Search Results (1,483)

This study evaluates the feasibility of fully renewable energy systems on El Hierro, the smallest and most isolated Canary Archipelago Island (Spain), contributing to the broader effort to decarbonize the European economy. By 2040, the island’s energy demand is projected to reach 80–110 GWh annually, assuming full economic decarbonization. Currently, El Hierro faces challenges due to its dependence on fossil fuels and inherent variability of renewable sources. To ensure system reliability, the study emphasizes the integration of renewable and storage technologies. Two scenarios are modeled using HOMER Pro 3.18.4 software with probabilistic methods to capture variability in generation and demand. The first scenario, BAU, represents the current system enhanced with electric vehicles. While the second, Efficiency, incorporates energy efficiency improvements and collective mobility policies. Both prioritize electrification and derive an optimal generation mix based on economic and technical constraints, to minimize Levelized Cost Of Energy (LCOE). The approach takes advantage of El Hierro’s abundant solar and wind resources, complemented by reversible pumped hydro storage and megabatteries. Fully renewable systems can meet demand reliably, producing about 30% energy surplus with an LCOE of roughly 10 c€/kWh. The final BAU scenario includes 53 MW of solar PV, 16 MW of wind, and a storage system of 40 MW–800 MWh. The Efficiency scenario has 42 MW of solar PV, 11.5 MW of wind, and 35 MW–550 MWh of storage. Uncertainty analysis indicates that maintaining system reliability requires an approximate 10% increase in both installed capacity and costs. This translates into an additional 7 MW of solar PV and 6 MW–23.5 MWh of batteries in the BAU, and 6 MW and 4 MW–16 MWh in the Efficiency. Full article

This study presents a comprehensive organizational carbon footprint assessment that integrates Scope 1, 2, and 3 emissions for a rubber plantation company, including often-overlooked non-energy sources such as fertilizer application, employee commuting, company-owned vehicle operations, and wastewater discharge. Using the Greenhouse Gas Protocol standard, IPCC 2006 guidelines, and locally adapted emission factors, the assessment quantified the company’s total organizational carbon footprint at 3125 tCO₂e—revealing a previously undocumented emission profile where methane from wastewater discharge, nitrous oxide from fertilizer application, and carbon dioxide from purchased electricity collectively account for over 75% of total emissions. This finding challenges conventional rubber industry practice, which has historically focused on energy-related emissions alone. Three targeted mitigation scenarios were evaluated: (1) optimized nutrient management to reduce fertilizer usage, (2) solar photovoltaic installation to offset grid electricity consumption, and (3) advanced wastewater treatment using Fenton’s reagent combined with activated carbon. Results demonstrate that substantial emission reductions are achievable while maintaining or enhancing productivity and profitability. By establishing a replicable methodological framework grounded in comprehensive emission accounting, this study advances environmental management practices in the rubber sector and provides actionable strategies for plantation-based industries to meet national sustainability agendas and international climate commitments. Full article

Europe, including Poland, is undergoing an energy transition. The use of renewable energy sources (RES) in the national energy sector is increasing significantly, and previously unused areas are increasingly developed for photovoltaic power plants. A specific type of housing common in Eastern European countries opens an additional opportunity for photovoltaic installations without occupying usable ground area. This article aims to analyze the potential for utilizing balconies and loggias in large-panel buildings, which are characteristic of major cities in Poland. Approximately 30% of the population resides in such housing. This presents significant potential for direct use of renewable energy by apartment residents. The article also explores the legal framework for such installations, both as individual investments by apartment owners and as collective initiatives managed by building administrators. The authors analyzed the potential performance of photovoltaic installations under varying azimuths and tilt angles, considering solar irradiation potential. The analyses also encompassed different photovoltaic module technologies, covering a spectrum of photovoltaic technologies, from commonly used monocrystalline panels to advanced transparent BIPV (Building-Integrated Photovoltaics) solutions. Furthermore, the study quantified the energy potential of such installations and compared the results with existing photovoltaic capacities and electricity demand in Poland. Full article

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu₂O, g-C₃N₄/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies. Full article

This study systematically investigated the effects of oxygen plasma treatment on oxygen vacancy defects in sputtered β-gallium oxide (β-Ga₂O₃) films and their corresponding ultraviolet (UV) detection performance. The sputtered β-Ga₂O₃ film subjected to 1 min of oxygen plasma treatment exhibited optimal photodetection properties. Compared to the untreated sample, the dark current was reduced by approximately one order of magnitude to 0.378 pA at 10 V bias. It exhibited an 86% (from 2.92 s to 0.41 s) decrease in response time, a 41.6% increase in photocurrent, a very high photo-to-dark current ratio of 9.18 × 10⁵, and a specific detectivity of 2.62 × 10¹⁰ cm·Hz^1/2W⁻¹ under 254 nm UV illumination intensity of 799 μW/cm² at 10 V bias. Notably, appropriate oxygen plasma treatment minimizes electron capture, enhances the separation and collection of photogenerated carriers, and suppresses the persistent photoconductivity (PPC) effect, thus ultimately shortening the response time. Oxygen plasma processing thus provides an effective approach to fabricating high-performance β-Ga₂O₃ solar-blind photodetectors (SBPDs). Full article

This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder (ED) architecture enhanced with residual connections and convolutional channel projection, which collectively improve the synergy among its core components. Based on this framework, we developed ED-ConvLSTM-Res, a multi-channel feature-based global ionospheric TEC prediction model. Comprehensive accuracy evaluation and comparative tests were carried out using datasets from the solar minimum year of 2019 and the current solar maximum year of 2024. The results indicate that the proposed model consistently achieves strong predictive performance compared with other models, along with a significantly enhanced feature representation capability. Specifically, the Root Mean Square Errors (RMSE) of the ED-ConvLSTM-Res model’s predictions in 2019 and 2024 are 1.28 TECU and 5.28 TECU, respectively, while the corresponding Mean Absolute Errors (MAE) are 0.87 and 3.87, and the coefficients of determination (R²) are 0.95 and 0.94. In the current high solar activity year 2024, the proposed model achieves error reductions of 13.6% in MAE and 11.6% in RMSE compared with the Center for Orbit Determination in Europe (CODE)’s one-day-ahead forecast product, c1pg. These results confirm that the proposed model not only outperforms the ConvLSTM model without additional indices and c1pg but also exhibits strong generalization capability, maintaining stable performance with low errors under both high and low solar activity conditions. Full article

Titanium dioxide (TiO₂) is widely used in solar cells and photocatalysts, given its excellent photoactivity, low cost, and high structural, electronic, and optical stability. Here, a novel TiO₂ composite was prepared by coating TiO₂ inverse opal (IO) with TiO₂ nanorods (NRs). With a porous three-dimensional network structure, the composite exhibited higher light absorption; enhanced the separation of the electron–hole pairs; deepened the infiltration of the electrolyte; better transported and collected charge carriers; and greatly improved the power conversion efficiency (PCE) of the quantum-dot sensitized solar cells (QDSSCs) based on it, while also boosting its own photocatalytic hydrogen generation efficiency. A very high PCE of 12.24% was achieved by QDSSCs utilizing CdS/CdSe sensitizer. Furthermore, the TiO₂ composite exhibited high photocatalytic activity with a H₂ release rate of 1080.2 μ mol h⁻¹ g⁻¹, several times that of bare TiO₂ IO or TiO₂ NRs. Full article

Reliable water access in remote and desert-like regions remains a challenge, particularly in areas with limited infrastructure. Solar-powered submersible pumps offer a promising solution; however, optimizing their performance under variable environmental conditions remains a challenging task. This research presents an Artificial Intelligence (AI)-driven digital twin framework for modeling and optimizing the performance of a solar-powered submersible pump system. The proposed system has three core components: (1) an AI model for predicting the inverter motor’s output frequency based on the current generated by the solar panels, (2) a predictive model for estimating the pump’s generated power based on the inverter motor’s output, and (3) a mathematical formulation for determining the volume of water lifted based on the system’s operational parameters. Moreover, a dataset comprising 6 months of environmental and system performance data was collected and utilized to train and evaluate multiple predictive models. Unlike previous works, this research integrates real-world data with a multi-phase AI modeling pipeline for real-time water output estimation. Performance assessments indicate that the Random Forest (RF) model outperformed alternative approaches, achieving the lowest error rates with a Mean Absolute Error (MAE) of 1.00 Hz for output frequency prediction and 1.39 kW for pump output power prediction. The framework successfully estimated annual water delivery of 166,132.77 m³, with peak monthly output of 18,276.96 m³ in July and minimum of 9784.20 m³ in January demonstrating practical applicability for agricultural water management planning in arid regions. Full article

The integration of residential solar photovoltaic (PV) systems with electric vehicle (EV) charging infrastructure offers significant potential for reducing carbon emissions and enhancing energy autonomy. This study presents a real-world case of a solar-powered EV charging system installed at a residential property in Dublin, Ireland. Unlike prior studies that rely solely on simulation, this work covers the complete process from digital design using OpenSolar to on-site installation and performance evaluation. The system includes 16 high-efficiency solar panels (435 W each), a 4 kW hybrid inverter, a 5.3 kWh lithium-ion battery, and a smart EV charger. Real-time monitoring tools were used to collect energy performance data post-installation. The results indicate that 67% of the household’s solar energy was self-consumed, leading to a 50% reduction in electricity costs. In summer 2024, the client achieved full grid independence and received a €90 credit through feed-in tariffs. The system also enabled free EV charging and generated environmental benefits equivalent to planting 315 trees. This study provides empirical evidence supporting the practical feasibility and economic–environmental advantages of integrated PV–EV systems in temperate climates. Full article

Accurately quantifying the impact of weather on dynamic grid carbon intensity is crucial for power system decarbonization. This study proposes a novel, interpretable machine learning framework integrating tree-based models with SHapley Additive exPlanations (SHAP) to quantify this impact across multiple timescales via a standardized “Weather Share” metric. Applied to city-level hourly data from China, the analysis reveals that meteorological variables collectively explain 21.64% of the hourly variation in carbon intensity, with air temperature and solar irradiance being the dominant drivers. Significant temporal variations are observed: the weather share is higher in summer (29.8%) and winter (23.5%) than in transition seasons and increases markedly to 32.7% during extreme high-temperature events. The proposed framework provides a robust, quantitative tool for grid operators, offering actionable insights for weather-aware carbon reduction strategies and highlighting critical time windows for targeted interventions. Full article

Accurate determination of opacity is critical for understanding radiation transport in both astrophysical and laboratory plasmas. We employ atomic data from R-Matrix calculations to investigate radiative properties in high-energy-density (HED) plasma sources, focusing on opacity variations under extreme plasma conditions. Specifically, we analyze environments such as the base of the convective zone (BCZ) of the Sun ($2\times {10}^6$ K, $N_e={10}^{23}$/cc), and radiative opacity data collected using the inertial confinement fusion (ICF) devices at the Sandia Z facility ($2.11\times {10}^6$ K, $N_e=3.16\times {10}^{22}$/cc) and the Lawrence Livermore National Laboratory National Ignition Facility. We calculate Rosseland Mean Opacities (RMO) within a range of temperatures and densities and analyze how they vary under different plasma conditions. A significant factor influencing opacity in these environments is line and resonance broadening due to plasma effects. Both radiative and collisional broadening modify line shapes, impacting the absorption and emission profiles that determine the RMO. In this study, we specifically focus on electron collisional and Stark ion microfield broadening effects, which play a dominant role in HED plasmas. We assume a Lorentzian profile factor to model combined broadening and investigate its impact on spectral line shapes, resonance behavior, and overall opacity values. Our results are relevant to astrophysical models, particularly in the context of the solar opacity problem, and provide insights into discrepancies between theoretical calculations and experimental measurements. In addition, we investigate the equation-of-state (EOS) and its impact on opacities. In particular, we examine the “chemical picture” Mihalas–Hummer–Däppen EOS with respect to level populations of excited levels included in the extensive R-matrix calculations. This study should contribute to improving opacity models of HED sources such as stellar interiors and laboratory plasma experiments. Full article

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools. Full article

This study focuses on the impact of wastewater discharges from the Besòs treatment plant on the coastal water quality of Barcelona, particularly under adverse weather conditions. A simplified mathematical model was developed to predict, in real time, the concentration of bacterial indicators (Enterococci and E. coli) along nearby beaches. This model aims to quickly detect contamination events and trigger alerts to evacuate swimming areas before water quality tests are completed. The simulator uses meteorological data—such as wind direction and speed, rainfall intensity, and solar irradiance, among others—to anticipate pollution levels without requiring immediate water sampling. The model was tested against real-world scenarios and validated with historical meteorological and bacteriological data collected over six years. The results show that bacterial pollution occurs mainly during intense rainfall events combined with specific wind conditions, particularly when winds blow from the southeast (SE) or east–southeast (ESE) at moderate to high speeds. These wind patterns carry under-treated wastewater toward the coast. Conversely, winds from the north or northwest tend to disperse the contaminants offshore, posing little to no risk to swimmers. This study confirms that pollution events are relatively rare—about two per year—but pose significant health risks when they do occur. The simulator proved reliable, accurately predicting contamination episodes without producing false alarms. Minor variables such as water temperature or suspended solids showed limited influence, with wind and sunlight being the most critical factors. The model’s rapid response capability allows public authorities to take swift action, significantly reducing the risk to beachgoers. This system enhances current water quality monitoring by offering a predictive, cost-effective, and preventive tool for beach management in urban coastal environments. Full article

Asteroids are remnants of primordial material from the early stages of solar system formation, approximately 4.6 billion years ago, and they preserve invaluable records of the processes underlying planetary evolution. Investigating asteroids provides critical insights into the mechanisms of planetary development and the potential origins of life. To enable efficient sample acquisition under vacuum and microgravity conditions, this study introduces a two-stage gas-driven asteroid sampling strategy. This approach mitigates the challenges posed by low-gravity environments and irregular asteroid topography. A coupled computational fluid dynamics–discrete element method (CFD–DEM) framework was employed to simulate the gas–solid two-phase flow during the sampling process. First, a model of the first-stage gas-driven sampling device was developed to establish the relationship between the inlet angle of the gas nozzle and the sampling efficiency, leading to the optimization of the nozzle’s structural parameters. Subsequently, a model of the integrated two-stage gas-driven sampling and sample-delivery system was constructed, through which the influence of the second-stage nozzle inlet angle on the total collected sample mass was investigated, and its design parameters were further refined. Simulation outcomes were validated against experimental data, confirming the reliability of the CFD–DEM coupling approach for predicting gas–solid two-phase interactions. The results demonstrate the feasibility of collecting asteroid regolith with the proposed two-stage gas-driven sampling and delivery system, thereby providing a practical pathway for extraterrestrial material acquisition. Full article

The energy sector holds critical importance for South Africa, particularly as a developing country grappling with persistent economic challenges and energy insecurity. These pressures have stimulated growing scientific and policy interest in renewable energy as a pathway to sustainable development. This study examines public perceptions and awareness of renewable energy technologies and estimates willingness to pay (WTP) for their increased integration into South Africa’s energy mix. By linking these objectives, the study provides insights into the social and economic factors that shape a just energy transition and informs targeted policies, investments, and engagement strategies to accelerate the adoption of renewable energy. A descriptive research design was employed, incorporating a systematic random sampling approach to ensure reliability and representativeness. Data were collected through structured questionnaire surveys conducted in both urban and rural households across Limpopo Province, South Africa. Findings reveal a generally positive public attitude toward the expansion of renewable energy, although knowledge levels remain moderate and are most pronounced with respect to solar energy systems. The mean household WTP for increased renewable energy penetration was estimated at ZAR 163.4 per annum. Binary logistic regression analysis identified eight statistically significant predictors of WTP: Education, Occupation, Income, Recognised Advantages (A1), Financial Incentive Schemes for RES (A3), Expansion Strategies for Renewable Energy (A4), Price Parity with Fossil Fuels (A7), and Interest-Free Financing Options (A8). These results highlight the importance of affordability, policy support, and tangible benefits in driving public acceptance. Overall, the findings highlight the potential for targeted policy and educational interventions to foster household participation and advance South Africa’s just energy transition. Full article