Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network

Abstract

An effective method for preventing spontaneous combustion of coal stockpiles on the ground is to control the air-flow in loose coal. In order to determine and predict accurately oxygen concentrations and temperatures within coal stockpiles, it is vital to obtain information of self-heating conditions and tendencies of spontaneous coal combustion. For laboratory conditions, we designed our own experimental equipment composed of a control-heating system, a coal column and an oxygen concentration and temperature monitoring system, for simulation of spontaneous combustion of block coal (13–25 mm) covered with fine coal (0–3 mm). A BP artificial neural network (ANN) with 150 training samples was gradually established over the course of our experiment. Heating time, relative position of measuring points, the ratio of fine coal thickness, artificial density, voidage and activation energy were selected as input variables and oxygen concentration and temperature of coal column as output variables. Then our trained network was applied to predict the trend on the untried experimental data. The results show that the oxygen concentration in the coal column could be reduced below the minimum still able to induce spontaneous combustion of coal — 6% by covering the coal pile with fine coal, which would meet the requirement to prevent spontaneous combustion of coal stockpiles. Based on the prediction of this ANN, the average errors of oxygen concentration and temperature were respectively 0.5% and 7 °C, which meet actual tolerances. The implementation of the method would provide a practical guide in understanding the course of self-heating and spontaneous combustion of coal stockpiles.