Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation

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

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    23 Citations (Scopus)


    This paper aims to present a kinetic study of formation of methane gas hydrate (GH) using artificial intelligence (AI) tools. Generally, study of the kinetics of GH formation will help to better understand this process in order to control it favorably. However, due to its complexity, this process is not fully understood yet. More studies in the literature are considering the thermodynamics of gas hydrate formation both experimentally and mathematically. However, there is no sufficient studies regarding the kinetics of gas hydrates and most of the experimental data and specifically the kinetic models in the literature are incomplete. That may be due to inherent stochastic behavior of GHs which makes it difficult to develop a deterministic model for it. Nowadays, Artificial Intelligence (AI) methods including Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been proved as a novel and potential tools with acceptable accuracy for modeling of engineering systems. Therefore, this paper aims to investigate the kinetics of gas hydrate formation using ANN and ANFIS when the relation between growth rate of methane hydrate, temperature and pressure has been modeled. Moreover, this can also be achieved using complicated governing equations but AI provides a less complex and easier way to accomplish this goal. Experimental data considering the methane hydrate growth rate as a function of pressure and temperature were used and ANFIS as well as ANN were employed to duplicate them. In this study, the results reveal that ANIFS could better predict the methane hydrate growth rate in the prevailing pressure and temperature conditions compared to ANN. Generally this study shows the effectiveness of AI based techniques for kinetics modeling of gas hydrates formation.
    Original languageEnglish
    Pages (from-to)2258-2264
    Number of pages7
    JournalJournal of the Taiwan Institute of Chemical Engineers
    Issue number5
    Early online date26 Aug 2014
    Publication statusPublished - Sept 2014


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