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Introduction Of The Group Contribution Concept Into The Nrtl Model

机译:将团体贡献概念引入Nrtl模型

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Generally the NRTL activity coefficient model has shown great capabilities inrnpredicting liquid-liquid or liquid-vapour phase equilibria. However its major drawbackrnis the non availability of the required molecular interaction parameters for a hugernnumber of chemical systems. Therefore in the present work, a group contributionrnapproach is introduced into the initial NRTL to give the GC-NRTL (Group ContributionrnNRTL) model. It is simply based on calculating group interaction parameters accordingrnto the NRTL equation, by minimizing an objective function made of the sum of thernsquared differences between the calculated values and the experimental ones reported inrnthe literature. The minimization method is based on the Genetic algorithm which hasrnproven to be very performing in reaching the optimum. As an illustration, the GCNRTLrnmodel was tested for 8 binary liquid- liquid systems mainly involving currentrnfunctional groups like CH, CH2, CH3, OH, COOH, ACH, ACOH, ACCH3, NO, etc. Thernagreement between the predicted results by means of the GC-NRTL and thernexperimental phase equilibrium data is encouraging and the interaction parameters tablernshould be completed to include a greater number of functional groups.
机译:通常,NRTL活度系数模型已显示出强大的预测液-液或液-汽相平衡的能力。然而,其主要缺点是无法为大量化学系统提供所需的分子相互作用参数。因此,在本工作中,将组贡献方法引入到初始NRTL中,以给出GC-NRTL(组贡献NRTL)模型。它仅基于根据NRTL方程计算组相互作用参数,通过最小化由计算值与文献报道的实验值之间的平方差之和构成的目标函数。最小化方法基于遗传算法,该算法已被证明在达到最佳效果方面表现出色。作为说明,对GCNRTLrn模型进行了8种二元液-液系统的测试,这些系统主要涉及当前的官能团,例如CH,CH2,CH3,OH,COOH,ACH,ACOH,ACCH3,NO等。通过GC预测结果之间的一致性-NRTL和实验相平衡数据是令人鼓舞的,应该完成相互作用参数表以包含更多的官能团。

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