提高布沼坝露天煤矿爆破块煤生产率及个别飞散物控制的试验研究
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摘要
随着煤炭资源需求的日益增大,块煤作为煤炭市场的一项主要产品,始终满足不了目前的社会需求。因此,提高煤矿块煤的生产率已成为煤炭企业经济发展的重要手段。露天煤矿开采的经济效益很大程度上取决于其块煤产出率的影响,其中煤岩的爆破块度是评价爆破效果、提高生产效益的重要指标。通常情况下,块煤的售价都高于粉煤,所以近些年很多工程技术人员对影响煤岩爆破块度的因素如：岩石性质、炸药单耗、起爆顺序、布孔方式等都进行了较多的研究。在满足安全生产的前提下,为提高块煤的生产率,获得好的经济效益,选择合理的爆破参数已成为生产爆破的重要环节。所以对于通过爆破手段提高露天煤矿块煤率的生产具有极大现实意义。
本论文结合《露天中深孔爆破计算机辅助设计系统的研究与开发》课题展开研究,根据小龙潭布沼坝露天煤矿的实际情况,历时半年多的时间,通过现场的大量试验,结合理论分析和试验数据统计、计算,并利用建立的BP神经网络模型对试验结果进行了预测验证。研究爆破块煤效果、个别飞散物控制与主要影响因素之间的关系,并得出了适合于该矿山安全、高效生产的爆破参数。
论文介绍了目前国内外爆破块煤生产的研究现状并进行了简要的评述,从相关理论上分析了提高爆破块煤的爆破机理和影响因素,分别论述了底盘抵抗线、炸药单耗、孔排距等爆破参数对块煤率的影响,经过现场多次的爆破试验确定了符合该矿山的爆破参数,通过参数的合理选择使炸药的能量对煤岩作用充分,减少了能量的损失,碎煤的比例也随之减少；通过CAD图形处理手段对大量的试验数据进行了处理,并通过VB程序对数据进行统计运算,探讨了合格块煤率的分布规律；为满足矿山安全生产的要求,论文还对爆破产生个别飞散物的影响因素进行了分析研究,找出了合理的填塞长度及抵抗线要求,基本满足了矿山安全生产对个别飞散物的控制；利用神经网络的特点建立了爆破块煤率的神经网络预测模型,对不同参数下爆破块煤率进行了初步预测,且预测结果与实际应用结果大致相同,证明了模型的实际应用性。
本文以课题为基础,结合实际生产内容,深入实践,为矿山企业带来了较好的经济效益。
Lump coal, as a major product in the coal market, can't meet the increasing society need of coal resource. So, improving the productivity of lump coal has been an importance mean of the economic development of coal industry. The economic benefit of opencast mine depend to a great extent on the yield of lump coal. The blasting fragmentation of coal rock is an important index for evaluating the blasting effect and increasing the production benefit. Usually, the selling price of lump coal is higher than that of powder coal. In the recent years, a lot of research on the effect factor, such as the habitude of rocks, the explosive ratio, the detonating sequence, and the way of design holes has been done. On the premise of meeting the safety production, the selection of reasonable blasting parameters has become an important part of the blasting production to increase the productivity of lump coal and obtain good economic benefit. So, it is significant to research the productivity improvement of opencast mine by blasting means.
Based on the project of the research and design of the open middle-deep hole computer aided design system and the practical condition of Xiaolongtan buzhaoba cole mine, the research of this paper was done through a large number of field tests for taking half the year. The tests result is also predicted and confirmed by the theory analysis, the statistics and calculation of experimental data, and the BP neural network model. The blasting parameter for the safety and high efficiency production of this mine is obtained by doing research on the primary factors of the effect of lump coal and the controlling of flying-stone in blasting.
In this paper, the recent progress of lump coal production in blasting at home and abroad is introduced and briefly reviewed. The blasting mechanism and the influential factors of improvement of lump coal blasting are analyzed according to the relevant theory. The effect of the burden in directional blasting, the explosive ratio, the hole arrangement and other blasting parameters on the lump coal ratio is discussed, respectively. The blasting factors fitted to this mine are confirmed through many field tests. The rational parameters can enhance the interaction between the explosive power and the coal-rock and reduce the energy loss and the ratio of powder coal. The large quantities test data and the distribution law of qualified lump coal ratio are processed and discussed by CAD and VB program, respectively. In order to satisfy the requirement of safety production and the control of flying-stone in blasting, this paper also analyze the influential factors to flying-stone in blasting and obtain the reasonable filling length and the burdenand requirement. The blasting lump coal ratio network model based on the property of BP neural network model is proposed and used to preliminary predict the blasting lump coal ratio at different parameters.
Based on the project and combined the practical production, this paper had gain good economical benefit for mine industry.
本论文结合《露天中深孔爆破计算机辅助设计系统的研究与开发》课题展开研究,根据小龙潭布沼坝露天煤矿的实际情况,历时半年多的时间,通过现场的大量试验,结合理论分析和试验数据统计、计算,并利用建立的BP神经网络模型对试验结果进行了预测验证。研究爆破块煤效果、个别飞散物控制与主要影响因素之间的关系,并得出了适合于该矿山安全、高效生产的爆破参数。
论文介绍了目前国内外爆破块煤生产的研究现状并进行了简要的评述,从相关理论上分析了提高爆破块煤的爆破机理和影响因素,分别论述了底盘抵抗线、炸药单耗、孔排距等爆破参数对块煤率的影响,经过现场多次的爆破试验确定了符合该矿山的爆破参数,通过参数的合理选择使炸药的能量对煤岩作用充分,减少了能量的损失,碎煤的比例也随之减少；通过CAD图形处理手段对大量的试验数据进行了处理,并通过VB程序对数据进行统计运算,探讨了合格块煤率的分布规律；为满足矿山安全生产的要求,论文还对爆破产生个别飞散物的影响因素进行了分析研究,找出了合理的填塞长度及抵抗线要求,基本满足了矿山安全生产对个别飞散物的控制；利用神经网络的特点建立了爆破块煤率的神经网络预测模型,对不同参数下爆破块煤率进行了初步预测,且预测结果与实际应用结果大致相同,证明了模型的实际应用性。
本文以课题为基础,结合实际生产内容,深入实践,为矿山企业带来了较好的经济效益。
Lump coal, as a major product in the coal market, can't meet the increasing society need of coal resource. So, improving the productivity of lump coal has been an importance mean of the economic development of coal industry. The economic benefit of opencast mine depend to a great extent on the yield of lump coal. The blasting fragmentation of coal rock is an important index for evaluating the blasting effect and increasing the production benefit. Usually, the selling price of lump coal is higher than that of powder coal. In the recent years, a lot of research on the effect factor, such as the habitude of rocks, the explosive ratio, the detonating sequence, and the way of design holes has been done. On the premise of meeting the safety production, the selection of reasonable blasting parameters has become an important part of the blasting production to increase the productivity of lump coal and obtain good economic benefit. So, it is significant to research the productivity improvement of opencast mine by blasting means.
Based on the project of the research and design of the open middle-deep hole computer aided design system and the practical condition of Xiaolongtan buzhaoba cole mine, the research of this paper was done through a large number of field tests for taking half the year. The tests result is also predicted and confirmed by the theory analysis, the statistics and calculation of experimental data, and the BP neural network model. The blasting parameter for the safety and high efficiency production of this mine is obtained by doing research on the primary factors of the effect of lump coal and the controlling of flying-stone in blasting.
In this paper, the recent progress of lump coal production in blasting at home and abroad is introduced and briefly reviewed. The blasting mechanism and the influential factors of improvement of lump coal blasting are analyzed according to the relevant theory. The effect of the burden in directional blasting, the explosive ratio, the hole arrangement and other blasting parameters on the lump coal ratio is discussed, respectively. The blasting factors fitted to this mine are confirmed through many field tests. The rational parameters can enhance the interaction between the explosive power and the coal-rock and reduce the energy loss and the ratio of powder coal. The large quantities test data and the distribution law of qualified lump coal ratio are processed and discussed by CAD and VB program, respectively. In order to satisfy the requirement of safety production and the control of flying-stone in blasting, this paper also analyze the influential factors to flying-stone in blasting and obtain the reasonable filling length and the burdenand requirement. The blasting lump coal ratio network model based on the property of BP neural network model is proposed and used to preliminary predict the blasting lump coal ratio at different parameters.
Based on the project and combined the practical production, this paper had gain good economical benefit for mine industry.
引文
[1]薛令光.大安山煤矿提高块煤率技术的应用研究：[硕士学位论文].阜新：辽宁工程技术大学,2004
[2]葛勇.宽孔距小抵抗线微差爆破技术研究：[硕士学位论文].阜新：辽宁工程技术大学,2004
[3]王有俊,崔立和,提高块煤率的技术方案研究,辽宁工程技术大学学报,2007,26：180-182
[4]中国爆破技术发展与展望工程爆破文集1997.
[5]庙延刚,栾龙发,爆破工程与安全技术.北京：化学工业出版社,2007
[6]于永江.炮采提高块煤率的机理研究：[硕士学位论文].阜新：辽宁工程技术大学,2003
[7]陈士海,乔卫国,赵有斌,提高块煤率的爆破技术,西部探矿工程,1999,11：62-64
[8]黄志辉.台阶爆破块度分布测定及其优化研究：[硕士学位论文].泉州：华侨大学,2005
[9]高善堂,露天深孔爆破飞石的控制,矿业研究与开发,1997,17：28-30
[10]陈学峰,李永苏,王旭东,提高块煤率的有效途径,河北煤炭,2002,1：5-6
[11]题正义,露天矿爆破后煤岩块度分布测试方法综述,阜新矿业学院学报(自然科学版),1994,13：38-43
[12]题正义.爆堆块度分布的自动与分形测试系统研究：[博士学位论文].阜新：辽宁工程技术大学,2001
[13]Dietze R J, Menz E. Rationales Verfah renzur Hauf werk analyse. Neue Bergbautechnik,1980,10:355-358
[14]Sirotyk G N. A method of calculating the fragment size composition of blasted rock from the given oversize fragment dimension. Soviet Ming Science,1970,1:692-474
[15]Schwate W, Bestimmung optimaler Sprengparameter unter Beachtung der Haufwerk and der Vorbrecherzuordnung, Neue Bergbautechnik,1979,9:692-695
[16]题正义,孙臣良,刘烟台,爆堆块度分布的实验室模型研究,煤炭学报,2000,25：173-175
[17]题正义,单图片摄影测试系统的煤岩块度分布计算方法,阜新矿业学院学报(自然 科学版),1994,13：14-16
[18]王军.乌龙泉矿降低粉矿率研究与块度图像处理：[硕士学位论文].武汉：武汉科技大,2003
[19]Just G D. The application of size distribution equations of rock breakage by explosives. National Symp. Rock Frag. Austrlia Geomech. Soc.,1973:18-23
[20]曾世奇,露天爆堆块度分布谱,金属矿山,1993,6：17-25
[21]Kemeny J M. Practical technique for determining the size distribution of blasted benches, wasted dumps and help leach sites, Mining Engineering,1994,11:1281-1284
[22]瑑世杰,矿岩块度图像分析法研究评述,金属矿山,1995,11：10-13
[23]向文飞,舒大强,条形药包爆破块度模型初控,爆破,2002,19(1)：14-16
[24]魏连春,大孔距小排距的孔网参数在中深孔爆破中的应用,天津冶金,2003,4：18-19
[25]刘为林,爆破飞石的控制分析,矿业工程,2001,2：24-25
[26]栾龙发,海州露天煤矿穿爆参数研究：[博士学位论文].北京：中国矿业大学,1988
[27]任祥,郭学斌,工程爆破飞石及其控制,西部探矿工程,2005,12：181-183
[28]罗建平,露天深孔爆破中飞石产生原因及控制,矿山技术,2002,3：69-71
[29]高文乐,李天忠,王桂春,岩石爆破中影响爆破飞石的主要因素,煤矿爆破,2000,3：18-20
[30]Vogt W A,数值图像处理露天矿岩石爆破破碎评定的一种方法,第四届国际岩石爆破破碎学术会议论文集.奥地利维也纳,1995
[31]Hunter G C, McDermott C. A review of image analysis techniques for measuring blasting fragmentation, Mining Science and Technology,1990,11:19-36
[32]Jong Y H, Lee C G, Application of Neural Networks to Prediction of Powder Factorand Peak Particle Velocity in Tunnel Blasting, Proceedings of the Annual Conference on Explosives and Blasting Technique. Ⅶ,2002:67-76
[33]Khandelwal M, Singh T N. Evaluation of blast—induced ground vibration predictors, Soil Dynamics and Earthquake Engineering,2007,27:116-125
[34]Singh T N, Kanchan R, Verma A K, Prediction of blast induced ground vibration andfrequency using an artificial intelligent technique, Noise and Vibration World wide, 2004,35:7-15
[35]Kuznetsov V M. The mean diameter of fragment stormed by blasting rock, Soviet Mining Seienee,1973,9
[36]Grady D L, Kipp M L. Continuum Modeling of Explosive Frnaceino:Shale. int. J. Roek Meeh Min.,1980,17:147-157
[37]周开利,康耀红,神经网络模型及其MATLAB仿真程序设计.北京：清华大学出版社,2005
[38]章海象.基于人工神经网络的爆破效果预测研究：[硕士学位论文].包头：内蒙古科技大学,2005
[39]朱太奇,史慧,人工神经网络的原理及应用.北京：科学技术出版社,2006
[40]胡守仁,神经网络导论.长沙：国防科技大学出版社,1993
[41]飞思科技产品研发中心,神经网络理论与Matlab7实现.北京：电子工业出版社,2005
[2]葛勇.宽孔距小抵抗线微差爆破技术研究：[硕士学位论文].阜新：辽宁工程技术大学,2004
[3]王有俊,崔立和,提高块煤率的技术方案研究,辽宁工程技术大学学报,2007,26：180-182
[4]中国爆破技术发展与展望工程爆破文集1997.
[5]庙延刚,栾龙发,爆破工程与安全技术.北京：化学工业出版社,2007
[6]于永江.炮采提高块煤率的机理研究：[硕士学位论文].阜新：辽宁工程技术大学,2003
[7]陈士海,乔卫国,赵有斌,提高块煤率的爆破技术,西部探矿工程,1999,11：62-64
[8]黄志辉.台阶爆破块度分布测定及其优化研究：[硕士学位论文].泉州：华侨大学,2005
[9]高善堂,露天深孔爆破飞石的控制,矿业研究与开发,1997,17：28-30
[10]陈学峰,李永苏,王旭东,提高块煤率的有效途径,河北煤炭,2002,1：5-6
[11]题正义,露天矿爆破后煤岩块度分布测试方法综述,阜新矿业学院学报(自然科学版),1994,13：38-43
[12]题正义.爆堆块度分布的自动与分形测试系统研究：[博士学位论文].阜新：辽宁工程技术大学,2001
[13]Dietze R J, Menz E. Rationales Verfah renzur Hauf werk analyse. Neue Bergbautechnik,1980,10:355-358
[14]Sirotyk G N. A method of calculating the fragment size composition of blasted rock from the given oversize fragment dimension. Soviet Ming Science,1970,1:692-474
[15]Schwate W, Bestimmung optimaler Sprengparameter unter Beachtung der Haufwerk and der Vorbrecherzuordnung, Neue Bergbautechnik,1979,9:692-695
[16]题正义,孙臣良,刘烟台,爆堆块度分布的实验室模型研究,煤炭学报,2000,25：173-175
[17]题正义,单图片摄影测试系统的煤岩块度分布计算方法,阜新矿业学院学报(自然 科学版),1994,13：14-16
[18]王军.乌龙泉矿降低粉矿率研究与块度图像处理：[硕士学位论文].武汉：武汉科技大,2003
[19]Just G D. The application of size distribution equations of rock breakage by explosives. National Symp. Rock Frag. Austrlia Geomech. Soc.,1973:18-23
[20]曾世奇,露天爆堆块度分布谱,金属矿山,1993,6：17-25
[21]Kemeny J M. Practical technique for determining the size distribution of blasted benches, wasted dumps and help leach sites, Mining Engineering,1994,11:1281-1284
[22]瑑世杰,矿岩块度图像分析法研究评述,金属矿山,1995,11：10-13
[23]向文飞,舒大强,条形药包爆破块度模型初控,爆破,2002,19(1)：14-16
[24]魏连春,大孔距小排距的孔网参数在中深孔爆破中的应用,天津冶金,2003,4：18-19
[25]刘为林,爆破飞石的控制分析,矿业工程,2001,2：24-25
[26]栾龙发,海州露天煤矿穿爆参数研究：[博士学位论文].北京：中国矿业大学,1988
[27]任祥,郭学斌,工程爆破飞石及其控制,西部探矿工程,2005,12：181-183
[28]罗建平,露天深孔爆破中飞石产生原因及控制,矿山技术,2002,3：69-71
[29]高文乐,李天忠,王桂春,岩石爆破中影响爆破飞石的主要因素,煤矿爆破,2000,3：18-20
[30]Vogt W A,数值图像处理露天矿岩石爆破破碎评定的一种方法,第四届国际岩石爆破破碎学术会议论文集.奥地利维也纳,1995
[31]Hunter G C, McDermott C. A review of image analysis techniques for measuring blasting fragmentation, Mining Science and Technology,1990,11:19-36
[32]Jong Y H, Lee C G, Application of Neural Networks to Prediction of Powder Factorand Peak Particle Velocity in Tunnel Blasting, Proceedings of the Annual Conference on Explosives and Blasting Technique. Ⅶ,2002:67-76
[33]Khandelwal M, Singh T N. Evaluation of blast—induced ground vibration predictors, Soil Dynamics and Earthquake Engineering,2007,27:116-125
[34]Singh T N, Kanchan R, Verma A K, Prediction of blast induced ground vibration andfrequency using an artificial intelligent technique, Noise and Vibration World wide, 2004,35:7-15
[35]Kuznetsov V M. The mean diameter of fragment stormed by blasting rock, Soviet Mining Seienee,1973,9
[36]Grady D L, Kipp M L. Continuum Modeling of Explosive Frnaceino:Shale. int. J. Roek Meeh Min.,1980,17:147-157
[37]周开利,康耀红,神经网络模型及其MATLAB仿真程序设计.北京：清华大学出版社,2005
[38]章海象.基于人工神经网络的爆破效果预测研究：[硕士学位论文].包头：内蒙古科技大学,2005
[39]朱太奇,史慧,人工神经网络的原理及应用.北京：科学技术出版社,2006
[40]胡守仁,神经网络导论.长沙：国防科技大学出版社,1993
[41]飞思科技产品研发中心,神经网络理论与Matlab7实现.北京：电子工业出版社,2005