Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks
Oil & Gas Science and Technology - Rev. IFP, 62 5 (2007) 701-706
Published online: 2007-10-30
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Predicting three phase (hydrate–liquid–vapour) equilibria of mixed hydrates in guest gas swapping: AI‐based approach versus physical modelling
Gauri Shankar Patel and Amiya K. JanaThe Canadian Journal of Chemical Engineering (2024)
https://doi.org/10.1002/cjce.25451
Research Advances in Machine Learning Techniques in Gas Hydrate Applications
Harrison Osei, Cornelius B. Bavoh and Bhajan LalACS Omega 9 (4) 4210 (2024)
https://doi.org/10.1021/acsomega.3c04825
Study of hydrate generation risk in gas-saturated oil-water emulsion system based on artificial intelligence
Xiaofang Lv, Shukai Chen, Wenguang Zeng, Yang Liu, Chuanshuo Wang, Shidong Zhou, Shangfei Song and Bohui ShiChemical Engineering Research and Design 204 137 (2024)
https://doi.org/10.1016/j.cherd.2024.02.034
Chemical Inhibitors in Gas Hydrate Formation: A Review of Modelling Approaches
Njabulo Mziwandile Zulu, Hamed Hashemi and Kaniki TumbaChemEngineering 8 (6) 124 (2024)
https://doi.org/10.3390/chemengineering8060124
Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines
Mustafa Karaköse and Özgün YücelEnergies 17 (21) 5306 (2024)
https://doi.org/10.3390/en17215306
Machine Learning and Flow Assurance in Oil and Gas Production
Ali Qasim and Bhajan LalMachine Learning and Flow Assurance in Oil and Gas Production 155 (2023)
https://doi.org/10.1007/978-3-031-24231-1_9
Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates
Elise Lunde Gjelsvik, Martin Fossen and Kristin TøndelFuel 334 126696 (2023)
https://doi.org/10.1016/j.fuel.2022.126696
Predicting Gas Hydrate Equilibria in Multicomponent Systems with a Machine Learning Approach
Qazi Nasir, Wameath S. Abdul Majeed and Humbul SulemanChemical Engineering & Technology 46 (9) 1854 (2023)
https://doi.org/10.1002/ceat.202300055
Machine Learning for Societal Improvement, Modernization, and Progress
Anupama Kumari, Mukund Madhaw and Vishnu S. PendyalaAdvances in Human and Social Aspects of Technology, Machine Learning for Societal Improvement, Modernization, and Progress 200 (2022)
https://doi.org/10.4018/978-1-6684-4045-2.ch010
International Conference on Artificial Intelligence for Smart Community
Wan Adli Nuaim Bin Wan Ahmad Nadzri, Omar Nashed, Bhajan Lal, Khor Siak Foo and Khalik Mohd SabilLecture Notes in Electrical Engineering, International Conference on Artificial Intelligence for Smart Community 758 859 (2022)
https://doi.org/10.1007/978-981-16-2183-3_81
Experimental investigation and ANN modelling on CO2 hydrate kinetics in multiphase pipeline systems
Nagoor Basha Shaik, Jai Krishna Sahith Sayani, Watit Benjapolakul, Widhyakorn Asdornwised and Surachai ChaitusaneyScientific Reports 12 (1) (2022)
https://doi.org/10.1038/s41598-022-17871-z
Applications of Nature-Inspired Computing in Renewable Energy Systems
Anupama Kumari, Mukund Madhaw, C. B. Majumder and Amit AroraAdvances in Environmental Engineering and Green Technologies, Applications of Nature-Inspired Computing in Renewable Energy Systems 95 (2022)
https://doi.org/10.4018/978-1-7998-8561-0.ch006
Integration of Physical Examination, Old and New Biomarkers, and Ultrasonography by Using Neural Networks for Pediatric Appendicitis
Fatma Akgül, Anıl Er, Emel Ulusoy, Aykut Çağlar, Hale Çitlenbik, Pembe Keskinoğlu, Ali R. Şişman, Osman Z. Karakuş, Erdener Özer, Murat Duman and Durgül YılmazPediatric Emergency Care 37 (12) e1075 (2021)
https://doi.org/10.1097/PEC.0000000000001904
Thermodynamic features-driven machine learning-based predictions of clathrate hydrate equilibria in the presence of electrolytes
Palash V. Acharya and Vaibhav BahadurFluid Phase Equilibria 530 112894 (2021)
https://doi.org/10.1016/j.fluid.2020.112894
A preliminary study of the gas hydrate stability zone in a gas hydrate potential region of China
Kun Xiao, Changchun Zou, Yaxin Yang, Hua Zhang, Hongxing Li and Zhen QinEnergy Science & Engineering 8 (4) 1080 (2020)
https://doi.org/10.1002/ese3.569
Gas hydrates in sustainable chemistry
Aliakbar Hassanpouryouzband, Edris Joonaki, Mehrdad Vasheghani Farahani, et al.Chemical Society Reviews 49 (15) 5225 (2020)
https://doi.org/10.1039/C8CS00989A
(2019)
https://doi.org/10.4043/29411-MS
Application of artificial intelligence techniques in the petroleum industry: a review
Hamid Rahmanifard and Tatyana PlaksinaArtificial Intelligence Review 52 (4) 2295 (2019)
https://doi.org/10.1007/s10462-018-9612-8
Prediction of natural gas hydrates formation using a combination of thermodynamic and neural network modeling
Noura Rebai, Ahmed Hadjadj, Abdelbaki Benmounah, Abdallah S. Berrouk and Salim M. BouallegJournal of Petroleum Science and Engineering 182 106270 (2019)
https://doi.org/10.1016/j.petrol.2019.106270
Toward a Robust, Universal Predictor of Gas Hydrate Equilibria by Means of a Deep Learning Regression
Michael K. B. Landgrebe and Diakanua NkaziACS Omega 4 (27) 22399 (2019)
https://doi.org/10.1021/acsomega.9b02961
Reliable modeling of constant volume depletion (CVD) behaviors in gas condensate reservoirs
Adel Najafi-Marghmaleki, Afshin Tatar, Ali Barati-Harooni, et al.Fuel 231 146 (2018)
https://doi.org/10.1016/j.fuel.2018.04.130
A new methodology for the production of furfural as a renewable energy source from bagasse in acidic aqueous media
M. Yazdizadeh, M. R. Jafari Nasr and A. K. SafekordiEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects 40 (2) 125 (2018)
https://doi.org/10.1080/15567036.2016.1248801
Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery
Mohammad Saber Karambeigi, Ali Haghighi Asl and Masoud NasiriOil & Gas Sciences and Technology – Revue d’IFP Energies nouvelles 73 4 (2018)
https://doi.org/10.2516/ogst/2017032
Prediction of sand production onset in petroleum reservoirs using a reliable classification approach
Farhad Gharagheizi, Amir H. Mohammadi, Milad Arabloo and Amin ShokrollahiPetroleum 3 (2) 280 (2017)
https://doi.org/10.1016/j.petlm.2016.02.001
Experimental Optimization and Modeling of Sodium Sulfide Production from H2S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network
Fatemeh Bashipour, Amir Rahimi, Saied Nouri Khorasani and Abbas NaderinikOil & Gas Science and Technology – Revue d’IFP Energies nouvelles 72 (2) 9 (2017)
https://doi.org/10.2516/ogst/2017004
Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature
Mohammad Mesbah, Ebrahim Soroush and Mashallah RezakazemiChinese Journal of Chemical Engineering 25 (9) 1238 (2017)
https://doi.org/10.1016/j.cjche.2016.09.007
A hybrid smart modeling approach for estimation of pure ionic liquids viscosity
Zohre Esmaeili-Jaghdan, Ahmad Shariati and Mohammad Reza Khosravi NikouJournal of Molecular Liquids 222 14 (2016)
https://doi.org/10.1016/j.molliq.2016.06.099
Evolving a robust modeling tool for prediction of natural gas hydrate formation conditions
Ebrahim Soroush, Mohammad Mesbah, Amin Shokrollahi, et al.Journal of Unconventional Oil and Gas Resources 12 45 (2015)
https://doi.org/10.1016/j.juogr.2015.09.002
Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach
Mahdi Kalantari Meybodi, Saeed Naseri, Amin Shokrollahi and Amin DaryasafarChemometrics and Intelligent Laboratory Systems 149 60 (2015)
https://doi.org/10.1016/j.chemolab.2015.10.001
Thermodynamic modeling of the KCl+formamide/glucose/proline+water ternary systems and activity coefficient prediction based on artificial neural network
Bahram Ghalami-Choobar and Behnam Ghalami ChoobarJournal of Molecular Liquids 207 136 (2015)
https://doi.org/10.1016/j.molliq.2015.03.028
Vapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm
Mohammad Mesbah, Ebrahim Soroush, Vahid Azari, et al.The Journal of Supercritical Fluids 97 256 (2015)
https://doi.org/10.1016/j.supflu.2014.12.011
Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach
Ramin Badrnezhad and Behrooz MirzaJournal of Industrial and Engineering Chemistry 20 (2) 528 (2014)
https://doi.org/10.1016/j.jiec.2013.05.012
A review on simulation models for exploration and exploitation of natural gas hydrate
Yonghong Sun, Xiaoshu Lü and Wei GuoArabian Journal of Geosciences 7 (6) 2199 (2014)
https://doi.org/10.1007/s12517-014-1294-1
Prediction of phase equilibrium of CO2/cyclic compound binary mixtures using a rigorous modeling approach
Mohammad Mesbah, Ebrahim Soroush, Amin Shokrollahi and Alireza BahadoriThe Journal of Supercritical Fluids 90 110 (2014)
https://doi.org/10.1016/j.supflu.2014.03.009
Prediction of Methane Uptake on Different Adsorbents in Adsorbed Natural Gas Technology Using a Rigorous Model
Ebrahim Soroush, Mohammad Mesbah, Amin Shokrollahi, Alireza Bahadori and Mohammad Hossein GhazanfariEnergy & Fuels 28 (10) 6299 (2014)
https://doi.org/10.1021/ef501550p
Experimental Measurements and Correlations of Excess Molar Enthalpies for the Binary and Ternary Mixtures of (Cyclohexane, 1,4-Dioxane and Piperidine) or (Cyclohexane, Morpholine and Piperidine) at 308.15 K and Atmospheric Pressure
Farid Brahim Belaribi, Ghénima Boukais-Belaribi, Atika Dahmoun, et al.Journal of Chemical & Engineering Data 59 (5) 1629 (2014)
https://doi.org/10.1021/je500088z
Experimental Study and Intelligent Modeling of Density of Crude Oils Diluted with Solvents
Mahdi Kalantari Meybodi and Jamshid MoghadasiJournal of Dispersion Science and Technology 141217111959003 (2014)
https://doi.org/10.1080/01932691.2014.941109
An assessment test for phase equilibrium data of water soluble and insoluble clathrate hydrate formers
Poorandokht Ilani-Kashkouli, Saeedeh Babaee, Farhad Gharagheizi, et al.Fluid Phase Equilibria 360 68 (2013)
https://doi.org/10.1016/j.fluid.2013.08.016
Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions
Mohammad Ghavipour, Mina Ghavipour, Minoo Chitsazan, Seyed Hessam Najibi and Saeed Shiry GhidaryChemical Engineering Research and Design 91 (2) 264 (2013)
https://doi.org/10.1016/j.cherd.2012.08.010
Intelligent model for prediction of CO2 – Reservoir oil minimum miscibility pressure
Amin Shokrollahi, Milad Arabloo, Farhad Gharagheizi and Amir H. MohammadiFuel 112 375 (2013)
https://doi.org/10.1016/j.fuel.2013.04.036
Robust Model for the Determination of Wax Deposition in Oil Systems
Arash Kamari, Abbas Khaksar-Manshad, Farhad Gharagheizi, Amir H. Mohammadi and Siavash AshooriIndustrial & Engineering Chemistry Research 52 (44) 15664 (2013)
https://doi.org/10.1021/ie402462q
Prediction of Equilibrium Conditions for Hydrate Formation in Binary Gaseous Systems Using Artificial Neural Networks
M. R. Moradi, K. Nazari , S. Alavi and M. MohaddesiEnergy Technology 1 (2-3) 171 (2013)
https://doi.org/10.1002/ente.201200056
Group contribution model for determination of molecular diffusivity of non-electrolyte organic compounds in air at ambient conditions
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonChemical Engineering Science 68 (1) 290 (2012)
https://doi.org/10.1016/j.ces.2011.09.035
Phase equilibrium modeling of clathrate hydrates of methane, carbon dioxide, nitrogen, and hydrogen+water soluble organic promoters using Support Vector Machine algorithm
Ali Eslamimanesh, Farhad Gharagheizi, Mohammad Illbeigi, et al.Fluid Phase Equilibria 316 34 (2012)
https://doi.org/10.1016/j.fluid.2011.11.029
Gas Hydrate Phase Equilibrium in Porous Media: Mathematical Modeling and Correlation
Amir H. Mohammadi, Ali Eslamimanesh, Dominique Richon, et al.Industrial & Engineering Chemistry Research 51 (2) 1062 (2012)
https://doi.org/10.1021/ie201904r
Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids
Ali Eslamimanesh, Farhad Gharagheizi, Amir H. Mohammadi and Dominique RichonChemical Engineering Science 66 (13) 3039 (2011)
https://doi.org/10.1016/j.ces.2011.03.016
Determination of Critical Properties and Acentric Factors of Pure Compounds Using the Artificial Neural Network Group Contribution Algorithm
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonJournal of Chemical & Engineering Data 56 (5) 2460 (2011)
https://doi.org/10.1021/je200019g
Handling a very large data set for determination of surface tension of chemical compounds using Quantitative Structure–Property Relationship strategy
Farhad Gharagheizi, Ali Eslamimanesh, Behnam Tirandazi, Amir H. Mohammadi and Dominique RichonChemical Engineering Science 66 (21) 4991 (2011)
https://doi.org/10.1016/j.ces.2011.06.052
Solubility Parameters of Nonelectrolyte Organic Compounds: Determination Using Quantitative Structure–Property Relationship Strategy
Farhad Gharagheizi, Ali Eslamimanesh, Farhad Farjood, Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 50 (19) 11382 (2011)
https://doi.org/10.1021/ie200962w
Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonJournal of Chemical & Engineering Data 56 (5) 1741 (2011)
https://doi.org/10.1021/je101190p
Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonJournal of Chemical & Engineering Data 56 (5) 2587 (2011)
https://doi.org/10.1021/je2001045
Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 50 (1) 221 (2011)
https://doi.org/10.1021/ie101545g
Determination of Parachor of Various Compounds Using an Artificial Neural Network−Group Contribution Method
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 50 (9) 5815 (2011)
https://doi.org/10.1021/ie102464t
Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network−Group Contribution Method
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonJournal of Chemical & Engineering Data 56 (4) 720 (2011)
https://doi.org/10.1021/je101061t
Phase Equilibrium Modeling of Structure H Clathrate Hydrates of Methane + Water “Insoluble” Hydrocarbon Promoter Using QSPR Molecular Approach
Ali Eslamimanesh, Farhad Gharagheizi, Amir H. Mohammadi and Dominique RichonJournal of Chemical & Engineering Data 56 (10) 3775 (2011)
https://doi.org/10.1021/je200444f
Group Contribution-Based Method for Determination of Solubility Parameter of Nonelectrolyte Organic Compounds
Farhad Gharagheizi, Ali Eslamimanesh, Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 50 (17) 10344 (2011)
https://doi.org/10.1021/ie201002e
400 (2011)
https://doi.org/10.1109/TMEE.2011.6199227
An accurate model for prediction of autoignition temperature of pure compounds
Farhad GharagheiziJournal of Hazardous Materials 189 (1-2) 211 (2011)
https://doi.org/10.1016/j.jhazmat.2011.02.014
Extension of an Artificial Neural Network Algorithm for Estimating Sulfur Content of Sour Gases at Elevated Temperatures and Pressures
Mehdi Mehrpooya, Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 49 (1) 439 (2010)
https://doi.org/10.1021/ie900399b
Experimental Measurements and Correlations of Excess Molar Enthalpies for the Binary and Ternary Mixtures of (Cyclohexane, Tetrahydropyran, and Morpholine) or (Cyclohexane, 1,4-Dioxane, and Morpholine) at 308.15 K and Atmospheric Pressure
Farid B. Belaribi, Atika Dahmoun, Aomar Dahmani, et al.Journal of Chemical & Engineering Data 55 (8) 2833 (2010)
https://doi.org/10.1021/je9010097
Excess Molar Enthalpies for the Binary and Ternary Mixtures of Cyclohexane, Tetrahydropyran, and Piperidine at 308.15 K and Atmospheric Pressure: Experimental Measurements and Correlations
Farid B. Belaribi, Ghénima Boukais-Belaribi, Amir H. Mohammadi and Dominique RichonJournal of Chemical & Engineering Data 55 (1) 303 (2010)
https://doi.org/10.1021/je900347f
Hydrate phase equilibria for hydrogen+water and hydrogen+tetrahydrofuran+water systems: Predictions of dissociation conditions using an artificial neural network algorithm
Amir H. Mohammadi and Dominique RichonChemical Engineering Science 65 (10) 3352 (2010)
https://doi.org/10.1016/j.ces.2010.02.015
Excess Molar Enthalpies for the Binary and Ternary Mixtures of Cyclohexane, Tetrahydropyran, and 1,4-Dioxane at 308.15 K and Atmospheric Pressure: Experimental Measurements and Correlations
Ghénima Boukais-Belaribi, Amir H. Mohammadi, Farid B. Belaribi and Dominique RichonJournal of Chemical & Engineering Data 54 (9) 2513 (2009)
https://doi.org/10.1021/je900077g
Development of Predictive Techniques for Estimating Liquid Water‐Hydrate Equilibrium of Water‐Hydrocarbon System
Amir H. Mohammadi, Dominique Richon and William AcreeJournal of Thermodynamics 2009 (1) (2009)
https://doi.org/10.1155/2009/120481
Estimating Sulfur Content of Hydrogen Sulfide at Elevated Temperatures and Pressures Using an Artificial Neural Network Algorithm
Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 47 (21) 8499 (2008)
https://doi.org/10.1021/ie8004463
A Mathematical Model Based on Artificial Neural Network Technique for Estimating Liquid Water−Hydrate Equilibrium of Water−Hydrocarbon System
Amir H. Mohammadi and Dominique RichonIndustrial & Engineering Chemistry Research 47 (14) 4966 (2008)
https://doi.org/10.1021/ie800235m