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Energy Sources, Part A: Recovery, Utilization, and Environmental Effects Volume 41, 2019 - Issue 4
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Research Article

Artificial neural network (ANN) modeling of thermal conductivity of supercritical ethane

Ling YangSchool of Information and Communication Engineering, North University of China, Taiyuan, China
&
Zhaoba WangSchool of Information and Communication Engineering, North University of China, Taiyuan, ChinaCorrespondence[email protected]
Pages 396-404 | Received 22 Jun 2018, Accepted 12 Aug 2018, Published online: 19 Sep 2018

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Read on this site (1)

Xubiao Wang. (2021) Rigorous Modeling of Solubility of Acid in Supercritical Carbon Dioxide Using Connectionist approach: Comparison between ANN and density based modeling. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 0:0, pages 1-14.
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Articles from other publishers (2)

Dabiao Wang, Shizhang Guo, Yuan Zhao, Sichong Li & Lanlan Li. (2023) Use of an artificial neural network to predict the heat transfer of supercritical R134a in a horizontal internally ribbed tube. Applied Thermal Engineering 228, pages 120429.
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Mohammad Hemmat Esfe & Masoud Afrand. (2019) Predicting thermophysical properties and flow characteristics of nanofluids using intelligent methods: focusing on ANN methods. Journal of Thermal Analysis and Calorimetry 140:2, pages 501-525.
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