Intelligent Prediction of CO2 Capture in Propyl Amine Methyl Imidazole Alanine Ionic Liquid: An Artificial Neural Network Model

Separation Science and Technology (Philadelphia)

Volumn 50, Issue 1, 2015, Pages 26-37

  Mirarab, Meysam ^a   Sharifi, Maysam ^b   Behzadi, Bahman ^b   Ghayyem, Mohammad Ali ^c  

^a MAPNA Group   (Iran)

^b IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^c PETROLEUM UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

artificial neural network; CO2 capture; gas liquid solubility; ionic liquid; synthesis

Indexed keywords

AMINO ACIDS; CARBON DIOXIDE; EQUILIBRIUM CONSTANTS; FORECASTING; LIQUIDS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SOLUBILITY; SYNTHESIS (CHEMICAL); TRANSFER FUNCTIONS;

ARTIFICIAL NEURAL NETWORK MODELING; ARTIFICIAL NEURAL NETWORK MODELS; GAS LIQUIDS; LINEAR TRANSFER FUNCTION; MULTI LAYER PERCEPTRON; PERFORMANCE COMPARISON; SIGMOID TRANSFER FUNCTION; TASK-SPECIFIC IONIC LIQUIDS;

IONIC LIQUIDS;

EID: 84924675166     PISSN: 01496395     EISSN: 15205754     Source Type: Journal    
DOI: 10.1080/01496395.2014.946145     Document Type: Article

References (49)

1. 2 capture. Combust. Sci. Technol., 184(7–8): 929–941.
2. Hwang, B.-J.; Park, S.-W.; Park, D.-W.; Oh, K.-J.; Kim, S.-S. (2009) Absorption of Carbon Dioxide into Ionic Liquid of 2-Hydroxy Ethylammonium Lactate. Sep. Sci. Technol., 44(7): 1574–1589.
3. 2 reduction strategies for the Northern Netherlands. Journal of Integrative Environmental Sciences, 8(3): 219–239.
4. 2 capture. Environ. Technol., 33(23): 2645–2651.
5. 2 capture/sequestration: a review. Energy Sources, Part A, 28(14): 1261–1279.
6. 2 Gas Capture. Sep. Sci. Technol., 47(11): 1596–1605.
7. 2 absorption behavior with a novel random packing: Super mini ring. Sep. Sci. Technol., 42(4): 701–716.
8. Anthony, J.L.; Maginn, E.J.; Brennecke, J.F. (2002) Solubilities and Thermodynamic Properties of Gases in the Ionic Liquid 1-n-Butyl-3-methylimidazolium Hexafluorophosphate. J. Phys. Chem. B, 106(29): 7315–7320.
9. 2N]. J. Chem. Eng. Data, 55(1): 165–172.
10. Baltus, R.E.; Culbertson, B.H.; Dai, S.; Luo, H.; DePaoli, D.W. (2003) Low-Pressure Solubility of Carbon Dioxide in Room-Temperature Ionic Liquids Measured with a Quartz Crystal Microbalance. J. Phys. Chem. B, 108(2): 721–727.
11. 2 in dialkylimidazolium dialkylphosphate ionic liquids. Thermochim. Acta, 494(1–2): 94–98.
12. Shin, E.-K.; Lee, B.-C.; Lim, J.S. (2008) High-pressure solubilities of carbon dioxide in ionic liquids: 1-Alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide. J. Supercrit. Fluids, 45(3): 282–292.
13. 4]. Ind. Eng. Chem. Res., 44(12): 4453–4464.
14. Hou, Y.; Baltus, R.E. (2007) Experimental Measurement of the Solubility and Diffusivity of CO2 in Room-Temperature Ionic Liquids Using a Transient Thin-Liquid-Film Method. Ind. Eng. Chem. Res., 46(24): 8166–8175.
15. Raeissi, S.; Peters, C.J. (2008) Carbon Dioxide Solubility in the Homologous 1-Alkyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide Family†. J. Chem. Eng. Data, 54(2): 382–386.
16. 2 capture—Development and progress. Chem. Eng. Process. Process Intensification, 49(4): 313–322.
17. Earle, M.J.; Seddon, K.R. (2000) Ionic liquids. Green solvents for the future. Pure Appl. Chem., 72(7): 1391–1398.
18. Ohno, H.; Fukumoto, K. (2007) Amino Acid Ionic Liquids. Acc. Chem. Res., 40(11): 1122–1129.
19. 2 Capture by a Task-Specific Ionic Liquid. J. Am. Chem. Soc., 124(6): 926–927.
20. 2 by tetrabutylphosphonium amino acid ionic liquids. Chemistry-a European Journal, 12(15): 4021–4026.
21. Xue, Z.; Zhang, Z.; Han, J.; Chen, Y.; Mu, T. (2011) Carbon dioxide capture by a dual amino ionic Liquid With Amino-Functionalized Imidazolium Cation And Taurine Anion. Int. J. Greenh. Gas Con., 5(4): 628–633.
22. 2 Capture. Chemistry-A European Journal, 15(12): 3003–3011.
23. Mohaghegh, S.; Ameri, A. (1995) Artificial neural network as a valuable tool for petroleum engineers. Paper SPE 29220, Prepared as an Unsolicited Paper for Society of Petroleum Engineers.
24. Rooki, R.; Doulati Ardejani, F.; Moradzadeh, A.; Kelessidis, V.C.; Nourozi, M. (2012) Prediction of terminal velocity of solid spheres falling through Newtonian and non-Newtonian pseudoplastic power law fluid using artificial neural network. Int. J. Miner. Process., 110–111 53–61.
25. Al-Fattah, S.M.; Startzman, R.A. (2001) Predicting natural gas production using artificial neural network. SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, Texas.
26. Irani, R.; Nasimi, R. (2011) Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling. J. Pet. Sci. Eng., 78: 6–12.
27. Farshad, F.; Garber, J.D.; Lorde, J.N. (1999) Predicting temperature profiles in producing oil wells using artificial neural networks. Latin American and Caribbean Petroleum Engineering Conference, Caracas, Venezuela.
28. Jeirani, Z.; Mohebbi, A. (2006) Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks. J. PetSci. Eng., 50(1): 11–20.
29. Karbalaee Akbari, M.; Jalali Farahani, F.; Abdy, Y. (2007) Using artificial neural network’s capability for estimation of gas condensate reservoir’s dew point pressure. EUROPEC/EAGE Conference and Exhibition, London, U.K.
30. Khaksar Manshad, A.; Ashoori, S.; Khaksar Manshad, M.; Omidvar, P. (2012) The prediction of wax precipitation by neural network and genetic algorithm and comparison with a multisolid model in crude oil systems. Pet. Sci. Technol., 30(13): 1369–1378.
31. Malinowski, P.; Sułowicz, M.; Bujak, J. (2011) Neural model for forecasting temperature in a distribution network of cooling water supplied to systems producing petroleum products. Int. J. Refrig., 34(4): 968–979.
32. ZareNezhad, B.; Aminian, A. (2011) An artificial neural network model for design of wellhead chokes in gas condensate production fields. Pet. Sci. Technol., 29(6): 579–587.
33. Romero, S.; Pamukcu, S. (1996) Using artificial neural networks to predict shear modulus of granular offshore sediments. The Sixth International Offshore and Polar Engineering Conference, Los Angeles, California, USA.
34. Jahanandish, I.; Salimifard, B.; Jalalifar, H. (2011) Predicting bottomhole pressure in vertical multiphase flowing wells using artificial neural networks. J. Pet. Sci. Eng., 75: 336–342.
35. Asadisaghandi, J.; Tahmasebi, P. (2011) Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields. J. Pet. Sci. Eng., 78: 464–475.
36. Bar, N.; Bandyopadhyay, T.K.; Biswas, M.N.; Das, S.K. (2010) Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components. J. Pet. Sci. Eng., 71: 187–194.
37. Zabihi, R.; Schaffie, M.; Nezamabadi-pour, H.; Ranjbar, M. (2011) Artificial neural network for permeability damage prediction due to sulfate scaling. J. Pet. Sci. Eng., 78: 575–581.
38. Malallah, A.; Nashawi, I.S. (2005) Estimating the fracture gradient coefficient using neural networks for a field in the Middle East. J. Pet. Sci. Eng., 49(3–4): 193–211.
39. Salehi, S.; Hareland, G.; Khademi Dehkordi, K.; Ganji, M.; Abdollahi, M. (2009) Casing collapse risk assessment and depth prediction with a neural network system approach. J. Pet. Sci. Eng., 69(1–2): 156–162.
40. 2 flooding. J. Pet. Sci. Eng., 37(1–2): 83–95.
41. Miri, R.; Sampaio, J.; Afshar, A.; Lourenco, A. (2007) Development of artificial neural networks to predict differential pipe sticking in Iranian offshore oil fields. International Oil Conference and Exhibition in Mexico, Veracruz, Mexico.
42. 4], and [bmim][Tf2N]. J. Chem. Eng. Data, 54(6): 1844–1849.
43. Tahmasebi, P.; Hezarkhani, A. (2012) A fast and independent architecture of artificial neural network for permeability prediction. J. Pet. Sci. Eng., 86-87: 118–126.
44. Karambeigi, M.S.; Zabihi, R.; Hekmat, Z. (2011) Neuro-simulation modeling of chemical flooding. J. Pet. Sci. Eng., 78: 208–219.
45. Nikravesh, M.; Aminzadeh, F. (2001) Mining and fusion of petroleum data with fuzzy logic and neural network agents. J. Pet. Sci. Eng., 29: 221–238.
46. Wu, J.D.; Liu, J.C. (2012) A forecasting system for car fuel consumption using a radial basis function neural network. Expert Syst. Appl., 39: 1883–1888.
47. Celikoglu, H.B. (2006) Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling. Math. Comput. Mode., 44: 640–658.
48. Mehrabi, S.; Maghsoudloo, M.; H., A.; Noormand, R.; Nozari, Y. (2009) Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases. Expert. Syst. Appl., 36: 6956–6959.
49. Prausnitz, J.M.; Lichtenthaler, R.N.; de Azevedo, E.G. (1998) Molecular thermodynamics of fluid-phase equilibria. Prentice Hall.