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Author

Hakim Djema

Institution: Sonatrach (Algeria)
OpenAlex ID: A5073488002
Modeling interfacial tension of the hydrogen-brine system using robust machine learning techniques: Implication for underground hydrogen storage
2022 · Cuthbert Shang Wui Ng, Hakim Djema, et al. · International Journal of Hydrogen Energy · 92
Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications
2024 · Menad Nait Amar, Hakim Djema, et al. · Energy & Fuels · 24
Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents
2025 · Saad Alatefi, Okorie Ekwe Agwu, et al. · Fuel · 23
Robust Ensemble Learning Models for Predicting Hydrogen Sulfide Solubility in Brine
2024 · Mohamed Riad Youcefi, Wei Wei, et al. · Energy & Fuels · 20
Advanced Smart Models for Predicting Interfacial Tension in Brine-Hydrogen/Cushion Gas Systems: Implication for Hydrogen Geo-Storage
2025 · Fahd Mohamad Alqahtani, Mohamed Riad Youcefi, et al. · Energy & Fuels · 15
Predicting the solubility of hydrogen in hydrocarbon fractions: Advanced data-driven machine learning approach and equation of state
2023 · Menad Nait Amar, Fahd Mohamad Alqahtani, et al. · Journal of the Taiwan Institute of Chemical Engineers · 15
Modeling wax disappearance temperature using robust white-box machine learning
2024 · Menad Nait Amar, Noureddine Zeraibi, et al. · Fuel · 13
A Reliable Model for Predicting Methane Solubility in Brine: Toward Effective Methane Emission Mitigation
2025 · Menad Nait Amar, Hakim Djema, et al. · Energy & Fuels · 12
An interpretable and explainable deep learning model for predicting hydrogen solubility in diverse chemicals
2024 · Mohamed Riad Youcefi, Fahd Mohamad Alqahtani, et al. · Chemical Engineering Science · 11
Rigorous Explainable Artificial Intelligence Models for Predicting CO2–Brine Interfacial Tension: Implications for CO2 Sequestration in Saline Aquifers
2025 · Menad Nait Amar, Mohamed Riad Youcefi, et al. · Energy & Fuels · 10
Improved explainable multi-gene genetic programming correlations for predicting carbon dioxide solubility in various brines
2025 · Mohamed Riad Youcefi, Fahd Mohamad Alqahtani, et al. · Desalination · 9
Robust ensemble learning frameworks for predicting minimum miscibility pressure in pure nitrogen and gas mixtures containing nitrogen–crude oil systems: Insights from explainable artificial intelligence
2025 · Menad Nait Amar, Noureddine Zeraibi, et al. · The Canadian Journal of Chemical Engineering · 8
A reliable model to predict mercury solubility in natural gas components: A robust machine learning framework and data assessment
2025 · Menad Nait Amar, Noureddine Zeraibi, et al. · Journal of Hazardous Materials · 8
Data‐driven framework for predicting the sorption capacity of carbon dioxide and methane in tight reservoirs
2024 · Fahd Mohamad Alqahtani, Mohamed Riad Youcefi, et al. · Greenhouse Gases Science and Technology · 8
An efficient super learner model for predicting wettability of the hydrogen/mineral/brine system: Implication for hydrogen geo-storage
2025 · Menad Nait Amar, Mohamed Riad Youcefi, et al. · International Journal of Hydrogen Energy · 7
Toward robust models for predicting carbon dioxide absorption by nanofluids
2022 · Menad Nait Amar, Hakim Djema, et al. · Greenhouse Gases Science and Technology · 6
Predicting Methane Hydrate Formation Temperature in the Presence of Diverse Brines Using Explainable Artificial Intelligence
2025 · Menad Nait Amar, Noureddine Zeraibi, et al. · Industrial & Engineering Chemistry Research · 5
Machine Learning–Based Estimation of Hydrogen Solubility in Brine for Underground Storage in Saline Aquifers
2025 · Fahd Mohamad Alqahtani, Menad Nait Amar, et al. · Greenhouse Gases Science and Technology · 4
Asphaltene Precipitation in Algerian Oilfields: Experimental Investigation and Field Mitigation
2014 · Mohamed Eutamene, Noureddine Mouloudj, et al. · Chemical engineering transactions · 4
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Robust group intelligent models for predicting hydrogen density and viscosity: Implication for hydrogen production, transportation, and storage
2025 · Fahd Mohamad Alqahtani, Mohamed Riad Youcefi, et al. · Journal of the Taiwan Institute of Chemical Engineers · 3
Predicting the viscosity of hydrogen – methane blends at high pressure for hydrogen transportation and geo-storage: Integration of robust white-box machine learning frameworks
2025 · Saad Alatefi, Mohamed Riad Youcefi, et al. · Digital Chemical Engineering · 1
Improved explicit data-driven frameworks for accurate prediction of carbon dioxide diffusivity in high viscosity hydrocarbon systems
2025 · Saad Alatefi, Okorie Ekwe Agwu, et al. · Unconventional Resources · 1
‘Corrigendum to “Modeling interfacial tension of the hydrogen-brine system using robust machine learning techniques: Implication for underground hydrogen storage” [Int J Hydrogen Energy 47(93), 1 December 2022, 39595–39605]’
2023 · Cuthbert Shang Wui Ng, Hakim Djema, et al. · International Journal of Hydrogen Energy · 1
Predicting viscosity of CO2–CH4 binary mixtures using robust white-box machine learning frameworks: implication for carbon capture, utilization, and storage
2025 · Saad Alatefi, Mohamed Riad Youcefi, et al. · Journal of Petroleum Exploration and Production Technology
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Developing a Robust Intelligent Model for Predicting Minimum Miscibility Pressure in Hydrocarbon Gas - Oil Systems: Implication for Miscible Gas Enhanced Oil Recovery Process
2025 · Fahd Mohamad Alqahtani, Menad Nait Amar, et al. · Middle East Oil, Gas and Geosciences Show (MEOS GEO)
Works count: 25
Citation count: 300
H-index: 10
I10-index: 10
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