Search Results (296)

Data-constrained island power systems with increasing shares of variable renewable energy (VRE) face growing challenges in maintaining reliability while preserving market efficiency. Existing reserve sizing practices typically rely on either fixed deterministic rules or data-intensive probabilistic methods, both presenting practical limitations in Small Island Developing States (SIDS). This paper develops a market-based framework for the co-optimization of energy and operating reserves in low-inertia island power systems, in which reserve requirements are established using historically observed extreme generation or load deviations that represent operationally validated high-risk system conditions, while reserve allocation and pricing emerge from the co-optimization process. By relying on observed operational variability, the proposed approach avoids explicit probabilistic uncertainty modeling while retaining sensitivity to system stress conditions. The approach is evaluated using a stylized island power system representative of Caribbean SIDS. Results show that reserve requirements are highly sensitive to operating conditions, reaching up to 26.7% of demand under high variability and significantly exceeding conventional fixed reserve criteria. The framework reduces non-served energy, improves reserve allocation efficiency, and generates scarcity-consistent reserve prices under stressed conditions. These findings demonstrate that the proposed methodology provides a practical intermediate solution between deterministic and probabilistic reserve sizing approaches while remaining suitable for data-constrained island power systems. Full article

To alleviate grid peak-shaving pressure from high-penetration renewable energy integration, coupling thermal energy storage (TES) with coal-fired power plants is an effective approach for enhancing operational flexibility. This paper systematically investigates the peak-shaving performance of a coal-fired unit integrated with molten salt storage and heat pump storage systems, focusing on load response characteristics, peak-shaving capability, and the influence of discharge strategies on thermodynamic performance under various rated turbine heat acceptance (THA) conditions. The results indicate that, under identical peak-shaving capacity, the molten salt system exhibits greater storage capacity, which increases with rising THA levels, whereas the heat pump storage capacity remains largely THA-independent. Regarding discharge strategies, replacing high-pressure extraction steam achieves the fastest ramp rate and largest incremental power output, introducing steam into the intermediate-pressure cylinder yields the slowest response but highest round-trip efficiency, and replacing low-pressure extraction steam delivers the smallest peak-shaving capacity and lowest round-trip efficiency. Although TES integration slightly reduces thermal efficiency due to heat exchange losses, this trade-off is justified by significant flexibility improvement, demonstrating clear engineering value for high-renewable grids. Full article

Lignin, an abundant and renewable aromatic biopolymer, represents a largely underutilized resource for the sustainable production of high-value chemicals. Among lignin-derived intermediates, protocatechuic acid (PCA) and catechol have emerged as key platform molecules due to their versatile applications in pharmaceuticals, polymers, and fine chemicals. This review provides a critical overview of microbial lignin valorization focusing on the microbial conversion of lignin-derived aromatics into PCA and catechol. It highlights recent advances in lignin depolymerization techniques, including thermochemical and biological approaches, and examines their influence on the generation of bioavailable aromatic feedstocks. We systematically discuss microbial biofunneling pathways that converge diverse lignin-derived compounds into PCA and catechol, emphasizing the role of central metabolic nodes and enzymatic transformations such as O-demethylation, hydroxylation, and decarboxylation. We treat protocatechuate decarboxylase (PCADC) as the central enzymatic bridge linking PCA and catechol. However, it should be noted that many reported microbial production strategies have been demonstrated using purified lignin-derived aromatic model compounds (e.g., ferulate, vanillate, p-coumarate, and PCA) rather than authentic lignin streams, highlighting the need for improved integration of lignin depolymerization and downstream bioconversion processes. Furthermore, the review explores state-of-the-art metabolic engineering strategies, including gene deletions, pathway rewiring, transporter engineering, and CRISPR-based regulation, to enhance product yields and selectivity. Despite significant progress, several challenges persist, including lignin recalcitrance, heterogeneity of depolymerization products, toxicity of intermediates, and limited enzyme efficiency. This review identifies key knowledge gaps and proposes future directions for integrating synthetic biology, adaptive evolution, and systems-level optimization to develop robust microbial cell factories. Overall, this work provides a strategic framework for advancing lignin bioconversion into PCA and catechol, contributing to the development of sustainable biorefineries and a circular bioeconomy. Full article

The growth of circular economy concepts has resulted in the need to develop methods for assessing circularity in geotechnical infrastructure systems. This paper proposes, for the first time, an initial framework for assessing circularity of geotechnical infrastructure systems and then uses it to assess the GeoBarrier System (GBS) as a case study. The framework considers five domains: water, energy, material, waste, and site quality. It was formulated based on a literature review, stakeholder requirements, and the case-specific characteristics of the GBS. Laboratory characterisation and numerical analyses were performed to assess the engineering performance of the system and support the circularity assessment. The results show that water circularity was the highest at 50.0%, meaning that half of the water in the system was effectively reused or recirculated. In contrast, energy circularity was absent due to the lack of renewable energy integration. Material and waste circularity performed at a moderate level, lower than water circularity, reflecting partial use of recycled materials and reuse of excavated soil. The site quality evaluation resulted in a score of 2.250, which, together with the other indicators, suggests an intermediate overall level of circularity performance. The assessment identified opportunities to improve circularity through greater on-site reuse of excavated waste, renewable energy integration, and improved site planning. The proposed framework is the first circularity/sustainability system specific to geotechnical infrastructure systems; therefore, apart from GBS, it is intended for potential applicability for evaluating circularity in other geotechnical systems. Full article

Background: This review aims to provide a comprehensive and practical overview of the evolving role of autologous transplantation in leukaemias, a strategy that was once largely abandoned but has recently regained interest in selected clinical settings. Methods: We reviewed the historical development of autologous transplantation in acute leukaemias, including the early period during which autologous transplantation was considered inferior to allogeneic approaches because of limited graft purification techniques and the inability to induce effective graft-versus-leukaemia (GVL)-like immune responses. We further summarise more recent experimental strategies aimed at improving stem cell purification and enhancing anti-leukaemic immune activity in autologous settings. In addition, we discuss how advances in measurable residual disease (MRD) assessment and molecular risk stratification have contributed to the renewed interest in autologous transplantation in selected subgroups of leukaemia patients. Results: This review identifies clinical situations in which autologous transplantation remains an important therapeutic option, including plasma cell leukaemia, where it continues to represent a standard first-line approach. We also discuss well-defined patient subgroups, particularly selected AML subtypes with intermediate-risk molecular profiles and acute promyelocytic leukaemia (APL) in second remission, in which outcomes following autologous transplantation may be comparable to, or occasionally superior to, those achieved with allogeneic transplantation. In contrast, autologous transplantation currently plays only a limited role in diseases such as chronic lymphocytic leukaemia (CLL) and chronic myeloid leukaemia (CML). Although attempts to induce potent anti-leukaemic immune effects in autologous settings have so far shown limited clinical efficacy, several emerging strategies appear promising and may further expand the role of autologous transplantation, particularly in elderly or frail patients. Discussion: Overall, current molecular and MRD-based risk stratification strategies, together with emerging immunological and graft-manipulation approaches, may redefine the role of autologous transplantation as a personalised therapeutic option in selected subgroups of leukaemia patients. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean $R^2=0.994$, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making. Full article

Driven by the growing demand for sustainable polymers, polylactic acid (PLA) has attracted increasing attention due to its renewable origin and biodegradability. Lactide, the key cyclic monomer for PLA production via ring-opening polymerization (ROP), plays a decisive role in determining the molecular weight, stereoregularity, and final performance of PLA materials. However, current lactide synthesis processes still face significant challenges, including competing side reactions under high-temperature and high-vacuum conditions, difficulties in controlling stereochemical purity, and relatively high energy consumption. In this review, recent advances in lactide synthesis are systematically analyzed by examining the two principal industrial routes: the one-step process based on the direct dehydration–cyclization of lactic acid (LA), and the two-step process involving prepolymerization of LA followed by depolymerization/cyclization of oligomeric intermediates. The reaction mechanisms, key intermediates, and major side reactions—including racemization, transesterification, and deep polycondensation—are discussed, together with the regulatory roles of catalytic systems and reaction–separation coupling strategies. Comparative analysis reveals that the one-step route offers advantages in process integration and potential energy efficiency, whereas the two-step route provides superior control over stereochemical purity and process stability. Future research directions focusing on green catalysts, process intensification, and sustainable lactide production are also highlighted. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

Intermediate crops, such as Brassica carinata and Camelina sativa, offer a promising pathway for expanding sustainable feedstock supply for advanced biofuels in Europe without competing with food and feed production. This study applies a competitive priority framework to assess the performance of intermediate crops in Italy and Spain, integrating agronomic, environmental, and regulatory dimensions. Using Member State-specific agroecological conditions, cost structures, and land-use profiles, the analysis identifies key challenges across land use and biomass-production stages and links them to measurable indicators and targeted optimisation strategies. Evidence from both experimental studies and modelling indicates that camelina can be seamlessly integrated into existing cropping systems without compromising crop yields or triggering soil carbon losses. These findings highlight the potential of intermediate crops to enhance soil health, to reduce erosion, and to stabilise yields under climate variability. This study also examines the policy conditions required to enable deployment, emphasising the need for region-specific crop calendars, digital traceability systems, and coherent implementation of RED III, CAP, ESCA, and CRCF frameworks. The distinction between volumetric and GHG-based targets is shown to be critical: intermediate crops perform strongly under GHG-based intensity reduction frameworks that reward soil carbon gains and sustainable cultivation. National instruments in Italy and Spain—including the Piano Strategico della PAC, Decreto Biocarburanti, Plan Estratégico de la PAC, and Real Decreto 376/2022—provide mechanisms for operationalising these strategies. Overall, the results demonstrate that intermediate crops can contribute meaningfully to both national and EU renewable energy, soil restoration, and climate mitigation objectives when supported by coherent agronomic and policy frameworks. Full article

The increasing global demand for renewable energy has intensified the search for high-efficiency and cost-effective solar cell technologies. Quantum dot-sensitized solar cells (QDSSCs) have emerged as promising candidates due to their tunable optoelectronic properties and enhanced light absorption. In this study, SnS quantum dots were synthesized from dithiocarbamate complexes using different ligands, namely m-toluidine (SnS1), aniline (SnS2), and p-toluidine (SnS3), to investigate the influence of precursor chemistry on material properties and device performance. Structural analysis confirmed the formation of an orthorhombic phase for all samples, while morphological studies revealed well-dispersed nanocrystals for SnS1 (5.93 nm), increased aggregation for SnS2 (8.57 nm), and partially fused domains with an intermediate size for SnS3 (6.67 nm). Optical measurements showed bandgap energies of 2.8, 2.2, and 2.7 eV for SnS1, SnS2, and SnS3, respectively, with SnS3 exhibiting reduced charge-recombination behaviour. Photovoltaic devices fabricated using these materials yielded power conversion efficiencies of 3.40, 2.03, and 7.63% for SnS1, SnS2, and SnS3, respectively, with no significant improvement observed for bifacial configurations. The superior performance of SnS3 is attributed to an optimal balance between light absorption, morphology, and charge transport properties, highlighting the critical role of precursor ligand selection in tuning quantum dot characteristics for improved QDSSC performance. Full article

In the renewable energy-driven “green electricity–green hydrogen–green ammonia” pathway, the development of low-temperature and low-energy-consumption ammonia synthesis technologies is of great significance. In this work, a plasma-catalytic ammonia synthesis system was established using a coaxial dielectric barrier discharge (DBD) reactor. The effects of different catalysts, including Ag, Cu, γ-Al₂O₃, BaTiO₃ and Co/BaTiO₃, Ni/BaTiO₃ on ammonia synthesis performance were systematically investigated. The reaction process was analyzed using voltage–current waveforms, Lissajous figures, and optical emission spectroscopy (OES). The results show that different catalytic systems have a significant influence on ammonia synthesis performance, with the promotional effect ranked as follows: Ni/BaTiO₃ > Co/BaTiO₃ > BaTiO₃ > Ag > γ-Al₂O₃ > Cu. Among them, Ni/BaTiO₃ exhibited the best performance. Under the conditions of N₂:H₂ = 1:1 and a gas flow rate of 2.5 L/min, the NH₃ synthesis rate reached 259.48 μmol/min, and the maximum energy efficiency reached 1.40 g-NH₃/kWh. Catalyst characterization results indicate that the BaTiO₃ support maintained a stable crystal structure, while the loaded metal species were highly dispersed and uniformly distributed on the support surface, which is beneficial for the adsorption and conversion of reactive species on the catalyst surface. Discharge characteristic analysis shows that the introduction of BaTiO₃ enhanced the local electric field and improved the uniformity of micro-discharges, while the further incorporation of metal active components strengthened the micro-discharge behavior. OES results reveal that the intensities of characteristic emission lines, such as NH, N₂⁺, and Hα, were significantly enhanced in the Ni/BaTiO₃ system, facilitating the formation and conversion of NH_x intermediates. The superior performance of Ni/BaTiO₃ is attributed to the coupling between BaTiO₃-induced dielectric enhancement and Ni-promoted surface hydrogenation and NH₃ desorption. This work provides mechanistic insight into catalyst-dependent DBD plasma-catalytic ammonia synthesis and offers an experimental basis for the further optimization of plasma-based ammonia production. Full article

Short-term wind speed forecasting plays a critical role in enabling the reliable integration of renewable energy and supporting the sustainable operation of power systems. However, traditional dual-frequency decomposition methods oversimplify wind speed dynamics by separating them into only high-frequency disturbances and low-frequency trends, making it difficult to capture intermediate-frequency transitional dynamics. Additionally, single models struggle to adapt to multi-scale temporal features, limiting forecasting performance. To address these issues, this paper proposes a tri-band frequency-aware heterogeneous expert collaboration framework. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed for signal denoising, followed by Particle Swarm Optimization-Time Varying Filtering-based Empirical Mode Decomposition (PSO-TVF-EMD) for multi-scale signal disentanglement. Then, Permutation Entropy (PE) is used to construct a tri-band structure consisting of high-, intermediate-, and low-frequency components. A frequency-aware expert routing mechanism assigns Bayesian Optimization Long Short-Term Memory (BO-LSTM), an improved Markov model, and Auto-Regressive Integrated Moving Average (ARIMA) to the corresponding frequency bands. Finally, a reliability-aware cooperative aggregation strategy integrates predictions from multiple experts. Experimental results show that representative baseline models, including BO-LSTM, Markov, ARIMA, Gated Recurrent Unit (GRU) and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), achieve MAE values ranging from 0.308 to 0.429, while the proposed framework reduces the Mean Absolute Error (MAE) to 0.193 and Root Mean Square Error (RMSE) to 0.274, with a Mean Absolute Percentage Error (MAPE) of 7.35% and R² of 0.927. Compared with the dual-frequency decomposition scheme (MAE = 0.266), the proposed tri-band framework achieves an average improvement of approximately 28.1%. The results suggest that explicitly modeling intermediate-frequency dynamics and aligning model inductive biases with multi-scale signal characteristics can effectively enhance short-term wind speed forecasting performance. Full article