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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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 9533 - 11 |
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19.03.2025
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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 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40108361$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1080/03019233.2021.1935143 10.1007/s11663-023-02753-0 10.2355/isijinternational.40.561 10.1080/03019233.2020.1771892 10.1007/s11663-011-9547-9 10.2355/isijinternational.ISIJINT-2017-735 10.1038/s41598-024-77058-6 10.1016/j.advengsoft.2016.01.008 10.1007/s12613-024-2950-4 10.48550/arXiv.1810.11363 |
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| Keywords | CaO FactSage Improved whale optimization algorithm Categorical boosting Multiple slag system Ion and molecule coexistence theory a(CaO) |
| Language | English |
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| References | XM Yang (93980_CR4) 2011; 42 ZC Xin (93980_CR2) 2021; 48 93980_CR5 CY Gu (93980_CR16) 2023; 47 93980_CR34 DJ Li (93980_CR25) 2018; 39 93980_CR32 BH Guo (93980_CR14) 2020 K Wu (93980_CR24) 2001; 37 S Mirjalili (93980_CR28) 2016; 95 QL Wen (93980_CR27) 2018; 58 NN Lu (93980_CR17) 2013; 34 ZC Xin (93980_CR33) 2024; 31 GZ Tang (93980_CR10) 2016; 37 H Xu (93980_CR22) 2015; 2 FM Qin (93980_CR38) 2024; 47 ZC Xin (93980_CR3) 2021; 48 KC Chou (93980_CR7) 1997; 33 GL Zhou (93980_CR30) 2024; 43 B Zhao (93980_CR19) 2022; 32 CY Jin (93980_CR15) 2023; 44 ZQ Huang (93980_CR20) 1987; 4 93980_CR21 JM Prausntiz (93980_CR6) 1986 RY Yin (93980_CR1) 2021; 56 YC Guo (93980_CR11) 2021; 42 M Li (93980_CR23) 2018; 40 CY Dai (93980_CR29) 2024; 46 ZC Xin (93980_CR13) 2023; 54 ZD Qu (93980_CR18) 2020; 36 L Wu (93980_CR12) 2008; 29 93980_CR8 S Li (93980_CR35) 2020; 44 ZB Yu (93980_CR36) 2024; 47 SL Li (93980_CR37) 2024; 39 ZY Chang (93980_CR9) 2018; 28 K Kume (93980_CR26) 2000; 40 Y Cai (93980_CR31) 2024; 14 |
| References_xml | – volume: 29 start-page: 1725 year: 2008 ident: 93980_CR12 publication-title: J. Northeast. Univ. (Nat. Sci.) – ident: 93980_CR21 – volume: 48 start-page: 1123 year: 2021 ident: 93980_CR3 publication-title: Ironmak. Steelmak. doi: 10.1080/03019233.2021.1935143 – volume: 47 start-page: 105 year: 2023 ident: 93980_CR16 publication-title: Autom. Electr. Power Syst. – volume: 4 start-page: 426 year: 1987 ident: 93980_CR20 publication-title: J. Northeast. Univ. Technol. – volume: 37 start-page: 127 year: 2016 ident: 93980_CR10 publication-title: Iron Steel Vanadium Titanium – volume: 2 start-page: 42 year: 2015 ident: 93980_CR22 publication-title: Metall. Eng. – volume: 44 start-page: 540 year: 2020 ident: 93980_CR35 publication-title: Chin. J. Rare Metals – ident: 93980_CR34 – volume: 54 start-page: 1181 year: 2023 ident: 93980_CR13 publication-title: Metall. Mater. Trans. B doi: 10.1007/s11663-023-02753-0 – volume: 39 start-page: 60 year: 2024 ident: 93980_CR37 publication-title: Autom. Instrum. – ident: 93980_CR8 – volume: 44 start-page: 1743 year: 2023 ident: 93980_CR15 publication-title: J. Northeast. Univ. (Nat. Sci.) – volume: 40 start-page: 561 year: 2000 ident: 93980_CR26 publication-title: ISIJ Int. doi: 10.2355/isijinternational.40.561 – volume: 42 start-page: 652 year: 2021 ident: 93980_CR11 publication-title: J. Northeast. Univ. (Nat. Sci.) – volume: 48 start-page: 275 year: 2021 ident: 93980_CR2 publication-title: Ironmak. Steelmak. doi: 10.1080/03019233.2020.1771892 – volume: 28 start-page: 6 year: 2018 ident: 93980_CR9 publication-title: China Metall. – volume-title: ECG Identification Based on Gradient Enhancement Machine Learning Algorithm year: 2020 ident: 93980_CR14 – volume: 32 start-page: 49 year: 2022 ident: 93980_CR19 publication-title: China Metall. – start-page: 193 volume-title: Molecular Thermodynamics of Fluid-Phase Equilibria year: 1986 ident: 93980_CR6 – volume: 42 start-page: 1150 year: 2011 ident: 93980_CR4 publication-title: Metall. Mater. Trans. B doi: 10.1007/s11663-011-9547-9 – volume: 37 start-page: 1069 year: 2001 ident: 93980_CR24 publication-title: ACTA Metall. Sin. – volume: 58 start-page: 792 year: 2018 ident: 93980_CR27 publication-title: ISIJ Int. doi: 10.2355/isijinternational.ISIJINT-2017-735 – volume: 33 start-page: 126 year: 1997 ident: 93980_CR7 publication-title: Acta Metall. Sin. – volume: 14 start-page: 25727 year: 2024 ident: 93980_CR31 publication-title: Sci. Rep. doi: 10.1038/s41598-024-77058-6 – volume: 47 start-page: 8 year: 2024 ident: 93980_CR38 publication-title: Guangxi Electr. Power – volume: 46 start-page: 1635 year: 2024 ident: 93980_CR29 publication-title: Comput. Eng. Sci. – volume: 43 start-page: 806 year: 2024 ident: 93980_CR30 publication-title: Mar. Environ. Sci. – volume: 36 start-page: 76 year: 2020 ident: 93980_CR18 publication-title: Steelmaking – volume: 56 start-page: 4 year: 2021 ident: 93980_CR1 publication-title: Iron Steel – volume: 95 start-page: 51 year: 2016 ident: 93980_CR28 publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 31 start-page: 2657 year: 2024 ident: 93980_CR33 publication-title: Int. J. Miner. Metall. Mater. doi: 10.1007/s12613-024-2950-4 – ident: 93980_CR5 – volume: 34 start-page: 1743 year: 2013 ident: 93980_CR17 publication-title: J. Northeast. Univ. Nat. Sci. – ident: 93980_CR32 doi: 10.48550/arXiv.1810.11363 – volume: 39 start-page: 32 year: 2018 ident: 93980_CR25 publication-title: Special Steel – volume: 40 start-page: 31 year: 2018 ident: 93980_CR23 publication-title: Chin. J. Eng. – volume: 47 start-page: 7 year: 2024 ident: 93980_CR36 publication-title: J. Univ. Sci. Technol. Liaoning |
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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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