AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing
| dc.contributor.author | Mishra, Amit Kumar | |
| dc.date.accessioned | 2021-10-25T11:28:42Z | |
| dc.date.available | 2021-10-25T11:28:42Z | |
| dc.date.issued | 2021-10-12 | |
| dc.date.updated | 2021-10-22T13:55:41Z | |
| dc.description.abstract | 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. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries. | en_US |
| dc.identifier | 10.3390/min11101118 | |
| dc.identifier.apacitation | Mishra, A. K. (2021). AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing. <i>Minerals</i>, 11(10), 1118. http://hdl.handle.net/11427/35288 | en_ZA |
| dc.identifier.chicagocitation | Mishra, Amit Kumar "AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing." <i>Minerals</i> 11, 10. (2021): 1118. http://hdl.handle.net/11427/35288 | en_ZA |
| dc.identifier.citation | Mishra, A.K. 2021. AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing. <i>Minerals.</i> 11(10):1118. http://hdl.handle.net/11427/35288 | en_ZA |
| dc.identifier.ris | TY - Journal Article AU - Mishra, Amit Kumar AB - 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. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries. DA - 2021-10-12 DB - OpenUCT DP - University of Cape Town IS - 10 J1 - Minerals LK - https://open.uct.ac.za PY - 2021 T1 - AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing TI - AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing UR - http://hdl.handle.net/11427/35288 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/35288 | |
| dc.identifier.vancouvercitation | Mishra AK. AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing. Minerals. 2021;11(10):1118. http://hdl.handle.net/11427/35288. | en_ZA |
| dc.language.iso | en | en_US |
| dc.publisher.department | Department of Electrical Engineering | en_US |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.source | Minerals | en_US |
| dc.source.journalissue | 10 | en_US |
| dc.source.journalvolume | 11 | en_US |
| dc.source.pagination | 1118 | en_US |
| dc.source.uri | https://www.mdpi.com/journal/minerals | |
| dc.title | AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing | en_US |
| dc.type | Journal Article | en_US |