Search Results (851)

Fractional-order models (FOMs) have been recognized as superior tools for capturing the complex electrochemical dynamics of lithium-ion batteries, outperforming integer-order models in accuracy, robustness, and adaptability. Parameter identification (PI) is essential for FOMs, as its accuracy directly affects the model’s ability to predict battery behavior and estimate critical states such as state of charge (SOC) and state of health (SOH). In this study, a hybrid deep learning approach has been introduced for FOM PI, representing the first application of deep learning in this domain. A simulation platform was developed to generate datasets using Sobol and Monte Carlo sampling methods. Five deep learning models were constructed: long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (1DCNN), and hybrid models combining 1DCNN with LSTM and GRU. Hyperparameters were optimized using Optuna, and enhancements such as Huber loss for robustness to outliers, stochastic weight averaging, and exponential moving average for training stability were incorporated. The primary contribution lies in the hybrid architectures, which integrate convolutional feature extraction with recurrent temporal modeling, outperforming standalone models. On a test set of 1000 samples, the improved 1DCNN + GRU model achieved an overall root mean square error (RMSE) of 0.2223 and a mean absolute percentage error (MAPE) of 0.27%, representing nearly a 50% reduction in RMSE compared to its baseline. This performance surpasses that of the improved LSTM model, which yielded a MAPE of 9.50%, as evidenced by tighter scatter plot alignments along the diagonal and reduced error dispersion in box plots. Terminal voltage prediction was validated with an average RMSE of 0.002059 and mean absolute error (MAE) of 0.001387, demonstrating high-fidelity dynamic reconstruction. By advancing data-driven PI, this framework is well-positioned to enable real-time integration into battery management systems. Full article

Lightning strikes are a major cause of wind turbine (WT) damage, with approximately 80% of cloud-to-ground lightning strikes exhibiting a multi-stroke characteristic. Therefore, studying the transient overvoltages induced by multiple lightning strokes is essential for the effective lightning protection of offshore WTs. Firstly, a multiple-stroke lightning current model representative of Guangdong Province, China, is established based on data from the lightning location system and rocket-triggered lightning experiments. Simulations are then employed to analyze the transient overvoltage of a Guangdong offshore wind farm under multiple lightning strikes. Simulation results indicate that when a WT is subjected to a two-stroke lightning flash, with current amplitudes corresponding to a cumulative probability density of approximately 1%, the surge arrester A1 must be configured with four parallel columns to ensure the insulation safety of the equipment without sustaining damage. Additionally, adequate electrical clearance must be maintained between the power cable and the tower wall, or alternatively, a high-strength insulating material may be applied over the cable armor to prevent flashover. Moreover, it is observed that the front time of the impulse current flowing through the surge arrester is approximately 2 μs, significantly shorter than the front time specified in IEC 60099-4 for the repetitive charge transfer capability test of ZnO varistors. Hence, it is essential to consider local lightning intensity and distribution characteristics when studying the transient overvoltages in offshore wind farms, optimizing surge arrester configurations, and assessing the impulse withstand performance of ZnO varistors, in order to ensure the safe and stable operation of offshore WTs. Full article

Textile dye effluents, particularly cationic dyes, pose a major environmental challenge, demanding efficient and sustainable adsorbent materials to remove harmful synthetic dyes. In this study, a reference thiourea–formaldehyde (TU/FA) composite and a series of thiourea–poly(acrylic acid)–formaldehyde (TU/PAA/FA) composites were synthesized and systematically characterized. The composites were prepared by varying the volume of poly(acrylic acid) PAA (from 1 to 7.5 mL) to assess how PAA incorporation influences morphology, crystallinity, surface chemistry, charge, and thermal stability. Analytical techniques including SEM, XRD, FT-IR, particle size distribution, zeta potential, and TGA/DTG revealed that increasing PAA content induced more porous and amorphous microstructures, intensified carbonyl absorption, reduced particle size (optimal at 2.5–5 mL PAA), and shifted the zeta potential from near-neutral to highly negative values (−37 to −41 mV). From TU/PAA/FA composite analysis, it was depicted that the TU/PAA-5/FA material has the better characteristics as a potential cationic dye absorbent. Thus, the adsorption performance of this composite toward crystal violet dye was subsequently investigated and compared to the reference material thiourea–formaldehyde (TU/FA). The TU/PAA-5/FA material exhibited the highest capacity (145 mg/g), nearly twice that of TU/FA (74 mg/g), due to the higher density of carboxylic groups facilitating electrostatic attraction. Adsorption was pH-dependent, maximized at pH 6, and decreased with temperature, confirming an exothermic process. Kinetic data followed a pseudo-second-order model (R² = 0.99), implying chemisorption as the rate-limiting step, while Langmuir isotherms (R² > 0.97) indicated monolayer adsorption. Thermodynamic analysis (ΔH° < 0, ΔS° < 0, ΔG° > 0) further supported an exothermic, non-spontaneous mechanism. Overall, the TU/PAA-5/FA composite combines enhanced structural stability with high adsorption efficiency, highlighting its potential as a promising, low-cost material for the removal of cationic dyes from textile effluents. Full article

The increasing penetration of microgrids (MGs) in modern power distribution systems requires advanced operational strategies to ensure both economic efficiency and technical reliability. This study developed an optimal economic framework for battery energy storage in MG connected to distribution systems in order to minimize operational costs while considering renewable integration and battery charging and discharging cost and degradation cost as well, and their impact on grid technical constraint. An MG is interconnected to the IEEE-33 radial distribution feeder through an additional bus, where the BESS operates to minimize the total operating cost over a 24 h horizon. The formulation captures the charging and discharging dynamics of the BESS, BESS degradation, state-of-charge constraints, electricity price signals, and the network’s operational limits. The optimization problem is solved using Mixed Integer Linear Program (MILP) to obtain the optimal scheduling of BESS charging and discharging which minimizes the total operating cost and maintains grid constraint within the allowable limit by optimizing the power exchange between the MG and the distribution grid. Simulation results showed that the proposed approach reduces operational costs and optimize grid power exchange, while maintaining technical reliability of the distribution system by enhancing its voltage profiles, improving its feeder loading capability, and reducing the system losses. This study provides a practical tool for enhancing both economic and technical performance in MG-connected distribution systems. Full article

This study investigated the adsorption behavior of bisphenol A (BPA) onto a series of thermally and acid-activated biochars to elucidate the relationship between the surface properties and adsorption performance. Characterization analyses (FTIR, SEM, BET, elemental composition, and PZC) revealed that phosphoric acid activation significantly increased the surface area, pore development, and oxygen/phosphate functionalization, lowering the point of zero charge (PZC = 1.3) and enhancing the surface acidity. The kinetic data fitted well to the pseudo-second-order model, indicating a chemisorption-controlled process, while the equilibrium data were best described by the Langmuir model, with a maximum adsorption capacity (q_m = 262.28 ± 14.3 mg·g⁻¹) for the acid-activated biochar (LB₄₅₀-H₃PO₄). Thermodynamic analysis confirmed that the adsorption process is spontaneous and endothermic (ΔH° > 0), with a highly favorable entropy contribution. The effects of solution pH, adsorbent dosage, initial BPA concentration, and temperature demonstrated optimal removal under acidic to neutral conditions and moderate dosage (0.2 g·L⁻¹). Overall, the findings highlight that phosphoric acid activation effectively enhances surface functionality and charge properties, transforming biochar into a highly efficient and sustainable adsorbent for the removal of phenolic contaminants from aqueous solutions. Full article

Based on comprehensive experimental datasets—proximate/ultimate analyses, XPS, solid-state ¹³C NMR, and Raman spectroscopy—we constructed and optimized a compositionally faithful macromolecular model of SG coking coal. Using density-functional theory (DFT) calculations, we simulated electrostatic-potential (ESP) fields and frontier molecular orbitals (FMO) to probe elementary oxidation steps relevant to combustion, and focused on how heteroatom speciation and carbon ordering govern site-selective reactivity. Employing multi-peak deconvolution and parameter synthesis, we obtained an aromatic fraction f_a = 76.56%, a bridgehead-to-periphery ratio X_BP = 0.215, and Raman indices I_D1/I_G ≈ 1.45 (area) with FWHM(G) ≈ 86.7 cm⁻¹; the model composition C₁₉₀H₁₄₄N₂O₂₁S and its predicted ¹³C NMR envelope validated the structural assignment against experiment. ESP–FMO synergy revealed electron-rich hotspots at phenolic/ether/carboxyl and thiophenic domains and electron-poor belts at H-terminated edges/aliphatic bridges, rationalizing carbon-end oxidation of CO, weak electrostatic steering by O₂/CO₂, and a benzylic H-abstraction → edge addition → O-insertion/charge-transfer sequence toward CO₂/H₂O, with thiophenic sulfur comparatively robust. We quantified surface functionalities (C–O 65.46%, O–C=O 24.51%, C=O 10.03%; pyrrolic/pyridinic N dominant; thiophenic-S with minor oxidized S) and determined a naphthalene-dominant, stacked-polyaromatic architecture with sparse alkyl side chains after Materials Studio optimization. The findings are significant for mechanistic understanding and control of coking-coal oxidation, providing actionable hotspots and a reproducible workflow (multi-probe constraints → model building/optimization → DFT reactivity mapping → spectral back-validation) for blend design and targeted oxidation-inhibition strategies. Full article

This study examines dual-area load–frequency control (LFC) in the context of significant renewable energy integration, energy storage systems (ESSs), and collective electric vehicle (EV) involvement. We propose a RBBMO-FO-FuzzyPID+PI(1+DD) hybrid controller in which fractional-order fuzzy regulation shapes the ACE, while an auxiliary PI(1+DD) path adds damping without steady-state penalty. Across ideal linear plants, 3% governor-rate constraints (GRC), and stressed conditions that include contract violations in Area-2, renewable power variations, and partial EV State of Charge (SoC 50–70%), EV participation yields systematic gains for all controller families, and the magnitude depends on the control architecture. Baseline methods improve by 15–25% with EVs, whereas advanced designs—especially the proposed controller—benefit by 25–45%, revealing a clear synergy between controller intelligence and EV flexibility. With EV support, the proposed controller limits frequency overshoot to 0.055 Hz (a 20–55% reduction versus peers), caps the nadir at −0.065 Hz (15–41% better undershoot), and attains 3.5–4.5 s settling (25–61% faster than competitors), while holding tie-line deviations within ±0.02 Hz. Optimization studies confirm the algorithmic advantage: RBBMO achieves 30% lower cost than BBOA and converges 25% faster; EV integration further reduces cost by 15% and speeds convergence by 12%. A strong correlation between optimization cost and closed-loop performance (r² ≈ 0.95) validates the tuning strategy. Collectively, the results establish the proposed hybrid controller with EV participation as a new benchmark for robust, system-wide frequency regulation in renewable-rich multi-area grids. Full article

With the rapid advancement of battery technology, new energy hybrid tugboats have been progressively adopted. In order to align with the trend of electrifying tugboat fleets, a mixed-integer linear programming (MILP) model for the joint scheduling of new energy hybrid tugboats and berths has been established. The model incorporates the constraint imposed by the limited number of tugboat charging connectors. The objective is to minimize the total cost over the scheduling horizon, including ship waiting, delayed-departure costs, and the operating costs of both conventional diesel and hybrid tugboats. In light of the characteristics inherent to the problem, a hybrid solution approach combining CPLEX with a heuristic-enhanced whale optimization algorithm (WOA) is employed to solve the model. A case study was conducted using data on the energy consumption of tugboats at Xiamen Port. The effectiveness of the model and algorithm was then verified through a series of small-scale instance experiments. Finally, a comprehensive sensitivity analysis of key parameters is finally conducted, including the number of tugboat charging connectors, battery capacity, and charging rate. This analysis provides valuable guidance for port tugboat operations. Full article

Wettability significantly influences fluid distribution and flow behavior in hydrocarbon reservoirs, yet traditional macroscopic measurements fail to capture the micro- and nanoscale interfacial interactions that govern these processes. This study addresses a critical knowledge gap by employing molecular dynamics simulations to systematically investigate how salinity and mineral composition control wettability at the atomic scale—insights that are experimentally inaccessible yet essential for optimizing enhanced oil recovery strategies. We examined five typical reservoir minerals—kaolinite, montmorillonite, chlorite, quartz, and calcite—along with graphene as a model organic surface. Our findings reveal that while all minerals exhibit hydrophilicity (contact angles below 75°), increasing salinity weakens water wettability, with Ca²⁺ ions exerting the strongest effect due to their high charge density, which enhances electrostatic attraction with negatively charged mineral surfaces and promotes specific adsorption at the mineral–water interface, thereby displacing water molecules and reducing surface hydrophilicity. In oil–water–mineral systems, we discovered that graphene displays exceptional oleophilicity, with hydrocarbon interaction energies reaching −7043.61 kcal/mol for C₁₈H₃₈, whereas calcite and quartz maintain strong hydrophilicity. Temperature and pressure conditions modulate interfacial behavior distinctly: elevated pressure enhances molecular aggregation, while higher temperature promotes diffusion. Notably, mixed alkane simulations reveal that heavy hydrocarbons preferentially adsorb on mineral surfaces and form highly ordered structures on graphene, with diffusion rates inversely correlating with molecular size. These atomic-scale insights into wettability mechanisms provide fundamental understanding for designing salinity management and wettability alteration strategies in enhanced oil recovery operations. Full article

2,5-diformylfuran (DFF) is a significant biomass derivative that is employed in a variety of industries. One approach to synthesizing it is through the oxidation of 5-hydroxymethylfurfural (HMF). The challenges in DFF production arise from the need for extreme conditions, issues with overoxidation, and the limitations of noble materials used in neutral or acidic environments. By using a mildly alkaline electrolyte, DFF can be produced electrochemically alongside hydrogen gas generation, eliminating extreme conditions and allowing for the study of a wide range of transition metals. Moreover, the performance of bimetallic electrocatalysts has been studied, and it has been found to be more active in many kinds of processes, particularly Layered Double Hydroxides (LDH). Electrodeposition, once widely chosen among various LDH production methods, is preferred for producing controlled and uniform thin layers. This work examines the electrocatalytic properties of NiCo-LDH and NiFe-LDH in the production of DFF. Cobalt, which exhibits strong adsorption, will be compared to iron, which has a weak adsorption characteristic toward HMF. This study demonstrates that NiCo-LDH gives 1.49 V vs. RHE onset potential, 600 mV lower compared to NiFe-LDH (1.55 V vs. RHE) for HMF oxidation reaction. NiCo-LDH also converts twice the amount of HMF compared to NiFe-LDH for the same amount of charge passed at 0.25 mA/cm⁻² in 0.1 M Na₂B₄O₇. However, strong adsorption promotes reactant activation and reduces the energy barrier while reducing DFF selectivity in NiCo-LDH (23.4%) due to overoxidation, compared to NiFe-LDH (31.6%). In order to achieve optimal electrocatalyst performance, a careful balance of adsorption strength and reaction pathway management is required. Proper optimization of these parameters is essential to improve efficiency and selectivity in the electrocatalytic process. Full article

The accurate estimation of the state of charge (SOC) and state of health (SOH) is essential for the safety and reliability of electric vehicle batteries. Conventional single-state Kalman filters are prone to parameter drift caused by cell aging, which leads to persistent SOC estimation errors. This study compares two dual-estimator methods, the Dual Extended Kalman Filter (DEKF) and the Dual Unscented Kalman Filter (DUKF), for simultaneous SOC and SOH estimation using a second-order equivalent-circuit model. The process and measurement covariance matrices were tuned through a structured optimization procedure to ensure consistent performance under different drive cycles and initialization errors. To mitigate the weak voltage sensitivity to capacity, synthetic SOC–capacity coupling was introduced to enhance SOH observability and accelerate convergence. Simulations conducted under the Urban Dynamometer Driving Schedule (UDDS) and a real-world CLUST7 profile demonstrated SOC root-mean-square errors near 2% for both filters. The DUKF achieved faster and smoother convergence than the DEKF but required roughly fivefold higher computational cost. These findings provide quantitative evidence supporting dual Kalman filtering as an effective framework for accurate and robust SOC/SOH estimation in production battery management systems. Full article

We have investigated the evolution of CDW states and structural phases in a Cu-deficient Cu_1-δTe (δ = 0.016) by employing high-pressure experiments and first-principles calculations. Raman scattering results reveal that the vulcanite structure at ambient pressure starts to change into the Cu-deficient rickardite (r-CuTe) structure from 6.7 GPa, which then becomes fully stabilized above 8.3 GPa. Resistivity data show that T_CDW1 (≈333 K) is systematically suppressed under high pressure, reaching zero at 5.9 GPa. In the pressure range of 5.2–8.2 GPa, a sharp resistivity drop due to superconductivity occurs at the onset temperature T_C = ~2.0–3.2 K. The maximum T_C = 3.2 K achieved at 5.6 GPa is clearly higher than that of CuTe (2.3 K), suggesting the importance of charge fluctuation in the vicinity of CDW suppression. At 7.5 GPa, another resistivity anomaly appears due to the emergence of a second CDW (CDW2) ordering at T_CDW2 = ~176 K, which exhibits a gradual increase to ~203 K with pressure increase up to 11.3 GPa. First-principles calculations on the Cu-deficient Cu₁₁Te₁₂ with the r-CuTe structure show that including on-site Coulomb repulsion is essential for incurring an unstable phonon mode relevant for stabilizing the CDW2 order. These results point out the important role of charge fluctuation in optimizing the pressure-induced superconductivity and that of Coulomb interaction in creating the competing CDW order in the Cu-deficient CuTe system. Full article

This study transformed discarded courgette biomass into biochar (BC) via pyrolysis at 500 °C and employed it as an activator of potassium periodate (PI) for atrazine (ATZ) degradation. Characterization analyses confirmed that the synthesized BC possessed a porous structure, a high carbon content (76.13%), crystalline SiO₂, KCl, and CaCO₃ phases, as well as abundant oxygen-containing functional groups (–OH, C=O, C=C, –COOH), which are favorable for catalytic activation. The point of zero charge of 4.25 indicates that the BC surface carries a suitable charge distribution, promoting effective electrostatic interactions under near-neutral pH conditions. Under optimal operating conditions (neutral pH, [ATZ]_o = 7.3 mg/L, [PI]_o = 2.7 mM, [BC]_o = 0.55 g/L, and 25 ± 0.5 °C), the system achieved 99.35% ATZ removal (first-order kinetic rate constant = 0.0601 min⁻¹) and 64.23% TOC mineralization within 60 min. Quenching tests confirmed iodate radicals and singlet oxygen as the primary species, with hydroxyl and superoxide radicals playing secondary roles. The proposed mechanism suggests that electron transfer from oxygen-containing groups on the BC surface activates PI, leading to the generation of reactive oxygen species that facilitate ATZ degradation via synergistic radical and non-radical pathways. The BC catalyst exhibited strong recyclability, with only ~9% efficiency loss after five cycles. The BC/PI system also demonstrated high removal of tetracycline (79.54%) and bisphenol A (85.6%) within 60 min and complete Congo red dye degradation in just 30 min. Application to real industrial wastewater achieved 72.77% ATZ removal, 53.02% mineralization, and a treatment cost of 1.2173 $/m³, demonstrating the practicality and scalability of the BC/PI system for sustainable advanced wastewater treatment. Full article

In order to meet the dual requirements of low-energy heating and flexible operation, a comprehensive heating system with multi-mode and wide-load capabilities was constructed, incorporating a heat pump, a back-pressure turbine, and two 350 MW coal-fired condensing units. Based on the heat transfer characteristics of this system, the simulation model of this comprehensive thermal system was constructed through a commercial software (EBSILON). A dynamic coal consumption prediction method based on the non-equilibrium state parameters was first proposed, which was primarily designed for system operation optimization. Subsequently, the converted load and load change rate were integrated into the dynamic correction model to refine prediction accuracy. The results showed that while basic coal consumption primarily correlates with heat load and electricity load, dynamic coal consumption is influenced by both the converted load and the load change rate. Based on this, the three-dimensional surface plot of converted load, load charge rate, and dynamic coal consumption offset coefficient was calculated. Then, the accuracy of the prediction model was verified by the variable working condition parameter group, and its reliability was confirmed. Further, by developing online software, theoretical guidance for industrial production was realized. In a heating season case study, it was demonstrated the prediction method can effectively reflect the dynamic parameter deviation in the system, with the annual coal saving being able to reach 841.5 tons. It is expected to provide theoretical guidance for the research on multi-heat sources heating distribution and operation parameter optimization. Full article

Thermosyphon heat pipes (THPs) are increasingly employed in advanced thermal management applications due to their highly effective thermal conductivity, compact design, and passive operation. In this study, a numerical investigation was conducted on a copper or aluminum thermosyphon charged with different working fluids, with methanol serving as a reference case. A two-dimensional compressible CFD model was implemented in OpenFOAM, coupling the Volume of Fluid (VOF) method with a hybrid phase-change formulation that integrates the Lee and Tanasawa approaches. It provides, indeed, a balance between computational efficiency and physical fidelity. The vapor flow, considered as an ideal gas, was assumed compressible. The isoAdvector algorithm was applied as a reconstruction technique in order to improve interface capturing, to reduce spurious oscillations and parasitic currents, and to ensure more realistic simulation of boiling and condensation phenomena. The performance dependency on operating parameters such as the inclination angle, liquid filling ratio, and thermophysical properties of the working fluid is analyzed. The numerical predictions were validated against experimental measurements obtained from a dedicated test bench, showing discrepancies below 3% under vertical operation. This work provides new insights into the coupled influence of orientation, fluid inventory, and working fluid properties on THP behavior. Beyond the experimental validation, it establishes a robust computational framework for predicting two-phase heat and mass transfer phenomena by linearizing and treating the terms involved in thebalances to be satisfied implicitly. The results reveal a strong interplay between the inclination angle and filling ratio in determining the overall thermal resistance. At low filling ratios, the vertical operation led to insufficient liquid return and increased resistance, whereas inclined orientations enhanced the liquid spreading and promoted more efficient evaporation. An optimal filling ratio range of 40–60% was identified, minimizing the thermal resistance across the working fluids. In contrast, excessive liquid charge reduced the vapor space and degraded the performance due toflow restriction and evaporationflooding. Full article