CORROSION CONTROL APPROACH USING DATA MINING

Corrosion Science, 2010

A supervised neural network (NN) method was used as a data mining tool to predict corrosion behavior of metal alloys. The NN model learned the underlying laws that map the alloy's composition and environment to the corrosion rate. Existing corrosion data on corrosion allowable as well as corrosion resistive alloys were collected for both DC and AC corrosion experiments. The data mining results allow us to categorize and prioritize certain parameters (i.e. pH, temperature, time of exposure, electrolyte composition, metal composition, etc.) and help us understand the synergetic effects of the parameters and variables on electrochemical potentials and corrosion rates.

CORROSION, 2014

The objective of this work was to develop the foundation for an interactive corrosion risk management tool for assessing the probability of failure of equipment/infrastructure as a function of threats (such as pitting corrosion and coating degradation) and mitigation schemes (such as inhibitors and coatings). The application of this work was to assist with corrosion management and maintenance planning of equipment/infrastructure given dynamic changes in environmental conditions. Markov models are developed to estimate pitting damage accumulation density distributions as a function of input parameters for pit nucleation and growth rates. The input parameters are selected based upon characterization with experimental or field observations over a sufficiently long period of time. Model predictions are benchmarked against laboratory pitting corrosion tests and long-term atmospheric exposure data for aluminum alloys, obtained from the literature. The models are also used to examine hypothetical scenarios for the probability of failure in pipeline systems subject to sudden, gradual, and episodic events that change the corrosive conditions.

2015

Most metals and alloys exposed to the environment suffer deterioration due to the effects of atmospheric corrosion. This study presents results obtained for the corrosion of carbon steel, galvanised steel, copper and aluminium exposed to the environment for a period of 3 years, at 9 different sites around Chile. Mathematical models based on artificial neural networks are used to evaluate the corrosion of the metals and alloys as a function of meteorological variables (relative humidity, temperature and amount of rainfall), pollutants (chloride and sulphur dioxide) and time. The advantages of these models in predicting corrosion is also shown in comparison to traditional statistical regression models when considering the dependence of corrosion as a function of time alone.

This Office of the Secretary of Defense Corrosion Prevention and Control Program project developed a statistical model of atmospheric corrosion of selected metals. This model relates measured corrosion rates at test sites (mainly military bases) worldwide to critical environmental variables. These variables are (1) a measure of atmospheric chlorides, (2) rainfall, and (3) relative humidity values at several levels. The measured corrosion rates obtained at test sites over the period of CY05-CY07. Additionally this database includes much more data obtained from similar DoD monitoring activities over nearly the last decade. This serves to enhance the statistical relevance of the developed model. The model includes algorithms for several metals that have been routinely used in the monitoring work. These include copper, 6061 T6 aluminum, 7075 T6 aluminum, and a low carbon (1010) steel. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to * This information is provided as background for the interested technical reader, but understanding it is not necessary for the end user to take advantage of the model.

International journal of electrochemical science

Most metals and alloys exposed to the environment suffer deterioration due to the effects of atmospheric corrosion. This study presents results obtained for the corrosion of carbon steel, galvanised steel, copper and aluminium exposed to the environment for a period of 3 years, at 9 different sites around Chile. Mathematical models based on artificial neural networks are used to evaluate the corrosion of the metals and alloys as a function of meteorological variables (relative humidity, temperature and amount of rainfall), pollutants (chloride and sulphur dioxide) and time. The advantages of these models in predicting corrosion is also shown in comparison to traditional statistical regression models when considering the dependence of corrosion as a function of time alone.

2018

The main aim of the project is to develop an website for the benefit of the petroleum companies. Nowadays the companies are willing to have a knowledge of the corrosion occur in the transmission pipelines. But there is no way to predict the accurate corrosion time period so the companies face saviour loss in the transmission. To overcome this problem, an website is developed to calculate the corrosion timeperiod of the pipeline to yield a profit of the companies. It helps the companies to identify the correct time period of corrosion takes place in the pipeline and helps us to find the life cycle coasting of the pipelines.At the same time, it also ensures the previous years corrosion rate with the present rate of the corrosion. Firstly, carbon steel erosion strength model is proposed based on decision tree as we have mentioned above. Secondly the data which are obtained are feed into the excel to analyse the performance. Finally the graph of the Life cycle costing is gained with the...

This work looked at mathematical modeling as a tool for material corrosion property prediction. The weight loss technique was used to carry out the corrosion rate analysis for ductile Iron and mild steel and the data produced were used to carry out the mathematical analysis. It looked at how mathematical modeling can be used to predict corrosion and help to save resources and man-hour by using mathematical modeling. At the end, it was discovered that it has over 90% efficiency and concordance.

Engineer: Journal of the Institution of Engineers, Sri Lanka, 2015

Corrosion is defined as the degradation or loss of function of materials due to environmental effects. Corrosion has a huge impact on the economy of a country. This depends on the corrosive nature of environment of the country. Financial loss due to corrosion is inevitable but is controllable with the aid of proper corrosion management systems. Most of the countries use different methods for implementing corrosion management system in order to minimize the corrosion loss. In Sri Lanka the concern about corrosion is at a minimal stage but having a corrosion management system is becoming an essential requirement for future Sri Lanka. Implementation of corrosion management system has to be an all-country effort which has to be done with a much careful assessment of corrosive environment. There are several approaches for the assessment of corrosive environment and many researchers have been conducted all over the world. Evaluation of corrosivity as a function of environmental variables, which is known as corrosion modeling, and classification of corrosivity of atmosphere are widely used methods to assess the corrosive environment. In this paper the authors discuss about several existing environmental evaluation methods and models.

Long-term atmospheric corrosion forecasts often rely on the fulfilment of equations of the form C = At ~, where C is the corrosion after t years and A represents corrosion after the first year of exposure. Appropriate values must be assigned to constants A and n. In the first part of the work an analysis was performed on the possibility of expressing A as a function of usually available environmental parameters. In this second part data compiled in a comprehensive literature survey are used to determine whether the exponent n of the above equation can also be expressed as a function of such environmental parameters.

Bioscience Biotechnology Research Communications, 2020

This chapter provides an overview of corrosion of metals. It describes the general factors that influences metal corrosion. Over the years, there have been numerous studies on the rates of corrosion of metals in sea water. From various studies, it is now possible to identify the major factors that affect metal corrosion. These factors are metal composition, water composition, temperature, marine growth, seabed composition, and extent of water movement. The combined effect of all these complex and often interrelated factors is that each object must be considered individually when attempting to evaluate its corrosion history or when considering its recovered condition. Till date the available literature on corrosion factor prejudiced by the bacteria, which was predominantly focused on Sulfate Reducing Bacteria (SRB) that usually reside on sulfate (terminal electron acceptor) because SRB are often found at pitting sites.