scholarly journals Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Qing Li ◽  
Xiaojian Xia ◽  
Zibo Pei ◽  
Xuequn Cheng ◽  
Dawei Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Corrosion Rate ◽  
Atmospheric Corrosion ◽  
Chloride Concentration ◽  
Carbon Steels ◽  
Sensor Data ◽  
Corrosion Monitoring ◽  
Atmospheric Conditions ◽  
Data Points

AbstractIn this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.

Methodology to Improve Corrosion Rate Estimation Based on Atmospheric Corrosion Monitoring Sensors

CORROSION ◽  
10.5006/2234 ◽  
2017 ◽  
Vol 73 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Norikazu Fuse ◽  
Atsushi Naganuma ◽  
Tetsuo Fukuchi ◽  
Jun-ichi Tani ◽  
Yasuhiko Hori
Keyword(s):  
Corrosion Rate ◽  
Atmospheric Corrosion ◽  
Corrosion Monitoring ◽  
Rate Estimation

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

2020 ◽  
Vol 61 (12) ◽  
pp. 2348-2356
Author(s):  
Wanida Pongsaksawad ◽  
Namurata S. Palsson ◽  
Piya Khamsuk ◽  
Sikharin Sorachot ◽  
Amnuaysak Chianpairot ◽  
...  
Keyword(s):  
Corrosion Rate ◽  
Atmospheric Corrosion ◽  
Corrosion Monitoring ◽  
Rate Prediction

scholarly journals EDISON: An Edge-Native Method and Architecture for Distributed Interpolation

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2279
Author(s):  
Lauri Lovén ◽  
Tero Lähderanta ◽  
Leena Ruha ◽  
Ella Peltonen ◽  
Ilkka Launonen ◽  
...  
Keyword(s):  
Smart Cities ◽  
Sensor Data ◽  
Fine Grained ◽  
Distributed Approach ◽  
Computing Paradigm ◽  
Temporal Interpolation ◽  
Data Points ◽  
Alternative Approaches ◽  
Spatio Temporal

Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach.

scholarly journals Analysis of Environmental Factors Affecting the Atmospheric Corrosion Rate of Low-Alloy Steel Using Random Forest-Based Models

Materials ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3266 ◽  
Author(s):  
Luchun Yan ◽  
Yupeng Diao ◽  
Kewei Gao
Keyword(s):  
Random Forest ◽  
Environmental Factors ◽  
Corrosion Rate ◽  
Alloy Steel ◽  
Atmospheric Corrosion ◽  
Test Site ◽  
Training Data ◽  
Low Alloy Steel ◽  
Exposure Test ◽  
Corrosion Data

As one of the factors (e.g., material properties, surface quality, etc.) influencing the corrosion processes, researchers have always been exploring the role of environmental factors to understand the mechanism of atmospheric corrosion. This study proposes a random forest algorithm-based modeling method that successfully maps both the steel’s chemical composition and environmental factors to the corrosion rate of low-alloy steel under the corresponding environmental conditions. Using the random forest models based on the corrosion data of three different atmospheric environments, the environmental factors were proved to have different importance sequence in determining the environmental corrosivity of open and sheltered exposure test conditions. For each exposure test site, the importance of environmental features to the corrosion rate is also ranked and analyzed. Additionally, the feasibility of the random forest model to predict the corrosion rate of steel samples in the new environment is also demonstrated. The volume and representativeness of the corrosion data in the training data are considered to be the critical factors in determining its prediction performance. The above results prove that machine learning provides a useful tool for the analysis of atmospheric corrosion mechanisms and the evaluation of corrosion resistance.

scholarly journals Optimum selection of zinc-coated cable trunking systems for electrical installations based on atmospheric corrosion prediction

2018 ◽  
Vol 1 (2) ◽  
pp. 5-19
Keyword(s):  
Atmospheric Corrosion ◽  
Environmental Parameters ◽  
First Year ◽  
Logarithmic Function ◽  
Atmospheric Conditions ◽  
Optimum Selection ◽  
Selection Of

The study presents a methodology for the optimum selection of the most suitable zinc-based coatings in metallic trunking systems to fulfill the requirements related to atmospheric corrosion resistance. The current methodologies are based on heuristic procedures that do not consider the influence of the in situ atmospheric conditions, which are the main cause of most of the corrosion problems. The effect of corrosion over time is generally estimated using a logarithmic function, which depends on corrosion during the first year of exposure, as well as on environmental parameters (e.g. temperature, humidity, pollutants, etc.). Different mathematical models for the prediction of corrosion during the first year of exposure were analyzed. Ten of these models were selected and compared with actual tests determining the model that best fitted the actual values. From this first-year corrosion value, the long-term corrosion function was calculated for each relevant commercial coating. Finally, a case study was analyzed by means of the proposed methodology. The results show the importance of the corrosion function and its influence in the selection of the coating to minimize costs.

A Neural Based Fuzzy Logic Model to Determine Corrosion Rate for Carbon Steel Subject to Corrosion under Insulation

2015 ◽  
Vol 789-790 ◽  
pp. 526-530 ◽  
Author(s):  
Muhammad Mohsin Khan ◽  
Ainul Akmar Mokhtar ◽  
Hilmi Hussin
Keyword(s):  
Carbon Steel ◽  
Corrosion Rate ◽  
Degradation Mechanism ◽  
Fuzzy Model ◽  
American Petroleum Institute ◽  
Carbon Steels ◽  
Corrosion Monitoring ◽  
Absolute Deviation ◽  
External Corrosion ◽  
Corrosion Under Insulation

One of the most common external corrosion failures in petroleum and power industry is due to corrosion under insulation (CUI). The difficulty in corrosion monitoring has contributed to the scarcity of corrosion rate data to be used in Risk-Based Inspection (RBI) analysis for degradation mechanism due to CUI. Limited data for CUI presented in American Petroleum Institute standard, (API 581) reflected some uncertainty for both stainless steels and carbon steels which limits the use of the data for quantitative RBI analysis. The objective of this paper is to present an adaptive neural based fuzzy model to estimate CUI corrosion rate of carbon steel based on the API data. The simulation reveals that the model successfully predict the corrosion rates against the values given by API 581 with a mean absolute deviation ( MAD ) value of 0.0005, within that the model is also providing its outcomes for those values even for which API 581 has not given its results. The results from this model would provide the engineers to do necessary inferences in a more quantitative approach.

scholarly journals Corrosion Monitoring of Reinforced Steel Embedded in Cement Mortar under Wet-And-Dry Cycles by Electrochemical Impedance Spectroscopy

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 199
Author(s):  
Je-Kyoung Kim ◽  
Seong-Hoon Kee ◽  
Cybelle M. Futalan ◽  
Jurng-Jae Yee
Keyword(s):  
Corrosion Rate ◽  
Marine Environment ◽  
Cement Mortar ◽  
Electrochemical Impedance ◽  
Chloride Concentration ◽  
Primary Objective ◽  
Single Frequency ◽  
Corrosion Monitoring ◽  
Drying Time ◽  
Transfer Resistance

The primary objective of the present work is to measure the corrosion rate of reinforcing steel embedded in concrete structures in a simulated marine environment of high chloride concentration. The selection of a single frequency that corresponds to the solution resistance and single frequency that corresponds to the charge transfer resistance were performed and measurements were carried out in a relatively faster time. A total of seven cement mortar specimens were prepared. The effect of varying cover thickness (5–50 mm) and rebar distance (10–80 mm) on the electrical resistance of the concrete and corrosion rate of the steel was examined. To simulate the corrosion of reinforced concrete in a marine environment, cement mortars were exposed to 25 wet–dry cycles that involve an immersion for 8 h in 3 wt.% NaCl solution and drying time of 16 h under room temperature. Alternative current (AC) impedance measurements were carried out within a frequency range from 100 kHz to 1 mHz. Results show that the formation of rust layers on rebars has caused a significant decrease in the maximum phase shift to θ = −30°. An accelerated corrosion rate of the rebars was observed during drying stage.

scholarly journals Corrosion map of South Africa’s macro atmosphere

2019 ◽  
Vol 115 (7/8) ◽  
Author(s):  
Darelle T. Janse van Rensburg ◽  
Lesley A. Cornish ◽  
Josias van der Merwe
Keyword(s):  
South Africa ◽  
Corrosion Rate ◽  
Mild Steel ◽  
Atmospheric Corrosion ◽  
First Year ◽  
Corrosion Monitoring ◽  
Coastal Environments ◽  
Coastal Regions ◽  
Corrosion Rates ◽  
The Impact

The first atmospheric corrosion map of South Africa, produced by Callaghan in 1991, has become outdated, because it primarily focuses on the corrosivity of coastal environments, with little differentiation given concerning South Africa’s inland locations. To address this problem, a study was undertaken to develop a new corrosion map of the country, with the emphasis placed on providing greater detail concerning South Africa’s inland regions. Here we present this new corrosion map of South Africa’s macro atmosphere, based on 12-month corrosion rates of mild steel at more than 100 sites throughout the country. Assimilations and statistical analyses of the data (published, unpublished and new) show that the variability in the corrosion rate of mild steel decreases significantly moving inland. Accordingly, the average first-year corrosion rate of mild steel at the inland sites (at all corrosion monitoring spots located more than 30 km away from the ocean) measured 21±12 μm/a [95% CI: 18–23 μm/a]. The minimum inland figure was about 1.3 μm/a (recorded at Droërivier in the Central Karoo) and the maxima were approximately 51 μm/a and 50 μm/a in the industrial hearts of Germiston (Gauteng) and Sasolburg (Free State), respectively. The variability in the corrosion rate of mild steel also decreased by as much as 80% between 150 m and 1000 m from the coastline. Moreover, the impact of changing altitude on the corrosivity of the environment was confirmed, particularly along the coastal regions.

DYNALLOY

Alloy Digest ◽  
1957 ◽  
Vol 6 (4) ◽  
Keyword(s):  
Corrosion Resistance ◽  
Physical Properties ◽  
Tensile Properties ◽  
Atmospheric Corrosion ◽  
High Strength ◽  
Heat Treating ◽  
Carbon Steels ◽  
Rolled Steel ◽  
Bend Strength ◽  
Steel Company

Abstract DYNALLOY is a versatile low-alloy, high-strength, flat rolled steel which combines high physical properties with ductility and weldability. It has higher atmospheric corrosion resistance, and also higher resistance to abrasion, impact and fatigue than plain carbon steels. This datasheet provides information on composition, tensile properties, and bend strength as well as fatigue. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: SA-56. Producer or source: Alan Wood Steel Company.

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