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 AJAC  Vol.4 No.11 , November 2013
Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth
Abstract: Determining thermodynamic and kinetic conditions for natural gas hydrate formation is an interesting subject for many researches. At the present, suitable information including experimental data and the thermodynamic models of hydrate formation are available which predict the thermodynamic conditions of hydrate formation. Conversely, there is no sufficient study about the kinetics of natural gas hydrate and most of experimental data and kinetic models in the literature are incomplete. Artificial Intelligence (AI) having sub-branches such as artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) has been proved as a novel tool with acceptable accuracy for modeling of engineering systems. Therefore, this paper aims to investigate the kinetics of hydrate formation by predicting the relationship of growth rate of methane hydrate with temperature and pressure using ANN and ANFIS. This goal can also be achieved by solving complicated governing equations while artificial intelligence provides an easier way to accomplish this goal. The result has shown that ANIFS is a more potential tool in predication relationship of kinetics of hydrate formation with temperature and pressure in comparison of ANN in present work.
Cite this paper: J. Foroozesh, A. Khosravani, A. Mohsenzadeh and A. Mesbahi, "Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth," American Journal of Analytical Chemistry, Vol. 4 No. 11, 2013, pp. 616-622. doi: 10.4236/ajac.2013.411073.
References

[1]   D. Glew and M. Haggett, “Kinetics of Formation of Ethylene Oxide Hydrate. Part II. Incongruent Solutions and Discussion,” Canadian Journal of Chemistry, Vol. 46, No. 24, 1968, pp. 3867-3877.
http://dx.doi.org/10.1139/v68-640

[2]   P. Englezos, N. Kalogerakis, P. Dholabhai and P. Bishnoi, “Kinetics of Formation of Methane and Ethane Gas Hydrates,” Chemical Engineering Science, Vol. 42, No. 1, 1987, pp. 2647-2658.
http://dx.doi.org/10.1016/0009-2509(87)87015-X

[3]   A. Vysniauskas and P. Bishnoi, “A Kinetic Study of Methane Hydrateformation,” Chemical Engineering Science, Vol. 38, No. 7, 1983, pp. 1061-1072.
http://dx.doi.org/10.1016/0009-2509(83)80027-X

[4]   J. Herri, F. Gruy, J. Pic, M. Cournil, B. Cingotti and A. Sinquin, “Interest of in Situ Turbidimetry for the Characterization of Methane Hydrate Crystallization: Application to the Study of Kinetic Inhibitors,” Chemical Engineering Science, Vol. 54, No. 12, 1999, pp. 1849-1858.
http://dx.doi.org/10.1016/S0009-2509(98)00433-3

[5]   N. Gnanendran and R. Amin, “Modelling Hydrate Formation Kinetics of a Hydrate Promoter-Water-Natural Gas System in a Semi-Batch Sprayreactor,” Chemical Engineering Science, Vol. 59, No. 18, 2004, pp. 3849-3863.
http://dx.doi.org/10.1016/j.ces.2004.06.009

[6]   L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, No. 3, 1965, pp. 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X

[7]   M. Sugeno and T. Takagi, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, 1985, pp. 116-132.

[8]   C. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 2, 1990, pp. 404-435.
http://dx.doi.org/10.1109/21.52551

[9]   J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-685.
http://dx.doi.org/10.1109/21.256541

[10]   J. S. R. Jang, “Fuzzy Modeling Using Generalized neUral Networks and Kalman Filter Algorithm,” Proceedings of 9th National Conference on Artificial Intelligence (AAAI-91), Vol. 4, No. 1, 1991, pp. 762-767.
http://ieeexplore.ieee.org/xpl/abstractReferences.jsp?tp=&arnumber=182710&url=http%3A%2F%2Fieeexplore.
ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D182710


[11]   C. Ma, G. Chen and T. Guo, “Kinetics of Hydrate Formation Using Gas Bubble Suspended in Water,” Science in China Series B: Chemistry, Vol. 45, 2002, pp. 208-215. http://dx.doi.org/10.1360/02yb9028

[12]   “Determining Phase Diagrams of Tetrahydrofuran+ Methane, Carbon Dioxide or Nitrogen Clathrate Hydrates Using an Artificial Neural Network Algorithm,” Chemical Engineering Science, Vol. 65, No. 22, 2010, pp. 6059-6063. http://dx.doi.org/10.1016/j.ces.2010.07.013

[13]   A. Mohammadi and D. Richon, “Hydrate Phase Equilibria for Hydrogen+Water and Hydrogen+Tetrahydrofuran +Water Systems: Predictions of Dissociation Conditions Using an Artificial Neural Network Algorithm,” Chemical Engineering Science, Vol. 65, No. 10, 2010, pp. 3352-3355. http://dx.doi.org/10.1016/j.ces.2010.02.015

[14]   A. Mohammadi, “Use of an Artificial Neural Network Algorithm to Predict Hydrate Dissociation Conditions for Hydrogen+Water and Hydrogen+Tetra-n-Butyl Ammonium Bromide+Watersystems,” Chemical Engineering Science, Vol. 65, No. 14, 2010, pp. 4302-4305.
http://dx.doi.org/10.1016/j.ces.2010.04.026

[15]   G. Zahedi, Z. Karami and H. Yaghoobi, “Prediction of Hydrate Formation Temperature by Both Statistical Models and Artificial Neural Network Approaches,” Energy Conversion and Management, Vol. 50, No. 8, 2009, pp. 2052-2059. http://dx.doi.org/10.1016/j.enconman.2009.04.005

[16]   C. Y. Sun, G. J. Chen, C. F. Ma, Q. Huang, H. Luo and Q. P. Li, “The Growth Kinetics of Hydrate Film on the Surface of Gas Bubble Suspended in Water or Aqueous Surfactant Solution,” Journal of Crystal Growth, Vol. 306, No. 2, 2007, pp. 491-499.
http://dx.doi.org/10.1016/j.jcrysgro.2007.05.037

 
 
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