Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth - American Journal of Analytical Chemistry

AJAC > Vol.4 No.11, November 2013

American Journal of Analytical Chemistry

Volume 4, Issue 11 (November 2013)

ISSN Print: 2156-8251 ISSN Online: 2156-8278

Google-based Impact Factor: 1.71 Citations h5-index & Ranking

Application of Artificial Intelligence (AI) Modeling in Kinetics of Methane Hydrate Growth ()

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Download as PDF (Size: 1278KB) PP. 616-622

DOI: 10.4236/ajac.2013.411073 4,454 Downloads 7,314 Views Citations

Author(s)

Jalal Foroozesh, Abbas Khosravani, Adel Mohsenzadeh, Ali Haghighat Mesbahi

Affiliation(s)

Department of Chemical Engineering, Polymer Engineering Group, Isfahan University of Technology, Isfahan, Iran.
Department of Computer Science and Engineering, Amirkabir University of Technology, Tehran, Iran.
Heriot-Watt University, Institute of Petroleum Engineering, Edinburgh, UK.
School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, 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.

KEYWORDS

ANFIS; ANN; Kinetic; Growth Rate; Hydrate; Simulation

Share and Cite:

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.

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