西安科技大学学报2023，Vol.43Issue(6)：1088-1098,11.DOI:10.13800/j.cnki.xakjdxxb.2023.0607
煤自燃指标气体分析与分级预警
Index gas analysis and grading early warning of coal spontaneous combustion
摘要
Abstract
Coal spontaneous combustion seriously restricts the safety production of mines.In order to accurately predict the risk of coal spontaneous combustion,a temperature-programmed experimental system was adopted to test and analyze various gas products and their concentration changes in coal samples with different particle sizes in Shaqu No.1 Coal Mine.Furthermore,a random forest ensemble learning method was introduced to establish a grading and early warning model for the risk of coal spontaneous combustion,and the verification analysis was carried out by the spontaneous combustion test in Dafosi Coal Mine.The results show that the smaller the particle size,the larger the contact area between coal and oxygen,the more intense the coal oxygen reaction,and the higher the concentration of gas products.C2H6 belongs to the gas existing in the coal body,which appeared in the initial stage of the test.However,C2H4 only appears when the temperature rises to around 120 ℃,which is a product of coal oxidation cracking and can be used as an index gas for coal spontaneous combustion in Shaqu No.1 Coal Mine.The risk grading and early warning model for coal spontaneous combustion based on random forest has achieved 100％accuracy for training samples,and under the default parameter con-dition,the prediction accuracy of test samples is as high as 96.7％.The prediction accuracy of the test set is 98.9％through the validation and analysis of the spontaneous combustion test data,and the eval-uation results of the importance of variables show that CO and C2H4 gases have the greatest impact on the risk of coal spontaneous combustion,which is consistent with the actual situation on site.These in-dicate that random forest is ideal for dealing with the complex nonlinear relationship between the risk of coal spontaneous combustion and gas products,and is suitable for predicting the risk of coal spontane-ous combustion.关键词
煤自燃/指标气体/随机森林/分级预警/变量重要度评估Key words
coal spontaneous combustion/index gas/random forest/grading early warning/variable im-portance assessment分类
矿业与冶金引用本文复制引用
江莉娟,邓存宝,王彩萍,雷昌奎,年军..煤自燃指标气体分析与分级预警[J].西安科技大学学报,2023,43(6):1088-1098,11.基金项目
国家自然科学基金项目(52204229,52274220) （52204229,52274220）
山西省基础研究计划青年科学研究项目(20210302124349) （20210302124349）
山西省高等学校科技创新项目(2021L054) （2021L054）
