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

Erdal Yabalak, Büşra Külekçi, A. Gizir
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引用次数: 16

Abstract

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 H2O2. 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. F and P 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 R2, root mean square error and absolute average deviation values. Graphical Abstract
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基于响应面法和人工神经网络的超声辅助和亚临界水氧化法在Procion Crimson H-EXL矿化中的应用
摘要采用超声波辅助氧化(UAO)和亚临界水氧化(SWO)两种生态友好的方法,在H2O2存在下矿化了广泛使用的商业活性偶氮染料Procion Crimson H-EXL。UAO法和SWO法的总有机碳去除率分别为72.20%和72.86%。采用Box-Behnken设计(BBD)设计实验流程并对两种方法进行优化。采用方差分析和验证检验来评估所采用的模型。UAO方法的F和P值分别为36.72和<0.0001,SWO方法的F和P值分别为605.97和<0.0001。人工神经网络(ANN)分别应用于UAO和SWO方法中。通过R2、均方根误差和绝对平均偏差值对BBD和ANN模型的预测性能进行了评估和比较。图形抽象
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