Abstract The use of alternative fuels such as methane, methanol, and ammonia is quintessential for the shipping decarbonization. However, to minimize their carbon footprint and achieve net-zero targets, these fuels’ production must involve energy from renewable sources. This study aims to perform a comparative thermodynamic assessment of methane, methanol, and ammonia production pathways using renewable energy, determining the efficiencies of the renewable electric power conversion to chemical fuel power. The methodological approach defines the fuels production process boundaries and considers both electrical and thermal power demands to identify the dominant sources of irreversibility. A thermodynamic approach is applied considering the e-fuel production of 1,000 kg, along with the inflows of energy, compounds, and chemical reactions for each e-fuel. The required input parameters for the involved chemical conversion processes are identified by thorough literature review. The results demonstrate that the second-law efficiency for e-methane, e-methanol, and e-ammonia production is 29.2%, 32.7%. and 30.9%, respectively. Most irreversibilities occur due to the electrolysis required to produce the hydrogen feedstock. The derived results are used to discuss the e-fuels’ suitability for shipping, supporting system optimization, and evidence-based decision-making for maritime decarbonization strategies, while informing stakeholders and policy makers, hence providing, to the best of our knowledge, a first-of-its-kind input to policymakers pertaining to e-fuels production insights.
Abstract Photovoltaic (PV) power day-ahead forecasting is crucial for grid dispatch and energy management, yet its accuracy is severely challenged by the nonstationarity and multiscale periodicity of the power series. To address the limitations of existing transformer models in explicit periodic modeling and handling nonstationary disturbances, this paper proposes an enhanced iTransformer model that integrates frequency-domain decoupling and periodic perception mechanisms. The framework features two key contributions. First, a multiscale contextual temporal query attention mechanism is introduced to enhance the explicit characterization of periodic structures through learnable cycle-aware queries and contextual convolution. Second, an attention-enhanced frequency-informed normalization module is incorporated into the framework to decouple the series into periodic and residual components via Fourier transform. By employing cross-attention for feature enhancement and fusion, this module improves robustness against nonstationary disturbances, such as abrupt weather changes. Extensive day-ahead forecasting experiments on multiple real-world PV plant datasets demonstrate that the proposed model achieves competitive performance against various baseline models across mean squared error, mean absolute error, and weighted absolute percentage error metrics. These results validate the effectiveness and robustness of the proposed approach in modeling complex and nonstationary PV power generation patterns.
Abstract The deployment of renewable energy sources requires energy storage solutions to buffer mismatches between electricity supply and demand. Compressed air energy storage (CAES) is an appealing grid-scale scalable energy storage solution. Existing CAES models mostly focus on using human-made tanks or salt caverns as storage media, where the pressure responses are linear and instantaneous. However, such models are not applicable for CAES in porous media (PM-CAES), where gas pressure and flow rates are temporally and spatially changing and strongly depend on formation properties. This study investigates the feasibility of implementing an adiabatic CAES (A-CAES) system integrated with a solar power project in Australia. A daily operating schedule with 8 h of charging, 5.5 h of discharging, and a shut-in period was simulated over a 90-day summer season. A coupled CAES modeling framework is developed to integrate the surface power plant model, wellbore model, and subsurface geological storage model. Several scenarios of CAES in a saline aquifer are simulated to examine pressure and gas flow responses under varied key formation characteristics, including absolute permeability, anisotropy, wettability, and boundary conditions. Results highlighted that although target injection rates can be maintained, discharging performance is strongly influenced by boundary conditions, where closed-boundary systems outperform open-boundary cases. Reservoir permeability, especially in horizontal direction, is critical to enhance both injectivity and productivity. A 1,600-mD ( 1.58 × 10 − 12 m 2 ), open-boundary aquifer drilled with 13 wells could deliver 100 MW of continuous output for 5.5 h, corresponding to 550 MWh storage capacity, with a round-trip efficiency of 68.7%. This coupled CAES modeling framework considers formation properties to better guide optimized CAES design, well configuration, and charge/discharge scheduling.
Abstract High-temperature solid oxide electrolysis cells (SOECs) can achieve hydrogen production through water electrolysis at low voltage by introducing fuel gases to the anode side for oxidation reactions. However, research on the influence of different anode atmospheres and components on SOEC performance remains insufficient. In this study, a Ni-GDC/YSZ/Ni-YSZ electrolysis cell was employed to systematically compare the electrochemical behaviors under inert atmospheres and various reducing fuel atmospheres. The results show that reducing fuels significantly improves the electrolysis performance, with current densities of H 2 , CO, and CH 4 at 0.4 V reaching 0.73, 0.58, and 0.43 A / cm 2 , respectively. As for model biogas, as the CO 2 proportion increases ( CH 4 / CO 2 ratio from 1 ∶ 0 to 0.5 ∶ 0.5 ), the open-circuit voltage gradually approaches nearly 0 V, while the current density at 1.2 V remains within a decrease of 17.8% to 21.4% compared to that under pure methane atmosphere. This indicates that the Ni-GDC anode exhibits good tolerance and stability toward low-quality fuels containing CO 2 . This study provides a reference for the selection of anode fuels and research on the adaptability of SOECs to complex atmospheres.
Abstract Lithium-ion batteries have become the core energy carrier of electric vehicles and energy storage systems due to their high energy density and long cycle life. Their state of charge (SOC) is an important parameter in battery management systems, playing a key role in energy management, safety protection, and life prediction. However, the SOC cannot be measured directly, and it is difficult for traditional estimation algorithms to balance accuracy and real-time under nonlinear, non-Gaussian noise, multiworking conditions, and parameter time-varying conditions. This paper reviews the research progress of SOC estimation based on an improved particle filter (PF); systematically analyzes its comparison with direct measurement, data-driven, physical model, and mixed methods; and focuses on the fusion path of improved PF and the equivalent circuit model, online parameter identification, intelligent optimization algorithm, and deep learning. The results show that the improved PF algorithm can effectively alleviate the problem of particle degradation and significantly improve the estimation accuracy and robustness under the whole life cycle and complex working conditions. Among them, the nonlinear autoregressive neural network combined with particle filtering method has the best performance, achieving a root mean square error of about 0.02% and a maximum error of less than 0.07% under dynamic working conditions, which is significantly better than other methods. The results show that improving PF can not only break through the bottleneck of traditional methods but also provide an important direction for future high-precision SOC estimation. This paper suggests that subsequent research should further focus on intelligent optimization, multisource fusion, and embedded lightweight implementation to promote the large-scale engineering application of improved PF methods in electric vehicles and energy storage systems.
Abstract The cathode stoichiometry ( S ca ) is a key operational parameter that determines water-nitrogen management and durability of proton exchange membrane fuel cell with dead-ended anode (DEA-PEMFC). To clarify the role of S ca in DEA-PEMFC performance, this study employed a combination of numerical simulation and experimental testing. The results indicate that increasing S ca to the range of 2–2.5 significantly enhances water purge on the cathode side, mitigates the transmembrane water concentration gradient between the anode and cathode, and reduces liquid water saturation at the anode flow channel outlet, thereby alleviating local hydrogen mass transport limitations. At the same time, the increased total airflow and pressure drop at the cathode intensify the permeation and accumulation of N 2 across the membrane to the anode, resulting in a decrease in H 2 partial pressure at the anode outlet and suppression of local performance. During the initial startup, different S ca values exert little influence on the local current density distribution at the cathode. After 30 min of continuous operation, a moderate increase in S ca mitigates the local current density decay in the outlet region, whereas an excessively high S ca reduces membrane water content and increases impedance. Therefore, S ca must be carefully controlled to effectively optimize DEA-PEMFC performance.
Abstract Accurate prediction of fluid flow in subsurface reservoirs is critical for optimizing hydrocarbon recovery, particularly in heterogeneous formations where microscopic rock structure dictates macroscopic behavior. Traditional laboratory methods often struggle to capture the complex spatial variations inherent in these rocks. To address this problem, this study establishes an integrated digital rock physics framework that bridges the gap between microscale pore geometry and macroscale permeability. The research uses high-resolution X-ray computed tomography (CT) to image sandstone samples, followed by advanced image processing to reconstruct a high-fidelity three-dimensional digital rock model. From this model, multiple representative elementary volumes (REVs) are extracted to rigorously capture the internal structural heterogeneity. The methodology employs the FEM to solve the incompressible Stokes equations directly on the complex pore geometry, simulating fluid dynamics at the pore scale without relying on empirical simplifications. The results reveal significant variations in pore connectivity across the sample. Flow simulations demonstrate that permeability is not solely a function of porosity but is critically controlled by the topology of the pore network. Specifically, the study identifies distinct flow behaviors where well-connected high-permeability zones act as preferential flow channels, whereas adjacent tight zones serve as flow barriers. The numerical results are rigorously validated against experimental data, achieving a close agreement with an average deviation of approximately 11.3%, confirming the reliability of the computational workflow. These findings highlight that strong microscale heterogeneity is the root cause of macroscopic channeling issues, such as early water breakthrough during water flooding operations. Consequently, this work provides a robust theoretical basis for designing optimized reservoir management strategies, such as profile control and targeted stimulation, to improve sweep efficiency and maximize economic recovery in complex sandstone reservoirs.
Abstract Reconstructing missing or distorted well logs in geophysical well logging is crucial for accurate reservoir evaluation, particularly in tight sandstone formations like those in the Ordos Basin. Traditional methods often falter due to nonlinear couplings and noise, resulting in unreliable predictions. This study presents a hybrid deep-learning framework that integrates particle swarm optimization (PSO) for hyperparameter tuning, Inception modules for multiscale feature extraction, bidirectional long short-term memory (BiLSTM) networks for capturing bidirectional spatiotemporal dependencies, and efficient channel attention (ECA) for adaptive feature weighting. The model was trained and validated using well logging data from 10 exploration wells in the Ordos Basin, with inputs including true formation resistivity (RT), natural gamma ray (GR), and acoustic time (AC). The proposed model enables accurate density log reconstruction, showing significant improvements and low errors in derived reservoir parameters when validated against core data. This approach offers a cost-effective solution for reconstructing incomplete logs.
Abstract Moisture significantly influences methane ( CH 4 ) adsorption in coal seams, but its rank-dependent mechanisms remain poorly understood at the molecular scale. In this study, we integrated high-pressure CH 4 adsorption experiments with molecular simulation to address the moisture effect across low-, medium-, and high-rank coals. Experimental results reveal that moisture reduces the methane Langmuir adsorption capacity ( V L ) by 29.2% for the low-rank, 24.0% for the medium-rank, and 17.2% for the high-rank coal sample. Simulation results demonstrate that moisture inhibits CH 4 adsorption through two pathways: competitive displacement of CH 4 molecules and physical blockage of pore throats by water clusters. For the low-rank coal model, water binds to oxygen functional groups via hydrogen bonds, displacing CH 4 from O to N atoms. For the medium-rank coal model, water interacts strongly with oxygen and nitrogen functional groups through hydrogen bonds to occupy adsorption sites of CH 4 . For the high-rank coal model, with fewer oxygen functional groups compared with the low-rank, water attaches closely to O atoms. This pattern is distinct from that in the low-rank coal model, where water scatters due to the widespread O atoms. These findings provide a nanoscale explanation for the rank-dependent moisture effect on CH 4 adsorption in coals.
