摘要
为了精准快速预测矿井煤自燃等级,提出一种结合贝叶斯优化方法、极限梯度提升回归树(XGBoost)的煤自燃分级预警模型。以山东省某矿的煤自然发火实验数据为基础,依据指标气体曲线随温度的变化趋势,划分煤自燃预警等级。同时将数据划分为训练集与测试集,选取O_(2)、CO、C_(2)H_(4)、CO/ΔO_(2)、C_(2)H_(4)/C_(2)H_(6)指标气体作为XGBoost模型的输入,煤温等级作为模型的输出,同时采用贝叶斯优化方法对XGBoost模型中的学习率、n_estimators、最大深度等超参数寻优,构建基于贝叶斯优化的XGBoost煤自燃分级预警模型(BO-XGBoost),并将测试集数据带入到BO-XGBoost、XGBoost、BO-RF、BO-SVM、BO-KNN模型中进行比较分析,结果显示,BO-XGBOOST模型预测准确率为91%,较BO-RF、BO-SVM、BO-KNN准确率分别提高3%、9%、12%。通过在唐山东欢坨煤矿煤自燃分级预警中应用,进一步验证了BO-XGBoost模型的普适性与稳定性,表明建立的BO-XGBoost模型更适合煤自燃分级预警。
In order to accurately and quickly predict the level of coal spontaneous combustion in mine,a classification early warning method of coal spontaneous combustion combining with Bayesian optimization method and XGBoost model is proposed.Based on the experimental data of coal spontaneous combustion in a mine in Shandong province,firstly,the early warning levels of coal spontaneous combustion are classified according to the change trend of index gas curve with temperature.Secondly,the data are divided into training set and test set,and the index gases of O_(2),CO,C_(2)H_(4),CO/ΔO_(2),C_(2)H_(4)/C_(2)H_(6) are selected as the input of the XGBoost model,and the coal temperature grade is taken as the output of the model.At the same time,Bayesian optimization is used to optimize the parameters of XGBoost,such as learning rate,n_estimators,maximum depth.The XGBoost model of coal spontaneous combustion classification early warning based on Bayesian optimization algorithm(BO-XGBoost) is constructed.Then,the test set data is brought into BO-XGBoost,XGBoost,BO-RF,BO-SVM,BO-KNN for comparative analysis.The results show that the prediction accuracy of BO-XGBoost model is 91%,which increased 3%,9%,12% than that of BO-RF,BO-SVM and BO-KNN respectively.Finally,the applicability and stability of the BO-XGBoost Algorithm are further verified by its application in the early warning of spontaneous combustion classification in a mine in Tangshan.The results show that the BO-XGBoost model is more suitable for the early warning of coal spontaneous combustion.
作者
周旭
王认卓
代亚勋
张九零
孙玉雯
ZHOU Xu;WANG Ren-zhuo;DAI Ya-xun;ZHANG Jiu-ling;SUN Yu-wen(College of Mining Engineering,North China University of Science and Technology,Tangshan 063210,China;College of Artificial Intelligence,North China University of Science and Technology,Tangshan 063210,China)
出处
《煤炭工程》
北大核心
2022年第8期108-114,共7页
Coal Engineering
基金
河北省博士研究生创新资助项目(CXZZBS2021102)
河北省高校基本科研业务费(JQN2021011)。
关键词
煤自燃
指标气体
贝叶斯优化
XGBoost
coal spontaneous combustion
index gases
Bayesian optimization
XGBoost
