Prediction Of Corrosion Rate Research Articles

Recent Developments in Mechanistic Modeling of Mild Steel Corrosion in H2s Environments

The corrosion of mild steel in hydrogen sulfide (H2S) environments, as a serious concern in the oil and gas transmission pipelines, has been investigated over the past decades. However, the mechanisms of corrosion of mild steel in H2S environments have been studied in more detail only in recent years. In the present study, a comprehensive mathematical model was developed based on the recent advancement in the mechanistic understanding of corrosion processes in aqueous H2S environments in order to predict the corrosion rate and the polarization response of steel in this system. In this model, the cathodic reaction was developed based on H2S dissociation and its buffering effect. The only cathodic reaction in the system is hydrogen ion reduction. The results confirmed that H2S buffering capacity can thoroughly explain the “double wave” characteristics of the cathodic polarization as well as the increased cathodic limiting current densities of steel in this system. In addition, the anodic iron dissolution reaction was revised by considering the effect of hydrogen sulfide on this reaction. On that account, a reaction pathway with the sulfide intermediates was introduced parallel to the acidic iron dissolution reaction. The developed model was then compared with the available experimental data in the literature, and it was shown that it could reasonably represent the characteristic electrochemical behaviors of H2S corrosion of steel. Furthermore, the predicted corrosion rates are shown to be in good agreement with experimental data over a wide range of experimental conditions.

Assessment of the Local Mechano-Electrochemical Effect on Pipeline Defects by 2D and 3D Finite Element Models

Local corrosion at a defect on a pipeline was assessed using both 2-dimensional (2D) and 3-dimensional (3D) finite element models under mechano-electrochemical (M-E) interaction. While the M-E interaction increases corrosion activity at the defect, the assessment of M-E interaction would have different results using 2D and 3D models. Compared with the 3D model, the 2D model produces a greater local stress, a higher local plastic strain, a more negative corrosion potential and a higher anodic current density at the defect, and thus, a lower threshold internal pressure causing local yielding. The 3D model is more conservative for corrosion rate prediction of corroded pipelines. A new concept, rAZ (the ratio of the anodic zone length to defect length in the 2D model, or the ratio of the anodic zone area to the defect area in the 3D model), is proposed to define growth mode of the corrosion defect. There is a smaller rAZ produced in 2D model. At specific internal pressures, the 2D model predicts an ellipsoidal defect center area experiencing accelerated corrosion and potentially resulting in pipeline leaking.

Finite element modeling of local corrosion accelerated by the mechano-electrochemical coupling effect at defects on pipelines under combined effects of internal pressure and axial applied stress

A finite element model was built to assess the combined effects of internal pressure and axial applied stress $$\left( {\sigma_{A} } \right)$$ on localized stress concentration and electrochemical corrosion at defects on pipelines under the mechano-electrochemical (M-E) interaction. When defect angle $$\alpha$$ (i.e., included angle between the major axis of the defect and longitudinal direction of the pipe) is 90°, the maximum stress of the defect is enlarged remarkably with the increase in $$\sigma_{A}$$ , leading to electrochemical corrosion at the defect affected greatly under the M-E interaction. However, when $$\alpha$$ is 0°, the maximum stress and electrochemical corrosion are affected slightly by the $$\sigma_{A}$$ . Besides, when the hoop stress of the pipe is larger than its total axial stress, the largest corrosion rate at the defect with $$\alpha$$ of 0° would be higher than that at the defect with $$\alpha$$ of 90°. Thus, for the improvement of pipeline integrity management, the combined effects of internal pressure and $$\sigma_{A}$$ should be taken into account for a more accurate prediction of local corrosion rate at the defect especially with $$\alpha$$ of 90°.

Synergy effect modelling of cavitation and hard particle erosion: Implementation and validation

The prediction of the wear behaviour of components under conditions of cavitation and hard particle erosion requires the implementation of a new model capable of accounting for the accelerated damage due to the synergic effect of both phenomena. In a previous research, several simulations were performed to investigate why the damage on a surface is increased when a particle interacts with a collapsing bubble. Then those simulations were modified to develop a predictive equation that estimates the impact velocity of a particle when it is trapped by the microjet of a collapsing bubble near a solid wall. Subsequently, through the implementation in a computational fluid dynamics (CFD) software, the obtained expression is combined with the traditional Grant and Tabakoff hard particle erosion model for AISI 304 steel to develop a new model capable of predicting the synergic effect of cavitation damage and hard particle erosion. This new model is used in CFD simulations to calibrate and validate the predicted erosion rate with experimental results obtained using a slurry pot erosion tester under two conditions of particle concentration and two conditions of particle size combined with four triangular cavitation inducers that produced different cavitation conditions. Good agreement was found between the experimental and the predicted results using the developed model.

Photogrammetry based computational fluid dynamics of erosion of sand particles in water pipeline: Dynamic shape factors of 3D particles and minimization of erosion activity

Extraction and delivery of raw materials in engineering industries are inevitably polluted with sand particles that caused several flow assurance problems. Besides, designing a pipeline and estimating sand erosion is very complex in a multiphase flow due to several important factors needed to be considered, such as impact velocity, impact angle, size of particle, shape of particle, properties of particle, and properties of the target material. In this work, a modified E/CRC erosion model was developed to address the shape randomness in sand particles distribution. The photogrammetry technique was adopted to digitize sand particles into 3D models within the range of 400–500 μm. After quantitative and qualitative analysis, a database of shape factor for different spherical and roundness of sand particles was tabulated. Meanwhile, a high-fidelity simulation of a 90-degree elbow pipeline was conducted using the CFD-DEM method. Hydrodynamic characteristics of liquid flow, sand particle trajectories, the sensitivity of velocity, shape factor, and flow rate were comprehensively explored. Modification of the particle shape factor based on the generated database from photogrammetry resulted in overestimated erosion rate of the original E/CRC model with 124.4% accuracy improvement in predicting erosion rate on pipe bend at different collision angles. Lastly, through response surface methodology (RSM), a flow threshold condition was established to achieve minimum erosion damage.

FeCO3 Mineralization and Solubility – A Guiding Factor CO2 Corrosion and Storage

The CO2 capture and storage (CCS) technology is receiving increasing attention, as the need to reduce the emissions of greenhouse gases e.g. CO2, and CH4. CO2. These gases present in the process streams are highly corrosive towards carbon steel potentially causing a major problem in the industry. The occurrence of corrosion on the steel pipe will lead to significant production losses and costly shutdowns. Precipitated FeCO3 will deposit on the steel surface and create a protective barrier for further corrosion of pipelines and equipment. To predict the corrosion product, we need to understand the solubility of FeCO3 at different process parameters e.g. temperature, pressure, CO2 concentration and salinity. The solubility of FeCO3 is not well described in the literature. The overall objective of this work is to create a new fundamental understanding of FeCO3 solubility which will lead to better corrosion models, better prediction of corrosion rate, and better understanding of scale formation kinetics. The aim is to determine the FeCO3 solubility at different parameters; temperature, pressure, CO2 concentration, and in amine solutions. FeCO3 is not commercially available and must be synthesised under anoxic conditions before solubility measurement can be conducted.

A revision of mechanistic modeling of mild steel corrosion in H2S environments

In this study, a predictive model for the corrosion rate and the polarization response of bare steel in aqueous hydrogen sulfide (H2S) environments based on H2S dissociation and its buffering effect was developed. In this model, the hydrogen ion reduction is the only cathodic reaction. The results confirmed that the increased limiting cathodic current densities and the characteristic “double wave” behavior in the cathodic current of this system could be explained by hydrogen sulfide dissociation in the vicinity of the metal surface. The effect of hydrogen sulfide on the rate of iron dissolution reaction was also taken into account by introducing a reaction sequence involving sulfide intermediates into the calculations, parallel to the acidic iron dissolution reaction. The developed model was shown to be able to reasonably represent the characteristic electrochemical behavior of this system. Furthermore, the predicted corrosion rates are shown to be in good agreement with the available experimental data in the literature over a wide range of conditions.

The Effect Analysis of Coating Thickness Variation and Mixture Composition of Magnesium – Flake Glass on Epoxy Coating on Abrasive Resistance, Adhesion Strength, and Prediction of Corrosion Rate of ASTM A36 Steel Plate

Steel cannot be separated from the marine manufacturing industry. It is important to control the corrosion rate of steel used for offshore structures. The coating method can be used in mobilization areas and splash zones that have high corrosion rates due to sustained friction loads in the marine environment. The purpose of this study was to analyze the different in adhesion strength, abrasion resistance, and corrosion rate prediction of ASTM A36 low carbon steel plate with coating thickness variations of 300µm, 500µm, and 700µm and Magnesium Carbonate - Flake Glass mixture variations of 10%, 20%, and 30%. In the pull-off test, the highest value of adhesion strength was obtained from specimen with 300µm coating thickness and 10% Magnesium Carbonate - Flake Glass mixture with a value of 5.67 Mpa. The highest abrasion test value was obtained from specimen with 700µm coating thickness and 30% Magnesium Carbonate - Flake Glass mixture with a value of 0.91 Wear Cyces per Micrometer. Whereas in the three cells electrode test, the highest value was found at specimen with 700µm coating thickness and 30% Magnesium Carbonate - Flake Glass mixture with a value of 0,00010 mmpy.

A process-based method for predicting lateral erosion rates

An accurate means of predicting erosion rates is essential to improve the predictive capability of breach models. During breach growth, erosion rates are often determined with empirical equations. The predictive capability of empirical equations is governed by the range for which they have been validated and the accuracy with which empirical coefficients can be established. Most empirical equations thereby do not account for the impact of material texture, moisture content, and compaction energy on the erosion rates. The method presented in this paper acknowledges the impact of these parameters by accounting for the process of dilation during erosion. The paper shows how, given high surface shear stresses, the erosion rate can be quantified by applying the principles of soil mechanics. Key is thereby to identify that stress balance situation for which the dilatency induced inflow gives a maximum averaged shear resistance. The effectiveness of the model in predicting erosion rates is indicated by means of three validation test cases. A sensitivity analysis of the method is also provided to show that the predictions lie within the range of inaccuracy of the input parameters.

Corrosion simulation via coupling computational fluid dynamics and NORSOK CO2 corrosion rate prediction model for an outlet header piping of an air-cooled heat exchanger

In this study CO2 corrosion of outlet header pipe of an air-cooled heat exchanger in a natural gas dehydration unit of a gas refinery was investigated by simulation via coupling computational fluid dynamics and NORSOK M-506 CO2 corrosion prediction model. The Euler-Euler approach incorporated with k–ε model for the fluid turbulence was employed to simulate gas-liquid two-phase flow. The simulation results were utilized as the inputs of NORSOK CO2 corrosion prediction model to calculate CO2 corrosion rate. Fluid streamlines, velocity, viscosity, density and temperature profiles and also the distribution of gas and liquid phases through the investigated outlet header pipe were calculated and a three-dimensional corrosion rate map was presented. The results showed that there is a good agreement between the data obtained by corrosion simulation and field observation. It was found here that coupling computational fluid dynamics and CO2 corrosion prediction models can successfully predict regional corrosion rates and determine locations susceptible to CO2 corrosion.

Prediction of surface runoff and erosion rate using SWAT (soil water assesment tool) model in Selopamioro catchment as directions of soil and water conservation

Selopamioro catchment part of Oyo Watershed, is located in downstream area of Selopamioro village which consist of steep slope gradient, and has been constantly exposed to intensive farming activities. Thus, this watershed is susceptible to erosion. One of models to predict the impact of land use on hydrological and sedimentary conditions is SWAT (Soil Water Assessment Tool). Therefore, the purpose of this research was to apply the SWAT model in predicting surface runoff and erosion. The result of this study will be used as initial analysis to support the direction of soil and water conservation. Topography, land use, and climate data were collected to simulate erosion and surface runoff. Two adjacent sub-catchments namely DTA 1 and DTA 2 were analyzed. Surface runoff in DTA 1 is 333,660,000 liters/year and in DTA 2 is 316,920,000 liter/year. The erosion rate in DTA 1 is 24.6 mm/year and in DTA 2 is 21 mm/year. The result of SWAT model tends to be overestimate. This shows that SWAT model might not suitable to be used for erosion prediction in tropical agriculture country, such as Indonesia. Updates of SWAT data for physical characteristic in tropical country might need to be carried out for reliable result.

Experimental and numerical investigation of the abrasive waterjet machining of aluminum-7075-T6 for aerospace applications

The machining of hard-to-cut materials with a high degree of precision and high surface quality is one of the most critical considerations when fabricating various state-of-the-art engineered components. In this investigation, a comprehensive three-dimensional model was developed and numerically simulated to predict kerf profiles and material removal rates while drilling the aluminum-7075-T6 aerospace alloy. Kerf profile and material removal prediction involved three stages: jet dynamic flow modeling, abrasive particle tracking, and erosion rate prediction . Experimental investigations were conducted to validate the developed model. The results indicate that the jet dynamic characteristics and flow of abrasive particles alter the kerf profiles, where the top kerf diameter increases with increasing jet pressure and standoff distance. The kerf depth and hole aspect ratio increase with jet pressure, but decrease with standoff distance and machining time. Cross-sectional profiles were characterized by progressive edge rounding and parabolic shapes. Defects can be minimized by utilizing high jet pressure and small standoff distance. The material removal rate increases with increasing jet pressure, abrasive particle size, and exposure time, but decreases with increasing standoff distance.

Intelligent anti-corrosion expert system based on big data analysis

PurposeThe paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system.Design/methodology/approachThe system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management.FindingsThe proposed anti-corrosion system thoroughly considers a full use of corrosion data, relevant corrosion prediction and efficient corrosion management in one system. Therefore, this system can achieve an accurate prediction of corrosion rate, risk evaluation, risk alert and expert suggestion for equipment in petrochemical plants.Originality/valueCollectively, this present study has important ramifications for the more efficient and scientific management of corrosion data in enterprises and experts’ guidance in controlling corrosion status. At the same time, the digital management of corrosion data can provide a data support for related theoretical researches in corrosion field, and the intelligent system also offers examples in other fields to improve system by adding intelligence means.

Diagnostic and prognostic development of a mechanistic model for multiphase flow in oil-gas pipelines

A mechanistic model has been applied to predict corrosion rate in multiphase (water–oil-gas) flow regimes. It is applied due to the inadequacies of existing models to adequately address a wide variety of corrosion-dependent parameters with special emphasis on multiphase flow parameters. This model considers water cut, volumetric phase fraction of oil and gas, temperature, pressure, and other data collected relating to the pipe age, sand particle deposit, CO2 partial pressure, flow velocity, pH, chloride contents, mass transfer coefficients, and hydrogen sulfide. The applied model results show well above 91 percent prediction accuracy, while the prediction error ranges between 0.022 and 0.045 percent. When predicted results compared actual value (experimental data) with DeWaard and Milliams’ model; used in the oil and gas industries, it showed good agreement. It is seen that the resulting model produced an R2 value of 0.91 in comparison to 0.53, the value for Dewaard and Milliams’. It is obvious from the numerical simulation that the multiphase flow parameters have major effects on the entire rate of corrosion of oil and gas multiphase flow pipelines. Finally, this research concluded by showing the developed model performance, and a comparison was made with numerical results. It is expected that the outcome of this research will be of significant help in strengthening the application of mechanistic models for predicting corrosion rates. This model will be useful for oil and gas industries worldwide for accurate corrosion rate prediction.

Multilayer Perceptron Model for the prediction of corrosion rate of Aluminium Alloy 5083 in seawater via different training algorithms

Corrosion inhibitor is often opted as a corrosion protection method for various industries worldwide. The development of eco-friendly corrosion inhibitor has become a trending concern due to the various environmental regulations impose by several countries. However, a laboratory testing would be such a tedious, costly and time-consuming process. Therefore, artificial neural network (ANN) has been used extensively to predict the verdict based on the experimental values. In this study, 3-layered Multilayer Perceptron (MLP) models were developed with 3 inputs (Electrochemical Impedance Spectroscopy, Ω.cm2), (Potentiodynamic polarization, A/cm2), (weight loss, %), and one output (corrosion rate, mm.yr−1). The data were divided into three parts; 70%, 15%, and 15% for model development, model validation and model testing, respectively. Three training algorithms were tested during the model development, including the Levenberg-Marquadt (LM), Bayesian Regularization (BR), and Scale Conjugate Gradient (SCG). Results revealed that the best MLP models during the development were using neuron number 4 (r = 0.99272), 6 (r = 0.99155), and 2 (r = 0.98624) for LM, BR and SCG, respectively. Among the three training algorithms, LM is opted as the best training algorithm for the corrosion rate prediction which executed high correlation coefficient (R) values during development (R = 0.99272), validation (R = 0.99905), and testing (R = 0.97908). These findings will be an essential tool for the model development with the sole purposes of predicting the corrosion rate in line to ensure the exact time for repair and maintenance schedule.

Application of internal corrosion direct assessment in CO2 slug flow submarine pipelines

This paper aims to establish a direct method for evaluating the internal corrosion in submarine pipelines, including four steps: pre-assessment, indirect inspection, detailed inspection, and post-assessment. The evaluation of corrosion severity and the determination of key monitoring points are particularly important in indirect detection. Taking CO2 gathering and transmission pipeline of an offshore oil field as an example, the overall internal corrosion rate of the pipeline is calculated to be 0.253 mm/a. The 12–45 m long pipeline is selected as the focus of corrosion monitoring. Through the comparative analysis of internal detection data and loss wall thickness, it is found that the results of indirect evaluation and direct evaluation are consistent, indicating the correctness of corrosion rate prediction model and loss wall thickness calculation method. The pipeline reevaluation time is five years. The evaluation method can accurately predict the corrosion status of multiphase flow pipeline and locate the corrosion.

Prediction of crater depth, surface roughness and erosion rate during abrasive jet machining of glass

Understanding the mechanism for brittle material removal via solid particle impact is important for various areas such as energy and aerospace industries. The existing analytical formulation of erosion damage, consistent with indentation fracture theory, overestimates actual erosion by over 200%. This study complements an existing analysis by prescribing a realistic shape and mechanical properties to the impinging particle. We quantitatively show that a lateral crack in glass does not nucleate at the indenter's tip or the theoretical plastic depth, but at an intermediate depth. This depth can be found by implementing the actual particle penetration depth into Hill's ratio. Several new geometries of the particle's impacting tip are proposed as ways to achieve adequate particle penetration depth instead of an expanding hemispherical cavity. The crater depth, surface roughness, and erosion efficiency of borosilicate glass are predicted with 10%, 24%, and 23% error, respectively, using a simple analytical routine.

Response surface method and neural computation for the analysis and prediction of erosion response of glass-polyester composites filled with waste marble dust

Abstract Erosion wear response of waste marble dust filled glass-polyester composites is studied using response surface method (RSM) and neural computation. Marble dust is a construction/industrial waste generated from the processing of marble producing rocks. In the present investigation, convention hand lay-up technique is used for the preparation of hybrid composites consisting of 40% of glass fibers and 0, 16 and 32 wt% of waste marble dust respectively. The erosion behavior of the hybrid composites is investigated using an erosion tester as per ASTM G76. The experimental results are successfully analyzed using an analysis tool based on response surface method. Striking velocity, filler content and impingement angle in that sequence among the test parameters are found significant affecting the erosion loss of the composites. The morphologies of the eroded composite surfaces are analyzed and the predominant wear mechanisms are identified. Finally, an analysis and prediction tool working on artificial neural networks (ANN) is gainfully implemented for the analysis of experimental results and prediction of erosion rate for a wide combination of control factors within the experimental domain.

Mapping Atmospheric Corrosivity in Shandong

Air pollution can significantly accelerate the process of material corrosion, which may cause significant economic losses and serious safety incidents. Atmospheric corrosion maps provide atmospheric corrosivity in a given geographic scope, which can guide the designers to select the most suitable anti-corrosion materials for outdoor projects, also provide useful information for maintenance. This article investigated mapping of atmospheric corrosivity in Shandong Province, China. In order to obtain atmospheric corrosivity data, 100 exposure corrosion test sites were established in Shandong according to International Standard Organization (ISO) 8565. Hot-dip galvanized steel samples were exposed for 1 year in the test sites. Taking the results of exposure corrosion test as the data, inverse distance weighting (IDW) and ordinary kriging (OK) interpolation algorithm were used to estimate the atmospheric corrosivity of Shandong Province according to ISO 9223. The validity of OK and IDW was compared in developing atmospheric corrosion maps of Shandong Province on a 1 × 1 km resolution. The cross-validation results showed that OK interpolation algorithm with Gaussian semivariogram model get the best result in the prediction of corrosion rate. When the corrosion category was used as the criterion, the IDW interpolation algorithm of power 4 performed best, predicted results of 74 sites (n = 100) were consistent with observed. However, high mean relative errors (MRE more than 37%) and relatively low correlation (R2 about 24%) indicated that the prediction results of the two interpolation algorithms had a large error, which was caused by the low data density and the complicated corrosive factors of the atmosphere.

Atmospheric Corrosion Monitoring Sensor in Corrosion Rate Prediction of Carbon and Weathering Steels in Thailand

Atmospheric Corrosion Monitoring Sensor in Corrosion Rate Prediction of Carbon and Weathering Steels in Thailand