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Fuzzy TOPSIS method to primary crusher selection for Golegohar Iron Mine(Iran) 被引量:6
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作者 Mohammad Javad Rahimdel Mohammad Karamoozian 《Journal of Central South University》 SCIE EI CAS 2014年第11期4352-4359,共8页
Selection of the crusher required a great deal of design regarding to the mine planning. Selection of suitable primary crusher from all of available primary crushers is a multi-criterion decision making(MCDM) problem.... Selection of the crusher required a great deal of design regarding to the mine planning. Selection of suitable primary crusher from all of available primary crushers is a multi-criterion decision making(MCDM) problem. The present work explores the use of technique for order performance by similarity to ideal solution(TOPSIS) with fuzzy set theory to select best primary crusher for Golegohar Iron Mine in Iran. Gyratory, double toggle jaw, single toggle jaw, high speed roll crusher, low speed sizer, impact crusher, hammer mill and feeder breaker crushers have been considered as alternatives. Also, the capacity, feed size, product size, rock compressive strength, abrasion index and application of primary crusher for mobile plants were considered as criteria for solution of this MCDM problem. To determine the order of the alternatives, closeness coefficient is defined by calculating the distances to the fuzzy positive ideal solution(FPIS) and fuzzy negative ideal solution(FNIS). Results of our work based on fuzzy TOPSIS method show that the gyratory is the best primary crusher for the studied mine. 展开更多
关键词 primary crusher multi-criterion decision making(MCDM) technique for order performance by similarity to ideal solution fuzzy set theory golegohar Iron Mine gyratory crusher
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Application of analytical hierarchy process to selection of primary crusher 被引量:6
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作者 Rahimdel Mohammad Javad Ataei Mohammad 《International Journal of Mining Science and Technology》 SCIE EI 2014年第4期519-523,共5页
Selection of crusher required a great deal of design based on the mining plan and operation input. Selection of the best primary crusher from all of available primary crushers is a Multi-Criterion Decision Making (M... Selection of crusher required a great deal of design based on the mining plan and operation input. Selection of the best primary crusher from all of available primary crushers is a Multi-Criterion Decision Making (MCDM) problem, in this paper, the Analytical Hierarchy Process (AHP) method was used to selection of the best primary crusher for Golegohar Iron Mine in Iran. For this reason, gyratory, double toggle jaw, single toggle jaw, high speed roll crusher, low speed sizer, impactor, hammer mill and feeder breaker crushers were considered as alternatives and capacity, feed size, product size, rock compressive strength, abrasion index and mobility of crusher were considered as criteria. As a result of our study, the gyvratory crusher was offered as the best primary crusher for the studied mine. 展开更多
关键词 MCDMA nalytical Hierarchy Process Primary crusher selection golegohar Iron Mine
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Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks 被引量:5
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作者 Ghiasi Majid Askarnejad Nematollah +1 位作者 Dindarloo Saeid R. Shamsoddini Hamed 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2016年第2期183-184,共2页
The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boul... The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively. 展开更多
关键词 Blast boulder Artificial neural networks Multiple regression golegohar iron ore mine
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