A comprehensive review on the role of hydrogen in renewable energy systems

R Bhandari, N Adhikari - International Journal of Hydrogen Energy, 2024 - Elsevier
Hydrogen is emerging as a critical player in transitioning to sustainable and renewable
energy systems, serving roles in energy storage, grid balancing, and decarbonization. This …

Computational and Machine Learning Methods for CO2 Capture Using Metal–Organic Frameworks

H Mashhadimoslem, MA Abdol, P Karimi… - ACS …, 2024 - ACS Publications
Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …

Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques

M Rahimi, H Mashhadimoslem, HV Thanh, B Ranjbar… - Energy, 2023 - Elsevier
Pyrolysis, as a thermochemical conversion of biomass, is a superior biofuel production
procedure. The determining procedure for the optimal operational parameters, biomass …

Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects

HV Thanh, DS Dashtgoli, H Zhang, B Min - Energy, 2023 - Elsevier
Enhanced oil recovery (EOR) using CO 2 injection is promising with economic and
environmental benefits as an active climate-change mitigation approach. Nevertheless, the …

Data-driven machine learning models for the prediction of hydrogen solubility in aqueous systems of varying salinity: Implications for underground hydrogen storage

HV Thanh, H Zhang, Z Dai, T Zhang… - International Journal of …, 2024 - Elsevier
Hydrogen is a clean and sustainable renewable energy source with significant potential for
use in energy storage applications because of its high energy density. In particular …

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

HV Thanh, M Rahimi, S Tangparitkul… - International Journal of …, 2024 - Elsevier
This study introduces machine learning (ML) algorithms to predict hydrogen (H 2)
thermodynamic properties for geological storage, focusing on its mixtures with natural gas …

Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems

HV Thanh, H Zhang, M Rahimi, U Ashraf… - Journal of …, 2024 - Elsevier
This study investigates the application of machine learning techniques—specifically
convolutional neural networks, multilayer perceptrons and cascaded forward neural …

[HTML][HTML] Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions

AI Osman, M Nasr, AS Eltaweil, M Hosny… - International Journal of …, 2024 - Elsevier
The demand for clean and sustainable energy solutions is escalating as the global
population grows and economies develop. Fossil fuels, which currently dominate the energy …

Recent progress on advanced solid adsorbents for CO2 capture: from mechanism to machine learning

MS Khosrowshahi, AA Aghajari, M Rahimi… - Materials Today …, 2024 - Elsevier
Environmental pollution has become a serious issue due to the rapid development of
urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly …

Energy harvesting via thermoelectric generators for green hydrogen production: methods and techniques

SW Sharshir, A Joseph, MM Elsayad… - Process Safety and …, 2024 - Elsevier
The integration of presentectric generators (TEGs) into industrial processes and multi-
generation systems presents a promising solution for recovering low-grade waste heat. This …