Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Modeling industrial hydrocyclone operational variables by SHAP-CatBoost - A “conscious lab” approach
AU - Chehreh Chelgani, S.
AU - Nasiri, H.
AU - Tohry, A.
AU - Heidari, H. R.
N1 - Publisher Copyright: © 2023 The Authors
PY - 2023/4/15
Y1 - 2023/4/15
N2 - Undoubtedly hydrocyclones play a critical role in powder technology, which can considerably affect the plants' process efficiency. However, hydrocyclones were rarely modeled on an industrial scale, where a model can be used to train operators and minimize potential scale-up errors and lab costs. The novel approach for filling such a gap would be using conscious lab “CL” as a new concept that builds based on an industrial dataset and explainable artificial intelligence (XAI). As a novel approach, this study developed a CL and explored the interactions between hydrocyclone variables by the most recent XAI method called “SHapley Additive exPlanations (SHAP)”, and a novel machine-learning model, “CatBoost”. The hydrocyclone output and the particle size of the plant magnetic separator were modeled by SHAP-CatBoost. SHAP could successfully model all the relationships, and CatBoost could predict the O80 and K80 where outcomes had a higher accuracy (R2 ∼ 0.90) than other conventional AIs.
AB - Undoubtedly hydrocyclones play a critical role in powder technology, which can considerably affect the plants' process efficiency. However, hydrocyclones were rarely modeled on an industrial scale, where a model can be used to train operators and minimize potential scale-up errors and lab costs. The novel approach for filling such a gap would be using conscious lab “CL” as a new concept that builds based on an industrial dataset and explainable artificial intelligence (XAI). As a novel approach, this study developed a CL and explored the interactions between hydrocyclone variables by the most recent XAI method called “SHapley Additive exPlanations (SHAP)”, and a novel machine-learning model, “CatBoost”. The hydrocyclone output and the particle size of the plant magnetic separator were modeled by SHAP-CatBoost. SHAP could successfully model all the relationships, and CatBoost could predict the O80 and K80 where outcomes had a higher accuracy (R2 ∼ 0.90) than other conventional AIs.
KW - Hydrocyclone
KW - Random forest
KW - Support vector regression
KW - Ultrafine particles
KW - XGBoost
U2 - 10.1016/j.powtec.2023.118416
DO - 10.1016/j.powtec.2023.118416
M3 - Journal article
AN - SCOPUS:85149831670
VL - 420
JO - Powder Technology
JF - Powder Technology
SN - 0032-5910
M1 - 118416
ER -