煤与瓦斯突出事故链控制及应用研究
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摘要
随着煤矿开采深度的不断增加,煤层地应力增高,瓦斯压力、瓦斯含量增大,煤与瓦斯突出灾害日趋严重。论文依托国家自然科学基金重点项目“煤矿生产重大瓦斯事故的形成机理及其管理方法研究”(NO.70533050,起止时间:2006-2009年),结合事故致因理论、事故(灾害)链式理论和煤与瓦斯突出防治技术,对煤与瓦斯突出事故链进行了控制及应用研究。取得的主要研究成果如下:
1)将突出事故技术因素和管理因素作为煤与瓦斯突出事故链上的两个节点,其中突出事故技术因素包括技术基础工作、相关系统的可靠程度、防治技术、理念与技术创新和技术装备等,构成了技术致因链;突出事故管理因素包括瓦斯治理投入、瓦斯治理管理、瓦斯治理机构和瓦斯治理队伍等,构成了管理致因链。据此建立了煤与瓦斯突出事故链模型。
2)基于煤与瓦斯突出事故链模型,建立了煤与瓦斯突出事故评价指标体系,并结合煤与瓦斯突出防治的相关规定及科研结果,进行了安全标准的制定及打分,得出了煤与瓦斯突出事故评价指标体系安全标准表,从而实现了煤与瓦斯突出防治工程的规范化和程序化。
3)根据主成分原理(PCA),编写了煤与瓦斯突出事故关键因素求解算法。为验证该求解算法结果的可靠性,建立了煤与瓦斯突出预测的PCA-BP神经网络模型,应用国内典型的煤与瓦斯突出案例对该模型进行了训练和测试,仿真结果表明PCA-BP神经网络预测模型性能优于传统BP神经网络,即该求解算法可以用于确定煤与瓦斯突出事故关键链。
4)基于煤与瓦斯突出事故链模型,分析了不同突出危险煤层应采取的煤与瓦斯突出事故断链控制技术,提出了6种控制煤与瓦斯突出事故的区域性断链技术方案和管理致因链控制对策,从而实现了对技术致因链和管理致因链的控制,切断了煤与瓦斯突出事故链,达到了防治煤与瓦斯突出事故的目的。
5)进行了案例应用研究。根据煤与瓦斯突出事故评价指标体系和安全标准表,确定了大平煤矿煤与瓦斯突出事故指标对应的安全分值,利用编写的煤与瓦斯突出事故关键因素求解算法得出大平煤矿煤与瓦斯突出事故关键链由技术致因关键链(未掌握煤层瓦斯赋存规律、未及时进行突出危险性鉴定、瓦斯地质基础工作和近距离岩巷防突措施不到位)和管理致因关键链(无防突机构和防突队伍)组成,并与“10.20”特大型煤与瓦斯突出事故进行对比分析,与事故专家组调查结果基本一致。在此基础上,提出了适合大平煤矿的底板岩巷穿层钻孔结合工作面顺层钻孔瓦斯抽采区域性断链技术体系,结合16071工作面概况,进行了工作面顺层钻孔瓦斯抽采断链技术工程施工和效果考察。实践表明煤层顺层钻孔瓦斯抽采率达到31.5%以上,残余瓦斯含量降到了6.7m3/t以下,顺层钻孔抽采技术有效的切断了煤与瓦斯突出事故链,消除了试验区煤层的突出危险性。
With mining work getting deeper, the ground stress, gas pressure and gas content increase, coal-gas outburst disasters are becoming more serious. Relying on national natural science foundation key project“Research of Formation Mechanism and Management Method for Coal-gas Outburst Accidents”(NO.70533050,start/end time: 2006-2009), research was conducted into control and application of coal-gas outburst accident chains by using accident cause theory, accident chain theory and gas outburst control technology. The main conclusions are presented as following:
1) The technological and management factors are taken as two nodes of coal-gas outburst accident chains. The technical factors form the technical cause chain, which includes basic technological work, reliability of each system, prevention technology, innovation in ideology and technology and technical equipment. The management factors constitute the management cause chain, which consists of the input, management, institution and manpower for gas control. The simultaneous occurrence of technical and management cause chains would lead to coal-gas outburst accidents. The model for coal-gas outburst accident chains is built based on the theory.
2) An indicator system for coal-gas outburst accident was built by using the model for coal-gas outburst accident chain. Subsequently, safety standard establishment and grading were performed by referencing to the rules and research results relevant to coal-gas outburst prevention. Base on the above achivement, the standard safety table of the indicator system for coal-gas outburst accident was obtained, which thus realized the standardization process for coal-gas outburst prevention engineering.
3) An algorithm for critical factors of coal-gas outburst accidents was compiled based on principal component theory. To check its reliability, the algorithm was used to build the prediction model of PCA-BP neural network which was trained and tested by employing the typical coal-gas outburst cases in China. The simulation results showed that the prediction model of PCA-BP neural network was superior to the traditional BP network. Hence, the algorithm could be used to determine the critical chain of the coal-gas outburst accidents.
4) Chain-breaking technology for coal-gas outburst accident at coal seams with different outburst risk was analyzed by using the coal-gas outburst accident chain, six kinds of regional chain-breaking technologies for controlling coal-gas outburst accident were achieved, and management strategies for controlling coal-gas outburst accident chain were rendered. As a result, success was achieved in controlling the basic, technological and management factors, cutting off coal-gas outburst accident chain and preventing coal-gas outburst accidents.
5) Case study was investigated with the theory. The safety values corresponding to the indicators of Daping coal mine outburst accidents were determined using the indicator system for coal-gas outburst accident and standard safety table. By utilizing the compiled algorithm for critical factors of coal-gas outburst accidents, the accident critical chain was determined. Comparison with the‘10.20’extraordinarily serious coal-gas accidents revealed that the obtained accident critical chain agreed well with the investigation results of experts. Based on these results, several chain-breaking technologies for coal-gas outburst accident were proposed such as bedding layer bore gas drainage for unsteady‘three fragility’extremely-thick outburst coal seam. The chain-breaking technologies were also applied to Daping coal mine 16071 working face. The results showed that gas drainage rate for bedding layer bores could exceed 31.5%, and that the content of residual gas could decrease to below 6.7m3/t. The gas drainage technology for bedding layer bores successfully broke the coal-gas outburst accident chain and consequently ruled out the risk of outburst in the tested coal seam region.
1)将突出事故技术因素和管理因素作为煤与瓦斯突出事故链上的两个节点,其中突出事故技术因素包括技术基础工作、相关系统的可靠程度、防治技术、理念与技术创新和技术装备等,构成了技术致因链;突出事故管理因素包括瓦斯治理投入、瓦斯治理管理、瓦斯治理机构和瓦斯治理队伍等,构成了管理致因链。据此建立了煤与瓦斯突出事故链模型。
2)基于煤与瓦斯突出事故链模型,建立了煤与瓦斯突出事故评价指标体系,并结合煤与瓦斯突出防治的相关规定及科研结果,进行了安全标准的制定及打分,得出了煤与瓦斯突出事故评价指标体系安全标准表,从而实现了煤与瓦斯突出防治工程的规范化和程序化。
3)根据主成分原理(PCA),编写了煤与瓦斯突出事故关键因素求解算法。为验证该求解算法结果的可靠性,建立了煤与瓦斯突出预测的PCA-BP神经网络模型,应用国内典型的煤与瓦斯突出案例对该模型进行了训练和测试,仿真结果表明PCA-BP神经网络预测模型性能优于传统BP神经网络,即该求解算法可以用于确定煤与瓦斯突出事故关键链。
4)基于煤与瓦斯突出事故链模型,分析了不同突出危险煤层应采取的煤与瓦斯突出事故断链控制技术,提出了6种控制煤与瓦斯突出事故的区域性断链技术方案和管理致因链控制对策,从而实现了对技术致因链和管理致因链的控制,切断了煤与瓦斯突出事故链,达到了防治煤与瓦斯突出事故的目的。
5)进行了案例应用研究。根据煤与瓦斯突出事故评价指标体系和安全标准表,确定了大平煤矿煤与瓦斯突出事故指标对应的安全分值,利用编写的煤与瓦斯突出事故关键因素求解算法得出大平煤矿煤与瓦斯突出事故关键链由技术致因关键链(未掌握煤层瓦斯赋存规律、未及时进行突出危险性鉴定、瓦斯地质基础工作和近距离岩巷防突措施不到位)和管理致因关键链(无防突机构和防突队伍)组成,并与“10.20”特大型煤与瓦斯突出事故进行对比分析,与事故专家组调查结果基本一致。在此基础上,提出了适合大平煤矿的底板岩巷穿层钻孔结合工作面顺层钻孔瓦斯抽采区域性断链技术体系,结合16071工作面概况,进行了工作面顺层钻孔瓦斯抽采断链技术工程施工和效果考察。实践表明煤层顺层钻孔瓦斯抽采率达到31.5%以上,残余瓦斯含量降到了6.7m3/t以下,顺层钻孔抽采技术有效的切断了煤与瓦斯突出事故链,消除了试验区煤层的突出危险性。
With mining work getting deeper, the ground stress, gas pressure and gas content increase, coal-gas outburst disasters are becoming more serious. Relying on national natural science foundation key project“Research of Formation Mechanism and Management Method for Coal-gas Outburst Accidents”(NO.70533050,start/end time: 2006-2009), research was conducted into control and application of coal-gas outburst accident chains by using accident cause theory, accident chain theory and gas outburst control technology. The main conclusions are presented as following:
1) The technological and management factors are taken as two nodes of coal-gas outburst accident chains. The technical factors form the technical cause chain, which includes basic technological work, reliability of each system, prevention technology, innovation in ideology and technology and technical equipment. The management factors constitute the management cause chain, which consists of the input, management, institution and manpower for gas control. The simultaneous occurrence of technical and management cause chains would lead to coal-gas outburst accidents. The model for coal-gas outburst accident chains is built based on the theory.
2) An indicator system for coal-gas outburst accident was built by using the model for coal-gas outburst accident chain. Subsequently, safety standard establishment and grading were performed by referencing to the rules and research results relevant to coal-gas outburst prevention. Base on the above achivement, the standard safety table of the indicator system for coal-gas outburst accident was obtained, which thus realized the standardization process for coal-gas outburst prevention engineering.
3) An algorithm for critical factors of coal-gas outburst accidents was compiled based on principal component theory. To check its reliability, the algorithm was used to build the prediction model of PCA-BP neural network which was trained and tested by employing the typical coal-gas outburst cases in China. The simulation results showed that the prediction model of PCA-BP neural network was superior to the traditional BP network. Hence, the algorithm could be used to determine the critical chain of the coal-gas outburst accidents.
4) Chain-breaking technology for coal-gas outburst accident at coal seams with different outburst risk was analyzed by using the coal-gas outburst accident chain, six kinds of regional chain-breaking technologies for controlling coal-gas outburst accident were achieved, and management strategies for controlling coal-gas outburst accident chain were rendered. As a result, success was achieved in controlling the basic, technological and management factors, cutting off coal-gas outburst accident chain and preventing coal-gas outburst accidents.
5) Case study was investigated with the theory. The safety values corresponding to the indicators of Daping coal mine outburst accidents were determined using the indicator system for coal-gas outburst accident and standard safety table. By utilizing the compiled algorithm for critical factors of coal-gas outburst accidents, the accident critical chain was determined. Comparison with the‘10.20’extraordinarily serious coal-gas accidents revealed that the obtained accident critical chain agreed well with the investigation results of experts. Based on these results, several chain-breaking technologies for coal-gas outburst accident were proposed such as bedding layer bore gas drainage for unsteady‘three fragility’extremely-thick outburst coal seam. The chain-breaking technologies were also applied to Daping coal mine 16071 working face. The results showed that gas drainage rate for bedding layer bores could exceed 31.5%, and that the content of residual gas could decrease to below 6.7m3/t. The gas drainage technology for bedding layer bores successfully broke the coal-gas outburst accident chain and consequently ruled out the risk of outburst in the tested coal seam region.
引文
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