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...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 9533 - 11
Hauptverfasser:	Xin, Zi-cheng, Zhang, Jiang-shan, Liu, Qing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 19.03.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	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
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Abstract	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).
AbstractList	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 ), temperature, w(MgO), and w(Al O ). And the IWOA-CatBoost model achieved the highest R 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). Abstract 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(SiO2), temperature, w(MgO), and w(Al2O3). And the IWOA-CatBoost model achieved the highest R2 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). 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(SiO2), temperature, w(MgO), and w(Al2O3). And the IWOA-CatBoost model achieved the highest R2 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). 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). 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). 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(SiO2), temperature, w(MgO), and w(Al2O3). And the IWOA-CatBoost model achieved the highest R2 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).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(SiO2), temperature, w(MgO), and w(Al2O3). And the IWOA-CatBoost model achieved the highest R2 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).
ArticleNumber	9533
Author	Liu, Qing Xin, Zi-cheng Zhang, Jiang-shan
Author_xml	– sequence: 1 givenname: Zi-cheng surname: Xin fullname: Xin, Zi-cheng organization: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, School of Automation and Electrical Engineering, University of Science and Technology Beijing – sequence: 2 givenname: Jiang-shan surname: Zhang fullname: Zhang, Jiang-shan email: zjsustb@163.com organization: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing – sequence: 3 givenname: Qing surname: Liu fullname: Liu, Qing email: qliu@ustb.edu.cn organization: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing
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Keywords	CaO FactSage Improved whale optimization algorithm Categorical boosting Multiple slag system Ion and molecule coexistence theory a(CaO)
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Snippet	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... Abstract The activity of slag components is one of the primary factors influencing the thermodynamic properties of slag. In this study, a feasible model was...
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StartPage	9533
SubjectTerms	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
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Title	Predicting CaO activity in multiple slag system using improved whale optimization algorithm and categorical boosting
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