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Geometallurgy / Metallurgy / Mining / Kriging / Compositional data / Semivariance / QEMSCAN / Variogram / Statistics / Geostatistics / Economic geology


Improving processing by adaption to conditional geostatistical simulation of block compositions by R. Tolosana-Delgado*, U. Mueller†, K.G. van den Boogaart*‡, C. Ward**, and J. Gutzmer*‡
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Document Date: 2015-02-17 07:03:56


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File Size: 3,47 MB

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City

Bergakademie Freiberg / Dresden / Perth / /

Company

Southern Cross / /

Country

Germany / Australia / /

Currency

USD / /

Facility

The Southern African Institute of Mining / Technical University / Southern African Institute of Mining / Edith Cowan University / /

IndustryTerm

automated mineralogy systems / end-member algorithms / chemical components / framework accounting / toy processing / adaptive processing / computationally-intensive solution / classical multivariate geostatistical tools / observed chemical compositions / energy consumption / i.e. component-wise product / simulation algorithms / direct product / chemical compositions / mean chemical composition / chemical composition / energy requirements / chemical data / chemical and mineralogical compositions / chemical attributes / metal grade / process chain / systematic adaptive processing / lump product / downstream processing steps / mining / chemicals / bands algorithm / /

Organization

Southern African Institute of Mining and Metallurgy Helmholtz Zentrum Dresden-Rossendorf / Federal Government / Edith Cowan University / Perth / Southern African Institute of Mining / /

Person

Tolosana-Delgado / G. van den Boogaart / /

ProvinceOrState

Western Australia / Ohio / /

Region

Western Australia / /

Technology

turning bands algorithm / simulation algorithms / Dom / simulation / end-member algorithms / simulated separately using the turning bands algorithm / /

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