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.
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has been cited by the following article:

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

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

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

  JOURNAL NAME: American Journal of Analytical Chemistry, Vol.4 No.11, November 11, 2013

  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.