Microbial Fermentation Optimization Method Based on Particle Swarm Optimization

Abstract

The invention discloses a method for optimizing microbial fermentation based on a particle swarm algorithm. The method comprises the following steps: establishing a microbial fermentation data set, randomly dividing the microbial fermentation data set into two parts, a training data set and a testing data set, constructing a BP Neural network, training the BP neural network, binary encoding each of the microbial fermentation control parameters to obtain the initial population of particles, using the BP neural network as a fitness function to calculate the fitness value of each particle, and executing the particle's own extreme value The operator, the global extremum operator of the execution particle, and the operation operator of the speed-displacement model use the BP neural network as the fitness function to calculate the fitness value of the new generation of particles after updating the position, and obtain the optimal combination of control parameters. The present invention can obtain the optimal control parameter combination according to the existing fermentation data, without redesigning the experiment.

Description

Technical field [0001] The invention relates to the field of microbial fermentation, in particular to a method for optimizing microbial fermentation based on particle swarm algorithm. Background technique [0002] The microbial fermentation process is a highly complex and non-linear process, and it is difficult to model the fermentation process with an accurate mathematical model. In recent years, with the development of computational intelligence algorithms, more and more computational intelligence algorithms have been applied to modeling and optimal control of microbial fermentation processes. For example, using BP neural network and genetic algorithm to model and optimize the fermentation process. In the prior art, BP neural network and genetic algorithm are often used to model and optimize the fermentation process separately. There are also joint applications of BP neural network and genetic algorithm to model and optimize the fermentation process, but this joint applicatio...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the above-mentioned technical problems, the present invention provides a microbial fermentation optimization method based on particle swarm optimization algorithm, which is used to solve the need to pre-design fermentation experiments in the existing microbial fermentation control process, and it is not easy to obtain the optimal fermentation individual and optimal fermentation control parameters. question

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