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Leveraging machine learning in porous media
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …
The contribution of artificial intelligence to phase change materials in thermal energy storage: From prediction to optimization
Artificial Intelligence (AI) is leading the charge in revolutionizing research methodologies
within the field of latent heat storage (LHS) by using phase change materials (PCMs) and …
within the field of latent heat storage (LHS) by using phase change materials (PCMs) and …
Techno-economic comparison of power-to-ammonia and biomass-to-Ammonia plants using electrolyzer, CO2 capture and water-gas-shift membrane reactor
Conventional ammonia plants currently account for around 1.5% of global greenhouse gas
emissions. To effectively address this issue and reduce fossil fuel consumption, green and …
emissions. To effectively address this issue and reduce fossil fuel consumption, green and …
Organic catalysts for hydrogen production from noodle wastewater: Machine learning and deep learning-based analysis
Hydrogen production from the electrolysis of wastewater is an environmentally friendly and
highly efficient process. The performance of this process for instant noodle wastewater is …
highly efficient process. The performance of this process for instant noodle wastewater is …
Development of various machine learning and deep learning models to predict glycerol biorefining processes
Q Li, M Li, MR Safaei - International Journal of Hydrogen Energy, 2024 - Elsevier
Biorefining biological waste to produce eco-friendly fuels and by-products is essential in
transitioning from non-renewable energies. However, the analysis of the processes in the …
transitioning from non-renewable energies. However, the analysis of the processes in the …
Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and …
Background Reliable computational approaches to evaluate ceramic membrane
performance in wastewater treatment mark a transformative step towards optimizing …
performance in wastewater treatment mark a transformative step towards optimizing …
Machine learning-driven optimization for sustainable CO2-to-methanol conversion through catalytic hydrogenation
Growing concerns about greenhouse gas emissions have accelerated research into
converting CO 2 into valuable products like methanol. Catalytic hydrogenation, utilizing a …
converting CO 2 into valuable products like methanol. Catalytic hydrogenation, utilizing a …
Transfer study for efficient and accurate modeling of natural gas desulfurization process
S Wang, W Jiang, B Zheng, Q Liu, X Ji, G He - Journal of the Taiwan …, 2025 - Elsevier
Background Accurate modeling of the natural gas desulfurization process enables
enterprises to maintain stable production, optimize efficiency, improve product gas quality …
enterprises to maintain stable production, optimize efficiency, improve product gas quality …
Leveraging machine learning in porous media
B Ebrahimpour - Journal of Materials Chemistry A, 2024 - researchportal.port.ac.uk
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …
Solution gas-oil ratio estimation using histogram gradient boosting regression, machine learning, and mathematical models: a comparative analysis
H Yavari - Energy Sources, Part A: Recovery, Utilization, and …, 2024 - Taylor & Francis
Quantifying gas solubility in oil under reservoir conditions is essential in reservoir
engineering. Several laboratory techniques have been suggested to quantify it; …
engineering. Several laboratory techniques have been suggested to quantify it; …