Corrosion Prediction Model Research Articles

Spatial distribution of corrosion products from a bridge pier

AbstractThis paper studies the spatial distribution of corrosion by-products by a bridge pier within a conductive medium. An electrochemical impedance spectroscopy (EIS) technique was used to investigate an uncoated metallic bridge pier submerged in static distilled water. An equivalent circuit model, derived from EIS results, served as the foundation for the study. Further, the role of diffusion was analysed, considering its significance in characterising the transfer of particles from the pier into the surrounding water. This exploration revealed the complex interaction between the diffusion processes of various corrosion by-products as a function of distance. In addition, by evaluating the spatial distribution of iron (II) corrosion by-products and modelling nanoparticle diffusion, the research examined the impact of diffusion and concentration on corrosion particle transmission. The findings, analysed via Nyquist and Bode plots, demonstrate significant differences between theoretical and empirical diffusion coefficients. Results indicated that under natural corrosion conditions, the primary product of the corrosion reaction, iron (II), disperses into the medium when oxidation occurs. The elevated resistivity due to the presence of iron (II) underscores the diffusion effect, leading to corrosion product precipitation and reaching saturation level. Additionally, the results demonstrated ideal values for the diffusion coefficient, which are crucial for advanced corrosion modelling. The results emphasised the need for empirical data to improve corrosion prediction models and informed maintenance strategies for submerged structures.

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.

Prediction model for the bond behaviour of low-corrosion reinforced concrete considering corrosion time variability

Corrosion greatly affects the bond behaviour of reinforced concrete. In the case of reinforcing low-corrosion reinforced concrete, the bond strength of steel bars and concrete is at an enhanced stage. Accurate prediction of bond behaviour at this stage is crucial for the rational reinforcement. This study designed ten groups, totalling 30 pull-out specimens, were accelerated corroded with various current densities and electrification times. Through 3D scanning and image analysis technology, we observed that increasing the current density yielded less homogeneous corrosion, creating pit-like rust marks. Meanwhile, we developed a model for predicting the corrosion level. The model is applicable to the corrosion level design of accelerated corrosion. Pull-out tests showed that as corrosion increased, the specimen failure mode changed from pull-out to splitting and back to pull-out, with the bond strength first increasing and then decreasing. A bond-slip prediction model for corroded reinforced concrete was also established, considering the effects of corrosion on bond strength in different stages, especially the enhancement stage. The model performs well in predicting the bond behaviour of low-corrosion reinforced concrete and can be used in conjunction with corrosion prediction models and provides guidance for reinforcement.

Machine learning-based corrosion rate prediction of steel embedded in soil

Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were embedded in soil samples collected from different regions of the Wisconsin state. Variables including exposure time, moisture content, pH, electrical resistivity, chloride, sulfate content, and mean total organic carbon were measured through laboratory tests and were used as input variables for the model. The current density of steel was measured through polarization technique, and was employed as the output of the model. Of the various ML algorithms, the random forest (RF) model demonstrates the highest predictability (with an RMSE value of 0.01095 A/m2 and an R2 value of 0.987). In light of the feature selection method, the electrical resistivity is identified as the most significant feature. The combination of three features (resistivity, exposure time, and mean total organic carbon) is the optimal scenario for predicting the corrosion current density of soil-buried steel.

Data-driven atmospheric corrosion prediction model for alloys based on a two-stage machine learning approach

Alloy material corrosion is a comprehensive problem affected by multi-dimensional factors, including the time, environment, and material element. Its corrosion prediction has been facing the challenges of complex mechanism and small sample size. However, parametric methods are limited in terms of modeling accuracy and generalization performance for high-dimensional feature problems. Non-parametric machine learning algorithms, while having excellent modeling capabilities, need to be optimized in conjunction with the structure of the corrosion problem. Therefore, we develop a two-stage hybrid intelligent machine learning model for improving alloy corrosion prediction accuracy. The model takes the time, environment, and material information of the alloy corrosion process as inputs and the corrosion rate as output. The first stage of the model utilizes tree ensemble learning (TEL) models for initial prediction modeling and feature importance knowledge mining. In the second stage, the neural network (NN) is used to enhance the fusion of the multi-model prediction outputs, and it is optimized using feature importance modification (FIM). The proposed TEL-FIMNN model shows considerable performance improvement over the TEL and ANN models on real low-alloy steel corrosion dataset. Compared to other advanced multi-model fusion methods such as linear regression (LR), support vector regression (SVR), random forest (RF), and Bayesian model averaging (BMA), the proposed model also shows superior results. The TEL-FIMNN model provide a new structure from the perspectives of knowledge mining and fusion enhancement, which has good potential for multi-dimensional corrosion prediction.

Pipeline corrosion prediction and uncertainty analysis with an ensemble Bayesian neural network approach

This study developed an ensemble Bayesian Neural Network (BNN) model for pipeline corrosion prediction incorporating uncertainty analysis. The performance of the proposed BNN model was compared with different empirical and data-driven models. Shapley Additive Explanation (SHAP) values were used to quantify the importance of input factors. Results indicate that the ensemble BNN model demonstrates superior prediction accuracy, achieving the highest R2 value of 0.949, with the lowest Mean Absolute Error (MAE) of 0.33 mm and Root Mean Square Error (RMSE) of 0.449 mm on corrosion depth. It outperforms other models in predicting maximum corrosion depth and uniquely captures uncertainty information, which deterministic models cannot provide. Key factors influencing corrosion include chloride content, pH, pipe-to-soil potential, and pipeline age. Specifically, conditions such as a pipe-to-soil potential exceeding − 0.85 V, a pipeline age over 20 years, and pH values below 7 are found to significantly accelerate corrosion. The model also illustrates the effects of different influencing factors on corrosion growth uncertainty. Higher chloride content, lower pH values, and higher pipe-to-soil potentials may expand the uncertainty bounds. The proposed model presents the dynamic changes in prediction uncertainty over time, which is usually ignored in previous models. It can improve the precision and reliability of long-term predictions concerning pipeline corrosion depth. This knowledge of corrosion uncertainty is crucial for making informed decisions about pipeline inspection and maintenance strategies.

Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments

A series of advanced equipment exposed to sub-/supercritical water environments at high temperatures, high pressures, and extreme water chemistry with high salt and dissolved oxygen content faces serious corrosion problems. Obtaining on-site corrosion data for typical materials in harsh environments is crucial for operating and maintaining related equipment and optimizing various corrosion prediction models. First, this article introduces the advantages and disadvantages, usage scenarios, and future development potential of several in situ monitoring technologies, including ultrasonic thickness measurement, the infrared thermography method, microwave imaging, eddy current detection, and acoustic emission. Considering the importance of electrochemical corrosion data in revealing microscale and nanoscale corrosion mechanisms, in situ testing techniques such as electrical resistance probes, electrochemical corrosion potential, electrochemical impedance spectroscopy, and electrochemical noise that can be applied to sub-/supercritical water systems were systematically discussed. The testing platform and typical data obtained were discussed with thick and heavy colors to establish a mechanical prediction model for corrosion behavior. It is of great significance to promote the development of corrosion monitoring techniques, such as breaking through testing temperature limitations and broadening the industrial application scenarios and maturity.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Technology Focus: Production and Facilities (December 2023)

It seems that it has been a very productive period for production and facilities. I reviewed a significant number of abstracts on such an interesting variety of topics—new technology developments down- and midstream, improvements in corrosion detection and prediction, new systems for emulsion monitoring, and even downhole drones, as well as several optimization works using artificial intelligence and machine learning, for quality and control. In the materials-science aspect, composite and nonmetallic materials continue to be significantly researched, as well as additive manufacturing for fast replacement of parts. I also reviewed abstracts on new materials development for downstream applications, such as dielectric sealants and dissolvable rubbers. Some fundamental research on emulsion stability was also present, as well as new emulsion monitoring systems based on microwave, acoustic, and capacitance measurements. In the area of equipment reliability, a common trend is using digital methods on historical data for prediction of equipment failure or corrosion vulnerability. There were also inspection developments such as the use of chemical tracers to identify the location of equipment failure downstream. A couple of works in this area are suggested in paper SPE 205687, which provides an example of deep learning used for intelligent identification of equipment status, and paper SPE 205056, which is a more-fundamental work on corrosion-prediction models. My attention was particularly drawn to environmentally oriented submissions this year. Energy integration continues to be a topic of interest, with geothermal and even green hydrogen being considered for energy generation in production facilities, especially in remote locations. An example of an interesting energy integration viability study can be found in paper SPE 204551. Another relevant aspect addressed was waste management, with submissions regarding abandoned wells management and waste disposal. A very thorough review on how to deal with produced solids can be found in paper SPE 210003, which clearly explains all stages of handling, from separation to disposal, with case studies as examples. An interesting work, paper SPE 213000, combines two issues: the disposal of wind-turbine waste and the use of abandoned wells. Because of the toxicity of wind-turbine blades, abandoned wells and cement coprocessing are considered as disposal options. It is quite interesting how costs and emissions were carefully analyzed in this study. Paper SPE 211932 caught my attention for its social and economic impact. This work presents the use of modular refineries as an alternative, cheaper solution to increase refining capabilities. Many developing nations produce more oil than their internal market consumes but still need to import large quantities of refined products. This work points this out as an anomaly. While some refining companies may profit from this, deep social impacts are caused by the increased gas prices in nations that are petroleum-rich. It is nice to see developments in our field that can contribute to less social inequality in the world. Recommended additional reading at OnePetro: www.onepetro.org. SPE 205056 Possible Missing Link in CO2 Corrosion Prediction by Yves Gunaltun, Retired SPE 205687 A Deep-Learning Model To Intelligently Identify the Working Status of Screw Pumps for Oil Well Lifting by Zhen Wang, Luming Oil and Gas Exploration and Development, et al. SPE 204551 Challenges and Opportunities for Green Hydrogen Power Supply in Oil and Gas Remote Facilities by Salvador Alejandro Ruvalcaba Velarde, Heriot-Watt University

Studying Corrosion Failure Prediction Models and Methods for Submarine Oil and Gas Transport Pipelines

To predict the corrosion failure of carbon steel oil and gas pipelines more accurately, a new corrosion failure prediction model for submarine oil and gas transport pipelines was constructed. A corrosion failure prediction management system was also developed based on the constructed model. To construct the model, corrosion experiments were carried out to analyze the influences of temperature, partial pressure of CO2, pH value, and flow rate acting on the corrosion rate. Based on the analysis results and the corrosion experiment data, a new corrosion failure prediction model containing the time and flow rate for oil and gas pipelines was constructed. The model is based on the existing corrosion prediction model and has a determination coefficient, R2, of 0.9573, which indicates good prediction accuracy. A machine learning prediction model was also used to predict, and the prediction results are compared with that of the proposed model, which further verifies the accuracy and feasibility of the proposed model. A corrosion failure prediction management system for carbon steel oil and gas pipelines was developed based on the constructed model, which makes corrosion failure prediction more convenient and faster and provides a reference for the accurate prediction and efficient control of oil and gas pipeline corrosion failure.

Portable automatic detection system with infrared imaging for measuring steel wires corrosion damage

Accurate, cost-efficient and lightweight quantitative detection of steel wire corrosion in cable is an unsolvable issue that puzzles researchers and bridge owners. This paper proposed a portable automatic detection system for quantitative corrosion detection of replaced steel wires. The developed system consists of three parts (an automatic detection device, high-precision infrared imager, and corrosion evaluation algorithm). The proposed method is derived on the basis of Joule's law and the formula for calculating heat release. Then, the corroded specimens with different corrosion degrees and corresponding mass loss ratio were obtained by pre-corroded test to simulate industrial air corrosion and pickling test. The prior knowledge acquisition test utilizing presented automatic detection equipment was conducted to acquire the surface temperature of steel wires with various corrosion ratios by infrared camera after electric heating. Subsequently, the optimal quantitative corrosion prediction model after electric excitation is selected after infrared image processing, outlier filtering, theoretical method comparison, and model modification. Finally, the damaged steel wires from an in-service cable-stayed bridge are chosen to validate the proposed model's generalization ability and predictive accuracy. Furthermore, based on the discovery of characteristics on surface temperature of corroded steel wires after heating, the automatic detection device can be upgraded to realize the quantitative detection of in-situ corrosion of bridge cable.

Prediction of electrochemical corrosion behavior of magnesium alloy using machine learning methods

The corrosion of magnesium (Mg) alloys is a major challenge in many industrial applications because of their extreme reactivity, this greatly limits their applicability. The traditional methods in the prediction of corrosion rate depend on experimental or empirical models that can be time-consuming and expensive. Besides how to integrate experiments with faster machine-learning approaches is still a challenge of research. In recent years machine learning (ML) algorithms have proved and promised better magnesium corrosion predictability. In this study, different training algorithms such as linear regression (LR), decision tree (DT), extra tree (ET), random forest (RF), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and Artificial Neural Network (ANN) were used to develop and predict corrosion rate of magnesium alloys based on magnesium chemical alloy composition as independent input variables. The data used for model preparation and validation were obtained from literature and experimental studies respectively. The feature selection methods were used to identify the most important and influential input parameters for magnesium alloy thus Shapley Additive Explanations (SHAP) interpretation was employed in this manuscript to facilitate feature visualization of the model. The results of the study show that the random forest (RF) algorithm has the greatest prediction impact than other machine learning algorithms. The magnesium alloy corrosion prediction model provides a better ability for the prediction of magnesium corrosion alloys. The study demonstrates the potential of ML models in the prediction of the corrosion behavior of Mg alloys and these models can be used to optimize an alloy composition for designing improved corrosion resistance of new Mg alloys.

CO2 corrosion prediction on 20# steel under the influence of corrosion product film

Based on corrosion thermodynamics and kinetics, considering the multi-field coupling effects of fluid flow, electrochemical reaction and mass transfer process, a new corrosion prediction mechanistic model was proposed by introducing the influence factor of corrosion product film on diffusion coefficient of ion mass transfer, which is based on the CO 2 corrosion prediction model proposed by Nesic et al. The influence of temperature, flow rate and pH value on CO 2 corrosion behavior on 20# steel was studied by orthogonal tests. Scanning electron microscopy (SEM) and energy spectrum analysis (EDS) was used to analyze the surface and cross section morphology of the corrosion product film, and the thickness of the corrosion product film was measured. The results show that the introduced influence factor can simplify the ion mass transfer calculation in the presence of corrosion product film, and the relative error between the predicted value of the modified model and the experimental results is satisfactorily controlled less than 10%. Compared with the prediction model without considering the influence of corrosion product film, the influence factor can effectively correct the high prediction value of the mechanistic model under the influence of corrosion product film, improve the accuracy and applicability of corrosion prediction, and provide important theoretical guidance for the design, manufacturing, operation and maintenance of oil and gas production pipelines and related facilities.

Prediction of the corrosion depth of oil well cement corroded by carbon dioxide using GA-BP neural network

A corrosion prediction model was established based on the genetic algorithm (GA) and back propagation (BP) neural network to predict the long-term corrosion changes of oil well cement, Considering that the cement sheath is susceptible to corrosion and its corrosion degree is not easy to observe in acid gas wells and geological storage wells containing carbon dioxide (CO2). The initial weights and thresholds of the neural network were optimized by GA. The number of hidden layer nodes was selected by error verification, and the network was trained with an improved algorithm. The sample data was regression processed based on the empirical formula and was used in the network training. The simulation results shows that: The improved GA-BP network model (3–5-6–1) has a higher prediction accuracy with faster convergence and better fitting effect compared with the traditional BP neural network and the regression model (REG) in long-term prediction of corrosion depth in oil well cement. The regression coefficient (R2) of the prediction model is 0.9913, and the mean square error (MSE) of test samples is 0.0026. The modeling idea proposed in this paper can be applied to improve the accuracy of prediction models in predicting the corrosion of oil well cement.

Reliability Analysis of the Deep-Sea Horizontal Clamp Connector Based on Multi-Source Information from an Engineering Background

As a key piece of equipment in underwater production system, a reliability study of deep-sea connectors has important theoretical significance and engineering value for increasing fault-free operation time, improving engineering safety, and reducing maintenance costs. However, the diverse failure modes of connectors and the lack of high-quality and credible reliability data can lead to biased analysis outcomes. To tackle this problem, this study aims to establish a reliability model for deep-sea horizontal clamp connectors. Based on the actual engineering background, a fault tree model for deep-sea horizontal clamp connectors is developed, and the distribution types of bottom events are analyzed concerning the failure mechanism. To enhance the model’s credibility, a multi-source information approach is employed, combining prior product information, expert experience, and design information to quantitatively solve the reliability probability of the connector. The expert experience is quantified using the fuzzy quantitative analysis method, while the design information is estimated by developing a corrosion prediction model combined with grey theory. Thus, the reliability assessment of deep-sea horizontal clamp connectors is completed. Factory Acceptance Test (FAT) is performed on the improved connectors, and the closed-loop work of reliability analysis is completed.

Utilization of Rice Straw Extract as Corrosion Inhibitor to Decreasing Effect of Elevation on CS Pipeline using Olga Simulator

In many infrastructure of oil and gas industries, corrosion is one of the most problems that often occur, especially in pipelines. Corrosion is caused by an electrochemical reaction between material surface and corrosion fluid. This problem will affect the economy, safety, and conservation. In this case, we should give a solution such as a method or tools to minimize corrosion’s effect so, it can improve safety and minimize the potential problems caused by corrosion and loss more cost. One of the ways is using organic corrosion inhibitors such as biomass agriculture waste and plant extract. A corrosion prediction model can be used to predict the corrosion rate such as NORSOK and de Waard models. Both methods are available in Olga Simulation software by Schlumberger that were used in this paper. Some of the parameters that influence corrosion were also tested: Evaluation of 1°, 2°, 3°, 4 °, and 5° of pipeline elevation to corrosion rate. Meanwhile, 31.71% of rice straw extraction (RSE) with 75% IE was used as a corrosion inhibitor compared with 30% of mono-ethylene glycol with 26% IE and 38.97% of lignin with 61% IE to determine the period of pipeline per year and to design of the pipeline. RSE that contains lignocellulose was able to give higher efficiency to decreasing corrosion. Moreover, this organic inhibitor give low cost. Therefore, this biomass would be potential as an alternative inhibitor rather than a commercial corrosion inhibitor with high cost.

Calibrating a Physics-Based Corrosion Model with Field-Based Bridge Condition Data

This paper presents a novel method for calibrating a physics-based corrosion prediction model using a deterioration curve derived from field-based bridge condition assessments. The corrosion model used in this study considers inputs such as corrosion rate to predict outputs such as concrete crack width. Deterioration curves that present bridge element condition versus time are converted into crack width versus time using inspection guidance from transportation agencies. Input parameters of the corrosion model are calibrated by matching the crack widths predicted by the corrosion model with those predicted by the deterioration curves. The calibration method is demonstrated for a reinforced concrete bridge column in this article. The corrosion rate before cracking, the pitting factor, and the ratio of corrosion rates at critical crack width and before cracking significantly influenced the model results and were calibrated. Postcalibration input parameter ranges were narrower than but within the ranges reported in the literature. A physics-based corrosion model thus calibrated using field inspection data can potentially improve the predictions of future structural health and aid in asset management.

Mathematical Modelling of CO2 corrosion with NORSOK M 506

The consequences of corrosion is catastrophic also costs reached the global economy “$2.5 trillion, or world GDP’s 3.4%”. Despite the magnitude of the corrosion cost, it can be concluded that scientific studies on corrosion prevention are quite limited, with the exception of high-risk sectors such as aviation and the fuel oil industry. It is important to fight against corrosion in order to ensure the safe operation of oil-carrying pipelines under the sea; and to prevent accidents and environmental damage. As a result of developing industry conditions and increasing needs, modelling corrosion is a very effective method in the prevention of corrosion. Industry, research companies and universities have developed many corrosion rates, prediction models. One of them is the NORSOK M 506 model. In this study, the NORSOK M 506 CO2 corrosion prediction model and the experimental results conducted by Nešić, Solvi and Enerhaug in 1995 were compared in terms of CO2 corrosion rate. The results showed that the mathematical corrosion model calculated nearly six times higher than the experimental study within conformity.

A new approach for quantification of corrosion losses on steels exposed to an artificial seawater environment

ABSTRACT The selection methodology for thickness loss measurement is very important to determine the extent of corrosion damage, as well as in formulation of corrosion prediction models and inspection/maintenance plans for offshore structures. This paper introduces a more accurate corrosion measurement technique, based on the pre-exposure dimensional metrology and post-exposure optical microscopy/image analysis on the cross-sections of steel samples. During this corrosion test, the surface grinded and uncoated steel samples were submerged vertically in an artificial seawater solution, for a duration of up to a maximum of 365 days. The corrosion damage experienced on the steel samples means that the dimensional metrology can be more accurate, and useful approach to measure both uniform and localised corrosion losses simultaneously than the conventional average mass loss method.

Modelling the corrosion mechanism of steel bars in chloride-contaminated concrete with transverse cracks

The development of cracks in reinforced concrete structures can accelerate structural deterioration and reduce service life. A numerical model was established to analyse steel bar corrosion in chloride-contaminated concrete with transverse cracks. In the model, the dynamic distinction between anode and cathode regions on the steel surface is considered by introducing an empirical formula for the anodic Tafel slope related to chloride concentration. The time-dependent characteristics of the corroded region were examined using the proposed corrosion prediction model and the corrosion process of steel bars in cracked concrete was revealed. The effects of crack width and crack spacing on steel corrosion were analysed in detail in terms of the corrosion area and corrosion rate. The results showed a variation in the corrosion area adjacent to cracks over time and a positive correlation between corrosion area and the square root of time. According to the micro-cell and macro-cell contributions to the corrosion rate, the process of steel bar corrosion was determined to occur in three stages. The effects of transverse cracks on the corrosion rate were found to vary with exposure time and to depend on the control steps of the micro- or macro-cells.