International Journal of Hydrogen Energy最新文献

{"title":"Accounting for meteorological and load data uncertainty in the optimal design of off-grid hybrid renewable energy systems","authors":"Florent Struyven ,&nbsp;Mathieu Sellier ,&nbsp;Myeongsub Kim ,&nbsp;Rosalinda Inguanta ,&nbsp;Farkad A. Lattieff ,&nbsp;Philippe Mandin","doi":"10.1016/j.ijhydene.2025.150309","DOIUrl":"10.1016/j.ijhydene.2025.150309","url":null,"abstract":"<div><div>This study presents a methodological contribution to the optimal design of an off-grid hybrid renewable energy systems (HRES) producing both electricity and drinking water. Beyond simulating the operation of a system combining solar photovoltaic and wind generation with battery and hydrogen storage , the work focuses on a critical yet often overlooked issue: the uncertainty associated with meteorological and consumption input data. A multi-objective optimization model, implemented in Julia, is used to determine system configurations that minimize the cost of energy and water while maximizing the share of renewable energy. The analysis demonstrates that the selection of input data has a significant influence on system design results. A methodology is proposed to identify the most favorable and most unfavorable input datasets. A novel shortage indicator is introduced to quantify energy deficits during periods when renewable production is insufficient to meet demand. This indicator enables interpretation of the underlying causes of cost and sizing variations, by linking them to storage requirements. The methodology is applied to the island of Molène (France) using meteorological and consumption data from 2018 to 2023. The results highlight the strong sensitivity of system design to input variability, and provide a framework for robust analysis and planning under uncertainty.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150309"},"PeriodicalIF":8.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Production of sustainable aviation fuel: Influence of feedstock and plant capacity on fuel price","authors":"Pierre Guilloteau ,&nbsp;Niklas Groll ,&nbsp;Anker Degn Jensen ,&nbsp;Gürkan Sin","doi":"10.1016/j.ijhydene.2025.150494","DOIUrl":"10.1016/j.ijhydene.2025.150494","url":null,"abstract":"<div><div>This study investigates the economic and technical viability of producing Sustainable Aviation Fuels (SAF) using green hydrogen (H₂) and carbon dioxide (CO₂) first, before assessing the process of methanol-to-jet in the second part. Using 66 kt/year of H₂ and 480 kt/year of CO₂ as feedstock, a production of 55 kt/year of kerosene is achieved. Based on a Discounted Cash Flow Rate Model, the economic analysis reveals a Levelized Cost of Operations (LCO) of $8.17 ± 5.25/kg, significantly higher than the European conventional jet fuel price ($0.46–1.77/kg). Consistent with other research, a sensitivity analysis confirms the substantial impact of H₂ price on SAF costs, necessitating exploring new feedstocks. In this regard, bio-methanol emerges as promising with a mean LCO of $0.72/kg for economies of scales between 200 kt/year and 399 kt/year. Despite current limitations in bio-methanol production, the findings underscore the need for cost-effective solutions to provide sufficient supply of feedstock for SAF.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150494"},"PeriodicalIF":8.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stable bifunctional nanoflower-structured NiMnCu-LDH catalyst for selective and efficient methanol-to-formate electrocatalytic conversion and hydrogen evolution reaction","authors":"Yiping Wang,&nbsp;Yixin Xu,&nbsp;Zhaoqun Gao,&nbsp;Siraj Ullah,&nbsp;Ying Li,&nbsp;Na Su,&nbsp;Shuozhen Hu,&nbsp;Xinsheng Zhang","doi":"10.1016/j.ijhydene.2025.150434","DOIUrl":"10.1016/j.ijhydene.2025.150434","url":null,"abstract":"<div><div>Low formate Faradaic efficiency (FE) and poor methanol-to-formate electrooxidation reaction (MOR) stability under high potentials restrict commercialization of coupling MOR with hydrogen evolution reaction (HER), which generates hydrogen and high-value-added formate simultaneously with low energy consumption. Herein, we synthesized a nanoflower-structured NiMnCu-LDH as bifunctional catalyst for both MOR and HER. Adding Cu suppresses oxygen evolution reaction (OER) and elevates Ni³⁺ content by promoting electron transfer from Ni²⁺ to Mn³⁺. NiMnCu-LDH exhibits high MOR activity with potentials of 1.33/1.36 V_RHE at 10/100 mA cm⁻², high formate selectivity with over 95 % FE at potential exceeding 1.50 V_RHE, and superior durability for at least 120 h in alkaline media. Furthermore, NiMnCu-LDH exhibits excellent HER performance with an overpotential of 76.45 mV at 10 mA cm⁻² with the existence of methanol. Producing hydrogen and formate is successively achieved by applying NiMnCu-LDH as both anode and cathode simultaneously for methanol electrolysis in a non-diaphragm electrolyzer.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150434"},"PeriodicalIF":8.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a novel boil-off gas handling system for liquid hydrogen fuel cell vehicles","authors":"Zhiyong Li ,&nbsp;Jingxin Hou ,&nbsp;Frank Markert","doi":"10.1016/j.ijhydene.2025.150544","DOIUrl":"10.1016/j.ijhydene.2025.150544","url":null,"abstract":"<div><div>This study introduces a boil-off gas (BOG) handling system for fuel cell vehicles, incorporating a metal-organic framework (MOF) buffer storage tank to enhance the performance and safety of onboard liquid hydrogen (LH₂) storage. Unlike prior approaches focusing on insulation, cooling, or compression, this work explores a passive intermediate storage strategy that uses MOFs to temporarily adsorb and recycle boil-off hydrogen. Analysis indicates that the hydrogen recycling capacity improves by over 30 % at 0.8 MPa compared to 0.5 MPa, enabling smaller tank volumes at higher pressures. Although ambient heat transfer reduces performance, its impact is less pronounced at elevated pressures. Under 273–293 K conditions, the system can extend dormancy by one to several days depending on boil-off rate and MOF tank size. Additionally, the system assists with pressure stabilization and enables safer venting through auxiliary heating, facilitating emergency disposal. This work provides an early-stage feasibility assessment of a novel approach to managing BOG in vehicle-scale LH₂ systems.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150544"},"PeriodicalIF":8.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of N2 and CO2 on the flammability limit parameters and kinetic characteristics of hydrogen-blended natural gas mixtures","authors":"Jiachen Wang ,&nbsp;Minggao Yu ,&nbsp;Yu Wang ,&nbsp;Haitao Li ,&nbsp;Gege Hu ,&nbsp;Yihao Yao ,&nbsp;Shoutong Diao ,&nbsp;Chengcai Wei","doi":"10.1016/j.ijhydene.2025.150497","DOIUrl":"10.1016/j.ijhydene.2025.150497","url":null,"abstract":"<div><div>This study explores the flammability limits (FL) of hydrogen-blended natural gas (HBNG) under CO₂ and N₂ dilution using experiments, predictive modeling, and chemical kinetics. A limiting laminar flame speed method is applied, achieving upper flammability limit (UFL) prediction errors below 3 %. Experimentally, increasing the hydrogen blending ratio (R) from 0.1 to 0.3 raises the UFL by 4.165 % and the lower limit by 0.98 %. Kinetic analysis shows that CO₂ significantly reduces the peak mole fractions of ·H, ·OH, and ·O by 87.6 %, 87.0 %, and 81.3 %, respectively, while N₂ has a weaker effect. Furthermore, CO₂ alters dominant chain reactions, suppressing high-activation energy pathways. Grey relational analysis identifies ·OH and hydrogen content as the most influential factors for flammability. These findings clarify the suppression mechanism of inert gases and improve the quantitative prediction of flammability behavior in blended hydrogen fuels, offering valuable insight for hydrogen safety design and risk assessment in industrial applications.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150497"},"PeriodicalIF":8.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technical evaluation of plasma-assisted entrained flow gasification for hydrogen-rich syngas production from waste and biomass","authors":"Sebastian Bastek ,&nbsp;Marcel Dossow ,&nbsp;Andrius Tamošiūnas ,&nbsp;Kentaro Umeki ,&nbsp;Hartmut Spliethoff ,&nbsp;Sebastian Fendt","doi":"10.1016/j.ijhydene.2025.150184","DOIUrl":"10.1016/j.ijhydene.2025.150184","url":null,"abstract":"<div><div>Plasma-assisted entrained flow gasification (EFG) offers a potential solution to convert low-quality biomass and waste feedstocks into high-quality syngas. This study, therefore, evaluates the theoretical technical potential of steam plasma-assisted EFG using a novel Aspen Plus model (eGas), which integrates the simulation of thermodynamic plasma properties and dissociation phenomena into Aspen Plus. Simulation results show that increasing the electrification ratio (ELR) to 0.48, corresponding to full steam plasma gasification, raises the H₂/CO ratio to 1.03—more than double that of oxygen-blown EFG—while improving carbon conversion efficiency (CCE) to 95 % and reducing syngas CO₂ content by 79 %. The hydrogen-specific energy demand (HSED) reaches 181 MJ/kg H₂, outperforming proton exchange membrane (PEM) electrolysis (198 MJ/kg H₂) for H₂ addition to syngas. Plasma power conversion efficiencies exceed 85 %. Validation against NASA CEA and Cantera confirms the model's accuracy. This highlights plasma-assisted EFG as a promising future technology for hydrogen-rich syngas production.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150184"},"PeriodicalIF":8.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks based prediction of spatial hydrogen leakage concentration fields in hydrogen refueling stations","authors":"Shilu Wang ,&nbsp;Yubo Bi ,&nbsp;Chuntao Zhang ,&nbsp;Congcong Li ,&nbsp;Lili Ye ,&nbsp;Haiyong Cong ,&nbsp;Wei Gao ,&nbsp;Mingshu Bi","doi":"10.1016/j.ijhydene.2025.150365","DOIUrl":"10.1016/j.ijhydene.2025.150365","url":null,"abstract":"<div><div>Accurate and timely prediction of hydrogen leakage dispersion is essential for safety management in hydrogen refueling stations (HRS). This study proposes a physics-informed neural networks (PINNs)-based model that reconstructs the spatial hydrogen concentration field in real-time from sparse monitoring data. The model integrates the continuity equation, momentum conservation, and convection-diffusion equations as physical constraints, and is validated under two representative environmental wind scenarios: downwind and upwind. Numerical experiments show that the PINNs model achieves superior performance, particularly under limited training data. For instance, under complex upwind conditions, it attains an R² of 0.932 using only 5 % of the data, outperforming a conventional neural network trained on 20 % (R² = 0.905). This work establishes a fast, robust, and physically consistent framework for hydrogen risk monitoring, providing technical support for safe operation in hydrogen infrastructure and demonstrating strong potential for real-world deployment.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150365"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the thermophysical properties of alumina nanoparticles enhanced ionic liquids (NEILs) using advanced intelligent techniques","authors":"Sara Sahebalzamani ,&nbsp;Arefeh Naghizadeh ,&nbsp;Atena Mahmoudzadeh ,&nbsp;Sattar Ghader ,&nbsp;Abdolhossein Hemmati-Sarapardeh","doi":"10.1016/j.ijhydene.2025.150375","DOIUrl":"10.1016/j.ijhydene.2025.150375","url":null,"abstract":"<div><div>Ionic liquids (ILs) are promising alternatives to conventional heat transfer fluids (HTFs) in thermal energy systems. This paper uses advanced machine learning (ML) approaches, specifically Cascaded Forward Neural Networks (CFNN) and Generalized Regression Neural Networks (GRNN), to predict the thermophysical properties of Alumina (Al₂O₃) nanoparticles in a binary mixture of water and the ionic liquid [C₂mim][CH₃SO₃]. Various optimization methods, including Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), and Levenberg-Marquardt (LM), were applied to enhance CFNN model performance. Alumina mass concentration and temperature were used as input parameters to predict specific heat capacity, thermal conductivity, and density, whereas shear rate and Alumina mass fraction were used for viscosity prediction. Results demonstrated that the CFNN model optimized with the LM algorithm closely matched experimental data, achieving average absolute percentage relative errors (AAPRE) of 0.2519 %, 0.2910 %, 0.0088 %, and 0.5937 % for specific heat capacity, thermal conductivity, density, and viscosity, respectively. Sensitivity analysis showed Alumina concentration strongly affected viscosity, density, and conductivity (r = 0.26, 0.92, 0.91), while temperature most influenced heat capacity (r = 0.74). Trend analysis showed that the CFNN-LM model captured the actual trends in the thermophysical properties of nanoparticle-enhanced ionic liquids (NEILs), and the leverage method validated the data, confirming its authenticity.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150375"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset for classifying operational states in dry reforming of biogas processes","authors":"Renan Akira Nascimento Garcia Escribano ,&nbsp;Marcos Antonio Schreiner ,&nbsp;Luiz Eduardo Soares de Oliveira ,&nbsp;Guilherme Tamanho ,&nbsp;Julio Cezar da Silva Ferreira ,&nbsp;Izadora Costa da Silva ,&nbsp;Paola Cavalheiro Ponciano ,&nbsp;Helton José Alves","doi":"10.1016/j.ijhydene.2025.150314","DOIUrl":"10.1016/j.ijhydene.2025.150314","url":null,"abstract":"<div><div>Dry reforming of biogas (DR) converts methane and carbon dioxide into syngas, offering a sustainable solution for hydrogen production and greenhouse gas reduction. This study uses operational data from DR reactor sensors to predict process states: Activation, Reaction, and Irregularity. Nine reaction-specific datasets were analyzed via 11-fold cross-validation, ensuring test data independence. Machine learning (ML) models — k-nearest neighbors (KNN), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Random Forest (RF) — were evaluated, with RF performing best (88.40% accuracy, 89.04% F1-score for Irregularity). ML enables efficient monitoring by capturing complex variable relationships and responding to operational changes. Explainability analysis (SHAP and PDP) identified key variables, including record count, humidity, and pressure. The study provides a robust dataset and methodology for predicting DR states using operational data, supporting future research in fault prediction and process optimization. This approach enhances DR reactor control, advancing reliable and sustainable hydrogen production.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150314"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation on coupling mechanism of safety accident risk of hydrogen energy system based on N–K model","authors":"Shanshan Xing ,&nbsp;Fangping Ma ,&nbsp;Caizhi Zhang ,&nbsp;Fang Peng ,&nbsp;Mingjun Zhang ,&nbsp;Minglu Zheng ,&nbsp;Jiqiang Li ,&nbsp;Shuaifei Nan","doi":"10.1016/j.ijhydene.2025.150418","DOIUrl":"10.1016/j.ijhydene.2025.150418","url":null,"abstract":"<div><div>The rapidly growing hydrogen energy sector faces the challenge of maximizing its clean energy benefits while managing the associated safety risks. Currently, a comprehensive understanding of the interconnected risk factors contributing to accidents remains elusive, particularly impeding the formulation of robust safety measures. Here, this study addresses this gap by employing the N–K model to elucidate the formation mechanism of coupled risks in hydrogen energy systems. Firstly, the system is divided into four subsystems: human, machine, job and management, risk factors associated with safety accidents in each subsystem are analyzed. Secondly, risks are divided into three categories: single-factor, dual-factor, and multi-factor coupling. The formation mechanism of the coupling risk is analyzed based on the concept of triggers. Thirdly, utilizing the N–K model, the internal coupling relationships and their triggers are quantitatively analyzed. The hydrogen incidents and accidents database (HIAD 2.1) was used as the main source of data, and 92 accident cases with known causes were extracted from the database based on the criterion of whether there were fatal accidents, and an example study was carried out based on the N–K model. The findings of this study reveal that the probability of safety accidents is directly correlated with the magnitude of the risk coupling value, which is influenced by the number of interacting risk factors. Significantly, the machine factor emerged as the predominant determinant of risk coupling. This study highlights the necessity for hydrogen energy systems to strengthen risk prevention mechanisms, rigorously evaluate system designs, and proactively mitigate the potential for multi-factor risk couplings. Implementing these measures will significantly enhance the overall safety of hydrogen energy systems, contributing to the development of more reliable and secure energy solutions.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150418"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}