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<p>Many researchers have studied the application of titanium dioxide (TiO<sub>2</sub>​) 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<sub>2</sub> 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 (<i>k</i>), such as pollutant type, catalyst particle size, surface area, pollutant concentration, TiO<sub>2</sub> dosage, pH, light intensity, and temperature. Several machine learning models were trained and tested, among them the CatBoost model, demonstrating the prediction with <i>R</i><sup>2</sup> 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&#xa0;min<sup>− 1</sup> for methylene blue, which was experimentally validated as equivalent to the rate constant of 0.1278&#xa0;min<sup>− 1</sup>, confirming the potential of machine learning. This work confirms the potential of machine learning models for optimizing the photocatalysis efficiency of TiO<sub>2</sub> photocatalysts for environmental applications.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

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  • Moges Mitku,
  • Yunxiang Zhang,
  • Getachew G. Kebede,
  • Yuxin Zhang,
  • Wang Shou,
  • Qinfang Zhang,
  • Fekadu Gashaw Hone

摘要

Many researchers have studied the application of titanium dioxide (TiO2​) 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 TiO2 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, TiO2 dosage, pH, light intensity, and temperature. Several machine learning models were trained and tested, among them the CatBoost model, demonstrating the prediction with R2 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 TiO2 photocatalysts for environmental applications.