Optimization of Mining–Mineral Processing Integration Using Unsupervised Machine Learning Algorithms

Traditional ore-waste discrimination schemes often do not take into consideration the impact of fluctuations of the head grade, which have on the performance of mineral processing facilities. This research introduces the use of target grades for processing destinations as an alternative to cut off g...

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Vydané v:	Natural resources research (New York, N.Y.) Ročník 29; číslo 5; s. 3035 - 3046
Hlavní autori:	Li, Siyi, Sari, Yuksel Asli, Kumral, Mustafa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.10.2020 Springer Nature B.V
Predmet:	Algorithms Chemistry and Earth Sciences Cluster analysis Clustering Computer Science Cut off grades Divergence Earth and Environmental Science Earth Sciences Fossil Fuels (incl. Carbon Capture) Geography Learning algorithms Machine learning Mathematical Modeling and Industrial Mathematics Mineral processing Mineral Resources Original Paper Physics Reclassification Statistics for Engineering Sustainable Development Unsupervised learning Vector quantization Robust clustering Taguchi loss function Spectral clustering Target grades CLARA Mining and mineral processing
ISSN:	1520-7439, 1573-8981
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Abstract	Traditional ore-waste discrimination schemes often do not take into consideration the impact of fluctuations of the head grade, which have on the performance of mineral processing facilities. This research introduces the use of target grades for processing destinations as an alternative to cut off grade-based methods and models, in each processing destination, the losses due to deviation from targets via the Taguchi loss function. Three unsupervised learning algorithms, k -means clustering, CLARA and k -mean-based approximate spectral clustering, are presented to group mine planning blocks into clusters of similar grades with different processing destinations. In addition, a technique considering uncertainties associated with block grades is proposed to generate new sequences that reduce variation in processing capacities across the life of mine (LoM). The case study in this paper involves the treatment of a realistically large mining dataset. The results showed that clustering methods outperform cutoff grade-based method when divergence from target grades is penalized and that reclassification of blocks based on data from geostatistical simulations could achieve smoother capacities for processing streams across the LoM.
AbstractList	Traditional ore-waste discrimination schemes often do not take into consideration the impact of fluctuations of the head grade, which have on the performance of mineral processing facilities. This research introduces the use of target grades for processing destinations as an alternative to cut off grade-based methods and models, in each processing destination, the losses due to deviation from targets via the Taguchi loss function. Three unsupervised learning algorithms, k-means clustering, CLARA and k-mean-based approximate spectral clustering, are presented to group mine planning blocks into clusters of similar grades with different processing destinations. In addition, a technique considering uncertainties associated with block grades is proposed to generate new sequences that reduce variation in processing capacities across the life of mine (LoM). The case study in this paper involves the treatment of a realistically large mining dataset. The results showed that clustering methods outperform cutoff grade-based method when divergence from target grades is penalized and that reclassification of blocks based on data from geostatistical simulations could achieve smoother capacities for processing streams across the LoM. Traditional ore-waste discrimination schemes often do not take into consideration the impact of fluctuations of the head grade, which have on the performance of mineral processing facilities. This research introduces the use of target grades for processing destinations as an alternative to cut off grade-based methods and models, in each processing destination, the losses due to deviation from targets via the Taguchi loss function. Three unsupervised learning algorithms, k -means clustering, CLARA and k -mean-based approximate spectral clustering, are presented to group mine planning blocks into clusters of similar grades with different processing destinations. In addition, a technique considering uncertainties associated with block grades is proposed to generate new sequences that reduce variation in processing capacities across the life of mine (LoM). The case study in this paper involves the treatment of a realistically large mining dataset. The results showed that clustering methods outperform cutoff grade-based method when divergence from target grades is penalized and that reclassification of blocks based on data from geostatistical simulations could achieve smoother capacities for processing streams across the LoM.
Author	Li, Siyi Sari, Yuksel Asli Kumral, Mustafa
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Snippet	Traditional ore-waste discrimination schemes often do not take into consideration the impact of fluctuations of the head grade, which have on the performance...
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SubjectTerms	Algorithms Chemistry and Earth Sciences Cluster analysis Clustering Computer Science Cut off grades Divergence Earth and Environmental Science Earth Sciences Fossil Fuels (incl. Carbon Capture) Geography Learning algorithms Machine learning Mathematical Modeling and Industrial Mathematics Mineral processing Mineral Resources Original Paper Physics Reclassification Statistics for Engineering Sustainable Development Unsupervised learning Vector quantization
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Title	Optimization of Mining–Mineral Processing Integration Using Unsupervised Machine Learning Algorithms
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Volume	29
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