Scopus
YÖKSİS DOI Eşleşti
SJR Q2
Neural network-based correlations for the thermal conductivity of propane
Fluid Phase Equilibria · Ağustos 2007
YÖKSİS Kayıtları
Neural network based correlations for the thermal conductivity of propane
Fluid Phase Equilibria · 2007 SCI 1 atıf
Prof. Dr. ELİFE ÖZNUR KARABULUT →
Neural network based correlations for the thermal conductivity of propane
Fluid Phase Equilibria · 2007 SCI 1 atıf
Prof. Dr. ELİFE ÖZNUR KARABULUT →
Neural network based correlations for the thermal conductivity of propane
Fluid Phase Equilibria · 2007 SCI 1 atıf
Prof. Dr. MUSTAFA KOYUNCU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Neural network based correlations for the thermal conductivity of propane
2007 ISSN: 03783812 SCI 1 atıf
Prof. Dr. MUSTAFA KOYUNCU →
Excess properties of liquid mixtures from the perturbation theory of Barker Henderson
2002 ISSN: 03783812 SCI 8 atıf
Prof. Dr. MUSTAFA KOYUNCU →
Neural network based correlations for the thermal conductivity of propane
2007 ISSN: 03783812 SCI 1 atıf
Prof. Dr. ELİFE ÖZNUR KARABULUT →
Makale Bilgileri
Dergi
Fluid Phase Equilibria
ISSN03783812
Yayın TarihiAğustos 2007
Cilt / Sayfa257 · 6-17
Scopus ID2-s2.0-34250714771
Özet
An alternative approach, exploiting neural networks, is proposed to develop thermal conductivity correlation of propane for the first time. In order to test the accuracy of the proposed technique and demonstrate its utility in fitting the thermal conductivity surface of propane, we have established a thermal conductivity correlation in terms of temperature and density, and then compared its predictions with those obtained by the conventional method. The results obtained are so impressive that the neural network correlation has lower overall average absolute deviations (AADs) in each data set. The requirement of using a high accuracy equation of state (EoS) for the correlations which include density as a variable has been avoided by developing thermal conductivity equations as a function of temperature and pressure. For this purpose, three neural network models have been constructed for the liquid, vapour, and supercritical phases. It is found that neural network approach produces a much better correlation for the liquid region while the predictions of the other two models are in substantial agreement with the traditional results. Consequently, neural networks offer a powerful tool for the development of thermal conductivity correlations of fluids, no matter whether an EoS is used or not. © 2007 Elsevier B.V. All rights reserved.
Yazarlar (2)
1
Elife Ö Karabulut
2
Mustafa Koyuncu
Anahtar Kelimeler
Neural networks
Propane
Thermal conductivity
Transport properties correlation techniques
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Fluid Phase Equilibria
Q2
SJR Skoru0,531
H-Index148
YayıncıElsevier B.V.
ÜlkeNetherlands
Chemical Engineering (miscellaneous) (Q2)
Physical and Theoretical Chemistry (Q2)
Physics and Astronomy (miscellaneous) (Q2)
Metrikler
4
Atıf
2
Yazar
4
Anahtar Kelime