|本期目录/Table of Contents|

[1]邱冠豪,吴超,江时雨,等.金属矿井热害评价体系与评价方法[J].中国安全生产科学技术,2014,10(12):46-50.[doi:10.11731/j.issn.1673-193x.2014.12.008]
 QIU Guan-hao,WU Chao,JIANG Shi-yu,et al.Assessment system and method of thermal hazard in metal mines[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(12):46-50.[doi:10.11731/j.issn.1673-193x.2014.12.008]
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金属矿井热害评价体系与评价方法
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年12期
页码:
46-50
栏目:
学术论著
出版日期:
2014-12-31

文章信息/Info

Title:
Assessment system and method of thermal hazard in metal mines
作者:
邱冠豪吴超 江时雨杨国增
(中南大学 资源与安全工程学院,湖南长沙410083)
Author(s):
QIU Guan-hao WU Chao JIANG Shi-yu YANG Guo-zeng
(School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China)
关键词:
金属矿山热害评价层次分析法(AHP)物元分析法BP网络
Keywords:
metal mines thermal hazard assessment AHP matter element analysis BP neural network
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2014.12.008
文献标志码:
A
摘要:
为减小金属矿井热害对井下人员安全及井下开采工作的不利影响,需对井下热害进行评价和预测。基于文献调查和专家评价方法,结合工程实际,利用层次分析法构建金属矿井采矿热害评价体系,从生产能力、地质条件、矿井通风、地理环境四个方面提出17个评价指标。在分析层次分析法(AHP)确定权重不足的基础上,结合物元分析理论,建立确定金属矿井热害评价各因素权重的物元分析模型。在各评价因素权重确定的基础上,以BP神经网络作为评价工具,构建金属矿井热害综合评价预测模型。最后,以某矿山为例,进行评价和预测分析。结果表明,基于物元分析和AHP的BP深井网络评价模型预测误差最大只有3%。
Abstract:
In order to reduce the negative influence of thermal hazard in metal mines to personnel safety and the underground mining work, it is much necessary to evaluate and predict the risk of thermal hazard in underground mining. Based on the method of literature investigation and expert evaluation and combined with the engineering practice, an evaluation index system of thermal hazard in metal mines was established. 14 evaluation indexes were put forward from four aspects, such as production capacity, geological conditions, mine ventilation and geographical conditions. Based on analyzing the fit and unfit of the analytic hierarchy process (AHP), the matter element analysis model in determining weight of thermal hazard in metal mines was established through the matter element analysis. With BP neural network as assessment tool, the comprehensive evaluation and forecast model of thermal hazard in metal mines was constructed. Accordingly, the evaluation and forecast analysis was used to evaluate a practical mine as an example. The results showed that the maximum prediction error of BP neural network evaluation model based on the matter element analysis and AHP was only 3%.

参考文献/References:

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备注/Memo

备注/Memo:
国家安监总局安全科技“四个一批”项目,安监总厅科技〔2012〕142号
更新日期/Last Update: 2014-12-30