External Corrosion Research Articles

Applicability evaluation technology for steel small diameter pipes containing external surface corrosion defects

With the continuous operation of oil and gas stations and petrochemical equipment, it has been found that steel small bore pipelines have different degrees of corrosion defects, which have led to pipeline cracking and medium leakage, and have increasingly attracted great attention of their oil and gas and chemical enterprises. In order to ensure the safe operation of the small diameter pipes with external surface corrosion defects, the influence law of the size of corrosion defects on the bearing capacity of the small diameter pipes were studied by finite element analysis and test simulation method, and the damage mechanism and failure form were analyzed, and the failure pressure prediction formula of the small diameter pipes with defects was fitted. It provides technical guidance for the applicability evaluation of corrosion-containing steel small diameter pipes in oil and gas chemical stations or installations.

Consistency analysis of burst pressure in corroded pipelines

Abstract Due to the influence of the working environment and transport medium, corrosion defects inevitably occur on the inner and outer walls of natural gas pipelines. Scholars often studied the influence of internal and external corrosion defects on pipe burst pressure based on finite element method and experiment. However, it is not clear whether internal corrosion defects and external corrosion defects are consistent in burst pressure of pipelines. For this reason, this paper establishes the internal and external corrosion defect model of the pipe. Then, the burst pressures of pipelines with internal corrosion defects are compared with that with external corrosion defects. Finally, a burst pressure equation is proposed for the pipelines with internal corrosion defects and external corrosion defects. It was found that the burst pressure of the natural gas pipeline with internal and external corrosion defects was consistent. It provides a new idea for the establishment of a unified model to predict the burst pressure of corroded pipelines.

Treatment of turbine blades in electric power stations by adding nano oxides to the matrix material (Al80-Ni20)

Abstract A base material of Al80-Ni20 was used and mixed with variable proportions of Nano chromium oxide (Cr2O3) at percentages of 2, 4, 6, 8 and 10%, as well as with Nano magnesium oxide (MgO) at percentages of 2, 4, 6, 8 and 10%. These mixtures were then applied using the thermal spraying method with a flame. This particular method is commonly used for repairing cracks and protecting turbine blades in electric power stations from external corrosion. However, it is important to note that this method may face challenges when exposed to high-temperature water vapour, salts and other working conditions experienced by turbine blades. Samples were prepared by thermally sintering the coating at 1000 °C for two hours. Various measurements were performed to assess the structural properties using a scanning electron microscope (SEM), as well as other physical tests such as porosity, hardness, adhesion strength and frictional wear. The SEM analysis revealed that the presence of 10% Cr2O3 resulted in a surface that was uniformly free from external defects, whereas the addition of MgO led to a less homogeneous surface. The physical data obtained indicated a preference for Cr2O3, as evidenced by the porosity results (4.5%) observed after thermal sintering at 10%, as well as the hardness (193 HV), adhesion strength (40 MPa) and wear (2.90 × 10−5 g cm−1) measurements. Moreover, the analyses of the properties of MgO under the same conditions included porosity (10%), hardness (155 HV), adhesion strength (35 MPa) and wear (5.50 × 10−5 g cm−1).

Corrosion prediction model for long-distance pipelines based on NLFE-NGO-ELM

Abstract To improve the accuracy of buried pipeline corrosion rate prediction, we developed a Northern Goshawk Optimization-based Extreme Learning Machine (NLFE-NGO-ELM) model enhanced by Non-linear Feature Expansion. Feature selection was used to expand the small dataset and reduce the dimensionality of factors affecting pipeline corrosion. The Northern Goshawk algorithm was used, and 106 sets of pipeline external corrosion detection data were simulated in MATLAB as a case study. The ELM optimizer was analyzed using Grey Wolf Optimization (GWO-ELM), Northern Goshawk Optimization (NGO-ELM), and BP optimization as comparison models. Using NLFE-NGO-ELM significantly improved the model's prediction accuracy and speed. The NGO-ELM model was applied to predict the corrosion rates of 21 pipelines, achieving an absolute error of only 2.40% and an R value of 0.97255, surpassing the predictive performance of NGO-BP and GWO-ELM models. These results indicate that the current model exhibits superior learning performance and stronger fitting capabilities.

Theoretical calculation methods of stable bearing capacity for thin-walled shells with corrosion and variable temperature

AbstractThin-wall shells (steel plates, steel cylindrical shells, steel spherical shells, etc.) are widely used in many engineering fields such as construction, machinery, chemical industry, navigation, and aviation because of their light weight and high strength. Their failure modes under static pressure or impact dynamic load are mostly buckling instability, and the failure is very sudden, often causing structural failure or even catastrophic accidents without obvious symptoms. In this framework, the significance of this paper is that it considers the influence of external environment corrosion on steel shells' bearing capacity using plate and shell classical stability theory, and investigates the stable bearing capacity of thin-wall steel shells in view of corrosion impact. By this approach, a theoretical calculating method for the time-varying stable bearing capacity of plate and shell thin-walled steel members under the simultaneous action of corrosion and temperature changes is obtained, providing a useful theory for complex engineering practices such as corrosion and temperature changes, including fire actions. Noted that for this method with no analytical solution found, its numerical solutions are given in the appendixes.

A coupled phase-field model for sulfate-induced concrete cracking

The performance of concrete will decrease when subjected to external sulfate corrosion, and numerical models are effective means to analyze the mechanism. Most models cannot efficiently consider the effect between cracks and ionic transport because crack initiation and propagation are ignored. In this paper, a coupled chemical-transport-mechanical phase-field model is developed, in which the phase-field model is applied for the first time to predicate the cracking of sulfate-eroded concrete. The chemical-transport model is established based on the law of conservation of mass and chemical kinetics. The phase-field model equivalents the discrete sharp crack surface into a regularized crack, making it convenient to couple with the chemical-transport model. The crack driving energy in the phase-field model is computed by the expansion strain, which can be obtained from the chemical-transport model. The coupling of crack propagation and ionic transport is achieved by a theoretical equation, which considers both the effects of cracking and porosity. Complex erosion cracks can be automatically tracked by solving the phase-field model. The simulation results of the multi-field coupling model proposed in this paper are in good agreement with the experimental data. More importantly, the spalling phenomenon observed in physical experiments is reproduced, which has not been reported by any other numerical models yet, and new insight into the spalling mechanism is provided.

External stress switching water corrosion behavior of SiC (C-10.2)

External stress switching water corrosion behavior of SiC (C-10.2)

Deep neural networks for external corrosion classification in industrial above-ground storage tanks

This paper uses transfer learning and fine-tuning approaches to explore EfficientNet smaller models for a multiclassification task of corrosion types in above-ground storage tanks. Data augmentation was used to increment the data an oil and gas company provided, reaching a dataset of around 5000 images. The images were stored in Google Drive and imported into Colab to train the models using TensorFlow and Keras. After the hyperparameters’ tuning a transfer learning model was selected and explored with fine-tuning. The EfficientNetB0 model delivered from fine-tuning accomplished 94 % performance. This work is the first attempt to deploy an artificial vision automatic tool for being implemented during tank inspection in the industrial sector. In further development, this model can be coupled with one based on object detection to remotely identify failures due to external corrosion issues, improving safety and reliability in oil and gas operations.

Effect of seawater salinity, pH, and temperature on external corrosion behavior and microhardness of offshore oil and gas pipeline: RSM modelling and optimization

This research aims to investigate the effects of seawater parameters like salinity, pH, and temperature on the external corrosion behaviour and microhardness of offshore oil and gas carbon steel pipes. The immersion tests were performed for 28 days following ASTM G-1 standards, simulating controlled artificial marine environments with varying pH levels, salinities, and temperatures. Besides, Field emission scanning electron microscopy (FESEM) analysis is performed to study the corrosion morphology. Additionally, a Vickers microhardness tester was used for microhardness analysis. The results revealed that an increase in salinity from 33.18 to 61.10 ppt can reduce the corrosion rate by 28%. In contrast, variations in seawater pH have a significant effect on corrosion rate, with a pH decrease from 8.50 to 7 causing a 42.54% increase in corrosion rate. However, the temperature of seawater was found to be the most prominent parameter, resulting in a 76.13% increase in corrosion rate and a 10.99% reduction in the microhardness of offshore pipelines. Moreover, the response surface methodology (RSM) modelling is used to determine the optimal seawater parameters for carbon steel pipes. Furthermore, the desirability factor for these parameters was 0.999, and the experimental validation displays a good agreement with predicted model values, with around 4.65% error for corrosion rate and 1.36% error for microhardness.

Pipeline Circumferential Cracking in Near-Neutral pH Environment Under the Influence of Residual Stress: Dormancy and Crack Initiation

The purpose of this study is to identify the integrity challenges encountered by buried pipeline steels, specifically to address Circumferential Near-Neutral pH Corrosion Fatigue (C-NNpH-CF). Damage to the pipeline’s protective coating and corrosion conditions increase the risk of service failures caused by C-NNpH-CF. (Note that this mechanism has previously been termed near-neutral pH stress corrosion cracking.) Unlike axial cracking, circumferential cracking is primarily influenced by residual stress from pipeline bending, geohazards, and girth welds. External corrosion pits often lead to dormant cracks, with growth ceasing around 1 mm depth due to reduced dissolution rates. Investigating the impact of bending residual stress (an appropriate source of axial residual stress) and cyclic loading (simulated pipeline pressure fluctuation), the study employs the digital image correlation (DIC) method for stress distribution analysis. Factors like applied loading, initial notch depth, and bending conditions influence crack initiation and recovery from the dormancy stage by affecting stress distribution, stress cells, and stress concentration. Cross-sectional and fractographic images reveal time/stress-dependent mechanisms governing crack initiation, including dissolution rate and hydrogen-enhanced corrosion fatigue. The study emphasizes the role of various residual stress types and their interactions with axial cyclic loading in determining the threshold conditions for crack initiation.

Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS: Using Bayesian neural networks for external pitting corrosion modelling

AbstractCorrosion poses a great risk to the integrity of oil and gas pipelines, leading to substantial investments in corrosion control and management. Several studies have been conducted on accurately estimating the maximum pitting depth in oil and gas pipelines using available field data. Some of the frequently employed machine learning techniques include artificial neural networks, random forests, fuzzy logic, Bayesian belief networks, and support vector machines. Despite the ability of machine learning methods to address a variety of problems, traditional machine learning methods have evident limitations, such as overfitting, which can diminish the model's generalization capabilities. Additionally, traditional machine learning models that provide point estimations are incapable of addressing uncertainties. In the current study, a Bayesian neural network is proposed to include uncertainty in estimating the corrosion defect of a pipeline exposed to external pitting corrosion. The results are then incorporated into a Bayesian belief network for evaluating the probability of failure and its corresponding consequences in terms of social impact, thus forming a comprehensive risk assessment framework. The results of the Bayesian neural network are validated using field data and achieved a testing accuracy of 90%. The framework of the study offers a powerful decision‐making tool for the integrity management of pipelines against external corrosion.

An Investigation of Corrosion Behaviors of Thermally Sprayed Aluminum (TSA) at Elevated Temperatures Under Thermal Insulations and Autoclave Immersion Conditions

ABSTRACT Thermally Sprayed Aluminum (TSA) protects against internal and external corrosion in many industrial applications. Even though TSA coating has been the subject of many studies, there is still a need to gain better insights into the degradation mechanisms of the TSA especially under immersion conditions and moisture-saturated thermal insulations. This study addresses the corrosion behavior of TSA in a CUI simulation setup (per ASTM G189-07) and autoclave immersion. The corrosion tests were conducted for three and four days under isothermal wet (IW) and cyclic wet (CW) conditions. Linear polarization resistance (LPR) scans were conducted during both (i.e., CUI simulation and autoclave immersion tests) to better understand the corrosion behaviors of TSA coating. Following corrosion testing, thorough microstructural examinations were conducted employing confocal laser microscopy, 3D topography, scanning electron microscopy (SEM), and Energy dispersive spectroscopy (EDS) to understand the microstructural and tribological changes resulting from corrosion testing. TSA coating under the insulation showed significant degradation via flashing moisture and active dissolution of iron at the insulation-metal interface. Unlike immersion conditions, the wear of TSA due to flashing moisture under thermal insulation created the crevices that caused the active corrosion of the steel substrate.

Corrosion Prediction in the Oil Industry Using Deep Learning Techniques

Corrosion presents a significant challenge in the oil industry, causing both immediate and long-term damage. Effective early prediction and monitoring of corrosion are crucial to mitigating economic losses and environmental impacts. However, traditional methods for predicting and detecting corrosion are often time-consuming and inefficient. This study leverages convolutional neural networks (CNNs) within a deep learning framework to develop two automated detection models for internal and external corrosion. These models can extract hierarchical features directly from raw pixel data, enhancing prediction accuracy and efficiency. Our dataset, provided by the Iraqi Oil Company, includes drone-captured images (162 photos: 91 depicting corrosion and 71 showing no signs of corrosion) and ultrasonic sensor readings (250 rows of oil pipeline thickness measurements). We assess the performance of our CNN models using metrics such as accuracy, precision, recall, and F-score, and we perform regression analysis to evaluate prediction errors. This research introduces two innovative systems: a 2D CNN for classifying the presence or absence of external corrosion, and a 1D CNN for assessing internal corrosion levels, identifying areas with the highest corrosion rates, and estimating the remaining operational lifespan based on these rates. Additionally, we develop a user-friendly interface for these systems. Comparative analysis demonstrates the superior efficiency of our proposed approach over traditional and alternative methods. Our findings advance the understanding of artificial intelligence applications in corrosion prediction, offering robust models to prevent unexpected corrosion failures. Future work will explore the integration of additional factors, such as humidity and temperature sensors, to further enhance the system's accuracy and reliability.

Low-Cycle Corrosion Fatigue Deformation Mechanism for an α+β Ti-6Al-4V-0.55Fe Alloy

Titanium alloys with high strength and good corrosion resistance have become one of the critical bearing structural materials in marine engineering. But in service, corrosion fatigue would occur under the synergetic action of cyclic external load and corrosion environment, threatening the safety of components. In this study, compared with low-cycle fatigue in laboratory air, the low-cycle corrosion fatigue deformation mechanism and fracture characteristic of the Ti-6Al-4V-0.55Fe alloy were investigated in 3.5% NaCl corrosion solution under selected stress amplitudes. The results showed that under low stress amplitude, corrosion fatigue was determined by fatigue damage and corrosion damage, causing a reduction in fatigue life. The local stress concentration caused by corrosion pits and dislocations pile-up accelerated the initiation of fatigue cracks, and other corrosion behavior including crevice corrosion promoted fatigue crack propagation; the corrosion solution increased the surface damage. While under high stress amplitude, due to the short contact time between the sample and solution and higher applied stress, the fatigue life is determined by fatigue damage caused by multiple slips.

Oil and Gas Pipeline Integrity Monitoring and Alert System

The oil and gas industry face significant risks of explosions, often due to factors like gas and oil leakage as well as external corrosion. These substances are highly flammable, and even minor leaks can create volatile environments where the smallest spark can lead to catastrophic explosions. While internal corrosion in pipelines receives attention, external corrosion is often overlooked. Over time, pipeline coatings deteriorate, leaving them susceptible to external corrosion, which can result in leaks, endangering workers and the environment. Detecting and addressing corrosion promptly is crucial for preventing accidents and environmental harm. To address these challenges, a pipeline monitoring system has been developed using a Raspberry Pi Model 3B+ and advanced sensors. This system continuously monitors temperature, pressure, and external corrosion along pipeline routes. It utilizes a K-type thermocouple and a 1.2MPa pressure transducer for real-time data collection. Additionally, a laptop camera integrated with YOLOv8 enables visual inspection for detecting corrosion. The Raspberry Pi processes sensor data and images, triggering alerts for anomalies like temperature or pressure drops indicating leaks, or corrosion spots identified by YOLOv8. The system interfaces with ThingSpeak for remote data visualization and analysis, empowering operators to make informed decisions and take prompt corrective actions.

Perancangan Proteksi Katodik pada Tangki Olein Kapasitas 450 KL dengan Metode SACP

Cathodic protection is needed as corrosion mitigation in oil tanks on land to minimize internal and external corrosion. Corrosion problems that often arise in tanks are corrosion on the shell, roof and bottom. In this research, a corrosion protection system was designed for an olein storage tank with a capacity of 450 kL for one of the palm oil ship industries located in Kalimantan. The corrosion protection used is SACP (Sacrificial Anode Cathodic Protection) where active anodes are used to protect metal surfaces which are more passive against corrosion attacks. To provide good corrosion protection, sufficient current, number of anodes and correct placement are required. From the calculation results it was found that for the shell, the current required is 0.0102446 A with a total of 46 anodes and the placement of each anode is 5.36 m. For the same bottom and roof, each requires a current of 0.0103869 A with 7 anodes and a distance within a radius of 6.74 m.

Prediction of external corrosion rate for buried oil and gas pipelines: A novel deep learning method with DNN and attention mechanism

Buried pipelines is easily subjected to external corrosion affected by complex underground environment, such as soil resistivity, pH, dissolved ion concentration, water content and coatings condition. Therefore, external corrosion rate prediction is essential to ensure pipelines intrinsic safety. Generally numerous researches focused on mathematical models and degradation analysis are limited to considering comprehensive corrosion factors. In this work, a novel proposed deep learning method involves a deep neural network (DNN) and attention mechanism which has good robust generalization ability. DNN was used to predict external corrosion rate of buried pipelines with extracting corrosion characteristics data, and attention mechanism is carried on weighting operations of corrosion factors furtherly. The results show that main controlling factors are pipeline age and soil bulk density, sulfate ion concentration and redox potential have less effect by analyzing external corrosion factors dataset, and data reduction and standardization of DNN-attention mechanism model are carried out. The external corrosion rate value predicted by the DNN-attention mechanism model is the closest prediction with the real truth, which the evaluation index root mean square error (RMSE) is 0.0064, and prediction accuracy is 91.91 % compared with convolutional neural network (CNN), CNN-residual, DNN. It proves that it is reasonable to predict pipeline external corrosion rate using DNN-attention mechanism based on publicly pipeline corrosion dataset.

Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors

Pitting corrosion is a primary type of external corrosion that poses a critical challenge in the oil and gas industry, potentially leading to severe environmental, health, and economic consequences if left unaddressed. Researchers have attempted to comprehend and forecast it through diverse methods, including machine learning and artificial intelligence. However, achieving a precise estimation of pitting growth while considering the influence of various environmental and operational factors remains a concern. This study aims to develop a model by employing tree-based machine learning algorithms, including Decision Tree, Random Forest, LightGBM, CatBoost, and XGBoost, to enhance the prediction accuracy of the maximum depth of pitting corrosion in buried transmission pipelines while also facilitating interpretability. The Random Forest model is found to be the best-performing model with a mean absolute error (MAE) of 0.588. Beyond prediction accuracy, the present analysis contributes practical insights for industry application. First, the model identifies pipes at high risk of severe pitting corrosion under challenging environmental conditions, enabling targeted preventive measures. Secondly, the model's interpretation, utilising game-theoretic Shapely values and permutation importance, informs strategic decisions on the burial of new pipes, ultimately extending their operational lifespan. These applications emphasise the potential of our findings to significantly improve the safety, reliability, and risk mitigation strategies in oil and gas pipeline operations.

A mechanistic deterioration point assignment model for water pipe condition assessment

ABSTRACT A pipe condition assessment model is required to implement effective and economical planned maintenance of the water distribution system. The application of such a model requires sufficient accuracy, which, however, is limited by the complexity of the pipe deterioration process and data storage capacity of the water utility. The majority of previous studies have focused on the improvement of assessment algorithms for data mining. In this study, a mechanistic deterioration point assignment (MDPA) model is developed to make advancements in the modes of data input and result output to enhance the model's accuracy and application scope for cast iron and steel pipes. In this MDPA model, (1) indicators/sub-indicators on external corrosion, external load, internal corrosion, and internal load are constructed and can be obtained by data estimation or techniques and (2) assessment results include both pipe overall condition and detailed conditions on corrosion and load, offering evidence for primary maintenance measures. The weights of the indicators/sub-indicators are estimated using the Bayesian statistics theory. The modelling results of pipe samples demonstrate that this MDPA model is an effective tool for pipe condition assessment.

External Corrosion Analysis of Gas Pipeline Based on Gray Prediction Models

External Corrosion Analysis of Gas Pipeline Based on Gray Prediction Models