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Statistical modelling of endocrine disrupting compounds adsorption onto activated carbon prepared from wood using CCD-RSM and DE hybrid evolutionary optimization framework: Comparison of linear vs non-linear isotherm and kinetic parameters
Journal of Molecular Liquids ( IF 5.3 ) Pub Date : 2020-01-20 , DOI: 10.1016/j.molliq.2020.112526
Mohammad Hadi Dehghani , Rama Rao Karri , Zeinab Tafaroji Yeganeh , Amir Hossein Mahvi , Heshmatollah Nourmoradi , Mehdi Salari , Ahmad Zarei , Mika Sillanpää

In this research, the efficiency of two adsorbents, including powdered and granular activated carbon (obtained from wood) was investigated on BPA removal in a batch-mode reactor. ANOVA analysis based on the central composite design-response surface methodology (CCD-RSM) showed a good fit between quadratic model predictions with experimental values, thus resulting in R2 of 0.9992 and 0.9997 for PAC and GAC respectively. The proposed 3 layered backpropagation artificial neural network (ANN) model predictions results with R2 = 0.9839 and 0.9992 for PAC and GAC respectively. The CCD-RSM optimised results indicated a maximum removal efficiency of 99% BPA in the case of the PAC under the optimal conditions, whereas, it is 89% for GAC. Genetic algorithm (GA) is also implemented to find the optimal values that can result high removal efficiency. The set (pH, contact time, adsorbent dosage and initial BPA concentration) of GA based optimised values for both PAC and GAC are [7.18, 90 min, 18 mg/L, 1.6 mg/L] and [7.76, 90 min, 18 mg/L, 1.67 mg/L] respectively which results in 99% and 89.95% removal efficiency. In this study, the Qmax for powdered and granular activated carbon was found to be 93.89 and 74.62 mg/g, respectively. The adsorption process is following the Langmuir isotherm and Pseudo 2nd order kinetic models. The thermodynamic study also signifies a favourable and spontaneous removal process. Overall the results confirm that the low-cost powder activated carbon favours high removal efficiency of BPA from aqueous environment.



中文翻译:

使用CCD-RSM和DE混合进化优化框架对内分泌干扰物吸附到木材制得的活性炭上的统计模型:线性与非线性等温线和动力学参数的比较

在这项研究中,研究了两种吸附剂的效率,包括粉状和颗粒状活性炭(从木材中获得),在间歇式反应器中去除BPA的效率。基于中央复合设计响应表面方法(CCD-RSM)的ANOVA分析显示,二次模型预测值与实验值之间具有良好的拟合度,因此PAC和GAC的R 2分别为0.9992和0.9997。拟议的三层反向传播人工神经网络(ANN)模型预测结果与R 2 PAC和GAC分别为0.9839和0.9992。CCD-RSM优化结果表明,在最佳条件下,PAC的最大去除效率为99%BPA,而对于GAC则为89%。还实施了遗传算法(GA),以找到可导致较高去除效率的最佳值。基于GA的PAC和GAC的优化值的集合(pH,接触时间,吸附剂剂量和BPA初始浓度)分别为[7.18,90 min,18 mg / L,1.6 mg / L]和[7.76,90 min,18毫克/升,1.67毫克/升],去除效率分别为99%和89.95%。在这项研究中,Q max粉末状和颗粒状活性炭的最大含量分别为93.89和74.62 mg / g。吸附过程遵循Langmuir等温线和伪二级动力学模型。热力学研究还表明了良好且自发的去除过程。总的来说,结果证实了低成本粉末活性炭有利于从水性环境中去除BPA的高效率。

更新日期:2020-01-21
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