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超声辅助亚临界水氧化法在响应面法和人工神经网络作用下对活性翠蓝 H-EXL 的矿化作用

Application of ultrasound-assisted and subcritical water oxidation methods in the mineralisation of Procion Crimson H-EXL using response surface methodology and artificial neural network.

机构信息

Department of Chemistry, Faculty of Arts and Science, Mersin University, Mersin, Turkey.

出版信息

J Environ Sci Health A Tox Hazard Subst Environ Eng. 2019;54(14):1412-1422. doi: 10.1080/10934529.2019.1647749. Epub 2019 Aug 4.

Abstract

Eco-friendly methods, the ultrasound-assisted oxidation (UAO) and the subcritical water oxidation (SWO) methods, were applied to mineralise the widely used commercial reactive azo dye, Procion Crimson H-EXL in the presence of HO. 72.20% and 72.86% of total organic carbon removal were achieved in the UAO and SWO methods, respectively. The Box-Behnken design (BBD) was applied to design the experimental processes and optimise both methods. ANOVA and validation tests were performed to assess the employed models. and values were obtained as 36.72 and <0.0001 in the UAO method, respectively, and 605.97 and <0.0001 in the SWO method, respectively. The artificial neural network (ANN) was applied in both the UAO and the SWO methods. The predictive performance of the BBD and ANN models were evaluated and compared to each other over , root mean square error and absolute average deviation values.

摘要

环保方法,超声辅助氧化 (UAO) 和亚临界水氧化 (SWO) 方法,被应用于在 HO 存在下矿化广泛使用的商业活性偶氮染料 Procion Crimson H-EXL。UAO 和 SWO 方法分别实现了 72.20%和 72.86%的总有机碳去除率。Box-Behnken 设计 (BBD) 被应用于设计实验过程并优化这两种方法。ANOVA 和验证测试被用于评估所使用的模型。UAO 方法中得到 和 值分别为 36.72 和 <0.0001,SWO 方法中得到 605.97 和 <0.0001。人工神经网络 (ANN) 被应用于 UAO 和 SWO 方法中。BBD 和 ANN 模型的预测性能被评估,并通过 、均方根误差和绝对平均偏差值进行了相互比较。

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