An integrated intelligent framework for maximising SAG mill throughput: Incorporating expert knowledge, machine learning and evolutionary algorithms for parameter optimisation

Ghasemi, Z; Neshat, M; Aldrich, C; Karageorgos, J; Zanin, M; Neumann, F; Chen, L

An integrated intelligent framework for maximising SAG mill throughput: Incorporating expert knowledge, machine learning and evolutionary algorithms for parameter optimisation

Ghasemi, Z Neshat, M

Aldrich, C Karageorgos, J Zanin, M Neumann, F Chen, L

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Minerals Engineering, 2024, 212
Issue Date:: 2024-07-15

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Field	Value	Language
dc.contributor.author	Ghasemi, Z
dc.contributor.author	Neshat, M https://orcid.org/0000-0002-9537-9513
dc.contributor.author	Aldrich, C
dc.contributor.author	Karageorgos, J
dc.contributor.author	Zanin, M
dc.contributor.author	Neumann, F
dc.contributor.author	Chen, L
dc.date.accessioned	2025-03-12T01:14:44Z
dc.date.available	2025-03-12T01:14:44Z
dc.date.issued	2024-07-15
dc.identifier.citation	Minerals Engineering, 2024, 212
dc.identifier.issn	0892-6875
dc.identifier.uri	http://hdl.handle.net/10453/185697
dc.description.abstract	In mineral processing plants, grinding is a crucial step, accounting for approximately 50% of the total mineral processing costs. Semi-autogenous grinding (SAG) mills are extensively employed in the grinding circuit of mineral processing plants. Maximising SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces an intelligent framework leveraging expert knowledge, machine learning techniques and evolutionary algorithms to address this research need. In this study, an extensive industrial dataset comprising 36,743 records is utilised and relevant features are selected based on the insights of industry experts. Following the removal of erroneous data, an evaluation of 17 machine learning models is undertaken to identify the most accurate predictive model. To improve the performance of the model, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximise SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimisation algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Minerals Engineering
dc.relation.isbasedon	10.1016/j.mineng.2024.108733
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0306 Physical Chemistry (incl. Structural), 0904 Chemical Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Mining & Metallurgy
dc.subject.classification	4004 Chemical engineering
dc.subject.classification	4019 Resources engineering and extractive metallurgy
dc.title	An integrated intelligent framework for maximising SAG mill throughput: Incorporating expert knowledge, machine learning and evolutionary algorithms for parameter optimisation
dc.type	Journal Article
utslib.citation.volume	212
utslib.for	0306 Physical Chemistry (incl. Structural)
utslib.for	0904 Chemical Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-03-12T01:14:41Z
pubs.publication-status	Published
pubs.volume	212

Abstract:

In mineral processing plants, grinding is a crucial step, accounting for approximately 50% of the total mineral processing costs. Semi-autogenous grinding (SAG) mills are extensively employed in the grinding circuit of mineral processing plants. Maximising SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces an intelligent framework leveraging expert knowledge, machine learning techniques and evolutionary algorithms to address this research need. In this study, an extensive industrial dataset comprising 36,743 records is utilised and relevant features are selected based on the insights of industry experts. Following the removal of erroneous data, an evaluation of 17 machine learning models is undertaken to identify the most accurate predictive model. To improve the performance of the model, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximise SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimisation algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.

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http://hdl.handle.net/10453/185697