|本期目录/Table of Contents|

[1]张春,隋彦臣.基于网格优化双层随机森林的采空区煤氧化升温预测研究*[J].中国安全生产科学技术,2024,20(5):177-183.[doi:10.11731/j.issn.1673-193x.2024.05.024]
　ZHANG Chun,SUI Yanchen.Prediction of coal oxidation temperature rise in goaf based on grid optimization double-layer random forest[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(5):177-183.[doi:10.11731/j.issn.1673-193x.2024.05.024]
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基于网格优化双层随机森林的采空区煤氧化升温预测研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年5期
页码:
177-183
栏目:
职业安全卫生管理与技术
出版日期:
2024-05-31

文章信息/Info

Title:
Prediction of coal oxidation temperature rise in goaf based on grid optimization double-layer random forest
文章编号:
1673-193X(2024)-05-0177-07
作者:
张春; 隋彦臣
(1.辽宁工程技术大学 安全科学与工程学院,辽宁 阜新 123000；
2.辽宁工程技术大学 矿山热动力灾害与防治教育部重点实验室,辽宁 葫芦岛 125105）
Author(s):
ZHANG Chun;  SUI Yanchen
(1.School of Safety Science and Engineering,Liaoning Technical University,Fuxin Liaoning 123000,China;
2.Key Laboratory of Mine Power Disaster and Prevention of Ministry of Education,Liaoning Technical University,Huludao Liaoning 125105,China)
关键词:
采空区; 煤氧化升温; 温度预测; 网格优化双层随机森林
Keywords:
goaf;  coal oxidation temperature rise;  temperature prediction;  grid optimization double-layer random forest
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2024.05.024
文献标志码:
A
摘要:
为了对采空区煤氧化升温的温度进行预测，在内蒙古某煤矿16402综放工作面进行长期的采空区气体和温度观测实验，采集到准确的采空区煤氧化升温过程中气体及温度数据，提出1种基于网格优化双层随机森林（WG-DRF）的采空区煤氧化升温预测方法，用该方法构建预测模型并与传统随机森林、BP神经网络和支持向量回归模型的预测结果进行对比。研究结果表明：WG-DRF模型预测的平均绝对误差MAE，均方误差MSE，决定系数R2分别为1.725，6.158，0.903，优于其他模型。通过更换数据集对WG-DRF方法进行测试，验证双层随机森林模型具有较强的泛化性。研究结果可为采空区煤氧化升温的温度预测提供参考。
Abstract:
In order to predict the temperature of coal oxidation temperature rise in goaf,a long-term observation experiment of goaf gas and temperature was carried out on the 16402 fully mechanized caving face of a coal mine in Inner Mongolia to collect accurate gas and temperature data during the process of coal oxidation heating in goaf.A method for predicting the coal oxidation temperature rise in goaf based on the grid optimization double-layer random forest (WG-DRF) was proposed.The prediction model was constructed by this method and compared with the prediction results of traditional random forest,BP neural network and support vector regression model.The results show that the mean absolute error MAE,mean square error MSE and coefficient of determination R2 of WG-DRF model are 1.725,6.158 and 0.903,respectively,which are better than the other models.The WG-DRF method is tested by changing the data set,and it verified that the double-layer random forest model has strong generalization.The research results can provide reference for the temperature prediction of coal oxidation temperature rise in goaf.

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备注/Memo

备注/Memo:
收稿日期： 2023-12-28
* 基金项目： 国家自然科学基金项目（52174183，51774170）
作者简介： 张春，博士，副教授，主要研究方向为矿井灾害防治、矿井通风和冲击地压防治。
通信作者： 隋彦臣，硕士研究生，主要研究方向为矿井灾害防治。
更新日期/Last Update: 2024-05-30