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

•An intelligent framework is developed to frequently update set points in the grinding circuit, aiming to maximise SAG mill throughput.•The prediction model can be frequently retrained using data from process sensors and updated to adapt to changing conditions of the grinding process. This dynamic a...

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Veröffentlicht in:Minerals engineering Jg. 212; S. 108733
Hauptverfasser: Ghasemi, Zahra, Neshat, Mehdi, Aldrich, Chris, Karageorgos, John, Zanin, Max, Neumann, Frank, Chen, Lei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.07.2024
Schlagworte:
Ensemble models
Evolutionary algorithm (EA)
Machine learning (ML)
Meta-heuristic algorithm
Semi-autogenous grinding (SAG)
Throughput
Throughput
Machine learning (ML)
Ensemble models
Meta-heuristic algorithm
Semi-autogenous grinding (SAG)
Evolutionary algorithm (EA)
ISSN:0892-6875, 1872-9444
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Zusammenfassung:•An intelligent framework is developed to frequently update set points in the grinding circuit, aiming to maximise SAG mill throughput.•The prediction model can be frequently retrained using data from process sensors and updated to adapt to changing conditions of the grinding process. This dynamic approach distinguishes it from static modelling methods, which may experience reduced accuracy over time.•CatBoost is the most accurate predictor of SAG mill throughput among 17 machine learning models evaluated. Differential Evolution outperforms other optimisation algorithms. 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.
ISSN:0892-6875
1872-9444
DOI:10.1016/j.mineng.2024.108733