Search Results (2,436)

This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-minute intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R² score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications. Full article

The high-efficiency and clean utilization of coal resources is a key strategy for new energy development, and converting coal into carbon materials offers a promising route to valorize bituminous coal. However, fabricating high-performance bituminous coal-derived carbon for sodium ion (Na⁺) insertion/extraction remains a major challenge, as it is difficult to regulate the carbon’s microstructural properties to match Na⁺ storage demands. Herein, we propose a molten salt-assisted carbonization strategy to prepare bituminous coal-derived hard carbon (HC) for use as a sodium-ion battery (SIB) anode material, and we focus on regulating the structure of carbon. The results show that as-prepared HC exhibits significantly enhanced electrochemical performance for Na⁺ storage when the molar ratio of NaCl to KCl is 1:1. The optimized material achieves a reversible capacity of 366.7 mAh g⁻¹ at the current density of 100 mA g⁻¹ after 60 cycles and retains 99% of its initial capacity after 500 cycles at a current density of 1 A g⁻¹. The main finding is that the lattice spacing can be regulated by tuning the composition of the molten salt, and anode performance is enhanced remarkably by changes in the HC structure. This work provides a feasible strategy for designing and preparing a bituminous coal-derived carbon anode material for use in the energy storage field. Full article

Air liquefaction systems are essential in cryogenic engineering and energy storage, yet their performance is often constrained by significant exergy destruction. This study develops an exergy-based assessment of the Linde–Hampson air liquefaction cycle to identify dominant sources of inefficiency and explore strategies for improvement. The analysis shows that throttling (≈41%) and compression (≈40%) represent the major contributions to exergy losses, followed by finite-temperature heat transfer (≈15%) in the recuperative heat exchanger. To mitigate these losses, fractional throttling and optimized inlet conditions are proposed, leading to reduced compressor work and improved overall efficiency. A comparative study of a two-stage throttling configuration demonstrates a decrease in throttling-related exergy destruction to approximately 30%. Reverse Pinch analysis is employed to verify the thermal coupling of hot and cold streams and to determine the minimum feasible temperature difference. The design optimization of the recuperative heat exchanger identifies an optimal velocity ratio that minimizes pressure losses and quantifies how compression pressure affects the required heat transfer surface area. The results provide a systematic framework for improving the thermodynamic performance of air liquefaction cycles, highlighting exergy analysis as a powerful tool for guiding structural modifications and functional optimization. Full article

Electrocatalytic conversion of CO₂ to high-energy-density multicarbon products (C₂₊) offers a sustainable route for renewable energy storage and carbon neutrality. Precisely modulating Cu-based catalysts to enhance C₂₊ selectivity remains challenging due to uncontrollable reduction of Cu^δ+ active sites. Here, an efficient and stable Ge/Cu catalyst was developed for CO₂ reduction to ethanol via Ge modification. A Cu₂O/GeO₂/Cu core–shell composite was constructed by controlling Ge doping. The structure–performance relationship was elucidated through in situ characterization and theoretical calculations. Ge⁴⁺ stabilized Cu¹⁺ active sites and regulated the surface microenvironment via electronic effects. Ge modification simultaneously altered CO intermediate adsorption to promote asymmetric CO–CHO coupling, optimized water structure at the electrode/electrolyte interface, and inhibited over-reduction of Cu^δ+. This multi-scale synergistic effect enabled a significant ethanol Faradaic efficiency enhancement (11–20%) over a wide potential range, demonstrating promising applicability for renewable energy conversion. This study provides a strategy for designing efficient ECR catalysts and offers mechanistic insights into interfacial engineering for C–C coupling in sustainable fuel production. Full article

This paper presents a novel numerical–experimental workflow to evaluate the sealability of casing connections in geothermal and underground gas storage wells, where cyclic thermal and pressure loads challenge conventional qualification methods. The approach combines experimental make-up and cyclic loading tests with finite element analysis by explicitly modeling the connection geometry and the contact conditions. Validation against experimental data shows good agreement in seal ovality, roughness, and wear, confirming the predictive reliability of the model. Results indicate that initial geothermal discharge and seasonal storage cycles generate the highest von Mises stresses, expressed as a percentage of the material’s yield strength (%VMS), mainly under combined tensile and internal pressure loading. After the first make-up, subsequent cycles reduced seal contact pressure and length, increasing leakage risk; however, repeated loading improved tribological behavior, enhancing sealability despite occasional galling. The proposed framework enables accurate prediction of connection integrity under extreme cyclic conditions, offering a novel tool to optimize design and streamline qualification testing. Full article

Carbon Capture and Storage (CCS) is a key technology for achieving carbon neutrality goals. Relevant foreign research began in the 1970s, but overall it remains in the exploration and demonstration stage. Clarifying the geological parameters and characteristics of reservoir–caprock systems in CCS projects is of great significance to the effectiveness and safety of long-term storage. By reviewing 15 typical global CCS projects, this paper identifies that ideal reservoirs are gently structured sandstones with few faults (characterized by high porosity, high permeability, and large scale, which are conducive to CO₂ diffusion) or basalts (which can react with CO₂ for mineralization, enabling permanent storage). Caprocks are mainly composed of thick mudstone and shale; composite caprocks consisting of multi-layer low-permeability formations and tight interlayers within reservoirs have stronger sealing performance. Additionally, they should be far from faults, and sufficient caprock thickness is required to reduce leakage risks. Meanwhile, this paper points out the challenges faced by CCS technology, such as complex site selection, limitations in long-term monitoring, difficulties in designing injection parameters, and challenges in large-scale deployment. It proposes suggestions including establishing a quantitative site selection system, building a comprehensive monitoring network, and strengthening collaborative optimization of parameters, so as to provide a basis for safe site selection and assessment. Full article

This exploratory proof-of-concept study investigated the simultaneous deployment of autonomous, persona-driven Artificial Intelligence (AI) agents across multiple social media platforms using the ElizaOS framework. We developed three platform-specific agents with seven-layer character architectures and deployed them on Twitter/X, Discord, and Telegram for 18 days. The system processed 5389 interactions while gathering feedback from 28 volunteer participants. Addressing three research questions, we found that: (1) automation effectiveness was platform-dependent, with direct support platforms (Telegram, Discord) rated more useful than broadcast-oriented Twitter/X; (2) character design impact depended primarily on platform-persona alignment rather than architectural sophistication; and (3) technical performance showed platform-specific patterns, with median storage times ranging from 9.0 milliseconds (Twitter/X) to 61.5 milliseconds (Telegram) and high variability across all platforms. A notable finding was what we term the “Discord Paradox”—high quality ratings (4.05/5) but lowest preference (8.7%), suggesting platform familiarity and accessibility influence adoption more than agent quality. While the deployment demonstrated technical feasibility and revealed distinct user dynamics across platforms, the findings indicate that platform-specific optimization may be more effective than universal approaches. This exploratory study advances understanding of multi-platform agent deployment for marketing automation, identifying behavioral patterns and platform-specific dynamics that offer testable hypotheses for future systematic research. Full article

Underground hydrogen storage in aquifers is a promising solution to address the imbalance between energy supply and demand, yet its practical implementation requires optimized strategies to ensure high efficiency and economic viability. To improve the storage and production efficiency of hydrogen, it is essential to select the appropriate cushion gas and to study the influence of reservoir and process parameters. Based on the conceptual model of aquifer with single-well injection and production, three potential cushion gas (carbon dioxide, nitrogen and methane) were studied, and the changes in hydrogen recovery for each cushion gas were compared. The effects of temperature, initial pressure, porosity, horizontal permeability, vertical to horizontal permeability ratio, permeability gradient, hydrogen injection rate and hydrogen production rate on the purity of recovered hydrogen were investigated. Additionally, the impact of different well pattern on the purity of recovered hydrogen was studied. The results indicate that methane is the most effective cushion gas for improving hydrogen recovery in UHS. Different well patterns have significant impacts on the purity of recovered hydrogen. The mole fractions of methane in the produced gas for the single-well, line-drive pattern and five-spot pattern were 16.8%, 5%, and 3.05%, respectively. Considering the economic constraints, the five-spot well pattern is most suitable for hydrogen storage in aquifers. Reverse rhythm reservoirs with smaller permeability differences should be chosen to achieve relatively high hydrogen recovery and purity of recovered hydrogen. An increase in hydrogen production rate leads to a significant decrease in the purity of the recovered hydrogen. In contrast, hydrogen injection rate has only a minor effect. These findings provide actionable guidance for the selection of cushion gas, site selection, and operational design of aquifer-based hydrogen storage systems, contributing to the large-scale seasonal storage of hydrogen and the balance of energy supply and demand. Full article

Reservoir operation serves as a critical non-structural measure for real-time flood management, aimed to minimize downstream flood damage while ensuring dam safety. This study develops and evaluates a Gated Recurrent Unit (GRU)-based reservoir operation model with data integration (DI) to enhance flood management capabilities. Optimal reservoir outflows are first determined for historical flood events using the Interior Point Optimizer (IPOPT), a deterministic optimization model designed to minimize peak outflows. The optimized hydrographs are compared with observed outflows to assess the benefits of improved operational strategies. GRU models are then trained and validated using inflow hydrographs and resulting optimal reservoir storage and release data. Various input configurations are tested, incorporating DI of lagged observations and forecasted values to evaluate their influence on model accuracy. The study also examines multiple hyperparameter settings to identify the optimal configuration. The methodology is applied to the Namgang Dam in South Korea, simulating hourly operations during flood events. Results indicate that historical reservoir inflow and storage are the most influential inputs, while adding precipitation (historical or forecasted) and/or forecasted inflows does not improve model performance. The GRU model with DI successfully replicates optimized reservoir operations, demonstrating its reliability and efficiency in flood management. This framework supports timely and informed decision-making and offers a promising approach for enhancing flood risk mitigation through improved reservoir operations. Full article

Polyoxometalates (POMs) are nanoscale anionic clusters constructed from transition-metal oxide units with well-defined architectures and tunable electronic structures, offering abundant reversible redox sites and adjustable energy levels. Their diverse valence states and compositional flexibility of molecular architectures render them promising candidates for electrochemical energy storage. Rational molecular design and nano-structural engineering can significantly enhance the electrical conductivity, structural stability, and ion transport kinetics of POM-based materials, thus improving device performance. In solar cells, the tunable energy levels and light-harvesting capabilities contribute to enhanced photoconversion efficiency. In secondary batteries, the dense redox centers provide additional capacity. For supercapacitors, the rapid electron transfer supports high power density storage. This review systematically summarizes recent advances in POM-based functional nanomaterials, with an emphasis on material design strategies, energy storage mechanisms, performance optimization approaches, and structure–property relationships. Fundamental structures and properties of POMs are outlined, followed by synthesis and functionalization approaches. Key challenges such as dissolution, poor conductivity, and interfacial instability are discussed, together with progress in batteries and hybrid capacitors. Finally, future challenges and development directions are outlined to inspire further advancement in POM-based energy storage materials. Full article

Rainfall deficits and erratic dry spells pose major challenges in rainfed ecosystem. In-situ moisture conservation practices (MCP) like ridge–furrow methods, improve soil moisture but are inadequate during 2–3 week dry spells at critical crop stages (flowering and maturity), leading to yield loss. Supplemental irrigation (SI) using an ex-situ rainwater harvesting (RWH) pond can mitigate these effects, but optimizing the pond design is challenging due to limited runoff and storage losses. This study aims to design RWH pond for small farm holders with a 1.0 ha area and evaluate its efficient use for SI during intermittent dry spells and critical crop stages. The design volume was estimated using the SCS-CN method based on daily rainfall data (1974–2010) for the northeast monsoon. A pond with a capacity of 487.5 m³, constructed for a 1 ha micro-watershed, was used to observe the runoff for design validation. The harvested runoff can be used as SI for a cultivable area of 0.4 ha, based on the watershed-to-cultivable area ratio. Statistical analysis of observed and estimated runoff data from 2011 to 2023 revealed a strong correlation (r = 0.87), confirming the pond design. Harvested rainwater, applied through micro-irrigation (rain gun) at a depth of 50 mm during moisture stress periods, significantly improved cotton productivity. The combined use of harvested rainwater and MCP increased yield in the range of 3.8 to 25.3%, improved rainwater use efficiency (1.52 to 3.13 kg ha⁻¹ mm⁻¹), and had a higher benefit-cost ratio (1.15 to 2.43) over a 13-year period. This study concludes that integrating in-situ MCP with ex-situ RWH with micro-irrigation significantly improves rainfed crop productivity in vertisols. Full article

In the “cloud–edge–end” three-tier architecture of edge computing, the cloud, edge layer, and end-device layer collaborate to enable efficient data processing and task allocation. Certain computation-intensive tasks are decomposed into subtasks at the edge layer and assigned to terminal devices for execution. However, existing research has primarily focused on resource scheduling, paying insufficient attention to the specific requirements of tasks for computing and storage resources, as well as to constructing terminal clusters tailored to the needs of different subtasks.This study proposes a multi-objective optimization-based cluster construction method to address this gap, aiming to form matched clusters for each subtask. First, this study integrates the computing and storage resources of nodes into a unified concept termed the computing power resources of terminal nodes. A computing power metric model is then designed to quantitatively evaluate the heterogeneous resources of terminals, deriving a comprehensive computing power value for each node to assess its capability. Building upon this model, this study introduces an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) clustering algorithm. This algorithm incorporates simulated annealing and adaptive genetic operations to generate the initial population and employs a differential mutation strategy in place of traditional methods, thereby enhancing optimization efficiency and solution diversity. The experimental results demonstrate that the proposed algorithm consistently outperformed the optimal baseline algorithm across most scenarios, achieving average improvements of $18.07\%$, $7.82\%$, $15.25\%$, and $10\%$ across the four optimization objectives, respectively. A comprehensive comparative analysis against multiple benchmark algorithms further confirms the marked competitiveness of the method in multi-objective optimization. This approach enables more efficient construction of terminal clusters adapted to subtask requirements, thereby validating its efficacy and superior performance. Full article

The rapid proliferation of electric vehicles (EVs) demands lightweight yet efficient battery thermal management systems (BTMS). The fin-embedded phase-change material energy storage tube (PCM-EST) offers significant potential due to its high thermal energy density and passive operation, but conventional designs face a critical trade-off: enhancing heat transfer typically increases mass, conflicting with EV lightweight requirements. To resolve this conflict, this study proposes a multi-objective surrogate optimization framework integrating computational fluid dynamics (CFD) and Kriging modeling. Fin geometric parameters—number, height, and tube length—were rigorously analyzed via ANSYS (2020 R1) Fluent simulations to quantify their coupled effects on PCM melting/solidification dynamics and structural mass. The results reveal that fin configurations dominate both thermal behavior and weight. An enhanced multi-objective particle swarm optimization (MOPSO) algorithm was then deployed to simultaneously maximize heat transfer and minimize mass, generating a Pareto-optimal solution. The optimized design achieves 8.7% enhancement in heat exchange capability and 0.732 kg mass reduction—outperforming conventional single-parameter designs by 37% in weight savings. This work establishes a systematic methodology for synergistic thermal-structural optimization, advancing high-performance BTMS for sustainable EVs. Full article

Compared with other fossil energy sources, natural gas is characterized by compressibility, low energy density, high storage costs, and imbalanced usage. Natural gas pipeline supply systems possess unique attributes such as closed transportation and a highly integrated upstream, midstream, and downstream structure. Moreover, pipelines are almost the only economical means of onshore natural gas transportation. Given that the upstream of the pipeline features multi-entity and multi-channel supply including natural gas, coal-to-gas, and LNG vaporized gas, while the downstream presents a competitive landscape with multi-market and multi-user segments (e.g., urban residents, factories, power plants, and vehicles), there is an urgent social demand for non-discriminatory and fair opening of natural gas pipeline network infrastructure to third-party entities. However, after the fair opening of natural gas pipeline networks, the original “point-to-point” transaction model will be replaced by market-driven behaviors, making the verification and allocation of gas transmission capacity a key operational issue. Currently, neither pipeline operators nor government regulatory authorities have issued corresponding rules, regulations, or evaluation plans. To address this, this paper proposes a multi-dimensional quantitative evaluation model based on the Analytic Hierarchy Process (AHP), integrating both commercial and technical indicators. The model comprehensively considers six indicators: pipeline transportation fees, pipeline gas line pack, maximum gas storage capacity, pipeline pressure drop, energy consumption, and user satisfaction and constructs a quantitative evaluation system. Through the consistency check of the judgment matrix (CR = 0.06213 < 0.1), the weights of the respective indicators are determined as follows: 0.2584, 0.2054, 0.1419, 0.1166, 0.1419, and 0.1357. The specific score of each indicator is determined based on the deviation between each evaluation indicator and the theoretical optimal value under different gas volume allocation schemes. Combined with the weight proportion, the total score of each gas volume allocation scheme is finally calculated, thereby obtaining the recommended gas volume allocation scheme. The evaluation model was applied to a practical pipeline project. The evaluation results show that the AHP-based evaluation model can effectively quantify the advantages and disadvantages of different gas volume allocation schemes. Notably, the gas volume allocation scheme under normal operating conditions is not the optimal one; instead, it ranks last according to the scores, with a score 0.7 points lower than that of the optimal scheme. In addition, to facilitate rapid decision-making for gas volume allocation schemes, this paper designs a program using HTML and develops a gas volume allocation evaluation program with JavaScript based on the established model. This self-developed program has the function of automatically generating scheme scores once the proposed gas volume allocation for each station is input, providing a decision support tool for pipeline operators, shippers, and regulatory authorities. The evaluation model provides a theoretical and methodological basis for the dynamic optimization of natural gas pipeline gas volume allocation schemes under the fair opening model. It is expected to, on the one hand, provide a reference for transactions between pipeline network companies and shippers, and on the other hand, offer insights for regulatory authorities to further formulate detailed and fair gas transmission capacity transaction methods. Full article

Breast tissue-mimicking phantoms are essential tools for validating microwave imaging systems designed for early breast cancer detection. In this work, we report the fabrication and comprehensive characterization of oil-in-gelatin phantoms emulating normal, benign, and malignant breast tissues. The phantoms were manufactured with controlled mixtures of kerosene, safflower oil, and gelatin, and their dielectric properties were experimentally evaluated using a free-space transmission method with a Vector Network Analyzer across the 100 MHz–10 GHz range. Results demonstrated significant contrast in permittivity and conductivity among the different tissue types, consistent with values reported in the literature. Long-term stability was confirmed for up to six months under controlled storage. Additional structural and thermal characterization was performed using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA), providing insight into molecular composition and thermal response. The proposed method enables reproducible, low-cost, and stable phantom fabrication, offering reliable tissue models to support experimental validation and optimization of microwave-based breast cancer detection systems. Full article