Search Results (907)

Catalytic oxidation has proven to be an effective method for treating low-concentration ventilation air methane. However, regenerative catalytic oxidizers (RCOs) used for ventilation air methane (VAM) treatment often face engineering challenges such as low oxidation efficiency and uneven heat transfer, which limit their overall performance and reliability. This study proposes a CFD-based structural optimization approach that couples flow field, temperature field, concentration field, and chemical reaction processes to systematically analyze the heat transfer and reaction mechanisms within the RCO. The key operational parameters of the reaction process were further discussed. The research focuses on improving temperature uniformity and enhancing methane conversion efficiency to achieve superior thermal efficiency and more effective methane mitigation. The results show that increasing the number of the electric heating rods and rearranging their configuration improved the temperature uniformity of the catalyst layer by 0.2842 (from 0.5462 to 0.8304). Additionally, the installation of a flow distribution plate further enhanced temperature uniformity by 0.1481 (from 0.8304 to 0.9785). As a result of these structural optimizations, the methane conversion rate of the new system increased significantly from 65% to 95%. This study offers valuable insights for future RCO design and optimization, paving the way for more efficient and reliable VAM treatment technologies. Full article

This study focuses on the operational energy consumption of existing thermal power plant buildings in China’s hot-summer, cold-winter regions. Unlike conventional civil buildings, thermal power plant structures feature intense internal heat sources, large spatial dimensions, specialized ventilation requirements, and year-round industrial waste heat. Consequently, the energy consumption characteristics and energy-saving logic of their building envelopes remain understudied. This paper innovatively employs a combined experimental approach of field monitoring and energy consumption simulation to quantify the actual thermal performance of building envelopes (particularly exterior walls, doors, and windows) under current operating conditions, identifying key components for energy-saving retrofits of the main plant building envelope. Due to the fact that most thermal power plants were designed relatively early, their envelope structures generally have problems such as poor insulation performance and insufficient air tightness, resulting in severe energy loss under extreme weather conditions. An energy consumption simulation model was established using GBSEARE software. By focusing on heat transfer coefficients of exterior walls and windows as key parameters, a design scheme for energy-saving retrofits of building envelopes in thermal power plants located in hot-summer, cold-winter regions was proposed. The results show that there is a temperature gradient along the height direction inside the main plant, and the personnel activity area in the middle activity level of the steam engine room is the most unfavorable area of the thermal environment of the steam engine room. The heat transfer coefficient of the envelope structure does not meet the current code requirements. The over-standard rate of the exterior walls is 414.55%, and that of the exterior windows is 177.06%. An energy-saving renovation plan is proposed by adopting a composite color compression panel for the external wall, selecting 50 mm flame-retardant polystyrene EPS foam board for the heat preservation layer, adopting 6 high-transmittance Low-E + 12 air + 6 plastic double-cavity for the external windows, and adding movable shutter sunshade. The energy-saving rate of the building reached 55.32% after the renovation. This study provides guidance for energy-efficient retrofitting of existing thermal power plants and for establishing energy-efficient design standards and specifications for future new power plant construction. Full article

This paper presents a method for determining the equivalent circuit parameters of magnetically coupled air-core coils used in wireless power transfer (WPT) systems. The proposed approach enables fast and accurate modeling of inductively coupled energy transfer structures, which is essential for the design and optimization of high-efficiency wireless energy systems. The equivalent circuit of the analyzed system was developed using Cauer circuits, while a two-dimensional (2D) axisymmetric electromagnetic field model was employed to derive the equations. The model was implemented in proprietary software based on the edge-element finite element method (FEM) using the A–V formulation. The A–V formulation combines the magnetic vector potential A and the electric scalar potential V, enabling simultaneous representation of magnetic field distribution and current flow in conducting regions. The eddy currents in the conductors were considered in the electromagnetic field analysis. Simulations were carried out for two operating states: short-circuit and idle. The results were used to determine the parameters of the horizontal and magnetizing branches of the equivalent circuit of considered system and to analyze the frequency dependence of the resistances and inductances of the coupled coil system. The proposed modeling approach provides an effective and energy-oriented tool for the design of wireless power transfer systems with improved efficiency and reduced computational cost. The proposed method reproduces impedance characteristics with an accuracy of 0.2 × 10⁻³% in the idle state and 1.4 × 10⁻³% in the short-circuit state compared to the full FEM model, while significantly reducing the computation time. Full article

The model of a liquid desiccant dehumidification air conditioning (LDAC) system is one of the key foundations for achieving efficient cooling, dehumidification and regeneration, and saving energy consumption. The data-driven modeling method does not need to understand the complex heat and mass transfer mechanism and equipment physical information, thus the modeling complexity is greatly reduced. This paper proposes a temperature and humidity prediction model integrating the Black Kite Algorithm (BKA), Bidirectional Temporal Convolutional Network (BiTCN), Bidirectional Long Short-Term Memory (BiLSTM), and Self-Attention mechanism (SA). The model extracts local spatiotemporal features from sequence data through BiTCN, enhances the understanding of contextual dependencies in temporal data using BiLSTM, and employs the SA to assign dynamic weights to different time steps. Furthermore, BKA is adopted to optimize the hyperparameter combinations of the neural network, thereby improving prediction accuracy. To validate the model performance, an experimental platform for an LDAC system was established to collect operational data under multiple working conditions, constructing a comprehensive dataset for simulation analysis. Experimental results demonstrate that compared to conventional time-series prediction models, the proposed model achieves higher accuracy in predicting outlet temperature and humidity across various operating conditions, providing reliable technical support for system real-time control and performance optimization. Full article

The article presents the results of a comprehensive full-scale investigation of the influence of solar radiation on the thermal behavior of the exterior envelope systems of two residential buildings of different heights—a 9-storey building in Turkestan and a 25-storey building in Shymkent. The façade systems of both buildings consist of a multilayer enclosure with a ventilated air cavity, 100 mm wide in the 9-storey building and 50 mm wide in the 25-storey building. The objective of the study was to determine the diurnal and vertical dynamics of temperature fields, analyze the thermal inertia of the materials, and assess the effect of façade geometry on heat-transfer performance. Thermographic measurements were carried out during key periods of the day (7:00, 10:00, 13:00, and 17:00), which enabled coverage of the full solar-insolation cycle. The results showed that the maximum temperatures of the external cladding reached 48–52 °C for the 9-storey building and 53–58 °C for the 25-storey building, with a vertical temperature gradient of 3–7 °C. The temperature of the interior surface varied within 28–32 °C and 29–34 °C, respectively, reflecting the influence of both solar heating and the width of the ventilation cavity on heat transfer. It was found that reducing the air-gap width intensifies natural convection and decreases the thermal inertia of the system, resulting in sharper temperature fluctuations. The study demonstrates that current design standards insufficiently account for the vertical non-uniformity of solar exposure and the aerodynamic processes within the ventilation channel. The findings can be used in the design of energy-efficient façade systems, in the refinement of regulatory methodologies, and in the development of heat-transfer models for high-rise buildings under conditions of increased solar radiation. Full article

This study proposes a scalable cyber–physical system (CPS) framework utilizing a hierarchical five-layer architecture to enhance indoor environmental quality and energy efficiency. The methodology integrates a Random Forest-based predictive model trained on a 22-month longitudinal dataset (2024–2025) to separate climatic effects from occupancy-driven loads. This study prioritized the development of a high-precision and cost-effective monitoring architecture to address the persistent challenge of sustaining thermal comfort in subtropical academic laboratories. The proposed system achieved a validation mean absolute percentage error (MAPE) of 2.50%, indicating strong predictive reliability. Hardware expenditures were below USD 400, substantially reducing barriers to broader adoption. Field deployment confirmed an operational EUI of 188.6 kWh/m²·year, which is 28.5% lower than prevailing regional benchmarks, while consistently meeting stringent indoor air quality (IAQ) requirements. Additionally, simulation modules calibrated with the validated dataset indicated a further 15–20% reduction potential through the application of active control strategies. Collectively, these findings establish a transferable empirical reference for climate-responsive operational practice. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

The increasing demand for sustainable climate control has spurred research into our hydronic conditioning system with a patented radiant ceiling panel (AU 2024227462) inspired by biomimetic methodologies. This study develops a framework that utilizes natural systems for heating and cooling, enhancing system performance and environmental sustainability. Biometric analysis was the primary method for testing these systems, focusing on heat transfer mechanisms modeled after human biology. Findings indicate that the proposed hydronic system excels in cooling mode, achieving an average capacity of 95 W/m² while maintaining thermal comfort levels (PMV) with solar heat gains under 1.5 kW in an 18 m² space. However, in heating mode, the system shows a capacity of 85 W/m² but struggles with vertical air-temperature stratification, especially in the radiant ceiling component. This highlights the potential of biomimetic designs to enhance energy efficiency and comfort in sustainable development. The hydronic panel system parallels the human body in energy transfer; both can emit 75–90 W/m² through radiation. Convection over the panel can increase energy transfer by 50–80%, akin to the human body’s heat loss through convection. Notably, natural perspiration facilitates latent energy transfer of 20–25%. When the conditioned panel operates below the dew point, it generates water vapor, boosting cooling capacity by 5–15% and enhancing latent energy transfer. Overall, the heat transfer processes of the hydronic panel mimic certain aspects of human physiology, distinguishing it from conventional HVAC systems. Full article

This study selects typical existing office buildings in Zhengzhou, a region with a cold climate, as the research object and conducts a comparative analysis of the influencing factors of cooling energy consumption in west-facing and south-facing office spaces. A multi-stage sensitivity analysis methodology integrating global and local sensitivity methods is systematically applied to evaluate 13 key parameters across four categories: building morphology, envelope structure, shading measures, and active design strategies. Five parameters are consistently ranked among the top seven most sensitive parameters for both west- and south-facing orientations: the infiltration rate, the window-to-wall ratio, the cooling setpoint temperature, the number of shading boards, and building width. Two parameters exhibit orientation-specific differences, namely lighting power density and the external wall heat transfer coefficient in west-facing spaces, whereas shading board width and the external window heat transfer coefficient play a greater role in south-facing spaces. Local sensitivity analysis further reveals that within the parameter variation range, the five parameters with higher energy-saving rates for both orientations are air tightness, the window-to-wall ratio, the cooling setpoint temperature, the number of horizontal shading boards, and horizontal shading board width. By increasing the cooling setpoint temperature, south-facing spaces can achieve an energy-saving rate of 25.32%, which is significantly higher than the 21.77% achieved by west-facing spaces. Horizontal shading board width exhibits the most pronounced orientation difference, with south-facing spaces achieving an energy-saving rate of 16.69%, while west-facing spaces only reach 2.97%. The research findings offer quantitative scientific evidence for formulating targeted energy-saving retrofit strategies for existing office buildings in cold climate regions, thereby contributing to the meticulous development of building energy efficiency technologies. Full article

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and renewable energy systems has heightened the demand for efficient Battery Thermal Management Systems (BTMS). Effective thermal regulation is critical to prevent performance degradation, extend battery lifespan, and mitigate safety risks such as thermal runaway. Liquid cooling has become the dominant strategy in commercial EVs due to its superior thermal performance over air cooling. However, optimizing liquid cooling systems remains challenging due to the trade-off between heat transfer efficiency and pressure drop. Recent studies have explored various coolant selection, nanofluid enhancements, and complex channel geometries, an ideal balance remains difficult to achieve. While advanced methods such as topology optimization offer promising performance gains, they often introduce significant modeling and manufacturing complexity. In this study, we propose a practical alternative: an interconnected straight-channel cooling plate that introduces lateral passages to disrupt the thermal boundary layer and enhance mixing. Comparative analysis shows that the design improves temperature uniformity and reduces peak battery temperature, all while maintaining a moderate pressure drop. The proposed configuration offers a scalable and effective solution for next-generation BTMS, particularly in EV applications where thermal performance and manufacturability are both critical. Full article

In this study, the integration of SnO₂ with a perfluorinated Zn(II) porphyrin derivative, namely ZnTPPF₂₀CN, was explored as a strategy to enhance the performance of chemoresistive sensors toward gaseous acetone detection. The ZnTPPF₂₀CN molecule was specifically designed with an ethynylphenyl-cyanoacrylic anchoring group and a benzothiadiazole (BTD) spacer, enabling its chemisorption onto the SnO₂ surface. Hybrid materials containing three different ZnTPPF₂₀CN-to-SnO₂ ratios (1:4, 1:32, 1:64) were fabricated and tested for acetone detection at 120 °C, both under dark conditions and LED illumination. The sensing behavior of these hybrids was compared with that of previously studied SnO₂ composites, incorporating physisorbed, unsubstituted ZnTPPF₂₀. Among the tested ratios, the 1:32 ZnTPPF₂₀CN/SnO₂ demonstrated superior acetone sensitivity compared to its unmodified counterpart, despite showing a lower intrinsic conductivity in air and a reduced electron transfer efficiency. Density functional theory (DFT) calculations provided insights into the possible anchoring modes and interfacial electronic interactions, helping to rationalize this counterintuitive observation. The enhanced sensing response was attributed to a more favorable balance between charge injection and the availability of SnO₂ electronic states, facilitated by the chemisorbed anchoring of ZnTPPF₂₀CN. Overall, our findings highlight the importance of molecular engineering, particularly in terms of molecular design, loading ratio, and anchoring mechanism, in modulating charge dynamics and optimizing the sensing efficiency of porphyrin/SnO₂ nanocomposites. Full article

The machining of Ti-6Al-4V thin-walled parts is characterized by high cutting temperatures, significant force fluctuations, and complex thermomechanical coupling. Cryogenic Minimum Quantity Lubrication Technology (CMQL) uses bio-lubricant as the lubrication carrier, combined with the cooling characteristics of cryogenic temperature medium, showing good cooling and lubrication performance and environmental friendliness. However, the cooling and lubrication mechanism of different cryogenic media in synergy with bio-lubricants is still unclear. This paper establishes convective heat transfer coefficient and penetration models for cryogenic media in the cutting zone, based on the jet core theory and the continuum medium assumption. The model results show that cryogenic air has a higher heat transfer coefficient, while cryogenic CO₂ exhibits a better penetration ability in the cutting zone. Further milling experiments show that compared with cryogenic air, the average temperature rise, average cutting force and surface roughness of workpiece surface with cryogenic CO₂ as cryogenic medium are reduced by 23.6%, 32.8%, and 11.8%, respectively. It is considered that excellent permeability is the key to realize efficient cooling and lubrication in the cutting zone by Cryogenic CO₂ Minimum Quantity Lubrication Technology. This study provides a theoretical basis and technical reference for efficient precision machining of titanium alloy thin-walled parts. Full article

Electric vehicles (EVs) have garnered significant attention in recent years due to their near-zero carbon dioxide emissions and compatibility with sustainable transportation systems. However, the lack of high-performance batteries remains a major barrier to widespread EV adoption. This study examines the variations in heat transfer coefficient and surface temperature of prismatic lithium iron phosphate (LiFePO₄) battery packs during discharge operations. Experiments were conducted using both forced air convection and natural convection. A wind tunnel was constructed to maintain an ambient temperature of 25 °C. The air flow rates were set at 0, 40, 80, and 120 g/s, while the battery pack spacings were 5, 10, and 15 mm. Discharge rates of 0.50, 0.75, and 1.00 C-rate were also examined. The results reveal that increasing the discharge rate led to a significant and uniform rise in surface temperature across the battery pack. Additionally, the voltage decreased gradually until an approximately 90% depth of discharge, after which it declined rapidly until the battery pack was depleted. Under forced convection, the voltage drop occurred slightly faster than that under natural convection. Greater spacing between battery packs enhanced cooling efficiency. Higher air flow rates increased the convection coefficient, whereas an increased discharge rate elevated the heat generation but reduced the heat convection coefficient. The highest heat dissipation was observed at a battery pack spacing of 15 mm, a discharge rate of 1.00 C, and an air flow rate of 120 g/s. The highest convection coefficient was achieved under the same spacing and air flow rate, but with a discharge rate of 0.50 C-rate. Full article

Although numerous studies have investigated individual methods to improve the performance of solar air heaters (SAHs), such as flow obstruction barriers, porous media, nanofluids, and thermal energy storage units, the overall integration of these reinforcement strategies into a unified, sustainable system remains to be defined. The current study presents a hybrid solar air heating configuration that combines a solar air collector (SAC) with an electric air heater (EAH) powered by photovoltaic (PV) panels, aiming to stabilize outlet air temperature and enhance overall thermal efficiency. Experimental and numerical approaches were employed to evaluate the influence of barrier geometry (flat, trapezoidal, and V-groove) and airflow rate (53, 158, and 317 L/min) on system performance using three SAC models. Experimental results revealed that lower airflow rate promotes greater temperature rise (ΔT) due to longer air–surface contact, while V-groove barriers achieved the highest ΔT and collector efficiency among all configurations. At higher airflow rates, the absorbed energy factor Fc (τα) increased to approximately 0.73, whereas the heat loss factor FcU decreased, indicating reduced thermal losses and improved energy transfer. Model III demonstrated the most effective heat absorption, confirming its superior thermal design. The integrated SAC–EAH system exhibited improved overall efficiency, with the SAC functioning effectively as a preheating unit and the EAH sustaining thermal stability during variable solar conditions. Numerical results showed that the highest temperature difference occurs at the V-groove barriers at an air flow rate of 53 L/min. In contrast, the difference between inlet and outlet temperatures decreases across the remaining models, with reduced percentages of 11.8% and 12.7% for Model II and Model I, respectively. Numerical simulations ensured the experimental outcomes, showing close agreement with the temperature variation trends and validating the system’s enhanced thermal performance. Full article

Oil and grease (O&G) pollution in municipal effluents represents a critical environmental challenge. This study contributes a novel experimental assessment of how pressure and recirculation time influence oxygen transfer, microbubble generation, and pollutant removal in a pilot-scale DAF system, providing new insights into process optimization for municipal wastewater treatment. This study evaluated the efficiency of a DAF system in removing organic pollutants and solids from municipal effluent by varying gauge pressure (1–5 bar) and recirculation time (1–20 min). The initial concentrations present in the effluent were 800 mg/L total solids (TS), 590 mg/L total suspended solids (TSS), 450 mg/L oil and grease (O&G), 360 mg/L biochemical oxygen demand (BOD₅), and 710 mg/L chemical oxygen demand (COD). The concentration of dissolved air (interpreted as dissolved oxygen supersaturation) reached 102.3 mg/L and removal efficiencies of 84.4% for O&G, 88.9% for BOD₅, 88.7% for COD, and 85% for TSS were achieved, while pH and dissolved solids (DS) remained stable. The saturation factor (f = 0.8) confirmed efficient oxygen-liquid transfer, attributed to the use of Raschig rings in the absorption column. The significance of this work lies in demonstrating that operating conditions directly enhance oxygen dissolution and flotation performance, highlighting an optimization pathway rarely reported for municipal effluents. The results demonstrate that DAF is a robust, stable, and energy-efficient technology capable of effectively removing organic and lipid loads from municipal effluent, providing a sustainable alternative for the pretreatment and reuse of urban wastewater. Full article