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

References

• Abdi-Khanghah, M., A. Bemani, Z. Naserzadeh, and Z. Zhang.. 2018. Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN. Journal of CO2 Utilization 25:108–19
   Google Scholar
• Abdi Khanghah, M., R. M. Behbahani, T. Hamoule, and A. Baghban. 2017. Kinetic modeling and laboratory investigation of catalytic toluene methylation to para-xylene. Petroleum Science and Technology 35 (18):1866-72. doi:10.1080/10916466.2017.1369111.
   Web of Science ®Google Scholar
• Abdi-Khanghah, M., M. Adelizadeh, Z. Naserzadeh, H. Barati, and Z. Zhang. 2018. Methane hydrate formation in the presence of ZnO nanoparticle and SDS: application to transportation and storage. Journal of Natural Gas Science and Engineering 54:120-30. doi:10.1016/j.jngse.2018.04.005.
   Web of Science ®Google Scholar
• Al Dossary, M. A., and H. Nasrabadi. 2016. Well placement optimization using imperialist competitive algorithm. Review of Journal of Petroleum Science and Engineering 147:237–48. doi:10.1016/j.petrol.2016.06.017.
   Web of Science ®Google Scholar
• Alam, M. N., B. Das, and V. Pant. 2015. A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination. Review of Electric Power Systems Research 128:39–52. doi:10.1016/j.epsr.2015.06.018.
   Web of Science ®Google Scholar
• Amiri, M., J. Ghiasi-Freez, B. Golkar, and A. Hatampour. 2015. Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm–A case study. Review of Journal of Petroleum Science and Engineering 127:347–58. doi:10.1016/j.petrol.2015.01.013.
   Web of Science ®Google Scholar
• Antoniadis, K. D., G. J. Tertsinidou, M. J. Assael, and W. A. Wakeham. 2016. Necessary conditions for accurate, transient hot-wire measurements of the apparent thermal conductivity of nanofluids are seldom satisfied. Review of International Journal of Thermophysics 37 (8):78. doi:10.1007/s10765-016-2083-8.
   Web of Science ®Google Scholar
• Assael, M. J., A. Koutian, M. L. Huber, and R. A. Perkins. 2016. Reference correlations of the thermal conductivity of ethene and propene. Review of Journal of Physical and Chemical Reference Data 45 (3):033104. doi:10.1063/1.4958984.
   PubMed Web of Science ®Google Scholar
• De Castro, C. A., N. R. Tufeu, and B. Le Neindre. 1983. Thermal conductivity measurement of n-butane over wide temperature and pressure ranges. Review of International Journal of Thermophysics 4 (1):11–33. doi:10.1007/BF00504479.
   Google Scholar
• Eghtedaei, R., M. Abdi-khanghah, B. S. Najar, and A. Baghban. 2017. Viscosity estimation of mixed oil using RBF-ANN approach. Petroleum science and technology 35 (17):1731-6. doi:10.1080/10916466.2017.1365084.
   Google Scholar
• Esfe, M. H., A. Karimipour, W.-M. Yan, M. Akbari, M. R. Safaei, and M. Dahari. 2015. Experimental study on thermal conductivity of ethylene glycol based nanofluids containing Al2O3 nanoparticles. Review of International Journal of Heat and Mass Transfer 88:728–34. doi:10.1016/j.ijheatmasstransfer.2015.05.010.
   Web of Science ®Google Scholar
• Hammerschmidt, U., and W. Sabuga. 2000. Transient hot wire (THW) method: Uncertainty assessment. Review of International Journal of Thermophysics 21 (6):1255–78. doi:10.1023/A:1006649209044.
   Web of Science ®Google Scholar
• Hemmati-Sarapardeh, A., A. Varamesh, M. M. Husein, and K. Karan. 2018. On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment. Review of Renewable and Sustainable Energy Reviews 81:313–29. doi:10.1016/j.rser.2017.07.049.
   Web of Science ®Google Scholar
• Karkevandi-Talkhooncheh, A., S. Hajirezaie, A. Hemmati-Sarapardeh, M. M. Husein, K. Karan, and M. Sharifi. 2017. Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO2-crude oil minimum miscibility pressure. Review of Fuel 205:34–45. doi:10.1016/j.fuel.2017.05.026.
   Web of Science ®Google Scholar
• Le Neindre, B., Y. Garrabos, and M. Nikravech. 2014. Measurements of the thermal conductivity of propane in the supercritical region. Review of Journal of Chemical & Engineering Data 59 (11):3422–33. doi:10.1021/je500395f.
   Web of Science ®Google Scholar
• Letaief, A., R. Tufeu, Y. Garrabos, and B. Le Neindre. 1986. Rayleigh linewidth and thermal conductivity of SF6 in the supercritical range. Review of The Journal of Chemical Physics 84 (2):921–26. doi:10.1063/1.450538.
   Web of Science ®Google Scholar
• Millat, J., M. Ross, W. A. Wakeham, and M. Zalaf. 1988. The thermal conductivity of neon, methane and tetrafluoromethane. Review of Physica A: Statistical Mechanics and Its Applications 148 (1–2):124–52. doi:10.1016/0378-4371(88)90138-0.
   Web of Science ®Google Scholar
• Mohammadi, M., M. Sedighi, H. H. Kiasari, and S. M. Tohidi Hosseini. 2015. Genetic algorithm development for prediction of modified Langmuir Isotherm parameters of asphaltene adsorption onto metal surfaces: Using novel quartz crystal nanobalance. Review of Journal of Dispersion Science and Technology 36 (3):384–92. doi:10.1080/01932691.2014.903806.
   Web of Science ®Google Scholar
• Neindre, B. L., Y. Garrabos, F. Gumerov, and A. Sabirzianov. 2009. Measurements of the thermal conductivity of HFC-134a in the supercritical region. Review of Journal of Chemical & Engineering Data 54 (9):2678–88. doi:10.1021/je900210h.
   Web of Science ®Google Scholar
• Qasem, S. N., S. M. Shamsuddin, and A. M. Zain. 2012. Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Review of Knowledge-Based Systems 27:475–97. doi:10.1016/j.knosys.2011.10.001.
   Web of Science ®Google Scholar
• Rusconi, R., W. C. Williams, J. Buongiorno, R. Piazza, and H. Lin-Wen. 2007. Numerical analysis of convective instabilities in a transient short-hot-wire setup for measurement of liquid thermal conductivity. Review of International Journal of Thermophysics 28 (4):1131–46. doi:10.1007/s10765-007-0202-2.
   Web of Science ®Google Scholar
• Shen, W., X. Guo, W. Chao, and W. Desheng. 2011. Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Review of Knowledge-Based Systems 24 (3):378–85. doi:10.1016/j.knosys.2010.11.001.
   Web of Science ®Google Scholar
• Tatar, A., A. Shokrollahi, M. Mesbah, S. Rashid, M. Arabloo, and A. Bahadori. 2013. Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure. Review of Journal of Natural Gas Science and Engineering 15:82–92. doi:10.1016/j.jngse.2013.09.008.
   Web of Science ®Google Scholar
• Tufeu, R., D. Y. Ivanov, Y. Garrabos, and B. Le Neindre. 1984. Thermal conductivity of ammonia in a large temperature and pressure range including the critical region. Review of Berichte Der Bunsengesellschaft Für Physikalische Chemie 88 (4):422–27. doi:10.1002/bbpc.19840880421. Review of.
   Google Scholar