Energy Science & Engineering最新文献

{"title":"Application of GAN–CNN in Risk Assessment of Pipeline Failures in Multiphase Pipeline With Image Information Encoding Approach","authors":"Sihang Chen,&nbsp;Na Zhang,&nbsp;Biyuan Shui","doi":"10.1002/ese3.70524","DOIUrl":"https://doi.org/10.1002/ese3.70524","url":null,"abstract":"<p>In this paper, a novel approach for risk assessment of multiphase pipeline failures applying a generative adversarial network with a convolutional neural network (GAN–CNN) approach is proposed. To enhance the risk prediction ability of a machine learning model, generating cases and expanding the data set of multiphase pipeline data groups are ideal ways. The Image Information Encoding approach is employed to store the key features information in the grayscale images and reserve the relationships between different features. The GAN–CNN algorithm will generate the data group in the form of grayscale images, considering the relationship between key features of the data groups; meanwhile, the categories of the synthetic data types can be switched to adapt to the requirements of the practical application. The model performance of risk assessment will be compared based on different data sets, including the unexpanded one and the expanded one, and the relevant analysis will be presented.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3179-3194"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing Orthogonal-Arc Trajectories for Horizontal Wells to Reduce Drilling Cost in Development of Shale Oil/Gas Reservoirs","authors":"Jun Zhang,&nbsp;Boyun Guo,&nbsp;Sohail Nawab","doi":"10.1002/ese3.70568","DOIUrl":"https://doi.org/10.1002/ese3.70568","url":null,"abstract":"<p>Designing 3-dimensional (3D) horizontal well trajectories is critical for enhancing drilling efficiency in shale reservoirs. The objective of this work is to develop an easy-to-drill 3D trajectory to cut drilling cost in the development of shale oil/gas reservoirs. A new type of 3D trajectory is proposed to make well drillable with classical deflection tools rather than sophisticated modern defection tools. The new type of trajectory contains two curve sections in orthogonal orientation and is thus called Orthogonal-Arc Trajectory (OAT). The two curve sections have the same curvature so that they can be drilled with the same deflection tool of constant curvature. A complete set of equations in closed form and a procedure of using the equations to design OAT are presented. An example design of OAT is demonstrated. The feature of single constant curvature of the OAT enables the use of simple and economical deflection tools to reduce drilling costs, which is particularly significant for small-scale operators. It offers practical values for drilling optimization in regions where modern drilling tools, such as AutoTrak, Power Drive, and Geo-Pilot, are not accessible or too expensive to use. Comparing to the radius of curvature trajectory and the constant turn rate trajectory, the OAT gives the lowest drag and torque that will reduce drilling complications and thus increase drilling safety. The OAT is specially applicable to drilling geothermal wells where the hot temperature of earth rock prevents using sophisticated modern defection tools that contain delicate electronic parts with temperature limit.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3102-3108"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on the Combustion and Explosion Characteristics of Tributyl Phosphate and Kerosene Mixtures at Different Initial Pressures in High-Temperature Environments","authors":"Pincong Wu,&nbsp;Xijing Li,&nbsp;Jing Luo,&nbsp;Yuchen Ouyang,&nbsp;Yuanzhi Li,&nbsp;Longfei Hou,&nbsp;Pengliang Li","doi":"10.1002/ese3.70525","DOIUrl":"https://doi.org/10.1002/ese3.70525","url":null,"abstract":"<p>As critical materials in nuclear fuel reprocessing, kerosene and Tributyl phosphate are utilized in high-temperature, negative-pressure phases of reprocessing plant processes. The explosive limits of the mixture of Tributyl phosphate and kerosene are significantly influenced by the neighboring environment, which poses an explosion risk. At present, there is a lack of research in this field. This study utilizes an enhanced explosion limit testing system to examine the explosion limits and overpressure peak of mixed gases consisting of Tributyl phosphate and kerosene, with initial pressures varying from 100 to 70 kPa under elevated temperature conditions (120°C), thereby analyzing their combustion and explosion properties. According to the findings, the combined gases’ lower explosive limits are 7%, 5%, 3%, and 1% at 100–70 kPa (Δp = 10 kPa), while their upper explosive limits are 20%, 17%, 12%, and 10%, respectively. The explosive limits of a mixed gas constrict as the initial pressure falls, causing the gas concentration at the maximum overpressure produced by the explosion to migrate toward the upper explosive limit. In the same pressure drop range (Δp = 10 kPa), the sensitivity of the upper explosion limit surpasses that of the lower explosion limit; however, the maximum overpressure peak of the combination diminishes. The maximum overpressure peak diminishes most swiftly within the 90–80 kPa pressure range, exhibiting a decrease of 20.27%.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3195-3210"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of Wind-Powered Green Hydrogen Production via a Hybrid Graph Neural Network-Transformer Forecasting Model","authors":"Iman Baghaei,&nbsp;Mojtaba Mirhosseini,&nbsp;Alireza Zahedi","doi":"10.1002/ese3.70541","DOIUrl":"https://doi.org/10.1002/ese3.70541","url":null,"abstract":"<p>Accurate long-term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno-economic feasibility of wind-powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes a hybrid deep learning framework designed to enhance 1- to 10-year wind speed forecasts. The proposed architecture integrates graph neural networks (GNN) to extract inter-variable correlations and feature-space dynamics among meteorological parameters, coupled with advanced sequence modeling layers to capture temporal patterns. We rigorously evaluated the framework using multi-variable climate data from NASA's Power Data Access Viewer, comparing a GNN-Transformer model against a GNN-GRU variant, as well as standard baselines (LSTM, CNN) and state-of-the-art hybrids (e.g., MST-GNN). The results demonstrate that the proposed hybrid framework significantly outperforms standalone models. Specifically, the GNN-Transformer achieved a Mean Absolute Error (MAE) of 0.53 m/s for 10-year forecasts, representing a 30.27% improvement over a standard Transformer. Furthermore, our comparative analysis reveals that the GNN-GRU variant achieved superior practical performance with an MAE of 0.44 m/s. These findings provide two key contributions: (1) establishing a robust GNN-based framework that advances long-term forecasting accuracy for green hydrogen site planning, and (2) offering empirical evidence that while Transformers offer theoretical complexity, simpler recurrent architectures like GRU may yield better stability in specific long-term climatological tasks.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3434-3457"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Faults in Power Transformers Based on Machine-Learning Algorithms Compared With Other Techniques","authors":"Osama E. Gouda,&nbsp;Nourhan Ramadan,&nbsp;Matti Lehtonen,&nbsp;Mohamed M. F. Darwish","doi":"10.1002/ese3.70521","DOIUrl":"https://doi.org/10.1002/ese3.70521","url":null,"abstract":"<p>This paper implemented a comprehensive variety of modern machine-learning techniques, which were demonstrated to be effective in handling complex tabular data, generating accurate predictions, and ensuring high dependability in fault classification of oil-immersed power transformers. The primary objective of utilizing the techniques is to improve computing efficiency and generalization by using neural networks, gradient boosting models, and hybrid ensemble techniques. Light Gradient Boosting Machine (LGBM) was selected due to its rapidity and memory efficiency algorithms, integrating histogram-based learning and leafwise growth strategies to allow the effective handling of large-scale data sets. In parallel, Categorical Boosting was implemented because of its powerful ability to manage categorical data through adaptive categorical encoding and ordered boosting. These methods contribute to mitigating biases and overfitting. According to study comparisons, LGBM performs more accurately than several other boosting algorithms in a variety of real-life situations. Additionally, a Multilayer Perceptron was applied to capture complex nonlinear interactions that tree-based models frequently miss. On the basis of utilizing 700 samples reported by the International Technical Committee 10, Central Chemical Laboratories at Egyptian Electricity Holding Company, and related databases, it is concluded that the hybrid LGBM + Extra Trees Classifier model achieved the best performance, with accuracy values ranging between 93% and 98%. Furthermore, the accuracy of the hybrid LGBM + ET has been compared with the accuracy of different conventional and artificial intelligence techniques and has proven to be more accurate.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3132-3148"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148532797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixing Behavior of Natural Gas and Hydrogen in a High-Efficiency Vane (HEV) Static Mixer","authors":"Xiang Zhou,&nbsp;Jun Zheng,&nbsp;Weiqing Xu,&nbsp;Dewen Kong,&nbsp;Yan Shi,&nbsp;Maolin Cai","doi":"10.1002/ese3.70527","DOIUrl":"https://doi.org/10.1002/ese3.70527","url":null,"abstract":"<p>Blending hydrogen into natural gas pipelines is considered a practical approach for enabling large-scale hydrogen transport. Achieving a homogeneous hydrogen–natural gas mixture requires structural optimization of static mixers. In this study, a three-dimensional mixing model of natural gas and hydrogen within a HEV static mixer was developed using computational fluid dynamics, and its accuracy was validated against experimental data. A systematic investigation was conducted to evaluate the effects of vane angle (<i>α</i>), hydrogen injection strategy, vane number (<i>n</i>), and vane array spacing (<i>ξ</i>) on mixing uniformity and pressure drop. The results indicate that both mixing uniformity and pressure drop first increase and then decrease with increasing vane angle: at <i>α</i> = 60°, regions of intense turbulence are most concentrated, whereas at <i>α</i> = 120°, turbulent zones extend further downstream. Regarding the hydrogen injection strategy, counter-flow injection generates stronger flow disturbances, resulting in superior mixing performance. Increasing the number of vane arrays enhances both mixing uniformity and total pressure drop. Smaller vane array spacing promotes higher mixing uniformity over a shorter axial distance while maintaining a lower pressure drop. Based on these findings, the recommended HEV mixer configuration is <i>α</i> = 60°, <i>n</i> = 4, and <i>ξ</i> = 1<i>D</i>, combined with a reverse hydrogen injection strategy.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3211-3225"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exergy-Based Simulation and Optimization of Multiagent MSW Gasification for Sustainable Energy Recovery Using Aspen Plus","authors":"Mohammad Reza Gharib,&nbsp;Payam Danesh,&nbsp;Ali Delarami","doi":"10.1002/ese3.70535","DOIUrl":"https://doi.org/10.1002/ese3.70535","url":null,"abstract":"<p>Thermochemical gasification is one of the most efficient methods for producing solid waste synthesis gas. Therefore, to predict and analyze the process of gasification of municipal solid waste (MSW) in a downdraft fixed-bed gasification reactor with a thermal capacity of 2.1 kW, a comprehensive numerical modeling approach based on Gibbs free energy minimization was employed utilizing Aspen Plus simulation software. The developed computational framework facilitated the rigorous prediction and in-depth analysis of critical process performance indicators, including syngas composition, lower-heating value (LHV), and residual flux under a broad spectrum of gasification operational parameters. Specifically, the model enabled systematic investigation of the effects of varying gasification temperatures, as well as different types and ratios of gasifying factors, thereby providing nuanced insights into the optimization of gasification processes within diverse operational regimes. The validation outcomes demonstrate a strong correlation between experimental observations and the model's simulated outputs. The optimal gasification conditions were identified at an approximate temperature of 1300°C, with a preferred gasifying agent blend comprising water vapor and carbon dioxide alongside air at a combined ratio near 1.3. The best input residual flux for the production of synthetic gas was considered to be 18.1 kg/s. A gasifying agent composed of water vapor, carbon dioxide, and air is considered the most optimal for syngas production from MSW in Tehran, due to its ability to yield the highest LHV and the highest concentrations of hydrogen 52.46 mol% and carbon monoxide 47.9 mol%.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3118-3131"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blade Count and Rotational Speed Sensitivity of Vertical-Axis Turbines Under Guided Vehicle-Generated Transient Wakes","authors":"Aydin Ulus,&nbsp;Stefan Ilie Moldovan","doi":"10.1002/ese3.70533","DOIUrl":"https://doi.org/10.1002/ese3.70533","url":null,"abstract":"<p>Vehicle wakes generate short but energetic gusts that can be used by small roadside turbines, yet the operating conditions that allow a rotor to take full advantage of these events are still not fully clear. Flow-guiding structures that were previously shown to improve wake redirection and energy capture are employed in this study. However, the combined influence of blade count and prescribed rotational speed under true transient vehicle-induced inflow has not been examined in a systematic way. In the present study, vertical-axis turbines with two to six blades were evaluated numerically while a car traveling at 32 m/s produced the unsteady wake. The rotor speed was increased from the nominal value ω₀ = 6.677 rad/s (63.76 rpm, corresponding to a baseline tip-speed ratio of approximately 0.10) up to 2.2ω₀. The results show a strong dependence of performance on both blade number and rotational speed. The two-blade configuration provided the most consistent energy extraction and reached a maximum of 158.5 J at 2.1ω₀, representing a 24.5% increase relative to baseline speed. In contrast, higher-solidity rotors experienced significant performance deterioration at elevated speeds, and several cases produced negative net energy. This behavior was linked to repeated interception of the wake core by returning blades at unfavorable angular positions, leading to transient torque reversal. The findings suggest that, under short-duration vehicle wakes, low-solidity rotors operated at carefully selected rotational speeds are more reliable than higher-blade-count configurations.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3324-3345"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Waste to Hydrogen: Transforming Food Waste Into Biohythane (Bio-H2 + Bio-CH4) in a Two-Stage Reactor With the Aid of a Metal-Ion Catalyst","authors":"K. V. Sreedharan,&nbsp;Debabrata Barik,&nbsp;Ayyar Dinesh,&nbsp;D. Shanmugapriya,&nbsp;M. Santhamoorthy,&nbsp;N. Ashok","doi":"10.1002/ese3.70538","DOIUrl":"https://doi.org/10.1002/ese3.70538","url":null,"abstract":"<p>This study aimed to enhance biohythane (biohydrogen [bio-H₂] + biomethane [bio-CH₄]) production from food waste (FW) via dark fermentation (DF) using a two-stage anaerobic bioreactor system with specially developed seed cultures and metal-ion catalysts. The primary objective was to maximize sequential bio-H₂ and bio-CH₄ production through phase-separated DF and methanogenesis. Seed culture was prepared by selectively enriching <i>Clostridium thermocellum</i> from digestate slurry, using 2-bromoethanesulfonic acid. The bioreactors were operated for 45 days in batch mode and 50 days in continuous mode. Following the initial loading of 10 L substrate into the Stage-1 bioreactor, a continuous substrate loading rate of 0.08 L/day was maintained from day 36 onward under continuous mode. The digested effluent was subsequently transferred to the Stage-2 bioreactor at a loading rate of 0.16 L/day for bio-CH₄ production. The parameters impacting bio-H₂ yield have been analyzed, and the optimal pH and temperature were found to be 5.5°C and 40°C, respectively. The highest bio-H₂ production was 142 ± 5.48 L/day under batch mode and 212 ± 3.37 L/day under continuous mode, both observed at a NiCl₂ + FeCl₂ catalyst concentration of 75 mg/L. Gas chromatography analysis revealed a hydrogen (H₂) fraction of 75.81% at the optimal catalytic concentration under continuous mode. H₂ concentration and the total gas yield increased by about 30.8% and 77%, respectively, compared with the catalyst-free FW substrate. Microbial community structure and biochemical transformations in the FW substrate during DF were investigated using field-emission scanning electron microscopy and Fourier transform infrared spectroscopy. The integrated system at the optimal conditions demonstrated highly efficient performance in continuous mode. The first-stage DF reactor achieved 40.13% chemical oxygen demand (COD) removal efficiency with a specific bio-H₂ yield of 6005 L/kg COD. The second-stage methanogenic reactor attained a COD removal efficiency of 89.70%, corresponding to a specific bio-CH₄ yield of 2004 L/kg COD. Together, the dual-stage system reached an overall COD removal efficiency of 93.83% and delivered a total bioenergy recovery of about 75.21 MJ/kg COD. These results demonstrate effective phase separation, high substrate conversion efficiency, and superior reactor performance under continuous mode. The associated enhancement in renewable energy recovery and waste valorization firmly establishes the feasibility of large-scale biohythane production.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3388-3421"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bipolar \u0000 \u0000 \u0000 \u0000 q\u0000 \u0000 \u0000 -Fractional Fuzzy Aggregation Operators for Multi-Criteria Decision-Making in Optimal Renewable Energy Selection","authors":"Sagvan Y. Musa,&nbsp;Zanyar A. Ameen,&nbsp;Wafa Alagal,&nbsp;Baravan A. Asaad","doi":"10.1002/ese3.70529","DOIUrl":"https://doi.org/10.1002/ese3.70529","url":null,"abstract":"<p>Renewable energy sources, such as solar, wind, and hydropower, are critical for sustainable development, environmental protection, and long-term energy security. Selecting the most suitable renewable energy sources involves complex multi-criteria decision-making, considering economic, environmental, techn ological, and reliability aspects. This paper introduces a bipolar <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math>-fractional fuzzy set model along with corresponding aggregation operators for multi-criteria decision-making under uncertainty. The proposed bipolar <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math>-fractional fuzzy sets combine the flexibility of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math>-fractional fuzzy sets with the dual-perspective capability of bipolar fuzzy sets, enabling simultaneous representation of positive and negative evaluations. To aggregate bipolar <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math>-fractional fuzzy information across multiple criteria, two operators are defined: the bipolar <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math>-fractional fuzzy weighted average and the bipolar <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow>\u0000 </mrow>\u0000 </semantics></math>-fractional fuzzy weighted geometric operators. Their key properties, including idempotency, monotonicity, and boundedness, are also established. A systematic decision-making algorithm is proposed, integrating these operators with score and accuracy functions to rank alternatives effectively. A practical example on renewable energy sources selection demonstrates the model's ability to preserve ranking consistency, maintain smooth score trends, and provide interpretable results. Comparative and sensitivity analyses further illustrate the robustness of the proposed approach.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 7","pages":"3250-3271"},"PeriodicalIF":3.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148522907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}