Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125216
Xiaojin Song, Heming Han, Fusheng Zha, Shan Wu, Bo Kang, Long Xu, Heng Shun
Soil thermal conductivity is a key thermophysical parameter in the synergistic development of urban subsurface space and geothermal energy. However, existing studies lack a mapping between thermal response temperature and soil parameters, and interference with the construction process. In this study, a distributed fiber-optic in situ inversion method for ground thermal conductivity is proposed based on the thermal perturbation generated during pile foundation engineering, which organically integrates construction with geothermal parameter testing. A distributed fiber optic sensing network is employed to monitor the heat diffusion process of hydration in grouted piles in real time. The mapping relationship between the distribution of ground thermal conductivity and the temperature gradient is derived using an analytical model of the temperature field under variable heat source conditions. The reliability of the heat conduction inverse inversion algorithm is verified through numerical simulations, and a field trial was conducted at an underground comprehensive project in Anhui. The results demonstrate that the diffusion boundary of the hydration heat of the piles is 2.4 m. The maximum deviation between the field-measured and laboratory-tested thermal conductivity values is only 0.029 W/(m·K). This study offers a cost-effective and innovative solution for the thermophysical investigation of underground spaces in smart cities.
{"title":"Distributed fiber-optic in situ inversion method for ground thermal conductivity based on thermal perturbation of pile foundation engineering","authors":"Xiaojin Song, Heming Han, Fusheng Zha, Shan Wu, Bo Kang, Long Xu, Heng Shun","doi":"10.1016/j.renene.2026.125216","DOIUrl":"10.1016/j.renene.2026.125216","url":null,"abstract":"<div><div>Soil thermal conductivity is a key thermophysical parameter in the synergistic development of urban subsurface space and geothermal energy. However, existing studies lack a mapping between thermal response temperature and soil parameters, and interference with the construction process. In this study, a distributed fiber-optic in situ inversion method for ground thermal conductivity is proposed based on the thermal perturbation generated during pile foundation engineering, which organically integrates construction with geothermal parameter testing. A distributed fiber optic sensing network is employed to monitor the heat diffusion process of hydration in grouted piles in real time. The mapping relationship between the distribution of ground thermal conductivity and the temperature gradient is derived using an analytical model of the temperature field under variable heat source conditions. The reliability of the heat conduction inverse inversion algorithm is verified through numerical simulations, and a field trial was conducted at an underground comprehensive project in Anhui. The results demonstrate that the diffusion boundary of the hydration heat of the piles is 2.4 m. The maximum deviation between the field-measured and laboratory-tested thermal conductivity values is only 0.029 W/(m·K). This study offers a cost-effective and innovative solution for the thermophysical investigation of underground spaces in smart cities.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"260 ","pages":"Article 125216"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125229
Emine Ilhan , Forooza Samadi , Hazhir Karimi
Large-scale photovoltaic deployment requires a national assessment of suitable lands for utility-scale facilities. This study presents a Geographic Information System-based Fuzzy Analytic Hierarchy Process to map photovoltaic suitability across the Contiguous United States at 90-m resolution. Ten criteria, including climate, environmental limitations, topographic conditions, and accessibility-related factors, were normalized using fuzzy membership functions and combined with Fuzzy Analytical Hierarchy Process-weighted overlay. High–very high suitability totals 2.51 million km2 (32.1 %), concentrated in the U.S. Southwest. Low suitability occurs mainly in mountainous and densely populated regions. Compared with 5680 existing installations and 55,977 background points, presence scores are higher (mean/median 0.60/0.62 vs 0.50/0.57), and 81 % of facilities fall in high–very high classes (≥0.60). Installations are strongly clustered (nearest-neighbor ratio 0.346; z = −94.34; p < 0.01); Getis–Ord Gi∗ identifies persistent Southwest hot spots across percentile thresholds (top 10–20 %). Receiver Operator Characteristic analysis yields the Area Under the Curve of 0.59, reflecting screening-scale discrimination; unmodeled economic and regulatory factors remain important. Sensitivity ranks irradiance as the dominant driver, followed by slope and transmission proximity. These maps support strategic planning, transmission upgrades, and site screening.
大规模光伏发电的部署需要国家对公用事业规模设施的合适土地进行评估。本研究提出了一种基于地理信息系统的模糊层次分析法，以90米分辨率绘制美国相邻地区的光伏适宜性图。采用模糊隶属函数对气候、环境限制、地形条件和可达性相关因素等10个标准进行归一化，并结合模糊层次分析法加权叠加。高-非常高适宜性总面积为251万平方公里（32.1%），集中在美国西南部。低适宜性主要发生在山区和人口稠密地区。与5680个现有设施和55,977个背景点相比，存在得分更高（平均/中位数0.60/0.62 vs 0.50/0.57）， 81%的设施属于高-非常高类别（≥0.60）。安装强烈聚集（最近邻比0.346;z = - 94.34; p < 0.01）；Getis-Ord Gi *确定西南地区持续存在的热点，跨越百分位数阈值（前10 - 20%）。接收算子特征分析得出曲线下面积为0.59，反映了筛选尺度的区别；未建模的经济和监管因素仍然很重要。灵敏度将辐照度列为主要驱动因素，其次是斜率和透射距离。这些地图支持战略规划、传输升级和站点筛选。
{"title":"Solar farm suitability mapping for the Contiguous United States using the fuzzy analytic hierarchy process and geospatial analysis","authors":"Emine Ilhan , Forooza Samadi , Hazhir Karimi","doi":"10.1016/j.renene.2026.125229","DOIUrl":"10.1016/j.renene.2026.125229","url":null,"abstract":"<div><div>Large-scale photovoltaic deployment requires a national assessment of suitable lands for utility-scale facilities. This study presents a Geographic Information System-based Fuzzy Analytic Hierarchy Process to map photovoltaic suitability across the Contiguous United States at 90-m resolution. Ten criteria, including climate, environmental limitations, topographic conditions, and accessibility-related factors, were normalized using fuzzy membership functions and combined with Fuzzy Analytical Hierarchy Process-weighted overlay. High–very high suitability totals 2.51 million km<sup>2</sup> (32.1 %), concentrated in the U.S. Southwest. Low suitability occurs mainly in mountainous and densely populated regions. Compared with 5680 existing installations and 55,977 background points, presence scores are higher (mean/median 0.60/0.62 vs 0.50/0.57), and 81 % of facilities fall in high–very high classes (≥0.60). Installations are strongly clustered (nearest-neighbor ratio 0.346; z = −94.34; p < 0.01); Getis–Ord Gi∗ identifies persistent Southwest hot spots across percentile thresholds (top 10–20 %). Receiver Operator Characteristic analysis yields the Area Under the Curve of 0.59, reflecting screening-scale discrimination; unmodeled economic and regulatory factors remain important. Sensitivity ranks irradiance as the dominant driver, followed by slope and transmission proximity. These maps support strategic planning, transmission upgrades, and site screening.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"260 ","pages":"Article 125229"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125239
Rui Yang, Hang Xu
To address global warming, nations globally have started implementing renewable energy policies (REPs) to encourage the growth of renewable energy sources and their replacement of fossil fuels. However, it is not clear whether the REPs alleviate carbon emissions. In light of this, this study empirically examines the effect of the REPs on carbon emissions using methods of two-way fixed effects model, instrumental variable estimation, and difference-in-differences with city-level panel data from China. According to the empirical analysis, the REPs can greatly aid in lowering carbon emissions. After re-estimating with an alternative variable and considering special cities and confounding carbon-related policies, this conclusion still holds. According to mechanism analysis, the REPs can encourage the reduction of carbon emissions through improving green technology innovation and lowering the marginal abatement cost. Furthermore, the REPs have a stronger effect on reducing carbon emissions in regions with better economic development conditions or old industrial bases. Therefore, implementation of REPs is a successful way to lower carbon emissions.
{"title":"The impact of renewable energy policies on carbon emissions: Empirical evidence from China","authors":"Rui Yang, Hang Xu","doi":"10.1016/j.renene.2026.125239","DOIUrl":"10.1016/j.renene.2026.125239","url":null,"abstract":"<div><div>To address global warming, nations globally have started implementing renewable energy policies (REPs) to encourage the growth of renewable energy sources and their replacement of fossil fuels. However, it is not clear whether the REPs alleviate carbon emissions. In light of this, this study empirically examines the effect of the REPs on carbon emissions using methods of two-way fixed effects model, instrumental variable estimation, and difference-in-differences with city-level panel data from China. According to the empirical analysis, the REPs can greatly aid in lowering carbon emissions. After re-estimating with an alternative variable and considering special cities and confounding carbon-related policies, this conclusion still holds. According to mechanism analysis, the REPs can encourage the reduction of carbon emissions through improving green technology innovation and lowering the marginal abatement cost. Furthermore, the REPs have a stronger effect on reducing carbon emissions in regions with better economic development conditions or old industrial bases. Therefore, implementation of REPs is a successful way to lower carbon emissions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"260 ","pages":"Article 125239"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125238
Adife Şeyda Yargıç
This study investigates the catalytic pyrolysis of tomato waste conducted in a Heinze reactor using Co/Al2O3 catalysts to produce valuable chemicals via thermochemical conversion. The research began with the characterization of co-precipitated Co/Al2O3 catalysts using scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX), X-ray powder diffraction (XRD), and N2 physisorption analyses. Subsequently, the influence of various experimental parameters related to catalyst application on the properties of the liquid product obtained from pyrolysis at 500 °C was examined through elemental analysis, Fourier-transform infrared spectroscopy (FT-IR), liquid column chromatography, and gas chromatography-mass spectrometry (GC/MS). Key variables included the cobalt content in the alumina-supported catalyst (5 and 10 wt%), the catalyst addition ratios (0, 5, 10, 15, and 20 wt% of catalyst-biomass mixture), and pyrolysis heating rates (10 and 40 °C/min), all of which affected the distribution of pyrolytic products and bio-oil yield. Additionally, the study employed a multilevel factorial experimental design to statistically model the effect of Co/Al2O3 catalyst usage on bio-oil yield. Although increasing cobalt content led to a reduction in overall bio-oil yield, column chromatography revealed that the Co/Al2O3 catalyst promoted deoxygenation reactions, thereby enhancing the proportion of polyaromatic and aromatic compounds in the bio-oil. Notably, the total phenolic content in the asphaltene and aromatic sub-fractions of bio-oil produced at a heating rate of 10 °C/min with the 5Co/Al2O3 catalyst reached 26.92 %. Thus, despite a lower yield, the catalytic pyrolysis process improved the quality and selectivity of the bio-oil. Analysis of variance (ANOVA) indicated that the catalyst addition ratio was the most significant factor influencing bio-oil yield (P-value = 0.013). These findings demonstrate that tomato processing waste can be effectively utilized as a feedstock for the production of phenolic-rich bio-oil and other valuable chemicals.
{"title":"Tuning bio-oil characteristics and applying a statistical modeling-optimization approach for bio-oil yield","authors":"Adife Şeyda Yargıç","doi":"10.1016/j.renene.2026.125238","DOIUrl":"10.1016/j.renene.2026.125238","url":null,"abstract":"<div><div>This study investigates the catalytic pyrolysis of tomato waste conducted in a Heinze reactor using Co/Al<sub>2</sub>O<sub>3</sub> catalysts to produce valuable chemicals via thermochemical conversion. The research began with the characterization of co-precipitated Co/Al<sub>2</sub>O<sub>3</sub> catalysts using scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX), X-ray powder diffraction (XRD), and N<sub>2</sub> physisorption analyses. Subsequently, the influence of various experimental parameters related to catalyst application on the properties of the liquid product obtained from pyrolysis at 500 °C was examined through elemental analysis, Fourier-transform infrared spectroscopy (FT-IR), liquid column chromatography, and gas chromatography-mass spectrometry (GC/MS). Key variables included the cobalt content in the alumina-supported catalyst (5 and 10 wt%), the catalyst addition ratios (0, 5, 10, 15, and 20 wt% of catalyst-biomass mixture), and pyrolysis heating rates (10 and 40 °C/min), all of which affected the distribution of pyrolytic products and bio-oil yield. Additionally, the study employed a multilevel factorial experimental design to statistically model the effect of Co/Al<sub>2</sub>O<sub>3</sub> catalyst usage on bio-oil yield. Although increasing cobalt content led to a reduction in overall bio-oil yield, column chromatography revealed that the Co/Al<sub>2</sub>O<sub>3</sub> catalyst promoted deoxygenation reactions, thereby enhancing the proportion of polyaromatic and aromatic compounds in the bio-oil. Notably, the total phenolic content in the asphaltene and aromatic sub-fractions of bio-oil produced at a heating rate of 10 °C/min with the 5Co/Al<sub>2</sub>O<sub>3</sub> catalyst reached 26.92 %. Thus, despite a lower yield, the catalytic pyrolysis process improved the quality and selectivity of the bio-oil. Analysis of variance (ANOVA) indicated that the catalyst addition ratio was the most significant factor influencing bio-oil yield (<em>P-value</em> = 0.013). These findings demonstrate that tomato processing waste can be effectively utilized as a feedstock for the production of phenolic-rich bio-oil and other valuable chemicals.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"261 ","pages":"Article 125238"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125234
Zhibin Zhang , Guosheng Jia , Zhendi Ma , Jianke Hao , Meng Zhang , Ying Cao , Liwen Jin
There has been growing interest in geothermal energy and solar energy combined heating systems with seasonal thermal energy storage. However, the long-term operational stability of such coupled systems remains challenging due to dynamic ground temperature variation around borehole heat exchangers (BHEs). This study develops a co-simulation model to evaluate a solar-assisted medium-deep geothermal heating system, analyzing 20-year thermal performance across 1500–2500 m BHE burial depths using a Shaanxi residential case. The results show that the introduction of solar thermal recharging leads to a temperature increase of 26 °C in the shallow ground layer, while the temperature decrease observed in the deep layer is significantly affected by the burial depth of the BHE. Meanwhile, the annual heat extraction decay rates reduce from 14.89 % to 2.62 % (1500 m) and 11.57 %–2.01 % (2500 m), demonstrating enhanced sustainability. Through multi-objective optimization of three key parameters—solar collector area, thermal storage tank volume and BHE burial depth—the optimized configuration achieves synergistic benefits, including an 9.02 % reduction in total energy consumption and a 20.75 % decrease lifecycle cost compared to conventional designs. The presented method can serve as a reference for the rational design of the medium-deep geothermal energy and solar energy coupled heating systems.
具有季节性热能储存的地热能和太阳能联合供暖系统越来越受到人们的关注。然而，由于井下热交换器（BHEs）周围的动态地温变化，这种耦合系统的长期运行稳定性仍然具有挑战性。本研究建立了太阳能辅助中深地热供暖系统的联合模拟模型，并以陕西某住宅为例，分析了1500-2500 m BHE埋深的20年热性能。结果表明：引入太阳热补给后，地表浅层温度升高26°C，深层温度下降受BHE埋深的显著影响。同时，年采热衰减率从14.89%下降到2.62% (1500 m)，从11.57%下降到2.01% (2500 m)，显示出增强的可持续性。通过对三个关键参数（太阳能集热器面积、储热罐容积和BHE埋深）的多目标优化，优化后的配置实现了协同效益，与传统设计相比，总能耗降低9.02%，生命周期成本降低20.75%。该方法可为中深层地热能和太阳能耦合供暖系统的合理设计提供参考。
{"title":"Parameter optimization and ground temperature analysis of a solar-assisted medium-deep geothermal heating system under long-term operation","authors":"Zhibin Zhang , Guosheng Jia , Zhendi Ma , Jianke Hao , Meng Zhang , Ying Cao , Liwen Jin","doi":"10.1016/j.renene.2026.125234","DOIUrl":"10.1016/j.renene.2026.125234","url":null,"abstract":"<div><div>There has been growing interest in geothermal energy and solar energy combined heating systems with seasonal thermal energy storage. However, the long-term operational stability of such coupled systems remains challenging due to dynamic ground temperature variation around borehole heat exchangers (BHEs). This study develops a co-simulation model to evaluate a solar-assisted medium-deep geothermal heating system, analyzing 20-year thermal performance across 1500–2500 m BHE burial depths using a Shaanxi residential case. The results show that the introduction of solar thermal recharging leads to a temperature increase of 26 °C in the shallow ground layer, while the temperature decrease observed in the deep layer is significantly affected by the burial depth of the BHE. Meanwhile, the annual heat extraction decay rates reduce from 14.89 % to 2.62 % (1500 m) and 11.57 %–2.01 % (2500 m), demonstrating enhanced sustainability. Through multi-objective optimization of three key parameters—solar collector area, thermal storage tank volume and BHE burial depth—the optimized configuration achieves synergistic benefits, including an 9.02 % reduction in total energy consumption and a 20.75 % decrease lifecycle cost compared to conventional designs. The presented method can serve as a reference for the rational design of the medium-deep geothermal energy and solar energy coupled heating systems.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"261 ","pages":"Article 125234"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125237
Naveed Ahmad , Shumaila Mustafa , Cui Ping
Electrochemical CO2 reduction (ECO2R) represents an effective approach for converting waste CO2 into value-added products using renewable energy. Despite extensive research on Ag-based electrocatalysts, their limited stability remains insufficient for industrial applications, leading to significant catalytic waste. This study presents a strategy to recycle spent Ag using metal ionic liquids (MILs). BMImCl:CuCl2 (Cu-MIL), BMImCl:FeCl3 (Fe-MIL), BMImCl:ZnCl2 (Zn-MIL), and BMImCl:NiCl2 (Ni-MIL) were employed to dissolve the spent Ag and convert it into AgCl. Among these, Cu-MIL and Fe-MIL demonstrated the highest Ag dissolution, reaching ∼82.85 ± 7 mg g−1-MIL and ∼73 ± 12 mg g−1-MIL, respectively. The recovered AgCl was subsequently reduced to metallic Ag using electrochemical and chemical reduction methods. The impact of these reduction methods on product selectivity was subsequently analyzed. Chemically reduced Ag catalysts (Ag-III) exhibited approximately ∼82 % FECO with a CO partial current density (jCO) of −7.5 mA cm−2, while electrochemically reduced Ag0.96Cu0.04 (Ag-II) demonstrated 65 % HCOO− Faradaic efficiency (FEHCOO−) with jHCOO− of −16 mA cm−2 in the H-cell. Furthermore, the Ag-II catalyst demonstrated jHCOO− up to −213.5 mA cm−2, achieving 59 % FEHCOO−, while the Ag-III catalyst exhibited a jCO of −175 mA cm−2 with 58.4 % FECO in the flow cell. These findings demonstrate a facile method for converting spent Ag into a high-performance catalyst.
电化学二氧化碳还原（ECO2R）是利用可再生能源将二氧化碳转化为增值产品的有效途径。尽管对银基电催化剂进行了广泛的研究，但其有限的稳定性仍然不足以用于工业应用，导致大量的催化浪费。提出了一种利用金属离子液体回收废银的方法。采用BMImCl:CuCl2 （Cu-MIL）、BMImCl:FeCl3 （Fe-MIL）、BMImCl:ZnCl2 （Zn-MIL）和BMImCl:NiCl2 （Ni-MIL）溶解废银并将其转化为AgCl。其中Cu-MIL和Fe-MIL表现出最高的Ag溶解，分别达到~ 82.85±7 mg g - 1-MIL和~ 73±12 mg g - 1-MIL。回收的AgCl随后通过电化学和化学还原方法还原为金属银。随后分析了这些还原方法对产物选择性的影响。化学还原的Ag催化剂（Ag- iii）表现出约82%的FECO， CO偏电流密度（jCO）为−7.5 mA cm−2，而电化学还原的Ag0.96Cu0.04 （Ag- ii）在h电池中表现出65%的HCOO -法拉第效率（FEHCOO−），jHCOO−为−16 mA cm−2。此外，Ag-II催化剂的jHCOO−高达- 213.5 mA cm−2，达到59%的FEHCOO−，而Ag-III催化剂的jCO为- 175 mA cm−2,58.4%的FECO。这些发现证明了一种将废银转化为高性能催化剂的简便方法。
{"title":"Recycling spent silver electrodes into high-performance catalyst using chlorine-coordinated metal-ionic liquids","authors":"Naveed Ahmad , Shumaila Mustafa , Cui Ping","doi":"10.1016/j.renene.2026.125237","DOIUrl":"10.1016/j.renene.2026.125237","url":null,"abstract":"<div><div>Electrochemical CO<sub>2</sub> reduction (ECO<sub>2</sub>R) represents an effective approach for converting waste CO<sub>2</sub> into value-added products using renewable energy. Despite extensive research on Ag-based electrocatalysts, their limited stability remains insufficient for industrial applications, leading to significant catalytic waste. This study presents a strategy to recycle spent Ag using metal ionic liquids (MILs). BMImCl:CuCl<sub>2</sub> (Cu-MIL), BMImCl:FeCl<sub>3</sub> (Fe-MIL), BMImCl:ZnCl<sub>2</sub> (Zn-MIL), and BMImCl:NiCl<sub>2</sub> (Ni-MIL) were employed to dissolve the spent Ag and convert it into AgCl. Among these, Cu-MIL and Fe-MIL demonstrated the highest Ag dissolution, reaching ∼82.85 ± 7 mg g<sup>−1</sup>-MIL and ∼73 ± 12 mg g<sup>−1</sup>-MIL, respectively. The recovered AgCl was subsequently reduced to metallic Ag using electrochemical and chemical reduction methods. The impact of these reduction methods on product selectivity was subsequently analyzed. Chemically reduced Ag catalysts (Ag-III) exhibited approximately ∼82 % FE<sub>CO</sub> with a CO partial current density (j<sub>CO</sub>) of −7.5 mA cm<sup>−2</sup>, while electrochemically reduced Ag<sub>0.96</sub>Cu<sub>0.04</sub> (Ag-II) demonstrated 65 % HCOO<sup>−</sup> Faradaic efficiency (FE<sub>HCOO</sub><sup>−</sup>) with j<sub>HCOO</sub><sup>−</sup> of −16 mA cm<sup>−2</sup> in the H-cell. Furthermore, the Ag-II catalyst demonstrated j<sub>HCOO</sub><sup>−</sup> up to −213.5 mA cm<sup>−2</sup>, achieving 59 % FE<sub>HCOO</sub><sup>−</sup>, while the Ag-III catalyst exhibited a j<sub>CO</sub> of −175 mA cm<sup>−2</sup> with 58.4 % FE<sub>CO</sub> in the flow cell. These findings demonstrate a facile method for converting spent Ag into a high-performance catalyst.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"261 ","pages":"Article 125237"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125231
Kai He, Arindam Banerjee
Understanding flow-body interactions under oscillation is crucial for enhancing energy harvesting or maintaining structural stability. Elevated levels of inflow turbulence further complicate the problem, as they can potentially affect the dynamic behavior of structures; however, their specific impacts on energy harvesting mechanisms and oscillation patterns remain underexplored. In this experimental study, flow-induced motions under elevated turbulent inflow conditions are examined using an active grid turbulence generator that creates turbulence intensities of 13.4 % and 19.4 % in a range of Reynolds numbers between 1800 and 32000. Plain and surface-modified cylinders were examined for their oscillation behaviors in the transverse direction. It was observed that turbulence leads to oscillation suppression at most inflow velocities. Moreover, at the onset of the VIV upper branch, a slight decrease in power by ∼25 % is observed for the turbulent inflow cases compared to the laminar baseline. For a surface-modified cylinder (H/D = 1.6 %), turbulence enhances the mechanical power output as the system hits galloping compared to the laminar case, which transitions to the lower branch. An increase of up to 190 % of mechanical power output is observed. However, as the surface thickness increases (H/D = 3.2 % or higher), mechanical power output under elevated inflow turbulence is up to 40 % lower. A higher coefficient of variation of mechanical power under turbulent inflow conditions indicates decreased oscillation stability.
{"title":"Dynamic behavior and energy harvesting potential of a circular cylinder experiencing flow-induced motions in elevated turbulent inflow","authors":"Kai He, Arindam Banerjee","doi":"10.1016/j.renene.2026.125231","DOIUrl":"10.1016/j.renene.2026.125231","url":null,"abstract":"<div><div>Understanding flow-body interactions under oscillation is crucial for enhancing energy harvesting or maintaining structural stability. Elevated levels of inflow turbulence further complicate the problem, as they can potentially affect the dynamic behavior of structures; however, their specific impacts on energy harvesting mechanisms and oscillation patterns remain underexplored. In this experimental study, flow-induced motions under elevated turbulent inflow conditions are examined using an active grid turbulence generator that creates turbulence intensities of 13.4 % and 19.4 % in a range of Reynolds numbers between 1800 and 32000. Plain and surface-modified cylinders were examined for their oscillation behaviors in the transverse direction. It was observed that turbulence leads to oscillation suppression at most inflow velocities. Moreover, at the onset of the VIV upper branch, a slight decrease in power by ∼25 % is observed for the turbulent inflow cases compared to the laminar baseline. For a surface-modified cylinder (<em>H</em>/<em>D</em> = 1.6 %), turbulence enhances the mechanical power output as the system hits galloping compared to the laminar case, which transitions to the lower branch. An increase of up to 190 % of mechanical power output is observed. However, as the surface thickness increases (<em>H/D</em> = 3.2 % or higher), mechanical power output under elevated inflow turbulence is up to 40 % lower. A higher coefficient of variation of mechanical power under turbulent inflow conditions indicates decreased oscillation stability.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"261 ","pages":"Article 125231"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125236
Qiujie Wang , Hongning Mei , Hong Tan , Zhenxing Li , Hanli Weng , Fei Yan , Mohamed A. Mohamed
With the increasing demand for low-carbon energy transition, biomass energy has attracted significant attention as a renewable and carbon-neutral resource. Biomass-aided hydrogen production (BAHP) plays a crucial role in promoting the green development of energy structures. However, there is limited research focusing on developing comprehensive models for integrating BAHP into systems, especially in the context of optimizing energy production and distribution under uncertainty. To address this gap, this paper proposes a planning method for a low-carbon integrated energy system (IES) incorporating BAHP. First, based on the process characteristics of BAHP, the relationship between hydrogen production efficiency and temperature is established, and a BAHP model is proposed. Second, to tackle the uncertainties in distributed energy resources (DER), a distributionally robust chance-constrained (DRCC) planning model based on Wasserstein distance is constructed. Finally, the model is transformed into a mixed-integer linear programming model through piecewise linearization and conditional value-at-risk (CVaR) theory. The transformed model is solved using the column-and-constraint generation algorithm. Simulation verification is performed on an IES composed of an improved IEEE 33-node power grid and a 23-node thermal network. The results demonstrate that the proposed model reduces the annual carbon management cost by 6.6 % and the total annual operating cost by 4.4 %, thereby confirming its effectiveness and applicability.
{"title":"Planning method of a low-carbon integrated energy system with biomass-aided hydrogen production","authors":"Qiujie Wang , Hongning Mei , Hong Tan , Zhenxing Li , Hanli Weng , Fei Yan , Mohamed A. Mohamed","doi":"10.1016/j.renene.2026.125236","DOIUrl":"10.1016/j.renene.2026.125236","url":null,"abstract":"<div><div>With the increasing demand for low-carbon energy transition, biomass energy has attracted significant attention as a renewable and carbon-neutral resource. Biomass-aided hydrogen production (BAHP) plays a crucial role in promoting the green development of energy structures. However, there is limited research focusing on developing comprehensive models for integrating BAHP into systems, especially in the context of optimizing energy production and distribution under uncertainty. To address this gap, this paper proposes a planning method for a low-carbon integrated energy system (IES) incorporating BAHP. First, based on the process characteristics of BAHP, the relationship between hydrogen production efficiency and temperature is established, and a BAHP model is proposed. Second, to tackle the uncertainties in distributed energy resources (DER), a distributionally robust chance-constrained (DRCC) planning model based on Wasserstein distance is constructed. Finally, the model is transformed into a mixed-integer linear programming model through piecewise linearization and conditional value-at-risk (CVaR) theory. The transformed model is solved using the column-and-constraint generation algorithm. Simulation verification is performed on an IES composed of an improved IEEE 33-node power grid and a 23-node thermal network. The results demonstrate that the proposed model reduces the annual carbon management cost by 6.6 % and the total annual operating cost by 4.4 %, thereby confirming its effectiveness and applicability.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"261 ","pages":"Article 125236"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125232
Mingxiang Lin , Chaohong Guo , Zhigang Li , Yuming Zhu , Shiqiang Liang , Bo Wang , Xiang Xu
The supercritical carbon dioxide Brayton cycle is a closed system with a turbine that generates CO2 gas leaks when operating at high temperatures, pressures, and rotational speeds. Currently, dry gas seals are primarily used to mitigate these issues at the shaft end of rotating components. However, due to their limited heat resistance in high-temperature turbines and the operational challenges they face in such environments, it becomes necessary to apply active cooling measures to the turbine shaft. However, introducing cooling gas affects turbine performance and alters the state of the working fluid, which significantly impacts the overall system performance. This study evaluates the influence of turbine shaft cooling on real operational performance by using an actual system as the basis for analysis. The results show that as the turbine inlet temperature increases, the efficiency loss due to turbine shaft cooling also rises. When the compressor outlet pressure remains constant, an increase in the mass flow rate of working fluid reduces the efficiency loss caused by turbine shaft cooling. When the mass flow rate of the working fluid in the system remains constant, the efficiency loss due to turbine shaft cooling initially increases and then decreases as the compressor outlet pressure rises. Finally, when the cooling gas inlet temperature rises, the system efficiency loss due to turbine shaft cooling increases.
{"title":"Effect of turbine shaft cooling on the performance of supercritical carbon dioxide Brayton cycle system","authors":"Mingxiang Lin , Chaohong Guo , Zhigang Li , Yuming Zhu , Shiqiang Liang , Bo Wang , Xiang Xu","doi":"10.1016/j.renene.2026.125232","DOIUrl":"10.1016/j.renene.2026.125232","url":null,"abstract":"<div><div>The supercritical carbon dioxide Brayton cycle is a closed system with a turbine that generates CO<sub>2</sub> gas leaks when operating at high temperatures, pressures, and rotational speeds. Currently, dry gas seals are primarily used to mitigate these issues at the shaft end of rotating components. However, due to their limited heat resistance in high-temperature turbines and the operational challenges they face in such environments, it becomes necessary to apply active cooling measures to the turbine shaft. However, introducing cooling gas affects turbine performance and alters the state of the working fluid, which significantly impacts the overall system performance. This study evaluates the influence of turbine shaft cooling on real operational performance by using an actual system as the basis for analysis. The results show that as the turbine inlet temperature increases, the efficiency loss due to turbine shaft cooling also rises. When the compressor outlet pressure remains constant, an increase in the mass flow rate of working fluid reduces the efficiency loss caused by turbine shaft cooling. When the mass flow rate of the working fluid in the system remains constant, the efficiency loss due to turbine shaft cooling initially increases and then decreases as the compressor outlet pressure rises. Finally, when the cooling gas inlet temperature rises, the system efficiency loss due to turbine shaft cooling increases.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"260 ","pages":"Article 125232"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.renene.2026.125223
Menghui Li , Jie Wan , Jiakui Shi , Kun Yao , Guorui Ren
Data modal decomposition combined with deep learning networks has demonstrated high efficacy in short-term wind power prediction. However, traditional data modal decomposition methods are plagued by issues such as data leakage and struggle to balance multiple objectives. This study proposes a novel wind power prediction model integrating rolling adaptive successive variational mode decomposition with deep learning networks. In the proposed model, a rolling window mechanism is employed to safeguard future data during the decomposition process. Building upon successive variational mode decomposition, a multi-objective adaptive stopping criterion is incorporated to realize multi-objective optimization throughout the decomposition process. Subsequently, deep learning networks are utilized to fulfill the prediction tasks, with a specific analysis of the coupling relationship between data modal decomposition algorithms and deep learning networks. Based on open-source datasets, an extensive set of comparative experiments were carried out: The optimal rolling window size was determined via window irrelevance verification. Compared with coupled prediction models utilizing various alternative data modal decomposition algorithms, the proposed model demonstrates superior performance. Among models ensuring data reconstruction accuracy, it outperforms the second-ranked model by reducing RMSE, MAPE, and MSE by 26.81 %, 11.48 %, and 18.3 % respectively, while shortening the data modal decomposition time by 60 %. Among models exhibiting excellent predictive performance, it reduces the reconstruction error by approximately 80 % compared to the second-best model. Experiments investigating the coupling of modal decomposition algorithms with deep learning networks confirm that the proposed model and a simple deep learning network constitutes an optimal coupled prediction model.
{"title":"A wind power prediction method fusing deep learning with rolling adaptive successive variational mode decomposition","authors":"Menghui Li , Jie Wan , Jiakui Shi , Kun Yao , Guorui Ren","doi":"10.1016/j.renene.2026.125223","DOIUrl":"10.1016/j.renene.2026.125223","url":null,"abstract":"<div><div>Data modal decomposition combined with deep learning networks has demonstrated high efficacy in short-term wind power prediction. However, traditional data modal decomposition methods are plagued by issues such as data leakage and struggle to balance multiple objectives. This study proposes a novel wind power prediction model integrating rolling adaptive successive variational mode decomposition with deep learning networks. In the proposed model, a rolling window mechanism is employed to safeguard future data during the decomposition process. Building upon successive variational mode decomposition, a multi-objective adaptive stopping criterion is incorporated to realize multi-objective optimization throughout the decomposition process. Subsequently, deep learning networks are utilized to fulfill the prediction tasks, with a specific analysis of the coupling relationship between data modal decomposition algorithms and deep learning networks. Based on open-source datasets, an extensive set of comparative experiments were carried out: The optimal rolling window size was determined via window irrelevance verification. Compared with coupled prediction models utilizing various alternative data modal decomposition algorithms, the proposed model demonstrates superior performance. Among models ensuring data reconstruction accuracy, it outperforms the second-ranked model by reducing RMSE, MAPE, and MSE by 26.81 %, 11.48 %, and 18.3 % respectively, while shortening the data modal decomposition time by 60 %. Among models exhibiting excellent predictive performance, it reduces the reconstruction error by approximately 80 % compared to the second-best model. Experiments investigating the coupling of modal decomposition algorithms with deep learning networks confirm that the proposed model and a simple deep learning network constitutes an optimal coupled prediction model.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"261 ","pages":"Article 125223"},"PeriodicalIF":9.1,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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