Innovative Soft Computing Techniques for Analyzing Rate Constants in Artificial UV-Driven Photocatalysis Within Tubular Reactors

Accurate prediction of photocatalytic degradation in TiO2 reactors remains challenging due to the time-consuming, labor-intensive, and costly nature of experimental investigations, as well as the limited ability of conventional models. The degradation of benzoic acid was investigated in TiO2-immobilized tubular plug-flow reactors under artificial UV radiation. The effects of reactor diameter and flow rate on the reaction rate constant were experimentally investigated. The results showed a constant reaction rate under artificial UV irradiation, which is attributed to higher electron photoactivation resulting in high photocatalytic activity. Three machine learning models, namely kernel extreme learning machine (KELM), Crested Porcupine Optimizer–Support Vector Regression (CPO-SVR), and Harris Hawks Optimizer (HHO)–SVR, were designed and compared for accurate prediction of reaction rates. The Pearson correlation coefficient (PCC), Willmott index (WI), Nash–Sutcliffe efficiency (NSE), and Legates–McCabe index (LM) were used to evaluate model performance. The highest predictive accuracy, resulting in the best PCC, WI, NSE, and LM values, was obtained from the HHO-SVR model, with PCC values of 0.990 and 0.990, WI values of 0.999 and 0.999, NSE values of 0.999 and 0.998, and LM values of 0.982 and 0.971 during training and testing, respectively. The CPO-SVR model also demonstrated good predictive performance and outperformed the KELM model on its own. A five-fold cross-validation ensured the stability and uncertainty of the HHO-SVR model. Shapley Additive Explanations (SHAP) analysis was used to determine the relative importance of operating parameters that affect the reaction rate over time, thereby enhancing model interpretability. The results showed the predominant factors controlling photocatalytic degradation and how these factors influenced the model predictions.