The Influence of a CGA-BP Neural-Network-Based Aeration Oxygen Supply Prediction Model on the Maturity of Aerobic Composting

Abstract

In order to improve the problem of low oxygen supply efficiency during aerobic composting and prolong composting maturity, a genetic algorithm was used to optimize the initial weights and thresholds of the standard BP neural network and obtain the optimal parameters, and then a clonal selection algorithm was used to optimize the mutation operator in the genetic algorithm and duplicate the operator. A CGA-BP neural network based on an aeration oxygen supply prediction model was constructed, and the aeration oxygen supply predicted by the model was used to ferment the compost and accelerate the process of compost maturation. The results show that compared with the standard BP neural network algorithm and the GA-BP neural network algorithm, this model has accurate prediction performance in predicting aeration oxygen supply, with a prediction accuracy of 99.26%. The aeration oxygen supply predicted based on the CGA-BP model can effectively promote the composting maturity process and meet the needs of aeration oxygen supply throughout the entire fermentation process of aerobic compost.