Exploring the impact of thiol collectors system on copper sulfide flotation through machine learning-driven modeling

• Autorzy: Guner Mustafa K. , Akyildiz Ozge , Basarir Hakan , Kowalczuk Pshem

Identyfikatory

DOI

10.37190/ppmp/191709

• Języki publikacji: EN

Abstrakty

EN

Collector selection is a critical step in flotation, as it has a direct impact on product quality, flotation recovery, and selectivity. Collectors can consist of different components, and their effectiveness can vary depending on the type of ore being processed. The general practice in both literature and in industry is to use a mixture of collectors rather than a single collector. However, the use of a collector mixture introduces several complex issues. It is challenging to determine the specific effects of each collector on different minerals, as well as to understand the synergistic effects of mixed collectors in flotation. This study presents a novel investigation focusing on the impact of blends of NAX, AEROPHINE® 3422, and AERO® MX 5149, in varying dosages and combinations, on the flotation performance of Kupferschiefer copper ore. Kinetics flotation tests were conducted using a mechanical flotation cell with various combinations and dosages of listed collectors. For this investigation, different predictive models such as machine-learning (ML) and conventional regression analyses were developed. For model construction, a database including the results of comprehensive experimental results was constructed. The best performing model was selected considering statistical performance indicators and their performance on unseen data. A sensitivity analysis was conducted on the model to justify contributions of collectors on the copper recovery and grade. The results showed that the ML-based models provide compatible results with the expert opinions and have higher statistical performance than conventional modelling tools. According to the experimental results and models’ findings, it has shown that AEROPHINE® 3422 (a blend of isopropyl ethyl thionocarbamate and dithiophosphinate) was the most influential collector for the copper recovery. In addition, two ternary graphs were generated from the modeled data to formulate mixtures for different grades and recovery priorities.

Słowa kluczowe

EN

machine learning; thiol collector; copper flotation; random forest; dosage optimization

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2024
• Tom: Vol. 60, iss. 4
• Strony: art. no. 191709
• Opis fizyczny: Bibliogr. 50 poz., rys., tab., wykr.

Twórcy

autor

Guner Mustafa K.

• Department of Geoscience and Petroleum, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway

• https://orcid.org/0000-0001-6385-0238

autor

Akyildiz Ozge

• Department of Geoscience and Petroleum, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway

• https://orcid.org/0000-0002-5538-4211

autor

Basarir Hakan

• Department of Geoscience and Petroleum, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway

• https://orcid.org/0000-0003-4556-4953

autor

Kowalczuk Pshem

• Department of Geoscience and Petroleum, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway

• https://orcid.org/0000-0002-1432-030X

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