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OpenAlex Citation Counts

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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:

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state
Mohammad-Reza Mohammadi, Fahimeh Hadavimoghaddam, Saeid Atashrouz, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 22

Showing 22 citing articles:

Evolution of porosity in kerogen type I during hydrous and anhydrous pyrolysis: Experimental study, mechanistic understanding, and model development
Bo Liu, Mohammad-Reza Mohammadi, Zhongliang Ma, et al.
Fuel (2023) Vol. 338, pp. 127149-127149
Closed Access | Times Cited: 76
Modeling hydrogen solubility in water: Comparison of adaptive boosting support vector regression, gene expression programming, and cubic equations of state
Qichao Lv, Tianhao Zhou, Haimin Zheng, et al.
International Journal of Hydrogen Energy (2024) Vol. 57, pp. 637-650
Closed Access | Times Cited: 16
A Reliable Model for Predicting Methane Solubility in Brine: Toward Effective Methane Emission Mitigation
Menad Nait Amar, Hakim Djema, Fahd Mohamad Alqahtani, et al.
Energy & Fuels (2025)
Closed Access | Times Cited: 1
Exploring the performance of machine learning models to predict carbon monoxide solubility in underground pure/saline water
Behzad Vaferi, Mohsen Dehbashi, Ali Hosin Alibak, et al.
Marine and Petroleum Geology (2024) Vol. 162, pp. 106742-106742
Closed Access | Times Cited: 7
Toward Estimating CO2 Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO2 Dissolution Trapping in Saline Aquifers
Xinyuan Zou, Yingting Zhu, Jing Lv, et al.
ACS Omega (2024)
Open Access | Times Cited: 6
On the Evaluation of Coal Strength Alteration Induced by CO2 Injection Using Advanced Black-Box and White-Box Machine Learning Algorithms
Qichao Lv, Haimin Zheng, Xiaochen Li, et al.
SPE Journal (2024) Vol. 29, Iss. 03, pp. 1672-1691
Closed Access | Times Cited: 4
Application of group method of data handling and gene expression programming to modeling molecular diffusivity of CO2 in heavy crudes
Qichao Lv, Tianhao Zhou, Haimin Zheng, et al.
Geoenergy Science and Engineering (2024) Vol. 237, pp. 212789-212789
Closed Access | Times Cited: 3
Modeling of ionic liquids viscosity via advanced white-box machine learning
Sajad Kiani, Fahimeh Hadavimoghaddam, Saeid Atashrouz, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
The artificial neural network-based two-phase equilibrium calculation framework for fast compositional reservoir simulation of CO2 EOR
Liang-nan Li, Hongbin Jing, Jianqiao Liu, et al.
Fluid Phase Equilibria (2024) Vol. 585, pp. 114151-114151
Closed Access | Times Cited: 3
Predictive modeling of CO2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals
Mohammadreza Mohammadi, Aydin Larestani, Mahin Schaffie, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Applying feature selection and machine learning techniques to estimate the biomass higher heating value
Seyyed Amirreza Abdollahi, Seyyed Faramarz Ranjbar, Dorsa Razeghi Jahromi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 8
Modeling hydrogen solubility in alcohols using group method of data handling and genetic programming
Fahimeh Hadavimoghaddam, Mohammad-Reza Mohammadi, Saeid Atashrouz, et al.
International Journal of Hydrogen Energy (2022) Vol. 48, Iss. 7, pp. 2689-2704
Closed Access | Times Cited: 8
Machine learning prediction of methane, ethane, and propane solubility in pure water and electrolyte solutions: Implications for stray gas migration modeling
Ghazal Kooti, Reza Taherdangkoo, Chaofan Chen, et al.
Acta Geochimica (2024)
Open Access | Times Cited: 1
Modeling liquid rate through wellhead chokes using machine learning techniques
Mohammad-Saber Dabiri, Fahimeh Hadavimoghaddam, Sefatallah Ashoorian, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Modeling CO2 loading capacity of triethanolamine (TEA) aqueous solutions via a deep learning approach
Fahimeh Hadavimoghaddam, Behnam Amiri-Ramsheh, Saeid Atashrouz, et al.
Energy (2024), pp. 133476-133476
Open Access | Times Cited: 1
Random forest for the detection of unauthorized consumption in water supply systems: a case study in Southern Brazil
Marcos Roberto Stramari, Andreza Kalbusch, Elisa Henning
Urban Water Journal (2022) Vol. 20, Iss. 3, pp. 394-404
Closed Access | Times Cited: 3
Toward Predicting Interfacial Tension of Impure and Pure CO2-Brine Systems Using Robust Correlative Approaches
Chen Wei-yu, Lin Sun, Jiyong Zhou, et al.
ACS Omega (2024)
Open Access
Study of Methane Solubility Calculation Based on Modified Henry’s Law and BP Neural Network
Ying Zhao, Jiahao Yu, Hailei Shi, et al.
Processes (2024) Vol. 12, Iss. 6, pp. 1091-1091
Open Access
Predictive modeling of CO2 capture efficiency using piperazine solutions: a comparative study of white-box algorithms
Fahimeh Hadavimoghaddam, Jianguang Wei, Alexei Rozhenko, et al.
Deleted Journal (2024) Vol. 6, Iss. 11
Open Access
Solubilities and Self-Diffusion Coefficients of Light n-Alkanes in NaCl Solutions at the Temperature Range (278.15–308.15) K and Pressure Range (1–300) bar and Thermodynamics Properties of Their Corresponding Hydrates at (150–290) K and (1–7000) bar
Bin Fang, Parsa Habibi, Othonas A. Moultos, et al.
Journal of Chemical & Engineering Data (2023) Vol. 69, Iss. 10, pp. 3330-3346
Open Access | Times Cited: 1
Toward smart correlations for predicting in-situ stress: Application to evaluating subsurface energy structures
Fahimeh Hadavimoghaddam, Aboozar Garavand, Alexei Rozhenko, et al.
Geoenergy Science and Engineering (2023) Vol. 231, pp. 212292-212292
Closed Access | Times Cited: 1
AdaBoost-SVR Model-Based Transmitted Microwave Sensing in Wheat Moisture Prediction
Dong Dai, Xu Mao, Yehong Liu, et al.
IEEE Sensors Journal (2023) Vol. 24, Iss. 3, pp. 3988-3998
Closed Access | Times Cited: 1
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