QSPR prediction of the hydroxyl radical rate constant of water contaminants

• Hydroxyl radical rate constants of 457 water contaminants modeled.
• PSO-MLR and FFNN were utilized to develop the models.
• Various internal and external validation techniques used to validate the models.
• The proposed models were compared to those reported earlier.
• The AARE% for entire dataset were 2.78 and 2.01 for PSO-MLR and FFNN models.

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.

Figure optionsDownload high-quality image (200 K)Download as PowerPoint slide