Search Results (1,349)

Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily rising. Accurate forecasting methods are a crucial enabler for the operational existence of power systems that rely on renewable sources. And while in the pursuit of increased accuracy of predictions, many recent research works rely on artificial intelligence and machine learning techniques, this study proposes and adopts a more conventional approach with standardized mathematical models to address the problem of hydropower production forecasting. The model predicts the runoff–power relationship. It starts with the normalization of different rain phenomena as a part of the statistical characterization of runoff events. The system transforms rain occurrence to runoff events via the USDA SCS CN model and then feature vectors are composed, which are used to generate kernel coefficients via interpolation. Contrary to models based on artificial intelligence, the proposed approach has several practical advantages requiring a minimal set of input parameters, which significantly reduces data preprocessing demands and allows for a straightforward integration into existing systems, thereby lowering the cost and the implementation and deployment time. Furthermore, the simplicity and universality of the model make it so that it can be adapted across a wide range of hydropower plants of varying scales and with diverse hydrological and meteorological conditions. The model’s performance and prediction accuracy are evaluated using empirical data records of time series over a five-year period for the meteorological parameters and production of an existing real-life hydropower plant in Bulgaria. The performance of the newly proposed model is assessed using widely accepted statistical error metrics, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the Nash–Sutcliffe Efficiency (NSE) coefficient, and the Pearson correlation coefficient (R). These metrics provide a comprehensive assessment of the forecasts’ precision and effectiveness. The results show that the proposed model offers admissible accuracy with low computational effort. Thus, it can be successfully implemented in practice in a number of hydropower plant production forecasting applications. Full article

As a member state of the European Union, Bulgaria is committed to decarbonisation and the achievement of sustainable development goals. The country has a well-established energy sector and is a net exporter of electricity produced from diverse sources. Electricity generation relies mainly on two key pillars: lignite-fired Thermal Power Plants (TPPs) and the Nuclear Power Plant (NPP) in Kozloduy. This study examines the status of Bulgarian state-owned energy companies (SOEC) and their capacity to respond to the challenges of a sustainable transition towards low- or zero-emission electricity production. The study contributes to the existing literature by providing insights from a comparative analysis of state-owned thermal and nuclear power generation in Bulgaria, examined through the lens of sustainable development. From a practical standpoint it contributes by outlining possible pathways for the sustainable transformation of carbon-intensive TPPs. The analy-sis is based on key sustainability indicators covering the three pillars of sustainable development—economic, social and environmental performance. It includes not only an assessment of the financial performance of state-owned thermal power plants and the nuclear power plant over the past five years but also selected social and environmental indicators. The findings suggest that nuclear energy production in Bulgaria is largely consistent with the core principles of sustainability, while coal-based thermal power plants face increasing economic pressures and contribute to significant environmental impacts. The results highlight the need to transform the coal-based electricity sector into a more economically viable and socially responsible alternative, such as low-carbon generation technologies including nuclear energy. Full article

This study develops a simulation-based plant-level supervisory optimization framework for a Nanjing data center chilled-water plant by combining TRNSYS operating scenarios, fitted component-level surrogate models, and self-adaptive differential evolution (jDE). In the exported representative-case optimization, the decision variables are the cooling-tower approach temperature difference, cooling-water temperature difference, chilled-water temperature difference, and the common cooling-tower fan frequency, while chilled-water supply temperature is carried through as the matched scenario input and examined separately through a 14–18 °C sensitivity sweep. The system comprises three chillers, three cooling towers, three chilled-water pumps, and three cooling-water pumps. The retrained chiller support covers nominal chilled-water supply settings of 14, 15, 16, 17, and 18 °C with hourly sample counts of 8759, 8759, 8760, 8759, and 8759, respectively, yielding an in-support fitted range of approximately 14.0–18.0 °C. For 48 representative operating cases selected from four seasonal days, the optimized operation reduces total power by 17.24–33.55% (mean 27.34%) and increases system COP by 20.78–50.42% (mean 38.12%), relative to matched baseline values at the same timestamps. After enforcing the fitted chiller cooling-water support floor, the largest average relative gain appears in summer rather than winter. Component-level analysis shows that the corrected savings are generated mainly by pump and cooling-tower relief, with chiller-power changes remaining secondary and season dependent. All reported results are derived from matched simulation-based baseline and optimized evaluations rather than field measurements, and all 48 representative cases remain within the fitted chiller support, with optimized cooling-water supply temperatures ranging from 20.05 to 27.75 °C. Full article

In nuclear power, marine engineering, and other fields, a matching system composed of duplex steel and copper alloy is a common combination for rotating components in a seawater environment. However, this system is susceptible to galvanic corrosion that seriously threatens its service safety and service life, with ZCuAl10Fe5Ni5 being the main component corroded. Additionally, current corrosion research on this system has evident gaps. Specifically, the influence of area ratio on galvanic corrosion remains insufficiently understood, and the action mechanism of Cl⁻ on the ZCuAl10Fe5Ni5-based corrosion product film in seawater, as well as the product evolution path, has not been fully revealed, which restricts the development of targeted protection technologies. This study explores the degradation mechanism of ZCuAl10Fe5Ni5 in a specific high-salinity environment (20,000 mg/L Cl⁻), characteristic of nuclear power plant service conditions. The results show that due to the significant electrode potential difference between the SAF2507 duplex steel and ZCuAl10Fe5Ni5 copper alloy, a stable galvanic couple is formed, with ZCuAl10Fe5Ni5 acting as the anode and undergoing dissolution corrosion. When the area ratio of ZCuAl10Fe5Ni5 (anode) to SAF2507 duplex steel (cathode) is 1:50, a significantly stronger galvanic effect is observed. The high concentration of Cl⁻ in seawater can damage the surface of the ZCuAl10Fe5Ni5-based corrosion product film, leading to intensified local corrosion. The ZCuAl10Fe5Ni5-derived corrosion products have a layered structure mainly comprising a mixed system of Cu-Al-Mg oxides/hydroxides, and the corrosion process is accompanied by selective aluminum depletion corrosion. This study provides insight into the corrosion mechanism and key influencing factors of ZCuAl10Fe5Ni5 in the matching system, as well as a theoretical basis and technical support for the design of compatibility metal materials in a seawater environment and the control of corrosion in ZCuAl10Fe5Ni5. Full article

Estimating ground motion parameters at an unsampled site is challenging for seismologists and engineers alike. An attempt is made to apply Kriging interpolation to estimate peak ground accelerations at specific nuclear power plant sites. However, issues such as data quality and Kriging assumptions pose challenges to how practical and reasonable Kriging interpolation results may be in terms of estimating ground motion parameters. Peak ground acceleration data from the 2016 Gyeongju and 2017 Pohang earthquakes were taken from a local seismological agency. Peak ground acceleration, logarithms of the peak ground acceleration, and residuals between the recorded data and global and local ground motion models were used to select and derive empirical variogram models. The leave-one-out cross-validation process suggested estimating peak ground acceleration residuals from a locally developed ground motion model using an Exponential variogram model. Kriging estimates were compared to a site-specific ground motion model. These estimates appeared reasonable at one site but were significantly off at the other site. On the whole, Kriging estimates were lower than ground motion model predictions. When viewed relative to the nearest recordings, Kriging estimates appeared inconsistent across the two earthquake events. A nearest neighbor approach to computing Kriging estimates suggested a minimum of five data points but much more for modeling an empirical variogram. Results also suggest focusing on validation processes more than variogram selection. This suggests caution when applying Kriging for ground motion-related assessments in South Korea. Full article

The output power of photovoltaic (PV) systems is influenced by various environmental factors, exhibiting strong nonlinearity and non-stationarity, which poses significant challenges for accurate forecasting. To address these issues, this paper proposes a short-term PV power forecasting method based on wavelet convolutional neural networks and an improved Transformer. First, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original PV power sequence into several intrinsic mode functions (IMFs). Fuzzy entropy is then utilized to evaluate the complexity of each component, and subsequences with similar entropy values are reconstructed to reduce the non-stationarity of the original series. Subsequently, Pearson correlation coefficients and the maximal information coefficient (MIC) are applied to capture both linear and nonlinear relationships between each reconstructed component and meteorological features, enabling the selection of strongly correlated variables. On this basis, a wavelet convolutional network (WTConv) is introduced to perform multi-scale decomposition and frequency-band feature extraction on the reconstructed components by integrating wavelet transform with convolution operations, effectively expanding the receptive field and extracting deep-seated features of the sequences. Finally, an improved iTransformer model is adopted for time-series modeling, leveraging its inverted encoding structure and self-attention mechanism to fully capture long-term dependencies among multivariate variables. The proposed model is validated using actual power data from a PV plant in Ningxia, China, across four seasons. Comprehensive experiments, including ablation studies, comparative analyses, loss function convergence evaluation, and Diebold–Mariano significance tests, are conducted to thoroughly assess the model’s effectiveness and superiority. Experimental results demonstrate that the proposed model achieves excellent prediction accuracy and stability in spring, summer, autumn, and winter, showing strong potential for engineering applications. Full article

The prestressed concrete containment structure constitutes the core protective structure of a nuclear power plant. This paper utilises the prestressed concrete containment vessel (PCCV) of the Hualong Pressurised Reactor 1000 (HPR-1000)—a third-generation pressurised water reactor (PWR)—as the primary research prototype. Utilising ANSYS, a finite element model was established, with key points selected at critical locations such as the dome, cylinder, and base slab for stress analysis calculations. Reinforcement quantification derived from the design methodologies and analytical formulations prescribed in ACI 349 and ACI 359 were compared under various loading conditions. This investigation identified the core discrepancies and influencing factors between the two codes in reinforcement design, alongside a sensitivity analysis to identify key parameters affecting reinforcement design in different structural zones. The results indicate that discrepancies in reinforcement requirements stem primarily from the divergent design philosophies and strength assessment formulations, with this influence outweighing variations in load combinations. Furthermore, significant spatial differences exist in the sensitivity of reinforcement designs for key components to parameters such as the height-to-diameter ratio, shutdown seismic actions, accident pressure, and temperature effects. The conclusions of this study establish theoretical foundations and furnish empirical data to enhance the computational efficiency of prestressed concrete containment design for pressurised water reactor (PWR) facilities, while supporting the alignment of national and international regulatory standards. Furthermore, they serve as a technical reference for advancing nuclear power structural design practices. Full article

To explicitly illustrate the relationship between heliostat field optimization and power generation, a coupled model was established in Simulink. By optimizing the geometric layout of the heliostat field, the solar heat collection efficiency can be significantly improved, thereby increasing the thermal input to the system. The optimized heliostat field design can convert solar energy into thermal energy more efficiently and transfer it to the steam generator through the molten salt loop, thereby driving power generation in the Rankine cycle. In this process, the Rankine cycle is responsible for converting the thermal energy supplied by the molten salt loop into mechanical work and ultimately into electrical power output. At the same time, real meteorological data from a commercial heliostat field were introduced, and annual power generation simulations demonstrated that the integrated modeling of the heliostat field, thermal storage, and power block based on actual meteorological boundary conditions and system parameters can effectively reflect the power generation performance of a commercial tower solar thermal power plant. Meanwhile, research on heliostat field optimization should further evolve from identifying general patterns toward parameter design and overall system performance improvement. For molten-salt tower solar thermal power plants, key design variables such as receiver tower height, receiver dimensions, heliostat dimensions, and heliostat field spacing parameters affect not only the annual average optical efficiency of the heliostat field and the thermal power output of the receiver, but also the annual power generation of the entire plant. By integrating SOLARPILOT 1.5.2 and SAM 2025.4.16, the design variables were systematically analyzed to investigate their effects on the annual average optical efficiency of the heliostat field, the number of heliostats, the receiver output power, and the annual power generation, and the reasonable value ranges of the heliostat field parameters were determined accordingly. The established Rankine cycle power block model was then coupled with the parameter optimization results to carry out a secondary optimization of the initial heliostat field. Through the above study, the aim is to realize a shift from single-objective geometric optimization of the heliostat field to comprehensive optimization oriented toward annual plant power generation performance and scenario adaptability, thereby providing a basis for scheme design and parameter selection of molten-salt tower solar thermal power plants. For external validation, the annual generation predicted for the Delingha 50 MW commercial plant was 142.15 GWh, corresponding to a relative deviation of 2.64% from the published design value of 146 GWh. This indicates that the coupled framework can reasonably capture the integrated response of the heliostat field, thermal storage system, and power block at the plant level. The model is therefore suitable for generation-oriented parameter screening and preliminary design of tower molten-salt CSP plants, while detailed component-level transient design still requires higher-fidelity engineering models. Full article

Frequent co-occurrences of high temperature and drought in tropical regions make heat and drought tolerance of landscape plants core physiological traits that determine their landscape adaptability and community stability. However, systematic elucidation of the differentiation patterns of stress resistance between specialist and generalist tropical landscape plant species, the intrinsic correlations between heat and drought tolerance traits, and the regulatory mechanisms of leaf functional traits remains lacking. In this study, eight typical tropical landscape plant species in Xishuangbanna Tropical Botanical Garden were selected as research objects. By determining leaf chlorophyll fluorescence parameters, water relation parameters and leaf functional traits, we systematically analyzed the differences in heat and drought tolerance and interspecific differentiation characteristics between specialist and generalist species, and simultaneously elucidated the correlation patterns of drought-heat tolerance traits as well as the regulatory effects of leaf functional traits on these traits. The results showed that the turgor loss point water potential (Ψ_TLP) of generalist tropical landscape plant species was significantly higher than that of specialist species, with superior drought tolerance; in contrast, the half-lethal temperature of photosystem II (T50) of specialist species was significantly higher than that of generalist species, with stronger heat tolerance. Among the eight tested species, Bombax ceiba exhibited the strongest drought tolerance, while Baccaurea ramiflora had the optimal heat tolerance. The study also found that the drought and heat tolerance traits of tropical landscape plants exhibited stress-specific trade-offs; leaf functional traits had limited overall explanatory power for the stress resistance of tropical landscape plants and only exerted a certain regulatory effect on drought tolerance. This study clearly reveals the differences in heat and drought tolerance between specialist and generalist species. This finding not only enhances our mechanistic understanding of stress resistance in tropical plants but also provides data support for ecological restoration and conservation practices in tropical gardens. Full article

SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables. This procedure allowed for the identification of the modeling approaches that provide the most robust performance across varying operational regimes. The results show that XGB achieved the best predictive performance, with test RMSE and R² values of 87.98 and 97.35% for SAG Production, and 431.11 and 95.11% for SAG Power, respectively. Stochastic approaches provided complementary uncertainty quantification, supporting risk-informed decision making under variable operating conditions. The analysis by operational regime indicates that XGB presents better fit in the Thick hydraulic regime, for both responses’ variables, which could be explained why a dense pulp operation provides more predictable grinding dynamics. The comparative analysis reveals trade-offs between model complexity, interpretability, computational requirements, and predictive performance, offering practical guidance for selecting appropriate modeling frameworks based on specific operational objectives and data availability in mineral processing applications. Full article

S. Tomé and Principe (STP) islands have been studied in recent years for their wide range of medicinal plants which exhibit several biological activities of great medicinal interest for some diseases. Experimental design for optimization of several parameters was carried out by a full-factorial test of two levels of three factors for secondary metabolite extraction from Rauvolfia caffra leaves. The best conditions for highest extraction of phenolic compounds (i.e., 89.90 μmoles gallic acid equivalent/g leaves) were obtained at 25 °C in H₂O and at 5 days of incubation. Several phytochemical assays were performed for characterization of these plant extracts, and the highest levels of TFC, DPPH and reducing power were obtained with aqueous plant extraction at 25 °C and for 5 days of incubation, whereas leaf extraction with water at 40 °C for 5 days of incubation revealed the highest levels of ABTS scavenging activity. The levels of SOD and superoxide radical scavenging activities were highest in plant extraction, with hexane at 25 and 40 °C for 5 days of incubation, respectively. The present report consists of a novel and intrinsic synchronous fluorescence and phosphorescence characterization of secondary metabolites from this plant extract. Intrinsic and non-destructive synchronous fluorescence was carried out in the range of 250 to 750 nm with a Δλ range of 5–30 nm, which exhibited several fluorescence peaks in hexane and aqueous plant extracts. On the other hand, intrinsic and non-destructive synchronous phosphorescence was also performed which also exhibited several peaks in aqueous and hexane extracts. 3D spectra of secondary metabolites confirmed the fluorescence peaks observed in SFS in plant extracts. FTIR spectroscopy was selected to investigate the structural properties of secondary metabolites in these plant extracts. Therefore, the present work describes a novel characterization of secondary metabolites by a non-destructive and intrinsic synchronous fluorescence technique for plant extracts. Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

Background/Objectives: Onion (Allium cepa) peems are an underutilized by-product rich in polyphenols. This study evaluated the physicochemical profile, and bioactive potential (antidiabetic, antimicrobial, antioxidant, and anticoagulant) of Moroccan red onion peels using integrated in vivo, in vitro, and in silico approaches. Methods: Moisture, pH, ash content, and mineral elements were determined, followed by phytochemical screening and three extractions: decoction E0, aqueous Soxhlet E1, and hydroethanolic Soxhlet E2 (70/30; ethanol/water, v/v). The measurement of polyphenols, flavonoids, and tannins was carried out using colorimetric methods, while the molecular profile was studied by high-performance liquid chromatography coupled to ultraviolet detection and electrospray ionization mass spectrometry (HPLC/UV-ESI-MS). Biological activities were determined using 2,2-diphenyl-1-picrylhydrazyl, ferric reducing antioxidant power, and total antioxidant capacity assays (in vitro antioxidant); microdilution (antimicrobial); prothrombin time and activated partial thromboplastin time (anticoagulant); and α-amylase/α-glucosidase enzymatic inhibition and oral glucose tolerance tests on normoglycemic rats. Also, acute toxicity was evaluated, and molecular interactions between these proteins and ligands (docking, molecular dynamics, and MM-PBSA) were analyzed. Results: Physicochemical analyses showed an acidic pH (3.06) and high ash content (15.21%), with the concentration of regulated elements remaining within FAO/WHO limits. The extractive content was between 6.90% E0 and 19.18% E2. The E1 extract had the maximum amount of total polyphenols (178.95 mg GAE/g); on the other hand, E2 was the richest in flavonoids by 121.43 mg QE/g. The HPLC/ESI-MS analysis of E0 revealed 20 compounds, among which flavonoids (84.93%) were predominant, with isorhamnetin (30.26%), followed by quercetin and its glycosylated forms. E1 showed the most potent antioxidant effects (IC₅₀ DPPH, 22.38 µg/mL, as that of ascorbic acid). The antibacterial activity of E0 was especially potent towards Enterobacter cloacae and Pseudomonas aeruginosa (MIC 75 µg/mL). A mild dose-dependent anticoagulant effect was seen. Antidiabetic activity was found to be outstanding: α-amylase (IC₅₀ 62.75 µg/mL) and α-glucosidase (IC₅₀ 8.49 µg/mL, stronger than acarbose) inhibitions were corroborated in vivo by a considerable decrease in the glycemic area under the curve. The molecular docking study in silico demonstrated strong molecular interactions, especially for quercetin 4′-O-glucoside with good binding energies. Conclusions: A. cepa peels from Morocco can be considered a safe plant matrix containing bioactive flavonoids with strong antioxidant and selective antimicrobial activities and promising antidiabetic effects, supported by molecular modeling. Full article

This review focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. The rapid growth of the urban population and the increase in the frequency of extreme events related to climate change make novel approaches to the geophysical monitoring of urban areas and civil infrastructures essential in the context of programs for the sustainability and resilience of cities. In this scenario, there is a growing interest in using ground-based electromagnetic methods to investigate strategic infrastructures such as bridges, tunnels, dam embankments, power plants, energy plants and pipelines in a non-invasive way. The development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. This review critically analyzes the results relating to the application of ground-based electromagnetic methods in infrastructure monitoring and surveillance over the past 20 years by presenting a selection of best practice examples and studies planned to support programs for the resilience and maintenance of engineering infrastructures. The analysis reveals that these methods are highly effective in addressing a broad spectrum of monitoring issues in view of effective maintenance of civil infrastructures. In fact, these methods are essential for detecting the geometry of buried objects (e.g., bars and voids), enabling the early detection of degradation phenomena, and mapping water infiltration processes inside structures, as well as many other challenging applications. Finally, prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process and interpret data as well as to design smart operational guidelines for infrastructure management. Full article

Cultivated lettuce (Lactuca sativa L.) is a major leafy crop and an emerging model for functional genomics within the Asteraceae family, supported by high-quality reference genomes and efficient transformation systems. Although CRISPR/Cas technology offers powerful opportunities for crop improvement, editing efficiency depends on optimized construct architecture and reliable guide RNA (gRNA) validation. However, a rapid platform for evaluating CRISPR reagents in lettuce is still lacking. Here, we developed an efficient hairyroot-based system to accelerate CRISPR/Cas genome editing optimization in L. sativa. Four Agrobacterium rhizogenes strains were compared for hairy root induction in two cultivars, ‘Saladin’ and ‘Osiride’, identifying strain ATCC15834 as the most effective based on transformation frequency and root production. Using this platform, we evaluated multiple CRISPR construct configurations, including alternative promoters for nuclease and gRNA expression. A plant-derived promoter combined with At-pU6-26 variant significantly improved editing efficiency. As a proof of concept, we targeted LsHB2, the putative ortholog of Arabidopsis thaliana ATHB2, a key regulator of the shade avoidance response using SpCas9, SaCas9, and LbCas12a nucleases. The system enabled rapid genotyping and quantitative indel profiling. Overall, this workflow provides a robust framework for efficient guide selection and construct optimization in lettuce genome editing. Full article