Prediction Of Corrosion Rate Research Articles

A Novel Trajectory-Based Mechanistic Model for Predicting Solid Particle Erosion in Elbows

Abstract Solid particle erosion is a common and challenging phenomenon during the production and transport of particle-containing fluids and it is important to have models for predicting erosion rates accurately, especially for geometries such as elbows. Mechanistic models aim at predicting erosion accurately with low computational cost. In this study, a new particle trajectories-based mechanistic model is proposed to address the issues of liquid-dominated flows and the effect of particle size. Detailed flow and particle information for a standard elbow with water and air and different particle sizes are extracted from computational fluid dynamics (CFD) and analyzed to obtain a representative trajectory. The developed model includes various components that are sensitive to the particle size and flow conditions and accounts for the angle of impact and the turbulence in the flow. The proposed model is examined against CFD predictions for different pipe and particle sizes, and velocities with air, water, high-pressure air, and high-viscosity liquid. In comparison to an available mechanistic model, the new model provides relatively lower errors in predicting maximum erosion for many flow conditions. Moreover, the proposed model is found to be more consistent with CFD data for high-pressure air and higher-viscosity liquids. The model is further validated with experimental data for various conditions. Comparisons against numerical and experimental data suggest that the proposed model provides a significant improvement for liquid–solid flows and small particles.

Tafel-Piontelli model for the prediction of uniform corrosion rate of active metals in strongly acidic environments

Corrosion represents a threatening and expensive problem for every industrial sector. To address this issue, several predictive models were developed and categorized in empirical, semiempirical and mechanistic, according to their underlying hypotheses, their representation of major physicochemical phenomena, and their applicability limits. In this work, a mechanistic model strongly based on the theory of kinetics of electrochemical corrosion is presented and validated. The model, called Tafel-Piontelli, was firstly proposed in 2017 as a tool for the calculation of the corrosion rate of active metals in acidic environments, characterized by hydrogen evolution as the dominant cathodic process. Starting from its initial version, the base-equation of the model has been revised and the theoretical architecture improved. The model was validated according to the experimental data collected for a variety of strong acids in a wide range of temperature and pH sets. The kinetic parameters of the equation, such as the cathodic Tafel slope and the hydrogen evolution exchange current density, were determined via cathodic potentiodynamic polarization tests (Tafel extrapolation method), while the iron ion activity and the corrosion rate were determined from mass loss tests and application of the Faraday law. The results show a remarkable improvement in the representation of the iron ion activity and of its exponential dependence on pH and temperature with respect to the original formulation. Additionally, the estimated corrosion rates accurately capture the dynamics observed in the empirical data, thus confirming the validity of the model.

Atmospheric corrosion rate prediction of low-alloy steel using machine learning models

Corrosion mitigation is one of the indispensable needs in many industries and is currently being pursued by various methods like surface modification, corrosion inhibitor addition, and cathodic protection systems. Corrosion rate prediction can help in designing alloys with an optimized composition of materials such that it has a lower corrosion rate in the environment where they are exposed. Corrosion rate prediction can also help the manufacturers to plan the replacement of the sample used in advance. Machine learning, which is the science of making machines learn without being explicitly programmed and without using pre-determined equations, can help overcome challenges in predicting corrosion of various materials under a variety of environmental conditions. In this paper, three machine learning algorithms namely Support Vector Regression, Multiple Linear Regression, and Random Forest Regression are used to develop a Hybrid model to predict the corrosion rate of materials. Feature reduction is performed after feature importance calculation using Random Forest Regression model. The accuracy of the developed models were calculated using r2 scores as an evaluation metric for different train-test split ratios. The input data for various conditions such as open, sheltered, coastal. Etc. are fed to the model and the performance was evaluated. The results show that the proposed Hybrid model outperforms the other baseline approaches with an accuracy of 91.46%, for predicting corrosion rate of materials.

PREDIKSI LAJU EROSI PADA DAERAH TANGKAPAN AIR BENDUNGAN DENGAN METODE USLE (STUDI KASUS : BENDUNGAN TITAB)

Erosion is a serious problem in watershed management. The Saba watershed is a water catchment area at the Titab Dam in Buleleng Regency, Bali Province. The existence of the Titab Dam is accompanied by the development of activities of the surrounding community, both tourism, economic and daily activities. Unfortunately, this activity in the future will have a negative impact on the condition of the dam so that it poses a risk. This study aims to determine the amount of sediment inflow with land erosion approach at the Titab Dam using the USLE (Universal Soil Loss Equation) method. Erosion rate prediction was carried out using the USLE method and with the help of ArcGIS 10.8 software. From the results of the analysis, it was found that in the Titab Dam catchment area, only 3 classes were obtained. The results of the calculation of the erosion rate using the USLE method showed that the magnitude of the erosion was 439,962 tons/year or 53.05 tons/ha/year. In addition, the most influential factors on the rate of erosion are the length and slope factors. In addition, land cover is also one of the most influential factors.

Prediction of Corrosion Rates of Ni-TiN composite coating using a Radial Basis Function Neural Network

Prediction of Corrosion Rates of Ni-TiN composite coating using a Radial Basis Function Neural Network

APPLICATION AND COMPARISON OF ENSEMBLE MODELS TO PREDICT CORROSION RATE FOR AMINE REGENERATION UNIT IN REFINERY

Monitoring the corrosion activity of Amine Regeneration Units (ARU) in oil and gas refneries is crucial to avoid unexpected failures and unplanned shutdowns. Various corrosion monitoring devices are installed to provide real time metal loss readings translated into corrosion rates. However, existing corrosion monitoring devices do not ofer prediction capabilities, and a huge amount of supervised historical data is usually ignored due to its complex characteristics and incompleteness. By utilising ensemble models, machine learning can be adopted to generate value from the unattended historical data and provide prediction capabilities. This research work is mainly intended to process the historical data in Amine Regeneration Unit (ARU), implement Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) ensemble models for corrosion rate prediction, and compare the two to identify the most accurate model. An experiment was conducted via simulation through programming software that utilises Python as the programming language to process data from an undisclosed oil and gas refnery in Malaysia. Evaluation of accuracy was performed using statistical methods, which include R-squared (R2 ), Mean Average Error (MAE), Mean Squared Error (MSE), and Root-Mean-Square Error (RMSE). Upon model hyperparameter tuning and comparison between eight diferent models, the model utilising the XGBoost algorithm with K-Fold cross-validation was reported to provide the best accuracy in terms of R2 score at 65.4%. Future improvement for this research work includes exploration of various other corrosion groups in ARU among multiple oil and gas refneries and experimentation on various boosting tree hyperparameter tuning.
 Keywords: Amine, corrosion rate, LightGBM, oil and gas, predictive analytics, refinery, XGBoost

Numerical Simulation of Erosion Characteristics and Residual Life Prediction of Defective Pipelines Based on Extreme Learning Machine

Aiming to solve the problem that the residual life of defective elbows is difficult to predict and the prediction accuracy of a traditional extreme learning machine (ELM) is unsatisfactory, a genetic algorithm optimization neural network extreme learning machine method (GA-ELM) that can effectively predict erosion rate and residual life is proposed. In this method, the input weight and hidden layer node threshold of the hidden layer node is mapped to GA, and the input weight and threshold of the ELM network error is selected by GA, which improves the generalization performance of the ELM. Firstly, the effects of solid particle velocity, particle size, and mass flow rate on the erosion of elbow are studied, and the erosion rates under the conditions of point erosion defect, groove defect, and double groove erosion defect are calculated. On this basis, the optimized GA-ELM network model is used to predict the residual life of the pipelines and then compared with the traditional ELM network model. The results show that the maximum erosion rate of defect free elbow is linearly correlated with solid particle velocity, particle size, and mass flow rate; The maximum erosion rate of defective bend is higher than that of nondefective bends, and the maximum erosion rate of defective bend is linearly related to mass flow rate, but nonlinear to solid particle flow rate and particle size; the GA-ELM model can effectively predict the erosion residual life of a defective elbow. The prediction accuracy and generalization ability of the GA-ELM model are better than those of the traditional ELM model.

Experimental simulation and new prediction model of sand control screen erosion performance in weakly consolidated heterogeneous reservoirs

Sand screens act as the key downhole equipment for sand control in weakly consolidated oil and gas reservoir, but always face the risk of erosion failure in sanding wells with high productivity. To develop an effective methodology to predict erosion performance in actual wells is a pressing demand of high reliability sand control. In this work, we conducted series of erosion experiments to investigate the failure mechanism and influencing factors of typical premium mesh screen with gas and liquid carrying sand. The results show that the protective shroud and the metal mesh medium show similar erosion laws under various sensitive factors. The erosion rate increases nonlinearly with fluid velocity, but increases linearly with sand particle size and concentration, and decreases linearly with erosion distance. In addition, erosion angles within 30°∼60° can result in higher erosion rates than other angles. With new proposed screen structure function and eroding condition function, an integrated model for erosion rate prediction of protective shroud and metal mesh medium was developed for gas and liquid carrying sand respectively by experiment data fitting. To make it easy to use the new model to predict the erosion performance of actual wells, an index of partial inflow factor (PIF) was furtherly put forward to describe the highest inflowing velocity in the heterogeneous inflow profile caused by the heterogeneity of reservoir properties. Considering the erosion of sand particles and the protection of the screen by sand accumulation, a systematic method was proposed to simulate the erosion performance of wells with sand control screen. Using this method, the final result of whether screen erosion failure can take place at the end of early stage the well production can be predicted, which depends on the relative speeds of screen erosion and sand accumulation in the wellbore annulus. The proposed methodology of erosion performance simulation takes into account not only the characteristics of screen itself, but also the actual inflow condition of well bottom hole. The models and methods are really very easy for practical application and offer a reliable access for sand control and well completion design, and integrity evaluation of completion string in weakly consolidated reservoirs.

Comparison of Response Surface Methodologies and Artificial Neural Network Approaches to Predict the Corrosion Rate of Carbon Steel in Soil

Soil corrosion is a critical problem that has recently interested many scientists. Several soil factors affect the corrosion rate of carbon steel, and they can all be relevant at the same time, thus making it difficult to maintain conditions across soil corrosion studies. There are currently two potential methods for predicting corrosion rates in a complex environment such as soils: the response surface methodology (RSM) and artificial neural network (ANN). RSM is the method using statistics to design experiments, while ANN predicts the corrosion rate through training based on human brain systems. In this study, these two methods will be implemented to predict the corrosion rate of carbon steel considering three factors: pH, temperature, and chloride. The prediction of corrosion rate is successful in both methods, and they have their own advantages and disadvantages.

Prediction of fluvial erosion rate in Jamuna River, Bangladesh

ABSTRACT River bank erosion of alluvial rivers is one of the major challenges for river management. This paper deals with the prediction of the fluvial erosion rate along the left bank of Jamuna River in Bangladesh using the excess shear stress method. Surface water modelling systems i.e. SMS and SRH-2D, were used to estimate the hydrodynamic effects on the riverbank along the 8.21 km reach of the river. Riverbank along the selected reach of Jamuna River consists of two layers of soil type. The upper layer soil is sandy silt with a median size of D 50 = 0.028 mm and the bottom layer is silty sand with D 50 = 0.167 mm. Results of the present analysis show that shear stress along the riverbank attains its maximum and initiates the bank erosion, when the flow rate is about 45,000 m3/s. Based on the results obtained from model run and empirical analysis, riverbank materials have been categorized as ‘erodible’ to ‘very erodible’, as far as erodibility parameters are concerned. Critical shear stress of bank soil is found to be varied between 0.15 and 0.22 Pa using erodibility co-efficient ranges between 5.05 and 6.03 cm3/N s. From this study, the maximum bank erosion rates have been estimated as 51.95 m/year to 69.82 m/year. However, average erosion rates have been estimated which range between 38.65 and 40.57 m/y. It is hoped that the results obtained from the study will be helpful in determining the riverbank stability for the implementation of appropriate river protective measures along the riverbank of Jamuna River.

Simulation of CO2 Corrosion of Carbon Steel in High Pressure and High Temperature Environment (HPHT)

In HPHT environments, the mechanism of CO2 corrosion faces a challenge as an effect of chemical-physical reactions on the metal surface. The presence of other elements in the CO2 system complicates corrosion behavior. To provide a realistic mechanism for corrosion process, some corrosion prediction models have developed software using fundamental theories such as electrochemical reactions and thermodynamics theories. Existing methods to predict corrosion rate models in HPHT environments have shown reasonable results. This paper reviews software of corrosion predictions which calculate corrosion rate based on mechanistic theories that study effects of H2S, acetic acid (HAc) concentrations, shear stress, pH in temperature from 25oC – 100oC and pressure from 1–10 bar. From the simulation, corrosion rate increased significantly in the high pressure CO2 environment. Corrosion rate at pH 4 increased to 30 mm/y at a temperature from 15oC to 90oC. While at pH 8 corrosion rate reached 4 mm/y. This lower corrosion rate indicated a tendency for deposits formation at higher pH. Corrosion rate behaves in a different mechanism at high temperatures. The corrosion rate decreased to 4 mm/y when the temperature increased to more than 90oC. Effects H2S gas and HAc were identified to increase corrosion rate. Both elements provide extra cathodic reaction and create limiting current density in the cathodic reaction process based on polarization sweep models. However, the polarization graph calculated using corrosion models could not display passive behavior in the anodic polarization process. Thus, further, improvement should be considered. From the data calculation, it can be shown that corrosion prediction software can predict corrosion rate in HPHT conditions.

CFD modelling of biomass ash deposition under multiple operation conditions using a 2D mass-conserving dynamic mesh approach

A dynamic CFD model with conjugate heat transfer that considers particle sticking and erosion is developed and used for predicting the effect of multiple operation conditions on ash deposition for a model biomass fly ash species (K2Si4O9) in a lab-scale entrained flow reactor. In order to achieve stable dynamic mesh morphing, a globally mass-conserving smooth method is proposed using a multiple-point weighted moving average algorithm and a growth scaling factor. A new method to estimate particle count for Lagrangian particle tracking is also proposed, which is based on particle impaction efficiency and face count of the mesh of deposition tube. Particle sticking is predicted based on the two-body collision method and particle erosion is evaluated using the empirical model that is dependent on temperature, particle diameter and velocity, and impaction angle. The proposed smoothing method incorporated with the particle count estimated can accomplish stable dynamic mesh morphing for all the 37 deposition cases without the need to loop the smoothing process and sub-group/averaging the growth rate. The prediction results using the proposed ash deposition model agree reasonably with the experimental observation of the effects of flue gas temperature, tube surface temperature, flue gas velocity, fly ash flux and deposition time on deposit formation rate. R2 of the predicted and measured deposit formation rates is approximately 0.68. The results also show that the predicted erosion rate essentially correlates negatively with the experimental deposit formation rate. Interestingly, coarse particles are numerically seen to reduce the deposition rate caused by smaller particles via erosion.

Modeling erosion process in elbows of petroleum pipelines using large eddy simulation

Elbows are widely used in piping systems to change the direction of flow. However, it is also extremely susceptible to erosive damage, which may lead to the leakage of petroleum and gas pipelines. Therefore, revealing the erosion process and accurately predicting erosion rate are of great importance to pipeline safety. In this study, the unsteady slurry erosion process in the 90° elbow is investigated numerically using LES Eulerian-Lagrangian methodology. The Large eddy simulation (LES) is coupled with Lagrangian particle tracking to simulate slurry flow and erosion process in elbows. The erosion prediction model is validated using experimental data before investigating the unsteady erosion process in elbows with different radius. The results show that the unsteady secondary flow can be precisely captured using LES and thus the prediction accuracy of the fluid flow velocities and turbulent intensities are improved by this model comparing to RANS. The particles movement coupled with the unsteady secondary flow and boundary separation in elbows with different radius can be successfully revealed using this methodology. The LES Eulerian-Langragian erosion model presented is helpful to understand the flow and slurry erosion in elbows and improve the accuracy of the CFD-based prediction of slurry erosion rate.

Prediction of the internal corrosion rate for oil and gas pipeline: Implementation of ensemble learning techniques

This paper proposes a practical implementation of robust ensemble learning models for accurate prediction of the internal corrosion rate in oil and gas pipelines. A correct assessment of the corrosion rate in fluid flowed oil and gas pipelines has a significant influence on the system's safety and the ability to control operation. The developed predictive data driven models include four ensemble learning approaches, namely random forest, adoptive boosting, gradient boosting regression tree, and extreme gradient boosting. The implementation procedure of these predictive models integrates a comprehensive database of eight system descriptors, extracted from the literature, while k-fold cross validation is employed to guarantee high performance and generalization. In addition, the obtained results of the internal corrosion rate are subjected to rigorous statistical and graphical analysis to evaluate the models performance and compare their abilities. The extreme gradient boosting model indicate the highest performance in the prediction of the internal corrosion rate in oil and gas pipelines based on the calculated single and global metrics, with a mathematical RMSE value of internal corrosion rate 0.031 mm/y and performance index, PI = 0.61. Besides, the significance of the input variables is determined through a sensitivity analyses by using feature importance criteria, whereas for the applied dataset strongest corrosion rate dependency to temperature and the pressure was shown beside the CO2 contribution. In overall, the ensemble learning models show a significant performance in the internal corrosion rate predictions, while the extreme gradient boosting model is beneficial to model the internal corrosion rate in oil and gas pipelines due to its high performance.

Linkages between Forestry Best Management Practices and erosion in the southeastern U.S.

Numerous studies have concluded that forestry Best Management Practices (BMPs) are effective at mitigating erosion and sedimentation caused by forest operations; however, the complex relationship between forestry BMPs and erosion is largely unexamined. In this study, BMP implementation rates, which are percentages ranging from 0 to 100% that indicates how well an operator instituted recommended practices in the field, and predicted erosion rates, obtained by using USLE-Forest, were calculated for 108 recent harvests in twelve states and three physiographic regions in the southeastern U.S. BMP implementation rates were subdivided into three levels of application: BMP+ (>90% implementation), BMP-standard (80–90% implementation), and BMP− (<80% implementation). Skid trails (86.5 Mg ha−1 yr−1) and haul roads (90.3 Mg ha−1 yr−1) eroded at relatively high rates at the BMP− level across the southeast. This emphasizes the importance of adequate BMP measures such as utilizing water diversion structures and cover management at these features to better protect water quality. The overall weighted average erosion estimates for all regions at the BMP-standard (10.4 Mg ha−1 yr−1) and BMP+ (6.6 Mg ha−1 yr−1) levels were <11 Mg ha−1 yr−1, indicating that water quality and site productivity are largely protected when adequate BMPs are implemented, and Streamside Management Zones (SMZs) are utilized along streams. Approximately 94% of the sites sampled were classified as either BMP-standard or BMP+, demonstrating that BMPs are being implemented consistently throughout the southeast. Spearman ρ correlation analyses were performed for all variables. Forestry BMP implementation and erosion estimates had significant negative correlations, especially for skid trails (Spearman ρ = −0.59, p-value < 0.0001) and haul roads (Spearman ρ = −0.39, p-value = < 0.0001), as well as for all regions across the southeast. These variables, however, were poorly correlated for stream crossings, indicating that current audit questions in the southeast may not fully address erosion. Additionally, BMP implementation and erosion estimates exhibited a significant negative correlation (R2 = 0.28, p-value < 0.0001) based on a quadratic regression line for all features, reinforcing that as BMP implementation increases, predicted erosion generally decreases.

Multi-factor mining and corrosion rate prediction model construction of carbon steel under dynamic atmospheric corrosion environment

For vehicles and vessels like cars and ships, the practical environmental conditions encountered are consequences of dynamic and static operation, include moving off and stopping. Therefore, it is necessary to perform dynamic atmospheric exposure corrosion tests on metal materials under vehicle operating conditions. In this paper, a carbon steel dynamic atmospheric corrosion test was designed considering vehicle operating conditions. After completion of the corrosion test, the corrosion data was collected, processed and predicted by a series of methods and algorithms. Firstly, multiple dedicated test vehicles operating in Tianjin were chosen as carriers for carbon steel dynamic atmospheric corrosion test. Secondly, the driving characteristics data was collected using an on-board positioning data collection and transmission system. Then, the dynamic fuzzy clustering algorithm was introduced to identify the key influencing factors in the parameters of vehicle driving characteristics and to optimize data dimension, the two key index parameters of the vehicle driving characteristics identified being the average vehicle speed and dynamic/static ratio. During the atmospheric corrosion test, a time-weighted weighting algorithm model was constructed and applied to the previously collected meteorological and pollutant data in order to fit the sampling interval of corrosion data. Finally, four models including GA-SVR, unoptimized SVR, GA-BPNN and unoptimized BPNN were constructed to predict the corrosion rate of carbon steel in the dynamic environment. The final results show that the root mean square error and average relative error reached their minimum for GA-SVR with the prediction result R2 = 0.9771, all indicators suggest that GA-SVR holds an advantage over the three models including SVR, GA-BPNN and BPNN.

A Global Rain-Driven Soil Erosion Investigation Based on Simulated Breakpoint Precipitation

HighlightsAn international dataset of simulated breakpoint precipitation climate stations was used to overcome the limitations of fixed-interval precipitation in global soil erosion applications.The international simulated breakpoint dataset was validated against collocated high-quality, high-resolution precipitation data from a ground network and other data sources.The process-based Rangeland Hydrology and Erosion Model (RHEM) was used to predict erosion based on global climate classifications and soil properties.Critical precipitation factors in RHEM scenarios were identified based on their ability to predict erosion rates.Abstract. Recent research has highlighted problems with erosion modeling applications that use coarser fixed-interval precipitation data as opposed to breakpoint precipitation data, which better preserves precipitation characteristics such as intensity and duration. Due to their wider availability, erosion modeling applications and risk assessments are typically based on fixed-interval data. However, these applications could be subject to substantial erosion underestimation bias related to time-averaged precipitation. Alternatively, this manuscript presents a novel approach to global-scale erosion assessment based on simulated breakpoint precipitation data. A point-scale stochastic weather generator, CLIGEN, was used to generate precipitation events with characteristics more similar to breakpoint precipitation data than fixed-interval alternatives. An international CLIGEN dataset of climate parameters from more than 10,000 long-term climate stations in numerous countries was evaluated for use in parameterizing CLIGEN simulations. CLIGEN-simulated event characteristics and derived statistics such as annual rainfall erosivity were compared to high-quality, high-resolution NOAA-ASOS precipitation data where available. The Rangeland Hydrology and Erosion Model (RHEM) was used to predict runoff and soil loss based on the same CLIGEN inputs for simple bare soil scenarios with site-specific soil textures taken from the global 250m SoilGrids product. Erosion results were analyzed according to climate type, revealing that predicted distributions of sediment yield and runoff were statistically unique for most global climate types. A multivariate regression model was developed to explore and understand the importance of various precipitation input factors. Peak precipitation intensity was the most critical climate factor for determining sediment yield, and combined CLIGEN precipitation factors had approximately as much predictive power as soil texture. Average annual rainfall erosivity values were calculated for each location and were 25% and 20% greater than values from RUSLE2 and Panagos et al. (2017) estimates, respectively. This finding is in agreement with the latest research on the topic. Keywords: ASOS, CLIGEN, Erosivity, Global soil erosion, Machine learning, RHEM, Simulated breakpoint precipitation.

Analysis of graphite nozzle throat erosion in liquid rockets using ethanol/LOX

Interstellar Technologies, Inc. has been developing a series of liquid rockets using ethanol/LOX called MOMO. These rockets use graphite nozzle throat inserts for which nozzle erosion, referring to the increase in diameter of the nozzle throat due to the chemical reactions between the combustion gas and the graphite, has become a problem. This study aims to develop a predictive equation for the nozzle erosion rate of liquid rockets using ethanol/LOX. The authors analyzed data from seven long-duration combustion experiments and calculated the nozzle erosion rate. Using the heat transfer coefficient of the nozzle throat, the heat transfer equation at the nozzle throat was solved and the nozzle wall temperature was also estimated. As a result, it was found that the nozzle erosion phenomenon of liquid rockets using ethanol/LOX has a similar trend to that of nozzle erosion in hybrid rockets using HDPE/LOX. A good correlation was obtained by using the nozzle erosion rate prediction equation of Kamps et al., which has chamber pressure, equivalence ratio and nozzle wall temperature as parameters.

Experimental investigation and empirical modeling for corrosion rate prediction of biodegradable zinc based composite considering the effect of constituent materials

Biodegradable zinc (Zn) has shown great potential in the area of biomedical applications. Though, the mechanical properties are decisive for the use of Zn for orthopedic and cardiovascular applications. Consequently, one needs to focus on improving the mechanical properties of Zn for its suitability in biomedical applications. Alloying of essential elements of the human body resulted in enhancement of Zn’s mechanical properties in recent years. The corrosion rate of pure Zn is ideal; however, the addition of other elements has resulted in a loss of its ideal corrosion rate. The inclusion of hydroxyapatite (HA) and iron (Fe) in Zn has also been reported in improving the mechanical properties. Hence, a need is raised for the development of a model which can predict the corrosion rate after adding HA along with Fe in Zn. In this research work, empirical based modeling is proposed to predict the corrosion rate, which incorporates the outcome of addition of Fe and HA in Zn. The Zn based materials were fabricated with the help of microwave sintering for developing the empirical model. The corrosion properties of the materials were assessed through a potentiodynamic polarization test in a simulated body fluid solution. The enhanced corrosion rate was attained with the rise in HA (wt%) and Fe (wt%) in Zn. An empirical correlation was established between the influencing controlling parameters (i.e., corrosion current, equivalent weight, and material density) of corrosion rate. Confirmation experiments were conducted to validate the developed model, and the highest error of 6.12% was obtained between the experimental and predicted values exhibiting the efficaciousness of the proposed model.

Macrocell Corrosion of Steel in Concrete under Carbonation, Internal Chloride Admixing and Accelerated Chloride Penetration Conditions.

Steel corrosion has become the main reason for the deterioration of reinforced concrete structures. Due to the heterogeneity of concrete and the spatial variation of environmental conditions, macrocell corrosion is often formed by localized corrosion, which is more detrimental if the anode is supported by large numbers of cathodes. The macrocell corrosion caused by concrete carbonation has been seldom studied. Furthermore, the influence of geometrical conditions on cathode-controlled corrosion in the chloride environment needs to be further clarified. In this work, the macrocell corrosion of steel embedded in concrete specimens exposed to accelerated carbonation, chloride contamination, and chloride penetration is studied using a modified ASTM G109 method. Concrete specimens with various binder types, geometrical parameters (i.e., concrete cover thickness and the diameter of embedded steel), and boundary conditions were tested. A simplified mathematical model for the prediction of the steel corrosion rate was developed considering two-dimensional oxygen diffusion. The results showed that, at the same level of anodic potential drops, the corrosion current rate in chloride-induced corrosion is higher than that of carbonation-induced corrosion. Chloride contamination is less detrimental to concrete incorporated with slag and pulverized fly ash than it is to pure ordinary Portland cement (OPC), likely due to enhanced chloride binding capacity. The results also indicated that the model considering two-dimensional diffusion can accurately predict the cathodic reaction process on corroded steel bars, which provides a theoretical basis for considering the correction coefficient of steel bar position in the establishment of a steel bar corrosion rate model.