硐室爆破岩石块度预测
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摘要
硐室爆破块度分布是定量评价工程爆破质量的重要指标之一。它不仅影响到矿山各种后续生产工序的效率和采矿生产总成本而且影响到实际工程的具体施工方案,如应用硐室爆破的方法形成矿山露天转地下工程的覆盖层。硐室爆破碎块的形成受多种因素影响,包括岩体内宏观节理、裂隙、断层等各种结构面和爆破参数、炸药参数等。
通过研究爆破漏斗的特点,将硐室爆破爆落体分为抛掷体和坍塌体两部分控制,抛掷体块度分布主要受装药量和爆破参数的影响,而坍塌体块度分布主要受岩体各种结构面影响。所以将硐室爆破块度预测模型分为抛掷体块度模型和坍塌体块度模型两部分进行研究。
借鉴深孔爆破块度预测的数学模型,分析硐室爆破与深孔爆破的相关参数,建立硐室爆破抛掷体块度预测的数学模型。通过图像分析方法研究岩体自然崩落爆堆块度和岩体原始块度的关系,以实际坍塌体爆堆块度分布和岩体结构面分布为依据,建立硐室爆破坍塌体块度预测的数学模型。再根据抛掷体与坍塌体的比例建立硐室爆破块度预测模型。
结合首钢杏山铁矿边帮硐室爆破方案,分别对硐室爆破形成的抛掷体和坍塌体进行了块度预测。坍塌体的块度比抛掷体要大,这与受爆破作用影响的程度有关。抛掷漏斗内岩体几乎承受了全部的爆炸能量,岩体破碎程度较大。而坍塌体主要受爆破震动影响,岩体主要沿节理面和弱面进行破碎,块度分布主要由岩体原始块度分布决定。
The distribution of underground chamber blasting fragmentation is one of the important quantificational evaluation indicators. It affects not only a variety of the efficiency of the following processes of the mining production, but also the impact on the total cost of specific construction programs of the actual project, such as the overburden formation mining transition from open pit to underground.
Underground chamber blasting explosive falling body was divided into throwing body which was affected by charge volume and collapse body which was affected by body mass size distribution of rock and various structural surface through studying the characteristics of blasting crater.
According to level block of deep-hole blasting prediction mathematical model, related parameters of chamber blasting and bench blasting were analysed and mathematical model of tossing body was established. The relationship between natural caving of rock burst and rock heap fragmentation degree was investigated by image analysis method. In accordance with the actual body explosion collapsed pile of rock fragment distribution and the distribution of structural surface, mathematical model of collapsed body was established. And then chamber blasting fragmentation prediction model was established under the ratio of the throwing body and collapsed body.
Combined Shougang Iron Slope Xingshan chamber blasting program, the formation of chamber blasting throwing body and collapse was predicted respectively. Collapsed body block was larger than tossing body, which was affected by the degree of the impact of blasting. Tossing funnel inner body almost suffered all explosion energy and rock fragmentation degree was greater. The collapsed body was mainly affected by the blasting vibration. Rock bursted along the joint plane and weak plane. Block size distribution was mainly determined by the original rock fragment.
通过研究爆破漏斗的特点,将硐室爆破爆落体分为抛掷体和坍塌体两部分控制,抛掷体块度分布主要受装药量和爆破参数的影响,而坍塌体块度分布主要受岩体各种结构面影响。所以将硐室爆破块度预测模型分为抛掷体块度模型和坍塌体块度模型两部分进行研究。
借鉴深孔爆破块度预测的数学模型,分析硐室爆破与深孔爆破的相关参数,建立硐室爆破抛掷体块度预测的数学模型。通过图像分析方法研究岩体自然崩落爆堆块度和岩体原始块度的关系,以实际坍塌体爆堆块度分布和岩体结构面分布为依据,建立硐室爆破坍塌体块度预测的数学模型。再根据抛掷体与坍塌体的比例建立硐室爆破块度预测模型。
结合首钢杏山铁矿边帮硐室爆破方案,分别对硐室爆破形成的抛掷体和坍塌体进行了块度预测。坍塌体的块度比抛掷体要大,这与受爆破作用影响的程度有关。抛掷漏斗内岩体几乎承受了全部的爆炸能量,岩体破碎程度较大。而坍塌体主要受爆破震动影响,岩体主要沿节理面和弱面进行破碎,块度分布主要由岩体原始块度分布决定。
The distribution of underground chamber blasting fragmentation is one of the important quantificational evaluation indicators. It affects not only a variety of the efficiency of the following processes of the mining production, but also the impact on the total cost of specific construction programs of the actual project, such as the overburden formation mining transition from open pit to underground.
Underground chamber blasting explosive falling body was divided into throwing body which was affected by charge volume and collapse body which was affected by body mass size distribution of rock and various structural surface through studying the characteristics of blasting crater.
According to level block of deep-hole blasting prediction mathematical model, related parameters of chamber blasting and bench blasting were analysed and mathematical model of tossing body was established. The relationship between natural caving of rock burst and rock heap fragmentation degree was investigated by image analysis method. In accordance with the actual body explosion collapsed pile of rock fragment distribution and the distribution of structural surface, mathematical model of collapsed body was established. And then chamber blasting fragmentation prediction model was established under the ratio of the throwing body and collapsed body.
Combined Shougang Iron Slope Xingshan chamber blasting program, the formation of chamber blasting throwing body and collapse was predicted respectively. Collapsed body block was larger than tossing body, which was affected by the degree of the impact of blasting. Tossing funnel inner body almost suffered all explosion energy and rock fragmentation degree was greater. The collapsed body was mainly affected by the blasting vibration. Rock bursted along the joint plane and weak plane. Block size distribution was mainly determined by the original rock fragment.
引文
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[2] Favreau R F.台阶爆破岩石位移速度[M].第一届爆破破岩国际会议论文集,1983.
[3] Harries. A mathematical model of cratering and blasting[C],15th Aplom Symposium,Austrilia,1979.
[4] Harries. The calculation of the fragmentation of rock from createring[C],15th Aplom Symposium,Brisbar,Austrilia,1977.
[5]题正义.爆堆块度分布的自动与分形测试系统研究[D].辽宁工程技术大学,2001.
[6]汪义龙,牛海成,李奇等.节理岩体爆破块度预测模型分析[J].建井技术,2006,(2):19-22.
[7]肖富国,李启月,李夕兵.节理岩体爆破块度预测模型综述[J].矿业研究与开发,2001,(3):42-44.
[8] Mchugh S.动力引起的破坏和破碎的数值模拟.第一届爆破破岩国际会议论文集(译文集) [C],1983:234-243.
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[12] Gama. Microcomputer simulation of rock blasting to predict fragmentation[C]. Research Coordinator. DMGA-IP.T.Brazil,1982.
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[14]邹定祥.计算露天矿台阶爆破块度分布的三维数学模型[J].爆炸与冲击,1984,(4):48-59.
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[17] Pogliani C. The role of exopolymers in the bioleaching of a nun-ferrous metal sulphide[J]. Joumal of Industrial Micro-biology & Biotechnology,1999,(22):88-92.
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[19] Nemati M. Biological oxidation of ferrous sulphate by Thiobacilfua ferrooxidans[J]. Bi-achemical Engineering,1998,(1):171-190.
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[23] Chrlce T. Importance of eaopolymets fromThiobacillus jenooxidans and Geptospirillum jerrooxidana for bioleachin[J]. Appl Environ Microbio1,1995,(43):961-966.
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[35]李长洪.爆破碎块分布的分形模型研究及爆破模型优选[J].爆破,1997,(2):9-12.
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[37]王祥厚,杨用华,王作强.台阶爆破爆堆的计算与模拟的探讨[J].煤炭科学技术2000,(7):154-158.
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[42]亨利奇.J.爆炸动力学及其应用[M].北京:科学出版社,1987.
[43]叶海旺.土岩爆破智能化系统研究[D].武汉:武汉理工大学,2003.
[44]吴腾芳.爆破材料与爆破技术[M].北京:国防工业出版社,2008.
[45]李英龙.基于神经网络与弹性分析的因素分析方法及其在采矿中的应用[J].有色金属(矿山部分),1992,(1):29-32.
[46]段宝福,费鸿禄.神经网络模型在台阶爆破块度预测中的应用[J].工程爆破,2005,(4):25-29.
[47]郭连军,王智静,牛成俊.爆破优化的神经网络模型[J].工程爆破,2006,(2):11-15.
[48]黄光球.水平面上矿体自动圈定的神经网络方法[J].矿冶,1996(2):1-9.
[49]李仁璞,王正欧.一种结构自适应的神经网络特征选择方法[J].计算机研究与发展,2006,(12):1613–1617.
[50]李守巨,刘迎曦,何翔,等.基于人工神经网络的爆炸冲击荷载参数识别方法[J].武汉:岩石力学与工程学报,2006,(11):1870–1873.
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[2] Favreau R F.台阶爆破岩石位移速度[M].第一届爆破破岩国际会议论文集,1983.
[3] Harries. A mathematical model of cratering and blasting[C],15th Aplom Symposium,Austrilia,1979.
[4] Harries. The calculation of the fragmentation of rock from createring[C],15th Aplom Symposium,Brisbar,Austrilia,1977.
[5]题正义.爆堆块度分布的自动与分形测试系统研究[D].辽宁工程技术大学,2001.
[6]汪义龙,牛海成,李奇等.节理岩体爆破块度预测模型分析[J].建井技术,2006,(2):19-22.
[7]肖富国,李启月,李夕兵.节理岩体爆破块度预测模型综述[J].矿业研究与开发,2001,(3):42-44.
[8] Mchugh S.动力引起的破坏和破碎的数值模拟.第一届爆破破岩国际会议论文集(译文集) [C],1983:234-243.
[9] Cuningham.预估爆破破碎的KUZ-RAM模型[C].长沙:第一届爆破破岩国际会议论文集,1985.
[10] Danell R E.岩石爆破断裂和爆破的计算机模拟[C].长沙:第二届爆破破岩国际会议论文集.长江科学院,1990:303-311.
[11] Kuznetsov.V.M.. The mean diameter of fragments formed by blasting rock[C],Soviet Mining Science,1973.
[12] Gama. Microcomputer simulation of rock blasting to predict fragmentation[C]. Research Coordinator. DMGA-IP.T.Brazil,1982.
[13] Grady D L,Kipp M L. Continuum modeling of explosive france in shaleint[J]. Rock Mech.Min.SCI,1980,(17):147-157.
[14]邹定祥.计算露天矿台阶爆破块度分布的三维数学模型[J].爆炸与冲击,1984,(4):48-59.
[15] Cunningham C.预测爆破破碎的KUZ-RAM模型[C].第一界爆破破岩国际会议论文集,1984:251-257.
[16] Morgan J W. A comparative study of the nature of hiopolymera extraded from anaerobic and activated sludges[J].Wat Res,1990,(24):743-730.
[17] Pogliani C. The role of exopolymers in the bioleaching of a nun-ferrous metal sulphide[J]. Joumal of Industrial Micro-biology & Biotechnology,1999,(22):88-92.
[18] Rodrigue Z M. Studies on nativeswains of Thiobacillus ferrooxidans[J]. Biotelmol Appl Biochem,1986,(8):292-297.
[19] Nemati M. Biological oxidation of ferrous sulphate by Thiobacilfua ferrooxidans[J]. Bi-achemical Engineering,1998,(1):171-190.
[20] Kinzler K. Bioleaching a result of interfacial processes caused by extracellular polymeric substances(EPS)[J]. Hydrometallurgy,2005,(71):83-88.
[21] Gehrke T. Importance of extracellular polymeric subatances from thiobaciflus jerrooxidans for bioleaching[J]. Appl Environ Microbio1,1998,(7):2743-2747.
[22] Blake R C. Does aponisticyanin mediate the edhesion of thiobacillus jerrooxidans to pyrite[J]. Hydrometallurgy,2001,(59)57-72.
[23] Chrlce T. Importance of eaopolymets fromThiobacillus jenooxidans and Geptospirillum jerrooxidana for bioleachin[J]. Appl Environ Microbio1,1995,(43):961-966.
[24] Gancarczyk J J. Structure of activated sludge flocs[J]. Biotech Bioeng,1990,(35):57-65.
[25]杨军,陈鹏万,胡刚.现代爆破技术[M].北京:北京工业大学出版社,2005.
[26]张继春.节理岩体爆破的块度研究[D].沈阳:东北大学,1995.
[27]张继春.岩体爆破的块度理论及其应用[M].成都:西南交通大学出版社,2001.
[28]郭学彬,张继春.爆破工程[M].北京:人民交通出版社,2007.
[29]张云鹏.露天台阶爆破岩石破碎与抛掷的计算机模拟[D].北京:北京科技大学硕士学位论文,1994.
[30]翁春林,叶加冕.工程爆破[M].北京:化学工业出版社,2008.
[31]黄志辉.台阶爆破块度分布测定与优化分析[D].南京:华侨大学硕士学位论,2006.
[32]焦淑华,夏冰,徐海静等. BP神经网络预测的MATLAB实现[J].哈尔滨金融高等专科学校学报,2009,(1):55-56.
[33]东兆星,邵鹏.爆破工程[M].北京:中国建筑工业出版社,2005.
[34]廖成孟,马建军,蔡路军,等.无底柱分段崩落法回采爆破参数计算机辅助设计[J].矿业研究与开发,2005,(7):160-164.
[35]李长洪.爆破碎块分布的分形模型研究及爆破模型优选[J].爆破,1997,(2):9-12.
[36]璩世杰.露天矿爆破设计与模拟Blast-Code模型及其在水厂铁矿的应用[J].工程爆破,2001,(2):18-24.
[37]王祥厚,杨用华,王作强.台阶爆破爆堆的计算与模拟的探讨[J].煤炭科学技术2000,(7):154-158.
[38]刘新波,盛建龙.爆破数学模型浅析[J].北京:爆破,1999,(3)22-24.
[39]张奇,杨永琦,于滨.岩石爆破破碎时间及微差起爆延时优化[J].爆炸与冲击,1998,(4):63-66.
[40]高金石,张奇.爆破理论与爆破优化[M].西安:西安地图出版社,1992.
[41]文永胜,方颜空,吕力行.露天矿深孔控制爆破实践[J].有色金属,2009,(4):52-54.
[42]亨利奇.J.爆炸动力学及其应用[M].北京:科学出版社,1987.
[43]叶海旺.土岩爆破智能化系统研究[D].武汉:武汉理工大学,2003.
[44]吴腾芳.爆破材料与爆破技术[M].北京:国防工业出版社,2008.
[45]李英龙.基于神经网络与弹性分析的因素分析方法及其在采矿中的应用[J].有色金属(矿山部分),1992,(1):29-32.
[46]段宝福,费鸿禄.神经网络模型在台阶爆破块度预测中的应用[J].工程爆破,2005,(4):25-29.
[47]郭连军,王智静,牛成俊.爆破优化的神经网络模型[J].工程爆破,2006,(2):11-15.
[48]黄光球.水平面上矿体自动圈定的神经网络方法[J].矿冶,1996(2):1-9.
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