硫化矿石自燃灾害预警的RBF神经网络模型及应用
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摘要
为了科学准确地确定硫化矿石自燃灾害预警等级,进而为灾害防控提供决策,减少矿山损失。通过深入分析硫化矿石自燃典型案例,按照预警指标选取原则,从矿山生产人员、硫化矿石自燃倾向性、环境条件、管理水平等4个方面构建硫化矿石自燃灾害预警系统指标体系。运用RBF预警模型对硫化矿石自燃灾害预警等级进行预测,采用所选的21组样本数据完成了RBF神经网络的学习与训练。应用学习好的预警模型对江西某高硫矿山的硫化矿石自燃灾害预警等级进行预测。该矿山硫化矿石自燃灾害预警等级为III级,即自燃危险性一般,与该采场的实际状况相一致。通过现场案例验证了该模型的适用性,能应用于硫化矿石自燃灾害预警等级的预测,对类似灾害事件预警也有借鉴作用。
In order to scientifically and accurately determine the early warning level of spontaneous combustion hazard of sulfide ore,reduce the loss of mine disaster and provide decision-making for disaster early warning.Through in-depth analysis of the typical cases of spontaneous combustion of sulfide ore,according to the early warning index selection,the index system of early warning system for spontaneous combustion disaster of sulfide ore was constructed from four aspects of mine production personnel,spontaneous combustion tendency of sulfide ore,environmental conditions,and management level.The RBF early warning model was used to predict the warning level of spontaneous combustion hazard of sulfide ore,and 21 sets of sample data selected were used to complete the learning and training of RBF neural network.Then the well-preserved early warning model was used in a high-sulfur mine in Jiangxi.The result showed that the warning level of spontaneous combustion disaster of sulfide ore in this mine is grade III and the risk of spontaneous combustion is generally the same,which is consistent with the actual situation of the stope.The field case shows the applicability of the model.Therefore the model can be applied to the prediction of early warning grade of spontaneous combustion disaster of sulfide ore,and it can also be used as a reference for early warning of similar disaster events.
In order to scientifically and accurately determine the early warning level of spontaneous combustion hazard of sulfide ore,reduce the loss of mine disaster and provide decision-making for disaster early warning.Through in-depth analysis of the typical cases of spontaneous combustion of sulfide ore,according to the early warning index selection,the index system of early warning system for spontaneous combustion disaster of sulfide ore was constructed from four aspects of mine production personnel,spontaneous combustion tendency of sulfide ore,environmental conditions,and management level.The RBF early warning model was used to predict the warning level of spontaneous combustion hazard of sulfide ore,and 21 sets of sample data selected were used to complete the learning and training of RBF neural network.Then the well-preserved early warning model was used in a high-sulfur mine in Jiangxi.The result showed that the warning level of spontaneous combustion disaster of sulfide ore in this mine is grade III and the risk of spontaneous combustion is generally the same,which is consistent with the actual situation of the stope.The field case shows the applicability of the model.Therefore the model can be applied to the prediction of early warning grade of spontaneous combustion disaster of sulfide ore,and it can also be used as a reference for early warning of similar disaster events.
引文
[1]李孜军.硫化矿石自燃机理及其预防关键技术研究[D].长沙:中南大学,2007.LI Zijun.Investigation on the mechanism of spontaneous combustion of sulphide ores and the key technologies for preventing fire[D]. Changsha:Central South University,2007.
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[5]许春明,吴超,陈沅江.硫化矿石堆自燃的灰色预测研究[J].安全与环境学报,2008,8(4):125-127.XU Chunming,WU Chao,CHEN Yuanjiang.Grey system approach in application to the prediction of spontaneous combustion of sulfide ore residue[J].Journal of Safety and Environment,2008,8(4):125-127.
[6]阳富强,朱伟方,刘晓霞.硫化矿石自燃倾向性分级的云模型及其应用[J].自然灾害学报,2018,27(1):208-214.YANG Fuqiang,ZHU Weifang,LIU Xiaoxia.Cloud model and its application of classifying spontaneous combustion tendency of sulfide ores[J].Journal of Natural Disasters,2018,27(1):208-214.
[7]潘伟,吴超,李孜军,等.运用趋势混沌预测模型预测硫化矿石堆自热升温过程[J].中南大学学报(自然科学版),2015,46(3):901-907.PAN Wei,WU Chao,LI Zijun,et al.Prediction of selfheating process of sulfide ore heap using trend and chaos prediction model[J].Journal of Central South University(Science and Technology),2015,46(3):901-907.
[8]LEE C C,CHIANG Y C,SHIH C Y,et al.Noisy Time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques[J].Expert System with Applications,2009,36(3):4717-4724.
[9]孟凡荣,赵芳.煤矿安全预警系统体系构建[J].微计算机信息,2008,24(30):60-61.MENG Fanrong,ZHAO Fang.Construction of prewarning system of mine safety[J].Microcomputer Information,2008,24(30):60-61.
[10]邹秀清,莫国辉,刘杨倩宇,等.地方政府土地财政风险评估及预警研究[J].中国土地科学,2017,31(9):70-79.ZOU Xiuqing,MO Guohui,LIU Yangqianyu,et al.A study on risk evaluation and pre-warning of local government land finance[J].China Land Sciences,2017,31(9):70-79.
[11]张雅洁,张杰,卞晓峰.基于RBF的安徽省资源环境压力动态预警[J].中国农学通报,2015,31(1):174-179.ZHANG Yajie,ZHANG Jie,BIAN Xiaofeng.Earlywarning of resource and environment pressure in Anhui province based on RBF[J].Chinese Agricultural Science Bulletin,2015,31(1):174-179.
[12]郑剑锋,焦继东,孙力平.基于神经网络的城市内湖水华预警综合建模方法研究[J].中国环境科学,2017,37(5):1872-1878.ZHENG Jianfeng,JIAO Jidong,SUN Liping.A modeling approach for early-warning of water bloom risk in urban lake based on neural network[J].Chinese Environmental Science,2017,37(5):1872-1878.
[13]谢振华,梁莎莎,张雪冬.基于RBF神经网络的露天矿山边坡失稳预警方法[J].金属矿山,2014(9):7-10.XIE Zhenhua,LIANG Shasha,ZHANG Xuedong.Early warning method of slope instability of open-pit mine based on RBF neural network[J].Metal Mine,2014(9):7-10.
[14]谢第斌,胡明形.基于相关分析和主成分分析的国有林场分类指标体系设计研究[J].中南林业科技大学学报,2016,36(8):141-146.XIE Dibin,HU Mingxing.Research on the classiifcation index system design of the state-owned forest farms based on correlation analysis and principal component analysis[J].Journal of Central South University of Forestry&Technology,2016,36(8):141-146.
[15]WU CHAO.Fault tree analysis of spontaneous combustion of sulphide ores and its risk assessment[J].Journal of Central South University of Technology,1995,2(2):77-80.
[16]阳富强,吴超.硫化矿自燃预测预报理论与技术[M].北京:冶金工业出版社,2011.YANG Fuqiang,WU Chao.Prediction and forecast of spontaneous combustion of sulfide minerals-theory and technology[M]. Beijing:Metallurgical Industry Press,2011.
[17]阳富强,吴超,李孜军.硫化矿石自燃倾向性综合判定的物元模型及其应用[J].中南大学学报(自然科学版),2011,42(11):3459-3464.YANG Fuqiang,WU Chao,LI Zijun.Matter-element model and its application to comprehensive determination on spontaneous combustion tendency of sulfide ores[J].Journal of Central South University(Science and Technology),2011,42(11):3459-3464.
[2]王利岗,余乐文,赵冰峰.极端环境下矿山安全监测系统综合保障技术[J].有色金属工程,2018,8(4):105-109.WANG Ligang,YU Lewen,ZHAO Bingfeng,et al.Comprehensive safeguard technology of mine safety monitoring system in extreme environment[J].Nonferrous Metals Engineering,2018,8(4):105-109.
[3]古德生,周科平.现代金属矿业的发展主题[J].金属矿山,2012(7):1-8.GU Desheng,ZHOU Keping.Development theme of the modern metal mining[J].Metal Mine,2012,41(7):1-8.
[4]邬长福.高硫金属矿床内因火灾及其灭火措施[J].矿业安全与环保,2002,29(2):21-22.WU Changfu.Spontaneous combustion in high-sulfur metal mine and its control measures[J].Mining Safety and Environmental Protection,2002,29(2):21-22.
[5]许春明,吴超,陈沅江.硫化矿石堆自燃的灰色预测研究[J].安全与环境学报,2008,8(4):125-127.XU Chunming,WU Chao,CHEN Yuanjiang.Grey system approach in application to the prediction of spontaneous combustion of sulfide ore residue[J].Journal of Safety and Environment,2008,8(4):125-127.
[6]阳富强,朱伟方,刘晓霞.硫化矿石自燃倾向性分级的云模型及其应用[J].自然灾害学报,2018,27(1):208-214.YANG Fuqiang,ZHU Weifang,LIU Xiaoxia.Cloud model and its application of classifying spontaneous combustion tendency of sulfide ores[J].Journal of Natural Disasters,2018,27(1):208-214.
[7]潘伟,吴超,李孜军,等.运用趋势混沌预测模型预测硫化矿石堆自热升温过程[J].中南大学学报(自然科学版),2015,46(3):901-907.PAN Wei,WU Chao,LI Zijun,et al.Prediction of selfheating process of sulfide ore heap using trend and chaos prediction model[J].Journal of Central South University(Science and Technology),2015,46(3):901-907.
[8]LEE C C,CHIANG Y C,SHIH C Y,et al.Noisy Time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques[J].Expert System with Applications,2009,36(3):4717-4724.
[9]孟凡荣,赵芳.煤矿安全预警系统体系构建[J].微计算机信息,2008,24(30):60-61.MENG Fanrong,ZHAO Fang.Construction of prewarning system of mine safety[J].Microcomputer Information,2008,24(30):60-61.
[10]邹秀清,莫国辉,刘杨倩宇,等.地方政府土地财政风险评估及预警研究[J].中国土地科学,2017,31(9):70-79.ZOU Xiuqing,MO Guohui,LIU Yangqianyu,et al.A study on risk evaluation and pre-warning of local government land finance[J].China Land Sciences,2017,31(9):70-79.
[11]张雅洁,张杰,卞晓峰.基于RBF的安徽省资源环境压力动态预警[J].中国农学通报,2015,31(1):174-179.ZHANG Yajie,ZHANG Jie,BIAN Xiaofeng.Earlywarning of resource and environment pressure in Anhui province based on RBF[J].Chinese Agricultural Science Bulletin,2015,31(1):174-179.
[12]郑剑锋,焦继东,孙力平.基于神经网络的城市内湖水华预警综合建模方法研究[J].中国环境科学,2017,37(5):1872-1878.ZHENG Jianfeng,JIAO Jidong,SUN Liping.A modeling approach for early-warning of water bloom risk in urban lake based on neural network[J].Chinese Environmental Science,2017,37(5):1872-1878.
[13]谢振华,梁莎莎,张雪冬.基于RBF神经网络的露天矿山边坡失稳预警方法[J].金属矿山,2014(9):7-10.XIE Zhenhua,LIANG Shasha,ZHANG Xuedong.Early warning method of slope instability of open-pit mine based on RBF neural network[J].Metal Mine,2014(9):7-10.
[14]谢第斌,胡明形.基于相关分析和主成分分析的国有林场分类指标体系设计研究[J].中南林业科技大学学报,2016,36(8):141-146.XIE Dibin,HU Mingxing.Research on the classiifcation index system design of the state-owned forest farms based on correlation analysis and principal component analysis[J].Journal of Central South University of Forestry&Technology,2016,36(8):141-146.
[15]WU CHAO.Fault tree analysis of spontaneous combustion of sulphide ores and its risk assessment[J].Journal of Central South University of Technology,1995,2(2):77-80.
[16]阳富强,吴超.硫化矿自燃预测预报理论与技术[M].北京:冶金工业出版社,2011.YANG Fuqiang,WU Chao.Prediction and forecast of spontaneous combustion of sulfide minerals-theory and technology[M]. Beijing:Metallurgical Industry Press,2011.
[17]阳富强,吴超,李孜军.硫化矿石自燃倾向性综合判定的物元模型及其应用[J].中南大学学报(自然科学版),2011,42(11):3459-3464.YANG Fuqiang,WU Chao,LI Zijun.Matter-element model and its application to comprehensive determination on spontaneous combustion tendency of sulfide ores[J].Journal of Central South University(Science and Technology),2011,42(11):3459-3464.