Computing Entropy for Long-Chain Alkanes Using Linear Regression: Application to Hydroisomerization
Entropies for alkane isomers longer than C<sub>10</sub> are computed using our recently developed linear regression model for thermochemical properties which is based on second-order group contributions. The computed entropies show excellent agreement with experimental data and data from Scott's tables which are obtained from a statistical mechanics-based correlation. Entropy production and heat input are calculated for the hydroisomerization of C<sub>7</sub> isomers in various zeolites (FAU-, ITQ-29-, BEA-, MEL-, MFI-, MTW-, and MRE-types) at 500 K at chemical equilibrium. Small variations in these properties are observed because of the differences in reaction equilibrium distributions for these zeolites. The effect of chain length on heat input and entropy production is also studied for the hydroisomerization of C<sub>7</sub>, C<sub>8</sub>, C<sub>10</sub>, and C<sub>14</sub> isomers in MTW-type zeolite at 500 K. For longer chains, both heat input and entropy production increase. Enthalpies and absolute entropies of C<sub>7</sub> hydroisomerization reaction products in MTW-type zeolite increase with higher temperatures. These findings highlight the accuracy of our linear regression model in computing entropies for alkanes and provide insight for designing and optimizing zeolite-catalyzed hydroisomerization processes.
Authors
- Thijs J. H. Vlugt (ORCID: https://orcid.org/0000-0003-3059-8712)
- Marcello Rigutto (ORCID: https://orcid.org/0000-0002-3671-3446)
- Sofı́a Calero (ORCID: https://orcid.org/0000-0001-9535-057X)
- Erik Zuidema (ORCID: https://orcid.org/0000-0002-2650-0412)
- Umang Agarwal (ORCID: https://orcid.org/0000-0002-5182-3141)
- Shrinjay Sharma (ORCID: https://orcid.org/0000-0001-8345-7433)
- David Dubbeldam (ORCID: https://orcid.org/0000-0002-4382-1509)
- R. Baur
Institutions
- Shell (United States) (US)
- Shell (Netherlands) (NL)
- Eindhoven University of Technology (NL)
- University of Amsterdam (NL)
- Delft University of Technology (NL)
Publication Details
- Journal
- Entropy
- Published
- 2024-12-21
- DOI
- https://doi.org/10.3390/e26121120
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.12
Funders
- Ministerie van Economische Zaken
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Delft Research Centres
- Ministerie van Economische Zaken en Klimaat