Abstract

The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m^–2 Bhatti, M. S.; Kapoor, D.; Kalia, R. K.; Reddy, A. S.; Thukral, A. K. RSM and ANN Modeling for Electrocoagulation of Copper from Simulated Wastewater: Multi Objective Optimization Using Genetic Algorithm Approach. Desalination 2011, 274, 74–80. DOI: https://doi.org/10.1016/j.desal.2011.01.083.[Crossref], [Web of Science ®] , [Google Scholar], 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.

摘要

这里报告的工作重点是通过电凝聚过程从废水中去除油和油脂，并使用建模和优化来获得考虑四个主要操作参数的结果，即。电流密度、pH、电极距离和反应时间。通过响应面法（RSM）的实验设计（DOE）设计了31个实验，方差分析（ANOVA）研究证实了实验结果的一致性。人工神经网络 (ANN) 也用于确定使用神经网络进行 4-10-1 排列的预测响应。RSM 和 ANN 预测的响应均与实验结果一致。^{在 80 A m – 2}的工作参数下达到 78% 的最大去除率 巴蒂，女士；卡普尔，D。卡利亚，RK；雷迪，AS；Thukral、AK RSM 和 ANN 模拟废水中铜的电凝聚建模：使用遗传算法方法的多目标优化。海水淡化 2011 , 274 , 74 – 80 . DOI：https://doi.org/10.1016/j.desal.2011.01.083。[Crossref]、[Web of Science ®]  、[Google Scholar]、3.6 pH、0.005 m 的电极距离和 20 分钟的反应时间。