Guide for Formats for Collection and Compilation of Corrosion Data for Metals for Computerized Database Input

An automated corrosion monitor, named the Internet of Things atmospheric corrosion monitor (IoT ACM) has been developed. IoT ACM is based on electrical resistance sensor and enables accurate and continuous measurement of corrosion data of metallic materials. The objective of this research is to study the characteristics of atmospheric corrosion by analyzing the acquired corrosion data from IoT ACM. Employing data processing and data analysis methods to research the acquired corrosion data of steel, the atmospheric corrosion characteristics implied in the corrosion data can be discovered. Comparing the experiment results with the phenomenon of previous laboratory experiment and conclusions of previously published reports, the research results are tested and verified. The experiment results show that the change regulation of atmospheric corrosion data in the actual environment is reasonable and normal. The variation of corrosion depth is obviously influenced by relative humidity, temperature and part of air pollutants. It can be concluded that IoT ACM can be well applied to the conditions of atmospheric corrosion monitoring of metallic materials and the study of atmospheric corrosion by applying IoT ACM is effective and instructive under an actual atmospheric environment.

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Research on Acoustic Emission In-Service Inspection for Large Above-Ground Storage Tank Floors

2010 8th International Pipeline Conference, Volume 3 ◽

10.1115/ipc2010-31080 ◽

2010 ◽

Cited By ~ 1

Author(s):

Mingchun Lin ◽

Yewei Kang ◽

Weibin Wang ◽

Lei Zhang ◽

Yi Sun

Keyword(s):

Acoustic Emission ◽

Storage Tank ◽

Magnetic Flux Leakage ◽

Analysis Method ◽

Inspection Method ◽

Oil Tank ◽

Flux Leakage ◽

Service Inspection ◽

Service Field ◽

Corrosion Data

Much manpower is needed and a lot of materials are wasted when the floor of large above-ground storage tank (AST) is inspected with conventional methods which need to shut down the tank, then to empty and clean it before inspection. Due to the disadvantages of that, an in-service inspection method using acoustic emission (AE) technology is presented. By this mean the rational inspection plan and integrity evaluation of tank floors can be constructed. First, specific inspection steps are established based on the acoustic emission principle for large AST’s floors and the practical condition of AST in order to acquire the AE corrosion data. Second, analysis method of acoustic emission dataset is studied. Finally, maintenance proposes are provided based on results of analysis for the corrosion status of the tank floors. In order to evaluate the performance of our method, an in-service field inspection is practiced on product oil tank with a volume of 5000 cubic meters. Then a traditional inspection procedure using magnetic flux leakage (MFL) technology is followed up. Comparative analysis of the results of the two inspection methods shows that there is consistency in localizing the position of corrosion between them. The feasibility of inservice inspection of AST’s floors with AE is demonstrated.

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

Anti-Corrosion Methods and Materials ◽

10.1108/acmm-11-2017-1858 ◽

2019 ◽

Vol 66 (4) ◽

pp. 403-411 ◽

Cited By ~ 1

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.

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