基于人工神经网络的发酵过程动态模式及优化

DYNAMIC MODELING AND OPTIMIZATION OF FERMENTATION PROCESS BASED ON THE ARTIFICIAL NEURAL NETWORKS

人工神经网络从植物数据中学习非线性基本过程的能力可有助于发酵过程的控制。本文旨在研究柠檬发酵过程，并建立了两个柠檬发酵过程的人工神经网络模型。我们研究了网络模型在优化和状态跟踪上的应用并得出优化的柠檬发酵过程控制。用这种方法，可缩短发酵时间，降低能量消耗。

The ability of artificial neural networks to leam essential process of non linearities from plant data may provide a means by which to assist fermentation process in being controlled. In this paper, the citric fermentation process was studied and two different artificial neural networks models of citric fermentation were set up. The uses of the network models for optimization and state trace were studied and citric fermentation were carried out on the basis of the optimization. Using this method, the yield of citric was increased, the time of fermentation was shortened and the energy consumption was reduced.