Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth
- 1 Heriot-Watt University, Institute of Petroleum Engineering, Edinburgh, UK
- 2 Department of Computer Science and Engineering, Amirkabir University of Technology, Tehran, Iran
- 3 School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran
- 4 Department of Chemical Engineering, Polymer Engineering Group, Isfahan University of Technology, Isfahan, Iran
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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