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Artificial Intelligence Review

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18-04-2020

Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector

Authors: Danish Ali, Samuel Frimpong

Published in: Artificial Intelligence Review | Issue 8/2020

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Abstract

Machine learning and artificial intelligence are the two fields of computer science dealing with the innovative idea of inducing smartness and intelligence in machines and automating complex tasks and operations through modern learning algorithms. While the rest of the operational fields have been diligent in developing new technologies, the mining industry has been lacking when it comes to applying these innovative methodologies to achieve operation autonomy with intelligence. However, this trend is beginning to change with a few researchers adopting the fields of machine learning and artificial intelligence to improve the existing technologies. This study was an attempt to review and analyze all the recent automation related work in every sector of the mining industry including mineral prospecting and exploration, mine planning, equipment selection, underground and surface equipment operation, drilling and blasting, mineral processing, etc., for establishing the existing frontiers of technological advancement. Shortcomings and challenges were identified within the current research work. Recommendations were provided to progress the existing technology by implementing deep learning, machine learning, and artificial intelligence for smart and intelligence-based evolution in the mining sector. With all of this innovative development and implementation of smart automation systems, the foundation for the mine of the future could be built, thus creating efficient, effective, and safer machines with sustainable mining operations.

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Title: Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector
Authors: Danish Ali
Samuel Frimpong
Publication date: 18-04-2020
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 8/2020
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09841-6

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