Production & Supply Chain

NH3 cracking is gaining attention as a promising route for on-demand carbon-free H2 production particularly in off-grid or distributed energy applications. Nevertheless its practical implementation hinges on the development of catalysts not only highly active but also cost-effective and thermally efficient. Starting from the state-of-theart catalyst for NH3 decomposition (nickel-based) the most promising catalytic systems (ruthenium-based) are critically reviewed with a focus on the interplay between catalyst activation energy thermal duty and operating conditions. In view of discussing whether the implementation of noble-based catalysts can be practical or not a technical analysis of the cracking furnace with different Ru-based catalytic systems is presented referring to a decentralized application representative of compact yet industrially relevant units. The trade-off between technical and economic performance is quantified with the aim of offering design guidelines for developing scalable NH3 cracking.

With the promise of increased economics and improved safety advanced nuclear reactors such as the Natrium design by TerraPower and GE Hitachi can help many electricity energy markets transition to carbon-free power smoothly. Operating at higher temperatures the Natrium design based on a sodium fast reactor is suitable for co-located hydrogen production using high temperature steam electrolysis. This study models and analyzes three Natrium integrated energy systems with thermal energy storage and co-located hydrogen production. The first two configurations focus on improving thermal efficiency of the reheat Rankine cycle used in the Natrium design while the final configuration improves hydrogen production efficiency. Results indicate that coupling the Natrium system with hydrogen production can boost its energy efficiency by 1% and using low grade steam directly from the Natrium steam cycle for hydrogen production significantly reduces system complexity and increases the overall system efficiency by 3%.

The transition to carbon-neutral energy has renewed interest in hydrogen as a gas turbine fuel in the form of fuel blends with hydrocarbons. However the distinct fluid properties and chemical kinetics of hydrogen and hydrocarbon blends necessitate redeveloped combustor designs. While conventional combustor design and emissions estimation through computational fluid dynamics (CFD) is preferred it is computationally intensive and impractical for system-level simulations. To alleviate this a thermofluid network model was developed to predict the performance of a MGT combustor operating on pure and fuel blends of propane and hydrogen. It incorporates sub-component pressure losses and heat transfer and presents the first implementation of well-stirred and plug-flow reactors into Flownex SE. A 3-D CFD study of the combustor revealed that hydrogen addition improved combustion efficiency and reduced wall temperatures. However although it produces less CO2 it leads to 70 % more CO and 80 % more NO than for propane-only operation. Validated against the 3-D CFD data the network model predicted the combustor outlet total temperature and pressure within 0.55 % and 0.26 % respectively. The change in total pressure across subcomponents (<6 %) and the mass flow distribution showed similarly strong agreement. Major species mass fractions CO₂ and H₂O were predicted accurately. However by assuming that the temperature and composition are uniform within combustion zones zone and wall temperatures and pollutant predictions deviated considerably. NO was overpredicted by a factor of 8.2–10.7 and CO was overpredicted for propane-only but underpredicted for blended cases. The network model achieved this performance 420 times faster than CFD making it suitable for rapid design exploration.

In this work a plasma fluidized bed reactor has been studied as an electrified methane decomposition reactor for sustainable hydrogen production. A combined 3D rotating gliding arc/fluidized bed reactor assembly demonstrates a stable operation with a CH4/Ar mixture containing up to 8 vol% of CH4. The reactor provides a 97.2 % H2 selectivity at a methane conversion of 16.6 % and energy costs of 10.6 kJ L− 1 . This performance provides a new benchmark for electrified H2 production with a potential to utilise renewable electricity. In addition carbon materials are produced. The characterizations show difference in the morphology of the materials collected in different reactor zones.

Separation of H2 from CO2 is crucial in industry since they are the products of water gas shift reaction. In addition the demand for pure H2 as well as the potential reuse of CO2 as reactant are increasing as a consequence of the transition from fossil fuels to decarbonization processes. In this scenario this work aims to propose a possible solution to get simultaneously pure H2 and CO2 meeting the world’s requirements in terms of reduction of CO2 emissions and transition to cleaner energy. A simulated plant combining Pd-based and SAPO-34 membrane modules is able to provide pure H2 with a final recovery higher than 97%. In addition the entire CO2 fed to SAPO-34 unit is recovered in the permeate stream with a concentration of 97.7%. A cost analysis shows that feed gas gives a higher contribution than compression heat exchange and membranes (e.g. 70 20 3 and 7% respectively). Net profit and net present value are positive within a specific feed gas price range (e.g. net profit up to 0.10 and 0.155 $/Nm3 depending on the labour cost set) showing that the process can be cost-effective and profitable. H2 purification cost ranges between 2.6 and 7.8 $/kg.

In light of increased international efforts to combat climate change sustainable infrastructure is shifting toward green hydrogen produced through renewable-powered electrolysis. Still it is challenging to forecast the production of green hydrogen because environmental and system factors are variable both in time and space. We introduce a new system that utilizes a Spatio-Temporal Graph Convolutional Network (STGCN) and a novel algorithm the Ninja Optimization Algorithm (NiOA) to address this issue. Using the framework binary NiOA performs feature selection while continuous NiOA optimizes both the model architecture and the number of variables in the data simultaneously. It is clear from the research that forecasting results have shown significant improvement. The STGCN model achieved an R2 of 0.8769 and an MSE of 0.00375 whereas the STGCN with NiOA reached an R2 of 0.9815 and an MSE of only 7.48 × 10−8. Due to these improvements adaptive metaheuristics show even greater promise in delivering more accurate forecasting and reduced computational requirements for addressing critical environmental issues. The suggested strategy can be followed repeatedly providing a solid framework for the effective modeling of renewable energy systems and making green hydrogen projects more dependable.

Dating back to more than one century ago the photocatalysis process has demonstrated great promise in addressing environmental problems and the energy crisis. Nevertheless some single or binary composite materials cannot meet the requirements of large-scale implementations owing to their limited photocatalytic efficiencies. Since 2021 dual S-scheme heterojunctionbased nanocomposites have been undertaken as highly efficient photoactive materials for green energy production and environmental applications in order to overcome limitations faced in traditional photocatalysts. Herein state-of-the-art protocols designed for the synthesis of dual S-scheme heterojunctions are described. How the combined three semiconductors in dual S-scheme heterojunctions can benefit from one another to achieve high energy production and efficient oxidative removal of various pollutants is deeply explained. Photocatalytic reaction mechanisms by paying special attention to the creation of Fermi levels (Ef ) and charge carriers transfer between the three semiconductors in dual S-scheme heterojunctions are discussed. An entire section has been dedicated to some examples of preparation and applications of double S-scheme heterojunction-based nanocomposites for several photocatalytic applications such as soluble pollutants photodegradation bacteria disinfection artificial photosynthesis H2 generation H2O2 production CO2 reduction and ammonia synthesis. Lastly the current challenges of dual S-scheme heterojunctions are presented and future research directions are presented. To sum up dual S-scheme heterojunction nanocomposites are promising photocatalytic materials in the pursuit of sustainable energy production and environmental remediation. In the future dual S-scheme heterojunctions are highly recommended for photoreactors engineering instead of single or binary photocatalysts to drive forward photocatalysis processes for practical green energy production and environmental protection.

Hydrogen (H2) is an attractive fuel due to its high specific energy and zero direct carbon emissions. Membraneless electrolyzers (MEs) offer a lower-cost route to hydrogen production but their operation is complex and current efficiencies are modest. Although multi-objective optimization is widely used its heavy compute demands and weak integration with modern learning methods limit scalability and adaptability. We introduce a practical ML-guided way to design Tesla-valve (TV) membraneless electrolyzers by building diodicity (Di) directly into the geometry search. Using multilayer-perceptron surrogates trained on 150 high-fidelity simulations (R2 > 0.95) we link four design knobs (We Wc Wd Di) to pressure drop (Δp) and ohmic loss. A Genetic Algorithm (GA)-based multi-objective search over realistic ranges delivers 60 Pareto-optimal designs that make the Δp–ohmic trade-off explicit; TOPSIS then selects a balanced geometry (We = 1.708 mm Wc = 0.200 mm Wd = 1.012 mm Di = 1.618) with ohmic loss 4.069 V and Δp 6.169 Pa. The approach delivers faster lower-cost design maps and is supported by experimental checks pointing to an actionable route for scalable interpretable optimization of sustainable hydrogen production.

This work develops a replicable method for designing the optimal renewable hydrogen production facility applicable to any site and based on technical parameters and actual equipment costs. The solution is based on the integration of photovoltaic (PV) energy with lithium-ion battery storage systems which maximizes electrolyzer operating hours and significantly reduces the Levelized Cost of Hydrogen (LCOH). This study shows that increasing the inclination of the photovoltaic modules reduces the need for storage optimizing operation and extending the electrolyzer’s annual operating hours. In the Seville case study with current costs and efficiencies a minimum LCOH of €4.43/kg was achieved a value well below market benchmarks opening the door to a potentially competitive industrial business. The analysis confirms that electrolyzer efficiency—particularly specific power consumption—is the most important factor in reducing costs while technological progress in photovoltaics storage and equipment promises further reductions in the coming years. Overall the proposed methodology offers a practical and scalable tool to accelerate the economic viability of green hydrogen in a variety of contexts.

The rational design of Cu-based catalysts with tailored interfacial structures and electronic states remains challenging yet essential for advancing hydrogen production via methanol steam reforming (MSR). Here we developed a samarium-mediated strategy to construct a 30Sm-CuAl catalyst. The introduction of Sm promotes Cu dispersion and induces strong metal-support interactions resulting in the formation of Sm2O3- encapsulated Cu nanoparticles enriched with Cu+ -O-Sm interfaces. The optimized 30Sm-CuAl demonstrates exceptional MSR performance achieving a hydrogen production rate of 1126 mmol gcat− 1 h− 1 at 250◦C. Mechanistic studies revealed that the reaction follows the formate pathway in xSm-CuAl with formate accumulation identified as the primary reason for the deactivation of 30Sm-CuAl. Dynamic regeneration of 30SmCuAl through redox treatment restores its activity thereby enabling cyclic operation. These findings provide insights into rare-earth oxide regulation of Cu-based catalysts and lay the foundation for targeted resolution of formate intermediate accumulation to enhance MSR stability.