Corrosion data analysis

Recommended practice for applying statistics to analysis of corrosion data Practice for operating light- and water-exposure apparatus (carbon-arc Type) for exposure of nonmetallic materials Method for detecting susceptibility to intergranular attack in wrought nickel-rich, chromium-bearing alloys... 

Measurements of electrochemical noise and AC impedance of coated metal substrates are under development (indeed have been used for quite some time). These measurements relate to the corrosion protection afforded by the coating and can, in principle, be made continuously. The complexity of the electrochemical reactions require sophisticated data analysis for extraction of useful information and relationships. 

Data Analysis. Data analysis is, of course, directly related to data acquisition. However, not all good data is or can be completely analyzed. For example, McIntyre (J5) has observed that "a broad base in chemical shift data has been slow in developing" for XPS data. Until such a data base existed, it was difficult for both expert and non-expert to interpret spectra from corrosion products, particularly on complex alloys. The Handbook of X-Ray Photoelectron Spectroscopy (27) and collections of Auger parameter data (32 ) are examples of data compilations very useful to a researcher trying to interpret measurements of corrosion products. 

The traditional way is to measure the impedance curve, Z(co), point-after-point, i.e., by measuring the response to each individual sinusoidal perturbation with a frequency, to. Recently, nonconventional approaches to measure the impedance function, Z(a>), have been developed based on the simultaneous imposition of a set of various sinusoidal harmonics, or noise, or a small-amplitude potential step etc, with subsequent Fourier- and Laplace transform data analysis. The self-consistency of the measured spectra is tested with the use of the Kramers-Kronig transformations [iii, iv] whose violation testifies in favor of a non-steady state character of the studied system (e.g., in corrosion). An alternative development is in the area of impedance spectroscopy for nonstationary systems in which the properties of the system change with time. 

In the laboratory portion of the project, the students quantify iron in real and artificial surface water samples by UV-Vis spectroscopy. The iron is complexed to the o-phenanthroline (phen) ligands in a buffered solution to create a highly colored orange complex, [Fe(phen)3] The intensity of the complex color is proportional to the concentration, following Beer s Law. Students create a standard series and prepare a surface water sample using modified standard protocols (21). We use autodispensers to dispense corrosive reagents and provide the stock iron solutions this equipment reduces exposure and ensures that the experimental work can fit within the three hour laboratory period. Students measure of the absorbance of their standard series as well as their surface water sample on a spectrometer at X = 508 nm. Students complete the experimental write-up, calculations, data analysis, and assessment during the subsequent laboratory period. 

The analysis of experimental data shows that the average value 111 g/m2 of all corrosivity data (improved by rejecting outliers) corresponds to the value 140 40 g/m2 indicated in the standard. For the evaluation of the expanded combined uncertainty U with factor k=2 the corrosivity measurement gives the value of 215 g/m2 (at 95% confidence). It means that our data uncertainty is five-times higher than that specified in the standard as the data scattering interval 40 g/m2 and seven times as wide compared the statistic confidence interval in our own experimental data corrosivity (Table 2a and 2b). The main components of the combined uncertainty are mass loss and surface area determination. 

The usefulness of existing long term exposure metals corrosion data thus depends upon reconstruction of the meteorological and chemical histories which are relevant to corrosion. To do so involves analysis of data on meteorology and pollutant emissions in conjunction with data interpolation tools, i.e. pollutant dispersion models. This report discusses the current status of such an effort at Washington University, and examines the existing exposure data for evidence of key features which may clarify the likely importance of manmade pollutants in metals corrosion. 

The inability to estimate dry deposition velocities and the lack of knowledge of the compounds that are likely to contribute to corrosion makes Equation 3 difficult to apply in evaluating corrosion data. The results of the experiments reported here will address both of these issues and will be used to develop the framework for a model for analysis of galvanized steel corrosion data obtained from field studies. 

M.W. Kendig, U. Bertocci and J.E. Strutt (Eds.), Computer Aided Acquisition and Analysis of Corrosion Data, The Electrochemical Society, New York, 1985. 

This expression was derived in Chapter 1.3. An experimental polarization curve, such as that shown in Fig. 3(b), can be fitted by nonlinear least squares fitting to this expression. Such a fit will yield values for the corrosion rate, corrosion potential, and anodic and cathodic Tafel slopes. Most modern software packages for analysis of corrosion data have this capability. 

Internal condition of pipes can be assessed through visual inspection photomicrographs, weight loss, pitting potential measurements, scale analysis, and corrosion probe data. From the corrosion data, service life of the pipes can be estimated. 

Macdonald summarized the hmitations of EIS technique when used to measure the corrosion current (corrosion rates) of metals [79]. A high level of mathematics is required to analyze data and interpret properties of the corrosion system. Analysis of impedance data results in determination of the polarization resistance. However, it requires obtaining a large number of low-frequency data for an accurate estimate. It is necessary to extract the noise from the data obtained at low frequency ranges to obtain meaningful mechanistic information. To calculate the corrosion rate using the Stem-Geary equation, the Tafel method should be used to estimate the Tafel slopes as a function of time. Due to the variation of porosity of corrosion products on metals, the corrosion products (oxides and hydroxides) contributions to the overall impedance spectra are difficult to evaluate. 

R. E. Ricker, Analysis of Pipeline Steel Corrosion Data front NBS (NIST) Studies Conducted between 1922-1940 and Relevance to Pipeline Management, NISTER 7415, National Institute of Standards and Technology, U.S. Department of Commerce, May 2,2007, pp. 1-2. 

B. Savova-Stoynov and Z. Stoynov [1985] Computer Analysis of Non-Stationary Impedance Data, Proc. Symp., Computer Aided Acquisition and Analysis of Corrosion Data, ed. M. W. Kendig, U. Bertocci, and J. E. Strutt, Electrochem. Soc. Inc., Pennington, Vol. 85-3, 152-158. 

Statistics in Research by Bernard Ostle [7] is an excellent text that can be used as a handbook. In addition. Standard G 16 (Guide for Applying Statistics to Analysis of Corrosion Data) is a useful document. Statistical techniques are particularly useful in planning and designing experiments. This chapter describes some of the advantages and limitations of applying statistical techniques in corrosion research. 

Three statistical methods that are often important in corrosion experiments are (1) probability distributions, (2) design of experiments, and (3) analysis of data. Most investigators use some kind of data analysis technique. Less attention is given to experimental design, and probability distribution is the most neglected of the three methods. 

The normal distribution has the familiar symmetrical beU shape as shown in Fig. 1 and is the basis for the most common statistical techniques of experimental design and data analysis. The characteristics of this distribution are described in the section on Terminology. Mass loss, mass gain, thickness loss, corrosion potential, corrosion rate, and pitting area may have a normal distribution. Although this may not be an established fact, in the past many researchers have assumed normal distributions for such data with apparent success. 

Most of the popular spreadsheet programs can perform multiple regression analysis, and most of the information that is needed can be obtained from this process. Regression analysis as well as other techniques that deal with normal statistics are based on two basic assumptions that are seldom completely accurate these are (1) independent variables upon which dependent variables are r ressed are truly independent, or not associated with each other in any way, and (2) the values of the independent variables are fixed (that is, each one is not just a sample of a distribution of values and thus is not subject to error). In the real world of corrosion research, it is extremely difficult to design or conduct an experiment where these criteria are met. Thus, it is necessary to evaluate how these assumptions might affect the data analysis. The different statistical techniques discussed in the foUowing paragraphs consider the effects of these assumptions. 

Corrosion researchers can gain a greater degree of confidence in their experimental results if they have a basic understanding and use statistical techniques of experimental design and data analysis. Statisticians cannot property design... 

Eden, D. A. and Rothwell, A. N., Electrochemical Noise Data Analysis and Interpretation, Paper 92 in the Proceedings of the Corrosion Conference, National Association of Corrosion Engineers, Houston, TX, 1992. 

G 16 Guide for Applying Statistics to Analysis of Corrosion Data... 

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