities that lie ahead in the realm of bioengineering.