Prediction of Respirable Dust Concentration in Coal Mine Based on Neural Network

Abstract

Pneumoconiosis is the most important occupational disease in China, and respiratory respirable dust is the main cause of pneumoconiosis. It can effectively reduce the incidence of pneumoconiosis by improving the monitoring and supervision level of respiratory dust concentration in the workplace. In order to solve the shortcomings of obtaining the concentration of respirable dust in mines by methods such as sampling by respirable dust samplers and numerical simulation experiments, an artificial neural network is proposed to predict the concentration of respirable dust. The factors affecting the concentration of respirable dust in coal mining face were analyzed, and the neural network structure for predicting respirable dust was established in this paper. Through training by selecting measured data, it was found that the error between the predicted result and the measured concentration was less than 15%, which was better than the error of regulations of dust measuring instruments. The results of the study have a certain reference effect on the prediction and prevention of respiratory dust in coal mines and the reduction of the incidence of pneumoconiosis.