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The choice of mining method is a complex systematic engineering, which is featured by multiple decision factors, coexistence of certainty and uncertainty, and at the same time the traditional experience contrast method has subjective randomness. To aim at the evaluation and selection of mining methods, an information entropy and fuzzy matter element analysis method is proposed in this paper. In the process of evaluation and selection, decision factors such as mining cost, dilution rate, and loss rate are selected. the detailed steps are as follows. Firstly, the factors of selected mining methods are transformed to evaluation matrix; secondly, the weight vectors of the factors are obtained by the information entropy and fuzzy matter element analysis method; lastly, the relative closeness of all the methods are given by utilizing entropy fuzzy matter element theory. A domestic mine is taken as an example. Three mining schemes are evaluated by the proposed method in terms of, security, technology, and economic indexes. Their relative closeness are 96.18%, 81.02% and 92.18%, respectively, and the optimal method is then determined, which has better consistency with the fuzzy optimum seeking method. 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科技导报
| 研究论文 2014, 32(2): 30-33
熵权模糊物元分析法在采矿方法选择中的应用
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陈建宏, 邓伟夏
作者信息
Entropy Fuzzy Matter Element Analysis Method of Mining Method Selection
Affiliations
出版时间: 2014-01-18
doi: 10.3981/j.issn.1000-7857.2014.2.004
文章导航
采矿方法的选择关系到矿山的经济效益和资源效益,是矿山建设和生产的核心内容。采矿方法选择是一个复杂的系统工程,具有决策多属性、确定性与不确定性共存的特点,同时传统的经验对比法具有较大的主观随意性,本文将信息论和模糊物元分析理论有机地结合,针对采矿方法的评价优选,提出了信息熵模糊物元分析法。评判优选过程中,选择如采矿成本、损失率、贫化率等决策指标;将待选择方法指标转换评判矩阵,通过信息熵模糊物元分析法得到各因素权重向量,利用熵模糊物元原理计算出各方法的相对贴近度。选取国内某矿山采矿方法为例,从安全、技术、经济3个方面综合考虑评判指标,对拟选用的3种方案进行综合评判,计算得出3种待选方法的相对贴近度分别为96.18%、81.02%、92.18%,从而确定最优方法,为矿山采矿方法的优选提供依据。该优选方法与模糊优选法有较好的一致性,且计算方法简单,评价结果直观、准确可靠,具有信息利用率高等优点。
采矿方法
/
信息熵
/
模糊物元
/
方法优选
/
贴近度
The selection of mining method is related to the economic and resource benefits of mines, and is the core of mine construction and production. The choice of mining method is a complex systematic engineering, which is featured by multiple decision factors, coexistence of certainty and uncertainty, and at the same time the traditional experience contrast method has subjective randomness. To aim at the evaluation and selection of mining methods, an information entropy and fuzzy matter element analysis method is proposed in this paper. In the process of evaluation and selection, decision factors such as mining cost, dilution rate, and loss rate are selected. the detailed steps are as follows. Firstly, the factors of selected mining methods are transformed to evaluation matrix; secondly, the weight vectors of the factors are obtained by the information entropy and fuzzy matter element analysis method; lastly, the relative closeness of all the methods are given by utilizing entropy fuzzy matter element theory. A domestic mine is taken as an example. Three mining schemes are evaluated by the proposed method in terms of, security, technology, and economic indexes. Their relative closeness are 96.18%, 81.02% and 92.18%, respectively, and the optimal method is then determined, which has better consistency with the fuzzy optimum seeking method. The new method lays a basis for the selection of mining method, and is simple, intuitive, accurate and reliable, with a high utilization of information.
mining methods
/
information entropy
/
fuzzy matter element
/
method optimigation
/
closeness
陈建宏, 邓伟夏.
熵权模糊物元分析法在采矿方法选择中的应用.
科技导报,
2014
, 32
(2)
: 30
-33
.
DOI: 10.3981/j.issn.1000-7857.2014.2.004
CHEN Jianhong, DENG Weixia.
Entropy Fuzzy Matter Element Analysis Method of Mining Method Selection[J].
Science & Technology Review ,
2014
, 32
(2)
: 30
-33
.
DOI: 10.3981/j.issn.1000-7857.2014.2.004
2014年第32卷第2期
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文章信息
doi: 10.3981/j.issn.1000-7857.2014.2.004
接收时间:2013-08-27
首发时间:2014-02-14
出版时间:2014-01-18
收稿日期:2013-08-27
修回日期:2013-10-25
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2014.2.004
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2种不同金属材料的力学参数
科 Family 属数 Number of genus 种数 Number of species 占总种数比例 Percentage of total species (%) 属 Genus 种数 Number of species 占总种数比例 Percentage of total species (%) 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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