Search Results (11,248)

The rapid growth in data storage and computing demand has substantially increased the energy consumption and carbon emissions of the data center (DC). Energy storage systems facilitate multi-source energy coordination, while DCs are playing an expanding role in the demand response (DR) program. This paper proposes an optimal energy and workload dispatch model for a DC cluster (DCC) integrating distributed energy storage (DES) and shared energy storage (SES) under the carbon trading mechanism. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated to minimize the economic objective, including the electricity-related cost and carbon trading cost. Case study results show that, compared with a DES-only configuration, the DCC incorporating both DES and SES can achieve a 46.19% reduction in cost and a 54.02% reduction in carbon emission. DR participation yields further reductions in both cost and carbon emission. The scalability of the proposed model is validated by evaluating its computational performance across DCC instances with varying numbers of constituent DCs. The study also examines the effects of carbon prices and energy storage capacities on DCC performance. A higher carbon price reduces carbon emissions but increases costs, whereas larger DES and SES capacities reduce both. The proposed model proves solvable and well-behaved across a wide range of renewable energy generation conditions and computing workloads. This research provides practical implications for the sustainable development of DCs under the carbon trading mechanism. Full article

In Fusion Deposition Modeling-based manufacturing, process parameters affect the mechanical and physical properties of the print. Considering these properties, accurately predicting print quality is essential. This is where machine learning (ML) models for three-dimensional (3D) print quality prediction come to the forefront. In this study, a dataset was prepared under strict operational measurement standards—utilizing the Interquartile Range (IQR) method for data sanitization—encompassing 10 material types, 2 printer types, and 4 printing parameters. Five hundred different sample combinations were prepared and printed in sets of three according to ISO 527-2 Type 4 standard dimensions. Tensile, hardness, and surface roughness tests were applied to the prepared samples. Using this validated dataset, a Hybrid Multi-Material Quality–Ensemble System–Stacking–Gradient Boosting Regressor (HMQ-ES-Stack-GBR) architecture is proposed as a diagnostic framework for multi-output quality prediction. Particularly in terms of quality outputs such as tensile strength, hardness, and surface roughness, while also providing a quantitative analysis of the effect of material type on print quality. Furthermore, a multi-objective optimization pipeline integrating three distinct meta-heuristic algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—was coupled with the framework to systematically derive material-specific optimal processing parameter configurations. Furthermore, the study shows that open-system printers exhibit higher prediction errors than closed-system printers. Reflecting system-induced variability rather than full hardware independence. Although the study is limited to internal validation within the current experimental design and includes material imbalance across filament groups, the findings suggest that the proposed framework provides a promising diagnostic decision-support tool for pre-print quality estimation within the studied dataset. By accurately reflecting rather than physically overcoming manufacturing variability, it supports decision-making processes through pre-print quality estimation, thereby enabling proactive interventions that reduce raw material, time, and energy losses. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

The accessory gearbox of an aero-engine operates under high-speed and heavy-load conditions, where insufficient lubrication may lead to oil-film failure, increased frictional losses, and reduced transmission reliability. Therefore, understanding oil-jet injection and tooth surface oil-film spreading characteristics is essential for improving lubrication performance. In this study, a three-dimensional geometric model incorporating the meshing region and oil nozzles was established based on a typical accessory gear pair. The model employs the VOF multiphase flow approach and the standard k-ε turbulence model, coupled with dynamic mesh techniques to accurately capture the transient interactions between gear rotation and oil–air two-phase flow. Numerical simulations reveal the dynamic evolution of oil injection, impingement on the tooth surface, oil-film spreading, and transport into the meshing zone, while the effects of injection velocity and nozzle length on lubrication performance are quantitatively analyzed. Results indicate that an injection velocity of 45–55 m/s yields optimal oil-film coverage and uniformity, and a nozzle length of h = 30 mm minimizes jet energy decay and airflow interference, achieving uniform oil filling in the meshing zone. The optimal lubrication performance for accessory gears is obtained at an injection velocity of 45–55 m/s and a nozzle length of 30 mm. This study provides a reference for the design optimization of accessory gear lubrication systems. Full article

Permanent magnet synchronous motors (PMSMs) used in hybrid shunting locomotive traction systems operate under complex conditions, and their electrical parameters may vary with temperature rise, load disturbance and magnetic saturation. To improve online parameter tracking under such conditions, this paper investigates a forgetting-factor recursive least squares (FFRLS)-based identification method for stator resistance, stator inductance and permanent magnet flux linkage. The main contribution lies in the traction-oriented formulation of the identification model, DSP28335-based real-time implementation, and simulation/experimental validation of three-parameter online tracking. Simulation results show that the proposed method can track the three key parameters under selected perturbation conditions. The experimental results provide algorithm-level evidence for the real-time implementation and three-parameter tracking capability of the proposed method on a scaled-down PMSM platform, thereby establishing a basis for subsequent full-scale validation and studies on traction-control robustness and energy-efficiency optimization. Full article

The global packaging industry is highly energy intensive, with liquid carton packaging facing growing pressure to improve sustainability through energy efficiency. The objective of this review is to synthesize and critically evaluate existing literature on energy performance metrics, energy auditing practices, optimization frameworks, and renewable energy integration in liquid carton packaging manufacturing. Unlike previous studies that focus on individual aspects of industrial energy management, the review adopts an integrated system-level perspective that combines process-level energy analysis, auxiliary utility systems, energy performance indicators, digital monitoring approaches, optimization tools, and renewable energy integration. This holistic approach provides a more comprehensive understanding of energy performance improvement opportunities within liquid carton packaging manufacturing. The study examines global energy trends, system inefficiencies, and best practices in implementing energy management systems, modeling tools, and solar photovoltaic adoption. A qualitative approach was applied, analyzing peer-reviewed articles, industry reports, and case studies to identify key themes and comparative strategies. Findings indicate that energy-intensive processes such as extrusion coating and flexographic printing dominate consumption, while auxiliary systems contribute significantly to non-process energy use. Despite advancements in monitoring and renewable integration, gaps persist in standardized performance metrics, real-time data utilization, and regional representation, particularly in Africa, Latin America, and other developing regions where packaging manufacturing systems remain underrepresented in the literature. The findings provide a practical reference guide for energy managers, manufacturing engineers, and sustainability practitioners seeking to implement ISO 50001-based energy management systems, real-time energy monitoring frameworks, and renewable energy integration strategies within packaging manufacturing facilities. The review further highlights the need for standardized performance metrics and region-specific studies to support sustainable and energy-efficient packaging operations. Full article

Decarbonising municipal solid waste (MSW) is a defining algorithmic challenge for smart cities: waste-to-energy (WtE), composting and material-recovery investments must be committed years ahead under deep uncertainty about household source-separation uptake and the governing policy instruments (landfill taxes, carbon prices, compost subsidies). We present a two-stage stochastic mixed-integer linear programming (MILP) framework, applied to the Attica region of Greece (Athens; 5122 t/day MSW) and calibrated to confirmed 2024 weighbridge data. The contribution is an integration strategy rather than a new technique: endogenous WtE capacity sizing (Special Ordered Sets of Type 2 (SOS2) piecewise-linear cost, economies-of-scale exponent 0.85), the bilinear capacity–build coupling linearised exactly by McCormick envelopes (one factor being binary), and Pigouvian shadow-price recovery of the optimal policy instruments are combined in a single-shot, gap-bounded MILP and embedded in a 10,000-run Latin-hypercube Monte Carlo layer over 12 parameters with Spearman sensitivity indices. The individual components are established; their joint formulation is, to our knowledge, new. The pipeline solves 10,415 MILP instances. Three results are policy-relevant: investment is robust to rollout uncertainty (VSS ≈ €0; EVPI ≈ €5.4 M, 0.15%); the carbon price alone explains ~80% of cost variance (ρ = +0.891); and, under the model’s calibration, the implied Pigouvian-optimal landfill tax (€1100–3300/t) indicates a binding landfill cap is needed to secure diversion. The framework transfers to any metropolitan MSW system facing decarbonisation and circular-economy mandates. Full article

The high penetration of sustainable energy integration leads to severe source-side uncertainty challenges in power system dispatch, and two-stage robust optimization is a critical tool for addressing this issue. However, traditional solution methods suffer from significant iterative computational bottlenecks and fail to meet the timeliness requirements of real-time grid dispatch. Therefore, this paper proposes a data-model hybrid-driven fast solution method for two-stage robust optimization. First, the method abandons the traditional iterative solution mode and directly identifies the worst-case scenarios within the uncertainty set based on a conditional diffusion model, which transforms the two-stage robust optimization into a single-stage deterministic optimization. Then, it constructs a physics-guided conditional diffusion model and proposes a worst-case scenario physics-guided operator to apply directional adversarial guidance during the reverse denoising stage to approach the worst-case operational boundary. Finally, by establishing a sensitivity allocation model for global active power imbalance, it reformulates the non-differentiable risk constraints in the gradient operator into efficient algebraic matrix operations. Experimental results show that the proposed method accurately generates worst-case scenarios and reduces the computation time to approximately 20% of the original while ensuring near-optimal economy, which provides a new solution paradigm for balancing decision robustness and timeliness. Full article

Data centers are facing rapidly increasing electricity demand and carbon emissions, while the intermittency of renewable energy creates a significant temporal mismatch between renewable generation and data center load demand. To bridge this temporal mismatch, we propose a coordinated optimization strategy that integrates shared hydrogen energy storage facilities with load scheduling mechanisms. A multi-objective MILP model is formulated to minimize annualized cost, renewable energy curtailment, and carbon emissions. Simulation results show that, compared with the no-shared-station case, the proposed electricity–hydrogen coordination strategy with load shifting yields significant benefits: the annualized total cost decreases from 13.65 to 4.74 million yuan; annual carbon emissions are reduced from 6297 to 1432 tons; and peak-period electricity purchases are reduced from 5373 to 906 MWh. Under the representative daily forecast condition, Scenario S4 achieves zero renewable curtailment when grid export is permitted; therefore, the renewable-electricity utilization rate reaches 100.00% within the model boundary. When grid export is prohibited, the utilization rate decreases to 98.59%, with 179,100 kWh of annualized renewable curtailment. The research findings indicate that integrating shared hydrogen energy storage with the load flexibility of data centers can effectively reduce the system’s overall operating costs, promote the integration of renewable energy, and achieve low-carbon operation. Full article

Wet grinding is an important operation in the technological process of ceramic tile production. The properties of the slurry obtained from grinding (slip) are conditioned by the raw materials (the type and characteristics of raw material in mixture, recipes for mixture), and by the operating parameters in grinding (technical characteristics of mill, type of grinding system, mill charge, grinding media body, grinding time, etc.). The optimal selection of these influential parameters results in satisfactory properties of slip, i.e., in efficient grinding as process operation, and, consequently, in smooth and efficient realisation of subsequent operations in the process, particularly spray drying. At the end of the technological process, the final goal is to obtain a ceramic tile of satisfactory quality. Multi-criteria decision-making (MCDM) is an increasingly applied tool for selecting optimal technological parameters for the purpose of optimisation, problem solving and improvement of technological processes. This paper presents the application of the symmetry point of criterion—ranking alternatives by perimeter similarity (SPC-RAPS) as an MCDM hybrid method for the selection of optimal technological parameters in grinding in the ceramic tile production process. The ranking and selection of alternatives (raw materials, grinding balls and grinding time) were performed according to various criteria. In addition to the technological parameters related to the characteristics of the products from the grinding (slip), and to the technical characteristics of the final product (ceramic tiles), the criteria also included economic parameters (the market price of raw material and specific energy consumption in grinding). The developed mathematical model enabled the selection of the best alternative as a solution for this problem. Full article

Liquid desiccant dehumidification, an energy-efficient technology for air humidity control, has gained significant attention in recent years. In this study, the air temperature and humidity prediction models of liquid desiccant cooling and dehumidification (LDCD) system are built based on the proposed ITSO-TCN-BiGRU-SA. In the proposed model, the TCN is employed to obtain local features within sequence and improve the learning ability of temporal dependencies; BiGRU strengthens the model through global and bidirectional contextual relationships; self-attention mechanism assigns different weights to each time step. The ITSO algorithm, which combines the nonlinear adaptive weights and Levy flight strategy, is proposed to find the optimal hyperparameters of network. Accordingly, the model prediction accuracy is improved. Through comprehensive comparative analysis with other models under a series of experiment results, the superior performance of the developed models was systematically validated. Furthermore, based on the model predictions and experimental results, a comprehensive analysis was performed to systematically investigate the impact of system inlet parameters on cooling and dehumidification capacity and efficiency, which can provide valuable guidance for system control and operation. Full article

High photovoltaic (PV) penetration introduces rapid variability, voltage deviations, and increased real-power losses in distribution networks, necessitating control strategies that remain effective under forecast uncertainty. This paper presents a unified Particle Swarm Optimization-based Receding-Horizon Control (PSO-RHC) framework for optimal coordination of multiple Battery Energy Storage Systems (BESSs) in a PV-rich distribution feeder. The controller employs a receding-horizon structure—using horizon-based forecasts, constraint enforcement, and stepwise decision updates—while PSO serves as the optimization engine that computes BESS power setpoints at each prediction step. Deterministic PV and load forecasts are perturbed with stochastic noise to emulate realistic uncertainty, and each candidate solution is evaluated using a forward–backward sweep load-flow model. Simulation results on the IEEE-69 bus system show that the proposed PSO-RHC scheme reduces total daily energy losses from 1467.50 kWh to 1310.19 kWh (10.72% reduction), improves weakest-bus voltages by 1–4%, and maintains all BESS units within operational limits. The normalized objective components remain small (below 0.5%), indicating balanced operation without excessive cycling. These findings demonstrate the effectiveness and simulation-level effectiveness of PSO-based receding-horizon control for enhancing distribution-network performance under uncertain and dynamic PV conditions. Full article

The paper presents a simulation and experimental study of a three-phase 12/8 Switched Reluctance Motor (SRM) operating in motoring mode. The operation of the asymmetric bridge converter is analyzed, and the mathematical equations describing the phase-current change under various commutation states in soft-switching mode (modulation of the upper transistors) are derived. An analytical model is used to examine the energy exchange between the battery, the power switches, and the phase inductance. For the purposes of the study, a simulation model was developed in the MATLAB/Simulink R2025b environment, including models of the battery, the power converter, and the SRM. Simulation studies were conducted under various load conditions and phase current values, yielding time-domain waveforms of the phase currents and voltages, as well as the electromagnetic torque. Experimental waveforms of the phase current and voltage, measured under conditions corresponding to those in the simulation studies, are presented. A comparative analysis was performed between the simulation and experimental results, with tabular and graphical dependencies presented, and the relative error between them determined. The results obtained show good agreement between the simulation model and the actual system, which confirms the applicability of the developed approach for the analysis and optimization of SRM drives. Full article

Centrifugal pumps serve as key equipment in water conveyance systems. Different tee inlet structures distort the internal inflow field, induce extra hydraulic losses and reduce system energy efficiency, since pump power consumption and operational stability are highly sensitive to tee pipeline layouts. Pressure pulsation directly reflects internal flow disorder and reveals the root causes of hydraulic performance attenuation, so it is adopted as the core analytical tool in this work. Using the Shear Stress Transport (SST) k-ω turbulence model, numerical simulations are carried out on an 80 mm centrifugal pump with four inlet structures: straight pipe (SP), reducing tee (RT), reducing wye (RW), and asymmetric reducing wye (ARW). Combined with pressure pulsation signals, this study reveals the propagation rules of flow disturbances induced by tee inlet structures and their inherent energy loss mechanisms. The results show that tee inlet structures barely affect overall pump performance, except RT, which reduces the hydraulic head by 0.44 m and efficiency by 1.47%, accompanied by higher power consumption. Impeller pulsation intensity increases from the leading edge to the trailing edge, with the mid-passage leading edge being the most sensitive region. Impeller spectra are dominated by low-order shaft frequency harmonics, while volute signals are dominated by blade frequency (BF) harmonics. Different tee inlet structures have little impact on circumferential volute pulsation but significantly alter flow characteristics at the volute tongue and outlet diffuser, where 2BF becomes the primary dominant frequency. Disturbance propagation laws differ greatly between the impeller and downstream volute. Centered on low-energy pump system design, this study provides theoretical support for inlet pipeline optimization and energy-saving operation of municipal, industrial, marine and water conservancy pumping facilities. Full article

Drying is a key operation for extending the shelf life of agricultural products and maintaining food quality, and its efficiency and product outcomes are governed by coupled heat and mass transfer. This review critically summarizes the mechanisms, technological characteristics, research methods and application prospects of agricultural-product drying from a heat- and mass-transfer perspective. The moisture-migration pathways, including surface evaporation, internal diffusion, capillary flow, vapor diffusion and bound-water desorption, are first discussed within a porous-medium framework. Governing equations based on Fourier’s law, Fick’s law, energy conservation and convective transfer are then introduced to clarify the theoretical basis of drying models. Typical convective, radiative, conductive and combined drying technologies are compared in terms of transfer mechanisms, drying efficiency, energy consumption, product-quality retention, carbon-footprint potential and industrial feasibility. Particular attention is given to the effects of drying-induced heat and mass transfer on color, texture, rehydration, bioactive compounds, antioxidant activity and microstructure. Current theoretical, experimental, numerical and data-driven research methods are further reviewed, and the limitations of existing studies are identified, including simplified homogeneous assumptions, insufficient model validation, limited quantitative comparison and weak scale-up applicability. Finally, future directions are proposed, including refined multi-scale and multi-field coupled models, advanced in situ characterization, multi-energy-field synergistic drying, digital twins, predictive modeling and multi-objective intelligent optimization. This review aims to provide a more mechanism-based and application-oriented reference for developing efficient, low-carbon and quality-preserving drying systems for agricultural products. Full article