Oxidation kinetics of water contaminants: New insights from artificial intelligence

Degradation of water contaminants through the advanced oxidation processes (AOP) has become the focus of strategists of environmental science and technology. Hydroxyl radicals are regarded as promising oxidants for the efficient decomposition of the organic contaminants. Nevertheless, understanding and monitoring the kinetics of the hydroxyl radical reaction has remained cumbersome to be deciphered by the aid of mathematical and statistical analyses. Herein, a new stochastic gradient boosting (SGB) decision tree technique based on the quantitative structure–property relationship (QSPR) method was developed to model and capture an image of the degradation rate constant of the hydroxyl radicals. An artificial model was constructed, trained, and tested on the bedrock of 457 different cases of water contaminants from 27 chemical structures. Several statistical techniques, including outlier detection, regression, sensitivity, and error analyses were served to validate the reliability of the proposed model. The outcomes showed that the developed model could appropriately estimate the logarithmic hydroxyl radical rate constants of numerous water contaminants with the promising R-squared of 0.97, and a quite low mean absolute relative error of 0.86%. Moreover, a sensitivity analysis revealed that Burden eigenvalue was found to be the most effective parameter as the input of the model. Finally, a comparison study was performed between the proposed QSPR and the models previously suggested, where the superiority of the present model was uncovered.