Predicting CaO activity in multiple slag system using improved whale optimization algorithm and categorical boosting

The activity of slag components is one of the primary factors influencing the thermodynamic properties of slag. In this study, a feasible model was established to predict the a (CaO) using improved whale optimization algorithm (IWOA) and Categorical Boosting (CatBoost). The effects of other variable...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 15; číslo 1; s. 9533 - 11
Hlavní autoři: Xin, Zi-cheng, Zhang, Jiang-shan, Liu, Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 19.03.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/705
a(CaO)
Algorithms
Aluminum oxide
Categorical boosting
FactSage
Humanities and Social Sciences
Improved whale optimization algorithm
Ion and molecule coexistence theory
Molecules
multidisciplinary
Multiple slag system
Neural networks
Optimization algorithms
Prediction models
Science
Science (multidisciplinary)
Silicon dioxide
Slag
CaO
FactSage
Improved whale optimization algorithm
Categorical boosting
Multiple slag system
Ion and molecule coexistence theory
a(CaO)
ISSN:2045-2322, 2045-2322
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The activity of slag components is one of the primary factors influencing the thermodynamic properties of slag. In this study, a feasible model was established to predict the a (CaO) using improved whale optimization algorithm (IWOA) and Categorical Boosting (CatBoost). The effects of other variables on a (CaO) were listed in descending order of influence as follows: w (CaO), w (SiO 2 ), temperature, w (MgO), and w (Al 2 O 3 ). And the IWOA-CatBoost model achieved the highest R 2 value of 0.9200, lowest RMSE of 0.0042, and lowest MAE of 0.0030 in predicting the a (CaO). The performance of the optimal IWOA-CatBoost model was evaluated and compared with that of known models. The results demonstrate that the IWOA-CatBoost model outperformed existing models and methods, such as the Factsage, ion and molecule coexistence theory, and genetic algorithm—backpropagation neural network. The accurate calculation of slag component activity is of great significance to the analysis of the thermodynamic properties of slag. Meanwhile, the approach and algorithm used to develop the a(CaO) prediction model can also be applied to predicting the activity of other slag components or other metallurgical applications (e.g., predicting molten steel temperature, steel composition, and alloy yield).
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-93980-9