Search Results (114)

In the Republic of Moldova, orchard biomass represents an important resource for the production of densified solid biofuels, with peach having the highest sustainable energy potential (33.5 ± 6.54 GJ·ha⁻¹). However, the quality of solid biofuels derived from orchard biomass is often constrained by heterogeneity in moisture content, uneven particle size distribution, and inadequate drying or blending practices along the supply chain. Optimizing the solid biofuel supply chain is therefore essential to minimize feedstock variability, ensure consistent densification quality, and reduce production costs. The aim of this study was to improve the process of producing densified solid biofuels from orchard biomass. Specifically, the study investigated how raw material moisture and particle size influence briquette density and durability, and how ternary mixtures of peach biomass, wheat straw, and sunflower residues can be optimized for enhanced energy performance. All experimental determinations were performed using validated methods and calibrated equipment. The results showed that optimal performance is achieved by shredding the biomass with 4–8 mm sieves and maintaining the moisture content between 6 and 14%, resulting in briquettes with the density of 1.00–1.05 g·cm⁻³, ash content below 3–5%, and an energy yield of 18.4–19.2 MJ·kg⁻¹. Ternary diagrams confirmed the decisive role of peach lignocellulosic residues in achieving high density, low ash content, and increased energy yield, while wheat straw and sunflower residues can be used in controlled proportions to diversify resources and reduce costs. These findings provide quantitative insights into how mixture formulation and process parameters influence the briquette quality, contributing to the optimization of solid biofuel supply chains for orchard and agricultural residues. Overall, this study demonstrates that competitive solid biofuels can be produced through careful balancing of mixture composition and optimization of technological parameters, offering practical guidelines for sustainable bioenergy development in regions with abundant orchard residues. Full article

This work focuses on the development of a diagnostic approach for detecting and localizing sensor faults in an autonomous photovoltaic system. The considered system is composed of a photovoltaic module and a resistive load. However, an adaptation stage formed by a DC/DC voltage boost converter is necessary to transfer energy from the source to the load. The diagnostic scheme is based on a sliding mode observer (SMO) that is robust to uncertainties and parametric variations. The SMO incorporates adaptive gains optimized via parametric adaptation laws, with stability rigorously verified through Lyapunov analysis. The method effectively identifies both independent and simultaneous sensor faults, employing an optimized threshold selection strategy to balance detection sensitivity and false alarm resistance. Simulation results under varying environmental conditions, system parameter fluctuations, and noisy measurement demonstrate the approach’s superior performance, achieving a 20% reduction in mean absolute percentage error (MAPE) and 90% faster settling time compared to existing techniques. These enhancements immediately increase the dependability, efficiency, and lifetime of the PV system, which are critical for lowering carbon emissions and ensuring the economic feasibility of solar energy investments. Key innovations include a novel residual generation mechanism, seamless integration with backstepping sliding mode maximum power point tracking (MPPT) control, and enhanced transient response characteristics. Full article

Rice bran, a rice milling by-product, is a rich source of bioactives such as tocopherols and γ-oryzanol, with promising antioxidant properties. This study compared three extraction techniques—Soxhlet, maceration, and supercritical CO₂ (SC-CO₂)—to identify the method offering the best balance of rice bran oil (RBO) recovery, composition, and sustainability. Although all methods yielded similar oil quantities (~9.5–10.8%), SC-CO₂ extraction achieved superior preservation of bioactives, with the highest tocopherol (116.9 µg/g) and γ-oryzanol (13.2 mg/g) levels. Antioxidant capacity, assessed via FRAP, ABTS, and DPPH assays, was consistently higher in SC-CO₂-extracted oil. The fatty acid profile further confirmed the advantages of SC-CO₂ extraction, with the oil showing a high proportion of unsaturated fatty acids (86.3%) and low saturated content (13.6%). In contrast, Soxhlet- and maceration-extracted oils contained higher saturated fractions (56.5% and 60.1%, respectively) and lower unsaturated content, reflecting the impact of thermal and solvent exposure on the lipid composition. Environmental impacts were quantified through cradle-to-gate life cycle assessment (LCA), showing that SC-CO₂ extraction led to the lowest ecological burden due to its solvent-free process and lower energy demand. Normalizing impacts on both oil yield and bioactive content further highlighted its advantages. These findings place SC-CO₂ extraction as a green, efficient alternative for valorizing rice bran, yielding a high-quality, antioxidant-rich oil suitable for food and cosmetic applications. The integrated chemical and environmental evaluation underscores the potential for a sustainable bioeconomy, effectively turning agricultural residue into functional ingredients. Full article

As the global energy system accelerates its transition towards high penetration of renewable energy and high penetration of power electronic devices, regional power grids have undergone profound changes in their structural forms and component composition compared to traditional power grids. Conventional dynamic equivalencing methods struggle to balance modeling accuracy and computational efficiency simultaneously. To address this challenge, this paper focuses on the dynamic equivalencing of regional power grids and proposes a dynamic equivalencing scheme considering multiple feature constraints. First, based on the structural characteristics and the evolution of dynamic attributes of regional power grids, three key constraint conditions are identified: network topology, spatial characteristics of frequency response, and nodal residual voltage levels. Secondly, a comprehensive equivalencing scheme integrating multiple constraints is designed, which specifically includes delineating the retained region through multi-objective optimization, optimizing the internal system based on coherent aggregation and the current sinks reduction (CSR) method, and constructing a grey-box external equivalent model composed of synchronous generators and composite loads to accurately fit the electrical characteristics of the external power grid. Finally, the proposed methodology is validated on a Back-to-Back VSC-HVDC-connected regional power grid in Eastern Guangdong, China. Results demonstrate that the equivalent system reproduces the original power-flow profile and short-circuit capacity with negligible deviation, while its transient signatures under both AC and DC faults exhibit high consistency with those of the reference system. Full article

Olive tree pruning (OTP), a widely available agricultural residue in Mediterranean countries, represents a promising lignocellulosic feedstock for anaerobic digestion. However, its recalcitrant structure limits its biodegradability and methane yields, necessitating effective pretreatment approaches. In this context, hydrogen peroxide in combination with ultraviolet (UV) radiation (UV/H₂O₂) at ambient temperature was used as a pretreatment method for enhancing methane production from OTP. Three concentrations of H₂O₂ (0, 1, and 3% w/w) alone or in combination with UV radiation, at different retention times (8, 14, and 20 h), were evaluated to enhance OTP depolymerization and methane generation. In addition, the combination of UV/H₂O₂ with alkali (UV/H₂O₂/NaOH) was compared with the typical alkaline pretreatment (NaOH) in terms of lignocellulosic biomass fractionation and biochemical methane potential (BMP). Results showed that increasing H₂O₂ concentration during UV/H₂O₂ pretreatment enhanced hemicellulose solubilization. Both NaOH and UV/H₂O₂/NaOH pretreatment promoted lignin reduction (37.3% and 37.8%), resulting in enhanced BMP values of 330.5 and 337.9 L CH₄/kg TS, respectively. Considering operational energy requirements (heating at 80 °C and irradiance for 20 h) and methane energy recovery, net energy balances of 45.52 kJ and 66.65 kJ were obtained for NaOH and UV/H₂O₂/NaOH, respectively. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Natural gas, a key low-emission energy source with significant strategic value in modern energy systems, necessitates accurate forecasting of its market price to ensure effective policy planning and economic stability. This paper proposes an ensemble framework to enhance natural gas price forecasting accuracy across multiple temporal scales (weekly and monthly) by constructing hybrid models and exploring diverse ensemble strategies, while balancing model complexity and computational efficiency. For weekly data, an Autoregressive Integrated Moving Average (ARIMA) model optimized via 5-fold cross-validation captures linear patterns, while the Long Short-Term Memory (LSTM) network captures nonlinear dependencies in the residual component after seasonal and trend decomposition based on LOESS (STL). For monthly data, the superior-performing model (ARIMA or SARIMA) is integrated with LSTM to address seasonality and trend characteristics. To further improve forecasting performance, three diverse ensemble techniques including stacking, bagging, and weighted averaging are individually implemented to synthesize the predictions of the two baseline models. The bagging ensemble method slightly outperforms other models on both weekly and monthly data, achieving MAPE, MAE, RMSE, and $R^2$ values of 9.60%, 0.3865, 0.5780, and 0.8287 for the weekly data, and 11.43%, 0.5302, 0.6944, and 0.7813 for the monthly data, respectively. The accurate forecasting of natural gas prices is critical for energy market stability and the realization of sustainable development goals. Full article

Global wine production reached about 226 million hectolitres in 2024, with Europe as the largest producer. The winemaking industry generates substantial amounts of by-products, presenting both economic and environmental challenges, as approximately 30% of processed grapes are discarded as waste. This study evaluates various polyphenol extraction techniques from wine residues, utilising data from the literature. Techniques assessed include subcritical water extraction, ultrasound-assisted extraction, conventional solvent extraction, and microwave-assisted extraction, each preceded by a suitable pretreatment. Results show that the extraction method, temperature, solvent, and feedstock type have a strong influence on environmental impacts. Microwave extraction from exhausted grape marc had the highest impact due to its low yields and high energy use during freeze drying. In contrast, subcritical water extraction from red wine residues was the most sustainable, benefiting from its high efficiency, use of water as a solvent, and the rich polyphenol content of red grape residues. When included, drying was the primary contributor to greenhouse gas emissions. Climate change and energy demand were key impact categories, with a renewable energy scenario potentially reducing impacts by up to 90%. Results demonstrate that no single extraction method is universally best; choices must balance efficiency and energy use. This work supports optimising sustainable polyphenol recovery within circular economy and climate goals. Full article

Cascade biorefineries have demonstrated potential due to their ability to further valorize a wide variety of waste, including agricultural residues such as avocado hard waste. The intrinsic safety aspects of these technologies have been scarcely studied. Therefore, an inherent safety analysis was applied to identify and assess the risks of an avocado cascade biorefinery in the Amazon region. Several available databases (online) were used to determine the safety data of the substances in the process, such as CameoChemicals, INCHEM, and NIOSH. Additionally, data from extended mass and energy balance (based on the literature) were collected to assess the process operating conditions. The results show that the process is slightly unsafe, with an overall inherent safety score of 25, and that it achieved a performance of 96% relative to the neutral operating point (24). Chemical risks represented the most critical challenges of the process, with a score of 16, with exothermic reactions, hazardous substances, and dangerous chemical interactions being the most significant sources of risks. On the other hand, the process safety indicator scored 9, indicating that these aspects are not a major source of risk, as the process had mostly low operating conditions (near-environment temperatures and pressures; low inventory), with equipment being the only significant risk factor. Nonetheless, the safety structure subindex for this process was 2, as no clear and recognizable risks existed (at least in the literature) for this type of scheme at the scale analyzed (small scale). This score needs to be studied to properly assess the risks in bioprocesses like cascade biorefineries. Finally, replacing acid hydrolysis with enzymatic hydrolysis, along with another method for bioactive extraction, is recommended to reduce the inherent risks. Full article

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m⁻². Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations. Full article

Multi-UAV cooperative missions demand millisecond-level coordination across three key resource dimensions—battery energy, wireless bandwidth, and onboard computing power—where traditional Min or linearly weighted schedulers struggle to balance safety with efficiency. We propose a prediction-enhanced two-stage T-norm–Choquet–OWA resource aggregator. First, an LSTM-EMA model forecasts resource trajectories 3 s ahead; next, a first-stage T-norm (min) pinpoints the bottleneck resource, and a second-stage Choquet–OWA, driven by an adaptive interaction measure $\varphi$, elastically compensates according to instantaneous power usage, achieving a “bottleneck-first, efficiency-recovery” coordination strategy. Theoretical analysis establishes monotonicity, tight bounds, bottleneck prioritization, and Lyapunov stability, with node-level complexity of only $O\left(1\right)$. In joint simulations involving 360 UAVs, the method holds the average round-trip time (RTT) at 55 ms, cutting latency by 5%, 10%, 15%, and 20% relative to Min, DRL-PPO, single-layer OWA, and WSM, respectively. Jitter remains within 11 ms, the packet-loss rate stays below 0.03%, and residual battery increases by about 12% over the best heuristic baseline. These results confirm the low-latency, high-stability benefits of the prediction-based peak-shaving plus two-stage fuzzy aggregation approach for large-scale UAV swarms. Full article

Background: Pancreatic lipase (PL), the principal enzyme catalyzing the hydrolysis of dietary triacylglycerols in the intestinal lumen, is pivotal for efficient lipid absorption and plays a central role in metabolic homeostasis. Enhanced PL activity promotes excessive lipid assimilation and contributes to positive energy balance, key pathophysiological mechanisms underlying the escalating global prevalence of obesity—a complex, multifactorial condition strongly associated with metabolic disorders, including type 2 diabetes mellitus and cardiovascular disease. Inhibition of pancreatic lipase (PL) constitutes a well-established therapeutic approach for attenuating dietary lipid absorption and mitigating obesity. Methods: With the aim to identify putative PL inhibitors, a Structure-Based Virtual Screening (SBVS) of PhytoHub database naturally occurring derivatives was performed. A refined library of 10,404 phytochemicals was virtually screened against a crystal structure of pancreatic lipase. Candidates were filtered out based on binding affinity, Lipinski’s Rule of Five, and structural clustering, resulting in six lead compounds. Results: In vitro, enzymatic assays confirmed theoretical suggestions, highlighting Pinoresinol as the best PL inhibitor. Molecular dynamics simulations, performed to investigate the stability of protein–ligand complexes, revealed key interactions, such as persistent hydrogen bonding to catalytic residues. Conclusions: This integrative computational–experimental workflow highlighted new promising natural PL inhibitors, laying the foundation for future development of safe, plant-derived anti-obesity therapeutics. Full article

To address the issues of cluster imbalance and inefficient energy utilization in IoT-based microgrids with static nodes, a new routing protocol, called the link efficiency and available-energy-driven routing protocol (LEAD-RP), has been developed. This protocol introduces a flexible method for determining the optimal number of cluster heads by minimizing overall energy consumption during both cluster formation and steady-state transmission phases, thus improving network efficiency. The protocol refines the cluster head selection process by integrating residual energy and distance factors, resulting in a more balanced energy distribution and strategically placed cluster heads. Simulation results demonstrate that the LEAD-RP significantly extends the network’s operational lifetime. Full article

The Saiss plain in northern Morocco covers an area of 2300 km² and is one of the main agricultural contributors to the national economy. However, climate change and water scarcity reduce the region’s agricultural yields. Conventional methods of estimating evapotranspiration (ET) provide localized results but cannot capture regional-scale variations. This study aims to estimate the spatiotemporal evolution of daily crop ET (olives, fruit trees, cereals, and vegetables) across the Saiss plain. The METRIC model was adapted for the region using Landsat 8 data and was calibrated and validated using in situ flux tower measurements. The methodology employed an energy balance approach to calculate ET as a residual of net radiation, soil heat flux, and sensible heat flux by using hot and cold pixels for calibration. METRIC-ET ranged from 0.1 to 11 mm/day, demonstrating strong agreement with reference ET (R² = 0.76, RMSE = 1, MAE = 0.78) and outperforming MODIS-ET in accuracy and spatial resolution. Olives and fruit trees showed higher ET values compared to vegetables and cereals. The results indicated a significant impact of ET on water availability, with spatiotemporal patterns being influenced by vegetation cover, climate, and water resources. This study could support the development of adaptive agricultural strategies. Full article

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article