Search Results (704)

In response to the reduced system inertia and increased frequency security risks in high-renewable power systems, as well as the limitations of single energy storage technologies, a coordinated optimal scheduling method for electrochemical energy storage (EES) and compressed air energy storage (CAES) considering the minimum inertia requirement (MIR) is proposed. The method constructs a coordination framework, leveraging the fast response of EES and the sustained support and equivalent inertia contribution of CAES. An MIR evaluation model considering RoCoF and frequency nadir constraints is established, and the inertia deficit is converted into fast reserve demand, forming an inertia–reserve coupling mechanism. To address nonlinear frequency constraints, an adaptive piecewise linearization method is adopted to transform the model into a mixed-integer linear programming problem. Case studies show that, compared with the benchmark hybrid energy storage scheduling strategy without inertia–reserve coordination, the proposed method reduces thermal generation cost by 4.5% and renewable curtailment by 74.8%. Moreover, the proposed APWL method improves computational efficiency by 47% compared with the conventional PWL method. Full article

Landfill leachate membrane concentrate (LLMC) is a high-salinity and high-organic wastewater stream that poses significant treatment challenges to conventional evaporation technologies. This study investigated the treatment performance and operating costs of a low-temperature atmospheric evaporation (LTAE) system for LLMC treatment under mild operating conditions. The effects of key operational parameters—including evaporation temperature (60–95 °C), pH (5–11), air–liquid mass ratio (A/L = 0.5–10), and concentration factor (CF = 5–20)—were systematically evaluated based on condensate quality parameters (UV₂₅₄, COD_Cr, and NH₃–N). Results demonstrated that the LTAE system achieved a higher concentration ratio (CF = 20) compared to the on-site mechanical vapor compression (MVC) system (CF ≈ 10). The optimal operating conditions for meeting effluent discharge standards were determined to be 70 °C, pH: 5, A/L = 5 and CF = 20. Under these conditions, the condensate contained ~5.6 mg/L NH₃–N and ~91.6 mg/L COD_Cr, while the concentrate reached ~4200 mg/L NH₃–N and ~38,000 mg/L COD_Cr, indicating that some organic matter and ammonia nitrogen escaped from the system and a gas scrubbing unit is recommended to minimize secondary pollution. Within the experimental range, the system achieved the highest K_cA = 22,871.25 kW/(m³·°C) and the highest K_dA reached 6.52 kg/m³·s. Economic analysis revealed a specific energy consumption of 110.5 kWh/t of freshwater produced. Despite the relatively high energy consumption, the LTAE system demonstrates considerable potential for the advanced treatment of high-organic wastewater, offering enhanced freshwater recovery under mild thermal conditions. This study provides theoretical and data support for the application of LTAE technology in LLMC treatment and similar challenging organic wastewater. Full article

Traditional ready-to-wear garments can mostly not conform to different body shapes because of the adoption of the generic sizing system, which leads to the local strain of concentration and morphological misfit. Auxetic structures, which have a negative Poisson’s ratio, permit enhanced redistribution of stress and geometry and allow deformation. Two auxetic knitted structures were developed by using 100% polyester and 100% nylon yarns with a fabric density of 41 Wales and 40 courses per inch. Characterization of the initial fabrics involved checking the behavior of negative Poisson’s ratio (NPR) where the polyester line (P1) structure shows the highest auxeticity, with a NPR of approximately −0.4 and peak strain reductions of 80–90%, as well as air permeability, moisture management, bend test, compression, roughness, friction properties and stiffness tests to check the mechanical and comfort-related performances. The standardized tunic garment was modeled in CLO 3D on three female body shapes—hourglass, pear and rectangle—with a constant size of 34. The fit map showed a strain of 91.49% in auxetic and 509.75% in single-jersey fabric at the hip area of the pear body shape when measuring fabric and body interaction. The findings indicate lower peak strain levels, which ascertain that increased adaptability is possible and support its use in the development of adaptive ready-to-wear garments. Full article

Hydraulic transients in pressurized pipe systems are significantly influenced by the presence of entrapped air, which alters wave propagation through increased compressibility and energy dissipation. Traditional discrete cavity models, such as the Discrete Gas Cavity Model (DGCM), often assume a constant wave celerity, which limits their accuracy under high gas content conditions. This study evaluated different approaches for representing the effects of gas cavities and unsteady friction in closed pipe transients. The work introduces the Adjustable-celerity Gas Cavity Model (AGCM), a formulation that explicitly couples local air volume and pressure to dynamically adjusted celerity values. Two variants are considered, a non-iterative (AGCM.v1) and an iterative approach (AGCM.v2), the latter ensuring consistency between pressure head and celerity at each time step. The models were evaluated through numerical simulations using both experimental datasets and a hypothetical test case with increasing air fractions. Results show that the AGCM was able to represent celerity magnitudes in unsteady flows with large fractions of air. Also, while constant-celerity models perform well under low-air conditions, variable-celerity formulations offer superior consistency in predicting wave amplitudes and celerity dynamics as gas content increases. These findings underscore the importance of dynamic celerity coupling in transient flow modeling and validate the AGCM as a useful approach for transient modeling in conditions with higher air phase fractions. Full article

This study addresses the low operational efficiency and high energy storage cost of wind–solar hybrid energy storage systems due to the strong volatility and intermittency of wind and photovoltaic power. Instead, the authors propose a dual-layer optimization model based on convolutional neural network–long short-term memory–attention mechanism (CNN-LSTM-AM) forecasting. First, a CNN-LSTM-AM forecasting model is constructed based on convolutional neural networks and long short-term memory networks. Then, the model is applied to wind and solar power forecasting to dynamically optimize the output power ratio of renewable sources and batteries based on predicted power, thereby reducing the start–stop frequency of compressed air energy storage (CAES) and improving operational efficiency. For lower-layer optimization, a weight evaluation model based on AHP is constructed and subsequently used to optimize the capacity configuration of the hybrid energy storage system to achieve overall system optimality. Case studies indicate that after upper-layer optimization, the number of CAES start–stop cycles decreases from 25 to 17, and further declines to 14 after optimization of the lower-layer capacity configuration, while the energy storage cost is reduced by 5.43% and the curtailment rate decreases by 0.15%. This validates the effectiveness of the proposed model in improving the economic performance and stability of renewable hybrid energy storage systems. Full article

Compressed air leakages represent a major source of energy waste and financial loss in industrial facilities. However, accurately detecting and quantifying these leaks remains challenging due to the wide variation in the accuracy, cost, usability, and practical applicability of available methods. This paper presents a structured review and evaluation of leakage localization and quantification methods for compressed air systems (CASs), categorized into hardware-, software-, and non-technical-based approaches. Based on expert interviews and a comprehensive literature review, a set of evaluation criteria was defined and applied within a multi-criteria decision analysis (MCDA) framework. The Analytic Hierarchy Process (AHP) was used to derive criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the alternatives separately for localization and quantification tasks. To enhance practical relevance, five expert interviews were conducted with industrial stakeholders from diverse professional backgrounds, including maintenance engineers and energy managers. A questionnaire was also distributed to assess the methods. The results provide illustrative insights into the relative suitability of different methods. Within the scope of this exploratory study, from a practical industrial perspective, the compressor duty cycle method and non-intrusive load monitoring (NILM) appear to be promising approaches to leakage quantification, while ultrasonic detection is preferred for localization. Notably, discrepancies between questionnaire-based rankings and expert interview insights highlight the limitations of purely survey-driven evaluations. The proposed framework supports industrial decision-makers in selecting leakage detection and quantification methods by balancing technical performance, implementation effort, and operational constraints, thereby contributing to reduced energy losses and improved system efficiency. Full article

Ensuring the required microclimate parameters is the most critical task in hot climates. In pig farms, air cooling is provided by means of steam-compression chillers or evaporative cooling, which is the simplest way to cool the air. The implementation of evaporative cooling depends largely on the interaction of the media involved in this process. This paper considers the process of interaction of cooling water with the surface of a cellular polycarbonate heat exchanger. A mathematical model describing the process of wetting the sprayed surface of the heat exchanger is obtained. The authors determined the theoretical water flow rate required to provide air cooling for a given operation mode. Experimental trials of a recuperative heat recovery unit with a heat exchanger made of cellular polycarbonate equipped with a water evaporative cooling system were carried out. The authors conducted a comparative assessment to evaluate the effectiveness of evaporative cooling in a heat recovery unit equipped with a polycarbonate heat exchanger versus panel evaporative systems using wetted paper pads at pig farms in the Vladimir and Tambov regions of Russia. The panel evaporative coolers provided a temperature reduction of 11.3 °C without any splashing effect. Under the same operating conditions, the heat recovery unit achieved an inlet air temperature reduction of 10.5 °C, accompanied by splashing. When the water flow rate supplied for evaporation was reduced until the splashing ceased, the cooling temperature drop decreased to 10.1 °C, which is 11% lower, compared with the paper pads. The study revealed characteristic operating modes for the unit that ensure effective air cooling, depending on the cooling water flow rate. Since the prevailing temperature during the system’s main operating time is significantly lower than the design temperature (the absolute temperature maximum), to achieve effective cooling of the supply air without splashing or excessive water waste, the cooling circuit water should circulate at a flow rate within 40 to 63% of the maximum design value. Alternatively, an automated control system should be employed to regulate the water supply based on outdoor air temperature and humidity. Full article

An improved optimizer based on projection-iterative methods (IPIMO) is proposed to address the optimal configuration problem of multi-type energy storage systems (MT-ESS), with the objective of achieving synergistic minimization of comprehensive costs, including both investment and operational expenditures. A comprehensive energy system model is established, integrating photovoltaic power, wind power, and six typical energy storage technologies—lithium-ion battery, flywheel energy storage, supercapacitors, valve-regulated lead-acid battery, compressed air energy storage, and redox flow battery. Four typical operational scenarios are designed to validate the adaptability and robustness of the algorithm. A systematic evaluation of IPIMO’s comprehensive performance is conducted by comparing it with the weighted average method (WA), the single-energy storage optimization method (SEO), the projection-iterative-methods-based optimizer algorithm (PIMO), and the genetic algorithm (GA). Simulation results demonstrate that IPIMO exhibits superior convergence performance, achieving stable convergence rapidly and significantly outperforming PIMO and GA. Moreover, IPIMO achieves the lowest total cost across all four scenarios, with an average of $46,837, representing reductions of 6.54% compared to the benchmark weighted average method and 11.8% compared to the SEO. Additionally, IPIMO adaptively adjusts the allocation ratios of energy storage types based on scenario characteristics, prioritizing energy-type storage in stable scenarios while increasing the proportion of fast-response storage to 49.1% in fluctuating scenarios, thereby demonstrating its strong scenario adaptability. Full article

To investigate the interlayer contact mechanism of foamed cold-recycled asphalt mixture under static loads, a three-layer asphalt pavement discrete element model (DEM) was established, with the surface layer composed of asphalt concrete-13 (AC-13), asphalt concrete-20 (AC-20) and asphalt-treated base-25 (ATB-25) foamed cold-recycled asphalt mixture and cement-stabilized macadam as the base. Based on mortar theory, the pavement was divided into coarse aggregate, asphalt mastic and air void phases, and the Burgers Model, Linear Parallel Bond Model and Linear Model were adopted to characterize the bonding of asphalt-aggregate, cement contact interface and subgrade-surface layer, respectively. Static loads of 0.7 MPa, 1.1 MPa, 1.5 MPa and 1.9 MPa were applied to analyze the mechanical responses of asphalt-based and cement-based pavement systems from tensile strain, vertical compressive stress and vertical displacement. Results showed that mechanical indices of the pavement increase monotonically with static load and present obvious layered distribution. The cement-stabilized macadam base provides rigid support, significantly reducing tensile strain (TS) and vertical displacement (VD) of asphalt layers, while the asphalt-based system has flexible stress transfer and superior stress dissipation in the bottom layer. The two systems exhibit respective structural advantages, with the cement-based system outstanding in deformation control and the asphalt-based system suitable for flexible stress adaptation working conditions. Full article

Addressing the challenges in 3D reconstruction of large-scale open-pit mines, such as dramatic terrain undulations, complex texture features, and the difficulty of balancing geometric accuracy with real-time rendering efficiency using traditional methods, this paper proposes a high-fidelity reconstruction framework integrating UAV multi-modal data with the state-of-the-art 3D Gaussian Splatting (3DGS) architecture. First, an integrated air-ground multi-modal data acquisition system is established. Using a UAV equipped with LiDAR and a high-resolution camera, high-quality geometric and textural data of the mining area are acquired through terrain-adaptive flight planning. Second, to tackle the VRAM bottlenecks and loose geometric structures inherent in original 3DGS for large scenes, we adopt the advanced CityGaussianV2 architecture as our core reconstruction engine. By leveraging its divide-and-conquer parallel training strategy, 2DGS planar geometric constraints, and Decomposed Gradient Densification (DGD) mechanism, this framework effectively overcomes memory limitations and significantly enhances the geometric sharpness of slope crests and toes. Finally, engineering validation was conducted at Kambove Mining. Experimental results demonstrate that the proposed method achieves centimeter-level geometric accuracy, a real-time web rendering frame rate exceeding 60 FPS, and a model storage compression rate of over 90%. The digital twin control platform built upon this model successfully achieves deep fusion and visual scheduling of multi-source heterogeneous data, providing a novel technical path for constructing high-precision reality-based foundations for smart mines. Full article

This study investigates how outlet pressure influences the fire suppression performance of a compressed air foam system (CAFS), with the aim of supporting system optimization and engineering applications. An experimental apparatus for foam performance testing is used to measure changes in foam flow rate, expansion, initial velocity, initial momentum, and drainage time at different outlet pressures. On the basis of relevant theoretical models, the factors causing discrepancies between model predictions and experimental results are examined, and the models are then refined. How the outlet pressure of CAFS affects foam performance is thereby clarified. The results show that foam flow rate increases as outlet pressure increases. At higher pressures, shear-thinning and intensified gas–liquid mixing affect the foam. As a result, the growth of flow rate in the range of 0.01–0.03 MPa is significantly higher than that in the range of 0.06–0.10 MPa. Both initial velocity and initial momentum increase significantly with increasing pressure, whereas the expansion decreases. Within the outlet pressure range of 0.01–0.10 MPa, the initial velocity increases from 1.23 m/s to 6.65 m/s, the initial momentum rises from 4.6 kg·m/s to 34.1 kg·m/s, and the expansion decreases from 9.2 to 5.4, indicating reduced foam stability. Drainage time and drained mass vary non-monotonically with outlet pressure. The longest drainage time and the smallest drained mass occur at 0.06 MPa. Fire suppression performance improves as outlet pressure increases. A higher outlet pressure enables the foam solution to penetrate the flame zone more effectively and to cover the surface of the burning material. In addition, changes in foam properties enhance the thermal insulation and smothering effects of the foam layer, as well as its heat absorption and cooling capacity. These effects together improve the efficiency of fire source cooling. Full article

Central business district (CBD) hospitals must sustain reliable pressure relationships in critical rooms while reducing whole-facility carbon under tight space and disruption constraints. We developed an ontology-grounded semantic digital twin that normalizes building automation system (BAS) and building management system (BMS) telemetry into a unified semantic store consistent with Brick Schema, enabling portable asset discovery via query and thereby supporting forecasting, anomaly detection, and multi-objective optimization without dependence on vendor point naming conventions. Whole-facility impacts were verified using International Performance Measurement and Verification Protocol Option C–style measurement and verification with an S0-calibrated baseline model and residual-based savings attribution. Relative to the baseline (S0), the intervention (S3) produced a step increase in the critical-room pressure-compliance pass rate, tighter room-to-corridor differential-pressure (ΔP) control across airborne infection isolation and open room strata, and intent-aligned ventilation delivery (air changes per hour ratio distribution concentrated near unity; p < 0.05 where letter groups differ). Operational-state discrimination improved (AUC 0.649→0.696) and issue-resolution times shortened (left-shifted cumulative distribution function), indicating reduced service burden. Option C verification showed energy residuals shifting negative under S3, consistent with net savings versus baseline expectations. Across progressive maturity (S0→S3), time-to-value and burden fractions decreased, carbon intensity (tCO₂e m⁻²) decreased, long-tail exposure compressed (log-scale horizon), and composite performance indices increased (p < 0.05). These results demonstrate a verifiable pathway to pressure-reliable, decarbonized hospital operations at the whole-facility boundary while making the semantic layer’s utility explicit through query-driven, ontology-grounded asset discovery. We present an IPMVP Option-C–verifiable semantic digital-twin governance framework that links audited operational evidence (telemetry → actions → verification) to whole-facility energy and carbon outcomes while maintaining critical-room pressure-relationship reliability. Optimization benchmarking (including quantum annealing) is used as supporting decision-support evaluation, rather than as the central contribution. Full article

The implementation of Australia’s 2024 waste export ban has increased pressure on domestic recycling systems, resulting in an additional 650,000 tonnes of waste annually. This emphasises the urgent need for high volume landfill waste material recovery, especially in sustainable construction materials such as geopolymer concrete (GPC). Geopolymer concrete is recognised as a sustainable construction material; however, the scientific understanding of the compatibility between landfill waste and the geopolymer matrix, particularly under harsh environments, remains unknown. This paper presents an experimental investigation on five types of geopolymer concrete (GPC) mixes. The study included a control mix with natural stone chips and four additional mixes in which stone chips were 100% replaced with waste materials including shredded plastic, cardboard, crushed glass, and granular crumb rubber as fine aggregates. The mechanical performance, durability behaviour and stress-strain characteristics of these mixes were evaluated. Concrete samples were exposed to normal air, a saline environment with 10% salinity, and a hygrothermal environment at 60 °C and 98% humidity for four months to assess durability performance. The results demonstrate that GPC is compatible with landfill waste aggregates and enables the production of a workable mixture. As a result of saline environments, waste aggregate-based geopolymer concrete reduces compressive strength by 15%, while natural stone chips-based geopolymer concrete decreases strength by 45% during the same period, indicating that waste aggregates are more appropriate than natural aggregates in marine environments. Although the inclusion of waste aggregates reduces the strength and stiffness of the GPC, the materials continue to meet the mechanical property requirements for non-structural applications. A theoretical model considering the elastic modulus, ultimate strength and corresponding strain has been developed to predict compressive stress–strain behaviour of waste-based GPC. High modulus aggregates, typically ranging from approximately 10.0 GPa to 85.0 GPa such as stone chips and glass sand demonstrate parabolic stress–strain behaviour. In contrast low modulus aggregates, generally ranging from 1.0 GPa to 5.0 GPa including plastic, cardboard, and crumb rubber, exhibit a bilinear stress–strain response. Full article

Integrating Joint Source–Channel Coding (JSCC) with the LoRa Chirp Spread Spectrum (CSS) physical layer (PHY) presents a significant challenge due to the complexity of joint optimization, which remains underexplored despite the known advantages of JSCC. Traditional LoRa systems rely on decoupled source and channel coding, resulting in redundant overhead and limited adaptability under dynamic Wireless Body Area Network (WBAN) conditions. To address these limitations, we propose a novel LoRa–JSCC framework: a fully learned, end-to-end differentiable architecture that jointly optimizes source compression and channel redundancy. The proposed system integrates a Denoising Autoencoder (DAE) for non-linear source compression with learned neural channel encoder and decoder modules, trained via backpropagation to minimize reconstruction distortion under noisy channel conditions. Rigorous Monte Carlo simulations conducted under unified and reproducible channel conditions demonstrate consistent performance improvements across LoRa configurations. The proposed approach achieves an average 25–30% improvement in goodput across moderate-to-high SNR regimes, with gains exceeding 100% under noise-limited conditions. It further reduces Time on Air (ToA) by approximately 30–35%, enhancing spectral efficiency and lowering effective energy cost per delivered bit. In the transitional Bit Error Rate (BER) region, the proposed LoRa–JSCC framework exhibits an effective SNR gain of approximately 18–20 dB relative to conventional LoRa, corresponding to multiple orders-of-magnitude reduction in BER. These results indicate substantial improvements in reliability, coverage robustness, and energy efficiency for WBAN and IoT deployments. Full article

Background/Objectives: Blast-induced hearing loss (BIHL) is a major concern, particularly for military personnel, and is linked to impaired auditory neuron survival and synaptic plasticity. This study investigates the potential of the TrkB agonist 7,8-dihydroxyflavone (7,8-DHF) to reduce the severity of BIHL and promote recovery in a mouse model. Methods: Eight-week-old male C57BL/6J mice were used. A custom-built, compressed air-driven system utilizing a modified paintball apparatus was employed to deliver controlled unilateral double blasts (~22 psi exposure pressure) to the left ear. The blasts were administered 30 min apart. Immediately following the second blast, mice received either 7,8-DHF (10 mg/kg) or vehicle (10% DMSO) via intraperitoneal injection. Auditory brainstem responses (ABRs) were measured in both ears at baseline (pre-blast) and at several post-exposure time points. Results: The consecutive blast exposure induced a significant elevation in ABR thresholds, indicative of hearing loss, in both the ipsilateral (exposed) and contralateral (unexposed) ears of vehicle-treated mice. Notably, mice treated with 7,8-DHF demonstrated a marked improvement in hearing recovery compared to the vehicle group. Significant reductions in ABR thresholds were observed in the ipsilateral ear at 4 weeks post-blast (p < 0.0001) and in the contralateral ear as early as 1-week post-blast (p = 0.0236). However, the recovery was partial, with ABR thresholds plateauing after 4 weeks. Conclusions: A controlled blast model demonstrates that systemic administration of the TrkB agonist 7,8-DHF exerts a protective effect, partially restoring auditory function after blast injury. This supports the therapeutic potential of targeting the BDNF-TrkB signaling pathway for managing BIHL. Full article