Mohammad Jooshaki, Alona Nad
Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including the mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature.
@article{10f98b26-f025-4848-a642-705f774d6f66,
title={A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry},
author={Mohammad Jooshaki and Alona Nad},
year={2021},
language={en}
}TY - JOUR TI - A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry AU - Mohammad Jooshaki AU - Alona Nad PY - 2021 LA - en ER -
Rajesh Jha, Bimal Kumar Jha
This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t
The Australasian Institute of Mining and Metallurgy
This conference, held in Perth, Western Australia, focuses on the latest advancements in metallurgical processing plant design and operational strateg
P Card, J Canterford
This document presents a collection of papers from the METPLANT 2011 conference focusing on metallurgical plant design and operating strategies. The o
Deepak Malhotra
This work discusses the essential components of a rigorous auditing process aimed at enhancing metallurgical plant performance. The objective is to id