Machine learning modeling to predict the photocatalytic degradation rate constant in wastewater using TiO2

Many researchers have studied the application of titanium dioxide (TiO 2 ​) for photodegradation, but optimization is time-consuming and resource-intensive due to the complexity of various experimental parameters. In this study we developed a machine learning model for enhancing the photocatalytic efficiency of TiO 2 photocatalyst for pollutant degradation. Therefore, we compiled a dataset of 150 experimental results from published literature. This dataset was compiled based on various parameters that influence the reaction rate constant ( k ), such as pollutant type, catalyst particle size, surface area, pollutant concentration, TiO 2 dosage, pH, light intensity, and temperature. Several machine learning models were trained and tested, among them the CatBoost model, demonstrating the prediction with R 2 of 0.9044 and MAE of 0.1052. Analysis revealed that catalyst load, light intensity, and pollutant concentration were the most influential factors in the model’s predictions. The optimized CatBoost model successfully predicted a degradation rate constant of 0.1383 min − 1 for methylene blue, which was experimentally validated as equivalent to the rate constant of 0.1278 min − 1 , confirming the potential of machine learning. This work confirms the potential of machine learning models for optimizing the photocatalysis efficiency of TiO 2 photocatalysts for environmental applications.