Prediction of properties of new halogenated olefins using two group contribution approaches

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• 作者：Maria E. Mondejar^a ; ^{maemmo@mek.dtu.dk} ; Stefano Cignitti^b ; Jens Abildskov^b ; John M. Woodley^b ; Fredrik Haglind^a
• 关键词：Olefins ; Group contribution method ; Neural network ; Critical temperature ; Critical pressure ; Normal boiling temperature ; Acentric factor ; Ideal gas heat capacity
• 刊名：Fluid Phase Equilibria
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：433
• 期：Complete
• 页码：79-96
• 全文大小：2107 K
• 卷排序：433

文摘

Two methods for the prediction of properties of halogenated compounds are presented. The first method follows a classical group contribution method approach. The second method combines the use of neural networks with group contributions. Both methods yield a better prediction accuracy compared to equivalent methods. The neural network approach improves the accuracy, except for the acentric factor.