Search Results (18,031)

Traditional polysaccharide extraction suffers from low efficiency and high energy consumption, while deep eutectic solvents (DESs) are promising sustainable solvents. This study used DES ChCl-LA (1:2) with ultrasonic assistance to extract polysaccharides from red alga A.taxiformis. Optimized via single-factor experiments and response surface methodology (350 W, 1:30 g/mL, 75 °C), the yield reached 11.28% ± 0.50% (1.5 times higher than that obtained by water extraction). Structural characterization revealed that the DES extract was an acidic polysaccharide, mainly composed of galactose (89.2%), glucose (4.9%), xylose (4.9%), and glucuronic acid (1.0%), with a weight-average molecular weight of 99.88 kDa. Density functional theory and molecular dynamics simulations showed that ChCl-LA enhanced galactose solubility via stronger hydrogen bonding (−25.33 vs. −5.06 kcal/mol for water). Notably, the immunological activity of the DES-extracted polysaccharide was significantly compromised compared to the water-extracted counterpart (p < 0.05). At a concentration of 0.25 mg/mL, the water-extracted polysaccharide-treated group exhibited a 33.98% higher neutral red phagocytosis rate in macrophages, a nitric oxide (NO) secretion level of 34.14 μmol/L (94.98% higher) compared with the DES-extracted polysaccharide group, as well as significantly higher secretion levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). The observed disparity in bioactivity is likely due to the distinct chemical profiles resulting from the two extraction methods, including the significantly reduced molecular weight and potential alterations of sulfation degree, monosaccharide composition, and protein content in the DES-extracted polysaccharide. This mechanistic perspective is supported by the relevant literature on the structure–activity relationships of polysaccharides. This study demonstrates the potential of ChCl-LA and elucidates the complex effects of extraction methods on polysaccharide’s structure and function, thereby informing the high-value utilization of A. taxiformis in functional foods. Full article

Non-Intrusive Load Monitoring is an effective method for disaggregating the power consumption of individual appliances from the aggregate load data of a building. The advent of smart meters, Internet of Things devices, and artificial intelligence technologies has significantly advanced the capabilities of non-intrusive load monitoring. However, challenges such as varying sampling frequencies and measurement sensitivities remain. This paper introduces an innovative model incorporating an Adaptive Multi-Scale Attention Integration Module (AMSAIM) to address these issues. The model leverages deep learning and attention mechanisms to improve the accuracy and real-time performance of non-intrusive load monitoring. Validated on the standard UK-DALE dataset, the model consistently demonstrated superior performance. In seen scenarios, our model achieved average F1-scores approximating 0.94 and notably reduced Mean Absolute Error (MAE) values. For washing machines, it achieved an F1-score of 0.99 and MAE of 41.64, outperforming the next best method’s F1-score by 1 percentage point. In challenging unseen scenarios, the model showcased strong generalization, achieving an F1-score of 0.91 for washing machines and reducing MAE to 7.66. Furthermore, an ablation study rigorously confirmed the necessity of the AMSAIM module, showing that the synergistic integration of the efficient multi-scale attention (EMA) and the selective kernel (SK) adaptive receptive field unit is crucial for enhancing model robustness and generalization. Our results highlight the model’s potential for enhancing energy efficiency and providing actionable insights for energy management across various conditions. Full article

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

Under the dual imperatives of carbon neutrality and marine energy transition, hydrogen has emerged as an emerging energy storage carrier, offering a new pathway for offshore wind power consumption. This study addresses the critical challenges of offshore wind power intermittency and hydrogen transport efficiency bottlenecks by proposing an innovative solution. A coordinated planning method for far-offshore wind–hydrogen systems considering nearshore relay points is developed, establishing a multi-stage optimization framework of “offshore hydrogen production—relay point storage and transportation—hierarchical vessel delivery”. By optimizing hydrogen transport routes through coordinated allocation of electrolyzers, storage tanks, and vessel transportation, and designing a hierarchical transportation model that differentiates between ocean-going and nearshore vessels, the simulation results of a coastal area in China demonstrate that, compared with traditional methods, the proposed approach reduces investment costs and operation costs by nearly 10% while decreasing the monthly wind curtailment rate by 10.53%. Full article

Tungsten is a critical metal for advanced industrial applications, yet its supply is challenged by the depletion of high-grade ores. This study presents a comprehensive optimization and mechanistic analysis of the alkaline fusion process for extracting tungsten from wolframite ((Fe,Mn)WO₄) using sodium carbonate (Na₂CO₃). Experimental investigations systematically evaluated the effects of alkali-to-ore ratio, reaction temperature (650–1000 °C), and reaction duration (30–270 min). Optimal conditions were established at a 2:1 Na₂CO₃-to-ore molar ratio, a reaction temperature of 750 °C, and a holding time of 30 min, achieving a tungsten extraction efficiency exceeding 99.9%. This represents a significant improvement in energy and process efficiency over conventional methods. A novel kinetic analysis reveals a two-stage reaction mechanism, transitioning from a slow, diffusion-controlled solid-state reaction (E_a = 243 kJ/mol) to a rapid, autocatalytic liquid-phase reaction (E_a = 212 kJ/mol) upon the formation of a Na₂WO₄–Na₂CO₃ eutectic above approximately 590 °C. The optimal temperature of 750 °C is rationalized as the point that ensures operation within this kinetically favorable liquid-phase regime. Furthermore, a thermochemical analysis of ore impurities indicates that silicon, lead, sulfur, and calcium are effectively sequestered into the slag phase as stable silicates, insoluble lead compounds, and sulfates, highlighting an intrinsic purification benefit. X-ray fluorescence (XRF) and X-ray diffraction (XRD) analyses confirmed minimal residual tungsten in the processed slag. This streamlined process, supported by a robust mechanistic understanding, reduces alkaline consumption, shortens reaction times, and maintains high yields, offering a sustainable and efficient pathway for leveraging declining wolframite resources. Full article

Wooden waste railroad ties preserved with coal tar creosote oil represent a specific source of polluting substances. The aim of this study was to investigate and compare extraction capacity due to solvent extraction of fifteen frequently used organic solvents for the purpose of decontamination treatment of waste wooden railroad ties, while recovering wood for reuse. Pure organic solvents, ethanol 96%, propan-2-ol, deionized water, dichloromethane, acetone, n-hexane, mixture n-hexane/acetone (V/V = 1/1), cyclohexane, methanol, N,N-dimethyl formamide, toluene, ethyl acetate, acetonitrile, amyl acetate, medical gasoline, n-pentane and n-butyl acetate were for leaching pollutants from waste railroad ties. The highest extraction capacity was achieved using dichloromethane, where 7.50 to 7.89 wt.% of total sixteen polycyclic aromatic hydrocarbons were extracted from waste railroad tie chips. The most promising solvents for the treatment exhibited extraction efficiency which decreases in a series dichloromethane > n-hexane/acetone > acetone > methanol > ethanol 96% > propan-2-ol > cyclohexane > toluene > n-hexane. Solvent extraction represents a novel approach for treatment of wooden waste railroad ties. The experiments are based on the search for a management process for the treatment of wood waste railroad ties that is simple, low energy consumption, efficient and could potentially be applied for large scale. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

Residential buildings in Mediterranean regions remain major contributors to energy consumption and greenhouse gas emissions. Existing studies often assess renewable energy technologies or innovative building solutions in isolation, with limited attention to their combined performance across different residential typologies. This study evaluates the integrated impact of solar renewable energy systems and smart building technologies on the energy performance of residential buildings in Cyprus. A typology-based methodology is applied to three representative residential building types—detached, semi-detached, and apartment buildings—using dynamic energy simulation and scenario analysis. Results show that solar photovoltaic systems achieve higher standalone reductions than solar thermal systems, while smart building technologies significantly enhance operational efficiency and photovoltaic self-consumption. Integrated solar–smart scenarios achieve up to 58% reductions in primary energy demand and 55% reductions in CO₂ emissions, and 25–30 percentage-point increases in PV self-consumption, enabling detached and semi-detached houses to approach national nearly zero-energy building (nZEB) performance thresholds. The study provides climate-specific, quantitative evidence supporting integrated solar–smart strategies for Mediterranean residential buildings and offers actionable insights for policy-making, design, and sustainable residential development. Full article

In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article

Surface mulching and nitrogen (N) application are widely used to enhance crop yield and water productivity (WP). However, their combined effects remain unclear. Here, a three-year field experiment was conducted to comprehensively assess the effects of surface mulching (no mulching, B; straw mulching, S; and plastic film mulching, F) and N fertilization (no N application, N0; split application of urea, N1; 1:2 mixture of controlled-release urea and urea, N2) on maize growth, yield, and WP on the Loess Plateau. Application of nitrogen (N) significantly increased evapotranspiration (ET), grain yield, and WP by 4.58%, 176% (from 5215.43 kg ha⁻¹ in N0 to 14,548.21 kg ha⁻¹ in N2), and 166% (from 11.36 kg ha⁻¹ mm⁻¹ in N0 to 30.63 kg ha⁻¹ mm⁻¹ in N2), respectively. Compared with B and S, F increased ET during the pre-silking stage by 16.75% and 23.99%, respectively, and shortened the vegetative period of maize by 3–9 days but extended the duration from the milky stage (R3) to physiological maturity (R6) in the reproductive period by 5–13 days. F significantly increased yield and WP by 9.18% and 8.26% compared with S. Under F combined with N application, deep soil water (100–200 cm) consumption during R1–R3 increased by 15.75 mm and 13.15 mm compared with B and S, respectively. The combination of F and N2 achieved the highest yield (15,648.28 kg ha⁻¹) and WP (32.44 kg ha⁻¹ mm⁻¹) without causing detectable depletion of soil water within the 0–200 cm profile during the study period, providing an effective strategy for enhancing crop yield and improving water–fertilizer use efficiency in semi-arid regions. Full article

In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn–Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios. Full article

Direct-starting of industrial motors has problems such as large current impact (five to eight times the rated current), mechanical stress damage, and low energy efficiency. This paper explores the technological innovations in motor soft-start driven by intelligent control and wide-bandgap semiconductors, and constructs a highly reliable and low energy consumption solution. Firstly, based on a material–device–algorithm system framework, a comparative study is conducted on the performance breakthroughs of SiC/GaN in replacing silicon-based devices. Secondly, an intelligent control model is established and a highly reliable system architecture is developed. A comprehensive review of recent literature indicates that SiC devices can reduce switching losses by up to 80%, and intelligent algorithms significantly improve control accuracy. System-level solutions reported in the industry demonstrate the capability to limit current to 1.5–3 times the rated current and achieve substantial carbon emission reductions. These technologies provide key technical support for the intelligent upgrading of industrial motor systems and the dual-carbon goal. In the future, development will continue to evolve in the direction of device miniaturization and other directions. Full article

Aquaponic systems are the integration of aquaculture and hydroponic systems to enhance productivity, reduce land use, and improve sustainability. This review focused on commonly used life cycle assessment (LCA) methodologies, system boundaries, and functional units used in aquaponics, standard impact categories, and identified hotspots. The scope is worldwide and encompasses a variety of aquaponic designs, fish species, and crops, illustrating the diversity of the systems examined. The analysis indicates that aquaponics provides the considerable environmental advantages of decreased fertilizer consumption and water conservation in comparison with aquaculture and hydroponic system. However, aquaponics systems are characterized by high energy consumption and may produce greater greenhouse gas (GHG) emissions compared to traditional farming methods when reliant on fossil fuel energy sources. Studies show that fish feed production, system infrastructure, and electricity usage for pumps, lights, heating, and other controls are hotspots. Harmonized comparisons of previous studies show methodological differences, especially in fish–plant co-production. Despite these variations, most believe that energy efficiency, renewable energy, feed optimization, and waste reuse may make aquaponics more sustainable. The study recommends the inclusion of broader environmental and social impacts. Also, future focus might be on making a standard functional unit or specifying system boundaries which might provide different accurate outcomes. Full article

This study investigates the influence of the additive illite on the thermal, tribological, and energy efficiency characteristics of calcium grease (CG) at different concentrations (0.05 wt.%, 0.1 wt.%, 0.2 wt.%, 0.4 wt.%, 0.6 wt.%, and 0.8 wt.%). Thermo-gravimetric analysis under inert and oxidative atmospheres revealed that illite enhances thermal stability by increasing inorganic residue under N₂, but promotes oxidative degradation under O₂, limiting practical thermal use to around 400 °C. Grease with 0.1 wt.% illite (CGI2) performed well in tribological tests by reducing the coefficient of friction and wear scar diameter by 53% and 57%, respectively, compared to the base grease. Fleischer’s energy-based wear model showed that all grease samples operated within the mixed friction regime, and CGI2 exhibited a 93% higher apparent frictional energy density and a substantially lower wear intensity that was 47% lower than the base grease, indicating improved energy dissipation and wear resistance. All samples had the same weld load (1568 N), but CGI2 had a 21% higher load–wear index than the base grease in the extreme-pressure test, indicating better load-carrying capacity. In the energy consumption test, a 6% reduction in current consumption was observed in CGI2 in comparison with the base grease. Overall, illite at an optimal concentration significantly enhances lubrication performance, wear protection, and energy efficiency. Full article

Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency. Full article