Search Results (2,173)

In response to the urgent need for sustainable energy solutions and efficient fossil resource utilization, the current research is conducted to examine the catalytic co-pyrolysis of four typical Chinese oil shales. The study assesses the ability of synergistic interactions, which are the result of organic and inorganic components, to improve the aspect of thermal behavior, decrease the activation energy and improve the shale oil quality. Thermogravimetric analysis in conjunction as Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS) and integral master-plots approaches showed that there were low activation energies and better reaction kinetics in blended systems. Fischer assay and GC-MS were utilized in product distribution and product composition evaluation, respectively. Optimization increased gas yield and oil composition stabilization in the blended gas, which is found due to the catalytic functions of AAEMs and clay minerals. This contribution facilitates the development of catalytic co-processing solutions where better conversion and reduced carbon intensity are achieved in the production of fossil-based energy. Full article

This article provides a conceptual and exploratory examination of Scope 3 greenhouse gas (GHG) emissions, focusing on the complexities associated with their nature, measurement, reporting, and verification. It examines the emerging role of artificial intelligence (AI) in addressing these complexities, particularly considering the fragmented, opaque, and often inaccessible nature of Scope 3 data. The paper introduces Critical Systems Thinking (CST) as a foundational framework for considering the practicality of utilization of AI in this context. CST emphasizes three key principles: critical awareness of assumptions and contexts, emancipation through attention to power dynamics and continuous improvement, and methodological pluralism, to engage with complexity through diverse analytical approaches. Due to the complex nature of GHG emissions reporting and assurance, AI application for this purpose remains limited. While Scope 3 reporting has made progress in certain sectors and regions, overall maturity remains uneven—particularly in developing and emerging markets. Although AI applications in Scope 3 reporting are still at an early stage, they hold significant potential to enhance both reporting quality and assurance processes. A key factor that needs to be addressed in the future utilization of AI for Scope 3 emissions reporting and assurance is the integration of CST into the development and implementation of AI tools. This paper proposes such integration as a necessary step forward. At present, there are substantial gaps in Scope 3 emissions measurement and reporting due to the inherently highly complex, distributed, and fragmented nature of value chain emissions. This gap poses risks to data quality and consistency, which in turn can hinder the implementation of reporting legislation and informed decision making by management and stakeholders. Systemic fragmentation, power asymmetries in data access, and methodological inconsistencies present substantial challenges to traditional forms of validation. Rather than offering a predictive model or finalized solution, the paper aims to lay a conceptual foundation for future empirical research and highlights the importance of systems-based approaches in advancing the credibility and utility of Scope 3 GHG disclosures. This is a key limitation relating to this paper, as it mainly focuses on the CST framework and the potential incapacities of artificial intelligence in relation to the implementation of CST, rather than applications of CST, as they are limited at present. Full article

Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning—an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications. Full article

The development of large-scale, flexible, and safe hydrogen storage is critical for enabling a low-carbon energy system. Deep saline aquifers (DSAs) offer substantial theoretical capacity and broad geographic distribution, making them attractive options for underground hydrogen storage. However, hydrogen storage in DSAs presents complex technical, geochemical, microbial, geomechanical, and economic challenges that must be addressed to ensure efficiency, safety, and recoverability. This study synthesizes current knowledge on hydrogen behavior in DSAs, focusing on multiphase flow dynamics, capillary trapping, fingering phenomena, geochemical reactions, microbial consumption, cushion gas requirements, and operational constraints. Advanced numerical simulations and experimental observations highlight the role of reservoir heterogeneity, relative permeability hysteresis, buoyancy-driven migration, and redox-driven hydrogen loss in shaping storage performance. Economic analysis emphasizes the significant influence of cushion gas volumes and hydrogen recovery efficiency on the levelized cost of storage, while pilot studies reveal strategies for mitigating operational and geochemical risks. The findings underscore the importance of integrated, coupled-process modeling and comprehensive site characterization to optimize hydrogen storage design and operation. This work provides a roadmap for developing scalable, safe, and economically viable hydrogen storage in DSAs, bridging the gap between laboratory research, pilot demonstration, and commercial deployment. Full article

The pursuit of higher current densities and device miniaturization intensifies gas evolution in alkaline water electrolysis, thereby reducing catalyst utilization and degrading system performance. In this work, a visualized alkaline electrolysis system was developed to investigate bubble dynamics on vertically oriented nickel wire electrodes. High-speed imaging coupled with a Yolov8 deep learning model enabled quantitative analysis of oxygen evolution behavior, revealing distinct bubble evolution modes such as isolated growth and coalescence. Systematic experiments demonstrated that current density, electrode diameter, and KOH concentration exert significant influences on bubble size distribution. Further correlation with electrochemical performance showed that increases in bubble population and size result in higher overpotentials, while bubble volume exhibits a strong linear relationship with the system’s ohmic resistance. These findings provide mechanistic insights into the coupling between bubble evolution and electrochemical performance, offering guidance for the design of efficient alkaline electrolyzers. Full article

To address the “metering blind zone” problem in distribution networks caused by flood disasters, this paper proposes an optimal deployment strategy for low-voltage instrument transformers (LVITs) based on time-varying risk assessment. A comprehensive model quantifying real-time node importance during disaster progression is established, considering cascading faults and dynamic load fluctuations. A multi-objective optimization model minimizes deployment costs while maximizing fault coverage, incorporating dynamic response constraints. A Genetic-Greedy Hybrid Algorithm (GGHA) with intelligent initialization and elite retention mechanisms is proposed to solve the complex spatiotemporal coupling problem. Simulation results demonstrate that GGHA achieves solution quality of 0.847, outperforming PSO, GA, and GD by 7.5%, 11.7%, and 8.7%, respectively, with convergence stability within ±2.5%. The strategy maintains 100% normal coverage and 73.3–95.5% disaster coverage across flood severity levels, exhibiting strong feasibility and generalizability on IEEE 123-node and 33-node test systems. Full article

With growing global demand for sustainable decarbonization, hydrogen energy systems have emerged as a key pillar in achieving carbon neutrality. This study assesses the greenhouse gas (GHG) reduction efficiency of Republic of Korea’s hydrogen ecosystem from a life-cycle perspective, focusing on production and utilization stages. Using empirical data—including the national hydrogen supply structure, fuel cell electric vehicle (FCEV) deployment, and hydrogen power generation records, the analysis compares hydrogen-based systems with conventional fossil fuel systems. Results show that current hydrogen production methods, mainly by-product and reforming-based hydrogen, emit an average of 6.31 kg CO₂-eq per kg H₂, providing modest GHG benefits over low-carbon fossil fuels but enabling up to a 77% reduction when replacing high-emission sources like anthracite. In the utilization phase, grey hydrogen-fueled stationary fuel cells emit more GHGs than the national grid. By contrast, FCEVs demonstrate a 58.2% GHG reduction compared to internal combustion vehicles, with regional variability. Importantly, this study omits the distribution phase (storage and transport) due to data heterogeneity and a lack of reliable datasets, which limits the comprehensiveness of the LCA. Future research should incorporate sensitivity or scenario-based analyses such as comparisons between pipeline transport and liquefied hydrogen transport to better capture distribution-phase impacts. The study concludes that the environmental benefit of hydrogen systems is highly dependent on production pathways, end-use sectors, and regional conditions. Strategic deployment of green hydrogen, regional optimization, and the explicit integration of distribution and storage in future assessments are essential to enhancing hydrogen’s contribution to national carbon neutrality goals. Full article

This study evaluates the application of an electronic nose (E-nose) system as a non-destructive tool for the early detection of Monilinia laxa infection in yellow nectarines (Prunus persica var. nectarine, cv. “Kinolea”) through the analysis of volatile organic compounds (VOCs). Two experimental groups were established: a control group of healthy fruit and a treatment group inoculated with the pathogen. The VOCs emitted by both groups were identified and quantified using gas chromatography-mass spectrometry (GC-MS). Simultaneously, the responses of the E-nose were recorded at three critical stages of fungal development: early, intermediate, and advanced. The electronic nose used consists of a set of 11 commercial metal oxide semiconductor (MOX) sensors. The signals from these sensors showed a strong correlation with the VOC profiles associated with fungal deterioration. Linear discriminant analysis (LDA) models based on E-nose data successfully distinguished between healthy and infected samples with 97% accuracy. Furthermore, the system accurately classified samples into three stages of contamination—control, early infection, and advanced infection—with 96% classification accuracy. These findings demonstrate that E-nose technology is an effective, rapid, and non-invasive method for the real-time monitoring of post-harvest fungal contamination in nectarines, offering significant potential for improving quality control during storage and distribution. Full article

Plastic pollution, driven by the durability and widespread use of polyolefins such as polypropylene (PP) and high-density polyethylene (HDPE), poses a formidable environmental challenge. To address this issue, we have developed an integrated multiscale framework that combines thermocatalytic experimentation, process-scale simulation, and molecular-level modeling to optimize the catalytic pyrolysis of PP and HDPE waste. Under the identified optimal conditions (300 °C, 10 wt % HMOR zeolite), liquid-oil yields of 60.8% for PP and 87.3% for HDPE were achieved, accompanied by high energy densities (44.2 MJ/kg, RON 97.5 for PP; 43.7 MJ/kg, RON 115.2 for HDPE). These values significantly surpass those typically reported for uncatalyzed pyrolysis, demonstrating the efficacy of HMOR in directing product selectivity toward valuable liquids. Above 400 °C, the process undergoes a pronounced shift toward gas generation, with gas fractions exceeding 50 wt % by 441 °C, underscoring the critical influence of temperature on product distribution. Gas-phase analysis revealed that PP-derived syngas contains primarily methane (20%) and ethylene (19.5%), whereas HDPE-derived gas features propylene (1.9%) and hydrogen (1.5%), highlighting intrinsic differences in bond-scission pathways governed by polymer architectures. Aspen Plus process simulations, calibrated against experimental data, reliably predict product distributions with deviations below 20%, offering a rapid, cost-effective tool for reactor design and scale-up. Complementary density functional theory (DFT) calculations elucidate the temperature-dependent energetics of C–C bond cleavage and radical formation, revealing that system entropy increases sharply at 500–550 °C, favoring the generation of both liquid and gaseous intermediates. By directly correlating catalyst acidity, molecular reaction mechanisms, and process-scale performance, this study fills a critical gap in plastic-waste valorization research. The resulting predictive platform enables rational design of catalysts and operating conditions for circular economy applications, paving the way for scalable, efficient recovery of fuels and chemicals from mixed polyolefin waste. Full article

Polylactic acid (PLA) is considered as an attractive polymer due to its renewable origin, biodegradability, and promising tensile strength and modulus. However, its inherent brittleness, characterized by a low impact resistance and elongation at break, can significantly restrict its application. This work proposes a new insight to improve the toughness of PLA while keeping its biocompatibility by incorporating two biocompatible ionic liquids (ILs), 1-ethyl-3-methylimidazolium ethyl sulfate ([emim][EtSO₄]), and tris(2-hydroxyethyl) methylammonium methylsulfate ([Tris][MeSO₄]). The modified PLA systems were thoroughly characterized to evaluate their mechanical and thermal behavior. Results demonstrated that the addition of 1 wt% of either IL resulted in significant improvement in modulus. Increasing the amount of IL resulted in an increase in the toughness while maintaining the material’s original stiffness and also the thermal stability. Furthermore, the foaming potential of the modified PLA using supercritical CO₂ was investigated as an environmentally friendly processing method. The ionic liquids contributed positively to the foamability of the material, suggesting improved gas solubility and cell nucleation during the foaming process. The addition of both IL decreased the cell size and resulted in narrower cell size distribution. These findings highlight the potential of ionic liquid-modified PLA systems for the processing of lightweight, and high-performance packaging materials. Full article

Reservoirs in the Orinoco Heavy Oil Belt, Venezuela, typically hold natural foamy oil. Gas liberation during depletion leads to a sharp increase in viscosity, adversely impacting development efficiency. Therefore, this paper proposes a natural gas (CH₄)–chemical synergistic huff-and-puff method (CCHP). It utilizes the synergism between a stable foam plugging system and natural gas to supplement reservoir energy and promote the generation of secondary foamy oil. To evaluate the performance of 20 types of foam stabilizers (polymers and surfactants), elucidate the influence on production and properties of key parameters, and reveal the flow characteristics of produced fluids, 24 sets of foam performance evaluation tests were conducted using a high-temperature foam instrument. Moreover, 15 sets of core experiments with production fluid visualization were performed. The results demonstrate that, in terms of individual components, XTG and HPAM-20M demonstrated the best foam-stabilizing performance, achieving an initial foam volume of 280 mL and a foam half-life of 48 h. Conversely, the polymer–surfactant composite of XTG-CBM-DA elevated the initial foam volume to 330 mL while maintaining a comparable half-life, further enhancing the performance of foaming capacity for a stable foam system. For further application in the CCHP, oil production shows a positive correlation with both post-depletion pressure and chemical agent concentration; however, the foam gas–liquid ratio (GLR) exhibits an inflection point, with the optimal ratio found to be 1.2 m³/m³. During the huff-and-puff process, the density and viscosity of the produced oil decrease cycle by cycle, while resin and asphaltene content show a significant reduction. Furthermore, visualization results reveal that the foam becomes finer, more stable, and more uniformly distributed under precise parameter control, leading to enhanced foamy oil effects and improved plugging capacity. Moreover, the foam structure transitions from an oil-rich state to a homogeneous and stable configuration throughout the CCHP process. This study provides valuable insights for achieving stable and sustainable development in natural foamy oil reservoirs. Full article

Ultra-high-voltage direct current (UHVDC) wall bushings are critical components in DC transmission systems, ensuring insulation integrity and operational reliability. In recent years, surface discharge incidents induced by charge accumulation at the gas–solid interface have become increasingly prominent. A comprehensive understanding of the electric field distribution and charge accumulation behavior of wall bushings under UHVDC is therefore essential for improving their safety and stability. In this work, an electrostatic field model of a ±800 kV UHVDC wall bushing core was developed using COMSOL Multiphysics 6.3. Based on this, a geometrically scaled model of the bushing core was further established to investigate charge distribution characteristics along the gas–solid interface under varying voltage amplitudes, application durations, and practical operating conditions. The results reveal that the maximum surface charge density occurs near the geometric corner of the core, with charge accumulation increasing as the applied voltage amplitude rises. Over time, the accumulation exhibits a saturation trend, approaching a steady state after approximately 480 min. Moreover, under actual operating conditions, the charge accumulation at the gas–solid interface increases by approximately 40%. These findings provide valuable insights for the design optimization of UHVDC wall bushings, thereby contributing to improved insulation performance and enhanced long-term operational reliability of DC transmission systems. Full article

Permeability evolution is one of the key parameters influencing the efficient exploitation of deep unconventional energy resources, as it reflects the dynamic development of pore-fracture structures under complex engineering effects. Using fractal geometry to describe the pore-fracture system, rock permeability enhancement can be quantitatively evaluated. In this study, fractured coal specimens were analyzed under simulated mining-induced stress relief and CH₄ release conditions based on fractal geometry theory. The permeability-enhancement rate was derived and verified through CT (Computed Tomography) characterization of the pore-fracture network. The fractal dimension of the fracture aperture distribution and the tortuosity of fracture paths were determined to establish a fractal permeability-enhancement model, and its sensitivity was analyzed. The results indicate that permeability evolution undergoes four distinct stages: a stable stage, a slow-growth stage, a rapid-growth stage, and a stable or declining stage. The mining-induced stress relief and gas desorption effects significantly accelerate permeability enhancement, providing new insights into the mechanisms governing gas flow and pressure relief in deep coal seams. The proposed model, highly sensitive to the fracture aperture ratio (λ_min/λ_max), reveals that a smaller aperture span leads to greater permeability enhancement during the damage and fracture stage. These findings offer practical guidance for predicting permeability evolution, optimizing gas drainage design, and enhancing the safety and efficiency of coal mining and methane extraction operations. Full article

This study aims to reveal the influence mechanisms of particles to provide a basis for screening high-efficiency foam stabilizers of nanoparticle (NP) and surfactant (SF). Molecular simulation was used, including Stretching Molecular Dynamics (SMD) for liquid film rupture, Mean Squared Displacement (MSD)/Radial Distribution Function (RDF) for water molecule behavior, NP interface tendency analysis, and interface traction force analysis. The system used silica (SiO₂) NPs (silane-modified to adjust hydrophilicity–hydrophobicity), three SFs [DTAB, CHSB, BS12; single/mixed systems], water (liquid phase), and nitrogen (gas phase). NPs need balanced hydrophilicity (to adsorb water) and hydrophobicity (to stay at the gas–liquid interface); 10% silane-modified NPs performed best, with 44% higher critical traction force for film rupture than unmodified NPs, effective water adsorption (molecules within 0.3–0.4 nm), and 12% interface presence probability. SFs (especially mixed systems like DTAB + BS12) attracted NPs to form stable composites, binding more tightly than single SFs and reducing SF mobility. The NP-SF system showed superior stability: DTAB + BS12 + NPs had the highest critical traction force (816.11 kJ·mol⁻¹·nm⁻¹) and longest rupture time (1.61 ns); the traction work required to pull NPs in the composite (2443.87 kJ·mol⁻¹) was much higher than that required for pure NPs (991.63 kJ·mol⁻¹). Finally, an experiment was conducted to measure the initial foam volume and drainage half-life of different systems to verify the simulation results. Full article

To enhance the accuracy and efficiency of reliability analyses for an aero-engine vectoring exhaust nozzle (VEN), a dual-stochastic extreme response surface method based on the genetic algorithm (DSERSM-GA) is developed by integrating the genetic algorithm, the random extremum response surface method, and the dual response surface method in the paper. In the proposed method, a limited set of Monte Carlo samples is strategically utilized to construct and optimize a population-based response surface model, forming a robust mathematical framework for reliability prediction. The uncertainty sources considered include aerodynamic loads acting on the vector nozzle, material densities of the expansion plate and triangular link, as well as the elastic moduli of these components. Stress and deformation responses of both the expansion plate and triangular link are employed as the performance metrics. The proposed DSERSM-GA methodology is validated through dynamic reliability simulations applied to a vector nozzle system, yielding distributions and corresponding reliability indices of critical responses. Comparative analyses against traditional Monte Carlo Simulation (MCS) and conventional Extreme Response Surface Methods (ERSM) demonstrate that the DSERSM-GA significantly reduces computational costs while preserving high predictive accuracy. Full article