Prediction of coal spontaneous combustion temperature with multi-gas index based on machine learning

Spontaneous combustion of coal in goaf is one of the main factors inducing mine fire, which accounts for 90% of mine fire. In order to achieve accurate prevention and control of spontaneous combustion in goaf, it is necessary to accurately obtain the high temperature point temperature in goaf. Based on Yanbei Coal Mine as the engineering background, this paper analyzes the index gases produced during the oxidation and heating process of coal samples, establishes the index system of coal spontaneous combustion temperature prediction, and then carries out the research of multi-index gas coal spontaneous combustion temperature prediction based on deep learning. Firstly, the coal samples collected from Yanbei Coal Mine were tested for coal oxidation and temperature rise. According to the experimental results, they were divided into single gas index and compound gas index. The change law of each index with temperature rise was analyzed, and the appropriate index was determined as the prediction index of coal spontaneous combustion temperature. The prediction index of coal spontaneous combustion temperature is processed by multi-source data processing method, the data is cleaned by Cook distance method and multiple interpolation method, and the prediction index system of coal spontaneous combustion temperature is established by grey correlation analysis method. Elman neural network was used to build a prediction model, and after determining the model structure and hyper-parameters, a prediction model of coal spontaneous combustion temperature was established to obtain an accurate prediction of the temperature in the process of coal oxidation and heating.