Green Energy and Resources最新文献

{"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, 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":"2025-05-05","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":"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":"2025-04-10","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":"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":"2025-03-01","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}

{"title":"Using machine learning methods for long-term technical and economic evaluation of wind power plants","authors":"Ali Omidkar,&nbsp;Razieh Es'haghian,&nbsp;Hua Song","doi":"10.1016/j.gerr.2025.100115","DOIUrl":"10.1016/j.gerr.2025.100115","url":null,"abstract":"<div><div>The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465255","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":"Potentials and effects of electricity cogeneration via ORC integration in small-scale biomass district heating system","authors":"Truong Nguyen ,&nbsp;Leteng Lin","doi":"10.1016/j.gerr.2024.100113","DOIUrl":"10.1016/j.gerr.2024.100113","url":null,"abstract":"<div><div>This study explores the potential and impact of electricity cogeneration using Organic Rankine Cycle (ORC) integrated with small-scale biomass boilers within district heating systems. An analysis is conducted on a 3 MW_th biomass-fired district heating plant in southern Sweden. Process monitoring data, collected over a one-year period from the plant, serves as the basis for simulation and analysis. The study examines operational changes and fuel usage at a local level, together with an extension to a regional scale considering both short-term and long-term energy system implications. The results show that integrating a 200 kW_e ORC unit with the existing boiler having a flue gas condenser is cost-optimal and could cogenerate approximately 1.1 GWh electricity annually, with a levelized electricity cost of €64.4 per MWh. This is equivalent to a system power-to-heat ratio of 7.5%. From a broader energy system perspective, this efficient integration could potentially reduce CO₂ emissions by 234∼454 tons per year when the saved energy locally is used to replace fossil fuels in the energy system, depending on how biomass is utilized and what type of fossil fuels are replaced. Increasing installed capacity of ORC unit to maximize electricity co-generation could result in a carbon abatement cost ranging from €204 to €79 per ton CO₂. This cost fluctuates depending on the installed capacity, operation of the ORC units, and prevailing electricity prices. The study highlights the trade-off between financial gains and CO₂ emission reductions, underscoring the complex decision-making involved in energy system optimization.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152920","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":"Investigation of highly efficient CO2 hydrogenation at ambient conditions using dielectric barrier discharge plasma","authors":"Zhihao Zeng ,&nbsp;Yujiao Li ,&nbsp;Yunfei Ma ,&nbsp;Xiaoqing Lin ,&nbsp;Xiangbo Zou ,&nbsp;Hao Zhang ,&nbsp;Xiaodong Li ,&nbsp;Qingyang Lin ,&nbsp;Ming-Liang Qu ,&nbsp;Zengyi Ma ,&nbsp;Angjian Wu","doi":"10.1016/j.gerr.2024.100102","DOIUrl":"10.1016/j.gerr.2024.100102","url":null,"abstract":"<div><div>The increasing utilization of CO₂ for synthesizing high-value fuels or essential chemicals is a potentially effective approach to mitigating global warming and climate change. Compared to thermal catalytic CO₂ conversion under harsh operating conditions (400∼500°C, 10 MPa), non-thermal plasma can overcome kinetic barriers and trigger reactions beyond thermal equilibrium at ambient temperature and pressure. In this study, the effects of operating conditions (discharge frequency, input power, and gas flow rate) and geometrical parameters (discharge length, discharge gap, and dielectric materials) have been extensively analyzed using typical cylindrical dielectric barrier discharge (DBD) plasma. The discharge characteristics changed by operating conditions (including waveforms of applied voltage and current) are compared, indicating higher applied voltage and lower gas flow rate can strengthen the filamentary discharges. The results demonstrate CO₂ conversion rate increases with the increase of applied voltage and the decrease of CO₂/H₂ ratio, achieving its maximum value of 43.0% at 20 mL/min. The highest energy efficiency of 3771.9 μg/kJ for CO generation is obtained at the applied voltage of 5.5 kV and gas flow rate of 40 mL/min, respectively. Besides, the structure of plasma reactor also impacts the performance of CO₂ conversion. On the one hand, the discharge gap has a significant role in the variation of CO₂ conversion and product selectivity, which is attributed to the electric field density and corresponding electron-induced reaction. On the other hand, the circulating water-cooling jacket was used to find out the influence of reaction temperature, which switched the product from CO to CH₄. This work will pave the way for a sustainable alternative towards future CO₂ conversion and utilization.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745815","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}