Title

Artificial Intelligence, Machine Learning and Process Automation: Existing Knowledge Frontier and Way Forward for Mining Sector

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.

Department(s)

Mining and Nuclear Engineering

Comments

Article in press

Keywords and Phrases

Artificial intelligence; Autonomous haul trucks; Autonomous technology; Deep learning; Fuzzy logic; Machine learning; Mining; Process automation

International Standard Serial Number (ISSN)

0269-2821; 1573-7462

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Springer, All rights reserved.

Publication Date

01 Apr 2020

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