2026-09-20T13:43:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/978562022-01-13T07:54:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nicolas G. Hadjiconstantinou.
Swisher, Mathew M
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2015-07-17T19:53:54Z
2015-07-17T19:53:54Z
2015
2015
http://hdl.handle.net/1721.1/97856
913747305
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 77-78).
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.
by Mathew M. Swisher.
S.M.
78 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Representing liquid-vapor equilibria of Ternary systems using neural networks
Thesis
application/pdf
Publication
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<Abstract>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.</Abstract>
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