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CatBoost-SHAP for modeling industrial operational flotation variables – A “conscious lab” approach
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering. Wallenberg Initiative Materials Science for Sustainability, Department of Civil, Environmental and Natural Resources Engineering, Swedish School of Mines, Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-2265-6321
Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.ORCID iD: 0000-0002-8932-5340
Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.ORCID iD: 0000-0002-9279-6063
Delijan Copper Flotation Company, Delijan, Iran.
2024 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 213, article id 108754Article in journal (Refereed) Published
Abstract [en]

Flotation separation is the most important upgrading critical raw material technique. Measuring interactions within flotation variables and modeling their metallurgical responses (grade and recovery) is quite challenging on the industrial scale. These challenges are because flotation separation includes several sub-micron processes, and their monitoring won't be possible for the processing plants. Since many flotation plants are still manually operating and maintaining, understanding interactions within operational variables and their effect on the metallurgical responses would be crucial. As a unique approach, this study used the “Conscious Lab” concept for modeling flotation responses of an industrial copper upgrading plant when Potassium Amyl Xanthate substituted the secondary collector (Sodium Ethyl Xanthate) in the process. The main aim is to understand and compare interactions before and after the collector substitution. For the first time, the conscious lab was constructed based on the most advanced explainable artificial intelligence model, Shapley Additive Explanations, and Catboost. Catboost- Shapley Additive Explanations could accurately model flotation responses (less than 2% error between actual and predicted values) and illustrate variations of complex interactions through the substitution. Through a comparative study, Catboost could generate more precise outcomes than other known artificial intelligence models (Random Forest, Support Vector Regression, Extreme Gradient Boosting, and Convolutional Neural Network). In general, substituting Sodium Ethyl Xanthate by Potassium Amyl Xanthate reduced process predictability, although Potassium Amyl Xanthate could slightly increase the copper recovery.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 213, article id 108754
Keywords [en]
Extreme gradient boosting, Flotation circuit, Potassium amyl xanthate, Random forest, Sodium ethyl xanthate, Support vector regression
National Category
Metallurgy and Metallic Materials Physical Chemistry Mineral and Mine Engineering
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-106150DOI: 10.1016/j.mineng.2024.108754Scopus ID: 2-s2.0-85194407159OAI: oai:DiVA.org:ltu-106150DiVA, id: diva2:1867340
Note

Validerad;2024;Nivå 2;2024-06-10 (signyg);

Funder: Array (WISE-WASP);

Full text license: CC BY

Available from: 2024-06-10 Created: 2024-06-10 Last updated: 2024-06-10Bibliographically approved

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Chehreh Chelgani, Saeed

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