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Prediction of multicomponent ION exchange equilibria by using the e-NRTL model for computing the activity coefficients in solution

机译:通过使用E-NRTL模型计算解决方案中活动系数的E-NRTL模型预测多组分离子交换平衡

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The e-NRTL model for computing the activity coefficients in solution of electrolytes was used for the first time, as the best of our knowledge, together with the Wilson's equation for evaluating the non-ideal behavior of the resin phase, in the prediction of multicomponent ion exchange equilibria. For this purpose, experimental data for the binary systems Ni2+/Na+ and Zn2+/Na+ and the ternary system Ni2+/Zn2+/Na+ using Amberlite IR-120 reported by Franco et al. [1] were considered. The results obtained showed that the major impact over the equilibrium modelling was caused by the inclusion of the Wilson's equation demonstrating the predominant effect of the non-ideality of the resin phase. No significant differences in the results (for the systems and ionic concentrations explored here) were observed when Bromley, Pitzer and e-NRTL equations were used to predict the activity coefficients in solution. Regarding the prediction of the multicomponent equilibria, it can be said that the most complex models: Pitzer's and e-NRTL ones, yield the best results. Again, the major impact in the improvement of the prediction was the inclusion of the Wilson's parameters. Finally, since the e-NRTL model allows to indistinctly work with both electrolytes and non-electrolytes, it could be possible to design modules for simulating the ion exchange operation in process simulations software like Aspen Plus (R). (C) 2019 Elsevier B.V. All rights reserved.
机译:用于计算电解质溶液中的活性系数的E-NRTL模型首次使用,作为我们的知识,以及威尔逊的等式,以评估树脂相的非理想行为,在多组分预测中离子交换平衡。为此目的,使用Franco等人报告的二元系统Ni2 + / Na +和Zn2 + / Na +和三元系Ni2 + / Zn2 + / Na +的实验数据。 [1]被考虑。所得到的结果表明,通过包含威尔逊的方程来表明树脂相的非理想性的主要效果引起了对平衡建模的主要影响。当使用溴利,PITZER和E-NRTL方程时,观察到结果中没有显着差异(本文探索的系统和离子浓度),用于预测溶液中的活性系数。关于对多组分均衡的预测,可以说最复杂的模型:Pitzer和E-NRTL系列,产生最佳结果。同样,在改进预测方面的主要影响是包含威尔逊的参数。最后,由于E-NRTL模型允许模糊地与电解质和非电解质一起工作,因此可以设计用于模拟过程模拟软件中的离子交换操作的模块,例如Aspen Plus(R)。 (c)2019年Elsevier B.V.保留所有权利。

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