Gases, Volume 5, Issue 2 (June 2025) – 3 articles

Hydrogen is increasingly recognized as a key contributor to a low-carbon energy future, and machine learning (ML) is emerging as a valuable tool to optimize hydrogen production processes. This review presents a comprehensive analysis of ML applications across various hydrogen production pathways, including gray, blue, and green hydrogen, with additional insights into pink, turquoise, white, and black/brown hydrogen. A total of 51 peer-reviewed studies published between 2012 and 2025 were systematically reviewed. Among these, green hydrogen—particularly via water electrolysis and biomass gasification—received the most attention, reflecting its central role in decarbonization strategies. ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. Despite promising results, real-world deployment remains limited due to data sparsity, model integration challenges, and economic barriers. Nonetheless, this review identifies significant opportunities for ML to accelerate innovation across the hydrogen value chain. By highlighting trends, key methodologies, and current gaps, this study offers strategic guidance for future research and development in intelligent hydrogen systems aimed at achieving sustainable and cost-effective energy solutions. Full article

The LNG maritime industry started to anticipate offshore LNG production in tandem with increasing demand for FLNG platforms as offshore gas resources were developed further. The rapid expansion of FLNG deployment demands equipment and procedures that handle challenges associated with weight and space constraints. The chemical composition of LNG will result in slightly fewer CO₂ emissions. While not significant, another crucial aspect is that LNG predominantly comprises methane, which is acknowledged as a greenhouse gas and is more harmful than CO₂. This requires investigation into clean energy fuel supply for power generation systems, carbon emissions from the process, and thermodynamic analysis and optimisation. Focus on supplying fuel for FLNG power generation to reduce the essential management of boil-off fuel gas, which can be researched on the direct reforming method of hydrogen as a marine fuel gas to support the power generation system. The principal reason for choosing hydrogen over other energy sources is its exceptional energy-to-mass ratio (H/C ratio). The most effective method for hydrogen production is the methane reforming process, recognised for generating significant quantities of hydrogen. To optimise the small-scale plant with a carbon capture system (CCS) as integrated into the reforming process to produce blue hydrogen fuel with zero carbon emissions, this research selection focuses on two alternative processes: steam methane reforming (SMR) and autothermal reforming (ATR). Furthermore, the research article will contribute to other floating production platforms, such as FPSOs and FSRUs, and will be committed to clean energy policies that mandate the support of green alternatives in substitution of hydrocarbon fuel utilisation. Full article

The total quantity and energy delivered through a gas grid is calculated using simple formulæ that sum the increments measured at regular time intervals. These calculations are described in international standards (e.g., ISO 15112 and EN 1776) and guidelines (e.g., OIML R140). These guidelines recommend that the associated measurement uncertainty is evaluated assuming the measurement results to be mutually independent. This assumption leads to the underestimation of the measurement uncertainty. To address the growing concern among transmission and distribution system operators, the underlying assumptions of these uncertainty evaluations are revisited and reworked to be more adequate. The dependence of measurement results coming from, e.g., the same flow meter and gas chromatograph will be assessed for correlations, as well as other effects, such as the effect of the chosen mathematical approximation of the totalisation integral and fluctuations in the flow rate and gas quality. In this paper, an outline is given for improvements that can be implemented in the measurement models to render them more responsive to the error structure of the measurement data, temporal effects in these data, and the fluctuations in the gas quality and gas quantity. By impact assessment using a simple scenario involving the injection of (renewable) hydrogen into a natural gas grid, it is shown that these improvements lead to a substantive difference. This preliminary work demonstrates that correlations occur both in the instrumental measurement uncertainty and due to temporal effects in the gas grid. To obtain a fit-for-purpose uncertainty budget for custody transfer and grid balancing, it is key to enhance the current models and standards accordingly. Full article