International Journal of Hydrogen Energy最新文献

{"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}

{"title":"Effect of distance of cylinder and release pressure on the hydrogen jet flame temperature distribution induced by impingement of ignited release of hydrogen","authors":"Yongzheng Yao ,&nbsp;Fang Chen ,&nbsp;Yi Jiang ,&nbsp;Aolan Pan ,&nbsp;Luyao Tan ,&nbsp;Yiyuan Wang ,&nbsp;Liang Gong","doi":"10.1016/j.ijhydene.2025.150517","DOIUrl":"10.1016/j.ijhydene.2025.150517","url":null,"abstract":"<div><div>Hydrogen is clean, green, and environmentally friendly, and it can be beneficial in solving the climate problem of global warming. However, hydrogen is flammable and explosive, and it is crucial to study the characteristics of hydrogen jet flame. At present, the temperature decay law of hydrogen jet flame after hitting the cylinder is not clear. In this paper, the temperature distribution of the jet flame formed on the surface of the cylinder after hydrogen leakage was experimentally investigated at a nozzle diameter of 1.50 mm, a leakage pressure of 0.15–0.30 MPa, and a nozzle distance of 0.20–0.40 m. The ground is approximately 0.16 m from the lower surface of the cylinder. The results of the study showed that the flame expansion region after hitting the cylinder and the maximum over-temperature increases with increasing initial pressure and decreasing cylinder-to-nozzle distance. In the longitudinal direction along the cylinder, relationship between dimensionless distance and dimensionless over-temperature satisfying Gaussian distribution. And when the longitudinal distance is greater than 0.10 m, the over-temperature increases with the distance from the cylinder to the nozzle. In the vertical direction, an exponential relationship is satisfied between dimensionless distance and dimensionless over-temperature. These temperature prediction models are only applicable to the boundary conditions in the paper. The results of the study contribute to the improvement of hydrogen safety.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150517"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665810","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":"Optimization of catalyst for electrolysis and sono-electrolysis process for hydrogen production","authors":"Yew Heng Teoh ,&nbsp;Pak Hen Soon ,&nbsp;Heoy Geok How ,&nbsp;Haseeb Yaqoob ,&nbsp;Mohamad Yusof Idroas ,&nbsp;Muhammad Ahmad Jamil ,&nbsp;Saad Uddin Mahmud ,&nbsp;Thanh Danh Le ,&nbsp;Hafiz Muhammad Ali ,&nbsp;Muhammad Wakil Shahzad","doi":"10.1016/j.ijhydene.2025.150508","DOIUrl":"10.1016/j.ijhydene.2025.150508","url":null,"abstract":"<div><div>This study explores hydrogen production and energy efficiency optimization in electrolysis and sono-electrocatalysis using ZnO, Cu₂O, and graphene catalysts, where an ultrasonic bath, operating at 110W and a constant frequency of 40 kHz, was used as the ultrasound source. For electrolysis and sonoelectrolysis, a total of 26 experimental runs were conducted, including 13 runs for electrolysis and 13 for sonoelectrocatalysis, each lasting 5 min. The research indicates that sono-electrolysis can boost hydrogen production by 10–20 %. However, energy efficiency must be monitored due to the increased current and ultrasonic energy requirements. The study also evaluates the impact of different catalysts and their concentrations on maximizing hydrogen production and energy efficiency. Employing the Design of Experiments (DOE) approach, Response Surface Methodology (RSM), and Analysis of Variance (ANOVA), the study optimized both the sono-electrocatalysis and electrocatalysis processes. Optimal condition for electrocatalysis was found with a ZnO catalyst concentration of 2.668 g/L, achieving a hydrogen production rate of 57.6 cm³/h and an energy efficiency of 7.85 %. The predictions made by the model closely aligned with the experimental results, confirming the model's accuracy. In sono-electrocatalysis, the use of 0.1 g/L of graphene led to a hydrogen production rate of 66.4 cm³/h and an energy efficiency of 2.43 %, with minimal experimental errors observed. These findings highlight the potential of these optimized processes for practical applications in sustainable energy solutions.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150508"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665811","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":"Power loss tracking for the PEM electrolyser using multiphysics dynamical bond graph model","authors":"Mahdi Boukerdja ,&nbsp;Sumit Sood ,&nbsp;Belkacem Ould-Bouamama ,&nbsp;Anne-Lise Gehin ,&nbsp;Abd Essalam Badoud","doi":"10.1016/j.ijhydene.2025.150343","DOIUrl":"10.1016/j.ijhydene.2025.150343","url":null,"abstract":"<div><div>Green hydrogen generation using intermittent renewable sources through electrolysis faces challenges related to efficiency and reliability, largely due to material limitations and the fluctuating nature of energy inputs. These fluctuations disrupt continuous hydrogen production and increase the degradation rate of various components of the electrolyser, leading to power losses and diminished performance. To address this, a bond graph model-based power loss tracking approach is proposed to study the impact of degradation on Proton Exchange Membrane (PEM) electrolyser performance. This approach enables real-time tracking of power losses at different subcomponent and physical phenomenon levels by accurately representing the system’s reaction kinetics and complex, nonlinear, multi-physical dynamics. Implemented in the 20-Sim software, the model benefits from automatic generation of governing analytical equations, enhancing usability and insight. A sensitivity study of the model has also been performed to analyse the responsiveness of the power loss trackers to the change in parameters. The model can serve as a valuable tool during the design phase, allowing engineers to analyse and estimate power losses under various operating conditions. A simulation-based validation was conducted within a green hydrogen production multisource platform, confirming the model’s capabilities. Due to its causal and structural properties, the developed approach has the potential to support diagnostics and prognostics of a PEM electrolyser.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150343"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666083","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 quantitative risk assessment method for hydrogen fuel cell vehicles in residential garages","authors":"Xu Zhu ,&nbsp;Changjian Wang ,&nbsp;Qimiao Xie ,&nbsp;Aifeng Zhang","doi":"10.1016/j.ijhydene.2025.150250","DOIUrl":"10.1016/j.ijhydene.2025.150250","url":null,"abstract":"<div><div>Since hydrogen fuel cell vehicle (HFCV) has zero emission and no pollution, it is one of main development directions of new energy vehicles in the future. Parking time of the vehicle in the garage far exceeds the driving time. The ventilation in residential garage is insufficient and therefore the leaked hydrogen is more likely to be accumulated. Jet fire and explosion bring serious casualties and property losses. It may also cause oxygen concentration reduction leading to hypoxia asphyxiation in the residential garage scenario. A quantitative risk assessment method based on Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) assessment framework and Latin Hypercube sampling (LHS) method is proposed. The uncertainty of hydrogen leak frequency, leak detected and isolated probability, ignition probability and asphyxiation probability are fully quantified. The randomness of occupant locations is considered by LHS method. Risk metrics are calculated to measure the severity of accidents, and whether the risk is within the acceptable risk range is judged. The assessment results show that the risk contribution rate of jet fire is significantly higher than that of explosion and asphyxiation. Average individual risk at each pressure and HFCV is far below 10⁻⁵ in current case, which is within the acceptable risk level of the staff. The most effective factors for reducing system unreliability and the main sources of risk are identified.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"157 ","pages":"Article 150250"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665807","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":"Electrophoretic deposition of novel hybrid MnCo2O4: Mn1·7CuFe0·3O4 spinel protective coating on stainless-steel metallic interconnects for SOFCs application","authors":"Omid Ekhlasiosgouei ,&nbsp;Maciej Bik ,&nbsp;Federico Smeacetto ,&nbsp;Piotr Jasinski ,&nbsp;Sebastian Molin","doi":"10.1016/j.ijhydene.2025.150569","DOIUrl":"10.1016/j.ijhydene.2025.150569","url":null,"abstract":"<div><div>An innovative hybrid spinel coating, composed of MnCo₂O₄ and Mn₁_·₇CuFe₀_·₃O₄ spinel materials in varying ratios (1:0, 1:3, 1:1, 3:1, and 0:1 wt%), is applied on AISI 441 stainless-steel interconnects by electrophoretic deposition method, to improve electrical conductivity, and inhibit the migration and evaporation of chromium. Stainless steel have been coated with dense, uniform, and crack-free coatings using EPD method. The cross-sectional analysis reveals that the densification of hybrid coatings (1:1 wt%) sintered under reduction treatment (1000 °C for 2 h in H₂), followed by a subsequent oxidation treatment (900 °C for 2 h in air) is greater (29 %) than those sintered solely under oxidation treatment (900 °C for 4 h in air). What is more, Raman and XRD study suggests that the applied procedure provides a precise control over the phase composition of the hybrid coating materials. The electrical conductivity of the hybrid materials (1:1 wt%) is higher (45 %) than that of the MnCo₂O₄ spinel material but lower (36 %) than that of the Mn₁_·₇CuFe₀_·₃O₄ spinel material at 600 °C. The novel hybrid spinel coating presents a promising candidate for protective coating on metallic interconnects, due to its higher electrical conductivity, and higher sinterability as compared to Mn–Co spinel coating.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"158 ","pages":"Article 150569"},"PeriodicalIF":8.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662220","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}