Applying SVM scheme for modeling of natural gas dew point

References

• Ahmadi, M. A., M. Z. Hasanvand, and A. Bahadori. 2015. A LSSVM approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems. International Journal Ambient Energy 38:122–29.
   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.
   Web of Science ®Google Scholar
• Ahmadi, M.-A., and A. Bahadori. 2015. A LSSVM approach for determining well placement and conning phenomena in horizontal wells. Fuel 153:276–83.
   Web of Science ®Google Scholar
• Ahmadi, M. A., and M. Ebadi. 2014. Evolving smart approach for determination dew point pressure through condensate gas reservoirs. Fuel 117 (Part B (0)):1074–84. doi:10.1016/j.fuel.2013.10.010.
   Google Scholar
• Ahmadi, M. A., M. Ebadi, and S. M. Hosseini. 2014. Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach. Fuel 117 (Part A (0)):579–89. doi:10.1016/j.fuel.2013.09.071.
   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 50:115–22.
   Web of Science ®Google Scholar
• Ahmadi, M. A., R. Soleimani, and A. Bahadori. 2014. A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems. Fuel 137:145–54.
   Web of Science ®Google Scholar
• Ahmadi, M. H., M. A. Ahmadi, S. A. Sadatsakkak, and M. Feidt. 2015. Connectionist intelligent model estimates output power and torque of stirling engine. Renewable and Sustainable Energy Reviews 50:871–83.
   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. Journal of Petroleum Science and Engineering. doi: 10.1016/j.petrol.2015.01.013.
   PubMed Web of Science ®Google Scholar
• Anguita, D., A. Ghio, N. Greco, L. Oneto, and S. Ridella. 2010. Model selection for support vector machines: Advantages and disadvantages of the machine learning theory. Paper presented at the Neural Networks (IJCNN), The 2010 International Joint Conference on, Barcelona, Spain, 18-23 July 2010.
   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 101:184–92.
   Web of Science ®Google Scholar
• Baghban, A., M. A. Ahmadi, and B. H. Shahraki. 2015. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. The Journal of Supercritical Fluids 98:50–64.
   Web of Science ®Google Scholar
• Baghban, A., and A. Bahadori. 2016. Determination of efficient surfactants in the oil and gas production units using the SVM approach. Petroleum Science and Technology 34 (20):1691–97.
   Web of Science ®Google Scholar
• Baghban, A., A. Bahadori, A. H. Mohammadi, and A. Behbahaninia. 2017. Prediction of CO2 loading capacities of aqueous solutions of absorbents using different computational schemes. International Journal of Greenhouse Gas Control 57:143–61.
   Web of Science ®Google Scholar
• Baghban, A., A. H. Mohammadi, and M. S. Taleghani. 2017. Rigorous modeling of CO2 equilibrium absorption in ionic liquids. International Journal of Greenhouse Gas Control 58:19–41. doi:10.1016/j.ijggc.2016.12.009.
   Web of Science ®Google Scholar
• Bahadori, A., and H. B. Vuthaluru. 2009. Rapid estimation of equilibrium water dew point of natural gas in TEG dehydration systems. Journal of Natural Gas Science and Engineering 1 (3):68–71.
   Web of Science ®Google Scholar
• Fei, S.-W., M.-J. Wang, Y.-B. Miao, J. Tu, and C.-L. Liu. 2009. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil. Energy Conversion and Management 50 (6):1604–09. doi:10.1016/j.enconman.2009.02.004.
   Web of Science ®Google Scholar
• Herskowitz, M., and M. Gottlieb. 1984. Vapor-liquid equilibrium in aqueous solutions of various glycols and polyethylene glycols. 1. Triethylene glycol. Journal of Chemical and Engineering Data 29 (2):173–75.
   Web of Science ®Google Scholar
• Li, Q., Q. Meng, J. Cai, H. Yoshino, and A. Mochida. 2009. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Conversion and Management 50 (1):90–96. doi:10.1016/j.enconman.2008.08.033.
   Web of Science ®Google Scholar
• Makogon, I. F., J. F. Makogon, R. Ingénieur, and J. F. Makogon, Russia Engineer. 1981. Hydrates of natural gas. Tulsa, Oklahoma: PennWell Books.
   Google Scholar
• Momeni, E., D. J. Armaghani, M. Hajihassani, and M. Amin. 2015. Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63. doi:10.1016/j.measurement.2014.09.075.
   Web of Science ®Google Scholar
• Parrish, W. R., K. W. Won, and M. E. Baltatu. 1986. Phase behavior of the triethylene glycol-water system and dehydration/regeneration design for extremely low dew point requirements. Paper presented at the Proceedings of the 65th Annual GPA Convention, San Antonio, TX.
   Google Scholar
• Sahoo, G. B., and C. Ray. 2006. Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms. Journal of Membrane Science 283 (1–2):147–57. doi:10.1016/j.memsci.2006.06.019.
   Web of Science ®Google Scholar
• Sloan, E. D. 2003. Fundamental principles and applications of natural gas hydrates. Nature 426 (6964):353–63.
   PubMed Web of Science ®Google Scholar
• Twu, C. H., W. D. Sim, and V. Tassone. 2002. A versatile liquid activity model for SRK, PR and a new cubic equation-of-state TST. Fluid Phase Equilibria 194:385–99.
   Web of Science ®Google Scholar
• Twu, C. H., V. Tassone, W. D. Sim, and S. Watanasiri. 2005. Advanced equation of state method for modeling TEG–Water for glycol gas dehydration. Fluid Phase Equilibria 228:213–21.
   Web of Science ®Google Scholar
• Vapnik, V. 1998. Statistical learning theory. New York: Wiley.
   Google Scholar
• Yun, H. 2001. Discuss of the TEG dehydration process. China Offshore Oil and Gas (Engineering) 3:005.
   Google Scholar