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

Zi-Cheng Xin1,2, Jiang-Shan Zhang3, Qing Liu4

  • 1State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing, 100083, China.

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|March 20, 2025
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Summary
This summary is machine-generated.

This study introduces a novel model using the improved whale optimization algorithm (IWOA) and Categorical Boosting (CatBoost) to accurately predict slag component activity (a(CaO)). The IWOA-CatBoost model significantly outperforms existing methods for thermodynamic property analysis.

Keywords:
a(CaO)Categorical boostingFactSageImproved whale optimization algorithmIon and molecule coexistence theoryMultiple slag system

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Area of Science:

  • Metallurgical thermodynamics
  • Materials science
  • Computational chemistry

Background:

  • Slag component activity is crucial for understanding thermodynamic properties.
  • Accurate prediction of activity is essential for metallurgical processes.

Purpose of the Study:

  • To develop a robust model for predicting calcium oxide activity (a(CaO)) in slag.
  • To compare the model's performance against established methods.

Main Methods:

  • Utilized an improved whale optimization algorithm (IWOA) integrated with Categorical Boosting (CatBoost).
  • Analyzed the influence of slag composition (w(CaO), w(SiO2), w(MgO), w(Al2O3)) and temperature on a(CaO).

Main Results:

  • The IWOA-CatBoost model achieved high accuracy with R² = 0.9200, RMSE = 0.0042, and MAE = 0.0030.
  • Demonstrated superior performance compared to Factsage, ion and molecule coexistence theory, and GA-BPNN models.
  • Identified w(CaO) as the most influential variable, followed by w(SiO2), temperature, w(MgO), and w(Al2O3).

Conclusions:

  • The developed IWOA-CatBoost model provides a highly accurate method for predicting a(CaO).
  • This approach offers a significant advancement in analyzing slag thermodynamic properties.
  • The methodology is adaptable for predicting other slag component activities and various metallurgical applications.