Green Energy and Resources最新文献

{"title":"Dynamic process of hydrogen flow and spontaneous combustion in tubes featuring different configurations after leakage from 35 and 70 MPa","authors":"Qin Huang ,&nbsp;Zuo-Yu Sun ,&nbsp;Ya-Long Du ,&nbsp;Jia-Ying Li","doi":"10.1016/j.gerr.2025.100127","DOIUrl":"10.1016/j.gerr.2025.100127","url":null,"abstract":"<div><div>Hydrogen, as a green energy resource, presents a crucial opportunity to reduce emissions and facilitate the transition to sustainable energy, particularly in the shipping industry. The storage pressure for hydrogen gas (like 35 MPa for metal-composite Type III vessels and 70 MPa for polymer-composite Type IV vessels) is prone to leakage or even rupture, and hydrogen could be spontaneously ignited during pressurized leakage; thus, investigating the dynamics of spontaneous hydrogen combustion is essential for safely advancing hydrogen energy in marine applications. This study numerically examined the development of shockwaves and the spontaneous combustion process during pressurized leakage within tubes featuring various configurations (L-shaped and T-shaped, which are commonly found in actual pipelines) at pressures of 35 and 70 MPa. The results indicated that, upon release from the tested pressures, hydrogen would spontaneously ignite within the upstream sections of the tubes beyond the leakage port, with the flame propagating downstream along with the shockwave development. Notably, shockwave and spontaneous combustion characteristics variations differed across the two tube configurations. Velocity measurements showed that values would be lowest near the corner of the L-shaped tube, whereas they would consistently decline downstream in the T-shaped tube. This suggested that measures to mitigate shockwave effects (thus reducing the likelihood of spontaneous combustion) should be implemented in the upstream section of the tubes, regardless of the configuration. Additionally, pressure readings were highest near the corner of the L-shaped tube and showed a consistent decline downstream in the T-shaped tube. Therefore, protective measures addressing stress intensity should focus on the L-shaped tube's corner and the T-shaped tube's upstream section.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ‘Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis’ [Green Energy Res. 2 (2024) 100089]","authors":"Avinash Alagumalai,&nbsp;Hua Song","doi":"10.1016/j.gerr.2025.100117","DOIUrl":"10.1016/j.gerr.2025.100117","url":null,"abstract":"","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-series signal analysis of sustainable process intensification: Characterization method development of gas-solid fluidized bed hydrodynamics towards AI-enhanced algorithms","authors":"Yue Yuan ,&nbsp;Silu Chen ,&nbsp;Meifeng Li ,&nbsp;Jesse Zhu ,&nbsp;Lihui Feng ,&nbsp;Tinghui Zhang ,&nbsp;Kaiqiao Wu ,&nbsp;Donovan Chaffart","doi":"10.1016/j.gerr.2025.100128","DOIUrl":"10.1016/j.gerr.2025.100128","url":null,"abstract":"<div><div>Sustainable manufacturing is pivotal to promoting societal advancements that balance the progressive growth of human needs with the gradual exhaustion of natural resources and the environmental impact of current manufacturing technologies. Gas-solid fluidization, a key process intensification technique, has advanced sustainability for over a century. The complex nature of these systems has led to numerous analysis algorithms for assessing time-series signals critical to observe the fluidization hydrodynamics. This work reviews widely used signal analysis methods for processing the commonly-measured time-series signals for fluidization, specifically focusing on pressure drop and optical signals. Despite their widespread implementation, these methods have limited potential due to the limited visibility of optical signals and the inability of pressure signals to provide localized fluidization system information. Veritably, the traditional algorithms cannot consider all influencing factors and handle flawed, large-scale signals.</div><div>Artificial intelligence (AI) has emerged as a promising solution to overcome these limitations. Nevertheless, AI-enhanced methods for fluidization signal analysis are still nascent. This work emphasizes the potential of AI to enhance understanding of complex fluidization behavior, particularly heterogeneous agglomerations, through reviewing signal analysis methods from traditional numerical methods to AI-driven approaches. Furthermore, this study highlights the future steps necessary to adequately expand upon machine learning-based analysis methodologies and extends a call to arms for future research establishment within this field. These advancements will support the development of sustainable manufacturing technologies that balance industrial progress with environmental responsibility.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the relationship between climate change, air pollutants and human health: Impacts, adaptation, and mitigation strategies","authors":"Gibson Owhoro Ofremu ,&nbsp;Babatunde Yusuf Raimi ,&nbsp;Samuel Omokhafe Yusuf ,&nbsp;Beatrice Akorfa Dziwornu ,&nbsp;Somtochukwu Godfrey Nnabuife ,&nbsp;Adaeze Mary Eze ,&nbsp;Chisom Assumpta Nnajiofor","doi":"10.1016/j.gerr.2024.100074","DOIUrl":"10.1016/j.gerr.2024.100074","url":null,"abstract":"<div><div>The innumerable impact of climate change is a global menace to human health. This paper conveys a comprehensive review of scientific literature to explore the relationship between climate change, air pollutants, and human health. The integral relationship between climate change and health is complex and has a significant impact on every facet of human life. The impact can either be direct (e.g., exposures due to extreme heat, storms, flooding, and air pollution) or indirect (e.g., displacement, food security, and variation in water). The rising temperature of the planet could lead to increasingly severe health impacts from climate change in the future. It is important to take stringent climate actions to mitigate the climate change risk and adapt to the impacts that are already happening. To lessen the speed and severity of climate change, mitigation focuses on cutting greenhouse gas emissions. Options for adaptation include things like advancing to higher ground to stop sea levels from increasing, growing new crops that can grow in a new environment, or using novel construction methods. Investing in novel or enhanced technology, infrastructure, and research is frequently required for adaptation. The review emphasized the importance of considering both short-term and long-term adaptation strategies as well as mitigation efforts, which call for steps to address the root cause by halting or reducing the growth in fossil fuel emissions that might severely and completely increase the earth's scorching temperatures. The results of this study provide insightful viewpoints on adaptation measures, and mitigation strategies for decision-makers, experts in public health, and researchers working in the field of climate change and its effects on human health.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141052124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZnCl2-activated S/N-doped biochar for low-temperature NH3-SCR of NOx: Performance and pathway analysis","authors":"Shuangshuang Yan ,&nbsp;Dongmei Bi ,&nbsp;Chengxizi Zhang ,&nbsp;Zhisen He ,&nbsp;Yu Ni ,&nbsp;Kang Yue ,&nbsp;Shanjian Liu","doi":"10.1016/j.gerr.2025.100133","DOIUrl":"10.1016/j.gerr.2025.100133","url":null,"abstract":"<div><div>Carbon-based catalysts for low-temperature denitrification were prepared from wheat straw via ZnCl₂ activation and thiourea doping. The catalysts were systematically characterized using BET surface area analysis, NH₃-TPD, XPS, and transient response experiments. The ZnCl₂-activated catalyst exhibited a NO_<em>x</em> reduction efficiency of 45.1%. To further enhance the denitrification performance, the Z_1.2 biochar was co-doped with sulfur and nitrogen. Experimental results demonstrated that the SN_2.5Z_1.2/AC biochar catalyst achieved a maximum NO conversion of 88% within the temperature range of 50–260°C and exhibited stable activity in long-term durability tests. Sulfur and nitrogen co-doping markedly increased the number of strong acid sites and surface chemisorbed oxygen (O_α), thereby facilitating the formation of N-6 functional groups. The presence of C-SO₃-H species may be a critical factor contributing to the enhanced NO_x conversion. The denitrification process over sulfur- and nitrogen-doped biochar follows both the Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) mechanisms, wherein •NH₂ radicals play a pivotal role in the reduction of NO to its gaseous and adsorbed forms.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar dryers: A review of mechanism, methods and critical analysis of transport models applicable in solar drying of product","authors":"Onyinyechi Nnamchi ,&nbsp;Cyprian Tom ,&nbsp;Godwin Akpan ,&nbsp;Murphy Umunna ,&nbsp;David Ubong ,&nbsp;Mathew Ibeh ,&nbsp;Adindu Linus–Chibuezeh ,&nbsp;Leonard Akuwueke ,&nbsp;Stephen Nnamchi ,&nbsp;Augustine Ben ,&nbsp;Macmanus Ndukwu","doi":"10.1016/j.gerr.2025.100118","DOIUrl":"10.1016/j.gerr.2025.100118","url":null,"abstract":"<div><div>As the world transitions towards green energy sources solar drying has become a vital technology for sustainable agricultural production, offering a cleaner, more efficient alternative to traditional drying methods. Solar drying has been demonstrated to be a sustainable and eco-friendly drying process for drying and preserving agricultural products, offering advantages over traditional methods that include faster drying rates, improved product quality, and reduced energy costs. This review examines the mechanisms and methods applicable to solar drying, including indirect and direct solar drying, hybrid systems combining solar drying with other heating sources, and thermal storage materials to address challenges such as intermittent solar radiation. The designs of solar drying systems include various solar collector configurations, drying chamber geometries, and air conveyance mechanisms crucial for efficient drying. This review therefore explores different design approaches and their effects on drying performance, highlighting the importance of understanding the complex interactions between system components. Additionally, the approach for Energy and exergy analysis of solar drying systems was explored, providing insights into energy utilization and efficiency. Finally, this review elucidates the complex transport phenomena governing solar drying, including moisture diffusion, heat and mass transfer, and airflow patterns. It identifies knowledge gaps in existing models and future research directions in transport modelling phenomena to advance sustainable, efficient, and scalable solar drying techniques.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes","authors":"Kelly Joel Gurubel Tun ,&nbsp;Elizabeth León-Becerril ,&nbsp;Octavio García-Depraect","doi":"10.1016/j.gerr.2024.100112","DOIUrl":"10.1016/j.gerr.2024.100112","url":null,"abstract":"<div><div>Dark fermentation process from low-cost renewable substrates for simultaneous wastewater treatment and hydrogen production (H₂) is suitable due to economic viability and environmental sustainability. This work explores the application of an innovative control strategy in a scale fermentation bioreactor designed for energy recovery from organic wastes. This approach not only promotes low carbon emissions but also offers significant potential for industrial application. Machine learning (ML) and optimization methods are used to model the nonlinear process and then, a neural predictive control (NPC) strategy to drive the system to its optimal operating order under varying influent conditions is developed. Predictive control uses the Newton-Raphson as the optimization algorithm and a multi-layer feedforward neural network for the state prediction. This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H₂ production. These optimal operating parameters are then used to create reference trajectory signals within a NPC scheme to achieve the optimal hydrogen production rate. The control strategy led to an HPR mean of 12.35 ± 1.2 NL H₂/L-d under pseudo-steady state with hydrogen content in the gaseous phase of 63 % <em>v/v</em>, and a maximum COD recovery of 90 ± 2.8 %. The results demonstrate that this innovative control method can significantly improve the performance and efficiency of biogas plants, showing viability for large-scale industrial implementation.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment","authors":"Oluwatobi Adeleke ,&nbsp;Obafemi O. Olatunji ,&nbsp;Tien-Chien Jen ,&nbsp;Iretioluwa Olawuyi","doi":"10.1016/j.gerr.2025.100119","DOIUrl":"10.1016/j.gerr.2025.100119","url":null,"abstract":"<div><div>This study proposes a hybrid network of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA) to predict the higher heating value (HHV) of municipal solid waste (MSW). To enhance the robustness and accuracy of the model and optimize its ability to capture the complex non-linear relationship in the MSW dataset, eight membership functions (MF)-type of the grid partitioning (GP) clustering approach were tested. Moreover, understanding the relative importance and contribution of different waste properties to HHV prediction is critical for improving the model's predictive capability and optimizing the waste-to-energy (WTE) process. To this end, the feature importance analysis of MSW input variables was carried out using the decision tree regressor with the Gini importance (GI) metrics to identify the most influential variable. Key waste properties, including ultimate analysis data, ash and moisture content were used as input variables for the model. The result shows that the GP-clustered GA-ANFIS model based on triangular-shaped MF-type (tri-MF) has the most accurate HHV predictions with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Deviation (MAD) values of 0.7642, 13.677, 1.5913 and 0.9821 at the training and 0.6364, 16.216, 1.2437 and 0.7821 at the testing stage. Feature importance assessment revealed ash content as the most important predictor of HHV based on GI-value of 0.519668 (about 50% cumulative importance). Additionally, sulphur and nitrogen, along with ash content, dominated the HHV prediction and exhibited the highest predictive power on HHV with about 80% cumulative importance. The robust integrated approach of hybrid neuro-fuzzy model, with decision tree-based feature importance assessment, offers an effective approach for enhancing the prediction of HHV of MSW. The outcome of the study enhances WTE systems, facilitating more efficient and sustainable energy recovery from MSW.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis and normalization of single-hole vs. multi-hole spray characteristics: 1st report on spray characteristic comparison","authors":"Chang Zhai ,&nbsp;Junyu Zhang ,&nbsp;Kuichun Li ,&nbsp;Pengbo Dong ,&nbsp;Yu Jin ,&nbsp;Feixiang Chang ,&nbsp;Hongliang Luo","doi":"10.1016/j.gerr.2025.100120","DOIUrl":"10.1016/j.gerr.2025.100120","url":null,"abstract":"<div><div>The single hole injector, known for its simple design and ease of measurement, is widely utilized in optical spray experiments; however, multi-hole injectors are commonly applied in real engine applications. The structural differences between the two leads to variations in spray characteristics. This series of studies, based on the principles of similarity and normalization, proposes a theory for the transformation of spray characteristics between different hole numbers injectors. The 1st report investigates the spray characteristics of different hole numbers injectors under super high injection pressure conditions. Using the Diffuser Background Imaging (DBI) method, the experimental pressure range covers 100∼300 MPa. The research indicate that the single-hole injector exhibits a shorter initial injection delay, while the multi-hole injector demonstrates a more stable injection flow rate and greater penetration. At higher pressures, the velocity increase, especially at 300 MPa. Higher ambient density has a suppressive effect on spray tip velocity and alters spray morphology. Moreover, it was observed that while the initial spray velocity of the single-hole injector is relatively higher, the penetration of the multi-hole injector significantly exceeds that of the single-hole injector in the later stages. For multi-hole injectors, interactions between adjacent sprays lead to a relatively narrower spray angle. The ratio of spray angle to cone angle for both injectors remain nearly unaffected by changes in density and injection pressure. In general, the Naber and Siebers model is better suited for predicting penetration in single-hole injectors under conditions of high density and ultra-high injection pressure (200∼300MPa). This study not only highlights the distinctive spray characteristics under super high pressure conditions but also offers valuable theoretical foundations and experimental insights for optimizing diesel engine design.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parametric study of the decomposition of methane for COx-free H2 and high valued carbon using Ni-based catalyst via machine-learning simulation","authors":"Dinghao Xue ,&nbsp;Pingyang Zhang ,&nbsp;Yuanyuan Lin ,&nbsp;Wenshuo Wang ,&nbsp;Jiachang Shi ,&nbsp;Qiang Hu ,&nbsp;Gartzen Lopez ,&nbsp;Cristina Moliner ,&nbsp;Jin Sun ,&nbsp;Tao Wang ,&nbsp;Xinyan Zhang ,&nbsp;Yingping Pang ,&nbsp;Xiqiang Zhao ,&nbsp;Yanpeng Mao ,&nbsp;Zhanlong Song ,&nbsp;Ziliang Wang ,&nbsp;Wenlong Wang","doi":"10.1016/j.gerr.2025.100114","DOIUrl":"10.1016/j.gerr.2025.100114","url":null,"abstract":"<div><div>With industrial informatization, abundant data provides solutions for the digital design of methane-based hydrogen production. Catalytic methane decomposition (CMD) is a promising strategy for COx-free hydrogen production, with high-value carbon products generated. However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. In this study, five machine learning methods were utilized for the precise prediction of methane conversion using Ni-based catalysts. Combined with SHAP method and univariate analysis method, XGBoost model with the best accuracy (with R² = 0.894, RSME = 7.724) was selected for the exploration of the reaction impact of active phase loading, support loading, and reaction conditions in methane convention, hydrogen production, carbon yield, and carbon quality. The result shows that methane conversion rate is mainly influenced by space velocity, reaction temperature, nickel loading, and methane percentage. Copper doping significantly affects carbon yield and its quality, and there is a strong bond between Ni and Al₂O₃, contributing the most to the reaction. This work would provide a guidance for the efficient catalyst design and effective hydrogen production.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}