A systematic review on the application of machine learning in exploiting mineralogical data in mining and mineral industry
M Jooshaki, A Nad, S Michaux - Minerals, 2021 - mdpi.com
Machine learning is a subcategory of artificial intelligence, which aims to make computers
capable of solving complex problems without being explicitly programmed. Availability of …
capable of solving complex problems without being explicitly programmed. Availability of …
AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing
AK Mishra - Minerals, 2021 - mdpi.com
In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI),
have been ubiquitous in both popular science media as well as the academic literature …
have been ubiquitous in both popular science media as well as the academic literature …
Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion
Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared
Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric …
Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric …
[HTML][HTML] Material fingerprinting as a tool to investigate between and within material type variability with a focus on material hardness
JR van Duijvenbode, LM Cloete, MS Shishvan… - Minerals …, 2022 - Elsevier
Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been
used to define Au-mineralised fingerprints. VNIR-SWIR spectral data were represented by …
used to define Au-mineralised fingerprints. VNIR-SWIR spectral data were represented by …
[HTML][HTML] Interpretation of run-of-mine comminution and recovery parameters using multi-element geochemical data clustering
JR Van Duijvenbode, LM Cloete, MS Shishvan… - Minerals …, 2022 - Elsevier
Multi-element (ME) datasets provide comprehensive geochemical signatures of an orebody
and are commonly used to gain insight into the mineralogy, lithology, alteration patterns and …
and are commonly used to gain insight into the mineralogy, lithology, alteration patterns and …
[PDF][PDF] A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry. Minerals 2021, 11, 816
M Jooshaki, A Nad, S Michaux, U König, Y Choi - mdpi. com, 2021 - academia.edu
Machine learning is a subcategory of artificial intelligence, which aims to make computers
capable of solving complex problems without being explicitly programmed. Availability of …
capable of solving complex problems without being explicitly programmed. Availability of …
[PDF][PDF] Determining the lowest quantity of variables required for geometallurgical machine learning-based modelling: A review
EE Mack, BP von der Heyden, M Tadie, TM Louw - saimm.co.za
An important aspect of geometallurgy is predicting the metallurgical performance of mineral
deposits, often including extraction and processing characteristics. The use of machine …
deposits, often including extraction and processing characteristics. The use of machine …
[PDF][PDF] The novel application of continuous wavelet tessellation and data mosaic method to assess relationships between material fingerprints and time series plant …
LM Cloete, JR van Duijvenbode, MS Shishvan… - saimm.co.za
Geochemical and mineralogical datasets collected from grade control drilling and blast hole
samples at AngloGold Ashanti and Regis Resources' Tropicana Gold Mine in Australia were …
samples at AngloGold Ashanti and Regis Resources' Tropicana Gold Mine in Australia were …