Search Results (157)

Hydrophobic and superhydrophobic surfaces have emerged as key solutions for fluid transport, biofouling prevention, and energy efficiency, with market forecasts projecting a compound annual growth rate (CAGR) of over 15% through 2030 due to their broad range of applications. This review critically examines the principles of natural hydrophobicity, as exemplified by lotus leaves and shark skin, and their translation into engineered surfaces via micro/nanofabrication techniques, such as laser patterning, etching, and self-assembly. Recent advances in hybrid nanomaterials have demonstrated WCAs in the range of 140–160°, along with enhanced mechanical strength and chemical stability, enabling applications in self-cleaning, anti-corrosion, and oil–water separation technologies. Superhydrophobic coatings are particularly important for reducing ice adhesion by more than 80%, while drag reduction in pipelines can reach up to 30%, contributing to energy savings. Despite these advances, challenges remain in achieving long-term stability under harsh environmental conditions, minimizing environmental impact, and developing cost-effective, scalable fabrication techniques. Future directions focus on environmentally friendly, multifunctional nanocomposites with switchable wettability, including pH- and light-responsive coatings capable of reversibly transitioning between superhydrophilic (<5°) and superhydrophobic (>150°) states, paving the way for sustainable and adaptable surface technologies. Full article

With the increase in operation time of DC traction systems due to the environment of tunnel and stress rupture, the insulation between the rail and ground inevitably decreases, causing increased stray current leakage. In view of this, we present an analytical and electrochemical study of stray current behavior and its corrosion impact arising from local rail-to-ground insulation damage in DC urban rail systems. A two-layer rail–earth continuous model of stray current distribution is developed (unilateral and bilateral supply cases) using Kirchhoff network formulations with insulation damage boundary conditions. Numerical simulations quantify the effects of damage location and grounding resistance on rail potential shifts, abrupt changes in rail and stray currents, and total leakage. To assess electrochemical consequences for nearby buried pipelines, the electrical model is proposed in this work with an impedance-informed corrosion model and Monte Carlo sampling of operational and electrical uncertainties to estimate dynamic corrosion rates and pitting evolution. The results show that single–point insulation faults shift the rail zero potential toward the fault, leading to instantaneous jumps in leakage and rail currents whose magnitude grows as damaged-point resistance decreases, markedly increasing pipeline corrosion risk. The integrated electrical-electrochemical framework provides a tool for detection, risk assessment, and mitigation planning for stray current-induced pipeline corrosion. 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

With the global advancement of carbon peaking and carbon neutrality goals, the importance of carbon capture, utilization, and storage (CCUS) technologies has become increasingly prominent. As a critical component of CCUS systems, gaseous CO₂ pipeline transportation has emerged as a research hotspot due to its efficiency and cost effectiveness. However, there are invariably corrosion problems when it comes to gaseous CO₂ pipeline transportation. These issues pose a significant threat to both the safety and economic viability of pipeline operations. Therefore, it is of importance to investigate gaseous CO₂ corrosion during pipeline transportation. In this work, based on recent domestic and international research achievements, research progress in the field of gaseous CO₂ corrosion during pipeline transportation is systematically reviewed. First, the corrosion mechanisms and corrosion characteristics during gaseous CO₂ pipeline transportation are studied, and the synergistic mechanisms by which key parameters such as impurities, temperature, pressure, flow velocity, and water content jointly influence pipeline wall corrosion behavior are elucidated. Then, corrosion products in CO₂ transportation pipelines are analyzed, and protective measures applicable to gaseous CO₂ pipeline systems are synthesized. Finally, future research goals are proposed to promote research on gaseous CO₂ corrosion during pipeline transportation: the impact of interactions among multiple impurities on corrosion behavior should be clarified; the inhibitory effects of the dynamic evolution of product films on mass transfer processes should be considered in corrosion rate calculation models; and more economical and efficient anti-corrosion technologies should be developed to meet diverse operational requirements. This work can provide guidance for the corrosion protection of gaseous CO₂ pipeline transportation. Full article

A 700 m pilot-scale cast iron pipeline reactor was operated for 120 days to investigate corrosion-stage evolution under reclaimed-water conveyance conditions. Sampling points were arranged at 50, 250, 450, and 650 m, and water-quality monitoring, coupon weight-loss tests, scanning electron microscopy (SEM), and high-throughput 16S rRNA sequencing were combined to characterize corrosion-rate variation, corrosion-product morphology, and microbial community succession. During transport, NH₄⁺ generally decreased while NO₃⁻ increased, indicating nitrification-related nitrogen transformation under aerobic conditions; meanwhile, PO₄³⁻ declined and DOC fluctuated, reflecting coupled physicochemical and biological processes. SEM observations showed a transition from loose porous deposits to relatively compact layered corrosion products, followed by local deterioration and renewed porous structures in the later period. The corrosion rate followed an increase–decrease–re-increase pattern rather than a monotonic trend. Therefore, corrosion acceleration (CA = dc/dt) was introduced as an auxiliary diagnostic indicator to identify whether corrosion activity was increasing, decreasing, or temporarily stabilizing. Microbial community analysis showed stage-associated variation in biofilm and nitrogen-transformation-related taxa, supporting the interpretation that corrosion evolution was jointly affected by water-quality change, corrosion-product development, and microbial succession. Overall, the combined interpretation of corrosion rate, CA, water quality, SEM morphology, and microbial succession provides a more informative basis for diagnosing corrosion-stage transitions in reclaimed-water cast iron pipelines. From a sustainability perspective, this diagnostic framework can support long-term operation, maintenance planning, and risk monitoring of urban reclaimed-water distribution infrastructure, thereby improving pipeline durability, reducing leakage and maintenance risks, and enhancing the reliability of reclaimed-water reuse systems. Full article

Corrosion of pipelines by flue gas desulfurization (FGD) wastewater compromises the normal operation of the desulfurization tower, and corrosion under high-chloride conditions in particular severely damages the tower’s internal structure. To further elucidate the corrosion mechanism at elevated Cl⁻ concentrations, the corrosion behavior of 20 steel exposed to high-chloride FGD wastewater at different Cl⁻ concentrations was investigated through weight-loss measurements, electrochemical tests, immersion corrosion experiments, composition analysis, and microscopic morphology characterization. The results revealed that higher Cl⁻ concentrations corresponded to lower corrosion rates: the corrosion rate reached 0.1964 mm/y in the absence of Cl⁻, but decreased to 0.1537 mm/y at a Cl⁻ concentration of 100,000 mg/L. XPS analysis showed that as the Cl⁻ concentration increased, the corrosion film gradually transformed from porous FeOOH into dense Fe₃O₄. Localized pitting analysis indicated a positive correlation between Cl⁻ concentration and pitting susceptibility. At Cl⁻ concentrations of 0 and 100,000 mg/L, the corrosion current density decreased from 32.44 μA/cm² to 6.43 μA/cm² after 72 h, decreasing by a factor of approximately 5.05. This behavior is attributed to the fact that Cl⁻ increases solution conductivity in high-chloride environments, thereby promoting the formation rate of the corrosion film. Additionally, high Cl⁻ levels reduce dissolved oxygen in the solution, causing the corrosion film to progressively react and form denser Fe₃O₄. Nevertheless, the high penetrability of Cl⁻ continues to aggravate pitting corrosion of 20 steel. Full article

Accurate corrosion-rate prediction for buried pipelines is fundamental to sustainable integrity management, yet industrial corrosion datasets are typically small and heterogeneous, making reliable model training challenging. This study proposes CARE-Boost (Context-Aware Restrained-Ensemble Boosting), a compact method designed for exactly this setting. The algorithm fuses three complementary components: a practical-variable gradient-boosting branch trained on directly measurable pipeline predictors; a spatial-neighborhood context branch that encodes short-range continuity from adjacent stake-point predictors; and a restrained regime-focused augmentation scheme stabilized by fixed convex blending. The engineering dataset was collected from a natural-gas pipeline in Central Asia and organized as a one-dimensional spatial sequence. Under repeated $5\times 2$ cross-validation, CARE-Boost achieves RMSE $=0.0577\mathrm{mm}/\mathrm{year}$, MAE $=0.0314\mathrm{mm}/\mathrm{year}$, and $R^2=0.472$, outperforming XGBoost ($0.0599$, $0.0320$, $0.432$) and LightGBM ($0.0618$, $0.0333$, $0.385$); the improvement over XGBoost is statistically significant ($p=0.0068$, splitwise Wilcoxon). Split-conformal intervals achieve $95.0\%$ empirical coverage at the nominal $90\%$ level. SHAP attribution identifies soil aggressiveness, pH, water content, and bicarbonate as the dominant corrosion drivers, and the mean fit–predict cycle completes in $1.80$ s, supporting deployment in routine integrity workflows. These findings position CARE-Boost as a practically viable uncertainty-aware corrosion predictor for sustainable integrity management under small-sample conditions, with its primary evidence lying in improved point prediction, calibrated uncertainty, and interpretable spatially informed inference. Full article

The article deals with the study of stress corrosion cracking (SCC) of X70 steel using corrosion-mechanical testing that simulates the operating conditions of underground pipelines. The tests were carried out under cyclic four-point bending at stresses close to the yield point, in electrolytes with various hydrogen charging capacities. The following model environments were used: NS4 solution and citrate buffer (pH 5.5). Hydrogen charging was controlled by the addition of thiourea and by varying the potential. It was shown that microcracks initiated at corrosion defects (pits) and then emerged at the surface to form narrow cracks. The incubation period depends on the environment: under corrosive conditions it is approximately two times shorter than in the air. The size and nature of stress concentrators play a significant role: natural pits (~hundreds of μm) lead to crack formation within 24–28 days, whereas artificial holes (0.6–1 mm) lead to crack formation within 5–7 days. The effect of hydrogen was established: the acceleration is insignificant under moderate hydrogen charging, whereas the incubation period decreases sharply at high hydrogen charging. Critical hydrogen concentrations where its effect becomes significant were determined. Methods for inhibiting stress corrosion cracking by means of organosilicon films (vinyl- and aminosilanes, as well as their mixtures with inhibitors—benzotriazole and amines) were considered. The most effective composition is vinylsilane + benzotriazole: the time to crack initiation increases from 5 to 36 days, and the crack growth rate decreases. Full article

Metal pipelines may have various defects due to long-term service, corrosion, external strikes, etc. Traditional closed-circuit television (CCTV) inspection techniques are capable of detecting these defects. However, substantial human resources are required and the detection results are subjected to human subjectivity. Thus, this study develops a deep learning-based intelligent defect detection model for metal pipeline images. CNNs (convolutional neural networks) are utilized to automatically extract defects, which may mitigate the interference of subjective factors and enhance the recognition capability of the defects in pipelines. The proposed model builds upon the original YOLOv8n model by incorporating the SCSA (Spatial and Channel Synergistic Attention) mechanism, LskBlock, and SlideLoss function, respectively. These enhancements improve the ability to detect small targets, increase recognition accuracy, and facilitate global optimization, respectively. The developed YOLO-LSS (YOLOv8n-LskBlock-SlideLoss-SCSA) model is compared with other deep learning models characterized by the following metrics: mAP50, mAP50:95, precision, recall rate, and F1-score, respectively. It is found that mAP50 achieves 79.05% (+2.86%), mAP50:95 53.7% (+7.19%), precision 81.6% (+5.30%), recall rate 75.5% (+2.90%), and F1-score 78.4 (+4.01), indicating that the proposed model effectively enhances the capability of detecting internal defects in pipelines. Full article

Corrosion and scaling critically threaten the safety and efficiency of oil–gas gathering stations. Through field inspections, water chemistry analysis, scale characterization, and corrosion simulation in Yanchang oilfield, this study identifies severe localized damage in key components—such as valves, bends, and injection pipelines—with service lives of only 1–2 years. Analysis of over 200 scale samples revealed that CaCO₃ (42 wt%) and CaSO₄ (23 wt%) were the predominant scale types. High salinity >56,000 mg/L, Cl⁻ >31,000 mg/L, and Ca²⁺ promote under-deposit pitting, galvanic corrosion (e.g., Cu–steel couples), and erosion-corrosion at high-velocity zones. Simulations based on OLI Analyzer Studio (a professional thermodynamic simulation software for electrolyte solution and high-salinity brine systems) reveal that the carbon steel (the primary material for the process pipelines and water injection pipelines in the studied oil–gas gathering and transportation stations) has a corrosion rate rising from 0.078 mm/year at 25 °C to 1.94 mm/year at 90 °C. Despite common use of coatings and cathodic protection, these measures often fail to address site-specific failure mechanisms. The study advocates a tailored mitigation strategy combining material compatibility, real-time water monitoring, optimized filtration, and component-level design. This integrated approach enhances asset reliability and operational safety in onshore oilfields. Full article

In response to corrosion challenges encountered during the gathering and transportation of wet natural gas, this study systematically investigates the corrosion behavior of L245NCS steel in environments containing O₂, H₂S, CO₂ and simulated oilfield-produced water. The research employs a combined approach involving high-pressure autoclave experiments and transparent flow loop simulations. Autoclave tests reproduce gas phase, liquid phase, and gas–liquid interface conditions under a controlled O₂-H₂S-CO₂ mixture, while a visual flow loop equipped with elbows and undulating sections is used to examine liquid accumulation behavior and flow characteristics under dynamic, real-world operating conditions. Results indicate that corrosion is most severe at the gas–liquid interface. H₂S is identified as the primary corrosive agent, exerting a stronger influence than CO₂ or O₂. Liquid accumulation is the main factor leading to non-uniform corrosion distribution, and its formation is influenced by water content, pressure, temperature difference, and pipeline shutdown and restart operations. Critical areas such as low-lying sections, downhill bottoms, and the beginning of uphill sections exhibit localized corrosion rates up to 61.4% higher than areas without liquid accumulation. This integrated methodology bridges mechanistic understanding with engineering practice, providing a basis for corrosion risk assessment, optimal monitoring point placement, and integrity management of wet gas pipelines. Full article

Due to long-term burial underground, oil and gas pipelines are susceptible to external surface corrosion influenced by time and soil conditions, which can lead to leakage and burst failures. Pipeline failure not only results in significant economic losses but also has catastrophic impacts on human safety and the environment. Therefore, modeling and analyzing the corrosion failure of these pipelines is of critical practical importance to ensure their safe operation during service. Addressing the insufficient research on correlation effects in current reliability evaluations of corroded pipelines, this paper proposes a calculation method for the failure probability of corroded oil and gas pipelines that considers the influence of two-layer correlations. Taking a specific segment of the Shaanxi–Beijing pipeline as a case study, the Monte Carlo sampling algorithm is employed to calculate the impact of two-layer correlations and the quantity of defect on the pipeline’s failure probability. Furthermore, a sensitivity analysis of the correlation coefficients is conducted. The results indicate that the influence of defect correlation on pipeline failure probability is significantly more pronounced than that of random variable correlation. The probabilities of pinhole leakage and burst failure decrease as the correlation coefficient between defects increases, while they increase with the number of defects. Random variable correlation exhibits no impact on pinhole leakage probability; however, the burst failure probability decreases with an increasing correlation coefficient between wall thickness and pipe diameter, but increases as the correlation between initial defect length and depth grows. Furthermore, the correlation coefficient between axial and radial defect growth rates exerts a bidirectional effect on burst failure probability: during the first 25 years of the prediction period, the failure probability increases with the correlation coefficient, whereas it subsequently decreases after approximately 25 years. These findings are applicable to the reliability evaluation of oil and gas pipelines containing multiple corrosion defects, providing valuable technical references for ensuring safe operation and the steady supply of energy resources. Full article

Offshore pipeline systems associated with floating platforms operate under complex environmental and operational conditions that significantly influence their structural integrity and inspection requirements. Limited accessibility, harsh marine environments, and time-dependent degradation mechanisms require inspection planning to be supported by structured decision-making frameworks capable of explicitly accounting for both degradation processes and failure consequences. In this study, a Risk-Based Inspection (RBI)-driven integrity assessment is applied to three carbon steel pipeline systems associated with a SPAR offshore platform. The analysis integrates system description, identification of dominant damage mechanisms, and RBI quantification to evaluate probability of failure and consequence-related risk under offshore operating conditions. Internal corrosion is identified as the dominant long-term degradation mechanism for all analyzed pipelines, while external corrosion governs short-term inspection interval definition due to its higher growth rate and sensitivity to insulation characteristics and environmental exposure. Although all pipelines are classified within the same overall qualitative risk category, significant differences in failure probability, risk intensity, and consequence-driven risk behavior are observed, reflecting variations in system configuration, insulation systems, length, and functional role within the offshore production infrastructure. To enable meaningful comparison between pipeline systems of significantly different total lengths, normalized risk indicators per unit length are introduced. These indicators provide additional insight into local risk intensity and spatial risk distribution that are not evident from absolute risk values alone. The results highlight the importance of treating risk as a dynamic quantity rather than a static classification and demonstrate that RBI-based assessment supported by normalized risk metrics can enhance inspection prioritization and maintenance decision-making for SPAR-associated offshore pipeline systems. Full article

The use of reclaimed water is a crucial strategy for water conservation. However, the quality of reclaimed water may induce corrosion in pipelines. Although UV (Ultraviolet) irradiation is a highly effective physical disinfection method that requires no chemical additives, it must be used in conjunction with NaClO (Sodium hypochlorite) disinfection because UV alone cannot provide continuous control of bacterial growth. This study monitored the concentrations of Cl⁻ and SO₄²⁻ in water samples collected from an annular biofilm coupon reactor, as well as the corrosion rate of cast iron coupons, to explore the corrosion characteristics of reclaimed water pipelines under different disinfection modes. The results showed that, when using NaClO as the sole disinfectant, the corrosion rate of the pipeline was the lowest at a NaClO dosage of 7 mg/L (corrosion rate: 0.85 mm/a at 72 h). For the UV-NaClO-combined disinfection, the optimal parameters were a UV dose of 120 mJ/cm² and a NaClO dosage of 5 mg/L, with a minimum corrosion rate of 0.62 mm/a at 72 h. The scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses revealed that a protective CaCO₃ layer forms on the pipe surface in the early corrosion stage, which effectively protects the metal pipeline. This study innovatively clarifies the synergistic effect of UV and NaClO on pipeline corrosion and identifies the optimal disinfection parameters, filling the research gap in the correlation between combined disinfection and cast iron pipe corrosion in reclaimed water systems. Full article

The corrosion behavior of pipeline steels in marine environments is strongly affected by hydrodynamic conditions and microbial activity, yet their coupled influence remains insufficiently understood. In this study, the corrosion behavior of X70 pipeline steel was systematically investigated in flowing artificial seawater over a velocity range of 0–1.5 m/s, under both sterile conditions and in the presence of Pseudomonas aeruginosa. Corrosion weight loss measurements, electrochemical techniques, and surface characterization were employed to evaluate flow-dependent corrosion evolution. The results show that flow velocity plays a dominant role in regulating corrosion behavior. Under sterile conditions, increasing flow velocity enhances mass transfer and surface renewal, leading to progressively increased corrosion severity. In the presence of P. aeruginosa, corrosion behavior exhibits a non-monotonic dependence on flow velocity. Lower flow velocities are associated with reduced corrosion rates and relatively uniform surface degradation, whereas moderate flow velocities promote localized corrosion and increased pitting severity. At higher flow velocities, strong hydrodynamic effects suppress the retention of corrosion products and microbe-associated surface layers, resulting in corrosion behavior primarily controlled by fluid flow. Overall, the results indicate that microbial presence modifies the flow–corrosion relationship of X70 steel by altering interfacial conditions under low-to-moderate flow regimes. Full article