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Enhancing Tetracycline Degradation Prediction with Hybrid DBO-XGBoost on g-C₃N₄ Photocatalysts
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DOI：10.1016/j.eti.2025.104711.png)
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Abstract
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• A novel hybrid DBO-XGBoost model yields R2 of 0.95 and RMSE of 0.11 for tetracycline degradation by g-C3N4, outperforming eight ML algorithms. • Analysis of 157 data points identifies surface area and light wavelength as drivers of photocatalytic efficiency, validated by SHAP and PD analyses. • The DBO algorithm converges faster ( 40 iterations) than GWO (50 iterations), enabling more efficient hyperparameter optimization in complex environmental systems overall. • Integrating ML predictions with photocatalytic kinetics, plus an economic-environmental framework, shows cost-effectiveness, reduced environmental impact, and actionable material design insights for g-C3N4.
Keywords:
Photocatalysis
Machine learning
Graphitic carbon nitride
Antibiotic degradation
DBO-XGBoost
Tetracycline
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