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- Chehreh Chelgani, Saeed, et al.
(författare)
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CatBoost-SHAP for modeling industrial operational flotation variables – A “conscious lab” approach
- 2024
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Ingår i: Minerals Engineering. - : Elsevier. - 0892-6875 .- 1872-9444. ; 213
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Fatahi, Rasoul, et al.
(författare)
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Modeling operational cement rotary kiln variables with explainable artificial intelligence methods–a “conscious lab” development
- 2023
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Ingår i: Particulate Science and Technology. - : Taylor & Francis. - 0272-6351 .- 1548-0046. ; 41:5, s. 715-724
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Tidskriftsartikel (refereegranskat)abstract
- Digitalizing cement production plants to improve operation parameters’ control might reduce energy consumption and increase process sustainabilities. Cement production plants are one of the extremest CO2 emissions, and the rotary kiln is a cement plant’s most energy-consuming and energy-wasting unit. Thus, enhancing its operation assessments adsorb attention. Since many factors would affect the clinker production quality and rotary kiln efficiency, controlling those variables is beyond operator capabilities. Constructing a conscious-lab “CL” (developing an explainable artificial intelligence “EAI” model based on the industrial operating dataset) can potentially tackle those critical issues, reduce laboratory costs, save time, improve process maintenance and help for better training operators. As a novel approach, this investigation examined extreme gradient boosting (XGBoost) coupled with SHAP (SHapley Additive exPlanations) “SHAP-XGBoost” for the modeling and prediction of the rotary kiln factors (feed rate and induced draft fan current) based on over 3,000 records collected from the Ilam cement plant. SHAP illustrated the relationships between each record and variables with the rotary kiln factors, demonstrated their correlation magnitude, and ranked them based on their importance. XGBoost accurately (R-square 0.96) could predict the rotary kiln factors where results showed higher exactness than typical EAI models.
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