Representing liquid-vapor equilibria of Ternary systems using neural networks

We develop a method based on neural networks for efficiently interpolating equations of state (EOS) for liquid-vapor equilibria of ternary mixtures. We investigate the performance of neural networks both when experimental data are available and when only simulation data are available. Simulation data are obtained from Gibbs Ensemble Monte Carlo simulations, using the TraPPE-EH molecular model. Our investigation uses the mixture of carbon dioxide, methane, and ethane as a validation example, for which experimental data exist. Analysis of the error in a neural-network-generated liquid-vapor coexistence curve shows that the resulting interpolation is robust and accurate, even in the case where the network is trained on a few data points. We use this observation to construct a methodology for accurately locating liquid-vapor equilibria of ternary mixures wihout using any experimetnal data.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Swisher, Mathew M

Advisor dc:contributor.advisor

• Nicolas G. Hadjiconstantinou.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/97856

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/97856