基于SSA-RBF神经网络的煤自然发火预测模型  

Prediction model of coal spontaneous combustion based on SSA-RBF neural network

作　　者：高飞[1,2] 梁宁[1] 贾喆 侯青 GAO Fei;LIANG Ning;JIA Zhe;HOU Qing(School of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125130,China;Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education,Huludao Liaoning 125130,China;Jizhong Energy Group,Xingtai Hebei 054099,China)

机构地区：[1]辽宁工程技术大学安全科学与工程学院,辽宁葫芦岛125130 [2]矿山热动力灾害与防治教育部重点实验室,辽宁葫芦岛125130 [3]河北冀中能源股份有限公司,河北邢台054099

出　　处：《中国安全科学学报》2024年第8期128-137,共10页China Safety Science Journal

基　　金：国家自然基金面上项目资助(51874161)。

摘　　要：为解决传统煤自燃预测模型预测状态单一和预测精度不高的问题,提出基于麻雀搜索算法(SSA)优化的径向基(RBF)神经网络煤自然发火预测模型。首先,采用程序升温试验分析煤样指标气随温度的变化特征,将煤自然发火过程按煤温分为缓慢(80≤t_(i)<120℃)、加速(120≤t_(i)<160℃)和激烈(t_(i)≥160℃)3个氧化阶段,同时分析这3个阶段指标气与煤温的灰色关联度;其次通过不同维度测试函数检验粒子群算法(PSO)、灰狼算法(GWO)和SSA算法性能;最后利用6个矿区数据验证基于SSA-RBF神经网络的煤自燃预测模型的优越性。结果显示,缓慢氧化阶段CO/ΔO_(2)、CO、C_(2)H_(4)这3种指标气体与煤温的灰色关联系数最大;而加速氧化阶段C_(2)H_(4)/C_(2)H_(6)、CO/ΔO_(2)、CO_(2)/CO_(3)种指标与煤温的灰色关联系数最大。3种不同维度函数的测试结果表明:SSA与PSO、GWO相比具有更好的全局搜索能力和稳定性,其收敛速度更快;神经元数量为5个、迭代次数为300次时,SSA-RBF神经网络预测模型对缓慢氧化和加速氧化阶段的预测准确性分别达到了99%和93%。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≤t_(i)<120℃),accelerated oxidation stage(120≤t_(i)<160℃)and intense oxidation stage(t_(i)≥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/ΔO_(2),CO and C_(2)H_(4) with coal temperature are the largest_(i)n the slow oxidation stage.The grey correlation coefficient between C_(2)H_(4)/C_(2)H_(6),CO/ΔO_(2),CO_(2)/CO and coal temperature is the largest_(i)n 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.

关 键 词：麻雀搜索算法(SSA) 径向基函数(RBF)神经网络 煤自然发火 预测模型 指标气 灰色关联度 

分 类 号：X936[环境科学与工程—安全科学]