Search Results (955)

This study investigates multi-horizon forecasting of solar-based green hydrogen (H₂) production in Southern Morocco (Dakhla: 23.7° N, 15.9° W; Laâyoune: 27.2° N, 13.2° W; and Guelmim: 29.0° N, 10.1° W) using daily meteorological time series (2010–2025) derived from NASA POWER and PVGIS. Daily H₂ production (kg/day) is estimated through a PV-to-hydrogen conversion model assuming a 100 MW PV plant, a performance ratio of 0.75, and a specific electricity consumption of 50 kWh/kg-H₂. We formulate a supervised learning problem to predict H₂ at multiple horizons (J + 1, J + 3, and J + 7), combining calendar features, physically motivated variables, and lagged/rolling statistics. Models are trained on 2010–2023 and evaluated on 2024–2025 using R², RMSE, and sMAPE. CatBoost, Random Forest, and LSTM are compared; additionally, a physically interpretable two-step framework is proposed. For J + 1, the best results reach R² values of 0.863 in Dakhla, 0.795 in Laâyoune, and 0.669 in Guelmim. At the J + 7 horizon, predictive performance remains robust with R² values of 0.792, 0.759, and 0.556, respectively. The proposed two-step approach yields comparable accuracy (e.g., Dakhla J + 1 R² = 0.862) while improving physical consistency. Full article

The present study proposes a configuration of a solar PV-battery-powered knapsack sprayer (SPKS) equipped with a rear-mounted multi-nozzle boom to serve as a sustainable, reliable and decentralized spraying system for smallholder farmers. This design aims to significantly reduce the physiological strain and inconsistent performance associated with conventional manual lever sprayers (MLSs). The SPKS was evaluated against the MLS during onion crop spraying in terms of hydraulic performance, field capacity, spray deposit uniformity, and operator ergonomics and biomechanics, alongside an economic and environmental sustainability assessment. Results of hydraulic tests revealed that the SPKS achieved the optimal spray distribution uniformity of C.V = 16.67% at an operating pressure of 350 kPa and a boom height of 40 cm. Field experiments demonstrated that the SPKS more than doubled the effective field capacity to 0.36 ha/h compared to 0.16 ha/h for the MLS, achieving a field efficiency of 67.55%. Moreover, the SPKS achieved high spray deposit coverage (88%) compared to the MLS (52.6%), while the integrated PV panel effectively doubled operational runtime by maintaining >50% battery state of charge under continuous load. Biomechanics and ergonomics pilot analyses indicated that the MLS operation imposed high musculoskeletal (RULA score = 7) and cardiac strain, whereas SPKS operation was classified as low-risk (RULA score = 3) with minimal cardiac strain. Economically, the SPKS saves approximately $8.70 USD per hectare in labor costs. Environmentally, it prevents ~17.0 kg of CO₂ emissions annually and reduces pesticide application volume by 26.5%. By simultaneously addressing energy limitations, ergonomic hazards, and operational inefficiencies, the SPKS offers a holistic and superior solution for sustainable smallholder agriculture. Full article

The reactive transport of hexavalent chromium (Cr(VI)) in anthropogenically disturbed sites (e.g., mine waste rock dumps, chromium salt industrial sites) is critically influenced by physico-chemical heterogeneity, yet the interplay between physical and chemical heterogeneities remains poorly understood. This study employed a series of experiments and numerical modeling to investigate the transport of Cr(VI), focusing on the implications of physical heterogeneity—represented by preferential flow paths—and chemical heterogeneity—characterized by reductive mineral lenses. Key findings indicate that physical heterogeneity accelerates Cr(VI) breakthrough by 1.4 to 2.1 pore volumes (PV) relative to homogeneous columns. The presence of pyrite lenses delays breakthrough by 0.6–1.2 PV under neutral pH and 1.6–2.0 PV under acidic pH. At a flow rate of 3.0 m/day, the apparent sorption capacity decreases by ~62.5% compared to 0.3 m/day, indicating that physical advection largely suppresses chemical retention under high-flux conditions. The above results demonstrate that physical heterogeneity governs flow paths and advection rates, whereas chemical heterogeneity impedes transport through heterogeneous adsorption and reduction in Cr(VI) to Cr(III) along these pathways. Furthermore, the presence of preferential paths leads to greater spatial variability, which subsequently influences the interaction dynamics between Cr(VI) and reactive minerals in the aqueous environment. The dominance shifts between physical/chemical controls based on flow rates and pH. At higher flow rates, the influence of physical heterogeneity becomes more pronounced, diminishing chemical reactions due to insufficient residence time of Cr(VI). Conversely, a lower pH environment enhances pyrite dissolution, which decouples the dependency on physical heterogeneity by promoting homogeneous reactions. Further evidence was obtained through X-ray photoelectron spectroscopy (XPS) analysis. The experimental observations are complemented by TOUGHREACT-based reactive transport simulations, which further reveal that the apparent dominance shifts arise from competing timescales between advection and surface reaction. The insights gained from the study emphasize the necessity of integrating both physical and chemical spatial variability in risk assessments, transport modeling, and designing targeted remediation strategies. Full article

With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems. Full article

Extreme weather couples spatially correlated component damage with photovoltaic (PV) derating, changing electric-vehicle (EV) demand, repair delay, and time-varying network topology. This paper develops a coordinated restoration architecture for multi-area feeders containing PV, battery energy storage, and charging stations. Its weather-facing layer constructs joint outage-risk, renewable-error, charging-demand, and voltage-vulnerability descriptors. Those descriptors parameterize a two-stage mixed-integer second-order-cone program with a finite-support optimal-transport ambiguity set that remains well defined for discontinuous mixed-integer recourse. Regional actor–critic agents propose five-minute corrections around the hourly robust schedule; constrained projection, non-linear AC power-flow screening, emergency fallback, and margin-tightened re-optimization retain the authority to accept or reject each proposal. The evaluation uses public 33-node and 123-node feeders together with synthetic 240-node and 850-node stress networks. A pre-fit manifest allocates 240 records to training, 80 to validation, and 320 to final testing, while aggregate operational outcomes cover 50 random streams. Within this controlled benchmark, accepted schedules restore 93.6% of critical-load energy (SD 2.1 percentage points), serve 96.7% of total demand (SD 1.8 percentage points), retain 82–86% of EV service across hazard classes, and reduce the modeled 24 h objective by 25.8% relative to deterministic dispatch. The full pipeline records two to four candidate-stage voltage-limit events by hazard, and 4.9% of candidates undergo tightened re-optimization before accepted schedules reach zero reported AC voltage-limit violations. Between-method comparisons are descriptive and unpaired; the larger synthetic cases are structural stress tests rather than feeder-transfer tests. Full article

To evaluate how composted agricultural and forestry organic materials function as components of horticultural growing media, a pot experiment was conducted with chrysanthemum (Chrysanthemum morifolium Ramat.) grown in a cinnamon-soil-based medium. The composted material, produced from chestnut shells, chicken manure, and spent mushroom substrate, was incorporated at 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50% (v/v). Water-extractable dissolved organic matter (WE-DOM) was characterized by excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis (EEM-PARAFAC), together with the fluorescence index (FI), biological index (BIX), humification index (HIX), and fluorescence regional integration (FRI). Growing-medium physicochemical properties, chrysanthemum traits, and an entropy-weighted comprehensive evaluation were also assessed. Compost amendment increased soil organic matter (SOM), dissolved organic carbon (DOC), total nitrogen, and total phosphorus, lowered pH, and was associated with improved porosity and water-retention characteristics. FI ranged from 1.810 to 2.371 and exceeded 1.9 at amendment rates of 30–35%, suggesting a greater contribution from microbially derived DOM. BIX, HIX, and P_V,n/P_III,n were generally higher in amended treatments than in the control, although their responses were non-monotonic across amendment rates, suggesting greater contributions from recently produced DOM and stronger humification-related fluorescence signals. EEM-PARAFAC resolved five fluorescent components. C1, C2, C3, and C5 were predominantly humic-like, whereas C4 displayed both protein-like and humic-like features. With increasing amendment rate, the relative contributions of C1–C4 generally increased, whereas that of C5 declined, suggesting a shift from the native soil fluorescence profile toward a more complex DOM composition influenced by compost inputs and subsequent biological transformation. Chrysanthemum height, stem diameter, flower number, and biomass were generally more favorable at amendment rates of 30–40%. The entropy-weighted evaluation yielded the highest overall response score at 30%, while the 35% treatment also maintained a high score. Considering WE-DOM fluorescence characteristics, growing-medium properties, and plant performance together, a volumetric amendment rate of 30–35% represents a relatively favorable range for the tested composted material under the present pot-experiment conditions. Full article

Pseudomonas syringae pv. actinidiae (Psa) is the most destructive pathogen of kiwifruit, and the absence of curative measures makes the management of Psa-induced kiwifruit bacterial canker (KBC) particularly challenging. Elettaria cardamomum produces an essential oil (CAR) rich in bioactive compounds with demonstrated potential to act directly against Psa, but its mechanisms of action remain insufficiently explored. Here, we investigated CAR’s mode of action in plants with established mild KBC symptoms, and assessed its potential as a plant elicitor. In the in planta assay, CAR application (0.1% w/v, applied 7 days after inoculation) reduced KBC symptoms, with the strongest effect observed 14 days after treatment (DAT). However, CAR did not significantly affect oxidative stress biomarkers, antioxidant system, pigments and primary metabolism, or the expression of target genes related to systemic acquired resistance or salicylic acid and jasmonic acid pathways. For instance, Psa inoculation significantly upregulated PR1 (≈5.4-fold) and PR5 (≈4.3–5.5-fold), irrespective of CAR application. Complementary in vitro assays revealed a transient, phase-dependent antimicrobial activity of CAR: although the effect disappeared by 32 h in liquid-phase assay and no inhibition was observed under vapor-phase exposure, a strong reduction in Psa viable cells (79.7%) was observed after 8 h exposure in the liquid phase. This study demonstrates that CAR exerts a direct, albeit transient, antibacterial effect against Psa, conferring curative activity when applied to plants with mild KBC symptoms. Consequently, repeated applications may be required to maintain disease suppression, as CAR does not appear to induce a sustained preventive defense response in the host. Full article

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems. Full article

Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fusion Transformer (RQR-TFT) that jointly estimates PV power quantiles and the probability of a future ramp event. Ramp labels are constructed from the normalized power change between adjacent sampling instants. A shared Temporal Fusion Transformer (TFT) encoder extracts temporal representations from historical PV power and meteorological variables, and two output branches perform quantile forecasting and ramp-event identification. Ramp-sample-weighted quantile loss and positive-class-weighted classification loss are jointly optimized to increase the influence of minority ramp samples. The proposed method is evaluated for 4 h ahead forecasting using measurements collected from a 50 MW PV power station during 2019–2020. For the nominal 90% prediction interval, RQR-TFT achieves a ramp-sample prediction interval coverage probability (${\mathrm{PICP}}_R$) of 0.864, an overall prediction interval normalized average width (PINAW) of 0.209, and an overall normalized interval score (NIS) of 0.365. The area under the precision–recall curve for ramp-event identification is 0.906. The results demonstrate improved ramp-sample coverage and overall interval quality, although ramp-sample coverage remains below the nominal level. Full article

This study directly compares a photovoltaic (PV)-powered vapor-compression chiller with a solar-thermal-driven absorption chiller for localized cooling of the tomato crop zone in a 1536 m² smart greenhouse under a hot-arid climate. The principal contribution is a controlled system-level comparison of two solar-cooling pathways under the same greenhouse load, solar-aperture area, terminal equipment, rated cooling capacity, and crop-zone temperature-control constraints. The previously validated greenhouse model was transitioned from EnergyPlus 8.9 to Version 23.1, after which the two alternative plants were connected to the same base model. Base-case annual simulations produced nearly identical chiller cooling energy (1669.3 and 1668.9 MWh) and was only 4 and 5 h above 28 °C. The PV-powered system required 101.6 MWh of net grid electricity, whereas the absorption system used 202.3 MWh of electricity and 253.2 MWh of natural gas and achieved an 84.23% solar fraction. Static operational primary energy was 331.2 and 912.7 MWhPE, respectively; HSDH28 was 0.50 and 0.81 °C·h; and peak grid import was 113.46 and 63.61 kW. The absorption case additionally required 10,103.6 m³/yr of cooling-tower makeup water. Storage/EMS sensitivity changed the absorption solar fraction from 58.17% to 88.30% and natural-gas use from 187.7 to 674.0 MWh/yr without materially changing cooling service. Matched 50–100 W/m² daytime latent-load sensitivity increased annual cooling by 14.7–28.8%. At the 100 W/m² bound, HSDH28 increased to 49.32 °C·h for PV and 8.06 °C·h for absorption, while the principal energy–infrastructure trade-off remained: static primary energy was 712.4 versus 1354.2 MWhPE and peak grid import was 137.46 versus 63.96 kW. A bounded hourly primary-energy-factor stress test did not reverse the technology ranking, and balanced TOPSIS scores were 0.766 for PV and 0.234 for absorption. The results show that PV vapor compression minimizes operational primary energy and cooling-water use, whereas solar-thermal absorption reduces electrical peak demand and shows greater thermal-control resilience at the highest tested latent-load bound. Full article

Kiwifruit bacterial canker, caused by Pseudomonas syringae pv. actinidiae (Psa), severely restricts the sustainable development of the kiwifruit industry. Screening stable resistant germplasm and establishing efficient disease resistance evaluation methods are core prerequisites for breeding resistant cultivars. In this study, 86 Actinidia accessions were systematically assessed for Psa susceptibility over three consecutive years using the reported detached shoot inoculation assay. Seven representative accessions with contrasting resistance phenotypes, namely ‘Cuiyu’, ‘Chuhong’, ‘Jinmei’, ‘Hongyang’, ‘Cuixiang’, ‘Avfs08’, and ‘G3’, were selected to measure the activities of four defense-related enzymes post Psa inoculation to dissect the physiological mechanisms driving divergent Psa resistance in kiwifruit. Lesion lengths across years exhibited a significant positive correlation, demonstrating that this inoculation method delivers repeatable, genetically stable phenotypic data with limited environmental interference. Two accessions belonging to A. valvata and A. eriantha exhibited stable high resistance via synergistic biochemical defenses. By contrast, the widely grown cultivar ‘Hongyang’ was highly susceptible, while moderately resistant materials such as ‘Cuiyu’ and ‘Yannong 3’ were discovered within the inherently susceptible species A. chinensis. Highly resistant accessions rapidly induced coordinated increases in SOD and PAL activity at 24 h post inoculation to maintain ROS homeostasis and lignin biosynthesis, whereas susceptible accessions displayed chaotic, ineffective enzymatic stress responses. Temporal synergy of PAL and POD may act as the key defensive regulatory mode. This study uncovered substantial interspecific variation in Psa resistance across Actinidia germplasm, identified elite donors with stable resistance, and elucidated the physiological mechanisms of kiwifruit resistance to Psa. These findings provided a theoretical foundation and valuable germplasm for subsequent resistance gene mining and disease resistance breeding. Full article

This work presents a novel method known as the Red Deer Algorithm-Based Artificial Neural Network (RDA-ANN) for managing real-time voltage and harmonic control in a cascade H-bridge multilevel inverter (CHB-MLI) that is fed by photovoltaic (PV) panels. The RDA-ANN technique proposed here computes the switching angles in real-time for selective harmonic elimination (SHE) on the output voltage of a multilevel inverter (MLI). In the proposed approach, a comprehensive lookup table containing 7776 permutations of switching angles was first generated offline using RDA optimization for a three-phase, 11-level CHB-MLI with five PV panels operating across a voltage range of 30 V to 35 V. This dataset was subsequently used to train a feed-forward ANN model capable of predicting optimal switching angles corresponding to any real-time voltage measurements from the panels. The SHE-PWM approach based on RDA-ANN targets the elimination of the 5th, 7th, 11th, and 13th order harmonics. This algorithm guarantees that the intended fundamental voltage is achieved, even during fluctuations in the voltages of the panels caused by varying irradiation and temperature conditions, while effectively removing the unwanted harmonics. The findings, validated under multiple environmental scenarios, illustrate that the RDA-ANN-based SHE-PWM technique successfully eliminates the selected harmonics from the load voltage with a fundamental voltage error not exceeding 0.18%, and results in a low total harmonic distortion (THD) value that complies with the IEEE 519-2022 standard across all tested conditions. Full article

Ultra-high-temperature ceramic (UHTC) coatings offer a promising route to extending cutting tool service life under severe conditions. This work evaluates the tribological behaviour and wear regime transitions of ZrB₂–SiC/ZrO₂ coatings, deposited by physical vapour deposition (PVD) onto ASSAB-17 high-speed steel tool bits, during dry turning of Ti-6Al-4V. Structural, microstructural, mechanical, and tribological characterisation was performed by X-ray diffraction (XRD), scanning electron microscopy (SEM), nanoindentation, and pin-on-disc testing under three pressure–velocity (PV) severity levels, with worn surfaces analysed by SEM and profilometry. The coating exhibited a nanostructured ZrB₂/β-SiC/t-ZrO₂ architecture with a hardness (H) of 24 ± 3 GPa, a hardness-to-reduced-elastic-modulus ratio (H/Er) of 0.100, and an elastic resistance to plastic deformation (H³/Er²) of 0.240 GPa. Three tribological regimes were identified: running-in, steady-state sliding, and progressive degradation, with the highest severity (PV = 6.0 N·m/s) triggering degradation beyond approximately 620 m, a more than one-order-of-magnitude rise in wear rate, and the only case exceeding the tool-life criterion of maximum flank wear (VBmax = 0.30 mm) according to ISO 3685. The main advantage of the proposed approach is that it condenses tool-life-relevant behaviour into a single, easily measurable severity parameter, the PV product, directly applicable to coating design and the selection of safe machining-condition windows. The overall behaviour is consistent with a mechanism governed by the stability and regeneration capacity of a protective tribofilm. As the composition of this layer was not directly characterised, this mechanism is proposed as a phenomenological interpretation, from which a PV threshold is derived as a design criterion for UHTC coatings. Full article

The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R² of 0.868 and the 1 h model an R² of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R² of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R² 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H₂/CH₄ blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO₂ emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications. Full article

AC/DC hybrid building microgrids require an energy management system (EMS) that coordinates distributed energy resources while maintaining fast local responses to communication and device faults. This study proposes a knowledge-graph-enhanced edge-cloud EMS for an AC/DC hybrid building microgrid. A 24 h linear-programming scheduler coordinates multi-resource dispatch in the cloud, while edge controllers enforce local safety constraints, correct setpoints and maintain fallback operation during link interruptions. The knowledge graph provides semantic context by linking assets, constraints, faults and admissible actions. Six operating scenarios, controlled V2G ablation tests and workday–weekend validation were used to assess the framework. Compared with rule-based EMS, the proposed method reduced peak demand by 28.0% and increased PV self-consumption from 78.4% to 95.4%. Compared with cloud-only MPC, it reduced the simulated mean control-loop latency from 7.54 s to 0.81 s. The eight-rule fault evaluation achieved a mean trigger accuracy of 94.6%. Under normal operation, however, the higher PV self-consumption was accompanied by a modest increase in operating cost and peak demand relative to non-KG edge-cloud MPC. These results support the complementary use of cloud scheduling, edge autonomy and semantic context within the tested simulation scope. Full article