Matches in SemOpenAlex for { <https://semopenalex.org/work/W4323350904> ?p ?o ?g. }
- W4323350904 endingPage "118416" @default.
- W4323350904 startingPage "118416" @default.
- W4323350904 abstract "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." @default.
- W4323350904 created "2023-03-08" @default.
- W4323350904 creator A5023442370 @default.
- W4323350904 creator A5048529791 @default.
- W4323350904 creator A5073803251 @default.
- W4323350904 creator A5077098010 @default.
- W4323350904 date "2023-04-01" @default.
- W4323350904 modified "2023-10-09" @default.
- W4323350904 title "Modeling industrial hydrocyclone operational variables by SHAP-CatBoost - A “conscious lab” approach" @default.
- W4323350904 cites W1964621912 @default.
- W4323350904 cites W1982474571 @default.
- W4323350904 cites W1983590312 @default.
- W4323350904 cites W1994242214 @default.
- W4323350904 cites W2020658996 @default.
- W4323350904 cites W2027386095 @default.
- W4323350904 cites W2066667672 @default.
- W4323350904 cites W2067470465 @default.
- W4323350904 cites W2077022536 @default.
- W4323350904 cites W2098661252 @default.
- W4323350904 cites W2494454509 @default.
- W4323350904 cites W2522459895 @default.
- W4323350904 cites W2607101430 @default.
- W4323350904 cites W2755756378 @default.
- W4323350904 cites W2766764243 @default.
- W4323350904 cites W2775270516 @default.
- W4323350904 cites W2901006410 @default.
- W4323350904 cites W2911964244 @default.
- W4323350904 cites W2935714482 @default.
- W4323350904 cites W2981572848 @default.
- W4323350904 cites W2981792167 @default.
- W4323350904 cites W3013498854 @default.
- W4323350904 cites W3021362913 @default.
- W4323350904 cites W3078061985 @default.
- W4323350904 cites W3081125651 @default.
- W4323350904 cites W3124266898 @default.
- W4323350904 cites W3127681039 @default.
- W4323350904 cites W3130219998 @default.
- W4323350904 cites W3132619335 @default.
- W4323350904 cites W3134563434 @default.
- W4323350904 cites W3138811411 @default.
- W4323350904 cites W3155270475 @default.
- W4323350904 cites W3173631909 @default.
- W4323350904 cites W3173903399 @default.
- W4323350904 cites W3191161603 @default.
- W4323350904 cites W3201482179 @default.
- W4323350904 cites W3209031292 @default.
- W4323350904 cites W3209846823 @default.
- W4323350904 cites W4205852257 @default.
- W4323350904 cites W4210437189 @default.
- W4323350904 cites W4224285150 @default.
- W4323350904 cites W4229376334 @default.
- W4323350904 cites W4292701963 @default.
- W4323350904 cites W4293255937 @default.
- W4323350904 cites W4293565759 @default.
- W4323350904 cites W4296432997 @default.
- W4323350904 cites W4296682282 @default.
- W4323350904 cites W4311091133 @default.
- W4323350904 doi "https://doi.org/10.1016/j.powtec.2023.118416" @default.
- W4323350904 hasPublicationYear "2023" @default.
- W4323350904 type Work @default.
- W4323350904 citedByCount "12" @default.
- W4323350904 countsByYear W43233509042023 @default.
- W4323350904 crossrefType "journal-article" @default.
- W4323350904 hasAuthorship W4323350904A5023442370 @default.
- W4323350904 hasAuthorship W4323350904A5048529791 @default.
- W4323350904 hasAuthorship W4323350904A5073803251 @default.
- W4323350904 hasAuthorship W4323350904A5077098010 @default.
- W4323350904 hasBestOaLocation W43233509041 @default.
- W4323350904 hasConcept C111919701 @default.
- W4323350904 hasConcept C119857082 @default.
- W4323350904 hasConcept C121332964 @default.
- W4323350904 hasConcept C127413603 @default.
- W4323350904 hasConcept C154945302 @default.
- W4323350904 hasConcept C21880701 @default.
- W4323350904 hasConcept C2778755073 @default.
- W4323350904 hasConcept C2780541163 @default.
- W4323350904 hasConcept C41008148 @default.
- W4323350904 hasConcept C62520636 @default.
- W4323350904 hasConcept C74650414 @default.
- W4323350904 hasConcept C98045186 @default.
- W4323350904 hasConceptScore W4323350904C111919701 @default.
- W4323350904 hasConceptScore W4323350904C119857082 @default.
- W4323350904 hasConceptScore W4323350904C121332964 @default.
- W4323350904 hasConceptScore W4323350904C127413603 @default.
- W4323350904 hasConceptScore W4323350904C154945302 @default.
- W4323350904 hasConceptScore W4323350904C21880701 @default.
- W4323350904 hasConceptScore W4323350904C2778755073 @default.
- W4323350904 hasConceptScore W4323350904C2780541163 @default.
- W4323350904 hasConceptScore W4323350904C41008148 @default.
- W4323350904 hasConceptScore W4323350904C62520636 @default.
- W4323350904 hasConceptScore W4323350904C74650414 @default.
- W4323350904 hasConceptScore W4323350904C98045186 @default.
- W4323350904 hasLocation W43233509041 @default.
- W4323350904 hasLocation W43233509042 @default.
- W4323350904 hasOpenAccess W4323350904 @default.
- W4323350904 hasPrimaryLocation W43233509041 @default.
- W4323350904 hasRelatedWork W2899084033 @default.
- W4323350904 hasRelatedWork W2961085424 @default.