Microbial fermentation optimal control method based on improved particle swarm optimization

Abstract

Microbial fermentation is a typical microbial fermentation process. Microbial bacteria ingest the nutrients of raw materials in the fermentation tank. Under appropriate conditions, enzymes in the body catalyze complex biochemical reactions to produce microorganisms. In order to guarantee the quality of modeling data and meet the accuracy, integrity, and consistency of data quality requirements, it needs to preprocess the input and output data. In this paper, the parameter model is solved by the particle swarm algorithm. Updating the parameter value of the next moment in real time constitutes a feedback correction to the prediction model. Theil inequality approach is adopted to test the tracking performance of the above model’s adaptive correction method. The Monte Carlo method is applied to generate multiple groups of different kinetic model values, which are substituted into the fermentation kinetic model as the real model parameter values. After the experimental analysis, the measured value of the model established by the method in this paper is closer to the predicted value, which has the effect of feedback correction and optimal control. The external conditions in the fermentation process are optimally controlled to achieve the effects of shortening the production period. It improves the yield of fermentation terminal target products and reduces the consumption of raw materials.