Search Results (4,277)

CO₂ methanation with renewable hydrogen is a promising strategy for carbon valorization and synthetic natural gas (SNG) production. However, the molecular mechanisms behind catalyst-dependent adsorption and mass transport in zeolite-confined spaces are still not fully elucidated. Herein, we performed comparative molecular simulations on HZSM-5, Ni/ZSM-5 and Ru/ZSM-5 by combining density functional theory (DFT), grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) methods, aiming to clarify the thermodynamic and mass transport mechanisms of reactant enrichment and product desorption in CO₂ methanation. The electronic structures of the three systems were systematically evaluated via Mulliken charge analysis, differential charge density mapping, and frontier molecular orbital calculations. We further quantified the adsorption thermodynamics and diffusion kinetics of reactants and products, focusing specifically on the effects of temperature and framework Si/Al ratio for Ni/ZSM-5. The results show that Ni doping greatly modulates the local electronic environment of the ZSM-5 framework, enhancing the adsorption of CO₂ (−121.9 kJ·mol⁻¹) and H₂ (−81.6 kJ·mol⁻¹) and weakening the adsorption of CH₄ and H₂O. A higher Si/Al ratio reduces CO₂ adsorption capacity, while elevated temperatures inhibit reactant adsorption and lower the diffusion selectivity of CH₄. This demonstrates that moderately low temperatures and moderate Si/Al ratios can optimize the adsorption and diffusion behaviors of reactants and products. This work provides molecular-level insights into the adsorption and diffusion behaviors of Ni/ZSM-5 and offers theoretical references for the rational development of high-performance CO₂ methanation catalysts. Full article

The practical application of Li–O₂ batteries is severely hindered by parasitic reactions on the cathode side, which generally lead to large charging over-potentials and degraded cyclic performance. To address this issue, it is essential to integrate high-efficiency catalysts into conventional carbon-based electrodes. Herein, we report a novel La_0.85Ca_0.15Cr_0.85O₃@Ru (LCC@R) hybrid catalyst with an ultralow Ru loading (6.55 wt.%), synthesized via a facile sol-gel combined with in-situ reduction-exsolution method. Mono-dispersed and ultrafine Ru nanocrystals (2–5 nm) are uniformly anchored on the LCC substrate and serve as the catalytically active sites. The Li–O₂ battery with the LCC@R catalyst exhibits a low charge potential of 3.75 V at a current density of 50 mAg⁻¹ with limited capacity of 500 mAhg⁻¹. Impressive cyclic stabilities of up to 80 cycles (at 1000 mAhg⁻¹) and 15 cycles (at 2000 mAhg⁻¹) are achieved. Moreover, a large specific capacity of 8630 mAhg⁻¹ is delivered at 50 mAg⁻¹. Mechanistic studies reveal that the intermediate discharge product LiO₂ can be absorbed on LCC@R, thereby inhibiting the parasitic reactions induced by LiO₂ attack on carbon. The as-prepared LCC@R hybrid material is a promising cathode catalyst for constructing long-cycle-life and low-over-potential Li–O₂ batteries. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent environmental contaminants whose mobility in soil and groundwater is strongly controlled by adsorption and desorption processes. In saturated clay-rich soils, these processes are complex because PFASs interact with hydrated mineral surfaces, organic matter, metal oxides, exchangeable cations, and pore-water constituents. This review synthesizes the current literature on PFAS adsorption and desorption in saturated soils, with an emphasis on clay mineralogy, mineral–water interfaces, pore-water chemistry, and electrochemical double layer (EDL) effects. PFAS retention is influenced by molecular properties such as chain length, functional head group, and charge state, as well as soil properties such as organic carbon content, clay mineral type, surface charge, cation exchange capacity, and Fe/Al oxide content. Longer-chain PFASs and sulfonate-based compounds generally show stronger retention, while shorter-chain PFASs tend to remain more mobile. This review focuses particularly on how an EDL affects PFAS behavior in saturated clay systems. Unlike dry clay surfaces, saturated clay surfaces are covered by structured water, exchangeable ions, and diffuse counterion layers. These hydrated interfacial conditions influence how closely anionic PFASs can approach negatively charged clay surfaces, how dissolved cations reduce electrostatic repulsion or promote cation-mediated binding, and how effectively short-range interactions such as hydrophobic association, van der Waals forces, hydrogen bonding, and surface association contribute to adsorption. Desorption is also emphasized because adsorption does not necessarily represent permanent immobilization. Changes in pH, ionic strength, cation composition, dissolved organic matter, or competing solutes can weaken retention and promote PFAS release. Overall, PFAS mobility in saturated clay-rich soils should be interpreted as a coupled interfacial process rather than simple partitioning to soil solids. Future work should better connect molecular-scale mechanisms, EDL behavior, adsorption–desorption experiments, and saturated transport studies to improve predictions of PFAS retention and long-term groundwater release. Full article

The large-scale integration of electric vehicles (EVs) has made the siting and sizing of electric vehicle-charging stations (EVCSs) a critical challenge in distribution systems, as inadequate deployment may compromise secure network operation due to voltage and thermal limit violations. This problem is formulated as a mixed-integer nonlinear programming (MINLP) model, where discrete variables define EVCS locations and charging capacities expressed in terms of the number of EVs served. To address this problem, this paper proposes a unified parallel AC-feasible optimization framework that maximizes EV hosting capacity while explicitly enforcing all operational constraints of the distribution system. Particle Swarm Optimization (PSO), a Population-based Continuous Genetic Algorithm (PGA), and Monte Carlo (MC) optimization are evaluated under a common decision-variable encoding, objective function, AC power-flow evaluator, and constraint-handling strategy, enabling a fair comparison among methodologies under identical operating conditions. The proposed framework is assessed on a modified 33-bus distribution system considering a representative weekly operating scenario and 100 independent runs. Results show that PSO achieves the highest hosting capacity, integrating up to 1246 EVs and an average of 1213.3 EVs, compared with 1225 and 1196.1 EVs for PGA and 1077 and 1049.4 EVs for MC, respectively. All methodologies exhibit standard deviations below 3%, confirming robust and repeatable performance, while requiring less than 1428 s on average to identify feasible planning solutions. In addition, the parallel implementation reduces computational times by 42.22%. These results demonstrate the effectiveness of the proposed framework for identifying high-capacity EVCS planning solutions while preserving secure network operation. Full article

In the context of the new power system, modular multilevel converter high-voltage direct current (MMC-HVDC) has become a key technical solution for the large-scale grid integration of wind power. However, when a fault occurs in the AC grid at the system receiving end, the high-voltage direct current (HVDC) system faces challenges such as wind power redundancy, DC overvoltage, and equipment overcurrent. To address this, this paper proposes an energy storage-coordinated fault ride-through (FRT) control strategy suitable for different fault scenarios. The strategy optimizes the allocation of energy storage capacity according to the state of charge (SOC) of the energy storage units (ESUs), preventing individual ESUs from prematurely shutting down and reducing energy dissipation. Finally, a comparison with a conventional DC dissipation resistor scheme on the PSCAD/EMTDC platform demonstrates that the proposed strategy provides smoother power regulation characteristics and smaller DC voltage fluctuations, thereby enhancing the economic efficiency and reliability of system operation. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Real-time scheduling in large-scale electric vehicle charging stations is challenged by stochastic vehicle arrivals, dynamic departures, limited charging resources, and station-level power constraints. To address this problem, this paper proposes a two-stage Supervised Service Matching and Reinforcement Power Dispatch (SMPD) framework, termed SMPD, which decomposes the original coupled scheduling problem into supervised service matching and reinforcement learning-based power dispatch. In the first stage, a supervised matching network learns EV-charger service suitability from historical charging-session records and determines service access decisions for feasible EV–charger pairs. In the second stage, a Soft Actor-Critic-based controller allocates continuous charging power to connected EVs under EV-side charging limits, charger capacity constraints, and the station-level total power constraint. The proposed framework is evaluated using public charging-session data from the ElaadNL dataset. Experimental results show that SMPD achieves lower average waiting time, higher average revenue, lower composite penalty, and comparable demand satisfaction compared with rule-based, single-stage reinforcement learning, and multi-agent baselines. Sensitivity and robustness analyses further indicate that SMPD maintains favorable scheduling performance and acceptable online decision time under the tested charger-scale settings and operational disturbance scenarios. These results suggest that the proposed two-stage design provides an effective and computationally tractable approach for real-time scheduling in large-scale EV charging stations. Full article

Towards achieving carbon neutrality, it is important to produce carbon-free hydrogen from renewables at an acceptable cost. At the same time, power retailers that own renewables must manage their imbalances between planned and actual generation. This paper proposes an economically viable carbon-free hydrogen method for such retailers, utilizing both positive imbalances of renewables and electricity from the market with non-fossil certificates. The proposed method enables geographically flexible hydrogen production through the power grid while utilizing renewable imbalances within actual power business operations. This paper develops solutions to an optimization problem that minimizes the hydrogen variable cost and offsets the imbalances using an electrolyzer and a battery while accounting for imbalance uncertainty. The case study in Tokyo, Japan demonstrates that imbalance compensation reduces the hydrogen variable cost by 30%. The minimum levelized cost of hydrogen (LCOH) is approximately 60 JPY/Nm³ when the electrolyzer operates at a 40% capacity factor. Furthermore, sensitivity analysis of market prices indicates that the LCOH can decline to 50 JPY/Nm³ under lower price conditions. The results suggest that market-independent cost components, such as wheeling and renewable energy charges and non-fossil certificates, remain major obstacles to further reducing hydrogen costs. Full article

Accurate remaining useful life (RUL) prediction is critical for the reliable operation of lithium-ion batteries. Traditional data-driven methods often suffer from parameter redundancy and error accumulation in state prediction. This paper proposes a hybrid data-driven RUL prediction framework based on Gaussian process regression (GPR) optimized by the lightning search algorithm (LSA). First, both local and global indirect health features (HFs) are extracted from the external characteristic parameter curves and the incremental capacity curves during battery charging/discharging. Second, the Pearson correlation coefficient is applied to select highly relevant features, forming a compact feature set. Third, a GPR model is developed, and the LSA is introduced to optimize its hyperparameters, overcoming the tendency of the conjugate gradient method to fall into local optima or fail to converge. Experimental results under identical conditions show that the proposed LSA–GPR model achieves a prediction error of 3% or less. Full article

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic. Full article

Cell degradation in second-life battery packs introduces heterogeneous capacity and internal resistance mismatch, reducing the effectiveness of conventional balancing approaches and limiting available pack runtime. Although equal state of charge (SoC) does not necessarily imply equal usable capacity, SoC-based control remains attractive for runtime-oriented operation. This paper proposes a target-mean controller for heterogeneous reconfigurable battery packs under constant-bus constraints that aims to improve runtime and achieve the cutoff-defined theoretical maximum capacity utilization limit. Using only real-time cell SoC measurements and legal switching actions, the controller selects the configuration that best reduces deviation from the pack-average SoC while preferentially loading cells above the mean. The online action selection requires no active balancing hardware, no explicit capacity or state of health (SoH) estimation, and no offline optimization; experimentally measured capacities are used only for calibrated Coulomb-counting SoC estimation. Simulation results on a heterogeneous five-cell reconfigurable battery pack show that the proposed controller reaches the cutoff-defined 90% theoretical utilization limit in the full-initial-SoC cases, while also extending runtime and reducing switching activity by up to 11.75% relative to the comparison methods. Hardware validation on a five-cell prototype further confirms this trend, achieving 89.12% experimental utilization, zero final SoC spread, and higher delivered energy than both comparison methods. A stepped-load hardware test further achieved 88.19% utilization from current integration, corresponding to 97.99% of the cutoff-defined 90% theoretical limit. The results suggest that, for heterogeneous second-life packs, SoC-based reconfiguration control can achieve both runtime improvement and near-maximum utilization without the added complexity of explicit SoH-aware balancing. Full article

Chitosan, produced through deacetylation of chitin from crustacean byproducts and, increasingly, fungal biomass and insects, is attracting food-sector interest because it combines antimicrobial activity, antioxidant capacity, biodegradability, and film-forming behavior in a single polymer. This review discusses how source, molecular weight (MW), degree of deacetylation, solubility, and charge density shape its performance in food systems. The paper then follows the main technological routes now tested or used: edible films and coatings, hydrogels, cryogels, nanoparticles, microcapsules, and hybrid matrices. These formats can protect fresh produce, meat, poultry, fish, seafood, and dairy foods, while also supporting beverage clarification, emulsion control, release of natural antimicrobials or antioxidants, and freshness monitoring in active or intelligent packaging. The evidence indicates strong promise, especially where microbial growth, lipid oxidation, moisture transfer, and short shelf life remain limiting factors. Yet, wider industrial use is still slowed by water sensitivity, sensory effects, raw-material variation, cost, process scale-up, and regulatory alignment. Future work should move beyond laboratory efficacy and address reproducible production, food-specific validation, and consumer acceptance. Full article

The rapid adoption of electric vehicles (EVs) creates a planning challenge for the Kampala-Wakiso metropolitan region in Uganda, where the electricity grid already faces local network constraints. This study applies the EVPV-Simulator, an open-source geospatial modelling framework that links mobility demand, charging demand, and EV-PV complementarity, to assess projected charging demand and solar integration potential in the Kampala-Wakiso metropolitan region. By simulating the charging requirements of a projected fleet of 60,000 EVs, the study identifies a pronounced evening charging peak concentrated in residential areas and weakly aligned with daytime solar availability. Under the base-case charging pattern, increasing PV capacity raises the self-sufficiency potential, but has limited influence on the evening peak. In the base-case with 40 MW of installed PV capacity, the self-sufficiency ratio reaches 39.6%, while peak demand falls by only 0.20%. A charging location sensitivity analysis then shows that temporal alignment improves substantially when charging shifts from home towards workplaces and Points of Interest (POI). In a selected daytime oriented scenario with 40% workplace charging and 60% POI charging, the self-sufficiency potential reaches 68.97% and the mean daily maximum net load falls to about 18 MW at 40 MW of installed PV capacity. These results show that the value of solar integration depends strongly on where charging occurs, and that daytime charging access should be treated as a central variable in EV infrastructure planning. The study provides a planning oriented basis for future work incorporating feeder level validation, explicit PV siting constraints, and storage. Full article

Hydrogen is a clean and renewable energy carrier, but its reversible storage near ambient conditions remains a major challenge. Here, density functional theory (DFT) combined with ab initio molecular dynamics (AIMD) is employed to assess the newly predicted 2D orthorhombic diboron dinitride (o-B₂N₂) monolayer, in pristine and Li-functionalized forms, as a hydrogen storage medium. On the pristine surface, H₂ physisorbs with binding energies of −0.158 to −0.174 eV. Li atoms anchor strongly at the hexagonal hollow sites ($E_{\mathrm{bind}}$ from −0.979 to −1.321 eV, strongest at the B-rich H1 site), donate 0.65–0.84 $\left|e\right|$ to the substrate, and render the semiconducting monolayer metallic. A positive cluster formation energy ($+0.171$ eV per Li pair) and a 5 ps AIMD simulation at 400 K confirm that the Li adatoms remain dispersed, without clustering. Each ${\mathrm{Li}}^{+}$ center polarizes and binds up to five H₂ molecules, with average adsorption energies of −0.207 to −0.336 eV/H₂, within the optimal window for room-temperature reversible storage. The 4Li@o-B₂N₂(20H₂) system attains a theoretical gravimetric capacity of 15.12 wt% and a practical capacity of 10.99 wt% under realistic operating conditions (charging at 30 atm/25 °C; release at 3 atm/100 °C). These results establish Li-functionalized o-B₂N₂ as a promising hydrogen storage material that merits experimental exploration. Full article

Hybrid renewable energy systems are regarded as one of the most promising solutions for the autonomous power supply of remote and weakly electrified sites, where diesel generation remains a costly and carbon-intensive energy source. This study presents the optimization of an off-grid PV/BESS/DGU microgrid for three representative regions of Kazakhstan—North, Central/East, and South/South-West—under different environmental scenarios. The aim of the study was to determine the optimal installed photovoltaic capacity, battery storage capacity, diesel generator rated power, and annual load coverage balance using the Generalized Reduced Gradient (GRG) method. The optimization was carried out using two objective functions: the conventional levelized cost of electricity, $LCOE$, and the environmentally adjusted cost of electricity, ${LCOE}_{\mathrm{e}\mathrm{n}\mathrm{v}}$, which includes the monetized cost of emissions associated with diesel generator operation. The model was formulated as a constrained nonlinear programming problem incorporating hourly energy balance, battery state-of-charge constraints, diesel generator operating constraints, and carbon price scenarios of 0, 25, 50, and 100 USD/tCO₂. The results show that an increase in the carbon price systematically shifts the optimum toward a higher share of photovoltaic generation and reduced diesel generator use in all regions. The strongest response is observed in the South/South-West region, followed by Central/East, whereas the North exhibits the lowest sensitivity due to the more pronounced seasonality of solar generation. Under the considered scenarios, the optimal PV capacity increases by approximately 24–28%, while the share of diesel generation in annual load coverage decreases by approximately 28% in the North, 44% in Central/East, and 61% in the South/South-West. At the same time, the rated diesel generator capacity remains unchanged in most scenarios, indicating the persistence of its backup function. The results confirm that the PV/BESS/DGU configuration constitutes a technically and economically justified baseline architecture for autonomous power supply under Kazakhstan’s conditions, while the inclusion of environmental costs supports the cost-effective displacement of diesel generation. The GRG method proved to be suitable for the transparent and efficient optimization of hybrid microgrid parameters. Full article