Prediction Of Corrosion Rate Research Articles

Investigation of Corrosion Rate of Mild Steel in Fruit Juice Environment Using Factorial Experimental Design

Although little attention is paid to corrosion study in food processing and production industry, it is highly important to take a critical look at what transpires at the production stages and how these can affect human health due to contamination of the final product. This research employs the design of experiment module of a Minitab software to analyze the corrosion effect of mild steel in fruit juice environment. Coupon method was adopted to calculate the rate of corrosion in orange, pineapple, and cashew fluid for a duration of 25 days with measurement taken at 5-day intervals. The highest corrosion rate of 0.71 mmpy was observed in cashew fluid followed by pineapple fluid, and the least rate of 0.08 mmpy was observed in orange fluid. The generalized model equation was obtained to predict corrosion rate of mild steel in a similar environment. In general, design of experiment offers a better way to analyze corrosion experiment result, and this innovative idea is shown in this work.

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

ABSTRACT The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (R2 values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.

A probability prediction model of erosion rate for Ti-6Al-4V on high-speed sand erosion

When a helicopter flies in sand and dust environment or encounters sandy weather, the sand entering the engine compressor can cause severe erosion damage to the blades. This paper aims to deduce an erosion rate prediction model for titanium alloy blade material which can take into account the probability factors such as particle size, velocity and sand fracture. Methods to judge whether sand fractured and quantify the effect of sand fragmentation on erosion damage were obtained. The probability relationship between particle size and mass ratio, impact kinetic energy and fracture rate, particle size range and impact velocity were studied. It is verified that the model can not only adapt to the high-speed erosion conditions, but also adapt to the probability characteristics of many impact factors. The angle of maximum erosion rate under relative high-speed erosion condition is significantly larger than that of under relatively low-speed erosion condition.

Computational fluid dynamic analysis for investigating the influence of pipe curvature on erosion rate prediction during crude oil production

The flow dynamics in pipes is a very complex system because it is largely affected by flow conditions. The transport of crude oil in pipelines within unconsolidated petroleum reservoirs is associated with presence of solid particles. These particles are often transported as dispersed phases during crude oil production and are therefore detrimental to the pipe surface integrity. This could lead to the occurrences of crevice corrosion due to pipe erosion. In relation to the above discussion, this paper is aimed at analyzing crude oil dynamics during flow through pipeline and identifying erosion hotspot for different pipe elbow curvatures. Reynolds Averaging Navier-Stokes (RANS) and Particle Tracing Modeling (PTM) approach were used. The focus is to simulate fluid dynamics and particle tracing, respectively. Post-processed results revealed that the fluid velocity magnitude was relatively high at the region with minimum curvature radius. The maximum static pressure and turbulence dissipation rate were experienced in areas with low-velocity magnitude. Also, the rate of erosive wear was relatively high at the elbow and the hotspot varied with pipe curvature. The particle flow rate, mass, and size were varied and it was found that erosion rate increased with an increase in particle properties.

Requirement and Calculation of Corrosion Allowance for Piping and Valves in the Oil and Gas Industry

“Acid Gases” refer to two of the undesirable by-products of oil and gas, namely CO2 and H2S$$.$$ The reaction of H2S and other sulphur compounds with water increases the degree of “sour corrosion” by forming sulphuric acid. CO2 corrosion of carbon and alloy steels is designated as “Sweet Corrosion” which is defined as the deterioration of metal components resulting from contact with gas or solutions including both CO2 and water. CO2 corrosion is an important problem in the oil and gas industry due to metal loss and its severe effects in terms of localized corrosion. Accurate prediction and modelling of CO2 corrosion rates for carbon and alloy steel pipes are vital tasks at the basic design phase of oil, gas and petrochemical projects in order to determine whether to consider additional wall thickness for the pipes and valves defined as “Corrosion Allowance (CA)” or to change the pipes’ or valves’ base materials to “Corrosion Resistance Alloys (CRA)”. Other corrosion mitigation approaches such as injecting the corrosion inhibitors and glycol or methanol can reduce the metal loss and CO2 corrosion. This paper proposes a practical model to calculate and select the corrosion allowance for piping and valves in oil and gas industry. Different process parameters such as scaling, fugacity, pH, glycol and corrosion inhibitor injection, water cut, operating pressure and temperature as well as CO2 partial pressure have been applied in the model.

A study to analysis of corrosion rate of uncoated and coated WC- Co- Cr naval brass materials

In this work, use of high velocity oxy-fuel (HVOF) spraying technique was explored as a way of producing coatings with a resistance to corrosion and erosion. Response surface methodology (RSM) was employed to develop the empirical relationships to predict corrosion rate of uncoated and coated WC-Co-Cr naval brass materials. Dissolution of naval brass and formation of naval hydroxide are the key factors for immersion corrosion. Atomic microscope analysis shows that the corroded surface of uncoated naval brass shows higher Ra value is owing to the surface damage. Coated naval brass show less undulations on the analyzed region.

Wind erosion rate for vegetated soil cover: A prediction model based on surface shear strength

Vegetation is regarded as the most effective measure for soil erosion control and sand control projects, which plays an important role in controlling soil wind erosion and sand disasters in arid and semiarid regions. Most of the existing models for soil wind erosion rate prediction are based on empirical results that consider factors such as vegetation coverage and wind speed but have rarely investigated the wind erosion rate from the shear strength perspective, which is also an important reason why it is difficult for existing wind erosion models to be popularized. In fact, the presence of vegetation alters the distribution of the near-surface airflow field and thus profoundly changes the magnitude and spatial distribution of shear stress generated by airflow on soil particles. According to existing theories, it is very difficult to describe these processes. Although the shear stress on a vegetation-covered soil surface may be measured using existing observation techniques, difficulties in technology make it impractical for conducting at a large scale. Consequently, research that combines soil wind erosion and the shear strength of vegetation-covered soil surfaces has rarely been reported. Therefore, the prediction of discrete wind erosion rates considering the uneven spatial distribution of the airflow field has gradually become a new trend in the study of the wind erosion of vegetation-covered soil surfaces. In this study, the model for the airflow field of a single plant and the conversion model for the airflow field from a single plant to multiple plants were examined, based on which parameters, such as vegetation characteristics and plant density in wind tunnel tests, were combined to simulate the spatial distribution of the airflow field over vegetation cover surface under four different vegetation coverages. A method for calculating the effective shear strength of the vegetation-covered soil surface was proposed, and the effective shear strength of the surface with different vegetation covers was determined. The effective shear strength of the vegetation-covered soil surface was found to increase with increasing wind speed, decrease with increasing vegetation coverage, and decrease with increasing particle size. On this basis, the relationship between the wind erosion rate q and the effective shear strength τeff¯ of the vegetation-covered soil surface was quantitatively established as q=kd/g·τeff¯, where k = 114.02. Findings of this study help to explain the wind erosion dynamics of a vegetation-covered soil surface and can also provide a solid foundation for constructing a wind erosion prediction model based on the theory of dynamics.

Soil erosion rates assessed by RUSLE and PESERA for a Chinese Loess Plateau catchment under land‐cover changes

ABSTRACTOn the Chinese Loess Plateau, soil erosion models are often employed to predict erosion rates and responses to land‐use/‐cover changes (LUCCs). Previous Loess Plateau studies employed individual models with specific emphases but model comparisons have not been undertaken so the relative performance of different models is not known. In this study we employed two extensively applied models (RUSLE and PESERA) to investigate the impact of LUCCs during 1990–2000 and 2000–2011 on soil erosion rates for a typical Loess Plateau catchment (i.e. Huangfuchuan), and compared their modelling results. Land‐cover patterns for 1990, 2000 and 2011 were derived from Landsat images. The catchment was dominated by grassland (over 70%) and experienced considerable LUCCs: vegetation coverage increased from 38.3% in 1990 to 48.7% in 2011. Modelling results suggested that mean soil erosion rates of the catchment increased under the 1990–2000 LUCC and decreased under the 2000–2011 LUCC. Sandy land and scrubland were found to suffer from most severe soil erosion and thus should be the focus of future conservation work. Mean soil erosion rates on steep slopes (i.e. > 25°) were predicted to increase under the 2000–2011 LUCC, implying that further work is still needed to study soil erosion processes and their conservation on steep slopes. Model comparisons showed that RUSLE predictions were higher than PESERA predictions for most areas (particularly for steep slopes), and the former were generally closer than the latter to check‐dam sediment yield measurements. RUSLE and PESERA results were not linearly correlated, possibly due to differences in their underlying principles and their sensitivity to crucial parameters – RUSLE is more sensitive to slope gradients while PESERA is more sensitive to vegetation coverage. PESERA needs improvement to better account for steep slope erosion processes on the Loess Plateau, while RUSLE needs improvement in the description of vegetation effects. © 2019 John Wiley & Sons, Ltd.

Predicting Corrosion rate in Chilled HVAC Pipe Network: Coupon vs Linear Polarisation Resistance method

Research has been undertaken to predict corrosion rate of conveyance pipes used in a Heating Ventilation and Air Conditioning (HVAC) system. This work focuses on comparing the performance of a conventional corrosion predicting technique known as the “coupon method” versus a relatively modern technique called Linear Polarisation Resistance (LPR). An experimental study has been conducted by designing a relatively modern rig that was representative of a typical HVAC system. It was found that both the coupon method and the LPR probe predicted similar corrosion rates with a difference of 8.3% which is insignificant in practical applications. Four highly important in-situ scenarios were simulated for a water temperature range of 12–32 °C, and it was found that an LPR probe with corroded electrodes over-predicted corrosion rate by 20% compared to a clean LPR probe. Also, a probe in use for 2 months over-predicted corrosion by 44% which is a significant error. A pipe network dosed with inhibitor was observed to reduce the rate of corrosion by 50% and 25% for water temperature of 12 °C and 32 °C respectively. It was also determined that one degree (oC) increase in water temperature will increase the corrosion rate by 3.5% on average. It has been identified that both coupon and LPR probes give similar results though both have limitations and continuous monitoring of corrosion is the way to insure a low maintenance HVAC pipe network.

Prediction of Corrosion Rate in Submarine Multiphase Flow Pipeline Based on PSO-SVM Model

In view of the internal corrosion rate of submarine multiphase flow pipelines, this paper analyzes the related factors affecting the corrosion rate of this type of pipeline, and introduces PSO algorithm and SVM algorithm respectively. Based on the PSO-SVM combination model, the 44 groups of data was used to study the influencing factors and corrosion rate, meanwhile the 10 groups of data was used to predict. The predictions are compared with the GA-SVM model, the LS-SVM model and the CV-SVM model to verify the advancement and feasibility of the proposed method. The results show that the temperature has a relatively large influence on the corrosion rate of the multiphase flow pipeline in the seabed. The influence of pressure on the corrosion rate of the multiphase flow pipeline in the seabed is relatively small. The PSO-SVM combined model is used in the submarine multiphase flow pipeline. The e error of corrosion rate prediction is only 1.848% on average, and the model training time is only 3.17s, both of which are smaller than other models. The research proves that the PSO-SVM combination model has strong advancement and feasibility for the prediction of the internal corrosion rate of submarine multiphase flow pipelines.

Remaining Life and Corrosion Rate Prediction of Well Casing Material in the CO2 Environment

Prediction of CO2 corrosion is required for predicting remaining life and determination of the required material selection. This paper give prediction of corrosion rate and remaining life of casing in the North Aceh well which is operated almost 14 years. Simulation method application is appropriate with the condition of North Aceh field since the well fluids contain high CO2 gas (35 – 37%) and small amount of H2S gas (0.016%). Based on historical data North Aceh well’s after production almost 14 years, it is possible that production packer leaks and degradation of cement could cause formation gas migrate through micro channelling inside cement. The CO2 gas which is accumulate in casing could cause CO2 corrosion. The corrosion rate calculation based on temperature 141°C, pressure 103.4 Barg and CO2 contain 37% is 0.871 mm/year. After calculating the thickness loss, we found thickness loss of casing K55 is 12.741 mm. meanwhile thickness of K55 casing 10.92 mm, it means the casing already leak before year 14th. Based on simulation and calculation the North Aceh well is assumed to have tubing and packer leaks in annulus #0 and channeling on annulus #1 so that the well is not recommended for re-production because of the communication between the annulus and the loss of the well barrier that can cause wellhead leakage. This condition is very dangerous because the well fluid is flammable and can cause pollution in the sea.

ARAHAN PENGELOLAAN DAERAH ALIRAN SUNGAI BERDASARKAN INDEKS BAHAYA EROSI (IBE) SUB DAS PITU RIASE KABUPATEN SIDRAP

The purpose of this study is 1)Analyzing the level of erosion that occurs in various land uses in the Pitu Riase sub-watershed, Sidrap Regency. 2)Assess tolerable erosion rates (TSL). 3)Analyze the erosion hazard index (E HI ).4)Determine recommendations for land use in the Pitu Riase sub-watershed in Sidrap Regency. This research was carried out in the Pitu Riase Sub-watershed, Sidenreng Rappang Regency from February to April 2018. As technology developed, there were several general methods that could be used to predict erosion rates. The USLE (Universal Soil Loss Equation) method is one of the commonly used methods. The results showed that erosion that occurred in the Pitu Riase sub-watershed was 1.8 tons/ha/year for Primary Dryland Forests, 4.97 tons/ha/year in secondary dryland forests, 85.37 tons/ha/year in mixed dryland agriculture, and 58.03 tons/ha/year in shrubs and erosion that can be tolerated is 11.99 tons/ha/year in primary dryland forests, 11.64 tons/ha/year in dryland forests secondary, 8.96 tons/ha/year in mixed dryland agriculture, and 11.35 tons /ha/year in shrubs. E HI in the Pitu Riase sub-watershed is classified as heavy on mixed dry land and scrub. The recommendations for land use in the Pitu Riase sub-watershed in Sidrap Regency for mixed dryland farming are the addition of mulch on agricultural land and the creation of a terrace to reduce erosion.

Submarine platform development by erosion of a Surtseyan cone at Capelinhos, Faial Island, Azores

AbstractErosion of volcanic islands ultimately creates shallow banks and guyots, but the ways in which erosion proceeds to create them over time and how the coastline retreat rate relates to wave conditions, rock mass strength and other factors are unclear. The Capelinhos volcano was formed in 1957/58 during a Surtseyan and partly effusive eruption that added an ~2.5 km2 tephra and lava promontory to the western end of Faial Island (Azores, central North Atlantic). Subsequent coastal and submarine erosion has reduced the subaerial area of the promontory and created a submarine platform. This study uses historical information, photos and marine geophysical data collected around the promontory to characterize how the submarine platform developed following the eruption. Historical coastline positions are supplemented with coastlines interpreted from 2004 and 2014 Google Earth images in order to work out the progression of coastline retreat rate and retreat distance for lava‐ and tephra‐dominated cliffs. Data from swath mapping sonars are used to characterize the submarine geometry of the resulting platform (position of the platform edge, gradient and morphology of the platform surface). Photographs collected during SCUBA and ROV dives on the submarine platform reveal a rugged surface now covered with boulders. The results show that coastal retreat rates decreased rapidly with time after the eruption and approximately follow an inverse power‐law relationship with coastal retreat distance. We develop a finite‐difference model for wave attenuation over dipping surfaces to predict how increasing wave attenuation contributed to this trend. The model is verified by reproducing the wave height variation over dipping rock platforms in the UK (platform gradient 1.2° to 1.8°) and Ireland (1.8°). Applying the model to the dipping platform around Capelinhos, using a diversity of cliff resistance predicted from known lithologies, we are able to predict erosion rate trends for some sectors of the edifice. We also explore wider implications of these results, such as how erosion creates shallow banks and guyots in reef‐less mid‐oceanic archipelagos like the Azores. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.

Soil Disturbance Effects from Tethered Forwarding on Steep Slopes in Brazilian Eucalyptus Plantations

Traditional timber harvests on steep slopes have been conducted through labor-intensive and sometimes environmentally impactful methods, such as manual felling with chainsaws and extraction using bladed skid trails, winching, or cable yarding. Ground-based mechanized harvesting and primary transportation methods such as cut-to-length harvesters and forwarders have emerged in some parts of the world as low-impact, safe, and efficient alternatives to the aforementioned systems. However, when mechanized operations are used on steep terrain, problems such as poor stability, loss of traction, and increased soil disturbance can occur. Tethered or winch-assisted logging practices are being tested and applied in several countries to adapt to challenges associated with operating equipment on steep slopes while minimizing environmental impact. To better understand the feasibility of these systems, we conducted a designed experiment to quantify changes in soil properties and predicted erosion resulting from varying numbers of passes and payload levels by a forwarder operating on slopes ranging from 27 to 38 degrees. The machine was equipped with two different track configurations, tethered by either a machine-mounted or self-contained winch, in eucalyptus plantations in Brazil. On low slopes, bulk density significantly increased, but it did not increase on steeper slopes; this demonstrates traction winches’ effectiveness at reducing concentrated ground pressures. Rut depths were minimal and decreased with increasing slope classes due to reduced track slippage. Predicted erosion rates were high, primarily due to the extremely steep, long slopes and lack of adequate cover in some portions of the trail, illustrating the importance of proper erosion management practices on steep slopes.

Erosion rate prediction model ssing SWAT and CA-Markov methods (case study: Upper Ci Catih Catchment Area)

Landuse has a vital role that affects the movement of water on hidrological cycle. Around Upper Ci Catih Catchment Area in the past 10 years, many of various industries (ranging from smal-scale to large-scale industries) have increased. Increasing built-up area, especially settlements and industries can increase runoff that have an impact on land degradation such as erosion. This study focuses on the hydrological modelling made by SWAT (Soil Water Assesment Tools) and landuse change prediction using CA-Markov (Cellular Automata-Markov Chain) to find the effects of landuse changes on the erosion rate changes. Erosion rate prediction in 2032 based on landuse changes prediction in that year. This study shows that variations of Hydrologic Response Unit (HRU) conditions affects the erosion rate in every subwatershed. The result of accuracy and calibration test satisfied, which NS and R2 mark for SWAT validation is 0,56 and 0,72, and kappa coefficient value for CA-Markov model validation by 89%. The changes of landuse especially forest and irrigated rice fields is predicted in 2032 will decrease in every Subbasin. It will affect the increase of average erosion rate in every Subbasin by 15% of the erosion rate in 2017 on Upper Ci Catih Catchment Area.

Experimental investigation on solid particle erosion behaviour of glass/epoxy Quasi-isotropic laminates

The erosion behaviour of glass/epoxy quasi-isotropic laminates were investigated using Aluminium Oxide as erodent. Mass loss of the target material was used to estimate the erosion rate. An erosion rate prediction model was developed using Response Surface Methodology by considering impact angle, impact velocity and feed rate as the input variables. Analysis of variance of the results predict that impact angle and velocity were the major independent parameters influencing the erosion rate. Higher impact angle and lower feed rate exhibited high erosion rate; lower velocity and high feed rate resulted in lower erosion rate. Surface morphology of the specimens with low and high erosion resistance were studied using scanning electron microscopic technique.

Prediction of corrosion rate in SA 283 material affected by differences soil type in controlled environments.

Research has been done on corrosion rates growth in SA 283 material which is influenced by differences in soil types in controlled environments. A283 grade D materials which are carbon steel formed into pipes with a diameter of 30" which is used to distribute raw water for industrial needs. Weight loss methods that are in accordance with the ASTM G 162 standard have been used in this study. The buried time of the test specimen is 720 hours as one of the variables in calculating the value of the corrosion rate that occurs. The study was conducted at the Laboratory by controlling the soil structure, reducing potential, moisture level, soil pH level so that it did not change. The results of the study on soil types with a potential redox value of 60.6 mV with a pH level of 5.67 showed the greatest corrosion rate in material A283 that is 19.67 MPY or equal to 0.499 mm/yr. The results of this study concluded that the type of soil and its environment greatly affect the corrosion rate of carbon steel, so special attention is needed in protecting the pipe from corrosion attack.

Pitting Depth Prediction Caused by SRB using Empirical Equation

Microbiologically influenced corrosion (MIC) is a serious problem in the oil and gas industry. The most common microorganism responsible for MIC is sulfate-reducing bacteria (SRB) which produces detrimental sulfide ions into the environment. Currently, there are some prediction models that develop to predict corrosion rate caused by SRB. However, among the models, the prediction is limited to predict the general corrosion rate, whereas, SRB caused localized corrosion. Thus, the objective of this work is to predict the pitting depth caused by SRB using available empirical equation. The study showed that the pitting depth increased with the increasing of sulfide concentration. In contrast, the pitting depth decreased with increasing sulfite concentration. The decreasing of pitting depth is related to the inhibitive FeS film formed, while the increasing of pitting depth is caused by the decreasing of the film thickness in the presence of sulfite.

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion.

Corrosion rate prediction for metals in biodiesel using artificial neural networks

The objective of this research was to develop a direct artificial neural network with the ability to predict a corrosion rate of metals in different biodiesel. Experimental values were obtained by the electrochemical noise technique, EN, as well as, information reported in the literature. A backpropagation model was proposed with three layers; metal and biodiesel composition, blend biodiesel/diesel, total acid number (TAN), temperature and exposure time were considered as input variables in the model. The best fitting training data were acquired with 24:4:1, considering a Levenberg –Marquardt learning algorithm, a hyperbolic tangent and linear transfer functions in the hidden and output layer respectively. Experimental and simulated data were compared satisfactorily through the linear regression model with a correlation coefficient of 0.9885 and a mean square error, MSE, of 2.15 × 10−4 in the validation stage. Furthermore, the model agreed the requirements of the slope and the intercept statistical test with a 99% confidence. The obtained results indicated that the ANN model could be attractive as corrosion rate estimator.