Publications

In response to environmental concerns related to carbon and nitrogen emissions hydrogen-powered aircraft (HPA) are poised for significant development over the coming decades driven by advances in power electronics technology. However HPA systems present complex multi-domain challenges encompassing electrical hydraulic mechanical and chemical disciplines necessitating efficient modeling and robust validation platforms. This paper introduces a physics-informed machine learning (PIML) approach for multi-domain HPA system modeling enhanced by hardware accelerated parallel hardware emulation to construct a real-time digital twin. It delves into the physical analysis of various HPA subsystems whose equations form the basis for both traditional numerical solution methods like Euler’s and Runge-Kutta methods (RKM) as well as the physics-informed neural networks (PINN) components developed herein. By comparing physics-feature neural networks (PFNN) and PINN with conventional neural network strategies this paper elucidates their advantages and limitations in practical applications. The final implementation on the Xilinx® UltraScale+™ VCU128 FPGA platform showcases the PIML method’s high efficiency accuracy data independence and adherence to established physical laws demonstrating its potential for advancing real-time multi-domain HPA emulation.

The growing challenges of climate change the depletion of fossil fuel reserves and the urgent need for carbon-neutral energy solutions have intensified the focus on renewable energy. In this perspective the generation of green hydrogen from renewable sources like biogas/landfill gas (LFG) offers an intriguing option providing the dual benefits of a sustainable hydrogen supply and enhanced waste management through energy innovation and valorization. Thus this review explores the production of green hydrogen from biogas/LFG through four conventional reforming processes specifically dry methane reforming (DMR) steam methane reforming (SMR) partial oxidation reforming (POX) and autothermal reforming (ATR) focusing on their mechanisms operating parameters and the role of catalysts in hydrogen production. This review further delves into both the environmental aspects specifically GWP (CO2 eq·kg−1 H2) emissions and the economic aspects of these processes examining their efficiency and impact. Additionally this review also explores hydrogen purification in biogas/LFG reforming and its integration into the CO2 capture utilization and storage roadmap for net-negative emissions. Lastly this review highlights future research directions focusing on improving SMR and DMR biogas/LFG reforming technologies through simulation and modeling to enhance hydrogen production efficiency thereby advancing understanding and informing future research and policy initiatives for sustainable energy solutions.

Hydrogen energy is a cornerstone of the future climate-neutral economy. Yet as undetected leaks easily generate dangerous atmospheres sensing systems must timely detect accumulated hydrogen to prevent ignitions and explosions. Eye-readable sensors (ERSs) displaying intuitive readouts promise to guarantee safe use and universal access to hydrogen-based technology. This review highlights the impact of reversible ERSs in hydrogen moni toring to contextualize their current and potential applicability. First sensing mechanisms for gasochromic tungsten oxide films and switchable metal hydrides are critically overviewed. Then pivotal strategies targeting real-time monitorization indoors and permanent leak recording outdoors are presented along with standard hydrogen leakage scenarios elucidating opportunities for ERSs. Finally important challenges and desirable userfriendly concepts are discussed with the purpose of narrowing the gap between this class of sensors and the forthcoming hydrogen society.

This study addresses cost-optimal sizing and energy management of a grid-integrated solar photovoltaic wind turbine hybrid renewable energy system integrated with electrolyzer and hydrogen storage tank to simultaneously meet electricity and hydrogen demands considering the case study of Dijon France. Mixed Integer Linear Programming optimization problem is formulated to evaluate two objective case scenarios: single objective and multi-objective minimizing total annual costs and grid carbon emission footprint. The study incorporates various technical economic and environmental indicators focusing on the impact of sensitivity lying on various grid electricity purchase rates within the French electricity market prices. The results highlight that rising grid prices drive increased integration of renewable sources while lower prices favor ultimate grid dependency. Constant hydrogen demand necessitates the installation of two electrolyzers. Notably grid electricity prices above 60 e/MWh result increase in the size of the hydrogen tank and electrolyzer operation to prevent renewable energy losses. Grid prices above 140 e/MWh depict 70% of electrical and 80% of electrolyzer demand provided by the renewable generation resulting in a carbon emission below 0.0416 Mt of CO2 and 0.643 kgCO2 /kgH2 . Conversely grid prices below 20 e/MWh lead ultimately to 100% grid dependency with a higher carbon emission of approximately 0.14 Mt of CO2 and 4.13 kgCO2 /kgH2 reducing the total annual cost to 41.63 Million e. Increase in grid prices from 20e/MWh to 180 e/MWh resulted in increase of hydrogen specific costs from 1.23 to 3.58 e/kgH2 . Finally the Pareto front diagram is employed to illustrate the trade-off between total annual cost and carbon emission due to grid imports aiding in informed decision-making.

Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion utilization and storage technologies with distributed wind and photovoltaic power generation techniques it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form of microgrids. However the diverse operational mechanisms varying capacities and distinct forms of distributed energy sources within hydrogen-coupled microgrids complicate their operational conditions making fine-tuned scheduling management and economic operation challenging. In response this paper proposes an energy management method for hydrogen-coupled microgrids based on the deep deterministic policy gradient (DDPG). This method leverages predictive information on photovoltaic power generation load power and other factors to simulate energy management strategies for hydrogen-coupled microgrids using deep neural networks and obtains the optimal strategy through reinforcement learning ultimately achieving optimized operation of hydrogen-coupled microgrids under complex conditions and uncertainties. The paper includes analysis using typical case studies and compares the optimization effects of the deep deterministic policy gradient and deep Q networks validating the effectiveness and robustness of the proposed method.

The surge in interest surrounding renewable energy stems from concerns regarding pollution and the finite supply ofnonrenewable resources. Solar PV and wind hybrid renewable energy systems (HRES) are increasingly recognized as practicaland cost-effective solutions particularly in remote areas. However the intermittent nature of solar and wind power presents achallenge. To address this incorporating a hydrogen source into the system has been proposed. This study focuses onmodelling and sizing a hybrid energy system tailored for remote areas accommodating both home and electric vehicle loads.The simulation is conducted for Siliguri West Bengal India with the goal of optimizing productivity minimizing expensesand considering economic factors using HOMER Pro software. The integration of green hydrogen-based power generationwith photovoltaic and wind HRES emerges as an effective solution. Solar power in particular showcases promisingopportunities for the electrolysis process and HRES systems. The presented work facilitates the modelling of a green hydrogen-based green energy system taking into account capacity cost and emission constraints. Various case studies are conducted toenhance system efficiency and reduce the costs of energy (COE). In this paper three cases of grid-connected and three cases ofoff-grid or grid-disconnected systems are considered for highlighting the benefits of hydrogen energy incorporation in bothtypes of systems. This research contributes to sustainable energy solutions advancing a greener and more efficient energylandscape especially in addressing the recent development in load combinations of home and electric vehicle loads in bothgrid-connected as well as grid-disconnected system.

The use of compression ignition engines (CIEs) is associated with increased greenhouse gas emissions. It is therefore necessary to research sustainable solutions and reduce the negative environmental impact of these engines. A widely studied alternative is the use of H2 in dual-fuel mode. This review has been developed to include the most recent studies on the subject to collect and compare their main conclusions on performance and emissions. Moreover this study includes most relevant emission control strategies that have not been extensively analyzed in other reviews on the subject. The main conclusion drawn from the literature is the negative effect of the addition of H2 on NOx. This is due to the increase in temperature during combustion which increases NOx formation as the thermal mechanism predominates. Therefore to reduce these emissions three strategies have been studied namely exhaust gas recirculation (EGR) water injection (WI) and compression ratio (CR) reduction. The effect of these techniques on NOx reduction together with their effect on other analyzed performance parameters have been deeply analyzed. The studies reviewed in this work indicate that hydrogen is an alternative fuel for CIEs when used in conjunction with techniques that have proven to be effective in reducing NOx.

Hydrogen has great growth potential due to its green carbon-neutral nature but public acceptance is low due to negative perceptions of the dangers associated with hydrogen energy. Safety concerns particularly related to its flammability and explosiveness are an obstacle to hydrogen energy policy. In South Korea recent hydrogen-related explosions have exacerbated these concerns undermining public confidence. This study developed public relations (PR) strategies to manage risk perception and promote hydrogen energy acceptance by analyzing the opinions of government officials and experts using SWOT factors the TOWS matrix and the analytic hierarchy process. The findings highlight the importance of addressing weaknesses and threats in PR efforts. Key weaknesses include Korea’s technological lag and the low localization of core hydrogen technologies both of which hinder competitiveness and negatively impact public perception of hydrogen energy. Notable threats include deteriorating energy dependency and expanding global carbon regulations. This information can be used to influence attitudes and foster public acceptance of hydrogen energy policies. Emphasizing weaknesses and threats may result in more effective PR strategies even if they do not directly address the primary concerns of scientific experts. The persuasive insights identified in this study can support future policy communication and PR strategies.

Although hydrogen is increasingly seen as a crucial energy carrier in future zero-carbon energy system a profitable exploitation of electrolysers requires still high amounts of subsidies. To analyze the profitability of electrolysers attention has to be paid not only to the costs but also to the interaction between electricity and hydrogen markets. Using a model of internationally integrated electricity and hydrogen markets this paper analyses the profitability of electrolysers plants in various future market circumstances. We find that in particular the future supply of renewable electricity the demand for electricity as well as the prices of natural gas and carbon strongly affect the profitability of electrolysis. In order to make massive investments in electrolysers profitable with significantly lower subsidy requirements the amount of renewable electricity generation needs to grow strongly and the carbon prices should be higher while the demand for electricity should not increase accordingly. This research underscores the critical role of market conditions in shaping the viability of hydrogen electrolysis providing valuable insights for policymakers and stakeholders in the transition to a zero-carbon energy system.

The formation of green hydrogen from water electrolysis is one of the supreme methodologies for understanding the well-organized consumption of sporadic renewable energies and the carbon-free future. Among the different electrolysis techniques the evolving anion exchange membrane water electrolysis (AEMWE) shows the utmost promise for manufacturing green hydrogen in an inexpensive way. In the present review we establish the most current and noteworthy achievements of AEMWE which include the advancements in increasing the ionic conductivity and understanding the mechanism of degradation of AEM and the most important topics regarding the designing of the electrocatalyst. The crucial issues that affect the AEMWE behavior are highlighted and future constraints and openings are also discussed. Furthermore this review article provides the appreciated strategies for producing extremely dynamic and robust electrocatalysts and evolving the construction of AEMWE equipment.