An Artificial Rabbit Optimization-Enhanced Support Vector Regression Framework for Sustainable Solar Photocatalytic Water Treatment

Abstract

Due to industrialization, urbanization, and the need for sustainable water resources, the treatment of organic water has become a major environmental concern. Semiconductor photocatalysts degrade organic contaminants through a clean, energy-efficient process called photocatalysis. In this study, batch reactor experiments were performed under natural solar irradiation to examine the influence of zinc oxide (ZnO) dosage, pH, and reaction time (inputs) on photocatalytic performance, with total organic carbon (TOC) as the output. Three machine learning algorithms, namely Artificial Rabbit Optimization–Support Vector Regression (ARO–SVR), Support Vector Regression (SVR), and AdaBoost, were proposed and compared. Model performance was assessed by the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE). The proposed ARO–SVR model outperformed the other models, with training and testing R2 values of 0.9782 and 0.9731, RMSE values of 0.040 and 0.0453, MAE values of 0.0402 and 0.046, and MSE values of 0.0016 and 0.0020. By contrast, conventional SVR and AdaBoost showed lower prediction accuracy and larger testing errors, indicating a lower generalization ability. The results show that combining photocatalytic technology in seawater treatment with an optimized machine learning framework can reliably and efficiently predict seawater treatment performance and minimize the need for extensive lab experiments.