金属矿矿岩可爆性评价及井下采场深孔爆破参数优化的理论与试验研究
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摘要
地下深孔采矿技术是以大孔径深孔爆破为特征的,开采强度大,生产能力高,是大型地下矿山广泛应用的一种大规模高效采矿技术。以往爆破参数的选择往往凭借经验或者依靠工程类比方法,缺乏科学性和系统性。本文依托云南省科技创新强省计划项目——“卡房钨多金属矿资源开发利用关键技术及产业化研究”,在试验矿山应用“采矿环境再造连续采矿嗣后充填采矿法”,尝试采用深孔爆破崩矿技术来达到预定的产能目标。论文重点着眼于采矿环境再造条件下的采场深孔爆破参数优化的理论和试验研究。主要研究内容包括：
(1)结合采矿环境再造技术和深孔爆破参数设计及优化的需要,对试验矿山地质结构进行了调查,对节理裂隙进行了统计；然后采用室内试验的方法测试了矿岩的物理力学性质并对试验矿山的岩石质量进行了总体评价。
(2)基于利文斯顿理论进行了爆破漏斗试验。选取与试验采场岩性尽可能相近的岩体分别进行单孔爆破漏斗试验和变孔距多孔同段爆破漏斗试验,初步确定了适合深孔爆破试验采场的爆破参数,即孔间距为2.0m、排距(最小抵抗线)为2.3m、炸药单耗在0.57kg/m3以上,并采用VC++6.0编制爆破漏斗体积计算程序,减轻了计算工作量。
(3)建立了爆破参数的粗糙集-BP神经网络预测模型,对爆破漏斗试验得出的主要参数进行验证和优化。研究认为在减少了样本属性的情况下,粗糙集-BP神经网络预测模型比BP神经网络预测模型的精度更高,预测得到的最佳爆破漏斗半径与试验值较为接近,证实了爆破漏斗试验的正确性。采用Excel2007软件和VBA语言编制了一个基于Excel2007的数据离散归一化软件用于处理粗糙集使用的样本数据,提高了工作效率。
(4)基于数据挖掘技术,在前人研究的基础上,采用粗糙集软件对收集整理的矿岩爆破性测试数据进行属性约简,并对约简规则和KNN分类准确性进行了检验。基于SPSS软件,采用多元非线性回归方法得到了修正后的矿岩爆破性指数计算公式,并对修正公式的可靠性进行了验证。建立了矿岩可爆性预测的BP神经网络模型和粗糙集-BP神经网络模型,并比较了两者的精度,认为应优先选择后者。采用修正的矿岩爆破性指数计算公式得到了试验矿山主要岩石的爆破性指数和爆破性级别,并用粗糙集-BP神经网络预测模型验证了上述计算和评价结果,最后采用经验公式估算得到井下采场深孔爆破的炸药单耗为0.62kg/m3。
(5)利用LS-DYNA软件对试验采场深孔爆破过程充填体的安全稳定性进行了数值模拟分析并对相关爆破参数进行了论证。按照预裂缝长度不同,分7种情况建立最大起爆段模型对充填矿柱中产生的振动强度和应力强度进行分析,基于最小二乘法原理对所得数据进行曲线拟合,优化后的预裂缝长度为12.0m,在此基础上对近点起爆段进行建模分析,得出主爆孔间毫秒微差延迟时间不低于35ms。
(6)应用以上理论与试验研究获得的主要爆破参数,成功在井下采场进行了深孔爆破试验,并运用TC-4850型爆破振动监测仪对主要保护对象进行了实时监测,回归得到了爆破振动衰减规律并采用小波分析方法对振动信号进行了辨识并对能量衰减规律进行了分析。
Characterized by large diameter borehole blasting, underground deep-hole mining technique is a large-scale and high efficient technique applied in major underground mines which has high mining intensity and high productivity. The blasting parameters often originate from experience and engineering analogy analysis, which is not quite scientific and systematic. The thesis based on the Science and Technology Innovation Project of Yunnan Province named "the Industrialization and Key Techniques Study of Kafang Tungsten Polymetallic Ore Development and Utilization" trys to apply "Mining Environment Regeneration and Continuous Mining with Subsequent Filling Mining Method" and deep-hole blasting technology in test mine and in order to reach the set productivity target. The thesis focuses on the theoretical and testing research of parameter optimization for deep-hole blasting under the condition of mining environment regeneration. The main research contents are as follows:
(1) Combining environment reconstructing technique and the design and optimization of deep-hole blasting parameters, the thesis investigates the geological structure of test mine and gathers statistics on joint fissure. Then it takes indoor experiments to test the physical and mechanical properties of mine rock and evaluate the rock quality of the test mine.
(2) The blasting crater test has been done based on Livingston theory. The mine rock similar to the test mine has been chosen to take on single-hole blasting crater tests and variable distance multi-hole simultaneous blasting crater test. The blasting parameters of deep-hole blasting test mine have been determined preliminarily, namely the interval of holes is2.0m, the distance between two rows (minimum resist line) is2.3m, and the unit explosive consumption is above0.57kg/m3. VC++6.0is used to establish a calculation program for blasting crater volume to reduce the workload.
(3) The blasting parameters's prediction model based on Rough Set and BP Neural Network is founded to testify and optimize the main parameters of blasting test experiment. It is assumed that the prediction model based on Rough Set and BP Neural Network is more accurate than the prediction model based on BP Neural Network and the predicted optimum crater radius is close to the experimental result, which proves the validity of blasting crater test. Excel2007and VBA language are used to design data discretization and normalization software based on Excel2007to process the sample data used in Rough Set which improve the efficiency.
(4) Based on the data mining technology and on the basis of previous studies, the original rock blastability test datas'attributes reduction have been done by using rough set software, and the accuracy of reduction rules and KNN classification are verified. Based on the SPSS software, the calculation formula of ore-rock's explosibility index is modified by using multiple nonlinear regression method, and the reliability of the modified formula is verified. The ore-rock's blastability prediction model are set up respectively by single BP Neural Network and joint Rough Set and BP neural network, and the two kinds of models' precision are compared, the study conclusion is that should give priority to the latter. The calculation results of the rock blasting index and explosive level in the test mine have been made by the modified formula and the results of calculation and evaluation are verified by the Rough Set and BP Neural Network prediction model, the final underground stope's estimated value of blasting explosive unit consumption is0.62kg/m3by using empirical formula.
(5) LS-DYNA is applied for numerical simulation analysis of test mine's backfill's security and stability in deep-hole blasting process and implements parameter argumentation. According to the different depth of pre-splitting crack, the7kinds of maximum initiating section blasting models are established, and the fill pillar's vibration amplitude and stress intensity are analyzed and the fitting curve is gotten by the obtained data based on the principle of least square method, the optimized pre-splitting length is12.0m. On the basis of the previous research, the near point initiating section blasting models are established and analyzed, and the study show that the millisecond minute difference which defers time is longer than35ms between the main blasting holes.
(6) Based on the main blasting parameters obtained by theory and experimental method, the deep-hole blasting experiment has been done in the test mine and the TC-4850blasting vibration monitors are used to monitor the main protected object in real time in which blasting vibration attenuation law is achieved and vibration signal and energy are analyzed through wavelet analysis.
(1)结合采矿环境再造技术和深孔爆破参数设计及优化的需要,对试验矿山地质结构进行了调查,对节理裂隙进行了统计；然后采用室内试验的方法测试了矿岩的物理力学性质并对试验矿山的岩石质量进行了总体评价。
(2)基于利文斯顿理论进行了爆破漏斗试验。选取与试验采场岩性尽可能相近的岩体分别进行单孔爆破漏斗试验和变孔距多孔同段爆破漏斗试验,初步确定了适合深孔爆破试验采场的爆破参数,即孔间距为2.0m、排距(最小抵抗线)为2.3m、炸药单耗在0.57kg/m3以上,并采用VC++6.0编制爆破漏斗体积计算程序,减轻了计算工作量。
(3)建立了爆破参数的粗糙集-BP神经网络预测模型,对爆破漏斗试验得出的主要参数进行验证和优化。研究认为在减少了样本属性的情况下,粗糙集-BP神经网络预测模型比BP神经网络预测模型的精度更高,预测得到的最佳爆破漏斗半径与试验值较为接近,证实了爆破漏斗试验的正确性。采用Excel2007软件和VBA语言编制了一个基于Excel2007的数据离散归一化软件用于处理粗糙集使用的样本数据,提高了工作效率。
(4)基于数据挖掘技术,在前人研究的基础上,采用粗糙集软件对收集整理的矿岩爆破性测试数据进行属性约简,并对约简规则和KNN分类准确性进行了检验。基于SPSS软件,采用多元非线性回归方法得到了修正后的矿岩爆破性指数计算公式,并对修正公式的可靠性进行了验证。建立了矿岩可爆性预测的BP神经网络模型和粗糙集-BP神经网络模型,并比较了两者的精度,认为应优先选择后者。采用修正的矿岩爆破性指数计算公式得到了试验矿山主要岩石的爆破性指数和爆破性级别,并用粗糙集-BP神经网络预测模型验证了上述计算和评价结果,最后采用经验公式估算得到井下采场深孔爆破的炸药单耗为0.62kg/m3。
(5)利用LS-DYNA软件对试验采场深孔爆破过程充填体的安全稳定性进行了数值模拟分析并对相关爆破参数进行了论证。按照预裂缝长度不同,分7种情况建立最大起爆段模型对充填矿柱中产生的振动强度和应力强度进行分析,基于最小二乘法原理对所得数据进行曲线拟合,优化后的预裂缝长度为12.0m,在此基础上对近点起爆段进行建模分析,得出主爆孔间毫秒微差延迟时间不低于35ms。
(6)应用以上理论与试验研究获得的主要爆破参数,成功在井下采场进行了深孔爆破试验,并运用TC-4850型爆破振动监测仪对主要保护对象进行了实时监测,回归得到了爆破振动衰减规律并采用小波分析方法对振动信号进行了辨识并对能量衰减规律进行了分析。
Characterized by large diameter borehole blasting, underground deep-hole mining technique is a large-scale and high efficient technique applied in major underground mines which has high mining intensity and high productivity. The blasting parameters often originate from experience and engineering analogy analysis, which is not quite scientific and systematic. The thesis based on the Science and Technology Innovation Project of Yunnan Province named "the Industrialization and Key Techniques Study of Kafang Tungsten Polymetallic Ore Development and Utilization" trys to apply "Mining Environment Regeneration and Continuous Mining with Subsequent Filling Mining Method" and deep-hole blasting technology in test mine and in order to reach the set productivity target. The thesis focuses on the theoretical and testing research of parameter optimization for deep-hole blasting under the condition of mining environment regeneration. The main research contents are as follows:
(1) Combining environment reconstructing technique and the design and optimization of deep-hole blasting parameters, the thesis investigates the geological structure of test mine and gathers statistics on joint fissure. Then it takes indoor experiments to test the physical and mechanical properties of mine rock and evaluate the rock quality of the test mine.
(2) The blasting crater test has been done based on Livingston theory. The mine rock similar to the test mine has been chosen to take on single-hole blasting crater tests and variable distance multi-hole simultaneous blasting crater test. The blasting parameters of deep-hole blasting test mine have been determined preliminarily, namely the interval of holes is2.0m, the distance between two rows (minimum resist line) is2.3m, and the unit explosive consumption is above0.57kg/m3. VC++6.0is used to establish a calculation program for blasting crater volume to reduce the workload.
(3) The blasting parameters's prediction model based on Rough Set and BP Neural Network is founded to testify and optimize the main parameters of blasting test experiment. It is assumed that the prediction model based on Rough Set and BP Neural Network is more accurate than the prediction model based on BP Neural Network and the predicted optimum crater radius is close to the experimental result, which proves the validity of blasting crater test. Excel2007and VBA language are used to design data discretization and normalization software based on Excel2007to process the sample data used in Rough Set which improve the efficiency.
(4) Based on the data mining technology and on the basis of previous studies, the original rock blastability test datas'attributes reduction have been done by using rough set software, and the accuracy of reduction rules and KNN classification are verified. Based on the SPSS software, the calculation formula of ore-rock's explosibility index is modified by using multiple nonlinear regression method, and the reliability of the modified formula is verified. The ore-rock's blastability prediction model are set up respectively by single BP Neural Network and joint Rough Set and BP neural network, and the two kinds of models' precision are compared, the study conclusion is that should give priority to the latter. The calculation results of the rock blasting index and explosive level in the test mine have been made by the modified formula and the results of calculation and evaluation are verified by the Rough Set and BP Neural Network prediction model, the final underground stope's estimated value of blasting explosive unit consumption is0.62kg/m3by using empirical formula.
(5) LS-DYNA is applied for numerical simulation analysis of test mine's backfill's security and stability in deep-hole blasting process and implements parameter argumentation. According to the different depth of pre-splitting crack, the7kinds of maximum initiating section blasting models are established, and the fill pillar's vibration amplitude and stress intensity are analyzed and the fitting curve is gotten by the obtained data based on the principle of least square method, the optimized pre-splitting length is12.0m. On the basis of the previous research, the near point initiating section blasting models are established and analyzed, and the study show that the millisecond minute difference which defers time is longer than35ms between the main blasting holes.
(6) Based on the main blasting parameters obtained by theory and experimental method, the deep-hole blasting experiment has been done in the test mine and the TC-4850blasting vibration monitors are used to monitor the main protected object in real time in which blasting vibration attenuation law is achieved and vibration signal and energy are analyzed through wavelet analysis.
引文
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