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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Natural resources research (New York, N.Y.) Jg. 29; H. 5; S. 3035 - 3046
Hauptverfasser:	Li, Siyi, Sari, Yuksel Asli, Kumral, Mustafa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2020 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!