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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1520-7439
1573-8981
DOI:10.1007/s11053-020-09628-0