KR20140070978A - Method for pridicting time-dependrnt corrosion wastage under corrosional environment - Google Patents

Abstract

The present invention relates to a method for predicting time-dependent corrosion wastage under a corrosional environment, capable of evaluating the safety of a ship and a marine structure and planning and executing various measures like repair, replacement, or waste in a proper period based on the predicted corrosion wastage by suggesting a practical method to predict the time-dependent corrosion wastage of various ships and the marine structure made of metal materials which are exposed to the corrosional environment like an oil well tube installed in a deep sea oil well to collect natural gas and crude oil with sulfur compound or the ship in contact with sea.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for predicting corrosion of a corrosion-

The present invention relates to a method and apparatus for monitoring various types of ships and marine structures exposed to a corrosive environment such as ships in contact with seawater, crude oil containing sulfur compounds, oil well tubes installed in deep sea oil wells to collect natural gas, , It is possible to evaluate the safety of ships and offshore structures by suggesting a practical method to predict the amount of corrosion according to the amount of corrosion and to plan and implement various measures such as repair, And a method for predicting the corrosion amount over time in a corrosive environment which enables the execution of the corrosion test.

Corrosion of metals in the ocean is an extremely complex phenomenon that depends on a wide variety of environmental and material factors. Environmental factors include bacteria, biofouling, oxygen supply, carbon dioxide, salinity, acidity, dissolved carbonate, contaminants, temperature, pressure, suspended matter, flow rate, and blue. The material parameters include the composition of the metal and the surface roughness. Furthermore, the phenomenon of corrosion is also associated with microbiological effects that depend on adequate bacterial colonies and the availability of both energy sources (electrons) and nutrients sufficient to sustain bacterial metabolism.

On the other hand, it is very important to predict the amount of corrosion of marine vessels and marine structures (hereinafter "marine structures").

Marine structures are subjected to thorough rustproofing in order to cope with corrosion, but since they have decades of operation life, they will eventually corrode and the loss of strength due to corrosion will seriously affect the stability of offshore structures.

For example, if a ship whose structural strength has deteriorated due to corrosion has failed to withstand a rough storm while operating on a deep sea, accident such as flooding or sinking can not be avoided. If the oil tanker transports crude oil, There is a secondary damage of enormous environmental pollution.

Or, the inside of the oil well tube installed in the deep well oil well is corroded by the sulfur compound contained in the crude oil or natural gas. Since the degree of corrosion inside the oil well tube can not be observed from outside, if the corrosion amount is not predicted in advance, It is very difficult to prevent accidents of pollution caused by spills.

However, as described above, the corrosion of metal in a marine environment is a very complicated phenomenon and it is not easy to predict because it is not corroded to a certain degree with time. Therefore, many attempts have been made yet.

As one of such attempts, Korean Patent No. 10-0219724 entitled "Method for predicting metal corrosion life in seawater atmosphere"

The present invention relates to a method for predicting the corrosion life of a stainless steel in a seawater corrosive atmosphere, wherein a composition of a stainless steel, a corrosion current density, a shape of a gap, an initial pH concentration, a Cl acid concentration, A pH change, and a potential change with time by inputting a diffusion coefficient, an electron number, and various constants of chemical ions derived from the composition obtained by the composition obtained by the above method, and if the calculated concentration exceeds a threshold value, More particularly, to a method for predicting corrosion life of metals, particularly stainless steel, under a seawater atmosphere in which a time required for a change to the concentration to exceed the threshold potential is measured as the corrosion start time.

This prediction method can be regarded as a numerical method based on a physical model. Since many assumptions and omission of some corrosion factors are involved in the process of modeling complex corrosion phenomena, There is a limit.

Korean Patent No. 10-0219724

The present invention provides a practical method for predicting the amount of corrosion of a particular metal structure exposed to various corrosive environments over time, thereby enabling evaluation of the safety of ships and offshore structures, And to plan and execute various measures such as repair, replacement, or disposal at an appropriate time.

The present invention relates to a method for predicting a corrosion amount over time in a corrosive environment in which a series of calculation processes are performed in a computing means provided in a computer,

A first step of inputting corrosion data of a specific metal structure collected for various corrosion exposure times;

A second step of determining a corrosion amount interval for which the average value is maximized and the coefficient of variation (COV) is the minimum value for the data input in the first step;

(3) calculating the shape parameter (α) and the dimensional parameter (β) by applying the Weibull function of the following equation (1) to the corrosion data for each corrosion exposure time for the corrosion amount interval determined in the second step step;

Calculating a line form or a polynomial expression for the corrosion exposure time that approximately expresses each parameter (?,?) From the shape parameter (?) And the dimensional parameter (?) For each of the plurality of corrosion exposure time calculated in the third step, step; And

The fifth step of completing the time-dependent subcategory prediction equation by applying a linear or polynomial equation of the shape parameter α and the dimension parameter β calculated in the fourth step to the Weibull function of the following equation (1) And a method for predicting the corrosion amount over time in the corrosive environment.

Equation (1):

{Wherein, d _c is the value that is calculated as a portion of food (defect depth), Y _e is Y _e = YY _c as the time (years) after the destruction of the coating layer (Y _c is the coating life, Y is the age of the metal structure) }

In an embodiment of the present invention, the coating life (Y _c ) of the formula (1) is assumed to be zero.

The second step may be determined by averaging the corrosion amount intervals which make the average value calculated by the corrosion exposure time maximum and the COV minimum value.

In the first embodiment of the present invention, the specific metal structure is a well tube, the corrosion amount data is a thickness reduction amount inside the well tube, and the shape parameter (alpha) and the dimensional parameter (beta) Is a linear or polynomial expression of the following equation (2).

Equation (2):

Meanwhile, in the second embodiment of the present invention, the specific metal structure is a seawater ballast tank, and the linear form or the polynomial equation of the shape parameter (alpha) and the dimensional parameter (beta) calculated in the third step is expressed by the following equation ).

Equation (3):

The present invention provides a generalized empirical formula that statistically treats the corrosion data of a particular metal structure collected over various corrosion exposure times to predict the amount of corrosion of a particular metal structure over time, And to make judgment criteria for planning and implementing various measures such as repair, replacement, or disposal at an appropriate time based on the estimated amount of corrosion.

Further, the present invention is also advantageous in that one empirical formula can be utilized universally for specific metal structures provided with the corrosion data.

1 is a flow chart illustrating the overall configuration of a method for predicting a corrosion rate over time in a corrosive environment according to the present invention;
Fig. 2 is a diagram schematically showing a change in an average value and COV according to a corrosion amount interval. Fig.
3 is a diagram showing a probability density distribution (a) and an accumulated density distribution (b) of a 5.1-year oil-tube in the first embodiment;
FIG. 4 is a graph showing a probability density distribution for each wellhead tube of different ages based on the Weibull distribution approximated in Example 1. FIG.
5 is a graph showing the actual values of the shape parameter (α) and the dimensional parameter (β) and the linear form or polynomial expression of each parameter derived therefrom in the first embodiment.
FIG. 6 is a graph showing an average and a standard deviation superimposed on the thickness reduction amount (red) actually measured in Example 1 and the thickness reduction amount (blue) calculated approximately by the empirical formula of the completed time dependent subcategory graph.
FIG. 7 is a graph showing the actual values of the shape parameter (?) And the dimensional parameter (?) And the linear form or polynomial expression of each parameter derived therefrom in Embodiment 2;
8 is a graph showing a probability density distribution for each ballast tank of different ages based on the Weibull distribution approximated in Example 2. Fig.
9 is a graph showing an average and a standard deviation of the thickness reduction amount (red) actually measured in Example 2 and the thickness reduction amount (blue) calculated approximate by the complete time dependent subcategory prediction empirical formula graph.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a preferred embodiment of a method for predicting corrosion amount according to time in a corrosive environment according to the present invention will be described in detail with reference to the accompanying drawings. It is to be understood that the present invention is not limited to the disclosed embodiments, but may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, It is provided to inform.

In addition, the present invention can be implemented as a program in which a series of calculation processes are performed in a computing means provided in a computer, and the components performing each step can be expressed as a kind of module.

Referring to FIG. 1, the overall construction of the corrosion prediction method according to the present invention over time in a corrosive environment according to the present invention will be described below.

The first step is to input the corrosion amount (reduced thickness or defect depth) data of the specific metal structure collected for various corrosion exposure times.

In the embodiment of the present invention to be described later, the metal structure is two types of oil well tube and ballast tank, and the corrosion amount data is inputted for each metal structure having a similar corrosion environment.

Accordingly, the present invention provides a time-dependent sub-body food-grade empirical formula for each specific metal structure.

In the second step, the average value of the data inputted in the first step is set to the maximum value, and the COV (coefficient of variation, COV = standard deviation / average, hereinafter collectively referred to as COV) .

In order to statistically process the corrosion data of the specific metal structure entered in the first step and to arrange it in the form of a frequency distribution table, the interval (rank) between variables, which are the variables, has a great influence on the statistical results.

Therefore, it is necessary to set an interval at which the COV becomes the minimum value while the average value is maximized.

Fig. 2 schematically shows changes in the mean value and the COV according to the corrosion amount interval. It should be noted in FIG. 2 that as the corrosion-free interval increases, it does not mean that the COV is reduced, but it means that there is a corrosion gap at which the COV becomes minimum.

The third step is to calculate the shape parameter (α) and the dimensional parameter (β) by applying a Weibull function to the corrosion data for each corrosion exposure time for the corrosion amount interval determined in the second step.

The corrosion data determined in the second step, ie, the class size, ie corrosion data sorted by corrosion exposure time, is expressed as a statistical function that can most approximate the distribution, so that it can be generally applied over a specific metal structure do.

The statistical functions that can represent the distribution of corrosion amount with the passage time of corrosion are Normal distribution, Lognormal distribution, Exponential distribution, 2-parameter Weibull distribution distribution, 3-parameter Weibull distribution, and gamma distribution.

In the embodiment of the present invention, Anderson-Darling goodness of fit test was performed for each of the above statistical functions. As a result, a 2-parameter and a wavelet distribution (hereinafter, simply referred to as a ' Was the most representative of the corrosion rate over time of corrosion.

The 3-parameter Weibull distribution is expressed by the following equation.

......식(1)

(1)

Here,? Is a shape parameter,? Is a scale parameter, and? Is a location parameter.

When the parameter γ (position parameter) is set to 0 (2-parameter) in the 3-parameter Weibull distribution, the Weibull distribution is obtained and expressed by the following equation (2).

......식(2)

(2)

Where d _c is the corrosion amount (depth of defect) and Y _e is the time elapsed since the fracture of the coating layer (year), calculated as Y _e = YY _c (where Y _c is the coating life and Y is the age of the metal structure) .

In the present invention, the coating lifetime Y _c is assumed to be zero because it is an unknown value that is difficult to generalize.

Applying the Weibull distribution (function) of Eq. (2) to each subclass, the shape parameter (α) and the dimension parameter (β) are obtained for each degree of corrosion exposure time.

In the fourth step, a linear form or a polynomial expression for the corrosion exposure time that approximates each parameter (?,?) From the shape parameter (?) And the dimensional parameter (?) For each of the plurality of corrosion exposure times calculated in the third step .

That is, by performing curve fitting on the values of the shape parameter? And the dimension parameter? Having a discontinuous distribution with respect to the corrosion exposure time (Y _e ), the shape parameter? And the dimension parameter? ) Is calculated in a linear form or polynomial with the corrosion exposure time as a variable.

The fifth step is to complete the time dependent subcategory prediction equation by applying a linear or polynomial equation of the shape parameter α and the dimension parameter β calculated in the fourth step to the Weibull function of the equation (2) .

Since the above-mentioned time-dependent dependent food prediction equation is a function of only the corrosion exposure time (Y _e ) as a variable, it is possible to obtain the corrosion amount (d _c ) immediately by inputting the corrosion exposure time (Y _e ) Based on these calculated values of the corrosion amount, it is possible to evaluate the safety of offshore structures or to plan and implement various measures such as repair, replacement or disposal at an appropriate time based on the predicted amount of corrosion.

Hereinafter, an example in which a series of steps of the method for predicting the corrosion amount according to the above-described time lapse described above is specifically applied will be described through two embodiments.

Example 1 relates to the derivation of a time dependent subcriteria prediction equation for predicting the amount of corrosion inside a well tube by a crude oil or a natural gas containing a sulfur compound by applying the present invention to a well tube installed in a deepwater well.

The corrosion amount inside the oil well tube was measured as the thickness decrease. The measurement results of 7 oil well tubes with different corrosion exposure times are summarized in the following Tables 1 and 2.

However, in order to measure the amount of corrosion inside the oil well tube installed in the deep sea, a special device for specifying the thickness of the tube while moving inside the tube was used. However, since the content of this device is out of the scope of the present invention, .

The basic information of the well tube which measured the thickness reduction Well tube Age (years) Depth (m) Number of measurements Tube Thickness (mm) A 5.1 1349.4 174 6.45 B 5.8 2610.1 287 6.45 C 9.1 1320.4 149 6.45 D 11.7 1419.0 197 5.51 E 15.3 2254.7 246 6.45 F 18.2 1364.6 184 6.45 G 22.8 1941.4 200 5.51

For reference, only the grade of the well tube D in Table 1 is AMS-28, and the rank of the remaining well tube is L-80.

The average thickness of the well tubes was measured to be 0.2 mm, while the average thickness of the well tubes was different from that of the other tubes. The results are summarized in Table 2.

Measurement result of thickness reduction of well tube Thickness reduction
Spacing (mm) Age (years) 5.1 5.8 9.1 11.7 15.3 18.2 22.8 0.0 to 0.2 69 162 24 27 8 0 45 0.2 to 0.4 55 54 78 72 76 23 68 0.4 to 0.6 39 38 36 72 82 76 26 0.6 to 0.8 10 20 9 13 64 37 10 0.8 to 1.0 One 10 2 7 13 24 5 1.0 to 1.2 0 3 0 4 3 10 One 1.2 to 1.4 0 0 0 0 0 7 3 1.4 to 1.6 0 0 0 0 0 3 3 1.6 to 1.8 0 0 0 One 0 4 0 1.8 to 2.0 0 0 0 0 0 0 2 2.0 to 2.2 0 0 0 0 0 0 6 2.2 to 2.4 0 0 0 0 0 0 12 2.4 ~ 2.6 0 0 0 0 0 0 6 2.6 to 2.8 0 0 0 0 0 0 3 2.8 to 3.0 0 0 0 0 0 0 One 3.0 to 3.2 0 0 0 0 0 0 2 3.2 ~ 3.4 0 0 0 0 0 0 5 3.4 to 3.6 0 0 0 0 0 0 One 3.6 to 3.8 0 0 0 0 0 0 0 3.8 to 4.0 0 0 0 0 0 0 0 4.0 ~ 4.2 0 0 0 0 0 0 One

3 shows an example of a probability density distribution (a) and a cumulative density distribution (b) of a 5.1-year oil-tube, and FIG. 3 shows another six oil-well tubes in the same manner.

FIG. 3 shows the distributions of the statistical functions of the normal distribution, the lognormal distribution, the exponential distribution, the Weibull distribution, the 3-parameter Weibull distribution and the gamma distribution together with the distribution of the thickness reduction amount.

The results of performing the Anderson-Darling fitness test on the six statistical functions are summarized in Tables 3 and 4. Table 3 shows the results of the fitness test. Table 4 summarizes the results of the fitness test. As the results and the ratio of the fitness test are smaller, it means that they are closer to the actual probability distribution. We can see that the weibull distribution is the most suitable statistical function for the prediction of corrosion.

Anderson-Darling fitness test results for each statistical function statistics
function Age (years) 5.1 5.8 9.1 11.7 15.3 18.2 22.8 Average Regular 4.51 15.59 4.74 5.21 2.54 7.59 23.91 9.16 Algebraic regular 12.14 7.36 12.24 9.91 1.89 1.73 4.06 7.05 Indices 7.63 5.37 24.32 23.72 43.42 30.43 9.75 20.66 Weibull 8.46 5.88 6.16 4.82 1.86 5.52 6.12 5.55 3- Weibull 21.39 17.09 5.53 4.80 1.56 2.21 6.00 8.37 gamma 8.24 6.11 8.68 5.67 1.31 3.19 7.12 5.76

Percentage of Anderson-Darling fitness test results by statistical function statistics
function Age (years) 5.1 5.8 9.1 11.7 15.3 18.2 22.8 Average Regular 0.21 1.00 0.19 0.22 0.06 0.25 1.00 0.367 Algebraic regular 0.57 0.47 0.50 0.42 0.04 0.06 0.17 0.279 Indices 0.36 0.34 1.00 1.00 1.00 1.00 0.41 0.639 Weibull 0.40 0.38 0.25 0.20 0.04 0.18 0.26 0.214 3- Weibull 1.00 1.10 0.23 0.20 0.04 0.07 0.25 0.361 gamma 0.39 0.39 0.36 0.24 0.03 0.10 0.30 0.226

4 is a graph showing a probability density distribution for each wellhead tube of different ages based on a Weibull distribution. As clearly shown in FIG. 4, it can be seen that the statistical characteristics indicating the progress of the corrosion are different depending on the age of the well tube.

Table 5 below shows the results of calculating the values of the shape parameter (?) And the dimensional parameter (?) When the formula (2) showing the Weibull distribution is applied to each well tube with different ages.

The shape parameter (α) and the dimension parameter (β) calculation result of the weave distribution for each well tube Equation (2) Age (years) 5.1 5.8 9.1 11.7 15.3 18.2 22.8 alpha 1.777 1.507 3.467 2.441 3.486 2.657 1.269 beta 0.3596 0.3471 0.4544 0.5490 0.6510 0.8336 1.100 Average (mm) 0.3300 0.3132 0.4086 0.4868 0.5856 0.7409 1.021 COV 0.5814 0.6761 0.3192 0.4342 0.3176 0.4052 0.7936

Based on the results of Table 5, the result of calculating the linear form or the polynomial expressing the approximation of the shape parameter (?) And the dimensional parameter (?) Is as shown in the following equation (3) The graphs together with the linear form or the polynomial expression of the formula (3) are shown in FIG.

......식(3)

(3)

Applying Equation (3) to Equation (2), which shows the Weibull distribution, a time dependent subcategory prediction equation is completed that can predict the amount of corrosion over time in the well tube.

6 is a graph showing an average and a standard deviation superimposed on the thickness reduction amount (red) actually measured and the thickness reduction amount (blue) calculated approximately by the complete time dependent subcategory prediction empirical formula. As shown, the mean and standard deviation of the actual and calculated approximations, respectively, show a fairly close result over the age range of the whole well tube, and therefore the time for the well tube derived from the first embodiment of the present invention It can be seen that empirical equations for predicting dependent food can be useful in practice.

Example 2 is the derivation of the time dependent subcategory prediction equation for ballast tanks containing seawater to provide sufficient restoring force to the ship and to adjust the draft and trim.

A total of 1935 measurement data were collected and input for various ballast tanks of different vessel age. The average corrosion depth interval (rank), which is the maximum value and the minimum value of COV, is calculated as 0.5 mm. Table 6 summarizes them.

Measurement result of thickness reduction of sea water ballast tank age
(year) Thickness reduction (mm) 0.0 to 0.5 0.5 to 1.0 1.0 to 1.5 1.5 to 2.0 2.0 to 2.5 2.5 to 3.0 3.0 to 3.5 3.5 ~ 4.0 11.0-11.5 2 0 0 0 0 0 0 0 11.5 to 12.0 18 5 0 0 0 0 0 0 12.0 to 12.5 6 3 9 0 0 0 0 0 12.5 to 13.0 23 2 0 0 0 0 0 0 13.0 to 13.5 16 28 30 2 0 0 0 0 13.5 to 14.0 9 0 0 0 0 0 0 0 14.0 to 14.5 3 3 0 0 0 0 0 0 14.5 to 15.0 One 2 0 0 0 0 0 0 15.0 to 15.5 22 13 10 3 2 0 0 0 15.5 to 16.0 9 One 0 0 0 0 0 0 16.0 to 16.5 5 0 0 0 0 0 0 0 16.5-17.0 12 8 5 2 One One 0 0 17.0 to 17.5 19 One 0 0 0 0 0 0 17.5 to 18.0 84 One 2 4 0 0 0 0 18.0 to 18.5 34 26 37 9 4 3 0 0 18.5 to 19.0 One 0 2 0 0 0 0 0 19.0 to 19.5 53 11 11 8 7 2 0 One 19.5-20.0 84 9 One 0 2 0 0 0 20.0 to 20.5 169 48 11 3 One 0 0 0 20.5 to 21.0 10 14 11 10 16 2 0 0 21.0 to 21.5 105 115 27 24 5 6 0 0 21.5 to 22.0 9 One One 2 2 0 0 0 22.0 to 22.5 44 39 4 9 7 5 3 0 22.5 to 23.0 8 18 One 3 0 0 0 0 23.0-23.5 67 46 11 5 3 5 0 0 23.5-24.0 8 3 One 0 0 0 0 0 24.0 to 24.5 41 27 8 2 0 0 0 0 24.5 to 25.0 18 15 2 0 0 0 0 0 25.0 to 25.5 30 49 48 57 40 2 2 One 25.5 to 26.0 10 One One 2 0 0 0 2 26.0 to 26.5 8 8 One 0 0 0 0 0 26.5 to 27.0 0 7 One 0 0 0 0 0

(2-parameter) Weibull distribution was found to be the most appropriate statistical function for predicting the corrosion rate, as in Example 1, and the equation (2) of the Weibull distribution for each corrosion exposure interval ) And the dimension parameter (?) When the shape parameter (?) And the dimensional parameter (?) Are applied and calculating the linear form or the polynomial expressing the shape parameter (?) And the dimensional parameter (4) below.

......식(4)

(4)

Fig. 7 is a graph showing actual parameters (alpha, beta) and linear form or polynomial expression of equation (4) together, and Fig. 8 is a graph showing the relationship between a linear form or a polynomial equation of equation (4) And the probability density of the Weibull distribution with respect to the corrosion exposure time at one-year intervals. As in the case of Example 1, the statistical characteristics of corrosion progress differ for different age types of ballast tanks.

9 is a graph showing an average and a standard deviation superimposed on the thickness reduction amount (red) of the actually measured ballast tank and the thickness reduction amount (blue) calculated approximately by the complete time-dependent weight food consumption prediction empirical formula . As shown, the mean and standard deviation of the calculated approximations present a moderate value for the actual measured values with large amplitudes, and therefore approximate values that can be used as a criterion for corrosion determination over the age range of the entire ballast tank Can be provided.

As described above, the method of predicting the corrosion amount according to the passage of time in the corrosive environment according to the present invention has been described with reference to the drawings. However, the present invention is not limited to the embodiments and drawings disclosed in the present specification, It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (5)

The present invention relates to a method for predicting a corrosion amount over time in a corrosive environment in which a series of calculation processes are performed in a computing means provided in a computer,
A first step of inputting corrosion data of a specific metal structure collected for various corrosion exposure times;
A second step of determining a corrosion amount interval for maximizing an average value and a minimum COV value for the data input in the first step;
(3) calculating the shape parameter (α) and the dimensional parameter (β) by applying the Weibull function of the following equation (1) to the corrosion data for each corrosion exposure time for the corrosion amount interval determined in the second step step;
Calculating a line form or a polynomial expression for the corrosion exposure time that approximately expresses each parameter (?,?) From the shape parameter (?) And the dimensional parameter (?) For each of the plurality of corrosion exposure time calculated in the third step, step; And
The fifth step of completing the time-dependent subcategory prediction equation by applying a linear or polynomial equation of the shape parameter α and the dimension parameter β calculated in the fourth step to the Weibull function of the following equation (1) ;
A method for predicting corrosion rate over time in a corrosive environment comprising:
Equation (1):

{Wherein, d _c is the value that is calculated as a portion of food (defect depth), Y _e is Y _e = YY _c as the time (years) after the destruction of the coating layer (Y _c is the coating life, Y is the age of the metal structure) } The method according to claim 1,
Wherein the coating life (Y _c ) of the formula (1) is assumed to be zero. The method according to claim 1,
Wherein the specific metal structure is a well tube and the corrosion amount data is a thickness reduction amount inside the well tube. The linear form or polynomial equation of the shape parameter (alpha) and the dimensional parameter (beta) (2). 2. The method of claim 1, wherein the step
Equation (2):

The method according to claim 1,
Characterized in that the specific metal structure is a seawater ballast tank and the linear form or polynomial expression of the shape parameter (alpha) and the dimensional parameter (beta) calculated in the third step is the following equation (3) Estimation of corrosion rate with time.
Equation (3):

The method according to claim 1,
And the second step is to determine the average value of the corrosion exposure time as a maximum value and the COV as a minimum value by averaging the corrosion amount intervals, thereby estimating the corrosion amount over time in the corrosive environment.

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