Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions

Ghavipour, Mohammad; Ghavipour, Mina; Chitsazan, Minoo; Najibi, Seyed Hesam; Ghidary, Saeed Shiry

doi:10.1016/j.cherd.2012.08.010

Cited by 70 publications

(27 citation statements)

References 42 publications

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“…A substantial part of the recent work is dedicated to properties of ionic liquids , , , , , that are hard to describe otherwise. Machine learning has also been used for describing the properties of crude oil, asphaltene, and natural gas , , , , , , , .…”

Section: A Preliminary Look Into Machine Learningmentioning

confidence: 99%

Digitalization in Thermodynamics

Forte

Jirasek

Bortz

et al. 2019

Chemie Ingenieur Technik

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Digitalization is about data and how they are used. This has always been a key topic in applied thermodynamics. In the present work, the influence of the current wave of digitalization on thermodynamics is analyzed. Thermodynamic modeling and simulation is changing as large amounts of data of different nature and quality become easily available. The power and complexity of thermodynamic models and simulation techniques is rapidly increasing, and new routes become viable to link them to the data. Machine learning opens new perspectives, when it is suitably combined with classical thermodynamic theory. Illustrated by examples, different aspects of digitalization in thermodynamics are discussed: strengths and weaknesses as well as opportunities and threats.

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Section: A Preliminary Look Into Machine Learningmentioning

confidence: 99%

Digitalization in Thermodynamics

Forte

Jirasek

Bortz

et al. 2019

Chemie Ingenieur Technik

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“…For example, the paper (Ghavipour et al, 2013) discussed an application of a neural network for hydrate formation prediction. In order to achieve an appropriate understanding of the gas hydrate behavior during formation and destabilization, series of laboratory experiments with six different gas mixtures were carried out, and more than 130 hydrate equilibrium points in the pressure range of about 450-3000 psia were recorded.…”

Section: Evaluation Of Hydrocarbon Reservoir Characteristicsmentioning

confidence: 99%

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

Tadeusiewicz¹

2015

Archives of Mining Sciences

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The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article -which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for.Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice.The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication

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“…Up to now, various approaches have been presented to determine hydrate phase equilibrium pressure or temperature. These approaches can be mainly divided into five types: laboratory tests [6,7], graphical methods [8,9], empirical correlations [10][11][12][13][14][15][16][17][18], thermodynamic models [19][20][21][22][23][24][25][26], and artificial intelligence algorithms [27][28][29][30]. It is well known that the experimental determination of the hydrate formation phase equilibrium conditions is expensive and time-consuming, which limit the application of this 2 Mathematical Problems in Engineering method.…”

Section: Introductionmentioning

confidence: 99%

A Graphical Alternating Conditional Expectation to Predict Hydrate Phase Equilibrium Conditions for Sweet and Sour Natural Gases

Zhong

Yao

Wang³

et al. 2019

Mathematical Problems in Engineering

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Natural gas hydrate has been widely of concern due to its great potential in application to address problems including gas storage, transmission, separation techniques, and also as energy resource. Accurate prediction of hydrate formation phase equilibrium conditions is essential for the optimized design during natural gas production, processing, and transportation. In this study, a novel graphical alternating conditional expectation (ACE) algorithm was proposed to predict hydrate formation phase equilibrium conditions for sweet and sour natural gases. The accuracy and performance of the presented ACE model were evaluated using 1055 data points (688, 249, and 118 data points for sweet natural gas, CO2-CH4, and H2S-CO2-CH4 systems, respectively) collected from literature. Meanwhile, a comparative study was conducted between the ACE model and commonly used correlations, including thirteen models for sweet natural gases, three models for CO2-CH4 binary system, and seven thermodynamic models for H2S-CO2-CH4 ternary system. The obtained results indicated that the proposed ACE model produces the best results in prediction of hydrate phase equilibrium temperature for sweet natural gases and pressure for CO2-CH4 system with average absolute relative deviation (AARD) of 0.134% and 2.75%, respectively. The proposed quick and explicit ACE model also provides a better performance in prediction of hydrate phase equilibrium pressure for H2S-CO2-CH4 ternary systems with AARD=5.20% compared with seven thermodynamic methods considered in this work, except for CPA/Electrolyte/Chen–Guo combined model (AARD=4.45%).

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Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions

Cited by 70 publications

References 42 publications

Digitalization in Thermodynamics

Digitalization in Thermodynamics

Neural Networks In Mining Sciences – General Overview And Some Representative Examples

A Graphical Alternating Conditional Expectation to Predict Hydrate Phase Equilibrium Conditions for Sweet and Sour Natural Gases

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