尾砂胶结充填体强度影响因素分析与研究
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摘要
资源问题和环境问题是当今人类社会面临的两大难题,矿产资源的开发利用,在资源减少的同时也将对环境造成一定的破坏。如何合理解决矿山开采后形成的采空区和选矿后大量的残留尾砂已经成为当今世界许多国家的重要研究课题。采空区的存在会引起一系列的安全隐患和经济问题：比如井下作业的安全、地表的下沉和塌陷、长期对采空区的监测和维护等；大量的残留尾砂污染地表环境,破坏土壤并且占用大量的地表用地。尾砂充填利用矿山大量的尾砂为原材料,与胶凝剂,水混合搅拌后冲入采空区承受地压压力,为井下生产产创造安全的作业环境,同时也将占用地表的尾砂送入地下,极大降低了尾砂对环境的破坏。
本文针对国内某矿山的矿柱尾砂胶结充填,为矿山在充填工作中提供数据和理论上的依据。在室内进行尾砂胶结充填体的抗压强度试验,在7cm×7cm×7cm的模具中浇注尾砂胶结充填体。具体的方案为：在料浆浓度(70%、72%)不同和尾砂粒级(全尾砂、分级尾砂)不同的条件下进行灰砂比为1：3、1：4、1：5、1：7、1：10、1：12、1：15、1：20的尾砂胶结充填体抗压强度试验。每种配比共浇注9个模块,分为3组,每组的模块数为3个,养护的时间为3d、7d、28d。本次试验共浇注模块288块。
得到尾砂胶结充填体抗压强度试验数据后,首先对数据进行直观的分析,找出尾砂胶结充填体与灰砂比、料浆浓度、尾砂粒级、养护时间的关系。
其次对养护时间为28d的尾砂胶结充填体抗压强度进行极差和方差的分析,找出灰砂比、料浆浓度、尾砂粒级哪个是主因素,哪个是次要因素。找出最优组合和因素的显著性。
最后使用matlab7.1软件建立尾砂胶结充填体抗压强度的BP神经网络模型和灰砂比的BP神经网络模型。比较室内试验数据和工程实际数据。计算两者的误差以及通过尾砂胶结充填体抗压强度的BP神经网络模型的预测数据和工程实际数据的比较。室内试验是否能为工程实际提供一定的参考和依据。
Resources and environmental problems are two major problems in our human society nowadays. With deduce of mine production, the development and utilization of mineral resources have the great impact to our surroundings and cause some damages indeed. How reasonable solutions should be formed to solve the gob problem after mining and the existence of large amount tailings have become the world's important research topics in many countries. The existence of gob areas will cause a security risk and economic issues:such as the safety of underground operations, surface subsidence and collapse, long-term monitoring of mined areas and maintenance; a lot of residue tailings pollution to surface environment, destroying soil structure and occupying a large number of surface sites. A large amount of tailings were made up as the raw materials, companied with the gelling agent as well as water, and then poured them into the mined area to hold the underground pressure. The purpose is to operate a safe underground working environment and also occupy the tailings into the ground to reduce the great damage to the environment.
In this paper,54-58 regarded as the research target to provide the exact data and theoretical basis. The compressive strength of cement tailing backfill tested indoors. The cement tailing backfill was poured into the module,7cm×7cm×7cm casting mold. The Specific programs as follows:8 classified gray sand rations (1:3,1:4,1:5,1:7,1:10,1:12,1:15,1:20) which under different condition of slurry concentration(70%,72%) and tail sand level (full tailings, classified tailings) were took into the cement tailing compressive test. Each ratio gets 9 modules which divided into 3 groups and each group has 3 modules. Curing time relates to 3days,7days, and 28 days. The experiments were pouring 288modules.
After the collection of compressive data from the cement tailing compressive strength test, the data analyzed directly to identify the relationship between cemented tailings backfill, gray sand ratio, slurry concentration, the sand level as well as the curing time.
Secondly, according to the analyze data of twisting pillar cement tailings in the curing time about 28 days by using range and variance analysis method to identify the gray sand ratio, slurry concentration and the level sand, which aims to make sure the main factor and the secondary factor. Find out the significance of optimum combination and factors. Finally, matlab7.1 software is used to establish the compressive strength of cemented tailings backfill of the BP neural network model and Than suggested BP neural network model
Comparative indoor test data and actual data. The error and calculated both by tailings cemented filling body of compressive strength of BP neural network models predict data and actual data comparison. For engineering laboratory test whether provide certain reference and basis.
本文针对国内某矿山的矿柱尾砂胶结充填,为矿山在充填工作中提供数据和理论上的依据。在室内进行尾砂胶结充填体的抗压强度试验,在7cm×7cm×7cm的模具中浇注尾砂胶结充填体。具体的方案为：在料浆浓度(70%、72%)不同和尾砂粒级(全尾砂、分级尾砂)不同的条件下进行灰砂比为1：3、1：4、1：5、1：7、1：10、1：12、1：15、1：20的尾砂胶结充填体抗压强度试验。每种配比共浇注9个模块,分为3组,每组的模块数为3个,养护的时间为3d、7d、28d。本次试验共浇注模块288块。
得到尾砂胶结充填体抗压强度试验数据后,首先对数据进行直观的分析,找出尾砂胶结充填体与灰砂比、料浆浓度、尾砂粒级、养护时间的关系。
其次对养护时间为28d的尾砂胶结充填体抗压强度进行极差和方差的分析,找出灰砂比、料浆浓度、尾砂粒级哪个是主因素,哪个是次要因素。找出最优组合和因素的显著性。
最后使用matlab7.1软件建立尾砂胶结充填体抗压强度的BP神经网络模型和灰砂比的BP神经网络模型。比较室内试验数据和工程实际数据。计算两者的误差以及通过尾砂胶结充填体抗压强度的BP神经网络模型的预测数据和工程实际数据的比较。室内试验是否能为工程实际提供一定的参考和依据。
Resources and environmental problems are two major problems in our human society nowadays. With deduce of mine production, the development and utilization of mineral resources have the great impact to our surroundings and cause some damages indeed. How reasonable solutions should be formed to solve the gob problem after mining and the existence of large amount tailings have become the world's important research topics in many countries. The existence of gob areas will cause a security risk and economic issues:such as the safety of underground operations, surface subsidence and collapse, long-term monitoring of mined areas and maintenance; a lot of residue tailings pollution to surface environment, destroying soil structure and occupying a large number of surface sites. A large amount of tailings were made up as the raw materials, companied with the gelling agent as well as water, and then poured them into the mined area to hold the underground pressure. The purpose is to operate a safe underground working environment and also occupy the tailings into the ground to reduce the great damage to the environment.
In this paper,54-58 regarded as the research target to provide the exact data and theoretical basis. The compressive strength of cement tailing backfill tested indoors. The cement tailing backfill was poured into the module,7cm×7cm×7cm casting mold. The Specific programs as follows:8 classified gray sand rations (1:3,1:4,1:5,1:7,1:10,1:12,1:15,1:20) which under different condition of slurry concentration(70%,72%) and tail sand level (full tailings, classified tailings) were took into the cement tailing compressive test. Each ratio gets 9 modules which divided into 3 groups and each group has 3 modules. Curing time relates to 3days,7days, and 28 days. The experiments were pouring 288modules.
After the collection of compressive data from the cement tailing compressive strength test, the data analyzed directly to identify the relationship between cemented tailings backfill, gray sand ratio, slurry concentration, the sand level as well as the curing time.
Secondly, according to the analyze data of twisting pillar cement tailings in the curing time about 28 days by using range and variance analysis method to identify the gray sand ratio, slurry concentration and the level sand, which aims to make sure the main factor and the secondary factor. Find out the significance of optimum combination and factors. Finally, matlab7.1 software is used to establish the compressive strength of cemented tailings backfill of the BP neural network model and Than suggested BP neural network model
Comparative indoor test data and actual data. The error and calculated both by tailings cemented filling body of compressive strength of BP neural network models predict data and actual data comparison. For engineering laboratory test whether provide certain reference and basis.
引文
[1]解世俊.金属矿床地下开采[M].北京：冶金工业出版社,2007
[2]刘同有.充填采矿及应用[M].北京：冶金工业出版社,2001
[3]周爱民.矿山废料胶结充填[M].北京：冶金工业出版社,2007
[4]刘同有.国外金属矿山充填采矿技术的研究与应用[M].北京：冶金工业出版社,1997
[5]周科平,古德生.安庆铜矿尾砂胶结充填灰砂配比的遗传优化设计[J].金属矿山,2001,301(7)：10-12
[6]舒广龙,刘继顺,凌同华.查干银矿1“矿体胶结充填材料配比及成本优化[J].金属矿山,2003,319(1)：4-7
[7]卢伟国,孟令刚,耿芳明.尾砂胶结充填采矿法在唐山亨达东安铁矿的应用[J].现代矿业,2009,486(10)：91-92
[8]朱天平.白银公司深部铜矿尾砂胶结充填系统[J].有色金属,2002,54(1)：95-97
[9]赵传卿,胡乃联.充填胶凝材料的发展与应用[J].采矿工程,2008,29(1)：25-29
[10]张闽祥.全尾砂胶结充填方案的评析[J].甘肃冶金,2007,29(3)：36-37
[11]杨思德.废石尾砂胶结充填技术[J].矿业快报,2008,476(12)：6-8
[12]郭利杰,杨小聪.废石尾砂胶结充填配比试验研究[J].2009增刊：226-228
[13]吴德权,郭忠林,孙国详.分段空场嗣后块石胶结充填采矿法在老厂锡矿的应用[J].采矿技术,9(2)：1-7.
[14]周瑞林.分级尾砂胶结充填在武山铜矿的应用[J].有色金属,2010,62(3)：1-2,5
[15]高泉,谭幼媛.高浓度全尾砂胶结充填料胶结机理研究[J].矿业研究与开发,1995,15(2)：1-4
[16]尹慰农,刘能国.高浓度全尾砂胶结充填新工艺[J].长沙矿山研究院季刊,1992,12(2)：1-7
[17]邓代强,高永涛,姚中亮.胶结充填材料力学特性影Ⅱ向因素回归分析[J].有色金属,2008,60(4)：120-124
[18]金文法,李永辉.胶结充填材料配比试验研究[J].采矿技术,2010,10(2)：15-16
[19]杨清平,冷复生.胶结充填工艺技术在铜绿山矿的应用[J].采矿技术,2010,1(1)：17-19
[20]潘文元,胶结充填技术在澳大利亚的应用[J].山东冶金,1991,(2)：17-21
[21]林国洪.尾砂胶结充填新工艺在武山铜矿的研究与应用[J].铜业工程,2008,(3)：7-19
[22]谢龙水.矿山胶结充填技术的发展[J].湖南有色金属,2003,19(4)：1-5
[23]裴启涛,陈建宏,王子哲等.胶结充填技术在矿山的应用[J].矿业快报,2008,469(5)： 92-94
[24]崔刚,陈文楷,段鸿杰.胶结充填料浆合理配比的确定[J].中国矿业大报,2004,33(3)：311-313
[25]李占军,姜维,王守德.全尾胶结充填与其它充填法的联合应用[J].矿产保护与利用,1998,(5)：45-48
[26]王新民,徐东升.胶结充填料优化配比研究及强度预测[J].矿业快报,2006,(12)：20-23
[27]刘宝友.矿山充填料的选择与应用[J].现代矿业,2010,2(2)：123-124
[28]孙恒虎,黄玉诚,杨宝贵.当代胶结充填技术[M].北京：冶金出版社.2002
[29]李兴尚.水砂胶结充填材料配合比的优化研究[D].昆明：昆明理工大学,2005
[30]刘凯.全尾砂充填空区的脱水试验研究[D].昆明：昆明理工大学,2009
[31]周爱民.中国充填技术概述[A].第八届国际充填采矿会议论文集[C].北京：中国有色金属学会,2004
[32]余斌.影响尾砂胶结充填体强度的若干因素分析[J].河北冶金,2001,123(1)：3-6
[33]L.HReneteder, adoption of High Denisty Hydralic Coveying in Backfill
[34]Putzmeister bnekfill in mines putzmiester sales information
[35]A.C.winore, baekfill Operations at Cooke 3shaft RE
[36]sunHenhun, Recearch summary of Creighton Backfill Projects(research RePort),2001
[37]J-Rbarrett Structural aspects of cemented fill behaviour, Proee of the Jubilee Symposium on Mine Filling, Mt.Isa/Queensland,1973
[38]Corous DonalR, Stabizition of hydraulic backfill with portlandcement, USBM R.3732.1970
[39]WaspEJ.solid-LiquidSluuryPipelineTransportation, 北京:水利出版社,1980
[40]REISNER, W.and EIGENHART, M.V, Bins and Bunker for handling bulk material.Teeh, Publ, Germany,1971
[41]CHEN, L.J, Mining Practice with backfill in Jiaojia MineNon-fermus Metal, China 1981
[42]Minefil 98 Proc.of 6th Interna, Symp.On Mining Backfill, Brisbanc, Australia,1998
[43]罗建祥.影响胶结充填体强度的因素分析[J].甘肃冶金,2005,27(2)：24-25
[44]刘明.内蒙查干银矿3“矿体上向进路尾砂胶结充填采矿方法试验研究[D].长沙：中南大学,2002
[45]王湘桂,唐开元.矿山充填采矿法综述[J].矿业快报,2008,476(12)：1-5
[46]张海波,宋卫东.评述国内外充填采矿技术发展现状[J].矿业快报,2009,18(12)：59-62
[47]李洪超.布沼坝露天煤矿西帮抢险治理工程爆破技术研究[D].昆明：昆明理工大学,2010
[48]冯夏庭.智能岩石力学导论[M].北京：科学出版社,2000
[49]傅鹤林,彭思甜,韩汝才等.岩土工程数值分析新方法[M].北京：中南大学出版社,2006
[50]杨建刚.神经网络实用教程[M].杭州：浙江大学出版社,2001
[51]韩力群.人工神经网络理论设计及应用[M].北京：化学工业出版社,2006
[52]蒋宗礼.人工神经网络导论[M].北京：高等教育出版社,2001
[53]艾艳.短期降水预报BP神经网络预报方法研究[D].郑州：郑州大学,2004
[54]廖志辉.基于模糊推理和BP神经网络的机械故障智能诊断系统的研发[D].重庆：重庆大学,2004
[55]金丕彦,芮勇.BP算法各种改进算法的研究及应用[J].南京航天航空大学学报,1994,26(11)：201-205
[56]李少华,董增川.BP神经网络模型应用于大坝原型观测研究[J].水利与建筑
[57]Ralf·Salomon, J·Leovan·Hemmen.Accelerating-Back-propagation-through-Dynamic Self-Adaptation.Neural·Network,1996,9(4):589-601
[58]余方威.基于BP神经网络的岩质边坡位移反分析及其应用研究[D]成都：西南交通大学,2007
[59]朱凯,王正林.精通MATLAB神经网络[M].北京：电子工业出版社,2010
[60]闻新MATLAB神经网络应用设计[M].北京：科技出版社,2000
[61]胡伍生.神经网络理论及其工程应用[M].北京：测绘出版社,2006
[2]刘同有.充填采矿及应用[M].北京：冶金工业出版社,2001
[3]周爱民.矿山废料胶结充填[M].北京：冶金工业出版社,2007
[4]刘同有.国外金属矿山充填采矿技术的研究与应用[M].北京：冶金工业出版社,1997
[5]周科平,古德生.安庆铜矿尾砂胶结充填灰砂配比的遗传优化设计[J].金属矿山,2001,301(7)：10-12
[6]舒广龙,刘继顺,凌同华.查干银矿1“矿体胶结充填材料配比及成本优化[J].金属矿山,2003,319(1)：4-7
[7]卢伟国,孟令刚,耿芳明.尾砂胶结充填采矿法在唐山亨达东安铁矿的应用[J].现代矿业,2009,486(10)：91-92
[8]朱天平.白银公司深部铜矿尾砂胶结充填系统[J].有色金属,2002,54(1)：95-97
[9]赵传卿,胡乃联.充填胶凝材料的发展与应用[J].采矿工程,2008,29(1)：25-29
[10]张闽祥.全尾砂胶结充填方案的评析[J].甘肃冶金,2007,29(3)：36-37
[11]杨思德.废石尾砂胶结充填技术[J].矿业快报,2008,476(12)：6-8
[12]郭利杰,杨小聪.废石尾砂胶结充填配比试验研究[J].2009增刊：226-228
[13]吴德权,郭忠林,孙国详.分段空场嗣后块石胶结充填采矿法在老厂锡矿的应用[J].采矿技术,9(2)：1-7.
[14]周瑞林.分级尾砂胶结充填在武山铜矿的应用[J].有色金属,2010,62(3)：1-2,5
[15]高泉,谭幼媛.高浓度全尾砂胶结充填料胶结机理研究[J].矿业研究与开发,1995,15(2)：1-4
[16]尹慰农,刘能国.高浓度全尾砂胶结充填新工艺[J].长沙矿山研究院季刊,1992,12(2)：1-7
[17]邓代强,高永涛,姚中亮.胶结充填材料力学特性影Ⅱ向因素回归分析[J].有色金属,2008,60(4)：120-124
[18]金文法,李永辉.胶结充填材料配比试验研究[J].采矿技术,2010,10(2)：15-16
[19]杨清平,冷复生.胶结充填工艺技术在铜绿山矿的应用[J].采矿技术,2010,1(1)：17-19
[20]潘文元,胶结充填技术在澳大利亚的应用[J].山东冶金,1991,(2)：17-21
[21]林国洪.尾砂胶结充填新工艺在武山铜矿的研究与应用[J].铜业工程,2008,(3)：7-19
[22]谢龙水.矿山胶结充填技术的发展[J].湖南有色金属,2003,19(4)：1-5
[23]裴启涛,陈建宏,王子哲等.胶结充填技术在矿山的应用[J].矿业快报,2008,469(5)： 92-94
[24]崔刚,陈文楷,段鸿杰.胶结充填料浆合理配比的确定[J].中国矿业大报,2004,33(3)：311-313
[25]李占军,姜维,王守德.全尾胶结充填与其它充填法的联合应用[J].矿产保护与利用,1998,(5)：45-48
[26]王新民,徐东升.胶结充填料优化配比研究及强度预测[J].矿业快报,2006,(12)：20-23
[27]刘宝友.矿山充填料的选择与应用[J].现代矿业,2010,2(2)：123-124
[28]孙恒虎,黄玉诚,杨宝贵.当代胶结充填技术[M].北京：冶金出版社.2002
[29]李兴尚.水砂胶结充填材料配合比的优化研究[D].昆明：昆明理工大学,2005
[30]刘凯.全尾砂充填空区的脱水试验研究[D].昆明：昆明理工大学,2009
[31]周爱民.中国充填技术概述[A].第八届国际充填采矿会议论文集[C].北京：中国有色金属学会,2004
[32]余斌.影响尾砂胶结充填体强度的若干因素分析[J].河北冶金,2001,123(1)：3-6
[33]L.HReneteder, adoption of High Denisty Hydralic Coveying in Backfill
[34]Putzmeister bnekfill in mines putzmiester sales information
[35]A.C.winore, baekfill Operations at Cooke 3shaft RE
[36]sunHenhun, Recearch summary of Creighton Backfill Projects(research RePort),2001
[37]J-Rbarrett Structural aspects of cemented fill behaviour, Proee of the Jubilee Symposium on Mine Filling, Mt.Isa/Queensland,1973
[38]Corous DonalR, Stabizition of hydraulic backfill with portlandcement, USBM R.3732.1970
[39]WaspEJ.solid-LiquidSluuryPipelineTransportation, 北京:水利出版社,1980
[40]REISNER, W.and EIGENHART, M.V, Bins and Bunker for handling bulk material.Teeh, Publ, Germany,1971
[41]CHEN, L.J, Mining Practice with backfill in Jiaojia MineNon-fermus Metal, China 1981
[42]Minefil 98 Proc.of 6th Interna, Symp.On Mining Backfill, Brisbanc, Australia,1998
[43]罗建祥.影响胶结充填体强度的因素分析[J].甘肃冶金,2005,27(2)：24-25
[44]刘明.内蒙查干银矿3“矿体上向进路尾砂胶结充填采矿方法试验研究[D].长沙：中南大学,2002
[45]王湘桂,唐开元.矿山充填采矿法综述[J].矿业快报,2008,476(12)：1-5
[46]张海波,宋卫东.评述国内外充填采矿技术发展现状[J].矿业快报,2009,18(12)：59-62
[47]李洪超.布沼坝露天煤矿西帮抢险治理工程爆破技术研究[D].昆明：昆明理工大学,2010
[48]冯夏庭.智能岩石力学导论[M].北京：科学出版社,2000
[49]傅鹤林,彭思甜,韩汝才等.岩土工程数值分析新方法[M].北京：中南大学出版社,2006
[50]杨建刚.神经网络实用教程[M].杭州：浙江大学出版社,2001
[51]韩力群.人工神经网络理论设计及应用[M].北京：化学工业出版社,2006
[52]蒋宗礼.人工神经网络导论[M].北京：高等教育出版社,2001
[53]艾艳.短期降水预报BP神经网络预报方法研究[D].郑州：郑州大学,2004
[54]廖志辉.基于模糊推理和BP神经网络的机械故障智能诊断系统的研发[D].重庆：重庆大学,2004
[55]金丕彦,芮勇.BP算法各种改进算法的研究及应用[J].南京航天航空大学学报,1994,26(11)：201-205
[56]李少华,董增川.BP神经网络模型应用于大坝原型观测研究[J].水利与建筑
[57]Ralf·Salomon, J·Leovan·Hemmen.Accelerating-Back-propagation-through-Dynamic Self-Adaptation.Neural·Network,1996,9(4):589-601
[58]余方威.基于BP神经网络的岩质边坡位移反分析及其应用研究[D]成都：西南交通大学,2007
[59]朱凯,王正林.精通MATLAB神经网络[M].北京：电子工业出版社,2010
[60]闻新MATLAB神经网络应用设计[M].北京：科技出版社,2000
[61]胡伍生.神经网络理论及其工程应用[M].北京：测绘出版社,2006