Modeling and optimization study on degradation of organic contaminants using nZVI activated persulfate based on response surface methodology and artificial neural network: a case study of benzene as the model pollutant

Abstract

Due to the complicated transport and reactive behavior of organic contamination in groundwater, the development of mathematical models to aid field remediation planning and implementation attracts increasing attentions. In this study, the approach coupling response surface methodology (RSM), artificial neural networks (ANN), and kinetic models was implemented to model the degradation effects of nano-zero-valent iron (nZVI) activated persulfate (PS) systems on benzene, a common organic pollutant in groundwater. The proposed model was applied to optimize the process parameters in order to help predict the effects of multiple factors on benzene degradation rate. Meanwhile, the chemical oxidation kinetics was developed based on batch experiments under the optimized reaction conditions to predict the temporal degradation of benzene. The results indicated that benzene (0.25 mmol) would be theoretically completely oxidized in 1.45 mM PS with the PS/nZVI molar ratio of 4:1 at pH 3.9°C and 21.9 C. The RSM model predicted well the effects of the four factors on benzene degradation rate (R2 = 0.948), and the ANN with a hidden layer structure of [8-8] performed better compared to the RSM (R2 = 0.980). In addition, the involved benzene degradation systems fit well with the Type-2 and Type-3 pseudo-second order (PSO) kinetic models with R2 > 0.999. It suggested that the proposed statistical and kinetic-based modeling approach is promising support for predicting the chemical oxidation performance of organic contaminants in groundwater under the influence of multiple factors.