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工作面瓦斯涌出量是采面通风设计及制定采面瓦斯防治措施的主要依据。在收集陈家山煤矿大量瓦斯地质资料基础上,分析了矿井主采4-2号煤层采面瓦斯涌出规律及其影响因素,研究认为,采面瓦斯涌出量为矿井主要瓦斯来源,其涌出量与煤层埋藏深度、煤层瓦斯含量、顶板含油气小街砂岩厚度及工作面日产量等主要控制因素呈正相关关系;采用数学建模方法建立了采面瓦斯涌出量预测模型,编制了采面瓦斯涌出量预测图,结果显示4-2号煤层采面绝对瓦斯涌出量总体呈现出由井田浅部向中部迅速增大,再由中部到深部逐渐减少的变化趋势。 相似文献
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在总结桑树坪煤矿瓦斯涌出量分布特征的基础上,通过定性与定量分析方法,研究了影响综采工作面瓦斯涌出量的主控因素,为该矿瓦斯涌出量定量预测研究奠定了基础。 相似文献
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林南仓属于低瓦斯矿井,但存在高瓦斯区域。煤层和采空区是瓦斯的主要来源,尤以采空涌出量大,给煤矿生产和安全带来了极大隐患。通过在1129综采工作面风道施工高位瓦斯孔,把钻孔打到采空区一侧煤层顶板以上冒落裂隙带内,用钻孔进行瓦斯抽放,使采空内的瓦斯通过裂隙带沿钻孔抽出,有效降低综采工作面瓦斯浓度,保证综采工作面正常回采和安全生产。 相似文献
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适当加大综放工作面长度已成为煤矿高产高效主要发展方向之一,但工作面采长加大后,瓦斯的涌出量加大,对安全生产带来隐患。通过分析阳泉煤矿三矿综放工作面采长增加后瓦斯涌出量的变化特征,对采长200m以下和200m以上的综放工作面瓦斯涌出影响因素进行了对比研究,认为综放工作面采长增大后,邻近层垮落卸压范围增大,吨煤瓦斯涌出量增加27%;瓦斯涌出总量的增加主要取决于卸压影响范围体积的增加和煤层开采产量的增大。 相似文献
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针对高强开采井田王家岭煤矿综放工作面瓦斯涌出量大、上隅角瓦斯积聚问题,基于岩层控制关键层理论,对顶板垮落走向与倾向采动裂隙发育进行研究。采用数值模拟的方法研究采空裂隙随工作面推进的演化过程,分析顶板裂隙发育高度,确定大直径高位定向长钻孔最佳布孔层位及钻孔结构,并进行工程实践。结果表明,依据关键层理论计算及采动裂隙数值模拟预测层位布设大直径高位定向长钻孔,抽采效果较好,单孔最大抽采纯量2.10 m3/min,最大抽采体积分数31.39%,4个定向长钻孔累计抽采瓦斯纯量28.99万m3,工作面上隅角瓦斯体积分数最低下降至0.46%,瓦斯治理效果显著,解决了上隅角瓦斯超限问题,保障了工作面的安全回采。 相似文献
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为了提高瓦斯涌出量预测精度,针对瓦斯涌出量影响因素的多重相关性、复杂性等问题,结合主成分分析法和分源预测理论,对开采层、邻近层、采空区的瓦斯涌出量数据分别进行主成分分析降维,得到预测指标。针对极限学习机(ELM)存在的输入权值矩阵与隐含层阈值随机生成的问题,利用模拟退火粒子群算法(SAPSO)对极限学习机的参数寻优,将新疆某煤矿回采工作面瓦斯涌出量及影响因素作为SAPSO-ELM模型的输入进行训练,再利用训练好的SAPSO-ELM模型对陕西某煤矿回采工作面的瓦斯涌出量进行验证预测,并对比原始ELM模型的预测结果。结果表明,SAPSO-ELM模型的平均相对误差为3.45%,ELM模型的平均相对误差为8.81%,与ELM模型相比,SAPSO-ELM模型预测精度及效率均优于原始ELM模型。分源预测理论和主成分分析法的结合有效解决了多因素间的多重相关性并降低了预测模型的复杂度,SAPSO-ELM预测模型实现了瓦斯涌出量的快速精准预测,对预防瓦斯事故发生和保障煤矿安全高效开采具有较好的指导作用。 相似文献
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丘陵地区地下水资源不丰富,依赖于地下水的生态环境也比较脆弱。在开采该地区的矿产资源过程中,采矿排水对周边地下水资源的影响应成为值得注意的问题。以安徽省庐江县某一铁矿为例,采用解析法和数值法分别对该矿矿坑涌水量进行预测,并将两种方法的预测结果进行了对比分析。结果表明:在-500 m开采条件下,计算结果分别为41991.44 m3/d和3150 m3/d,得出数值法更适合水文地质复杂条件下矿坑用水量的预测,且与实测结果较为吻合,具有一定实用性,可为矿山制定防排水方案提供依据。 相似文献
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基于BP神经网络方法的矿井涌水量预测 总被引:2,自引:0,他引:2
鉴于矿井涌水威胁煤矿安全生产及其影响因素的复杂性,提出基于BP神经网络的矿井涌水量预测方法.在充分分析新安煤矿+25m开采水平的涌水影响因素的基础上,选取大气降水、采空区面积和底板构造断裂和采动裂隙三个影响因子,建立了非线性人工神经网络预测模型,对+25m开采水平的正常涌水量进行了预计.其结果和实际观测数据能够较好地相吻合,表明采用人工神经网络预计矿井涌水量是可行的. 相似文献
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Predicting the destroyed floor depth caused by the mining of coal seams is of great importance in judging whether the mining of a deep coal seam can be safely performed above a confined aquifer and to prevent the inrush of water from the floor. Thirty sets of coal mining data on destroyed floor depth were selected for study. A comprehensive analysis of the factors that influence the depth of destruction of coal seam floor strata was performed and combined with the ability of a BP neural network to address dynamic nonlinear information. Then, a set of test samples was assembled and used to construct a predictive model using a BP neural network. The model was then used to predict the destroyed floor depth of the 7105 working face of the Baizhuang Coal Mine in the Feicheng coal field. To verify the effectiveness of the model, the depth of the destroyed strata comprising the coal seam floor was measured using equipment called the “Double Sided Sealed Borehole Water Injection Device.” By comparing the predictions made by the BP neural network with actual measurements, the conclusion was reached that a BP neural network model can effectively be used to predict the destroyed floor depth caused by the mining of a coal seam. 相似文献
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湖南道县铁锰矿Ⅱ矿体岩溶矿区矿坑涌水量分析 总被引:3,自引:1,他引:2
矿坑涌水是矿山地下开采面临的首要问题,合理的涌水量预测方法是目前国内外研究的热点和难点所在。本文以湖南省道县铁锰矿Ⅱ矿体-50 m开采水平为例,利用有限差分数值方法对矿坑开采的涌水量进行模拟与分析。计算结果表明,涌水量主要来自于车子江的补给,但东部、南部、北部及降雨的补给量所占比例增大,说明Ⅱ矿体涌水量来源较广泛。综合分析,数值法所计算出的不同季节、不同雨强特征的涌水量,更符合矿区实际条件,模拟结果可以作为矿区开采设计的依据。 相似文献
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In order to overcome the shortage that point-based data acquisition techniques cannot retrieve the whole basin subsidence caused by underground mining, and to avoid complex splicing of terrestrial 3D laser scanner (TLS) point cloud data and the errors caused by such splicing, GPS/TLS combined technology is employed for mining subsidence monitoring. The basic idea of the monitoring technology is put forward. In this article, an application of the method to a coal mining area in China is presented. Support vector machine (SVM) model for GPS level conversion in the mining area is established, and a comparative analysis of SVM, BP neural network and polynomial established local quasi-geoid in the mining area is conducted. Ground surface digital elevation model (DEM) of the mining area is established by using TLS point cloud data, and the ground surface dynamic subsidence basin is obtained through a subtraction of two DEMs. The results indicate that the quasi-geoid established by using SVM model features a relatively high level of stability and accuracy and that the established mining surface DEM and subsidence basin can provide the fundamental data for the reconstruction of ecological environment in the mining area. GPS/TLS combined monitoring technology is a new monitoring technology, which entangles the advantages of both GPS and TLS and could offset their disadvantages, thus obtaining complementary advantages. According to analysis on its application in the mining area, we conclude that the technology is feasible and has a great application prospect for the mining area purposes. 相似文献
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多元线性回归及BP神经网络是煤层含气量测井解释的常用方法。基于澳大利亚Galilee盆地和沁水盆地煤层测井资料和实测含气量数据,通过相关性分析和显著性检验,筛选了和含气量相关的测井参数,通过多元线性回归建立含气量与测井参数的解释模型;基于BP神经网络的理论,通过网络训练和测试,建立了煤层含气量和测井参数的非线性解释模型。讨论了多元线性回归模型的参数选择方法,并对两种解释方法的误差特点进行了分析,讨论了两种方法的适用性。结果显示:多元线性回归法和BP神经网络法是煤层含气量解释的常用方法,前者的解释误差比后者大;多元线性回归法解释精度与煤层含气量相关,适用于含气量较高的井;BP神经网络法解释精度普遍较高,在含气量高和低的井中均可适用,解释效果受输入层样本的数量和质量影响,样本数量越多,区域代表性越强,解释效果越好。 相似文献
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The West Mine of the Bayan Obo deposit, located in the northern‐central part of Inner Mongolia, China, is enriched in Nb, rare earth elements and iron (Nb‐REE‐Fe) mineral resources. This paper presents a combined method to explore metallogenic correlation of the Nb‐REE‐Fe mineralization at the Bayan Obo West Mine. The method integrates factor analysis and Back Propagation (BP) neural network technology into processing and modeling of geological data. In this study, the Nb and REE contents of samples were transformed into discrete values to analyze the correlations among the metallogenic elements. The results show weak mineralization correlations between Nb and REEs. Nb and U are closely related in the geochemical patterns, while Fe is closely related to both Th and Mn. LREEs are an important factor for the mineralization of the Bayan Obo deposit, while Fe and Nb can be considered as the results of passive mineralization. On the basis of a metallogenic correlation analysis, the factors affecting the Fe‐REE‐Nb mineralization were extracted, and the Nb mineralization model was established by the BP neural network. Based on the BP neural network data computing, the variability of the Nb concentration displays a coupled multi‐factor nonlinear relationship, which can be used to reveal the inherent metallogenic elemental regularities and predict the degree of element mineralization enrichment in the mining area. 相似文献
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矿区岩溶地表塌陷神经网络预测研究 总被引:3,自引:0,他引:3
针对近年来某矿岩溶地表塌陷频繁发生的现象,分析确定了影响地表塌陷的主要因素,构建了矿区岩溶地表塌陷预测BP神经网络模型,以训练后的BP网络模型对矿山帷幕注浆三期工程完成后可能形成的地表塌陷区的空间分布进行预测。并针对矿山现实塌陷情况,结合各区预测塌陷危险分级结果,提出了相应的岩溶地表塌陷灾害防治措施。实践表明,所建模型的预测结果与矿区地表塌陷实际情况相符,可为矿山后续帷幕注浆工程的设计与施工提供有益借鉴,为岩溶矿区地表塌陷灾害提供预警支持。 相似文献