Computerized Long-Term Corrosion Data

2009 ◽  
pp. 43-43-18
Author(s):  
DO Spatig ◽  
WH Ailor
Keyword(s):  
Corrosion Data

Long-term prediction on atmospheric corrosion data series of carbon steel in China based on NGBM(1,1) model and genetic algorithm

2019 ◽  
Vol 66 (4) ◽  
pp. 403-411 ◽  
Author(s):  
Yuanjie Zhi ◽  
Dongmei Fu ◽  
Tao Yang ◽  
Dawei Zhang ◽  
Xiaogang Li ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Carbon Steel ◽  
Atmospheric Corrosion ◽  
Data Series ◽  
Data Sets ◽  
Content Type ◽  
Term Prediction ◽  
Long Term Prediction ◽  
Corrosion Data

PurposeThis study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.Design/methodology/approachThis paper presents a new method, used to the collected corrosion data of carbon steel provided by the China Gateway to Corrosion and Protection, that combines non-linear gray Bernoulli model (NGBM(1,1) with genetic algorithm to attain the purpose of this study.FindingsResults of the experiments showed that the present study’s method is more accurate than other algorithms. In particular, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method in data sets are 9.15 per cent and 1.23 µm/a, respectively. Furthermore, this study illustrates that model parameter can be used to evaluate the similarity of curve tendency between two carbon steel data sets.Originality/valueCorrosion data are part of a typical small-sample data set, and these also belong to a gray system because corrosion has a clear outcome and an uncertainly occurrence mechanism. In this work, a new gray forecast model was proposed to achieve the goal of long-term prediction of carbon steel in China.

Long-Term Prediction of Atmospheric Corrosion Loss in Various Field Environments

CORROSION ◽  
10.5006/2706 ◽  
2018 ◽  
Vol 74 (6) ◽  
pp. 669-682 ◽  
Author(s):  
Yi-kun Cai ◽  
Yu Zhao ◽  
Xiao-bing Ma ◽  
Kun Zhou ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Linear Function ◽  
Environmental Variables ◽  
Atmospheric Corrosion ◽  
Accurate Estimation ◽  
Corrosion Loss ◽  
Surface Method ◽  
Long Term Prediction ◽  
Corrosion Data

This paper deals with the prediction of long-term atmospheric corrosion in different field environments using the power-linear function. A method for the calculation of exponent n and stationary corrosion rate α in the power-linear function is proposed based on the 1- and 8-y corrosion loss results (C1 and C8) of the ISO CORRAG program. The response surface method and the artificial neural network methodology are used to obtain the accurate estimation of C1 and C8 in different locations using environmental variables. Considering the uncertainty of the model and the experimental data, the confidence intervals of n and α are also calculated. It is shown that the long-term predictions obtained by the proposed method coincide with the actual corrosion loss within ±30% relative error. The estimations for the range of the long-term corrosion loss are also reliable. The proposed method is helpful in extrapolating the knowledge of corrosion management to different field environments where corrosion data are not available.

Non-equidistant GM(1,1) model based on GCHM_WBO and its application in corrosion rate prediction

2016 ◽  
Vol 6 (3) ◽  
pp. 365-374 ◽  
Author(s):  
Yuanjie Zhi ◽  
Dongmei Fu ◽  
Hanling Wang
Keyword(s):  
Corrosion Rate ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Small Sample Size ◽  
Real Data ◽  
Small Sample ◽  
Remaining Useful Life ◽  
Content Type ◽  
Corrosion Data

Purpose The purpose of this paper is to present a new model which combines the non-equidistant GM(1,1) model with GCHM_WBO (generalized contra-harmonic mean (GCHM); weakening buffer operator (WBO)). The authors use the model to solve the deadlock that for a large number of non-equidistant corrosion rate, it is difficult to establish a reasonable prediction model and improve the prediction accuracy. Design/methodology/approach This research consists of three parts: non-equidistant GM(1,1) model, GCHM_WBO operator, and the optimization of morphing parameter (contained in GCHM, control the intensity of the weakening operator). The methodology is explained as follows. First, the authors built a non-equidistant GM(1,1) model with GCHM_WBO weakened data, of which morphing parameter was randomly selected. Next, the authors calculated the error between prediction data of model and the real data, and adjusted the morphing parameter according to the error and property of GCHM. Then, the authors generated a new non-equidistant GM(1,1) based on new morphing parameter, and repeated the previous step until the termination condition was satisfied. Finally, the model with appropriate morphing parameter was used to implement the prediction of new data. Findings This paper finds a property of GCHM, which is a monotonic increasing function of morphing parameter in some specific conditions. Based on the property and the fixed point axiom of WBO, an algorithm was designed to search an appropriate morphing parameter. The appropriate morphing parameter was implemented for the purpose of improving the accuracy of the model. The model was applied to predict the corrosion rate of six steels at Guangzhou experimental station. The results showed that the proposed method can get more accuracy in prediction capability compared to the models with the original data and AWBO weakened data. The method is applicable to long-term forecasts in case of data scarcity. Practical implications Corrosion will cause huge economic loss to a country; therefore, it is important to judge the remaining useful life of a material or equipment; the foundation for judgement of which is the prediction of material corrosion rate. However, the prediction of corrosion rate is very difficult because of corrosion data’s features, such as small sample size, non-equidistant, etc. The proposed method can be used to implement long-term forecast of corrosion data with only one sample and non-equidistant samples. Originality/value This paper presented a model which combines the non-equidistant GM(1,1) model with GCHM_WBO to handle the problem of long-term forecasting of corrosion data. In the modelling process, the proposed morphing parameter searched through algorithm can improve the prediction accuracy of the model. Therefore, the model can provide effective and reliable result when data are of a small sample size and non-equidistant.

Predicting the Long Term Corrosion of Metal Containers for Nuclear Waste Disposal

1987 ◽  
Vol 112 ◽  
Author(s):  
G. P. Marsh
Keyword(s):  
Nuclear Waste ◽  
Experimental Approach ◽  
General Trend ◽  
General Corrosion ◽  
Mechanistic Studies ◽  
Materials Selection ◽  
Short Term ◽  
Worst Case ◽  
Corrosion Data

AbstractThe prediction of the long term corrosion of metal containers for nuclear waste under geological disposal conditions requires the extrapolation of corrosion data over several hundred years. There is a general trend to use either simulation or ‘worst case’ experiments as a means of materials selection, but neither really addresses the problem of long term extrapolation. It is proposed that such an extrapolation can only be done convincingly if it is based on a sound and generally accepted mechanistic understanding of the processes involved. If such knowledge does not exist the first step must be to acquire it through experimental mechanistic studies. Subsequently such knowledge should be formulated into mathematical models, which can be used to make long term predictions, and which can be validated by comparison with short term experimental results. The application of this combined mathematical modelling/experimental approach is illustrated for three corrosion processes which may affect carbon steel containers, namely general corrosion, localised attack and stress corrosion cracking.

A Contribution to the Modelling of the Corrosion Process for HLW Glasses

1984 ◽  
Vol 44 ◽  
Author(s):  
R. Conradt ◽  
H. Roggendorf ◽  
H. Scholze
Keyword(s):  
Corrosion Process ◽  
Slow Kinetics ◽  
Corrosion Data

AbstractAn evaluation of a broad set of HLWg lass corrosion data implies that adequate corrosion models must also take into account kinetics slower than √t. Solubility limits may not give a sufficient explanation for these cases. Before all, the long term leach rates are not infinitely small. A proposal is made how “slow” kinetics can be included in a model.

Analysis of the Excavated Archaeological Iron Using Xray-CT

2003 ◽  
Author(s):  
Hideki Yoshikawa ◽  
Kenichi Ueno ◽  
Takashi Honda ◽  
Shingo Yamaguchi ◽  
Mikazu Yui
Keyword(s):  
Cast Iron ◽  
Corrosion Behavior ◽  
Main Body ◽  
Natural Analogue ◽  
Corrosion Depth ◽  
X Ray ◽  
Reducing Conditions ◽  
Archaeological Iron ◽  
Corrosion Data

In order to evaluate the long-term corrosion behavior of carbon steel, we investigated the rust of archaeological iron buried in soil. It is difficult to obtain experimental data of the long-term corrosion in the laboratory. However, it is possible to obtain corrosion data over several hundred years by using archaeological iron and to develop a reliable model for the long-term corrosion behavior by using such natural analogue data. Since these archaeological samples are very rare and important, we can not get agreement to destroy it for analysis. The rust of the sample has been analyzed no-destructively and quantitatively using high-power X-ray computed tomography. The X-ray strength was developed in the two-demensional image. We observed a rust layer distinct from the inner iron metal as a main body by using X-ray map element concentration. A mass-balance quantity of rust calculation was performed from the amount of corroded layer. A sample of axe which was excavated in the Izumo-Taisha-ruin (Shimane prefecture) was analyzed by using the method. The region in Izumo is famous as the production area of the iron from ancient times in Japan. The axe is traditional Japanese type, made of iron, and probably used for a foundation ceremony of the building. The sample has been buried under the column of the shrine and enveloped by clay. It is assumed that the axe remained under reducing conditions until its discovery in 2001 for about 750 years. We have also investigated the corrosion of the gas pipe buried in the soil in several decades as natural analogue study. By the comparison of these data with the corrosion data of water pipe (cast iron) buried in clay soil at most for 100 year, the results of this study do not exceed the extrapolated pitting corrosion depth based on the corrosion depth of the cast iron pipe.

scholarly journals Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Dan Su ◽  
Ye Xia ◽  
Robert Yuan
Keyword(s):  
Sensor Network ◽  
Spatial Interpolation ◽  
Steel Reinforcement ◽  
Corrosion Process ◽  
Dynamic Prediction ◽  
Prediction Module ◽  
Key Issues ◽  
Self Powered ◽  
Corrosion Data

Corrosion is one of the key issues that affect the service life and hinders wide application of steel reinforcement. Moreover, corrosion is a long-term process and not visible for embedded reinforcement. Thus, this research aims at developing a self-powered smart sensor system with integrated innovative prediction module for forecasting corrosion process of embedded steel reinforcement. Vibration-based energy harvester is used to harvest energy for continuous corrosion data collection. Spatial interpolation module was developed to interpolate corrosion data at unmonitored locations. Dynamic prediction module is used to predict the long-term corrosion based on collected data. Utilizing this new sensor network, the corrosion process can be automated predicted and appropriate mitigation actions will be recommended accordingly.

Evaluation of Corrosivity in Atmospheric Environment by ACM (Atmospheric Corrosion Monitor) Type Corrosion Sensor

2005 ◽  
Vol 475-479 ◽  
pp. 61-64 ◽  
Author(s):  
Tadashi Shinohara ◽  
Shin-ichi Motoda ◽  
Wataru Oshikawa
Keyword(s):  
Atmospheric Corrosion ◽  
Atmospheric Environment ◽  
Indoor Environments ◽  
Sensor Output ◽  
Corrosion Sensor ◽  
Outdoor Environments ◽  
Corrosion Monitor ◽  
Indoor And Outdoor ◽  
Corrosion Data

An ACM (Atmospheric Corrosion Monitor) type corrosion sensor, consisting of a Fe-Ag galvanic couple was developed and applied for the evaluation of corrosivity of atmospheric environments. The sensor was designed considering mass-production and good reproducibility of results, making it convenient for long-term corrosion data acquisition. Besides the sensor output, I, temperature, relative humidity (RH) were also recorded by a microcomputer. By analyzing the magnitude and time variation of I, the occurrence and duration of rain, dew and dry periods, Train, Tdew and Tdry, respectively, could be distinguished and determined. And by referencing to the empirical I-RH calibrating curve, the amount of deposited sea salt, Ws, could also be estimated. It was also found that the corrosion loss could be estimated in both indoor and outdoor sites by analyzing sensor output. Corrosivities of some kinds of exposure sites, not only outdoor environments but also indoor environments, were evaluated by using the ACM sensor.

Therapy and prevention for mental health: What if mental diseases are mostly not brain disorders?

2019 ◽  
Vol 42 ◽  
Author(s):  
John P. A. Ioannidis
Keyword(s):  
Mental Health ◽  
Mental Disease ◽  
Brain Disorders ◽  
Social Stressors ◽  
Labor Education ◽  
Mental Diseases ◽  
Long Term Follow Up ◽  
Political Decisions

AbstractNeurobiology-based interventions for mental diseases and searches for useful biomarkers of treatment response have largely failed. Clinical trials should assess interventions related to environmental and social stressors, with long-term follow-up; social rather than biological endpoints; personalized outcomes; and suitable cluster, adaptive, and n-of-1 designs. Labor, education, financial, and other social/political decisions should be evaluated for their impacts on mental disease.

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