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首页> 外文期刊>Chemical Engineering Communications >Parametric Study and Process Evaluation of Fenton Oxidation: Application of Sequential Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System Computing Technique
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Parametric Study and Process Evaluation of Fenton Oxidation: Application of Sequential Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System Computing Technique

机译：芬顿氧化的参数研究和过程评价：顺序响应面方法的应用和自适应神经模糊推理系统计算技术

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The Fenton oxidation is rarely used industrially due to its high operating cost, large chemical consumption, excessive sludge production, and operability only within a narrow pH range. Therefore, there is a need to evaluate the Fenton oxidation to maximize its ability to degrade high-strength dye wastewater at reduced operating cost. Optimization tools are among the most commonly used tool to maximize the degradation of pollutants. The current study aims at evaluating the applicability of response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) to optimize the degradation of Remazol brilliant blue through the Fenton oxidation. The effects of four operating parameters including dye concentration, retention time, and mass ratios of Dye: Fe2+ and H2O2: Fe2+ were evaluated by applying RSM. According to the RSM results, color and chemical oxygen demand (COD) removal of 99.9% and 84%, respectively, were obtained at 120 min at the COD value of 795 mg/L, mass ratios of Dye: Fe2+ = 16, H2O2: Fe2+ = 15 and pH = 3. ANFIS was also used to evaluate the most influential operating parameters on the COD removal based on the RSM results. The ANFIS results showed that the mass ratio of H2O2: Fe2+ had the most significant contribution to the COD removal. High R-2 values (= 90%) indicated that the predictions of RSM and ANFIS models for COD removal were acceptable. In conclusion, this study demonstrated that RSM and ANFIS were able to determine the most significant operating parameters and optimum ratios of pollutant: oxidant: catalyst, which reduced the operating cost directly.
机译：由于其高运行成本，大型化学品消耗，过度污泥生产以及仅在窄的pH范围内，因此芬顿氧化很少使用。因此，需要评估芬顿氧化以使其在降低操作成本下降解高强度染料废水的能力。优化工具是最常用的工具中，以最大限度地提高污染物的劣化。目前的研究旨在评估响应面方法（RSM）和自适应神经模糊推理系统（ANFIS）的适用性，以优化通过芬顿氧化的雷达唑辉煌的降解。通过施加RSM评价包括染料浓度，保留时间和质量比的四种操作参数的影响，包括染料：Fe 2 +和H 2 O 2：Fe 2+。根据RSM结果，分别在120分钟的COD值为795mg / L，质量比的染料率：Fe2 + = 16，H 2 O 2：Fe2 + = 16，H2O2：COD的结果Fe2 + = 15和pH = 3. ANFIS还用于评估基于RSM结果的COD去除上最有影响力的操作参数。 ANFIS结果表明，H2O2：Fe2 +的质量比对COD去除具有最大的贡献。高R-2值（＆ = 90％）表明RSM和COD去除的ANFI模型的预测是可接受的。总之，本研究表明，RSM和ANFIS能够确定最重要的操作参数和污染物的最佳比率：氧化剂：催化剂，直接降低了运营成本。

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