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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.
摘要：
由于地下水中有机污染物的复杂迁移和反应行为，开发数学模型来帮助现场修复规划和实施越来越受到关注。在这项研究中,耦合响应面方法(RSM)，人工神经网络(ANN)，并采用动力学模型来模拟纳米零价铁（nZVI）活化过硫酸盐（PS）体系对苯的降解效果，地下水中常见的有机污染物。该模型用于优化工艺参数，以帮助预测多因素对苯降解速率的影响。同时,在优化的反应条件下，基于间歇实验，建立了化学氧化动力学，以预测苯的时间降解。结果表明，在pH3.9°C和21.9°C下，理论上苯（0.25mmol）在1.45mMPS中被完全氧化，PS/nZVI摩尔比为4：1。RSM模型很好地预测了四个因素对苯降解速率的影响（R2=0.948），与RSM（R2=0.980）相比，具有隐藏层结构[8-8]的ANN表现更好。此外,所涉及的苯降解系统与R2>0.999的2型和3型伪二阶（PSO）动力学模型非常吻合。这表明，所提出的基于统计和动力学的建模方法有望为预测多种因素影响下地下水中有机污染物的化学氧化性能提供支持。
