Computational and statistical modeling for parameters optimization of electrochemical decontamination of synozol red dye wastewater.,Chemosphere

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Computational and statistical modeling for parameters optimization of electrochemical decontamination of synozol red dye wastewater.
Chemosphere ( IF 8.1 ) Pub Date : 2020-04-05 , DOI: 10.1016/j.chemosphere.2020.126673
Saad Ullah Khan ₁ , Hammad Khan ₂ , Sajid Anwar ₃ , Sabir Khan ₄ , Maria V Boldrin Zanoni ₅ , Sajjad Hussain ₆

Affiliation

Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan; Institute of Chemistry Araraquara, São Paulo State University (UNESP), Av. Prof. Francisco Degni 55, Araraquara, SP, 14800-060, Brazil.
Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan.
Faculty of Computer Sciences and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan.
Institute of Chemistry Araraquara, São Paulo State University (UNESP), Av. Prof. Francisco Degni 55, Araraquara, SP, 14800-060, Brazil.
Institute of Chemistry Araraquara, São Paulo State University (UNESP), Av. Prof. Francisco Degni 55, Araraquara, SP, 14800-060, Brazil; National Institute for Alternative Technologies of Detection, Toxicological Evaluation and Removal of Micropollutants and Radioactivies (INCT-DATREM), São Paulo State University (UNESP), Institute of Chemistry, Araraquara, SP, 14800-060, Brazil.
Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan; Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, CEP 79070-900, Campo Grande, MS, Brazil.

In this study, computational and statistical models were applied to optimize the inherent parameters of an electrochemical decontamination of synozol red. The effect of various experimental variables such as current density, initial pH and concentration of electrolyte on degradation were assessed at Ti/RuO0·3TiO0·7O2 anode. Response surface methodology (RSM) based central composite design was applied to investigate interdependency of studied variables and train an artificial neural network (ANN) to envisage the experimental training data. The presence of fifteen neurons proved to have optimum performance based on maximum R2, mean absolute error, absolute average deviation and minimum mean square error. In comparison to RSM and empirical kinetics models, better prediction and interpretation of the experimental results were observed by ANN model. The sensitive analysis revealed the comparative significance of experimental variables are pH = 61.03%>current density = 17.29%>molar concentration of NaCl = 12.7%>time = 8.98%. The optimized process parameters obtained from genetic algorithm showed 98.6% discolorization of dye at pH 2.95, current density = 5.95 mA cm-2, NaCl of 0.075 M in 29.83 min of electrolysis. The obtained results revealed that the use of statistical and computational modeling is an adequate approach to optimize the process variables of electrochemical treatment.

中文翻译：

Synozol红染料废水电化学去污参数优化的计算和统计模型。

在这项研究中，计算和统计模型被用于优化Synozol红电化学去污的固有参数。在Ti / RuO0·3TiO0·7O2阳极上评估了电流密度，初始pH和电解质浓度等各种实验变量对降解的影响。基于响应面方法学（RSM）的中央复合设计用于研究研究变量的相互依赖性，并训练人工神经网络（ANN）来设想实验训练数据。事实证明，基于最大R2，平均绝对误差，绝对平均偏差和最小均方误差，十五个神经元的存在具有最佳性能。与RSM和经验动力学模型相比，通过ANN模型可以更好地预测和解释实验结果。敏感性分析表明，实验变量的相对意义为：pH = 61.03％>电流密度= 17.29％> NaCl的摩尔浓度= 12.7％>时间= 8.98％。从遗传算法获得的优化工艺参数表明，在29.83分钟的电解过程中，在pH 2.95，电流密度= 5.95 mA cm-2，NaCl为0.075 M的条件下，染料的变色率为98.6％。获得的结果表明，使用统计和计算模型是优化电化学处理过程变量的适当方法。电流密度= 5.95 mA cm-2，在29.83分钟的电解中，NaCl为0.075M。获得的结果表明，使用统计和计算模型是优化电化学处理过程变量的适当方法。电流密度= 5.95 mA cm-2，在29.83分钟的电解中，NaCl为0.075M。获得的结果表明，使用统计和计算模型是优化电化学处理过程变量的适当方法。

更新日期：2020-04-06

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