Search Results (549)

This paper evaluates the potential for green hydrogen production in Tunisia using nearly 3 GW of renewable electricity distributed across four strategically selected sites: Haouaria, Zriba, Sbikha, and Feriana. These locations were chosen for their proximity to the Trans-Mediterranean (TransMed) natural gas pipeline linking Algeria to Italy, as well as their strong but underexploited solar and wind energy resources. Each site was optimized according to land availability and renewable energy potential: Haouaria is wind-dominant, Zriba employs a hybrid solar-wind configuration, Sbikha focuses on solar, and Feriana integrates both solar and wind over a large area. The analysis reveals a total green hydrogen production capacity supported by approximately 3.1 GW of installed renewable power, with a base-case LCOH ranging from $1.21 to $2.05 per kilogram. El Haouaria emerges as the most cost-effective site due to its highly favorable wind conditions, while the sensitivity analysis shows that LCOH can reach up to approximately $3.8 per kilogram under higher CAPEX assumptions. The findings underscore the viability of a multi-site development strategy and highlight northern Tunisia’s comparative advantage for low-cost green hydrogen production, thanks to its superior resource mix, existing infrastructure, and better water availability relative to Tunisia’s southern regions. Full article

Soil aeration is a widely used field method to remove subsurface methane (CH₄) following natural gas (NG) pipeline leaks, reducing safety risks and enabling site recovery. However, conventional aeration practices often rely on generalized guidance and do not explicitly account for site-specific soil conditions, resulting in inefficient CH₄ removal and prolonged cleanup times. This study investigated the influence of soil properties and aeration system design on CH₄ removal using controlled field-scale experiments and a validated multiphase transport model. Six field-scale aeration experiments and 39 numerical simulations were conducted across representative soil types, soil moisture conditions, vacuum pressures, and bar hole configurations. Results show that CH₄ removal occurs in two distinct stages: an initial advection-dominated removal phase followed by a slower diffusion-controlled phase. More than 50% of the residual CH₄ mass was removed within the first 10 min of aeration in permeable soils, while greater than 90% removal was achieved within 30 min under favorable conditions. Increasing vacuum pressure improved CH₄ removal by approximately 15 percentage points after 60 min and increased the effective radius of influence of individual bar holes. Soil permeability exerted a primary control on performance, with high-permeability soils exhibiting substantially faster CH₄ removal and larger treatment zones than lower-permeability soils. Bar hole configuration was equally important; properly spaced bar holes improved plume coverage and removal efficiency, whereas excessive overlap reduced aeration effectiveness through airflow interference. Overall, the results demonstrate that CH₄ removal during NG soil aeration is governed by coupled interactions among soil properties, moisture conditions, vacuum pressure, and bar hole deployment. Incorporating these factors into aeration system design can improve removal efficiency, reduce aeration duration, and provide utilities with a quantitative basis for safer and more effective NG leak mitigation. Full article

Accurate prediction of natural gas thermophysical properties is essential for applications in production and transportation engineering, including reservoir simulation and flow modeling. Although machine learning (ML) techniques have been widely used, most studies focus on the estimation of these properties, with limited integration into practical applications. In this study, we propose a supervised model based on a Backpropagation Neural Network for simultaneous estimation of four interdependent properties: compressibility factor (Z), viscosity (μ), density (ρ) and gas formation volume factor (B_g). The multi-output model was trained on 58,165 data points generated from thermodynamic correlations, using pressure, temperature, composition (mole fractions of N₂, CO₂ and H₂S), and gas specific gravity as inputs. The results yielded RMSE values of 5.56 × 10⁻⁴, 3.24 × 10⁻⁵, 3.01 × 10⁻², and 6.33 × 10⁻⁴ for Z, μ, ρ and B_g, respectively, with R² coefficients close to unity. The model’s applicability was evaluated by integrating the Z-factor into pressure drop calculations in pipelines using the Cullender and Smith method, resulting in a mean percentage error of 3.78%, close to the traditional method (3.83%). The results indicate that the model is an efficient and consistent alternative, highlighting the potential for integrating ML with classical hydraulic models. Full article

This review examines hydrogen-assisted fatigue crack growth rate (HA-FCGR) in pipeline steels with a focus on implications for integrity assessment of hydrogen transport systems. Existing natural gas pipelines offer a cost-effective pathway for hydrogen transmission; however, hydrogen embrittlement (HE) significantly alters fatigue behavior. This paper integrates scientometric analysis with a systematic review to evaluate the influence of material microstructure, welds, loading conditions, hydrogen pressure, and environmental variables on fatigue crack growth rates (FCGR). The synthesis confirms that HA-FCGR is most pronounced in the Paris region and is strongly governed by hydrogen pressure and loading frequency, while the role of material strength is less definitive than traditionally assumed. Recent advances in machine learning demonstrate strong predictive capability for FCGR; however, their integration into risk-based inspection and pipeline integrity frameworks remains limited. To bridge the gap between laboratory-scale understanding and field implementation, the concept of a near-real-world hydrogen pipeline testbed is introduced, enabling synchronized measurement of pressure cycling, material degradation, and system-level response. The review identifies critical research needs, including weld-focused fatigue datasets, realistic pressure-cycle validation, uncertainty-aware modeling, and integration of physics-based and data-driven approaches for decision-making. These findings provide a pathway toward reliable and scalable integrity assessment for hydrogen transport in existing pipeline infrastructure. Full article

Hydrogen–steel interactions remain a critical concern for the safe deployment of hydrogen–natural gas mixtures in pipeline infrastructures. Thermally grown iron oxides may be a good barrier to hydrogen ingress into the crystalline lattice of pipeline steels, but their actual effectiveness depends strongly on their composition and stability under service conditions. Several experimental approaches have been proposed to investigate the correlation between thermally grown oxides and hydrogen permeation. Among these, electrochemical permeation testing offers a more complex but safer methodology compared to pressurized hydrogen gas tests. However, when the oxide is directly exposed to the charging side (cathodic charging conditions), permeation behaviour often appears comparable to that of bare steel, and rapid oxide degradation occurs. This study introduces an alternative permeation testing configuration that enables direct assessment of thin thermally grown oxides while preserving their structural integrity. By deliberately placing the oxide on the anodic detection side, mechanical removal during hydrogen evolution is suppressed, allowing the intrinsic resistance of the oxide to hydrogen transport to be evaluated. Carbon steel samples were thermally oxidized at 250 °C for controlled exposure times, and the resulting oxide scales were characterized by Raman spectroscopy, revealing variations in hematite and magnetite fractions. Hydrogen permeation was evaluated using a Devanathan–Stachurski cell by positioning the oxidized surface either on the cathodic charging side or on the anodic detection side. Under these conditions, significant variations in apparent steady-state permeation current density were observed as a function of oxidation time and oxide composition. Full article

Abandoned mine methane can remain active beneath post-mining cities long after mine closure, creating environmental and public-safety challenges when urban works reactivate subsurface gas-migration pathways. This study assesses methane hazard in Lupeni, Romania, where unexpected gas emissions were detected during commissioning tests for a new natural gas pipeline in August 2024. The analysis uses a monitoring database of 41 fixed points surveyed during three field campaigns (123 observations in total). Descriptive statistics showed a strongly right-skewed concentration field, with an overall mean of 5.65% vol., a median of 1% vol., and a maximum of 54% vol. Campaign-to-campaign correlations (Pearson r = 0.746–0.923) indicated short-term recurrence of hotspot behaviour over the monitored period. To translate these observations into decision-relevant risk information, a fully reproducible monitoring-driven probabilistic screening model was implemented. Consequence severity was mapped from measured methane concentration, while the probability component was derived from alert-threshold recurrence across the three campaigns using a Beta posterior and Monte Carlo resampling (50,000 iterations). The mean point-level risk was 6.97, indicating that the typical monitored location was not uniformly critical. However, a hotspot-envelope indicator defined as the 95th percentile of point-level risk across monitoring points had a mean of 19.40, and the model-derived probability that it exceeded the critical threshold of 17 was 97.6%. The findings suggest that the Lupeni hazard was localized but severe, supporting targeted monitoring, excavation control, and hotspot-focused intervention rather than generalized alarmism. More broadly, the workflow may provide a practical screening approach for other post-mining urban areas where repeated monitoring data are available but mechanistic subsurface characterization remains incomplete. Full article

Accurate healthy-state performance modeling under multiple operating conditions is essential for the condition assessment of industrial centrifugal compressors. However, conventional healthy-state baselines often struggle to meet the requirements of real-time condition assessment for centrifugal compressors operating under complex real-gas and multi-condition environments. To address this issue, this study proposes a two-layer framework combining a CFD-based physical baseline with data-driven residual correction using limited factory data. A three-dimensional full-machine CFD model was reconstructed and validated under real-gas conditions, then used to generate 1440 healthy-state operating points. XGBoost, LightGBM, Random Forest, and multilayer perceptron were evaluated as baseline surrogate models. A residual-correction model was subsequently trained to compensate for systematic discrepancies between CFD predictions and actual machine performance. Ablation tests compared the CFD baseline, a factory-data-only model, and the proposed hybrid model. Online computation requires only surrogate inference and residual correction, achieving an inference latency of 0.6585 ms per operating point on Intel64 Family, compatible with the 60 s SCADA sampling interval. After correction, the maximum errors in power, polytropic head, and polytropic efficiency were 0.611%, 0.481%, and 0.899%, respectively. Post-overhaul field validation yielded maximum errors of 1.650%, 3.048%, and 1.708% for outlet pressure, power, and polytropic efficiency. The framework provides a physically grounded and computationally efficient healthy-state reference, although its transferability requires validation using additional station-specific data. Full article

Citrus peel is examined here as a naturally evolved protective surface, with the goal of developing a transferable quantitative framework for extracting engineering-relevant descriptors from biological protective surfaces and using them as design templates for biomimetic counterparts. A single-specimen-per-species design is adopted to map intra-fruit geometric variation across regions and magnifications; absolute descriptor values are therefore reported as ordinal indicators of inter-species ranking rather than as population means. Five citrus species (lemon, orange, mandarin, grapefruit, and bitter orange) were characterised by mechanical testing (cutting, puncture, and compression; five replicates per fruit), gravimetric peel density and thickness, and scanning electron microscopy (SEM) at 100×–10,000×. The 135-image SEM dataset was processed with an automatic-calibration pipeline performing per-image scale-bar detection, multilevel-Otsu segmentation of albedo air space, cell-bounded surface segment (CBSS) and oil-gland segmentation on flavedo, and grey-level co-occurrence matrix (GLCM) texture analysis with a directional anisotropy index $A_F$. Calibration was consistent across all images (FoV × magnification $=\mathrm{403,273}\pm 410$ $\mathsf{\mu }$m$·\times$, $\pm 0.10\%$). Principal component analysis separated flavedo and albedo at every magnification (PC1 + PC2 = 84–92%). Within this dataset, grapefruit showed the densest CBSS cover (1072 ${\mathrm{mm}}^{-2}$) together with the highest oil-gland density (2.77 ${\mathrm{mm}}^{-2}$); bitter orange showed the largest CBSS area (23.7 $\mathsf{\mu }{\mathrm{m}}^2$) and the thickest peel (13.1 mm); mandarin showed the most directionally oriented flavedo film ($A_F=0.0885$); and lemon showed the most open albedo (${\phi }_{2D}=36.2\%$). Oil-gland equivalent diameter was essentially invariant (∼45 $\mathsf{\mu }$m) across the five fruits, while gland density varied 4.4-fold. The structural metrics define a layered descriptor space—a dense isotropic surface relief versus a thick cellular bulk—that supplies two distinct bioinspired-design priors: dense surface films as a structural prior for selective-permeability membranes and layered cellular cores as a prior for impact-absorbing panels. A modified-atmosphere packaging (MAP)-compatible biomimetic film is identified as one downstream design hypothesis requiring direct gas-permeability verification on synthetic membranes. Full article

To supply hydrogen to the geographically decoupled demand sites, efficient hydrogen transport is necessary. The existing natural gas pipelines represent a promising transport solution, with the blended hydrogen content expected to steadily increase. An open issue of hydrogen blending is the mixing behavior. Therefore, the effects of different geometric parameters (diameters, angles), operating conditions (velocities, concentrations), and injection layouts (single- and multi-point) on the mixture quality during direct injection of hydrogen into a natural gas pipeline are studied using 3D CFD. The main goal is to find parameters and layouts leading to sufficient mixing quality over a range of operating conditions. The mixing quality is determined based on the coefficient of variation (COV). The results show that the momentum flux ratio is a key parameter governing the mixing behavior. However, a high momentum flux ratio alone does not guarantee sufficient uniformity for all operating conditions. For the investigated range, single-point injection cannot ensure reliable mixing quality, whereas multi-point layouts with higher hydrogen inlet velocities achieve sufficient uniformity. Full article

Conventional depletion development and waterflooding are often ineffective in tight oil reservoirs because of their ultra-low permeability, complex fracture–matrix architecture, and limited fluid mobility. Although inter-fracture CO₂ flooding has demonstrated considerable potential for enhanced oil recovery (EOR), the coupled effects of key operational parameters on reservoir pressure evolution, fracture–matrix mass transfer, and oil mobilization remain inadequately understood. In this study, a multi-component compositional simulation model, constrained by detailed geological characterization and calibrated through production history matching of the Yuan 284 block in the Changqing Oilfield, was developed to systematically evaluate the effects of CO₂ injection rate, injection–production time ratio, and shut-in duration on recovery performance and reservoir response. The results show that increasing the CO₂ injection rate from 1000 to 50,000 m³/d improves the recovery factor from 40.49% to 49.90%; however, the incremental recovery gain decreases markedly beyond 30,000 m³/d, which is aggravated by enhanced gas channeling through high-conductivity fracture pathways. Analysis of the injection–production time ratio indicates that an optimal ratio of 0.50 provides the best balance between reservoir energy replenishment and oil displacement efficiency, whereas excessively small ratios result in insufficient pressure support and reduced recovery. In contrast, extending the shut-in duration consistently lowers recovery performance by weakening fracture–matrix mass transfer and promoting pressure dissipation, demonstrating that immediate production following injection is more effective than prolonged soaking under the investigated conditions. The optimized operating scheme yields a recovery factor of 48.87%, substantially exceeding the representative waterflooding recovery level of 35.20%. These findings clarify the mechanisms controlling pressure maintenance, CO₂ utilization efficiency, and volumetric sweep during inter-fracture asynchronous CO₂ flooding, and provide both theoretical insights and practical guidance for the efficient development of ultra-low-permeability fractured tight oil reservoirs. Full article

Metal hydrides can safely store hydrogen in the solid state at high volumetric density under moderate temperature and pressure. Their hydrogen sorption characteristics are represented by pressure–composition–temperature (PCT) curves. AB₅–type metal hydrides, which have low plateau pressures, store and release hydrogen at low pressures. AB₂–type metal hydrides, which have high plateau pressures, store and release hydrogen at relatively high pressures. Compared with AB₅–type metal hydrides, AB₂–type metal hydrides generally have lower raw material costs and higher hydrogen storage capacity. This makes them more suitable for storing large quantities of hydrogen. Green and blue hydrogen are produced using commercial alkaline water electrolyzers and natural gas reformers, respectively. After downstream purification, this hydrogen is typically supplied at pressures below 1 MPa. However, the high plateau pressures of AB₂–type metal hydrides make it difficult to store this low-pressure hydrogen directly. AB₅–type metal hydrides can store it but release it only at low pressures. A single hydride type therefore operates within a narrow pressure range for both storage and delivery. In this study, a multipurpose hydrogen storage system (MHSS) using AB₅– and AB₂–type metal hydrides was proposed to broaden the applications of metal hydride-based systems. The feasibility of the MHSS was experimentally evaluated through lab-scale tests. The AB₅ and AB₂ modules were first tested as standalone units. The integrated MHSS was then tested assuming that waste heat was continuously available. The MHSS can store a large quantity of low-pressure hydrogen and deliver it across a wide pressure range. This range covers diverse end uses, from fuel cells at 0.5 MPa to hydrogen pipelines at 4.0 MPa. Full article

Growing interest in transporting hydrogen via natural gas pipelines highlights the need to understand flame characteristics during accidental leakage. However, limited literature is available on addressing the flame horizontal projection length of hydrogen-blended natural gas jet fires under inclined conditions. Therefore, a series of experiments was conducted to investigate inclined H₂/CH₄ jet fires, with methane used as a surrogate for natural gas. Experiments with hydrogen content ranging from 0% to 20% were performed to examine the effects of inclination angle (0°, 30°, 45°, 60°, and 90°), nozzle diameter (2, 3, and 4 mm), and gas flow rate (4–25 L/min) on the flame morphological characteristics. It was found that the flame color evolves from a transparent blue base to a yellow luminous tip with increasing hydrogen content or fuel exit velocity, accompanied by soot enrichment in the luminous region. The flame horizontal projection length was quantified under different conditions. Results show it is only slightly affected when the hydrogen content is below 20%, whereas it increases with fuel exit velocity and nozzle diameter, and decreases with inclination angle. An explicit model was proposed by introducing the dimensionless heat release rate (${\dot{Q}}^{*}$), which predicts the flame horizontal projection length with good agreement with experimental data. The findings provide a basis for the safety design and risk assessment of hydrogen-blended natural gas pipelines. Full article

Accurate multistep-ahead prediction of natural gas pipeline-network states is essential for intelligent dispatching, yet such networks contain physically heterogeneous components (gas sources, pipelines, compressors, valves), and historical states and future dispatching commands are decoupled in both temporal scale and physical semantics. This causes conventional data-driven models to suffer from semantic entanglement and cumulative error during long-horizon forecasting. This study proposes a deep spatiotemporal surrogate model with three coordinated designs: (i) type-specific feature encoding combined with global latent-graph mapping and a shared graph convolutional network (GCN) to disentangle heterogeneous-equipment attributes and represent network-wide topological coupling; (ii) a residual-gated temporal coupling mechanism that adaptively fuses historical operating inertia with future external disturbances; and (iii) a temporal-gradient multi-objective loss with a 12-step autoregressive rolling strategy over a 6 h horizon to suppress cumulative divergence. On 85,248 samples built from field monitoring data and commercial mechanistic simulations, the model attains median relative errors of 1.15% for nodal pressure and 2.10% for pipeline flow, capturing macroscopic pressure decay and high-frequency transient flow induced by valve and compressor switching without noticeable delay, providing an efficient tool for online simulation, real-time warning, and decision support in complex natural gas pipeline networks. Full article

The acceleration and deceleration of high-speed gas flow within a pipeline, induced by the action of flow-restriction devices, frequently result in the emergence of unsteady flow phenomena. Consequently, the generated excitation forces provoke intense vibrations in the pipeline, thereby substantially elevating the operational risks of the pipeline system. To mitigate such risks, the pipeline is typically subjected to fixed constraints to reduce vibration. A pipeline designed to simulate unsteady airflow was developed for the purpose of validating the vibration attenuation effect. Within this context, the effects of binding and friction constraints were compared through fluid–structure interaction simulation, and their respective mechanisms of action were analyzed individually. The results demonstrate that the constraints, in conjunction with the original pipeline, will result in a higher first-order natural frequency, which constitutes one of the primary methods for mitigating resonance effects. Both friction constraints and binding constraints significantly elevate the first-order natural frequency of the pipeline system, with binding constraints demonstrating higher efficiency. This phenomenon is attributable to the arch-like bending deformation observed in such experimental pipelines during first-order resonance, as binding constraints effectively maximize the restriction on pipeline strain. Through a comparative analysis of the time-domain and frequency-domain results of outlet pipe 1 before and after constraint application, it was observed that the axial RMS value of the constrained pipe decreased by 21.8%, while the radial value diminished by 33%. This finding further substantiates that imposing binding constraints at the location of maximum strain can elevate the pipe’s natural frequency by reducing both strain and the effective length of the “beam”, thereby significantly alleviating pipe vibrations induced by unsteady flow. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article