|本期目录/Table of Contents|

[1]魏宁,孙亚胜男,邓立军,等.基于SVM的矿井通风阻力系数影响因素分析与预测[J].中国安全生产科学技术,2018,14(4):39-44.[doi:10.11731/j.issn.1673-193x.2018.04.006]
 WEI Ning,SUN Yashengnan,DENG Lijun,et al.Influence factors analysis and prediction on mine ventilation resistance coefficient based on SVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(4):39-44.[doi:10.11731/j.issn.1673-193x.2018.04.006]
点击复制

基于SVM的矿井通风阻力系数影响因素分析与预测
分享到:

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
14
期数:
2018年4期
页码:
39-44
栏目:
学术论著
出版日期:
2018-04-30

文章信息/Info

Title:
Influence factors analysis and prediction on mine ventilation resistance coefficient based on SVM
文章编号:
1673-193X(2018)-04-0039-06
作者:
魏宁12孙亚胜男12邓立军12黄德12郭欣12
(1.辽宁工程技术大学 安全科学与工程学院,辽宁 葫芦岛 125105;2.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):
WEI Ning12 SUN Yashengnan12 DENG Lijun12 HUANG De12 GUO Xin12
(1.College of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125105,China; 2.Key Laboratory of Mine Thermo-motive Disaster and Prevention,Ministry of Education,Huludao Liaoning 125105,China)
关键词:
矿井通风矿井通风阻力系数预测影响因子支持向量机(SVM)相关性分析
Keywords:
mine ventilation prediction of mine ventilation resistance coefficient influence factor support vector machine (SVM) correlation analysis
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2018.04.006
文献标志码:
A
摘要:
矿井通风阻力系数是通风安全最重要的基础参数之一,为了实现矿井通风阻力系数简单准确地预测,提出了利用支持向量机(SVM)来预测矿井通风阻力系数的方法。通过分析影响因子与矿井通风阻力系数的相关性关系,并利用MATLAB逐步建立单影响因素与矿井通风阻力系数、多影响因素与矿井通风阻力系数之间的SVM预测模型,对比分析各预测模型的相对误差,确定最佳矿井通风阻力系数预测模型,即当输入模型影响因素为巷道断面积、周长和支护方式时,预测相对误差小于10%的样本数占测试样本的76%,相对误差小于20%的样本数占测试样本的90%。结果表明:该预测方法在矿井通风阻力系数预测中是可行的,并具较高的准确性。
Abstract:
The mine ventilation resistance coefficient is one of the most important basic parameters of ventilation safety,and in order to realize the simple and accurate prediction of the mine ventilation resistance coefficient,a method to predict the mine ventilation resistance coefficient by using the support vector machine (SVM) was put forward. Through analyzing the correlation between the influence factors and the mine ventilation resistance coefficient,the SVM prediction models between the single influence factor and mine ventilation resistance coefficient and between the multiple influencing factors and mine ventilation resistance coefficient were established step by step by using MATLAB. The relative error of each prediction model were compared and analyzed,then the optimal prediction model of the mine ventilation resistance coefficient was determined,namely when the influence factors inputting the model were the sectional area,the perimeter and the support mode of roadway,the sample number with the relative error of prediction being less than 10% accounted for 76% of the test samples,and the sample number with the relative error of prediction being less than 20% accounted for 90% of the test samples. The results showed that this prediction method is feasible and accurate in the prediction of mine ventilation resistance coefficient.

参考文献/References:

[1]黄元平.矿井通风[M].徐州:中国矿业大学出版社.
[2]马恒,徐超,李宗翔,等.矿井通风井巷摩擦通风阻力的计算与研究[J].安全与环境学报,2011,11(5):172-174. MA Heng,XU Chao,LI Zongxiang,et al.Calculation and Research on friction wind resistance of mine ventilation shaft[J].Journal of safety and environment,2011,11(5):172-174.
[3]秦跃平,吴彪,毕永华,等.基于百米通风阻力的工作面阻力测定结果分析[J].矿业安全与环保,2015,42(1):90-93. QIN Yueping,WU Biao,BI Yonghua,et al.Analysis of resistance measurement of working face based on 100 meters wind resistance[J].Mining safety and environmental protection,2015,42(1):90-93.
[4]葛少成,刘雅俊,贾宝山.锚喷巷道通通风阻力力系数的计算式研究[J].辽宁工程技术大学学报(自然科学版),2000,19(5):466-469. GE Shaocheng,LIU Yajun,JIA Baoshan.Calculation study on ventilation resistance coefficient of anchorage and shotcrete[J].Journal of Liaoning Technical University (Natural Science ),2000,19(5):466-469.
[5]董学林,皮子坤,贾廷贵.五家沟煤矿工字钢巷道的摩擦阻力系数的研究[J].科技创新导报,2013(9):95-96,98. DONG Xuelin,PI Zikun,JIA Tinggui.Study on frictional resistance coefficient of i-i-steel roadway in five coal mines[J].Science and Technology Innovation Herald,2013(9):95-96,98.
[6]张攀.确定矿井巷道摩擦阻力系数的新方法研究[D].阜新:辽宁工程技术大学,2004.
[7]王思仪.基于多层前向神经网络的矿井巷道摩擦阻力系数的确定[D].阜新:辽宁工程技术大学,2014.
[8]刘俊娥,杨晓帆,郭章林.基于FIG-SVM的煤矿瓦斯浓度预测[J].中国安全科学学报,2013,23(2):80-84. LIU June,YANG Xiaofan,GUO Zhanglin.Prediction of coal mine gas concentration based on FIG-SVM[J].China Safety Science Journal,2013,23(2):80-84.
[9]秦洁璇,李翠平,李仲学,等.基于支持向量回归机的矿井突水量预测[J].中国安全科学学报,2013,23(5):114-119. QIN Jiexuan,LI Cuiping,LI Zhongxue,et al.Prediction of mine water inrush based on support vector regression machine[J].China Safety Science Journal,2013,23(5):114-119.
[10]汤国水,张宏伟,韩军.基于MABC-SVM的含瓦斯煤体渗透率预测模型[J].中国安全生产科学技术,2015,11(2):11-16. TANG Guoshui,ZHANG Hongwei,HAN Jun,et al.Gas permeability prediction model based on MABC-SVM[J].Journal of Safety Sciense and Technology,2015,11(2):11-16.
[11]陈祖云,张桂珍,邬长福,等.基于支持向量机的边坡稳定性预测研究[J].中国安全生产科学技术,2009,5(4):101-105. CHEN Zuyun,ZHANG Guizhen,WU Changfu,et al.Study on slope stability prediction based on support vector machine[J].Journal of Safety Sciense and Technology,2009,5(4):101-105.
[12]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42. ZHANG Xuegong. About statistical learning theory and support vector machine[J].Acta Automatica Sinica,2000,26(1):32-42.
[13]杨力,耿纪超,汪克亮.模糊支持向量机在煤与瓦斯突出预测中的研究[J].中国安全生产科学技术,2014,10(4):103-108. YANG Li,GENG Jichao,WANG Keliang.Research on prediction of coal and gas outburst by fuzzy support vector machine[J].Journal of Safety Sciense and Technology,2014,10(4):103-108.
[14]奉国和.SVM分类核函数及参数选择比较[J].计算机工程与应用,2011,47(3):123-124,128. FENG Guohe.Comparison of SVM kernel function and parameter selection[J].Computer Engineering and Applications,2011,47(3):123-124,128.
[15]董晓雷,贾进章,白洋,等.基于SVM耦合遗传算法的回采工作面瓦斯涌出量预测[J].安全与环境学报,2016,16(2):114-118. DONG Xiaolei,JIA Jinzhang,BAI Yang,et al.Prediction for gas-gushing amount from the working face of stope based on the SVM coupling genetic algorithm[J].Journal of Safety and Environment,2016,16(2):114-118.

相似文献/References:

[1]陈明生,陈伯辉,沈斐敏.矿井通风优化评价指标体系权重确定[J].中国安全生产科学技术,2011,7(3):22.
 CHEN Ming-sheng,CHEN Bo-hui,SHEN Fei-min.Weight determination of evaluation index system in mine ventilation optimization[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(4):22.
[2]张黔生,谢贤平,吴劭星,等.多风机多级机站通风网络的状态估计[J].中国安全生产科学技术,2010,6(3):75.
 ZHANG Qian-sheng,XIE Xian-ping,WU Shao-xing,et al.Static state estimation of ventilation system for multistage fan station[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2010,6(4):75.
[3]黄光球,孙鹏,陆秋琴.风窗对井下通风系统的影响及其调节与定位优化[J].中国安全生产科学技术,2014,10(3):160.[doi:10.11731/j.issn.1673-193x.2014.03.028]
 HUANG Guang qiu,SUN Peng,LU QIU qin.Influence of air windows on underground ventilation system and its adjusting and locating optimization[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(4):160.[doi:10.11731/j.issn.1673-193x.2014.03.028]
[4]张黔生,谢贤平,吴劭星.多风机多级机站通风系统优化的模糊群体决策法[J].中国安全生产科学技术,2009,5(5):88.
 ZHANG Qian sheng,XIE Xian ping,WU Shao xing.Study on optimization of mine ventilation system of multifan and multistage based on fuzzy population decision[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2009,5(4):88.
[5]刘剑,宋莹,刘明浩,等.基于LDA的均直巷道断面风速分布规律实验研究[J].中国安全生产科学技术,2015,11(12):65.[doi:10.11731/j.issn.1673-193x.2015.12.010]
 LIU Jian,SONG Ying,LIU Ming-hao,et al.Experimental study on distribution laws of wind velocity in straight roadway section based on LDA[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(4):65.[doi:10.11731/j.issn.1673-193x.2015.12.010]
[6]马恒,高巍,周西华.矿井气候多参数预测与通风网络自动解算算法研究[J].中国安全生产科学技术,2017,13(11):110.[doi:10.11731/j.issn.1673-193x.2017.11.018]
 MA Heng,GAO Wei,ZHOU Xihua.Study on multi-parameter prediction of mine climate and automatic solution algorithm of ventilation network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(4):110.[doi:10.11731/j.issn.1673-193x.2017.11.018]

备注/Memo

备注/Memo:
国家自然科学基金项目(51574142)
更新日期/Last Update: 2018-05-08