Search Results (887)

Steel-reinforced thermoplastic pipe is widely used for water transportation in sour gas fields. However, under the combined effects of corrosive media, internal high pressure, and long-term environmental aging, premature failures such as leakage and bursting often occur. To clarify the failure causes and primary contributing factors of the composite pipes, this study conducted a comprehensive analysis through microscopic morphology examination of different typical failure cases, differential scanning calorimetry, Fourier transform infrared spectroscopy, and mechanical property testing. The main failure mechanisms were investigated, and targeted protective measures are proposed. Key findings reveal that the typical failure modes are ductile cracking, aging-induced brittle cracking, and aging creep cracking. These failures follow a mechanism of degradation of the inner and outer polyethylene protective layers, penetration of the medium and corrosion of the steel wires, reduction in pressure-bearing capacity, and eventual structural damage or leakage propagation through the pipe wall. Notably, oxidation induction time values dropped as low as 1.4–17 min—far below the standard requirement of >20 min—indicating severe antioxidant depletion and material aging. The main controlling factors are poor material quality, external stress or mechanical damage, and long-term aging. The polyethylene used for the inner and outer protective layers is critical to the overall pipe performance; therefore, emphasis should be placed on evaluating its anti-aging properties and on protecting the pipe body during installation to ensure the long-term safety and stable operation of the pipeline system. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a multifactorial condition requiring multi-target therapeutic strategies beyond traditional single-marker approaches. In this work, we present a fully in silico nutraceutical screening pipeline that integrates molecular prediction, systemic aggregation, and technological design. A curated panel of ten MASLD-relevant targets, spanning nuclear receptors (FXR, PPAR-α/γ, THR-β), lipogenic and cholesterogenic enzymes (ACC1, FASN, DGAT2, HMGCR), and transport/regulatory proteins (LIPG, FABP4), was assembled from proteomic evidence. Bioactivity records were extracted from ChEMBL, structurally standardized, and converted into RDKit descriptors. Predictive modeling employed a stacked ensemble of Random Forest, XGBoost, and CatBoost with isotonic calibration, yielding robust performance (mean cross-validated ROC-AUC 0.834; independent test ROC-AUC 0.840). Calibrated probabilities were aggregated into total activity (TA) and weighted TA metrics, combined with structural clustering (six structural clusters, twelve MOA clusters) to ensure chemical diversity. We used physiologically based pharmacokinetic (PBPK) modeling to translate probabilistic profiles into minimum simulated doses (MSDs) and chrono-specific exposure (%T>IC50) for three prototype concepts: HepatoBlend (morning powder), LiverGuard Tea (evening aqueous form), and HDL-Chews (postprandial chew). Integration of physicochemical descriptors (MW, logP, TPSA) guided carrier and encapsulation choices, addressing stability and sensory constraints. The results demonstrate that a computationally integrated pipeline can rationally generate multi-target nutraceutical formulations, linking molecular predictions with systemic coverage and practical formulation specifications, and thus provides a transferable framework for MASLD and related metabolic conditions. 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

Emerging respiratory dimorphic fungi, including Coccidioides, pose a growing public health threat due to their ability to cause severe disease and the limited therapeutic options. A growing gap exists between rapidly expanding computational data and slower traditional experimental methods for virulence factor identification, limiting progress in fungal pathogenesis research and therapeutic development. This review presents a framework for integrating computational and experimental methodologies to accelerate virulence discovery in Coccidioides. We examine predictive tools for adhesins, transporters, secreted effectors, carbohydrate-active enzymes (CAZymes), and secondary metabolites, plus therapeutic target prioritization strategies based on druggability, selectivity, essentiality, and precedent. Examples from Coccidioides and other World Health Organization-designated emerging fungi highlight how computational pipelines clarify pathogenic mechanisms and guide experimental design. We also assess machine learning, structural prediction, and reverse vaccinology approaches for enhance target discovery. By applying computational advances to Coccidioides research with experimental validation, this integrated approach can guide future antifungal drug and vaccine development. Full article

Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures. Unlike uniform corrosion, which represents a symmetric form of material loss, pitting corrosion is a highly asymmetric and localized phenomenon. The inherent complexity and asymmetry of this process make its prediction a significant challenge. To address this, this study presents SSA-CNN-Attention, a deep learning model specifically designed to analyze the complex, nonlinear interactions among environmental factors. The model employs a Convolutional Neural Network (CNN) to extract local features, while a crucial attention mechanism allows it to asymmetrically weight the importance of these features, enhancing its ability to recognize intricate interactions. Additionally, the Sparrow Search Algorithm (SSA) optimizes the model’s hyperparameters for improved accuracy and stability. Furthermore, a post hoc interpretability analysis using the LIME framework validates that the model’s learned feature relationships are consistent with established corrosion science, revealing how the model accounts for the asymmetric influence of key variables. The experimental results demonstrate that the proposed model reduces mean squared error (MSE) by 61.3% and mean absolute error (MAE) by 26.6%, while improving the coefficient of determination (R²) by 28.2% compared to traditional CNNs. These findings highlight the model’s superior performance in predicting a fundamentally asymmetric process and provide valuable insights into the underlying corrosion mechanisms. Full article

Gangue slurry pumping backfill offers a cost-effective and environmentally sound solution for coal mine solid waste disposal. Addressing the poor pumpability of pure gangue slurry, this study applied the Talbot gradation theory to a non-cemented gangue system by designing various particle size gradations and water-solid ratios (W/S). Through tests on rheological properties, slump, spread, and bleeding rate, the optimal proportion for pumpability of pure gangue slurry (PGS) within the scope of this study was determined. Tests were conducted on rheology, slump, spread flow, and bleeding rate to determine the optimal mix proportion for pumpability. The results show that: The slurry in this study demonstrates a strong correlation with the characteristics of a Bingham fluid. Its yield stress increases significantly as the W/S decreases. At a gradation index (n) of 0.4, particle packing is densest, resulting in the lowest yield stress. Slump and spread flow decrease with a lower W/S. They initially increase and then decrease as the gradation index increases, with optimal fluidity observed at n = 0.4. Bleeding rate increases with a higher gradation index but decreases with a lower W/S. Comprehensive optimization determined the optimal mix proportion as gradation index n = 0.4 and W/S of 0.18. At this ratio: Yield stress = 144.25 Pa, Slump = 255 mm, Spread flow = 60.1 cm, Bleeding rate = 2.21%. This meets the pumping requirements (Slump > 180 mm, Bleeding rate < 3%). The research results provide important experimental value for the practical pipeline transportation of PGS and the reduction in pumping friction resistance. Full article

Offshore Carbon Capture, Utilization, and Storage (CCUS) is emerging as a critical strategy for achieving net-zero emissions, offering significant storage potential in depleted hydrocarbon reservoirs and deep saline aquifers while leveraging existing offshore infrastructure. This review summarizes recent advances in capture, transport, utilization, and storage technologies in the offshore industry. Case studies including Sleipner, Gorgon, and Northern Lights illustrate both the technical feasibility and the operational, economic, and regulatory challenges associated with large-scale deployment. While post-combustion capture and pipeline transport remain the most technologically mature approaches, significant uncertainties continue to exist regarding the logistics of marine transportation, reservoir integrity, and the robustness of monitoring frameworks. Policy and regulatory complexity, coupled with high capital costs and public acceptance issues, continue to constrain commercial viability. This review highlights that offshore CCUS holds significant promise but requires advances in monitoring technologies, cost reduction strategies, and harmonized international governance. Future research should focus on integrating CCUS with hydrogen production and renewable energy systems to accelerate large-scale deployment. Full article

This study investigates the impact of expanding and reducing deformation ratios on the performance of L360 straight-seam welded pipes formed by the JCO process for offshore oil and gas transportation. The Split Hopkinson Pressure Bar (SHPB) experimental technique was used to examine the stress/strain relationships, yield strength, and compressive strength of the pipe materials subjected to high strain rates. The results indicated that the impact performance of the pipes significantly improves with both expansion and reduction processes, demonstrating an enhancement effect of deformation. The impact of yield strength and compressive strength increases with higher expansion ratios, reaching maximum values of 1009 MPa and 847 MPa, respectively, at an expansion ratio of 1.2%. At a reduction ratio of 0.8%, the impact yield strength increased by 64% and the compressive strength by 14%. These findings not only provide theoretical support for optimizing the expansion and reduction processes and their associated equipment but also have direct and significant practical implications for enhancing the performance and safety of offshore oil and gas transmission pipelines, thereby contributing to the advancement of the field. Full article

This study generated an AI (Artificial Intelligence)-based prediction model for identifying high-risk groups of failures in urban district heating pipelines using pipeline attribute information and historical failure records. A total of 324,495 records from normally operating pipelines and 2293 failure cases were collected. Because the dataset exhibited severe imbalance, a KNN (K Nearest Neighbors)-based similarity selection was applied to reclassify the top 10% of normal data most similar to failure cases as high-risk. Input variables for model development included pipe diameter, purpose, insulation level, year of burial, and burial environment, supplemented with derived variables to enhance predictive capacity. The dataset was trained using XGBoost (eXtreme Gradient Boosting) v3.0.2, LightGBM (Light Gradient-Boosting Machine) v4.5.0, and an ensemble model (XGBoost + LightGBM), and the performance metrics were compared. The XGBoost model (K = 2) achieved the best results, with an F2-score of 0.921 and an AUC of 0.993. Variable importance analysis indicated that year of burial, insulation level, and purpose were the most influential features, highlighting pipeline aging and insulation condition as key determinants of high-risk classification. The proposed approach enables prioritization of failure risk management and identification of vulnerable sections using only attribute data, even in situations where sensor installation and infrared thermography are limited. Future research should consider distance functions suitable for mixed variables, sensitivity to unit length, and SHAP (Shapley Additive exPlanations)-based interpretability analysis to further generalize the model and enhance its field applicability. Full article

Supercritical CO₂ (sCO₂) pipeline transport is a critical link for the large-scale implementation of Carbon Capture, Utilization, and Storage (CCUS) technology, yet its safety is severely challenged by residual impurity gases (e.g., H₂O, O₂, SO₂, H₂S, and NO₂) from the capture process. This review systematically consolidates recent research advances, with the key findings being the following. Firstly, it reveals that the nonlinear synergistic effects among impurities are the primary cause of uncontrolled corrosion, whose destructive impact far exceeds the simple sum of individual effects. Secondly, it delineates the specific roles and critical thresholds of different impurities within the corrosion chain reaction, providing a theoretical basis for targeted control. Consequently, engineering management must enforce strict impurity concentration thresholds integrated with material upgrades and dynamic operational optimization. Future research should focus on developing multi-impurity reaction kinetic models, elucidating long-term corrosion product layer evolution, and establishing standardized experimental systems. This review provides crucial theoretical support for establishing impurity control standards and optimizing anti-corrosion strategies for the safe transport of CO₂ in supercritical CCUS pipelines. Full article

Application of low-emission hydrogen production methods in the decarbonization process remains a highly relevant topic, particularly in the context of sustainable hydrogen value chains. This study evaluates hydrogen applications beyond industry, focusing on its role as an energy carrier and applying multi-criteria decision analysis (MCDA) to assess economics, environmental impact, efficiency, and technological readiness. The analysis confirmed that hydrogen use for heating was the most competitive non-industrial application (ranking first in 66%), with favorable efficiency and costs. Power generation placed among the top two alternatives in 75% of cases. Transport end-use was less suitable due to compression requirements, raising emissions to 272–371 g CO₂/kg H₂ and levelizing the cost of hydrogen (LCOH) to 13–17 EUR/kg. When H₂ transport was included, new pipelines and compressed H₂ clearly outperformed other methods for short- and long-distances, adding only 3.2–3.9% to overall LCOH. Sensitivity analysis confirmed that electricity price variations had a stronger influence on LCOH than capital expenditures. Comparing electrolysis technologies yielded that, proton-exchange membrane and solid oxide reduced costs by 12–20% and CO₂ emissions by 15–25% compared to alkaline. The study highlights heating end-use and compressed hydrogen and pipeline transport, proving MCDA to be useful for selecting scalable pathways. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

As the transport of gaseous hydrogen and its use as a low carbon-footprint energy vector become increasingly likely scenarios, both the scientific literature and technical standards addressing the compatibility of pipeline steels with high-pressure hydrogen environments are rapidly expanding. This work presents a detailed review of the most relevant hydrogen embrittlement testing methodologies proposed in standards and the academic literature. The focus is placed on testing approaches that support design-oriented assessments, rather than simple alloy qualification for hydrogen service. Particular attention is given to tensile tests (conducted on smooth and notched specimens), as well as to J-integral and fatigue tests performed following the fracture mechanics’ approach. The influences of hydrogen partial pressure and deformation rate are critically examined, as these parameters are essential for ensuring meaningful comparisons across different studies. Full article

Pipelines are critical infrastructure for energy transportation, but long-term service under complex loading can cause local buckling failures. This study investigates the wrinkle behavior of API-X80 pipelines under combined internal pressure and bending using finite element analysis. The results show that increasing internal pressure significantly improves structural stability and delays wrinkle formation by suppressing cross-sectional ovalization. Wrinkle growth and protrusion height were quantified under various geometric and load conditions. Furthermore, a convex B-type sleeve repair method was modeled and optimized using response surface methodology and genetic algorithms. The optimized sleeve design effectively mitigates stress concentration around the defect area. This work provides a theoretical foundation for understanding wrinkle mechanisms and enhancing pipeline integrity under complex loads. Full article