Research On The Evaluation Method Of Aerobic Composting Maturity Based On Particle Swarm Optimization Algorith

With the further attention to the ecological environment,the effective use of agricultural organic waste is one of the ways to solve environmental pollution and realize resource recycling.Aerobic compost can rationally use agricultural organic waste,and carry out high-temperature composting,and the mature compost can be used as a soil amendment or organic fertilizer product.In view of this,exploring and studying the evaluation method of aerobic compost decay is of great significance to agriculture-guided composting,and the use of modern neural network model can further improve the efficiency of aerobic compost decay evaluation and provide more thinking directions for the study of aerobic compost decay.The main research contents in this paper are:1.Study the influencing factors in the fermentation process of aerobic compost and the methods and models for evaluating the ripeness of aerobic compost.Based on this,the weight of each index is calculated by the independence coefficient weight method,and the five indicators of temperature,humidity,p H,conductivity and aeration capacity are selected to comprehensively evaluate the ripeness of aerobic compost,and the compost decay evaluation system required by the research model in this paper is constructed.2.In this paper,the BP neural network model,particle swarm optimization BP neural network model and particle swarm optimization support vector machine model are constructed respectively,and the aerobic compost decay maturity is evaluated by the three models,and the prediction accuracy and performance of the three models are compared and studied.The experimental results show that the recognition accuracy of the PSO-BP neural network model and the PSO-SVM model is good,and the prediction accuracy of the models established by the three reactors(No.1 reactor: cow manure + straw,No.2 reactor: swamp sludge + straw,No.3reactor: chicken manure + straw)is more than 90%.The model can be selected based on the size of the sample data,and when the data set is a large sample,the PSO-BP model is selected,and the recognition accuracy reaches more than 93%.When the data set is a small sample,the PSO-SVM model is selected to achieve a recognition accuracy of more than 95%.3.Build an aerobic compost maturity evaluation software system based on experiments.The system includes two modules: automatic collection of compost data and evaluation of compost maturity.The automatic collection module can collect indicators such as compost temperature,humidity,room temperature,oxygen concentration,etc;The compost maturity evaluation module can evaluate the compost maturity based on the PSO-BP model and PSO-SVM model.Utilizing the aerobic compost maturity evaluation software system to improve the efficiency of compost maturity evaluation and provide guidance and assistance for effective fertilization in agriculture.