Bed blending design incorporating multiple regression modelling and genetic algorithms

Basit Öğe Kaydı
dc.contributor.authorKumral, M.
dc.date.accessioned2024-08-04T20:15:26Z
dc.date.available2024-08-04T20:15:26Z
dc.date.issued2006
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe efficiency of an ore-processing unit depends on the consistency of the characteristics of raw material entering the plant. When the mined ore is highly variable in quality, the only way to ensure consistency is to homogenize the ore prior to feeding to the processing plant. The homogenization can generally be achieved by the bed blending operation. Given that the stockpiling and reclamation processes are very expensive, it is necessary to design the process in such a way as to minimize variabilities of specified properties of raw material. in this paper, for alternative stacking types, optimal stockpile geometry is found in three stages: First, stockpile input is simulated by sequential Gaussian simulation, and then the variance reduction ratios (VRR) as a criterion of stockpile efficiency are calculated for various stockpile geometry scenarios by a stockpile simulator written in FORTRAN. Second, multiple regression analysis is performed to model the VRR by the use of stockpile length, the number of layers and stacker speed as the independent variables. Finally, the model is an optimization problem. Decision variables are the stockpile length, the number of layers, stacker speed and stacking type. The genetic algorithms (GA) are used to minimize the VRR. The approach was demonstrated on data from an iron orebody. The problem was to reduce fluctuations of iron, silica, alumina and lime contents in the stockpile output. The results showed that the approach could be used for the bed blending design efficiently.en_US
dc.identifier.endpage236en_US
dc.identifier.issn2225-6253
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-33646733692en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage229en_US
dc.identifier.urihttps://hdl.handle.net/11616/94385
dc.identifier.volume106en_US
dc.identifier.wosWOS:000237647900012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSouthern African Inst Mining Metallurgyen_US
dc.relation.ispartofJournal of The Southern African Institute of Mining and Metallurgyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleBed blending design incorporating multiple regression modelling and genetic algorithmsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu