France

The industry is advancing decarbonization in hydrogen production through water splitting technologies like water electrolysis which involves the hydrogen evolution reaction (HER) at the cathode and oxygen evolution reaction (OER) at the anode. Alkaline water electrolyser (AWE) is particularly suited for industrial applications due to its use of cost-effective and abundant nickel-based electrodes. However AWE faces significant challenges including energy losses from gas bubble coverage and poor detachment known as “bubble resistance”. Recent research highlights the role of power ultrasound in mitigating these issues by leveraging Bjerknes forces. These forces facilitate the ejection of larger bubbles and the coalescence of smaller ones enhancing gas removal. Additionally ultrasound improves mass transfer from the electrolyte to electrodes and boosts heat transfer via acoustic streaming and acoustic cavitation which the latter also enhances electrocatalytic properties for both HER and OER. However employing ultrasonic fields presents both benefits and challenges for scaling the system.

Green hydrogen a zero-carbon emission fuel has become a real competitor to transform the energy market thanks to improvements in the electrolysis process decreased costs and the presence of renewable energy resources. Energy industries have shown considerable progress in hydrogen production due to the incorporation of artificial intelligence (AI) knowledge through algorithms AI-based models and data programs. These techniques can greatly enhance the production storage and transportation of hydrogen fuel. The main goal of this article is to demonstrate the recent technological advancements and the influence of various AI techniques algorithms and models on the hydrogen energy sector along with this further examination of the energy policies of countries like China Japan India and South Korea. The key challenges related to these energy policies are addressed through standardized datasets AI models and optimized environmental conditions. This paper serves as a valuable resource for researchers engineers and practitioners interested in applying cutting-edge technologies to enhance hydrogen safety systems. AI-based models contribute to the overall shift towards a sustainable energy future by enhancing efficiency reducing costs and facilitating hydrogen energy commerce for Asian countries. This study accelerates the global investigation and tremendous applications of sophisticated machine-learning methodologies for producing renewable green hydrogen.

Developing new injection systems and combustion chambers for hydrogen is a central topic for the new generation of engines. In this effort simulations take a central role but methods developed for conventional hydrocarbons (methane kerosene) must be revisited for hydrogen. Validation then becomes an essential part and clean well documented experiments are needed to guaranty that computational fluid dynamics solvers are as predictive and accurate as expected. In this framework the HYLON case is a swirled hydrogen/air burner used by multiple groups worldwide to validate simulation methods for hydrogen combustion in configurations close to gas turbine burners with experimental data available through the TNF web site. The present study compares recent Raman spectroscopy and Particle Image Velocimetry measurements and Large Eddy Simulations (LES). The LES results are evaluated against a dataset comprising mean and RMS measurements of H2 N2 O2 H2O molar fractions temperature and velocity fields offering new insights into flame stabilization mechanisms. The simulations incorporate conjugate heat transfer to predict the combustor wall temperatures and are conducted for two atmospheric-pressure operating conditions each representing distinct combustion regimes diffusion and partially premixed. Novelty and significance statement Data on confined hydrogen flames in burner similar as industrial ones are limited. This work aims to fill this gap by performing multiple and simultaneous diagnostics on the swirled hydrogen-air flame called HYLON. For the first time in such a swirled configuration mean and RMS fields of temperature main species and velocities are compared to LES allowing new insight into the potential and limits of the models as well as the physics of these flames. These experimental results will be made available on TNF as over 30 research groups worldwide have expressed interest in using them.

Integrating microbial activity into underground hydrogen storage models is crucial for simulating longterm reservoir behavior. In this work we present a coupled framework that incorporates bio-geochemical reactions and compositional flow models within the Matlab Reservoir Simulation Toolbox (MRST). Microbial growth and decay are modeled using a double Monod formulation with populations influenced by hydrogen and carbon dioxide availability. First a refined Equation of State (EoS) is employed to accurately capture hydrogen dissolution thereby improving phase behavior and modeling of microbial activity. The model is then discretized using a cell-centered finite-volume method with implicit Euler time discretization. A fully coupled fully implicit strategy is considered. Our implementation builds upon MRST’s compositional module by incorporating the Søreide–Whitson EoS microbial reaction kinetics and specific effects such as bio-clogging and molecular diffusion. Through a series of 1D 2D and 3D simulations we analyze the effects of microbialinduced bio-geochemical transformations on underground hydrogen storage in porous media.These results highlight that accounting for bio-geochemical effects can substantially impact hydrogen loss purity and overall storage performance.

With the transition to a fully renewable energy grid arises the need for a green source of stability and baseload support which classical renewable generation such as wind and solar cannot offer due to their uncertain and highly-variable generation. In this paper we study whether green hydrogen can close this gap as a source of supplemental generation and storage. We design a two-stage mixed-integer stochastic optimization model that accounts for uncertainties in renewable generation. Our model considers the investment in renewable plants and hydrogen storage as well as the operational decisions for running the hydrogen storage systems. For the data considered we observe that a fully renewable network driven by green hydrogen has a greater potential to succeed when wind generation is high. In fact the main investment priorities revealed by the model are in wind generation and in liquid hydrogen storage. This long-term storage is more valuable for taking full advantage of hydrogen than shorter-term intraday hydrogen gas storage. In addition we note that the main driver for the potential and profitability of green hydrogen lies in the electricity demand and prices as opposed to those for gas. Our model and the investment solutions proposed are robust with respect to changes in the investment costs. All in all our results show that there is potential for green hydrogen as a source of baseload support in the transition to a fully renewable-powered energy grid.

Hydrogen is increasingly recognized as a key energy vector for achieving deep decarbonization across urban and industrial sectors. Supporting global efforts to reduce greenhouse gas (GHG) emissions and achieve the Sustainable Development Goals (SDGs) it is essential to understand the multi-sectoral role of the hydrogen value chain spanning production storage and end-use applications with particular emphasis on smart city systems and industrial processes. Green hydrogen production technologies including alkaline water electrolysis (AWE) proton exchange membrane (PEM) electrolysis anion exchange membrane (AEM) electrolysis and solid oxide electrolysis cells (SOECs) are evaluated in terms of efficiency scalability and integration potential. Storage pathways are examined across physical storage (compressed gas cryo-compressed and liquid hydrogen) material-based storage (solid-state absorption in metal hydrides and chemical carriers such as LOHCs and ammonia) and geological storage (salt caverns depleted gas reservoirs and deep saline aquifers) highlighting their suitability for urban and industrial contexts. In the smart city domain hydrogen is analyzed as an enabler of zero-emission transportation low-carbon residential and commercial heating and renewable-integrated smart grids with long-duration storage capabilities. System-level studies demonstrate that coordinated integration of these applications can deliver higher overall energy efficiency deeper reductions in life-cycle GHG emissions and improved resilience of urban energy systems compared with sector-specific approaches. Policy frameworks safety standards and digitalization strategies are reviewed to illustrate how hydrogen infrastructure can be embedded into interconnected urban energy systems. Furthermore industrial applications focus on hydrogen’s potential to decarbonize energy-intensive processes and enable sector coupling between electricity heat and manufacturing. The environmental implications of hydrogen deployment are also considered including resource efficiency life-cycle emissions and ecosystem impacts. In contrast to reviews addressing isolated aspects of hydrogen technologies this study synthesizes technological infrastructural and policy dimensions integrating insights from over 400 studies to highlight the multifaceted role of hydrogen in sustainable urban development and industrial decarbonization and the added benefits achievable through coordinated cross-sector deployment strategies.

Previous studies have demonstrated the potential benefit of a future hydrogen economy in terms of reducing CO2 emissions. The hydrogen leakage rate and the green hydrogen fraction in the mix were identified as key factors in maximising the climate benefit of this energy transition. This study highlights the importance of blue hydrogen production hypotheses for a climate-beneficial transition to a hydrogen economy. The benefits are substantial when blue hydrogen is produced properly using an efficient CO₂ sequestration hydrogen production plant and minimizing the rate of upstream CH₄ leakage. The rate of hydrogen leakage remains an important parameter to consider throughout the entire value chain. Based on various scenarios of the development of a 21st century hydrogen economy we estimate significant CO₂ emission reductions of 266–418 GtCO₂eq (up to 395–675 GtCO2eq in the case of a “high hydrogen demand” scenario) between 2030 and 2100. This cumulative reduction in CO₂ emissions translates into a reduction in global warming of 0.12–0.19 °C (0.18–0.30 °C for a “high hydrogen demand”) by the end of the century.