Evolving simple-to-apply models for estimating thermal conductivity of supercritical CO₂

References

• Ahmadi, M. A. 2011. “Prediction of Asphaltene Precipitation using Artificial Neural Network Optimized by Imperialist Competitive Algorithm.” Journal of Petroleum Exploration and Production Technology 1: 99–106. doi: 10.1007/s13202-011-0013-7
   Web of Science ®Google Scholar
• Ahmadi, M. A. 2012. “Neural Network Based Unified Particle Swarm Optimization for Prediction of Asphaltene Precipitation.” Fluid Phase Equilibria 314: 46–51. doi: 10.1016/j.fluid.2011.10.016
   Web of Science ®Google Scholar
• Ahmadi, M. A. 2015. “Connectionist Approach Estimates Gas-Oil Relative Permeability in Petroleum Reservoirs: Application to Reservoir Simulation.” Fuel 140: 429–439. doi: 10.1016/j.fuel.2014.09.058
   Web of Science ®Google Scholar
• Ahmadi, M. A., and A. Bahadori. 2015. “Determination of Oil Well Production Performance Using Artificial Neural Network (ANN) Linked to the Particle Swarm Optimization (PSO) Tool.” Petroleum. 10.1016/j.petlm.2015.06.004.
   Google Scholar
• Ahmadi, M. A., and A. Bahadori. 2015. “Prediction Performance of Natural Gas Dehydration Units for Water Removal Efficiency Using Least Square Support Vector Machine.” International Journal of Ambient Energy. doi:10.1080/01430750.2015.1004105.
   Web of Science ®Google Scholar
• Ahmadi, M. A., M. Ebadi, and A. Yazdanpanah. 2014. “Robust Intelligent Tool for Estimation Dew Point Pressure in Retrograded Condensate Gas Reservoirs: Application of Particle Swarm Optimization.” Journal of Petroleum Science and Engineering 123C: 5–17.
   Google Scholar
• Ahmadi, M. A., and M. Golshadi. 2012. “Neural Network Based Swarm Concept for Prediction Asphaltene Precipitation due Natural Depletion.” Journal of Petroleum Science and Engineering 98–99: 40–49. doi: 10.1016/j.petrol.2012.08.011
   Web of Science ®Google Scholar
• Ahmadi, M. A., M. Lee, and A. Bahadori. 2015. “Prediction of a Solid Desiccant Dehydrator Performance Using Least Squares Support Vector Machines Algorithm.” Journal of the Taiwan Institute of Chemical Engineers 50: 115–122. doi: 10.1016/j.jtice.2014.12.004
   Web of Science ®Google Scholar
• Ahmadi, M.-A., B. Pouladi, Y. Javvi, S. Alfkhani, and R. Soleimani. 2015. “Connectionist Technique Estimates H2S Solubility in Ionic Liquids Through a Low Parameter Approach.” The Journal of Supercritical Fluids 97: 81–87. doi: 10.1016/j.supflu.2014.11.009
   Web of Science ®Google Scholar
• Ahmadi, M. A., M. Zeinali Hasanvand, and A. Bahadori. “A LSSVM Approach to Predict Temperature Drop Accompanying a Given Pressure Drop for the Natural Gas Production and Processing Systems.” International Journal of Ambient Energy. doi:10.1080/01430750.2015.1055515.
   Google Scholar
• Ahmadi, M. A., S. Zendehboudi, A. Bahadori, A. Lohi, A. Elkamel, and I. Chatzis. 2014. “Recovery Rate of Vapor Extraction in Heavy Oil Reservoirs: Experimental, Statistical, and Modeling Studies.” Industrial & Engineering Chemistry Research 53 (41): 16091–16106. doi: 10.1021/ie502475t
   Web of Science ®Google Scholar
• Amooey, A. A. 2014. “A Simple Correlation to Predict Thermal Conductivity of Supercritical Carbon Dioxide.” The Journal of Supercritical Fluids 86: 1–3. doi: 10.1016/j.supflu.2013.11.016
   Web of Science ®Google Scholar
• Baghban, A., M. A. Ahmadi, B. Pouladi, and B. Amanna. 2015. “Phase Equilibrium Modeling of Semi-Clathrate Hydrates of Seven Commonly Gases in the Presence of TBAB Ionic Liquid Promoter Based on a Low Parameter Connectionist Technique.” The Journal of Supercritical Fluids 101: 184–192. doi: 10.1016/j.supflu.2015.03.004
   Web of Science ®Google Scholar
• Bahadori, A., and H. B. Vuthaluru. 2010. “Predictive Tool for an Accurate Estimation of Carbon Dioxide Transport Properties.” International Journal of Greenhouse Gas Control 4: 532–536. doi: 10.1016/j.ijggc.2009.12.007
   Web of Science ®Google Scholar
• Chen, F.-H., D.-J. Chi, and Y.-C. Wang. 2015. “Detecting Biotechnology Industry's Earnings Management using Bayesian Network, Principal Component Analysis, Back Propagation Neural Network, and Decision Tree.” Economic Modelling 46: 1–10. doi: 10.1016/j.econmod.2014.12.035
   Web of Science ®Google Scholar
• Chung, T. H., M. Ajlan, L. L. Lee, and K. E. Starling. 1988. “Generalized Multiparameter Correlation for Nonpolar and Polar Fluid Transport Properties.” Industrial & Engineering Chemistry Research 27: 671–679. doi: 10.1021/ie00076a024
   Web of Science ®Google Scholar
• Ely, J. F., and H. J. Hanley. 1983. “Prediction of Transport Properties. 2. Thermal Conductivity of Pure Fluids and Mixtures.” Industrial & Engineering Chemistry Fundamentals 22: 90–97. doi: 10.1021/i100009a016
   Web of Science ®Google Scholar
• Jarrahian, A., and E. Heidaryan. 2012. “A Novel Correlation Approach to Estimate Thermal Conductivity of Pure Carbon Dioxide in the Supercritical Region.” The Journal of Supercritical Fluids 64: 39–45. doi: 10.1016/j.supflu.2012.02.008
   Web of Science ®Google Scholar
• Johns, A. I., S. Rashid, J. T. Watson, and A. A. Clifford. 1986. “Thermal Conductivity of Argon, Nitrogen and Carbon Dioxide at Elevated Temperatures and Pressures.” Journal of the Chemical Society, Faraday Transactions 1: Physical Chemistry in Condensed Phases 82: 2235–2246. doi: 10.1039/f19868202235
   Google Scholar
• La, H., and S. E. Guigard. 2015. “Extraction of Hydrocarbons from Athabasca Oil Sand Slurry Using Supercritical Carbon Dioxide.” The Journal of Supercritical Fluids 100: 146–154. doi: 10.1016/j.supflu.2015.01.020
   Web of Science ®Google Scholar
• Leneindre, B., R. Tufeu, P. Bury, and J. Sengers. 1973. “Thermal Conductivity of Carbon Dioxide and Steam in the Supercritical Region.” Berichte der Bunsengesellschaft für physikalische Chemie 77: 262–275.
   Google Scholar
• Liao, S., and T. S. Zhao. 2002. “An Experimental Investigation of Convection Heat Transfer to Supercritical Carbon Dioxide in Miniature Tubes.” International Journal of Heat and Mass Transfer 45: 5025–5034. doi: 10.1016/S0017-9310(02)00206-5
   Web of Science ®Google Scholar
• Molero Gómez, A., C. Pereyra López, and E. Martinez de la Ossa. 1996. “Recovery of Grape Seed Oil by Liquid and Supercritical Carbon Dioxide Extraction: A Comparison with Conventional Solvent Extraction.” The Chemical Engineering Journal and the Biochemical Engineering Journal 61: 227–231. doi: 10.1016/0923-0467(95)03040-9
   Web of Science ®Google Scholar
• Mukhopadhyay, M. 2010. Natural Extracts using Supercritical Carbon Dioxide. USA: CRC press.
   Google Scholar
• Orr, F., and J. Taber. 1984. “Use of Carbon Dioxide in Enhanced Oil Recovery.” Science 224: 563–569. doi: 10.1126/science.224.4649.563
   PubMed Web of Science ®Google Scholar
• Pátek, J., J. Klomfar, L. Čapla, and P. Buryan. 2005. “Thermal Conductivity of Carbon Dioxide–Methane Mixtures at Temperatures Between 300 and 425 K and at Pressures up to 12 MPa.” International Journal of Thermophysics 26: 577–592. doi: 10.1007/s10765-005-5566-6
   Web of Science ®Google Scholar
• Pearson, A. 2005. “Carbon Dioxide—New Uses for an Old Refrigerant.” International Journal of Refrigeration 28: 1140–1148. doi: 10.1016/j.ijrefrig.2005.09.005
   Web of Science ®Google Scholar
• Pettersen, J., A. Hafner, G. Skaugen, and H. Rekstad. 1998. “Development of Compact Heat Exchangers for CO2 Air-Conditioning Systems.” International Journal of Refrigeration 21: 180–193. doi: 10.1016/S0140-7007(98)00013-9
   Web of Science ®Google Scholar
• Pioro, I. L., and R. B. Duffey. 2007. Heat-Transfer Enhancement at Supercritical Pressures. USA: ASME Press.
   Google Scholar
• Reverchon, E. 1997. “Supercritical Fluid Extraction and Fractionation of Essential Oils and Related Products.” The Journal of Supercritical Fluids 10: 1–37. doi: 10.1016/S0896-8446(97)00014-4
   Web of Science ®Google Scholar
• Scott, A. C., A. I. Johns, J. T. Watson, and A. A. Clifford. 1983. “Thermal Conductivity of Carbon Dioxide in the Temperature Range 300–348 K and Pressures up to 25 MPa.” Journal of the Chemical Society, Faraday Transactions 1: Physical Chemistry in Condensed Phases 79: 733–740. doi: 10.1039/f19837900733
   Google Scholar
• Vapnik, V. 1998. Statistical Learning Theory. New York: Wiley.
   Google Scholar