Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways
Amir H. Mohammadi, Saeed Hosseinpour, Zahra Jalilzadeh, Hossein Azarpira, Mahmood Yousefi
Abstract
This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), and Particle Swarm Optimization (PSO) models in improving the photocatalytic destruction of antibiotic utilizing a UV/ZrO₂/NaOCl system. The GBR model indicated the strength of exhibited precision, with high R² and Explained Variance Score (EVS) esteems, however gave indications of overfitting. Remarkably, the RR model obtained had many significant values and the model had a high data fitness with an R² of 0.81; however, error bars were observed in some areas that could be optimized by fine-tuning. Feature significance examination indicated that X2, X1, and X5 fundamentally affected the model's performance. At last, PSO was instrumental in finding the ideal limit mix to support removal performance. Altogether the presented models help to improve the photocatalytic degradation process and provide useful tools for further investigation in this field.