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

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