Research Article
Group Contribution Methods for Estimation of Ionic Liquid Heat Capacities: Critical Evaluation and Extension
Paul Nancarrow,
Moira Lewis,
Lamis AbouChacra,
Corresponding Author
Paul Nancarrow
American University of Sharjah, Department of Chemical Engineering, Sharjah, United Arab Emirates.
American University of Sharjah, Department of Chemical Engineering, Sharjah, United Arab Emirates.Search for more papers by this authorMoira Lewis
Queen's University Belfast, QUILL Research Centre, Northern Ireland, UK.
Search for more papers by this authorLamis AbouChacra
American University of Sharjah, Department of Chemical Engineering, Sharjah, United Arab Emirates.
Search for more papers by this authorPaul Nancarrow,
Moira Lewis,
Lamis AbouChacra,
Corresponding Author
Paul Nancarrow
American University of Sharjah, Department of Chemical Engineering, Sharjah, United Arab Emirates.
American University of Sharjah, Department of Chemical Engineering, Sharjah, United Arab Emirates.Search for more papers by this authorMoira Lewis
Queen's University Belfast, QUILL Research Centre, Northern Ireland, UK.
Search for more papers by this authorLamis AbouChacra
American University of Sharjah, Department of Chemical Engineering, Sharjah, United Arab Emirates.
Search for more papers by this authorAbstract
Recently, new group contribution method (GCM) models have been developed for ionic liquids (ILs). However, difficulty arises in choosing an appropriate GCM for a given property estimation due to the fact that the reported accuracies do not enable meaningful comparison since they have been tested against different and limited datasets. Furthermore, many GCMs lack the parameters necessary to predict heat capacities for many new ILs. To overcome these problems, an extensive database of IL heat capacity data as a function of temperature was compiled and used to evaluate two different GCM approaches, analogously named the Meccano and Lego approaches, based on the nature of the fundamental building blocks used in each model. Although the Meccano approach was found to be slightly more accurate, the Lego approach has much broader applicability and is compatible with process simulation and IL design.
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