Prediction of Airflow Temperature of Shafts with Water Dropping Based on PSO-BP Neural Network

In order to solve the problem of forecasting airflow temperature in the bottom of the shaft, a new model of forecasting airflow temperature in the bottom of the shaft with Matlab programming is built by taking BP neural network as model and using PSO algorithm to optimize the network weights and threshold. According to the analysis of the influencing factors of airflow temperature in the bottom of the shaft in a coal mine in Huainan, it is found that the airflow temperature, wet bulb temperature, atmospheric pressure on the ground and wet bulb temperature in the bottom of the shaft have greater influence. According to the test data that analyzed by the PSO-BP model and BP model, the results show that the model with fast convergence and high prediction accuracy is one of the most effective methods of forecasting nonlinear variation of airflow temperature in the bottom of the shaft.