dc.contributor.author | Bekri, Sezin | |
dc.contributor.author | Ozmen, Dilek | |
dc.contributor.author | Ozmen, Atilla | |
dc.date.accessioned | 2023-10-19T15:12:46Z | |
dc.date.available | 2023-10-19T15:12:46Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0104-6632 | |
dc.identifier.issn | 1678-4383 | |
dc.identifier.uri | https://doi.org/10.1007/s43153-023-00377-0 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12469/5526 | |
dc.description.abstract | Vapor-liquid equilibrium (VLE) data plays a vital role in the design, modeling and control of process equipment. In this study, to estimate the VLE data of binary systems, a deep neural network (DNN)-based combining rule was proposed based on the cross-term parameter (a(ij)) in the two-parameter Peng-Robinson cubic equation of state (PR-EoS) combined with the one-parameter classical van der Waals mixing and combining rule (1PVDW). Experimental VLE data of alternative binary refrigerant systems selected from the literature were calculated using both the PR + 1PVDW and the DNN-based model. Vapor phase mole fractions (y(i)) and equilibrium pressures (P) obtained from the proposed DNN-based and PR + 1PVDW models were compared in the terms of average percent deviations. For the DNN-based model, the vapor phase mole fractions give at least as good results as the models in the literature, and also it has been shown that a much better estimate of the equilibrium pressure (P) is obtained when compared with that of the literature. Results obtained using the proposed DNN-based model are presented with tables and graphs. For the equilibrium pressure, while the average percent deviation errors (Delta P/P%) calculated in the literature are less than 7.739, the errors obtained with the proposed DNN-based model are smaller than 3.455. And also, for vapor phase mole fractions, while the maximum error (Delta(y1)/(y1) %) in the literature is obtained as 6.142, the largest error calculated with DNN-based model is 3.545. It has been seen that the proposed DNN-based model makes more practical and less error-prone estimations than the methods in the literature. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer Heidelberg | en_US |
dc.relation.ispartof | Brazilian Journal of Chemical Engineering | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Equation-Of-State | En_Us |
dc.subject | Binary Interaction Parameters | En_Us |
dc.subject | Artificial Neural-Networks | En_Us |
dc.subject | Peng-Robinson | En_Us |
dc.subject | Phase-Equilibria | En_Us |
dc.subject | Refrigerant Mixtures | En_Us |
dc.subject | Pentafluoroethane R125 | En_Us |
dc.subject | Cubic Equations | En_Us |
dc.subject | Mixing Rules | En_Us |
dc.subject | Vle Data | En_Us |
dc.subject | Vapor-liquid equilibrium (VLE) | en_US |
dc.subject | Deep neural network (DNN) | en_US |
dc.subject | Peng-Robinson equation of state (PR-EoS) | en_US |
dc.subject | Van der Waals mixing and combining rule | en_US |
dc.subject | Refrigerant mixtures | en_US |
dc.title | Deep learning based combining rule for the estimation of vapor-liquid equilibrium | en_US |
dc.type | article | en_US |
dc.department | N/A | en_US |
dc.identifier.wos | WOS:001063519200001 | en_US |
dc.identifier.doi | 10.1007/s43153-023-00377-0 | en_US |
dc.identifier.scopus | 2-s2.0-85168655119 | en_US |
dc.institutionauthor | N/A | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.khas | 20231019-WoS | en_US |