Search Results (1,396)

This study investigates the optimization of airflow distribution and pressure drops in H14 HEPA minipleat filters through the introduction of the hot-melt cell as the fundamental hydraulic unit governing local flow behavior. A coupled analytical framework integrating Falkner–Skan boundary-layer theory, Darcy–Weisbach channel friction, and Darcy porous-medium flow was developed and experimentally tested using velocity measurements obtained in a 600 m³/h test bench operating at a frontal velocity of 0.45 m/s under laminar-flow conditions. Ten primary geometric configurations and 21 hot-melt distribution scenarios (totaling 31 cases plus an optimized design case) were systematically evaluated by varying cell width (W), inlet height (H_i), and pleat length (L). Experimental and analytical results reveal significant velocity heterogeneity in the vicinity of the filter surface, which progressively decreases with distance from the filter, while localized velocity amplification is observed near the hot-melt separators. The analysis demonstrates that hydraulic diameter, pleat angle, and hot-melt spacing are the dominant parameters governing pressure drop generation and flow redistribution. Among the configurations investigated, a model-predicted optimized design (W = 46.25 mm, L = 55.00 mm, 230 pleats) yields a theoretical pressure drop reduction of up to 29.23% without significantly compromising the effective filtration area. These results provide an analytical framework for pre-prototyping optimization, although experimental verification of physical prototype validation for mechanical integrity and the preservation of initial efficiency, among other governing physical quantities, remains essential. The results demonstrate that the proposed hot-melt cell concept provides a practical engineering framework for the aerodynamic optimization of minipleat HEPA filters, enabling improved flow uniformity and reduced energy consumption in cleanroom applications. Full article

Against the background of resource constraints and the difficulty of independent green technology innovation, green technology transfer (GTT) provides an important pathway for listed port companies to reduce energy-related emissions from their operational activities and advance low-carbon transformation. Based on panel data from 19 listed Chinese port companies from 2011 to 2024, this paper examines the effect and mechanisms of GTT on energy-related operational port carbon emissions (EOPCE) from both theoretical and empirical perspectives. The results show that (1) GTT significantly reduces EOPCE, and this finding remains robust after a series of robustness and endogeneity tests. (2) Mediation analysis indicates that GTT reduces EOPCE by promoting a cleaner energy consumption structure and technological progress. (3) Moderation analysis shows that environmental regulation, regional innovation support, and port financial health significantly strengthen the negative effect of GTT on EOPCE, while no statistically significant moderating effect of port technology absorptive capacity is identified under the current sample and proxy measure. (4) Heterogeneity analysis reveals that the effect of GTT on EOPCE varies across port regions and digitalization levels. Full article

Calcined clays are increasingly recognized as strategic supplementary cementitious materials for reducing clinker consumption and the environmental impacts of construction. However, life cycle assessments typically represent metakaolin production using a single carbon footprint value, despite substantial differences in calcination technology, energy supply, and feedstock characteristics. This study develops a parameterized cradle-to-gate carbon inventory for metakaolin production and evaluates how this variability affects the environmental assessment of low-carbon construction materials. The methodology combines process-based theoretical modeling, mass and energy balances, literature-derived industrial data, and life cycle assessment. A full-factorial scenario analysis was performed for three calcination technologies: rotary kiln, fluidized bed, and flash calcination, while systematically varying fuel source, electricity mix, kaolinite purity, feedstock moisture, and transport distance. The resulting inventories were subsequently propagated into representative LC3 cement and metakaolin-based geopolymer formulations to quantify their influence at the construction-material level. Across 648 production scenarios, the carbon footprint of metakaolin ranged from 34 to 578 kg CO₂e t⁻¹, demonstrating that production conditions define the environmental performance. Fuel selection was identified as the principal emission driver, while moisture content and feedstock purity produced secondary effects. The variability propagated to downstream products, resulting in carbon footprints of 363–527 kg CO₂e t⁻¹ for LC3 cement and 167–357 kg CO₂e m⁻³ for metakaolin-based geopolymers despite identical material compositions. For the building sector, the proposed framework enables designers, material producers, and LCA practitioners to select calcined clay production routes consistent with the carbon targets of concrete, mortar, masonry, precast elements, and other cement-based building applications. It therefore provides a practical basis for incorporating LC3 and geopolymer technologies into lower-carbon building projects while avoiding environmental benefits based on non-representative upstream assumptions. Full article

The global interest in utilizing waste from agro–industrial processes through the implementation of clean technologies is rapidly increasing. This study evaluates the technical, economic, and environmental feasibility of valorizing regional agro–industrial residues in Santander, Colombia, specifically coffee mucilage, cocoa mucilage, and pig manure, using integrated biorefinery scenarios. Modernizing these processes is essential given a theoretical energy potential of 75,000 TJ/year, which could generate up to 20,833 GWh/year. Three biorefinery scenarios were designed and simulated using Aspen Plus^® to produce bio-hydrogen, methane, and methanol. The total plant capacity was established at 31.7 t/day, distributed according to regional biomass availability: 4.62 t/day of cocoa mucilage, 1.83 t/day of coffee mucilage, and 25.25 t/day of pig manure. The technical results for Scenario 1 indicated a reformed H₂ production of 0.15 t/day with an associated preheating energy requirement of 193,365 kJ/h. Environmental impact assessment using the ReCiPe 2015 Midpoint methodology identified Scenario 3 as having the lowest direct CO₂ mass flow (9.28 t/day) due to carbon consumption during methanol synthesis. However, Scenario 1 reported a CO₂ equivalent (CO₂e) of 194.37 t/day, reflecting indirect emissions from thermal and energy requirements. Economic analysis, validated through engineering cost references, demonstrated that while Scenario 3 offers a diverse product portfolio, it is currently not cost-effective as the production costs exceed the total expected income. Consequently, Scenario 2 is identified as the most balanced alternative for the Santander context, offering a sustainable compromise between technical efficiency, environmental mitigation, and economic viability. Full article

Advanced manufacturing and cutting machinery often operate under variable material properties and uncertain load conditions, making real-time process optimization difficult when high-fidelity simulations and physical experiments are costly. To address this problem, this study proposes a physics-guided surrogate-assisted reinforcement learning framework for multi-objective speed regulation of coal–rock cutting machinery. The haulage speed and drum rotational speed are jointly optimized to balance production rate, cutting specific energy consumption, current load, vibration impact, and speed-regulation stability. First, an EDEM–RecurDyn–Simulink co-simulation model was established to obtain cutting current and vibration response data under different coal–rock structures and speed combinations. Similar-material cutting experiments were conducted to validate the vibration response, with root mean square (RMS) relative errors of 3.38%, 4.21%, 5.75%, and 5.03% under full-coal, single-gangue-layer, double-gangue-layer, and full-rock conditions, respectively. Based on these data, an improved physics-informed neural network (PINN) surrogate model was developed by embedding an equivalent coal–rock difficulty factor, a speed-matching factor, a semi-empirical current prior, and a vibration residual calibration mechanism. The surrogate model achieved R² values of 0.9623 and 0.9147 for cutting current and vibration kurtosis, respectively. It was then integrated into a Soft Actor–Critic (SAC) control environment to learn continuous dual-variable speed-regulation policies. Across five independent SAC training seeds, the improved SAC achieved an average theoretical productivity of 207.8033 ± 6.5641 t·h⁻¹ and a cutting specific energy consumption of 0.3387 ± 0.0114 kW·h·t⁻¹. Compared with the fixed-speed, empirical speed-regulation, and conventional SAC strategies, the proposed method increased the average theoretical productivity by 2.65%, 5.01%, and 1.77%, respectively, while reducing the corresponding specific cutting energy consumption by 1.37%, 4.05%, and 2.22%. These results demonstrate that the proposed framework provides an efficient intelligent optimization method for condition-aware speed regulation in complex industrial cutting processes. Full article

Commodity shocks can generate material cross-border exposure because energy, food, industrial metals, and critical minerals are essential inputs to production and consumption. This article develops a transparent, margin-based screening framework for eight commodity categories using 2024 WITS/UN Comtrade reporter-to-world top-country data. The empirical design combines export- and import-side concentration, normalized entropy, an explicit non-substitutability scenario parameter, and a dimensionless trade-scale adjustment. A maximum-entropy independence matrix, $p_{ij}^k = e_i^k m_j^k$, is retained only as a null exposure benchmark; because it is rank one and contains no bilateral information beyond the observed margins, the study does not interpret it as a recovered trade network and does not report graph centrality. The revised results identify copper ores as the highest scale-adjusted vulnerability layer, followed by crude petroleum and liquefied natural gas; lithium carbonates remain highly concentrated but rank lower once trade scale is normalized dimensionlessly. Cobalt ores are retained only as a diagnostic illustration of HS-proxy fragility and are excluded from headline country rankings. Country-level tables report observed exporter and importer shares directly rather than redundant composite transmitter and receiver scores. Sensitivity analysis shows that the commodity ordering is robust to uniform and compressed non-substitutability scenarios and to broad parameter perturbations. Historical plausibility checks against the 2021–2022 European gas shock, Indonesia’s nickel-ore export restrictions, and the 2022 wheat disruption are directionally consistent with the screening signals, although they do not constitute causal validation. The framework is therefore intended as an early-warning screening device, not a graph-theoretic propagation model or a macroeconomic-loss estimate. Full article

Heating, ventilation and air conditioning (HVAC) accounts for an overwhelmingly large proportion of building energy consumption. Mass low-grade cold and heat energy dissipates during system operation, endowing pulsating heat pipes (PHPs) with promising application prospects in building energy systems. Combining experimental tests and the phase-space reconstruction method, this paper investigates the intrinsic correlation between the flow behavior of working fluids and chaotic characteristics under varied working fluids, heating powers and liquid filling ratios. The working fluid type dominates the oscillation characteristics of pulsating heat pipes. When distilled water serves as the working fluid, the attractor presents a scattered distribution. In contrast, the PHP charged with HFE-7100 achieves stable unidirectional circulation with high-frequency, small-amplitude pulsation, forming a densely distributed attractor. Increasing the flow velocity of the working fluid drives the attractor distribution to evolve from scattered to concentrated. Intermittent flow of the working fluid induces a multi-temperature-zone distribution on the tube wall, and the attractor takes on a multi-region spiral morphology. A low liquid filling ratio triggers working fluid dry-out, and the attractor trajectory maintains a continuous unidirectional upward trend; by comparison, the attractor shows a multi-region spiral distribution under high filling ratio conditions. Research on chaotic dynamic characteristic identification, evolutionary law analysis and stable domain regulation of pulsating heat pipes can lay a theoretical foundation for structural optimization and operating condition adjustment of high-performance pulsating heat pipe devices for building waste heat recovery. Full article

This study characterizes the photovoltaic potential of three urban areas in Cuenca, Ecuador, located in the San Sebastián, Totoracocha, and Yanuncay parishes, through twelve technical, energy-related, socioeconomic, and environmental indicators. The analysis was complemented with grid simulations in CYME 9.2 to assess photovoltaic integration capacity in the distribution network and with a multicriteria synthesis using PROMETHEE II. The variables considered included solar irradiation, usable rooftop area, electricity consumption, population density, socioeconomic level, and the operating characteristics of distribution transformers. The results show that photovoltaic potential varies according to urban morphology, electricity demand, and the operating capacity of the grid. Totoracocha exhibited the highest theoretical potential and a self-sufficiency level of 99.1%, but it also recorded the lowest technical utilization factor, at 15.6%, because of transformer constraints. Yanuncay achieved the highest estimated technically available surplus under the evaluated operating scenarios, at 452,737 kWh/year, whereas San Sebastián exhibited the highest technical utilization factor, at 27.2%. The simulations confirmed that transformers constitute the main technical constraint on photovoltaic integration, while medium-voltage lines and voltage levels remained within their operating limits. The technically usable surplus energy could be allocated to induction cooking, electric mobility, or green hydrogen production as alternative, non-simultaneous scenarios. The energy allocated to self-sufficiency could avoid approximately 1022.5 tCO₂/year. Orientation analysis showed statistically significant but limited differences among the evaluated configurations, indicating that orientation should be considered together with other rooftop and grid-related factors rather than as a standalone feasibility criterion. Overall, the proposed integrated framework provides a comparative decision-support basis for sustainable urban photovoltaic planning by jointly considering rooftop potential, electricity demand, grid constraints, socioeconomic conditions, and environmental benefits. Full article

Research on energy poverty has advanced significantly over the past three decades, yet this progress has occurred along largely separate tracks. The Capability Approach (CA) examines how energy shapes household wellbeing, while the energy justice framework examines the equitable distribution, governance, and recognition of energy systems, with limited dialogue between the two. Measurement approaches have likewise evolved in parallel, moving from access and expenditure-based indicators to multidimensional and more recently, required consumption-based frameworks, yet no existing approach connects household-level deprivation to the structural and institutional conditions that produce it. This fragmentation leaves a critical gap: without an integrated theoretical and measurement approach, it remains unclear why energy poverty persists in rural South Africa despite decades of electrification. This paper addresses that gap through an integrative review that examines the conceptual evolution of energy poverty, evaluates existing measurement approaches, and synthesizes the CA, energy justice, and emerging evidence on social capital into a single framework. The review conceptualizes energy poverty as a dynamic process of household energy vulnerability, linking structural conditions, energy system performance, household characteristics, capability conversion, and welfare outcomes, offering a stronger foundation for measurement, interdisciplinary research, and policy aimed at household resilience. Full article

Human augmentation is an important branch of robotics research aimed at reducing metabolic energy consumption, delaying fatigue, and increasing body speed. However, existing evaluation protocols lack systematic frameworks for unpowered hip devices. This study aims to reduce the energy consumption of human movement without providing additional power and to develop a hip-assisted exoskeleton device. Through gait, plantar pressure, and electromyography tests, the system studied the assistance performance of exoskeletons in three wearing states: “No exo.”, “Exo. on”, and “Exo. off”. A comprehensive evaluation method of unpowered lower limb wearable exoskeleton (CE-ULLWE) is established, featuring the novel three-condition design that isolates the structural mass effect from true assistance via the “Exo. off” condition, and integrates multi-indicator metrics including kinematics, dynamics, plantar pressure, EMG, and metabolic simulations. Combining wearing and exercise testing to obtain the human–machine compatibility of exoskeletons and the subjective comfort of users when wearing exoskeletons. Experimental results demonstrate that wearing the exoskeleton increases peak hip and knee angular velocities by 18.5% and 9.0%, reduces joint power, decreases plantar pressure center excursion by 32.3%, and lowers total metabolic energy consumption by 16.0%, confirming its effectiveness in reducing metabolic cost and delaying fatigue. This achievement has important theoretical guidance and practical application value for the design, function, and performance evaluation of wearable assistive exoskeleton products. The proposed CE-ULLWE offers a replicable, multi-indicator framework that clarifies assistive efficacy and guides future exoskeleton optimization. Full article

The building heat demand and the energy system capacity are usually designed independently in solar heating systems for rural houses in cold regions. This leads to oversized system capacity and a lack of collaborative design between the building side and the energy system side. To address these issues, a building–PVT collaborative passive–active design framework was proposed for a typical rural house in Lanzhou, Gansu Province, China. First, a dynamic building thermal model was developed in EnergyPlus. Four typical building configurations, including a baseline house, an insulated house, a sunspace house, and a sunspace house with an intelligent thermal curtain, were established. The hourly heating demand over 8760 h was obtained for each configuration. Then, the hourly building heating demand was used as the unified boundary condition to develop a dynamic PVT heating system model in MATLAB. The responses of the PVT collector area, thermal storage tank volume, and auxiliary heater capacity to building heat demand were analyzed. A CSR was further proposed to establish a quantitative relationship between building heating load reduction and PVT system capacity reduction. Finally, a collaborative multi-objective optimization was carried out using the NSGA-II with the objectives of maximizing the SF and minimizing the LCC. The TOPSIS was adopted to determine the optimal compromise solution. The results show that the integrated passive design combining a sunspace with an intelligent thermal curtain reduces the annual heating energy consumption by 44.0% compared with the baseline house. The reduction in building heat demand simultaneously decreases the required PVT system capacity. The PVT collector area, thermal storage tank volume, and auxiliary heater capacity decrease by 44.14%, 27.55%, and 25.78%, respectively. This indicates that passive building energy-saving measures significantly change the design boundary of the energy system. The proposed CSR effectively quantifies the capacity substitution effect of building-side energy saving on the PVT heating system, and the integrated passive design achieves the highest overall CSR. The multi-objective optimization generates a uniformly distributed Pareto front. The optimal compromise solution achieves an SF of 86.41% with an LCC of 8.34 × 10⁴ CNY. The recommended configuration includes a 50 mm insulation layer, a 40 m² sunspace, a 12.62 m² PVT collector area, and a 1.23 m³ thermal storage tank. Compared with the initial design, the optimized configuration significantly improves solar energy utilization while maintaining a low LCC and further reducing the required auxiliary heater capacity. This study establishes a collaborative analysis framework between building heat demand and PVT system capacity. A quantitative mapping method between building heating load and system capacity is proposed. The framework enables integrated optimization of building thermal design and energy system capacity configuration. The proposed method provides a new theoretical approach and practical guidance for the design of solar heating systems for rural houses in cold regions. Full article

To address the mismatch between solid conveying efficiency and melting efficiency in grooved barrel single-screw extruders (SSEs), the effects of barrel groove and screw channel structural parameters on the melting start point and melting length were systematically investigated based on the groove-channel coupled melting (GCCM) theory. Three extruder configurations—smooth barrel, spiral-grooved IKV (Institut für Kunststoffverarbeitung), and GCCM—were designed and tested on a hydraulically driven clamshell barrel SSE platform with a screw diameter of 45 mm and a length-to-diameter ratio of 30:1. Low-density polyethylene (LDPE) grade 607 was used as the model material. The results demonstrate that increasing the barrel groove depth shifts both the melting start point and melting length downstream, whereas the groove width has negligible effects. A minimum melting start point is achieved at a groove pitch of 4D. At a screw speed of 30 r/min, the GCCM extruder equipped with a BARR barrier screw achieves a melting start point 24.0% earlier than the IKV extruder and a melting length shortened to 62.7% of the IKV value, representing reductions of 27–35% and 21–31% compared to reported barrier screw and Maddox screw melting lengths, respectively. The actual throughput reaches 93.7–95.7% of the theoretical solid conveying throughput, with specific energy consumption only 8.5% higher than the IKV extruder and throughput fluctuation reduced to 23.7%. Complete melting is achieved at a melting zone temperature of only 110 °C, confirming the dominant role of internal frictional heat. This study provides systematic experimental data and theoretical guidance for the structural optimization of grooved barrel SSEs. Full article

Thermoelectric air conditioning (TEAC) systems are gaining attention as refrigerant-free, solid-state cooling technologies due to their compactness, low noise, and environmental benefits. However, their widespread application is constrained by low energy efficiency and the limited experimental understanding of the coupled influence of electrical and aerodynamic operating parameters. Most existing studies address thermoelectric cooling under isolated conditions or rely on theoretical modeling, leaving a clear gap in the experimental quantification of the interactive effects of applied voltage and air velocity on system performance. To address this gap, the present study experimentally investigates a laboratory-scale TEAC system equipped with four thermoelectric cooler (TEC) modules (model TEC1-12706). The system was tested under controlled conditions by varying the input voltage from 6 to 12 V and the air velocity from 1 to 3 m/s. Key performance indicators, including cooling capacity, power consumption, cold-side temperature, and coefficient of performance (COP), were systematically measured and analyzed. The results show that increasing the applied voltage from 6 to 12 V enhances cooling capacity by approximately 50.4%, while significantly increasing electrical power consumption, leading to a 59% reduction in COP due to intensified Joule heating. Conversely, increasing air velocity improves convective heat transfer, resulting in a COP enhancement of about 24% with relatively stable power input. The findings highlight a clear trade-off between cooling capacity and energy efficiency and identify a practical operating region for balanced TEAC performance. Full article

Cabin temperature prediction is a key technique for improving occupant thermal comfort and reducing energy consumption of thermal management systems. Most existing cabin temperature prediction models do not integrate external meteorological information, resulting in poor adaptability to complex and variable environmental conditions. To address this issue, this paper proposes a multi-source data fusion prediction framework combined with regional meteorological information to achieve accurate cabin temperature prediction under real-world operating scenarios. Shapley additive explanations (SHAP) reveal key feature contributions and interaction mechanisms for dynamic cabin temperature prediction and improve the interpretability of the data-driven model. The precise prediction results quantify the changing patterns of cabin temperature rise and passive cooling. Based on these findings, adaptive strategies for low-temperature preheating and shutting down parking air conditioners early are designed to balance energy efficiency and occupant thermal comfort. Experimental results show that under snowy conditions, compared with the baseline model, the proposed framework reduces mean absolute errors (MAEs) by 13.05%, while the MAE decreases by 22.8% under sunny conditions. The introduction of meteorological data reduces the MAE of the XGBoost and GRU models by 14.9% and 23.28%, respectively. SHAP analysis further uncovered the feature interaction rules governing cabin temperature evolution. In addition, the proposed air-conditioning optimization strategy achieves significant energy savings without compromising occupant thermal comfort. Specifically, the steady-state energy consumption during sunny-morning preheating is reduced by 21.5%, and the pre-shutoff optimization yields energy-saving benefits ranging from 4.9% to 25.5% under typical winter conditions. This study provides theoretical support and serves as an engineering reference for the optimal design of intelligent vehicle thermal management systems. Full article

Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over $8.8\times$ lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately $4.7\times$ reduction obtained at a cost of only 2.29 percentage points in R@1. Full article