Publications

Water electrolysis for hydrogen production has garnered significant attention in the context of increasing global energy demands and the “dual-carbon” strategy. However practical implementation is hindered by challenges such as high overpotentials high catalysts costs and insufficient catalytic activity. In this study three mono and bimetallic metal−organic framework (MOFs)-derived electrocatalysts Fe-MOFs Fe/Co-MOFs and Fe/Mn-MOFs were synthesized via a one-step hydrothermal method using nitroterephthalic acid (NO2-BDC) as the ligand and NN-dimethylacetamide (DMA) as the solvent. Electrochemical tests demonstrated that the Fe/Mn-MOFs catalyst exhibited superior performance achieving an overpotential of 232.8 mV and a Tafel slope of 59.6 mV·dec−1 alongside the largest electrochemical active surface area (ECSA). In contrast Fe/Co-MOFs displayed moderate catalytic activity while Fe-MOFs exhibited the lowest efficiency. Stability tests revealed that Fe/Mn-MOFs retained 92.3% of its initial current density after 50 h of continuous operation highlighting its excellent durability for the oxygen evolution reaction (OER). These findings emphasize the enhanced catalytic performance of bimetallic MOFs compared to monometallic counterparts and provide valuable insights for the development of high-efficiency MOF-based electrocatalysts for sustainable hydrogen production.

This paper presents the design and implementation of an Intelligent Home Energy Management System in a smart home. The system is based on an economically decentralized hybrid concept that includes photovoltaic technology a proton exchange membrane fuel cell and a hydrogen refueling station which together provide a reliable secure and clean power supply for smart homes. The proposed design enables power transfer between Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) systems allowing electric vehicles to function as mobile energy storage devices at the grid level facilitating a more adaptable and autonomous network. Our approach employs Double Deep Q-networks for adaptive control and forecasting. A Multi-Agent System coordinates actions between home appliances energy storage systems electric vehicles and hydrogen power devices to ensure effective and cost-saving energy distribution for users of the smart grid. The design validation is carried out through MATLAB/Simulink-based simulations using meteorological data from Tunis. Ultimately the V2H/H2V system enhances the utilization reliability and cost-effectiveness of residential energy systems compared with other management systems and conventional networks.

This paper compares national hydrogen (H2) infrastructure plans in Canada the United States (the USA) Singapore and Sri Lanka four countries with varying geographic and economic outlooks but shared targets for reaching net-zero emissions by 2050. It examines how each country approaches hydrogen production pipeline infrastructure policy incentives and international collaboration. Canada focuses on large-scale hydrogen production utilizing natural resources and retrofitted natural gas pipelines supplemented by carbon capture technology. The USA promotes regional hydrogen hubs with federal investment and intersectoral collaboration. Singapore suggests an innovation-based import-dominant strategy featuring hydrogen-compatible infrastructure in a land-constrained region. Sri Lanka maintains an import-facilitated pilot-scale model facilitated by donor funding and foreign collaboration. This study identifies common challenges such as hydrogen embrittlement leakages and infrastructure scalability as well as fundamental differences based on local conditions. Based on these findings strategic frameworks are proposed including scalability adaptability partnership policy architecture digitalization and equity. The findings highlight the importance of localized hydrogen solutions supported by strong international cooperation and international partnerships.

Hydrogen energy one of the renewable energy sources plays a crucial role in combating climate change since its usage aims to reduce carbon emissions and enhance energy security. As the global energy trend moves toward cleaner alternatives countries start to adapt their energy strategies. In this transition hydrogen is one of the energy sources with the potential to increase long-term energy security. Developing countries face challenges such as high energy import dependency rising industrial demand and the need for infrastructure modernization making hydrogen valleys one of the viable solutions since they integrate hydrogen production storage distribution and utilization at one facility. However establishing small-scale hydrogen valleys requires a comprehensive decision-making strategy consisting of technical financial environmental social and political factors while addressing uncertainties in the system. To systematically manage the process this study proposes a Z-numberbased fuzzy cognitive mapping approach which models the interdependencies among success factors supported by Z-number Decision-Making Trial and Evaluation Laboratory for structured prioritization with a multi-expert perspective. The results indicate that Financial Factors emerged as the most critical category with Government Incentives Infrastructure Investment Cost and Land Acquisition Cost ranking as the top three sub-success factors. Availability of Skilled Workforce and Regional Energy Supply followed in importance which demonstrates the importance of social and technical dimensions in the hydrogen valley development. These findings demonstrate the critical role of policy support infrastructure readiness and workforce availability in the design process. Sensitivity analyses are also conducted to present robustness of the given decisions for the analysis of the results. Based on the results and analyses possible implications based on the policy and practical dimensions are also discussed. By integrating fuzzy logic and Z-numbers the study aims to minimize loss of information enhances the analytical background for decision-making and provides a strategic roadmap for hydrogen valley development.

Against the backdrop of the global transition towards clean energy deep-sea wind-power hydrogen production integrates offshore wind with green hydrogen technology. Addressing the technical coupling complexity and the impact of uncertain hydrogen prices this paper develops a capacity optimization model. The model incorporates floating wind turbine output the technical distinctions between alkaline (ALK) electrolyzers and proton exchange membrane (PEM) electrolyzers and the synergy with energy storage. Under three hydrogen price scenarios the results demonstrate that as the price increases from 26 CNY/kg to 30 CNY/kg the optimal ALK capacity decreases from 2.92 MW to 0.29 MW while the PEM capacity increases from 3.51 MW to 5.51 MW. Correspondingly the system’s Net Present Value (NPV) exhibits an upward trend. To address the limitations of traditional methods in handling multi-dimensional benefit correlations and information ambiguity a comprehensive benefit evaluation framework encompassing economic technical environmental and social synergies was constructed. Sensitivity analysis indicates that the comprehensive benefit level falls within a relatively high-efficiency interval. The numerical characteristics an entropy value of 3.29 and a hyper-entropy of 0.85 demonstrate compact result distribution and robust stability validating the applicability and stability of the proposed offshore wind–hydrogen benefit assessment model.

Under the dual pressures of global climate change and energy structure transition the offshore wind–solar–current energy coupling hydrogen production (OCWPHP) system has emerged as a promising integrated energy solution. However its complex multi-energy structure and harsh marine environment introduce systemic risks that are challenging to assess comprehensively using traditional methods. To address this we develop a novel risk assessment framework based on hesitant fuzzy sets (HFS) establishing a multidimensional risk criteria system covering economic technical social political and environmental aspects. A hybrid weighting method integrating AHP entropy weighting and consensus adjustment is proposed to determine expert weights while minimizing risk information loss. Two aggregation operators—AHFOWA and AHFOWG—are applied to enhance uncertainty modeling. A case study of an OCWPHP project in the East China Sea is conducted with the overall risk level assessed as “Medium.” Comparative analysis with the classical Cumulative Prospect Theory (CPT) method shows that our approach yields a risk value of 0.4764 closely aligning with the CPT result of 0.4745 thereby confirming the feasibility and credibility of the proposed framework. This study provides both theoretical support and practical guidance for early-stage risk assessment of OCWPHP projects.

This review provides a comprehensive examination of artificial intelligence methods applied to the design optimization and performance prediction of composite-based hydrogen storage vessels with a focus on composite overwrapped pressure vessels. Targeted at researchers engineers and industrial stakeholders in materials science mechanical engineering and renewable energy sectors the paper aims to bridge traditional mechanical modeling with evolving AI tools while emphasizing alignment with standardization and certification requirements to enhance safety efficiency and lifecycle integration in hydrogen infrastructure. The review begins by introducing HSV types their material compositions and key design challenges including high-pressure durability weight reduction hydrogen embrittlement leakage prevention and environmental sustainability. It then analyzes conventional approaches such as finite element analysis multiscale modeling and experimental testing which effectively address aspects like failure modes fracture strength liner damage dome thickness winding angle effects crash behavior crack propagation charging/discharging dynamics burst pressure durability reliability and fatigue life. On the other hand it has been shown that to optimize and predict the characteristics of hydrogen storage vessels it is necessary to combine the conventional methods with artificial intelligence methods as conventional methods often fall short in multi-objective optimization and rapid predictive analytics due to computational intensity and limitations in handling uncertainty or complex datasets. To overcome these gaps the paper evaluates hybrid frameworks that integrate traditional techniques with AI including machine learning deep learning artificial neural networks evolutionary algorithms and fuzzy logic. Recent studies demonstrate AI’s efficacy in failure prediction design optimization to mitigate structural risks structural health monitoring material property evaluation burst pressure forecasting crack detection composite lay-up arrangement weight minimization material distribution enhancement metal foam ratio optimization and optimal material selection. By synthesizing these advancements this work underscores AI’s potential to accelerate development reduce costs and improve HSV performance while advocating for physics-informed models robust datasets and regulatory alignment to facilitate industrial adoption.

Crude oil distillation is one of the most energy-intensive processes in petroleum refining consuming up to 20% of total refinery energy. Improving the energy efficiency of crude distillation units (CDUs) is essential for reducing costs lowering emissions and achieving sustainable refining. Current studies often examine energy savings operational flexibility or renewable energy integration separately. This review brings these aspects together focusing on heat integration advanced control systems and renewable energy options such as solar-assisted preheating and green hydrogen. Advanced column designs including dividing-wall and hybrid systems can cut energy use by 15–30% while AI-based optimization improves process stability and flexibility. Solar-assisted preheating can reduce fossil fuel demand by up to 20% and green hydrogen offers strong potential for decarbonization. Our findings highlight that integrated strategies including advanced simulation tools and machine learning significantly improve CDU performance. We recommend exploring hybrid algorithms renewable energy integration and sustainable technologies to address these challenges and achieve long-term environmental and economic benefits.

Hydrogen-based direct reduction of iron ore is a promising route to reduce CO2 emissions in steelmaking where uniform particle flow inside shaft furnaces is essential for efficient operation. In this study a full-scale three-dimensional Discrete Element Method (DEM) model of a shaft furnace was developed to investigate the effects of a diverter device on granular flow. By systematically varying the radial width and top/bottom diameters of the diverter particle descent velocity residence time compressive force distribution and collision energy dissipation were analyzed. The results demonstrate that introducing a diverter effectively suppresses funnel flow prolongs residence time and improves radial flow uniformity. Among the tested configurations the smaller central diameter diverter showed the most favorable performance achieving a faster and more uniform descent reduced compressive force concentration and lower collision energy dissipation. These findings highlight the critical role of diverter design in regulating particle dynamics and provide theoretical guidance for optimizing shaft furnace structures to enhance the efficiency of hydrogen-based direct reduction processes.

The present work focused on the comparison between HHO and hydrogen electrolyzers in design gas production and various parameters which affect the performance and efficiency of alkaline electrolyzers. The primary goal is to generate the highest possible hydrogen and HHO gas flow rates. Hydrogen and HHO were produced using 3 mm electrode of stainless steel 316L with 224 cm2 surface area. Hydroxy and hydrogen rates were affected by electrolyte content cell connection electric current operating time electrolyte temperature and voltage. Maximum HHO generation values were 1020 1076 1125 and 1175 mL min−1 n at 5 10 15 and 20 g L−1 of sodium hydroxide (NaOH) with supply currents of 15 15.3 15.6 and 16 A respectively. Once it stabilized after 30 min the temperature increased to 26 30 35 and 38 °C respectively and remained there. With currents of 18 18.45 18.7 19.2 19.5 and 19.8 A hydrogen output peak values after 60 min. stayed constant at 680 734 785 846 897 and 945 mL min-1. at 5 10 15 and 20 g L−1 NaOH catalyst concentrations. At 5 10 15 and 20 g L−1 catalyst ratios the temperatures were elevated to constant values of 28.5 32 37.9 40.5 41.4 and 43 °C respectively. With cell design [4C3A19N] electrolyte concentration of 5 g L−1 NaOH and current of 14 A maximum HHO productivity was 866 mL min−1. and 74.23% efficiency. In a cell design of [4C5A17N] with catalyst content of 10 g L−1 maximum productivity was 680 mL min−1 for hydrogen and highest production efficiency of 72.85% was attained at 18 A.