Search Results (2,400)

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement. Full article

In airborne synthetic aperture radar (SAR) imaging, slant-range errors vary across the swath, making phase errors range-dependent. However, the conventional phase gradient autofocus (PGA) method assumes a range-invariant phase model and becomes unreliable when range-dependent phase errors are pronounced. Although range-partitioned PGA can substantially improve focusing performance, it may still introduce block-dependent azimuth shifts after compensation, causing geometric distortion in the focused image. To address this problem, this paper proposes a lightweight post-autofocus distortion-correction method for SAR images processed by range-partitioned PGA. Instead of re-estimating the full residual phase, the method operates on the block-wise phase-error estimates after global linear-phase removal, extracts the distortion-related linear trend using a sliding-window fitting strategy, converts it into azimuth-shift profiles, and performs sinc-based realignment. The proposed method is validated using both simulation and real unmanned aerial vehicle (UAV) SAR data. Experimental results demonstrate that the method effectively corrects geometric distortion while preserving the focusing gain achieved by range-partitioned PGA. In two representative real-data regions, the azimuth misalignment is reduced from 20 pixels to 3 pixels and from 34 pixels to 2 pixels, respectively. Full article

Reliable multi-sensor bearing fault diagnosis is challenged by temporal leakage caused by window-level random splitting, limited modeling of cross-sensor dependencies, and inadequate integration of raw temporal dynamics with time-frequency representations. To address these issues, this study proposes a leakage-resistant multi-sensor diagnosis framework that combines a partition-before-windowing evaluation protocol with adaptive time-frequency graph learning and reliability-aware fusion. Continuous vibration records are first divided into disjoint temporal regions with guard intervals and overlap auditing to suppress time-neighbor leakage. The model then extracts complementary features from a raw-signal branch and a dual-resolution log-STFT branch, while adaptive graph learning captures sample-dependent inter-sensor couplings and sensor reliability weighting highlights informative channels. A cross-gated fusion module further integrates temporal and graph-domain representations in a sample-adaptive manner for final classification. Experiments on a reconstructed nine-class benchmark derived from the HUSTbearing dataset show that the proposed method achieves a Macro-Accuracy of 0.973, a Macro-Recall of 0.964, and a Macro-F1 of 0.954, outperforming representative raw-signal and STFT-based baselines under the same leakage-resistant protocol. These results demonstrate that jointly modeling multi-scale time-frequency structure, dynamic sensor relationships, and reliable evaluation yields an effective and interpretable solution for intelligent bearing fault diagnosis under complex operating conditions. Full article

Background: n-3 Docosapentaenoic acid (DPA; 22:5 n-3) is increasingly viewed as a distinct long-chain omega-3 fatty acid with biological activities that are not fully captured by eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA). However, progress remains limited by restricted access to high-purity DPA: most commercial sources contain DPA as a minor component, and published isolation strategies often yield only enriched mixtures or require multi-step workflows that are difficult to scale in standard laboratories. Objectives: We aimed to establish a robust, laboratory-accessible purification workflow to obtain DPA ethyl ester at high purity while preserving oxidative quality. Methods: Candidate lipid sources were screened to select an optimal DPA-containing feedstock. Oils were stabilized with antioxidants and pre-fractionated by cold crystallization (−20 °C) to reduce saturated lipids and oxidation by-products. Preparative separation used a stacked C18 flash system (15 μm + 45 μm in series) operated isocratically (methanol/water 92:8, v/v) at 120 mL/min. Fractions were analyzed by GC and iteratively reinjected to progressively enrich the DPA window. Solvent was recovered by distillation and reused. Results: Omegavie^® 4020EE (5.4% n-3 DPA) was identified as the best starting material. Pretreatment eliminated detectable TBARS-derived malondialdehyde. The isocratic purification-loop strategy produced tens of grams of DPA ethyl ester at >98% purity (GC–FID) defined as n-3 DPA area% of total identified fatty acid methyl esters by GC–FID, with per-cycle DPA recovery of 91–95%, overall recovery of 76% from the starting DPA content, and >90% solvent recycling. The workflow is scalable at the gram-to-tens-of-grams level for research laboratories, although solvent burden and column maintenance remain practical constraints for larger-scale implementation. Identity and purity were confirmed by GC–MS and ^1H NMR, and oxidation indices remained low (peroxide value < 0.2 meq/kg; p-anisidine < 3). Conclusions: This scalable, solvent-conscious protocol enables reliable access to high-purity DPA and should be adaptable to other low-abundance polyunsaturated fatty acids. Full article

Photovoltaic (PV) inverter fault prediction is critical for maintaining system reliability and minimizing energy loss. While recent studies have improved predictive accuracy using data-driven approaches, most evaluations remain focused on offline settings and do not address how probabilistic predictions are translated into operational decisions. This study investigates multi-horizon fault prediction for PV inverters under real-world constraints, with a particular focus on decision-level behavior. A modular prediction framework is implemented by combining transformer-based TimeXer embeddings with probabilistic classification using XGBoost. The model operates on sliding-window sensor data and produces fault probabilities across multiple future horizons. To support operational use, these probabilities are aggregated into a single risk score, and threshold-based alarm policies are evaluated through a systematic threshold sweep. The results show that predictive performance varies across horizons, with usable lead-time information concentrated in near-term predictions. Under severe class imbalance, imbalance-aware training significantly improves detection performance in precision–recall space, but performance remains sensitive to temporal variation. Most importantly, the threshold-sweep analysis reveals a structural trade-off between detection performance and alarm burden, where achieving moderate early-warning capability requires substantially increased alarm rates. These findings indicate that improving predictive accuracy alone is insufficient for practical deployment. Instead, decision-level behavior must be explicitly considered when designing predictive maintenance systems under operational constraints. Full article

Food waste filtrate (FW) and landfill leachate (LL) are high-strength organic wastewaters with complex compositions that pose significant challenges for conventional biological treatment. Anaerobic co-digestion is considered an effective strategy to improve process stability and methane recovery through substrate complementarity. In this study, an internal circulation (IC) anaerobic reactor was used to evaluate the co-digestion performance of FW and LL at different volumetric mixing ratios (3:7, 5:5, and 7:3). Methane production, COD removal, pH, volatile fatty acids (VFA), alkalinity, extracellular polymeric substances (EPS), enzyme activities, sludge morphology, and sludge structural and spectroscopic characteristics were analyzed to evaluate process performance and explore stability-related responses under different mixing ratios. The results showed that the 5:5 mixing ratio achieved the best overall performance. Under this condition, methane content remained at 78.79–81.60%, the volumetric methane production rate reached 893.38–1080.43 L CH₄/(m³·d), and methane yield was 0.219–0.265 L CH₄/g COD. COD removal efficiency was maintained at 86.93–88.35%. Meanwhile, the reactor operated within a relatively stable window, with pH of 6.98–7.80, VFA of 485.6–521.6 mg/L, alkalinity of 2000–3100 mg CaCO₃/L, and a VFA/TA ratio of 0.167–0.261. Compared with the other ratios, the 5:5 condition was associated with higher EPS levels, more favorable enzyme activity patterns, and a more compact sludge structure. Overall, FW-LL co-digestion exhibited clear ratio dependence, and the 5:5 mixing ratio provided the best balance between methane production, organic matter removal, and process stability. These findings offer quantitative support for substrate-ratio optimization and stable operation of anaerobic treatment systems for high-strength organic wastewaters. Full article

Hydroxylamine-modified transition-metal oxides (HA-TMOs) represent a promising class of catalysts for activating peroxymonosulfate (PMS) to degrade antibiotics. However, identifying energy-efficient operational conditions remains challenging. This study established a comprehensive dataset encompassing 600 experimental records from both in-house experiments and literature and systematically compared 12 machine learning algorithms for predicting the antibiotic degradation efficiency (%) in hydroxylamine-modified transition metal oxide/peroxymonosulfate (HA-TMO/PMS) systems. Among these models, CatBoost delivered the best generalization (test-set R² = 0.8110, RMSE = 8.92, MAE = 6.15) across repeated 80/20 stratified splits with 5-fold cross-validation, outperforming other ensembles as confirmed by cumulative distribution plots and error-metric analyses. Moreover, the permutation importance analysis identified PMS dosage, HA level, pH, initial pollutant concentration, and catalyst loading as the dominant drivers governing the pollutant removal performance. The partial-dependence plots, incorporating two-variable interactions, elucidated the response surfaces and enabled the discovery of operating windows that jointly maximize degradation efficiency and minimize electrical energy per order (EE/O). ML-guided optimization yielded optimal conditions, which were experimentally verified with sulfamethoxazole (SMZ). The HA-Co₃O₄/PMS system achieved the highest degradation rate constant (k = 0.11 min⁻¹) and the lowest EE/O value (6.84 kWh·m⁻³·order⁻¹), markedly improving kinetics and reducing energy consumption compared with non-optimized runs. This work establishes an interpretable ML framework that connects catalyst properties and reaction conditions to degradation kinetics and mechanisms, providing a practical strategy for the screening and scale-up of energy-efficient HA-TMOs/PMS-based advanced oxidation processes (AOPs). Full article

Silicon deficiency is widespread in soils of Southeastern China and may constrain nitrogen (N)-use efficiency and yield formation in oilseed rape; therefore, this study aimed to identify an N-reduction window enabled by silicon (Si) fertilization and to clarify the underlying mechanisms. Field experiments were conducted in Putian, Fujian Province (2023–2025), with five treatments: conventional N (T1, 300 kg·N·ha⁻¹), conventional N plus Si (T1+Si, 150 kg·Si·ha⁻¹), and three N rates (120%, 80%, and 60% of conventional N; T2+Si, T3+Si, and T4+Si) under a fixed Si input. Yield, N-use efficiency, plant physiological traits, soil quality index (SQI), and nitrogen-cycle ecosystem multifunctionality were assessed. Compared with T1, T1+Si and T3+Si increased yield by 6.55% and 6.06%, respectively, accompanied by higher dry matter translocation (27.20% and 34.60%) and improved N-use efficiency (28.86% and 39.66%). SQI increased by 31.42% (T1+Si) and 33.03% (T3+Si), while nitrogen-cycle multifunctionality increased by 32.42% and 58.42%, respectively. Correlation and path analyses indicated that Si promoted yield formation by simultaneously alleviating soil constraints (lower exchangeable acidity and Al³⁺; higher cation exchange capacity) and enhancing plant assimilation and allocation processes, thereby reducing potential N losses and strengthening N cycling. Overall, applying 150 kg·Si·ha⁻¹ combined with a 20% reduction in N (240 kg·N·ha⁻¹) achieved stable yield gains and coordinated improvements in soil quality, providing an operational fertilization window for Si-enabled N management in regional oilseed rape systems. Full article

Textile wastewater remains one of the most challenging industrial effluents to remediate due to its intense and persistent coloration, high organic load, elevated salinity, and fluctuating pH and the presence of recalcitrant dye structures and auxiliary chemicals. Conventional physicochemical and biological treatments frequently achieve incomplete removal, generate secondary wastes, or fail under high-salt and toxic dye matrices. Advanced oxidation processes (AOPs) provide molecular-level degradation via reactive oxygen species (ROS), yet their deployment is often constrained by narrow operating windows, catalyst instability, chemical/energy demand, and scale-up limitations. In this context, metal–organic frameworks (MOFs) have emerged as tunable porous catalytic platforms that integrate adsorption and oxidation within a single architecture through controllable metal nodes, functional linkers, and engineered pore environments. This critical review reimagines textile effluent treatment through the lens of MOF-based hybrid catalysts, synthesizing progress across Fenton/photo-Fenton catalysis, photocatalytic MOFs, persulfate activation, and MOF-derived/composite systems. Mechanistic pathways are discussed by linking pollutant enrichment, cyclic redox reactions, charge-transfer processes, and ROS-driven degradation toward mineralization, with emphasis on the distinction between rapid decolorization and true organic removal. A critical comparison highlights how hybridization improves charge transport, stability, and catalyst recovery, while persistent gaps remain in hydrolytic robustness, metal leaching control, intermediate toxicity assessment, real-wastewater validation, continuous-flow reactor integration, and techno-economic feasibility. Finally, the review outlines actionable research directions, including water-stable and defect-engineered MOFs, immobilized and structured catalysts, solar-driven operation, standardized performance metrics, and life-cycle-informed design, to accelerate translation toward scalable and sustainable textile wastewater remediation. By bridging material chemistry with reactor-level feasibility and sustainability assessment, this review provides an implementation-oriented perspective for next-generation textile wastewater treatment. Full article

Fine-grained energy management in smart-campus buildings requires accurate load forecasting together with reliable and interpretable anomaly diagnosis. This study presents an integrated forecasting–diagnosis framework for building energy systems. Hourly energy demand is modeled using a Transformer-based sequence-to-sequence architecture, in which a domain-aware attention mechanism is introduced to separately represent historical consumption dynamics, environmental influences, and temporal regularities commonly observed in building energy use. Anomaly diagnosis is conducted through a dual-scale strategy that supports both the timely detection of abrupt abnormal events and the identification of gradual performance degradation. Short-term anomalies are detected from forecasting residuals using adaptive thresholds, while long-term anomalies are identified by comparing current residual patterns with same-season historical baselines and validating multi-window trends over a 48 h horizon. The two detection streams are jointly used to distinguish point, pattern, and composite anomalies. To support practical operation and maintenance, SHAP-based explanations are provided to interpret both energy predictions and detected anomalies. Case studies on two educational buildings from the Building Data Genome Project 2 demonstrate that the proposed framework achieves the best overall forecasting performance against both conventional baselines and stronger recent Transformer-based models, with mean absolute percentage errors of approximately 3%. The results indicate that the proposed framework provides a practical solution for data-driven energy monitoring and decision support in smart buildings. Full article

Maintenance is crucial for the durability of the existing building stock and should be perceived as an opportunity to improve the built environment. The implementation of thermal retrofitting measures to the building’s envelope enhances global energy performance, which is economically and environmentally beneficial. Building-related energy consumption during the operation phase is key to tackling carbon neutrality and climate change. Introducing thermal retrofitting within the context of maintenance planning can be cost-optimizing, as it reveals the technical–economic synergy between building pathology and energy efficiency. Maintenance activities and energy demand throughout the building’s service life influence life-cycle costs (LCCs). Decision-making based on LCC awareness is an advantage for owners. This study discusses the impact of implementing an optimal retrofitting solution (ORS), according to different maintenance strategies, on the LCC of an existing single-family home. The ORS comprises the following measures: adding an external thermal insulation composite system (ETICS) to external walls, extruded polystyrene (XPS) panels to the roof, and replacing the existing windows with others with improved thermal performance. The three maintenance strategies involve different complexity levels, concerning the type, number and timing of activities. Moving beyond isolated assessments, this study develops an integrated framework that bridges based on two existing background methodologies, involving optimal thermal retrofitting and condition-based maintenance planning, which, combined with new research, enable the assessment of maintenance, energy and global LCC for a time horizon of 100 years. The evaluation of energy-related LCC is based on simulations. The results indicate that these costs represent the majority of the global LCC. The ORS has a considerable positive impact on energy and global LCC. Adopting a maintenance strategy characterized by fewer planned activities and an earlier schedule of replacement interventions, which determines the implementation of the retrofitting measures, is better in terms of LCC savings. Full article

This study presents the design, thermodynamic modelling, and numerical optimisation of a medium-scale (100 kW) transcritical CO₂ air-source heat pump water heater (ASHP-WH) intended to deliver 90 °C domestic hot water under sub-zero ambient conditions. A detailed component-sizing methodology was established and implemented in AMESim 2404 using REFPROP-based property calculations, with model convergence confirmed by the mass and energy balance closure. Parametric investigations covering the discharge pressure, refrigerant charge, ambient air temperature, and water outlet temperature were conducted through 140 steady-state simulations. The results show that the system achieved a heating capacity of 100–121 kW with a coefficient of performance (COP) of 2.7–3.3 across −15 °C to +10 °C ambient conditions. The optimal discharge pressure (≈11.2 MPa) and charge inventory (10 ± 2 kg) define a broad operating window that ensures COP stability (±2%) and avoids liquid carry-over. The exergetic efficiency remained above 0.75 throughout the tested climate range. Compared with published laboratory prototypes, the proposed 100 kW module demonstrates a superior performance at harsher sub-zero boundaries, highlighting its potential for commercial hot water and industrial applications. The findings provide actionable guidelines for component sizing, charge management, and adaptive pressure control, and establish a pathway from a numerical prototype to scalable field deployment of medium-scale transcritical CO₂ systems. Full article

Classifying which operation a Computer Numerical Control (CNC) machine is executing, not just detecting whether it is functioning correctly, is a monitoring challenge that existing sensor-based studies rarely address. Unlike tool wear estimation, operation-type classification must resolve toolpath strategies and cutting conditions within heterogeneous, noisy sensor streams in which modalities differ widely in their discriminative value. Which sensors are genuinely necessary, and how many can be removed before performance degrades, directly informs retrofit cost and monitoring system design. We present a systematic cross-validated ablation study for a nine-class CNC toolpath and condition classification task, using 120 operation files collected from a desktop CNC mill instrumented with six distributed sensor units spanning inertial, acoustic, environmental, and electrical modalities. To handle multimodal fusion under sensor noise, we introduce the Multimodal Denoising Temporal Attention Encoder with Long Short-Term Memory (MM-DTAE-LSTM), which combines learned modality weighting, cross-modal attention, and a self-supervised denoising objective, followed by recurrent temporal modeling for classification. We evaluate MM-DTAE-LSTM against five baseline model families across five cumulative sensor-ablation levels and ten temporal resolutions, using file-level cross-validation to prevent data leakage from overlapping windows. MM-DTAE-LSTM maintains 92.5% classification accuracy when nearly half the sensor channels are removed (56 of 110 features), whereas simpler baselines degrade by up to 10.7 percentage points under the same reduction. Analysis of variance reveals that pressure channels encode session-level atmospheric variation rather than machining dynamics, exposing how models that cannot suppress uninformative modalities rely on environmental confounds rather than machining physics. Together, these findings translate into concrete sensor-selection and deployment recommendations for cost-effective CNC process monitoring at under USD 500 in hardware, though generalization to industrial machines, diverse materials, and production environments requires further validation. Full article

The installation of floating offshore wind turbines presents significant operational challenges due to coupled vessel platform dynamics and sensitivity to environmental conditions. This study proposes a response-based methodology for defining allowable operational limits and assessing operability for floating wind turbine generator (WTG) installation using the Nordic Wind concept. The approach integrates hydrodynamic modelling, time-domain simulations, and probabilistic weather-window analysis to evaluate installation feasibility under realistic offshore conditions. The methodology explicitly accounts for coupled vessel spar interactions, heading-dependent system response, and response-based failure criteria, including relative motion, gripper forces, and impact velocity. Allowable sea-state limits are derived for key installation phases and applied to multiple case studies representing different geographical locations and project scales. The results show that installation operability is governed primarily by system response rather than environmental parameters alone. Peak wave period and wave heading are identified as dominant factors, with longer wave periods leading to reduced operability due to response amplification. Across all case studies, the mating phase is found to be the most restrictive operation, controlling overall installation feasibility. Head sea conditions generally provide improved operability, while following seas lead to increased relative motion and reduced performance. The comparative analysis further demonstrates that environmental severity and project scale jointly influence installation duration. Milder environments result in higher operability, whereas harsher conditions, particularly those characterised by long-period swell, significantly reduce feasible weather windows. Larger installation campaigns increase cumulative exposure to weather downtime, even under favourable conditions. The proposed framework extends existing operability assessment methods by incorporating coupled multi-body dynamics and response-based criteria specific to floating wind installations. The results provide a quantitative basis for defining operational limits and support improved planning and decision making for offshore wind turbine installation. Full article

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations. Full article