采矿与安全工程学报 ›› 2012, Vol. 29 ›› Issue (3): 416-420.

• 论文 • 上一篇    下一篇

煤层突出危险性的属性综合评价模型研究

  

  1. 中国矿业大学安全工程学院,江苏  徐州  221116
  • 收稿日期:2011-05-06 出版日期:2012-05-15 发布日期:2012-04-12
  • 作者简介:马衍坤(1985-),男,山东省肥城市人,博士,从事煤与瓦斯突出预测与防治方面的研究。 E-mail:mykunbest@126.com Tel:0516-83884695
  • 基金资助:

    国家自然科学基金项目(40904028)

    全国博士学位论文作者专项资金项目(201055)

    煤炭资源与安全开采国家重点实验室自主研究项目(SKLCRSM09X01)

    教育部新世纪优秀人才支持计划项目(NCET-10-0768)

Attribute Synthetic Evaluation Model for Predicting Risk of Coal and Gas Outburst

  • Received:2011-05-06 Online:2012-05-15 Published:2012-04-12

摘要: 针对目前煤层突出危险性程度难以准确判定的难题,基于属性数学理论和方法,建立了煤层突出危险性的属性综合评价模型。模型选用煤层瓦斯压力、瓦斯放散初速度、煤的坚固性系数、煤体破坏类型、综合指标D值和K值等6项指标作为煤层突出危险性的评价指标;应用属性数学理论,构造了各项指标的属性测度函数,定义了单指标属性测度和综合属性测度的计算方法;采用相似数和相似权的方法客观给各项指标赋权,用置信度准则进行属性识别,给出评价结果。通过贵州盘县的煤矿突出实例对模型进行了验证,结果表明该模型的评价结果与实际情况基本一致,可应用于煤层的突出危险性评价中。

关键词: 突出危险性, 属性综合评价, 属性数学, 属性测度, 相似权

Abstract: Based on attribute mathematics theory, we propose an attribute synthetic evaluation model for predicting the risk of coal and gas outburst in this paper. Six indexes are chosen to evaluate coal and gas outburst in the model, including the gas pressure of coal seam, the initial speed of methane diffusion, the firmness coefficient, types of the coal damage, comprehensive index of D and K. The attribute measurement functions of each index are constructed based on the attribute mathematics theory, while the calculation methods of the single index attribute measurement and comprehensive attribute measurement are provided, respectively. Meanwhile, the weight of each index is given with the method of similarity figure and similarity weight, while the evaluation results are determined by the reliability code. According to the application results of the model in some coal mines in Pan County, Guizhou province, the evaluation results are basically consistent with the actual situation, which is proved that the evaluation model can be used in outburst prediction.

Key words: risk of coal and gas outburst, attribute synthetic evaluation, attribute mathematics, attribute measure, similarity weight