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[1]刘超,雷晨,李树刚,等.基于CNN-GRU的瓦斯浓度预测模型及应用*[J].中国安全生产科学技术,2022,18(9):62-68.[doi:10.11731/j.issn.1673-193x.2022.09.009]
　LIU Chao,LEI Chen,LI Shugang,et al.Prediction model of gas concentration based on CNN-GRU and its application[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(9):62-68.[doi:10.11731/j.issn.1673-193x.2022.09.009]
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基于CNN-GRU的瓦斯浓度预测模型及应用*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年9期
页码:
62-68
栏目:
职业安全卫生管理与技术
出版日期:
2022-09-30

文章信息/Info

Title:
Prediction model of gas concentration based on CNN-GRU and its application
文章编号:
1673-193X(2022)-09-0062-07
作者:
刘超; 雷晨; 李树刚; 薛俊华; 张超
（1.西安科技大学 安全科学与工程学院，陕西 西安 710054；
2.西部矿井开采及灾害防治教育部重点实验室，陕西 西安 710054）
Author(s):
LIU Chao;  LEI Chen;  LI Shugang;  XUE Junhua;  ZHANG Chao
(1.College of Safety Science and Engineering,Xi’an University of Science and Technology,Xi’an Shaanxi 710054,China;
2.Key Laboratory of Western Mine Exploitation and Hazard Prevention of the Ministry of Education,Xi’an Shaanxi 710054,China)
关键词:
煤矿安全; 瓦斯治理; 深度学习; 瓦斯浓度预测
Keywords:
coal mine safety;  gas control;  deep learning;  gas concentration prediction
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.09.009
文献标志码:
A
摘要:
为解决传统瓦斯浓度预测方法预测精度低和适用性不强等问题，提出运用卷积神经网络（CNN）提取瓦斯浓度时间序列的变化趋势及局部关联特征，应用门自适应矩估计（Adam）优化的控循环单元神经网络（GRU），在关联特征基础上进行时序性预测的组合方法，并以铜川玉华煤矿监测数据为样本，对比CNN-GRU组合模型、传统机器学习模型LSTM和GRU模型的预测效果。研究结果表明：CNN-GRU模型的预测精度和收敛速度均优于LSTM和GRU模型；CNN-GRU平均绝对误差和均方根误差分别可降低至0.042，0.006，运行效率分别提高59.15%，35.04%，研究结果可为矿井瓦斯灾害防治提供依据。
Abstract:
Aiming at the problems of low prediction accuracy and weak applicability of the traditional methods for gas concentration prediction,the convolution neural network (CNN) was applied to extract the change trend and local correlation characteristics of gas concentration time series,and a combination method of time series prediction based on the correlation characteristics was put forward by using the controlled loop unit neural network (GRU) optimized by the gate adaptive moment estimation (Adam).Taking the monitoring data of Yuhua coal mine in Tongchuan as the sample,the prediction effect of CNN-GRU combined model was compared with those of the traditional machine learning LSTM model and GRU model.The results showed that the CNN-GRU model was better than LSTM model and GRU model in the prediction accuracy and convergence speed.The average absolute error and root mean square error of CNN-GRU could be reduced to 0.042 and 0.006 respectively,and the operation efficiency increased by 59.15% and 35.04% respectively,with higher application value.The results can provide basis for the gas disaster prevention and control in mines.

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备注/Memo

备注/Memo:
收稿日期： 2021-06-24
* 基金项目： 国家自然科学基金项目(51874233)
作者简介： 刘超，博士，教授，主要研究方向为矿井瓦斯灾害防治、矿山灾害力学与矿山应急救援。
更新日期/Last Update: 2022-10-14