Prediction model of coal spontaneous combustion based on SSA-RBF neural network
To solve the problems of single prediction state and insufficient prediction accuracy of the traditional coal spontaneous combustion prediction model,a prediction model based on RBF neural network optimized by SSA was proposed.Firstly,the temperature programmed test was used to analyze the variation characteristics of the index gas of coal samples with temperature.The coal spontaneous combustion process was divided into slow oxidation stage(80≤ti<120℃),accelerated oxidation stage(120≤ti<160℃)and intense oxidation stage(ti≥160℃)with coal temperature as the node.At the same time,the grey correlation degree between the index gas and coal temperature in each stage of coal spontaneous combustion was analyzed.Secondly,the performance of Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO)and SSA algorithm was tested by different dimension test functions.Finally,the superiority of the RBF neural network optimized by SSA algorithm to the coal spontaneous combustion prediction model was verified by using six mining area data.The results show that the grey correlation coefficients of CO/ΔO2,CO and C2H4 with coal temperature are the largest in the slow oxidation stage.The grey correlation coefficient between C2H4/C2H6,CO/ΔO2,CO2/CO and coal temperature is the largest in the accelerated oxidation stage.The test results of three different dimensional functions show that SSA has better global search ability,stability and faster convergence speed compared with PSO and GWO.When the number of neurons is 5 and the number of iterations is 300,the prediction accuracy of the SSA-RBF neural network prediction model for the slow and accelerated oxidation stages reaches 99％and 93％respectively.
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