煤尘爆炸影响因素试验与爆炸危险等级预测研究
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摘要
为研究不同工业分析指标对爆炸性火焰长度的影响,选用不同特质的煤样,利用全自动工业分析成分测试仪和煤尘爆炸性火焰长度测试仪器对这些煤样的水分、灰分、挥发分和爆炸性火焰长度进行测定,分析各个工业分析指标对爆炸性火焰长度的影响作用,并基于试验数据通过支持向量机建立分类预测模型预测爆炸危险等级,结果表明:煤尘粒径在一定范围内,挥发分的含量的增加、水分和灰分含量的降低均可以促进煤尘爆炸性火焰长度的增长;当粒径大到一定程度时,煤尘爆炸性火焰长度不再受工业指标的影响。分类预测模型预测结果的正确率为95.83%,可利用此方法对爆炸危险等级进行预测,有助于煤尘爆炸危险性的评估。
In order to study different industrial analysis indexes affected to the length of the explosion flame,with the application of different characteristic coal samples,a full automatic industrial analysis component test device and the coal dust explosion flame length test device were applied to measure the moisture content,ash content,volatile matter and explosion flame length of the coal samples. The paper had an analysis on each industrial analysis index affected to the length of the explosion flame. With the assistance of the support vector machine,the test data were applied to establish a classified prediction model to predict the grade of the explosion danger. The results showed that when the coal dust particle size was within a certain range,the volatile matter content increased and the moisture and ash contents decreased both would promote the length increased of the coal dust explosion flame. When the dust particle size was in a certain large degree,the length of the coal dust explosion flame would not be affected by the industrial index. The predicted result accuracy of the classified prediction model was 95.83%. The method could be applied to predict the explosion danger grade and could be assistance to the evaluation of the coal dust explosion risk.
In order to study different industrial analysis indexes affected to the length of the explosion flame,with the application of different characteristic coal samples,a full automatic industrial analysis component test device and the coal dust explosion flame length test device were applied to measure the moisture content,ash content,volatile matter and explosion flame length of the coal samples. The paper had an analysis on each industrial analysis index affected to the length of the explosion flame. With the assistance of the support vector machine,the test data were applied to establish a classified prediction model to predict the grade of the explosion danger. The results showed that when the coal dust particle size was within a certain range,the volatile matter content increased and the moisture and ash contents decreased both would promote the length increased of the coal dust explosion flame. When the dust particle size was in a certain large degree,the length of the coal dust explosion flame would not be affected by the industrial index. The predicted result accuracy of the classified prediction model was 95.83%. The method could be applied to predict the explosion danger grade and could be assistance to the evaluation of the coal dust explosion risk.
引文
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[14]刘立霞,马军海.基于LS-SVM的石油期货价格预测研究[J].计算机工程与应用,2008,44(32):230-231.LIU Lixia,MA Junhai.Squared support vector machine for petroleum futures price prediction[J].Computer Engineering and Applications,2008,44(32):230-231.
[15]LENG J F,JING S X.Mine fan fault diagnosis based on EMD and SVM[J].Applied Mechanics and Materials,2011,63(6):449-452.
[16]KARAN S K,SAMADDER S R.Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas[J].Environmental monitoring and assessment,2016,188(8):486-492.
[17]SNEHAMOY C,ANSUMAN D,SUKUMAR B.Ensemble support vector machine algorithm for reliability estimation of a mining machine[J].Quality and Reliability Engineering International,2014,31(8):1503-1516.
[18]朱天一,汪春秀,王定成.基于SVM的梅雨量预测方法[J].微计算机信息,2012,28(13):169-170.ZHU Tianyi,WANG Chunxiu,WANG Dingcheng.Prediction of Meiyu based on support vector machine[J].Microcomputer Information,2012,28(13):169-170.
[19]李雨成,刘天奇.煤质指标对煤尘爆炸性火焰长度影响作用的主成分分析[J].中国安全生产科学技术,2015,11(3):40-46.LI Yucheng,LIU Tianqi.Principal component analysis of impact of coal quality index on flame length in coal dust explosion[J].Journal of Safety Science and Technology,2015,11(3):40-46.
[20]王小川,史峰,郁磊,等.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013:102-104.
[2]李雨成,毕秋苹,刘天奇.混合煤质协同作用下煤尘爆炸性火焰特性研究[J].煤炭科学技术,2017,45(4):68-71.LI Yucheng,BI Qiuping,LIU Tianqi.Study on flame features of coal dust explosion under coordinated role of mixed coal qualities[J].Coal Science and Technology,2017,45(4):68-71.
[3]刘天奇,李雨成.煤质指标与煤尘爆炸性关联试验研究[D].葫芦岛:辽宁工程技术大学,2015:1-3.
[4]刘贞堂,郭汝林,喜润泽.煤尘爆炸特征参数影响因素研究[J].工矿自动化,2014,40(8):30-33.LIU Zhentang,GUO Rulin,XI Runze.Research of influence factors on characteristic parameters of coal dust explosion[J].Industry and Mine Automation,2014,40(8):30-33.
[5]关祥艾,于小翠,李文文.煤尘爆炸实验室检测方法分析[J].广东化工,2013,40(6):86-86.GUAN Xiangai,YU Xiaocui,LI Wenwen.The detection methods of the laboratory coal dust explosion[J].Guangdong Chemical,2013,40(6):86-86.
[6]常溪溪.煤工业分析与煤尘爆炸鉴定火焰长度相关性研究[J].煤炭与化工,2017,40(4):25-26.CHANG Xixi.Analysis of coal industry and study on correlation of flame length in coal dust explosion[J].Coal and Chemical Industry,2017,40(4):25-26.
[7]何朝远.矿井瓦斯煤尘爆炸后果严重度的模糊综合评价[J].矿业安全与环保,2000,27(6):31-32.HE Chaoyuan.Fuzzy comprehensive evaluation on the severity of mine gas dust explosion[J].Mining Safety and Environmental Protection,2000,27(6):31-32.
[8]李庆钊,翟成,吴海进,等.基于20L球形爆炸装置的煤尘爆炸特性研究[J].煤炭学报,2011,36(S1):119-124.LI Qingzhao,ZHAI Cheng,WU Haijin,et al.Investigation on coal dust explosion characteristics using 20 L explosion sphere vessels[J].Journal of China Coal Society,2011,36(S1):119-124.
[9]王育德,曲志明.煤尘浓度和粒度对煤尘燃烧爆炸特性影响的实验研究[J].中国矿业,2013(8):136-140.WANG Yude,QU Zhiming.Experimental study on the impact of coal dust concentration and particles on combustion and explosion characteristics[J].China Mining Magazine,2013(8):136-140.
[10]GB/T 483—2007,煤炭分析试验方法一般规定[S].
[11]AQ 1045—2007,煤尘爆炸性鉴定规范[S].
[12]王杰,黄卫,汪春梅.煤尘爆炸性鉴定中瞬间火焰长度检测技术的研究[J].中国安全科学学报,2009,19(12):79-84,201.WANG Jie,HUANG Wei,WANG Chunmei.Study on the technology of instaneous flame length detection in coal dust explosivity appraisal[J].China Safety Science Journal,2009,19(12):79-84,201.
[13]王艳斌,唐一科,张济生.基于图像处理的火焰监视控制系统[J].兵工自动化,2007(1):63-64.WANG Yanbin,TANG Yike,ZHANG Jisheng.Flame supervision and control system based on digital image processing[J].Automatic Measurement and Control,2007(1):63-64.
[14]刘立霞,马军海.基于LS-SVM的石油期货价格预测研究[J].计算机工程与应用,2008,44(32):230-231.LIU Lixia,MA Junhai.Squared support vector machine for petroleum futures price prediction[J].Computer Engineering and Applications,2008,44(32):230-231.
[15]LENG J F,JING S X.Mine fan fault diagnosis based on EMD and SVM[J].Applied Mechanics and Materials,2011,63(6):449-452.
[16]KARAN S K,SAMADDER S R.Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas[J].Environmental monitoring and assessment,2016,188(8):486-492.
[17]SNEHAMOY C,ANSUMAN D,SUKUMAR B.Ensemble support vector machine algorithm for reliability estimation of a mining machine[J].Quality and Reliability Engineering International,2014,31(8):1503-1516.
[18]朱天一,汪春秀,王定成.基于SVM的梅雨量预测方法[J].微计算机信息,2012,28(13):169-170.ZHU Tianyi,WANG Chunxiu,WANG Dingcheng.Prediction of Meiyu based on support vector machine[J].Microcomputer Information,2012,28(13):169-170.
[19]李雨成,刘天奇.煤质指标对煤尘爆炸性火焰长度影响作用的主成分分析[J].中国安全生产科学技术,2015,11(3):40-46.LI Yucheng,LIU Tianqi.Principal component analysis of impact of coal quality index on flame length in coal dust explosion[J].Journal of Safety Science and Technology,2015,11(3):40-46.
[20]王小川,史峰,郁磊,等.MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013:102-104.
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