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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Prediction of methane hydrate formation conditions in salt water using machine learning algorithms
Hongfei Xu, Zeren Jiao, Zhuoran Zhang, et al.
Computers & Chemical Engineering (2021) Vol. 151, pp. 107358-107358
Closed Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics
Xuping Yang, Chuan Yuan, Sirong He, et al.
Fuel (2022) Vol. 331, pp. 125718-125718
Closed Access | Times Cited: 71
Auto-MatRegressor: liberating machine learning alchemists
Yue Liu, Shuangyan Wang, Zhengwei Yang, et al.
Science Bulletin (2023) Vol. 68, Iss. 12, pp. 1259-1270
Closed Access | Times Cited: 26
Applications and interpretations of different machine learning models in runoff and sediment discharge simulations
Jindian Miao, Xiaoming Zhang, Guojun Zhang, et al.
CATENA (2024) Vol. 238, pp. 107848-107848
Closed Access | Times Cited: 9
In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications
Jian Liu, Wenjie Kuang, Jiaqiang Liu, et al.
Chemical Engineering Journal (2022) Vol. 438, pp. 135554-135554
Closed Access | Times Cited: 28
Three phase equilibria of the methane hydrate in NaCl solutions: A simulation study
S. Blazquez, Carlos Vega, M. M. Conde
Journal of Molecular Liquids (2023) Vol. 383, pp. 122031-122031
Open Access | Times Cited: 20
STUDY OF CONDITIONS FOR GAS HYDRATE AND ASPHALTENE-RESIN-PARAFFIN DEPOSITS FORMATION IN MECHANIZED OIL PRODUCTION
G. Y. Korobov, Andrey A. Vorontsov
Bulletin of the Tomsk Polytechnic University Geo Assets Engineering (2023) Vol. 334, Iss. 10, pp. 61-75
Open Access | Times Cited: 17
Gas storage via clathrate hydrates: Advances, challenges, and prospects
Xiao‐Bing Lan, Jun Chen, Dongdong Li, et al.
Gas Science and Engineering (2024) Vol. 129, pp. 205388-205388
Closed Access | Times Cited: 7
Predicting Phase Equilibria of CO2 Hydrate in Complex Systems Containing Salts and Organic Inhibitors for CO2 Storage: A Machine Learning Approach
Junghoon Mok, Woojin Go, Yongwon Seo
Energy & Fuels (2024) Vol. 38, Iss. 6, pp. 5322-5333
Closed Access | Times Cited: 6
Accelerated Design of Flame Retardant Polymeric Nanocomposites via Machine Learning Prediction
Zhuoran Zhang, Zeren Jiao, Ruiqing Shen, et al.
ACS Applied Engineering Materials (2022) Vol. 1, Iss. 1, pp. 596-605
Closed Access | Times Cited: 23
A reliable model to predict the methane-hydrate equilibrium: An updated database and machine learning approach
Mostafa Hosseini, Yuri Leonenko
Renewable and Sustainable Energy Reviews (2022) Vol. 173, pp. 113103-113103
Closed Access | Times Cited: 23
Research Advances in Machine Learning Techniques in Gas Hydrate Applications
Harrison Osei, Cornelius B. Bavoh, Bhajan Lal
ACS Omega (2024)
Open Access | Times Cited: 4
Robust and comprehensive predictive models for methane hydrate formation condition in the presence of brines using black-box and white-box intelligent techniques
Marzieh Rashnavadi Nezhad, M.A. Moradkhani, Behrouz Bayati, et al.
International Journal of Hydrogen Energy (2024) Vol. 77, pp. 612-624
Closed Access | Times Cited: 4
Development of machine learning model for the prediction of selectivity to light olefins from catalytic cracking of hydrocarbons
Iradat Hussain Mafat, Sumeet K. Sharma, Dadi Venkata Surya, et al.
Fuel (2024) Vol. 381, pp. 133682-133682
Closed Access | Times Cited: 4
Phase equilibria of gas hydrates: A review of experiments, modeling, and potential trends
Jin Cai, Han Tang, Teng-Hua Zhang, et al.
Renewable and Sustainable Energy Reviews (2025) Vol. 215, pp. 115612-115612
Closed Access
Study on the Factors Influencing CO2 Hydrate Sequestration in CH4 Hydrate-Bearing Reservoirs
Beù Ca Ùi Lôù, Shuxia Li, H. Sun, et al.
Energy (2025), pp. 135777-135777
Closed Access
Machine Learning to Predict Scale Inhibitor Squeeze Treatments Lifetime
Oscar Vazquez, Mahdi Kalantari Meybodi, Nathan Fowler, et al.
SPE International Conference on Oilfield Chemistry (2025)
Closed Access
Prediction of methane hydrate equilibrium in saline water solutions based on support vector machine and decision tree techniques
Chou‐Yi Hsu, Jorge Sebastián Buñay Guamán, Amit Ved, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
An Insight into the Prediction of Scale Precipitation in Harsh Conditions Using Different Machine Learning Algorithms
Reza Yousefzadeh, Amin Bemani, Alireza Kazemi, et al.
SPE Production & Operations (2022) Vol. 38, Iss. 02, pp. 286-304
Closed Access | Times Cited: 17
Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates
Elise Lunde Gjelsvik, Martin Fossen, Kristin Tøndel
Fuel (2022) Vol. 334, pp. 126696-126696
Open Access | Times Cited: 16
Machine learning for expediting next-generation of fire-retardant polymer composites
Pooya Jafari, Ruoran Zhang, Siqi Huo, et al.
Composites Communications (2023) Vol. 45, pp. 101806-101806
Open Access | Times Cited: 9
Forecasting short-term methane based on corrected numerical weather prediction outputs
Shuting Zhao, Lifeng Wu, Youzhen Xiang, et al.
Journal of Cleaner Production (2024) Vol. 460, pp. 142500-142500
Closed Access | Times Cited: 3
Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning
C.N. Vanitha, Sathishkumar Veerappampalayam Easwaramoorthy, Sai Krishna, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 8
Review of Heat Transfer Characteristics of Natural Gas Hydrate
Minghang Mao, Kefeng Yan, Xiao‐Sen Li, et al.
Energies (2024) Vol. 17, Iss. 3, pp. 717-717
Open Access | Times Cited: 2
Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate
Guozhong Wu, Frédéric Coulon, Jing‐Chun Feng, et al.
Journal of Molecular Liquids (2022) Vol. 370, pp. 120952-120952
Open Access | Times Cited: 11
Intelligent prediction of methane hydrate phase boundary conditions in ionic liquids using deep learning algorithms
Cornelius B. Bavoh, Chico Sambo, Ato Kwamena Quainoo, et al.
Petroleum Science and Technology (2023) Vol. 42, Iss. 20, pp. 2800-2819
Closed Access | Times Cited: 6
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