Search Results (54,654)

This paper evaluates a Sustainable Energy Efficiency Business Model (SEEBM) for small and medium sized enterprises (SMEs) in the European industrial sector. The sustainability-oriented model, developed by the authors, combines Energy Performance Contracting (EPC), Energy Supply Contracting (ESC), and Energy Saving Insurance (ESI) within a unified framework to support industrial decarbonisation. The study identifies key performance indicators and translates them into a System Dynamics model using a Design-Based Research approach. The model is built from secondary data drawn from 45 SME case studies in the European SMEmPower project and is validated through extreme condition testing and behavioural sensitivity analysis. Results indicate that the integrated model significantly enhances financial performance, reducing the average payback period from average 36 months to 10 months. Sensitivity analysis highlights the influence of contract duration, energy saving rates, and energy prices on both payback and emissions reduction outcomes. This research introduces a novel dynamic framework integrating EPC, ESC, and ESI, enabling time-based evaluation of investment viability and environmental impact. It offers a replicable decision support tool for policymakers and market actors seeking scalable, low risk pathways to SME decarbonisation. Overall, the model provides practical insights for improving investment decisions while accelerating the transition toward sustainable industrial systems across Europe. Full article

To address the problems of insufficient precision reserve, limited rotational speed, and excessive temperature rise in high-speed ultra-precision machine tool spindle bearings, the influence of frictional power loss on the thermo-mechanical behavior of the bearing system was investigated. Firstly, based on the analysis of the heat source of the bearing, the friction power consumption model of the bearing assembly is established, and the analysis of the bearing temperature field is realized by studying the heat energy transfer. Secondly, the test bench is built for experimental verification. Finally, through the study of thermal-mechanical coupling performance, the influence of different rotational speeds on bearing stress and life is analyzed. The results show that the friction power consumption generated by the spin sliding of the bearing rolling element accounts for the largest proportion, accounting for 31% of the total friction power consumption; the increase in bearing speed will increase the bearing temperature. At 55,000 r/min, the highest temperature at the rolling element is close to 75 °C, followed by the inner ring up to 68 °C, and the lowest outer ring temperature is 57 °C. The temperature has a great influence on the bearing performance. Under the same working conditions, the equivalent stress is increased by 21%, the contact pressure is increased by 25%, and the fatigue life of the bearing is reduced by 5.6%. Bearing performance is significantly affected by thermodynamic behavior. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

This study addresses the challenge of thermal accumulation and low efficiency in conventional ground heat exchangers for building heating and cooling applications. A novel direct-expansion CO₂ borehole heat exchanger (BHE) backfilled with well water is proposed to enhance heat transfer and mitigate soil thermal imbalance. A dynamic thermal resistance-capacity model (TRCM) coupling CO₂ phase change with natural convection in well water is developed and validated against full-scale field experiments (135 m depth), with prediction errors below 5% under cooling conditions (MAPE 2.29%, RMSE 2.49%). Quantitative analysis reveals that natural convection in well water enhances overall heat transfer by 14.9% compared to soil-backfilled systems, despite intensifying thermal short-circuiting. Two practical enhancement strategies for building energy efficiency are proposed: (1) adding insulation to the rising pipe, which increases the heat transfer rate by up to 35.1%; and (2) implementing artificial well-water circulation, which achieves up to 50.5% enhancement, with an equivalent coefficient of performance (COP) reaching 52.5 under intermittent operation. The proposed system and the parametric analysis of these strategies offer effective solutions for improving the energy performance of ground-source heat pumps in buildings, contributing to reduced operational energy consumption and enhanced system reliability. Full article

Ti-6Al-4V parts fabricated via selective laser melting (SLM) often exhibit severe surface irregularities that limit their direct engineering application. This study proposes a top-hat beam laser polishing method to improve surface quality. The results show that surface roughness (Sa) is reduced to 0.48 μm, a 95.3% decrease from the as-built condition. The uniform energy distribution of the top-hat beam stabilizes melt pool behavior, enabling effective surface leveling through valley filling and lateral melt flow. In contrast, Gaussian beam polishing induces strong Marangoni convection and wake effects, resulting in higher residual roughness. Microstructural analysis indicates an increased fraction of equiaxed α grains and a β-phase content of ~6% after top-hat polishing. The heat-affected zone likely exhibits a subcritical heat-treatment-like effect, promoting fine secondary α precipitation. Additionally, localized stresses induced by steep thermal gradients during SLM are effectively relieved. Overall, top-hat laser polishing is a promising post-processing technique for enhancing the surface quality of Ti-6Al-4V components. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

Aiming at the technical pain points of traditional manhole covers with low intelligence high cost and excessive power consumption, this study designs a TENG-based alarm device to enhance the safety and maintenance efficiency of urban infrastructure. The device integrates a water immersion sensor and a displacement sensor enabling real-time status monitoring through a unique TENG mechanism. The solid–liquid mode water immersion sensor detects seepage through the triboelectrification effect. Water droplets contact electrodes on the surface of FEP film and generate electric energy to trigger the detection circuit. The displacement sensor adopts the independent layer mode of TENG and combines with a mechanical tumbler mechanism to realize displacement detection. External force-induced manhole cover displacement drives internal balls to roll and rub against electrodes. Electric energy is then generated to activate the detection circuit. On the basis of the two sensors, an efficient and reliable intelligent alarm system is constructed. The system receives and analyzes displacement and water immersion-sensing signals in real time. It rapidly identifies potential safety hazards including displacement offset water accumulation and leakage. Signal analysis and early warning prompts are completed synchronously. This system provides accurate and real-time data support for public facility monitoring, pipe network operation and maintenance, and regional security in smart cities. It helps achieve early detection and early disposal of hidden dangers and improves the intelligent and refined level of smart city monitoring. Full article

This study addresses the increasing need to structure knowledge in the field of circular resource management, with a focus on energy efficiency and sustainable resilience. Previous studies have examined various taxonomies for the circular economy, yet a clear gap remains in understanding how energy efficiency and resilience serve as the main pillars for operational stability. This study is designed as a bibliometric analysis based on a selection of relevant scientific articles. The identified factors were extracted based on their frequency of occurrence in the literature and processed using statistical clustering techniques to group them into coherent categories. The results show that the field is defined by a set of interconnected factors that can be structured into distinct clusters, reflecting key dimensions such as operational performance, environmental impact, and system resilience. Specifically, the analysis demonstrates how energy-related attributes and resilience attributes act as stabilizing factors within closed-loop systems. Based on these findings, this study proposes a structured framework that organizes the identified factors into a clear configuration. This framework provides a reference point for researchers who aim to develop models in this area and for practitioners involved in the design and optimization of circular systems. This study contributes by offering a structured view of the field and by supporting the development of consistent analytical and decision-making approaches grounded in the necessity of balancing resource recovery with system stability. Full article

Background/Objectives: This secondary and exploratory meta-analysis re-evaluated 30 randomized controlled trials on free and added sugars (FS) detailed in the European Food Safety Authority’s (EFSA) report on the tolerable upper intake level for dietary sugars, focusing on the influence of food source (beverages, foods, or mixed) on cardiometabolic and anthropometric health. Methods: The EFSA’s method of analyzing the relative FS intake (difference between treatment and comparator arms, Δ%E^fs) was used, with further adjustment for the reported intake of all sources of FS and energy. The EFSA’s “high vs. low” random-effects meta-analysis comparing groups with the highest and lowest FS intake was replicated, and additional exploratory dose–response meta-regressions (linear and non-linear) were performed, stratified by food source. Given the secondary and observational nature of the analysis, all source-stratified findings should be interpreted as hypothesis-generating, rather than causal. Results: There were no interactions between Δ%E^fs and food source for any outcome, and within a source there were linearly positive and statistically significant regressions for body weight (mixed), low-density lipoprotein cholesterol (LDL-C, foods), and uric acid (beverages). Across 13 outcomes, Δ%E^fs was positively and linearly related to greater fasting glucose, high-density lipoprotein cholesterol (HDL-C), and LDL-C, and non-linearly to body weight. However, the data were limited in their representation of FS intake at typical population levels, and there were insufficient data to investigate the effect of FS from foods on most anthropometric outcomes. Conclusions: Meta-regressive dose–responses revealed little relationship between Δ%E^fs from specific food sources and health outcomes, but such effects might be masked by confounding factors. Future trials that test realistic intakes of FS across diverse food matrices and account for dietary compensation would help to overcome limitations in the body of evidence. Full article

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction. Full article

The rapid development of smart energy infrastructures and renewable energy systems requires advanced sensing solutions that provide high accuracy, expandability, and stability under real operating conditions. However, conventional gas monitoring systems are predominantly based on resistive or voltage-output sensors, which require complex analog front-end circuits and analog-to-digital conversion, leading to increased system complexity, cost, and susceptibility to electromagnetic interference. This paper tackles this limitation by proposing a frequency-domain sensing approach for multichannel monitoring of biogas plant parameters. The objective of this study is to develop and experimentally validate an extendable sensing architecture based on autogenerator microelectronic gas transducers with direct gas concentration–frequency conversion and FPGA-based digital acquisition. The proposed method is grounded in a physical–mathematical model of the space-charge capacitance of gas-sensitive semiconductor structures derived from Poisson’s equation, facilitating analytical formulation of conversion and sensitivity functions. A multichannel FPGA-based measurement system is implemented to process frequency signals without analog conditioning or ADC stages. Experimental validation was performed for CH₄ (0–85%), CO₂ (0–60%), H₂, NH₃, and H₂S (1–20,000 ppm). The results demonstrate measurement uncertainty within 0.25–0.5%, with sensitivity reaching 350–748 Hz/ppm for H₂, 455–750 Hz/ppm for NH₃, and 253–375 Hz/ppm for H₂S, while methane and carbon dioxide sensitivities reach up to 112 kHz/% and 98.7 kHz/%, respectively. Spectral analysis in the LTE-1800 band confirms improved noise immunity (up to 4.5×) and extended transmission capabilities. A 12-channel FPGA-based monitoring system (RDM-BP-1) with a 1 s sampling interval, IP67 protection, and wireless connectivity is developed and validated. The proposed architecture eliminates analog signal conditioning, reduces hardware complexity, and provides an easily expandable and reliable sensing solution for smart buildings, renewable energy systems, and cloud-integrated energy infrastructures. Full article

The widespread integration of wind energy conversion systems has fundamentally reshaped modern power grid architecture. However, the limited dynamic response of wind turbine (WT) converters during grid faults—particularly their inability to provide sufficient reactive current and maintain voltage stability under severe dips—necessitates a redefinition of the conventional low-voltage ride-through (LVRT) curve. This study addresses this challenge by proposing a Mutation-Adaptive Mean Variance Mapping Optimization (A-MVMO) algorithm for the control of grid-side converters (GSCs) in wind farms (WFs). To systematically assess post-fault voltage recovery, a Time-Segmented Analysis for Voltage Recovery (T-SAVR) approach is developed with a multi-objective function. The performance of the proposed A-MVMO is benchmarked against standard MVMO and conventional particle swarm optimization (PSO) under both moderate (0.7 pu) and severe (0.15 pu) voltage dips using the IEEE 39-bus system implemented in DIgSILENT/PowerFactory. The results demonstrate that A-MVMO achieves fast, oscillation-free voltage recovery with negligible overshoot (<1%) and lower current injection than PSO and MVMO, while satisfying all engineering constraints. Moreover, the co-optimization of Park-level and turbine-level controllers ensures seamless coordination, as evidenced by the close tracking between the farm-wide reactive power reference and the aggregated turbine response. The T-SAVR method proves essential for focusing optimization on controllable recovery dynamics, yielding a superior LVRT curve. Full article

Aiming to address the problems of low accuracy in coal–rock identification during coal mining, which lead to energy waste and safety hazards, a high-precision coal–rock medium identification method combining terahertz time-domain spectroscopy technology and multiple machine learning algorithms is proposed. By preparing coal–rock samples with a gradient change in coal content, terahertz time-domain spectroscopy data of coal–rock mixed media are collected, and optical parameters such as the refractive index and absorption coefficient are extracted. Principal component analysis is used to reduce the dimensionality of the terahertz data, and machine learning algorithms such as support vector machine, least squares support vector machine, artificial neural networks, and random forests are adopted for classification and identification. The study found that terahertz waves are more sensitive to coal–rock media in the 0.7–1.3 THz frequency band, and that the refractive index and absorption coefficient of coal–rock mixed media are significantly positively correlated with coal content within the range of 0–30%. After feature extraction and K-fold cross-validation, the random forest model achieved a coal–rock classification accuracy of over 96% on the test set, significantly outperforming other comparison algorithms. The research verifies the efficiency and practicality of terahertz technology combined with multiple machine learning algorithms in coal–rock identification, providing a new method for fields such as mineral separation. This method has, to a certain extent, broken through the accuracy bottleneck of traditional coal–rock identification technologies within its applicable range, providing a new solution for real-time detection of coal–rock interfaces and is expected to further reduce the risks of ineffective mining and roof accidents in the future. Full article

Poultry litter represents a promising feedstock for biogas production through anaerobic digestion (AD), offering potential benefits for both on-farm energy supply and organic waste management. This opportunity is particularly relevant in resource-constrained countries, where limited access to reliable energy and inadequate waste management remain critical challenges. This study investigates the integration of poultry litter-based biogas production into a decentralized energy system supplying a poultry farm and a nearby household in Yaoundé, Cameroon. A techno-economic optimization framework based on mixed-integer linear programming is used to determine the cost-optimal configuration of the energy system. The results show that anaerobic digesters are only selected when constraints on poultry litter disposal are introduced. Total annual system costs increase from approximately 2680 EUR·y⁻¹ in the unconstrained scenario to 3720 EUR·y⁻¹ when up to 50% of the poultry litter is valorized locally through AD. Increasing biogas production primarily substitutes liquefied petroleum gas (LPG) used for heating and progressively reduces electricity purchases from the grid. Overall, the analysis indicates that anaerobic digestion is currently not economically competitive when evaluated solely on energy supply benefits, mainly due to the high capital cost of digesters. However, when waste management objectives or external investment support are considered, poultry litter-based biogas systems can contribute to integrated energy–waste management strategies and support circular resource use in small-scale agricultural systems. Full article

This study reports that titanium nanoparticles can be used as a surface coating to enhance the corrosion resistance of 316 stainless steel. It specifically examines the influence of the deposition temperature (T_dep) on the coating’s structural and morphological properties, including corrosion behavior. TiO₂ nanoparticles were thoughtfully deposited on steel substrates at temperatures of 300, 400, and 500 °C using a horizontal hot-wall tubular reactor. This equipment was expertly engineered at the CIDETEQ laboratory through the metal–organic chemical vapor deposition (MOCVD) concept. Titanium isopropoxide [Ti(OC₃H₇)₄] was used as the precursor for the coating synthesis. Structural analysis was conducted using X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM). Corrosion performance was evaluated under accelerated conditions in 0.5 M H₂SO₄ using potentiodynamic anodic polarization (AP), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The corrosion test indicates that increasing T_dep significantly differentiates the coating morphology and improves corrosion resistance. AP revealed that the pitting potential (E_pit) shifted to more positive values, ranging from +1.4 to +1.5 V. CV voltammograms indicated that coated samples had lower passive current densities (I_p ≈ 10⁴ to 10⁵ A/cm²) than the bare substrate. EIS analysis demonstrated that the coating deposited at 500 °C processed the most favorable electrochemical performance, resisting corrosion for over 28 days. This coating achieved the highest electrical resistance (297 kΩ·cm²) and the lowest capacitance (2.7 μF/cm²), attributed to the formation of a crystalline anatase phase composed of pyramidal-like nanoparticle agglomerates (~40 nm). The dense packing structure effectively blocks charge-transfer pathways, restricting electron and ion transfer. Finally, MOCVD-based chemical surface modification with TiO₂ nanoparticles is considered an innovative method to improve the corrosion resistance of stainless steel, thereby prolonging its durability under accelerated sulfuric acid exposure. Full article