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Introduced the theory of three types of hazardous sources, and it recognized and analysed such three types of hazardous sources as the factor of inherent hazardous source, factor of inducing hazardous source and factor of men, which affect the safety and reliability of coal-dust explosion risk system and then builds up the risk factor indices of coal-dust explosion according to analysis of conditions inducing the coal-dust explosion. It fixes the risk degree of coal-dust explosion risk system by analyzing loss probability and loss scope of risk system and by means of the probabilistic hazard evaluation method and risk matrix method, etc.. According to the feature of strong capability of nonlinear approximation of BP neural network, the paper designed the structure of BP neural network for the risk evaluation of the mine coal-dust explosion with BP neural network. And the weight of the network was finally determined by training the given samples so that the risk degree of samples to be measured could be exactly evaluated and the risk of mine coal-dust explosion could be alarmed in good time. 相似文献
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瓦斯爆炸是矿山恶性事故之一。采用预先危险性分析进行矿井瓦斯爆炸危险度评价,可以找出影响矿井瓦斯爆炸的影响因素、事故隐患位置、触发条件及事故后果,针对不同危险源的危险等级,结合矿井的实际安全条件提出科学可行的安全对策措施,给企业安全管理提供依据。 相似文献
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为提高预测模型的可靠性,实现对煤层未采区域瓦斯含量的精确预测,以山阳煤矿5#煤层为研究对象,进行未采区瓦斯含量的预测。运用瓦斯地质学和多元线性回归分析法,得出基岩厚度、煤层厚度和埋深是影响该矿瓦斯赋存的主要因素,并将其作为BP神经网络模型的输入端神经元,初步构建出瓦斯含量预测模型;结合地勘时期瓦斯钻孔的实际数据,进行网络训练,再对预测模型的可靠性进行检验。结果表明:该预测模型预测瓦斯含量,精度较高,效果较好,能满足工程要求。采用多元线性回归-BP神经网络可以对未开采区域煤层瓦斯含量进行准确预测,为矿井瓦斯灾害防治提供一定的参考依据。 相似文献
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分析总结了煤体渗透率的3个主要影响因素--有效应力、温度和瓦斯压力,并结合煤体的力学特性建立了一个预测煤层瓦斯渗透率的BP神经网络模型。根据不同有效应力、不同温度和不同瓦斯压力条件下大量具有代表性的煤样渗透率数据来建立学习样本,并对该模型的精度进行了检验。该BP神经网络经过11 986次学习后精度满足要求,训练后BP神经网络模型所得预测结果的最大绝对误差为0.049×10-15 m2,最大相对误差为4.298%。根据所建立的BP神经网络模型得到的预测值与实测值吻合较好。 相似文献
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Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result. 相似文献
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针对煤矿瓦斯爆炸事故危险源具有多因素复杂性特征,运用对吻鱼骨图分析方式,系统剖析了煤矿瓦斯爆炸事故发生机理及其危害,结合模糊层次分析方法,建立了煤矿瓦斯爆炸风险综合评价模型,并以大同煤矿集团四台煤矿为例,分析验证了该方法对于煤矿瓦斯爆炸事故风险预控的可行性和科学性,从而为煤矿瓦斯爆炸危险源分级管理及安全预警提供了一种理论参考。 相似文献
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为了对高瓦斯矿井工作面煤自燃火区封闭后甲烷爆炸危险性进行有效预测,利用质量守恒定律对封闭火区在短时间内甲烷和氧气体积分数的变化规律进行推演,构建封闭火区内甲烷和氧气的积聚模型,分析火区封闭以后采空区内的气体体积分数的变化规律及关键影响因素,基于时间交集理论对火区封闭后甲烷爆炸危险性展开讨论,应用甲烷、氧气积聚模型对某矿火区封闭后的甲烷爆炸性进行估算,得到了甲烷爆炸的可能性及发生时间,及时采取相应的预防措施,成功地避免了火区封闭后甲烷爆炸事故。 相似文献
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为研究采煤工作面上隅角瓦斯爆炸在采面联巷内的传播特征,采用U型并联管道系统模拟爆炸在实际巷道内的传播。结果表明,上隅角瓦斯爆炸冲击波在采煤工作面不规则巷道中传播时,爆炸冲击波和火焰陡然变化,出现爆轰;进、回风巷内冲击波进入上下山巷道出现叠加;冲击波经过进风巷与回风巷传播特征存在较大差异,冲击波在回风巷内属燃烧爆炸传播,而在进风巷内属一般空气区传播,上下山巷道及工作面属爆炸破坏较严重区域,应强化预防措施,减少瓦斯爆炸带来的损失。 相似文献
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为了实现安全决策从经验驱动到数据量化驱动的转变,增强安全管理的针对性与有效性,引入循证安全管理方法,提出了最佳安全证据优化的煤矿瓦斯治理模式,构建了瓦斯爆炸事故循证安全管理体系,并选择瓦斯爆炸事故的时间数据为研究角度,通过分析获取时间相关的最佳安全证据,给出了基于最佳安全证据的安全决策。结果表明:循证安全管理应用于具体生产领域可有效优化现行的安全管理模式,提高安全决策的针对性,弥补安全对策过于笼统、灾害防治效率不高的缺陷。研究是循证安全理论在实践层面的首次尝试,亦为循证安全管理学的研究、实践与发展提供了可行的思路。 相似文献
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In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering. 相似文献