CN113806964B - Corrosion and scaling rate prediction method considering multi-factor coupling effect - Google Patents
Corrosion and scaling rate prediction method considering multi-factor coupling effect Download PDFInfo
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Abstract
The invention discloses a corrosion and scaling rate prediction method considering multi-factor coupling effect, and belongs to the technical field of corrosion protection of oil and gas fields. The method is characterized in that: firstly, carrying out correlation analysis on collected work area corrosion and scaling parameters and determining main control factors; simulating a metal corrosion and scaling experiment through orthogonal design, and obtaining a regression equation of corrosion and scaling rate by combining a multiple regression analysis method; and finally, the regression equation and the regression coefficient are checked, the experiment expansion sample can be increased by reducing the horizontal interval of the main control factor, and the prediction precision is improved. According to the experiment of the influence of the main control factors on the corrosion and scaling rates, the method is combined with a multiple regression analysis method, the corrosion and scaling rates under a wider range of working conditions can be predicted, guidance is provided for field corrosion protection work, and safety accidents and economic losses are reduced.
Description
Technical Field
The invention belongs to the technical field of corrosion protection of oil and gas fields, and particularly relates to a corrosion and scaling rate prediction method considering multi-factor coupling effect.
Background
In the process of oil and gas field development, corrosive media such as carbon dioxide, hydrogen sulfide gas and the like seriously corrode production equipment, metal corrosion failure is caused, and meanwhile, the corrosion degree is aggravated under the coupling action of conditions such as temperature, pressure, flow velocity, pH value, scaling and the like. In order to avoid safety accidents and economic losses, experts and scholars predict corrosion and scaling rates, and currently, a plurality of methods for predicting the corrosion and scaling rates are provided, but main control factor analysis and scaling rate prediction aiming at work area parameters are often lacked in the prediction method, and the actual corrosion and scaling rates on the site cannot be accurately reflected, so that the establishment of a novel method for predicting the corrosion and scaling rates by considering the multi-factor coupling effect is of great significance.
At present, aiming at a corrosion and scaling prediction method, a corrosion prediction model of an acid gas field considering multiple factors and a parameter determination method (application publication number: CN112668206A), data are mainly obtained from the site to carry out multivariate regression analysis, the influence of five factors on the corrosion rate is considered to carry out the prediction of the corrosion rate, the regression model is checked, and finally the prediction model is output. However, the field data change is small, so that the prediction accuracy in a small-range working condition is high, when the influence factor value exceeds the range of a common operation working condition, the prediction accuracy is reduced, and the scaling rate is not predicted. ' A CO taking multiple factors into account 2 A corrosion prediction plate establishing method (application publication number: CN111177947A) mainly analyzes temperature, carbon dioxide partial pressure and chloride ion concentration and fits a functional relation between a single factor and corrosion rate, and a nonlinear fitting is utilized to draw a two-factor plate for two-factor influence, but a mathematical model is not given in the method, and the corrosion rate prediction applicability is poor under the influence of more factors.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a corrosion and scaling rate prediction method considering multi-factor coupling effect, which screens main control factors of corrosion and scaling of a work area according to correlation coefficients, enlarges the value range of the main control factors, performs orthogonal test design, obtains a corrosion and scaling rate prediction equation under the influence of the main control factors, and provides a basis for corrosion protection work of an oil and gas field.
The technical problem to be solved by the invention is that the following technical scheme is adopted, and the corrosion and scaling rate prediction method considering the multi-factor coupling effect comprises the following steps:
the method comprises the following steps: collecting corrosion and scaling parameters of a work area and determining main control factors;
collecting corrosion and scaling parameters of a work area: the method comprises the following steps of (1) constructing a corrosion and scaling rate matrix A by using collected m corrosion and scaling rate influencing factors including influencing factors such as carbon dioxide partial pressure, temperature, flow velocity, chloride ion concentration, pH value, dissolved oxygen and bacteria, wherein the corrosion and scaling rate matrix A is as shown in a formula (1);
in the formula: a is a corrosion and scaling rate matrix; the front m columns are m collected corrosion and scaling rate influencing factors; a is ij J (j is more than or equal to 1 and less than or equal to m) th corrosion and scaling rate influence factor data in the ith group of data; column m +1 is reported as corrosion rate (fouling rate), mm/a (mg/m) 2 /h)；
Determining main control factors: calculating correlation coefficients of the collected m influencing factors with the corrosion rate and the scaling rate respectively; when the main control factor of the corrosion rate is analyzed, the corrosion rate data of the m +1 th column of the corrosion and scaling rate matrix A is taken and substituted into the formula (2) to obtain the relevant coefficient r of the corrosion rate i(m+1) (ii) a When the main control factor of the scaling rate is analyzed, the scaling rate data of the m +1 th column of the corrosion and scaling rate matrix A is taken and substituted into the formula (2) to obtain the scaling rate correlation coefficient r i(m+1) ；
In the formula: r is i(m+1) Representing the influence correlation of each factor and the corrosion rate or the scaling rate for the corresponding correlation coefficient when substituting the corrosion rate data or the scaling rate data, wherein the correlation is stronger when the absolute value is larger;
absolute value | r of a coefficient relating corrosion to fouling rate i(m+1) Sorting the I from large to small, and selecting three factors with strongest correlation as main control factors;
step two: determining the variation ranges of the three main control factor values, performing a multi-factor and multi-level experiment by using an orthogonal design method, simulating metal corrosion and scaling in a high-temperature high-pressure kettle, calculating the corrosion rate and the scaling rate, and establishing a corrosion and scaling rate matrix V as shown in formula (3);
in the formula: v is a corrosion and scaling rate matrix obtained by an experiment; x is the number of i1 The ith group of data is the first main control factor; x is the number of i2 The ith group of data is the second main control factor; x is the number of i3 The ith group of data is the third main control factor; x is the number of i4 Corrosion rate of group i, mm/a; x is the number of i5 Fouling Rate of group i, mg/m 2 /h；
Step three: establishing a corrosion and scaling multiple regression model with a general formula as shown in a formula (4);
y i ＝β 0 +β 1 z i1 +β 2 z i2 +β 3 z i3 +β 4 z i4 +β 5 z i5 +τ i (i＝1,2,...,n) (4)
when the model is a corrosion rate regression model, part of parameters in the formula (4) are shown as the formula (5);
in the formula: y is i Corrosion rate of group i, mm/a; beta is a j (j is more than or equal to 1 and less than or equal to 5) is a regression coefficient of the corrosion rate model; z is a radical of ij (j 1,2.. 5) converting the non-linear influence into an etch rate y in a regression model i The linearization term of (a); tau is i Is a residual error;
when the scaling rate regression model is adopted, part of parameters in the formula (4) are shown as (6);
in the formula: y is i For group i fouling rates, mg/m 2 /h；β j (j is more than or equal to 1 and less than or equal to 5) is a regression coefficient of the fouling rate model; z is a radical of ij (j 1,2.. 5) converting the non-linear influencing factors into fouling rate y in a regression model i A linearization term of (a); tau. i Is a residual error;
step four: normally testing the data of the influence factors of the corrosion and scaling rate;
and (4) performing data normality check on the data in the corrosion and scaling rate matrix V in the step two by using SPSS, wherein the checked data comprises x i1 、ln(x i1 )、
x i3 、
x i4 、ln(x i2 )、x i1 x i2 、x i5 If the test result passes the normal test, performing the fifth step; if the normal test is not passed, expanding the sample data, and returning to the step two;
step five: calculating regression coefficients of the corrosion and scaling rate model;
the general formulas of the multiple regression models established in the step three are respectively carried into the formula (5) and the formula (6) to represent a corrosion rate model and a scaling rate model, and the regression coefficients of the two models are uniformly calculated according to the formula (7);
in the formula:
is to the regression coefficient beta j An estimated value of (d); z when corrosion rate model regression coefficient is calculated ij (j ═ 1,2.. 5) substituting parameters for formula (6), y i Corrosion rate of the ith group; z when the regression coefficient of the fouling rate model is calculated ij (j ═ 1,2.. 5) substituting parameters for formula (7), y i Group i fouling rate;
step six: checking the regression model and the regression coefficient;
checking a regression model F: suppose that
Constructing a statistic F as in equation (8);
wherein:
in the formula: z when corrosion rate regression model is examined ij (j ═ 1,2.. 5) substituting parameters for formula (6),
f is the corrosion rate regression model test statistic; when examined by a regression model of fouling rate, z ij (j ═ 1,2.. 5) substituting parameters for formula (7),
for the ith group of fouling rates, F is the fouling rate regression model test statistic;
given significance level α 1 Looking up the table to obtain the critical value
When the temperature is higher than the set temperature
Then the original hypothesis H is rejected 0 Indicating that the overall regression effect of the dependent variable and the independent variable is obvious, and performing a sixth step; otherwise, expanding the sample data and returning to the step two;
testing the significance t of the regression coefficient: suppose that
Construct statistics t j As in equation (10);
in the formula: z in matrix Z when corrosion rate regression coefficients are examined ij (j ═ 1,2.. 5) substituting parameters into formula (6), and t j Regression coefficients for corrosion rate
The test statistic of (a); z in matrix Z when scale rate regression coefficients are examined ij (j ═ 1,2.. 5) substituting parameters for formula (7), t j Regression coefficients for fouling rate
The test statistic of (a); u. of jj Is matrix Z as the jth row and jth column elements of formula (11);
when significance level a is given 2 Looking up the table to obtain the critical value
When all are
Then the original hypothesis H is rejected 0 Step seven is carried out, wherein the independent variable influence is obvious; if there is only one t j Failed test, test
Corresponding z ij With any one of the other four z ik If the absolute value of the correlation coefficient between (k 1,2.. 5; k ≠ j) is greater than 0.7, the model will be used
Item elimination, step seven is carried out; otherwise, expanding the sample data and returning to the step two;
step seven: determining a corrosion and scaling rate prediction equation;
the corrosion rate prediction equation is formula (12);
in the formula: y is 1 Predicting the corrosion rate;
regression coefficient beta for corrosion rate j An estimated value of (d); x 1 、X 2 、X 3 The first, second and third main control factors are respectively;
the fouling rate prediction equation is formula (13);
in the formula: y is 2 Predicting the scaling rate;
regression coefficient beta for fouling rate j An estimated value of (d); x 1 、X 2 、X 3 The first, second and third main control factors are respectively.
And further, determining the variation range of the main control factors, wherein the lower limit of the value of each main control factor is 0.5-0.8 times of the parameter data of the normal operation working condition of the work area, and the upper limit of the value of each main control factor is 1.2-2 times of the parameter data of the ultimate operation working condition of the work area.
Further, the method for calculating the corrosion rate and the scaling rate in the second step specifically comprises the following steps: orthogonal design n groups of experiments, before the experiments, 4 metal hanging pieces of the ith group are weighed and m is recorded 1id (d is 1,2,3,4), after the experiment, the i-th group of 4 metal hanging pieces are dried by cold air, weighed and recorded 2id (d is 1,2,3,4), then the i group of 4 metal hanging sheets are weighed and recorded after being subjected to rust and scale removing treatment 3id (d ═ 1,2,3, 4); the corrosion rate v of each coupon of the set is obtained from equation (14) 1id (d is 1,2,3,4), and the same set of 4 coupons are etched at a rate v 1id (d is 1,2,3,4) and the average value is recorded as the corrosion rate x of the corresponding group number i4 (ii) a Equation (15) obtains the fouling rate v for each coupon in the set 2id (d-1, 2,3,4), the fouling rate v of 4 coupons in the same group will be measured 2id (d ═ 1,2,3,4) fouling rates x averaged for the corresponding groups i5 ；
In the formula: i is the ith group in n groups of experiments; rho is the density of the metal pendant, g/cm 3 (ii) a A is the surface area of the metal pendant in cm 2 (ii) a t is experimental time, h; m is 1id Is the initial mass of the metal hanging sheet, mg; m is 2id The mass of the metal hanging piece after the experiment is finished and dried by cold air is mg; m is 3id Mg is the mass of the metal hanging sheet after the rust and scale removal treatment; v. of 1id (d ═ 1,2,3,4) for 4 coupon corrosion rates, mm/a, respectively; v. of 2id (d-1, 2,3,4) is the fouling rate of 4 metal coupons, mg/m 2 /h。
And further, expanding the sample data in the fourth step, the sixth step and the seventh step, wherein the specific method is to reduce the horizontal spacing of the main control factors and increase an experimental data group during orthogonal test design.
Due to the adoption of the technical scheme, the invention has the following advantages:
(1) the method carries out correlation analysis on the influence factors of corrosion and scaling of the work area to obtain main control factors, designs multi-factor and multi-level metal corrosion and scaling experiments, and can effectively predict the corrosion and scaling rate under more complex working conditions and improve the applicability, wherein the designed working condition parameter range is larger than the actual operating working condition.
(2) The method considers corrosion and scaling at the same time, predicts the corrosion and scaling rate, is closer to the actual operation working condition of a work area, and provides guidance for avoiding corrosion and scaling damage.
Drawings
FIG. 1 is a flow chart of a corrosion and fouling prediction method that takes into account multi-factor coupling;
FIG. 2 shows the temperature x i1 A normal test chart;
FIG. 3 shows ln (x) i1 ) A normal test chart;
FIG. 4 is
A normal test chart;
FIG. 5 shows the chloride ion concentration x i3 A normal test chart;
FIG. 6 is
A normal test chart;
FIG. 7 shows the corrosion rate x i4 A normal test chart;
FIG. 8 shows ln (x) i2 ) A normal test chart;
FIG. 9 is x i1 x i2 A normal test chart;
FIG. 10 is fouling rate x i5 And (4) a normal test chart.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
The method comprises the following steps: collecting corrosion and scaling parameters of a certain work area, wherein 4 collected corrosion and scaling rate influence factors are temperature, carbon dioxide partial pressure and chloride ion concentration respectively, and 5 groups of data construct a statistical matrix A as shown in a formula (16);
in the formula: a is a corrosion and scaling rate matrix; the first column is temperature, deg.C; the second column is carbon dioxide partial pressure, MPa, and the third column is chloride ion concentration, mg/L; the fourth column is the flow velocity, m/s, the fifth column is the corrosion rate (fouling rate), mm/a (mg/m) 2 /h)；
When the main control factor of the corrosion rate is analyzed, the correlation coefficient | r is obtained by calculation 15 |＝0.692，|r 25 |＝0.3669，|r 35 |＝0.0714，|r 45 |＝0；When the main control factor of the scaling rate is adopted, the correlation coefficient | r is obtained by calculation 15 |＝0.6675，|r 25 |＝0.0607，|r 35 |＝0.0714，|r 45 0; analyzing to obtain main control factors of temperature, carbon dioxide partial pressure and chloride ion concentration as corrosion and scaling rates;
step two: taking the 20# steel of the work area as a test pipe, according to the data collected in the step one, the temperature interval [40,55], the carbon dioxide partial pressure interval [0.3,0.6] and the chloride ion concentration interval [9000,11000], according to the method of claim 2, expanding the values of the main control factors, and establishing a corrosion and scaling rate V matrix as shown in a formula (4) through test design; a first row temperature interval is [30,60], a second row carbon dioxide partial pressure interval is [0.5,1], a third row chlorine ion concentration is [5000,20000], 14 groups of data of corrosion and scaling rates are designed orthogonally, and a corrosion and scaling rate V matrix is established according to a formula (17);
step three: establishing a corrosion and scaling multiple regression model with a general formula as shown in a formula (4); formula (5) represents a corrosion rate model, formula (6) represents a fouling rate model;
step four: normally testing the data of the influence factors of the corrosion and scaling rate;
and (3) performing data normality check on the data in the corrosion and scaling rate matrix in the step two by using SPSS software, wherein the checked data comprises x i1 、ln(x i1 )、
x i3 、
x i4 、ln(x i2 )、x i1 x i2 、x i5 Obtaining normal test graphs which are respectively shown in the graphs 2-10, and performing a fifth step by using the results of the normal test;
step five: calculating regression coefficients of the corrosion and scaling rate model;
and (3) substituting the data in the matrix V and the formula (5) into the formula (7) to obtain an estimated value of the regression coefficient of the corrosion rate prediction equation:
and (3) substituting the data in the matrix V and the formula (6) into the formula (7) to obtain an estimated value of the regression coefficient of the fouling rate prediction equation:
step six: checking the regression model and the regression coefficient;
checking a regression model F;
and (4) checking the corrosion rate model F: the formula (8) obtains a corrosion rate Ftest statistic of 5.719, gives a significance level alpha of 0.05, and obtains a critical value F by table lookup 0.05 When (5,8) ≥ 3.687, F ≥ F α (5,8), the corrosion rate model passes the inspection;
fouling rate model F test: equation (8) gives a fouling rate fsample statistic of 8.365, gives a significance level α of 0.05, and looks up the critical value F 0.05 When (5,8) ≥ 3.687, F ≥ F α (5,8), the fouling rate model passes the test;
checking a regression coefficient t;
carrying out regression coefficient t test on the corrosion rate model: equation (10) obtains t test statistic t 0 ＝-2.34，t 1 ＝2.78，t 2 ＝2.743，t 3 ＝3.351，t 4 ＝2.824，t 5 -2.707, where a threshold t is found by looking up the table, giving a significance level α of 0.05 0.05 (8) 2.306 when | t j |≥t 0.05 (8) (j is more than or equal to 0 and less than or equal to 5) rejecting the original hypothesis H 0 Step seven is carried out, wherein the independent variable influence is obvious;
scaling rate model regression coefficient tpatch: equation (10) obtains t test statistic as t 0 ＝-2.795，t 1 ＝-1.73，t 2 ＝-2.719，t 3 ＝2.357，t 4 ＝2.786，t 5 Given a significance level α of 0.05, 3.007, the critical value t was found by looking up the table 0.05 (8)＝2.306, divide by t 1 All the z is obtained by checking and calculating by combining the formula (6) i1 And z ik Absolute values of correlation coefficients of (k 2,3,4,5) are 0.3126, 0.813, 0.3019, 0.1334, and z i1 ＝x i1 And z i3 ＝x i1 x i2 If the absolute value of the correlation coefficient is 0.813 and greater than 0.7, the correlation coefficient is rejected
Item, go to step seven;
step seven: determining a corrosion and scaling rate prediction equation;
firstly, a corrosion rate prediction equation is expressed as a formula (18);
secondly, the scaling rate prediction equation is expressed as a formula (19);
the basic method and main features of the present invention have been described above. It will be understood by those skilled in the art that while the present invention has been described in detail with reference to the preferred embodiments thereof, the present invention is susceptible to modification of part of the features of the invention and equivalents thereof without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is defined by the claims and their equivalents.
Claims (4)
1. A corrosion and scaling rate prediction method considering multi-factor coupling effect is characterized by comprising the following specific steps:
the method comprises the following steps: collecting corrosion and scaling parameters of a work area and determining main control factors;
collecting corrosion and scaling parameters of a work area: the method comprises the following steps of (1) constructing a corrosion and scaling rate matrix A by using collected m corrosion and scaling rate influencing factors including influencing factors such as carbon dioxide partial pressure, temperature, flow velocity, chloride ion concentration, pH value, dissolved oxygen and bacteria, wherein the corrosion and scaling rate matrix A is as shown in a formula (1);
in the formula: a is a corrosion and scaling rate matrix; the front m columns are m collected corrosion and scaling rate influencing factors; a is ij J is more than or equal to 1 and less than or equal to m in the ith group of data; column m +1 is reported as corrosion rate (fouling rate), mm/a (mg/m) 2 /h)；
Determining main control factors: calculating correlation coefficients of the collected m influencing factors with the corrosion rate and the scaling rate respectively; when the main control factor of the corrosion rate is analyzed, the corrosion rate data of the m +1 th column of the corrosion and scaling rate matrix A is taken and substituted into the formula (2) to obtain the relevant coefficient r of the corrosion rate i(m+1) (ii) a During the main control factor analysis of the scaling rate, the scaling rate data of the m +1 th column of the corrosion and scaling rate matrix A is substituted into a formula (2) to obtain a scaling rate correlation coefficient r i(m+1) ；
In the formula: r is a radical of hydrogen i(m+1) Representing the influence correlation of each factor and the corrosion rate or the scaling rate for the corresponding correlation coefficient when substituting the corrosion rate data or the scaling rate data, wherein the correlation is stronger when the absolute value is larger;
absolute value | r of a coefficient relating corrosion to fouling rate i(m+1) Sorting the I from large to small, and selecting three factors with strongest correlation as main control factors;
step two: determining the variation ranges of the three main control factor values, performing a multi-factor and multi-level experiment by using an orthogonal design method, simulating metal corrosion and scaling in a high-temperature high-pressure kettle, calculating the corrosion rate and the scaling rate, and establishing a corrosion and scaling rate matrix V as shown in formula (3);
in the formula: v is a corrosion and scaling rate matrix obtained by an experiment; x is the number of i1 The ith group of data is the first main control factor; x is the number of i2 The ith group of data is the second main control factor; x is the number of i3 The ith group of data is the third main control factor; x is the number of i4 Corrosion rate of group i, mm/a; x is the number of i5 Fouling Rate of group i, mg/m 2 /h；
Step three: establishing a corrosion and scaling multiple regression model with a general formula as shown in a formula (4);
y i ＝β 0 +β 1 z i1 +β 2 z i2 +β 3 z i3 +β 4 z i4 +β 5 z i5 +τ i (i＝1,2,...,n) (4)
when the model is a corrosion rate regression model, part of parameters in the formula (4) are shown as the formula (5);
in the formula: y is i Corrosion rate of group i, mm/a; beta is a j (j is more than or equal to 1 and less than or equal to 5) is a regression coefficient of the corrosion rate model; z is a radical of ij (j 1,2.. 5) converting the non-linear influence into an etch rate y in a regression model i A linearization term of (a); tau. i Is a residual error;
when the scaling rate regression model is adopted, part of parameters in the formula (4) are shown as (6);
in the formula: y is i For group i fouling rates, mg/m 2 /h；β j (j is more than or equal to 1 and less than or equal to 5) is a regression coefficient of the fouling rate model; z is a radical of ij (j 1,2.. 5) converting the non-linear influencing factors into fouling rate y in a regression model i A linearization term of (a); tau is i Is a residual error;
step four: normally testing the data of the influence factors of the corrosion and scaling rate;
and (4) performing data normality test on the data in the corrosion and scaling rate matrix V in the step two by using SPSS, wherein the tested data comprises x i1 、ln(x i1 )、
x i3 、
x i4 、ln(x i2 )、x i1 x i2 、x i5 If the normal test is passed, carrying out the step five; if the normal test is not passed, expanding the sample data, and returning to the step two;
step five: calculating regression coefficients of the corrosion and scaling rate model;
the general formulas of the multiple regression models established in the step three are respectively carried into the formula (5) and the formula (6) to represent a corrosion rate model and a scaling rate model, and the regression coefficients of the two models are uniformly calculated according to the formula (7);
in the formula:
is to the regression coefficient beta j An estimated value of (d); z when corrosion rate model regression coefficient is calculated ij (j ═ 1,2.. 5) substituting parameters for formula (6), y i Corrosion rate of the ith group; z when the regression coefficient of the fouling rate model is calculated ij (j ═ 1,2.. 5) substituting parameters for formula (7), y i Group i fouling rate;
step six: checking the regression model and the regression coefficient;
checking a regression model F: hypothesis H 0 :
Constructing a statistic F as shown in formula (8);
wherein:
in the formula: z when corrosion rate regression model is examined ij (j ═ 1,2.. 5) by substituting parameters for formula (6),
the regression value of the ith group of corrosion rate is obtained, and F is the test statistic of a corrosion rate regression model; when examined by a regression model of fouling rate, z ij (j ═ 1,2.. 5) substituting parameters for formula (7),
f is the fouling rate regression model test statistic for the ith group of fouling rates;
given significance level α 1 Looking up the table to obtain the critical value
When in use
Then the original hypothesis H is rejected 0 Indicating that the overall regression effect of the dependent variable and the independent variable is obvious, and performing a sixth step; otherwise, expanding the sample data and returning to the step two;
testing the significance t of the regression coefficient: hypothesis H 0 :
Construct statistics t j As in equation (10);
in the formula: z in matrix Z when corrosion rate regression coefficients are examined ij (j ═ 1,2.. 5) substituting parameters for formula (6), t j Regression coefficients for corrosion rate
The test statistic of (a); z in matrix Z when scale rate regression coefficients are examined ij (j ═ 1,2.. 5) substituting parameters for formula (7), t j Regression coefficients for fouling rate
The test statistic of (a); u. of jj Is the matrix Z as the jth row and jth column elements of formula (11);
when significance level a is given 2 Looking up the table to obtain the critical value
When all are
Then the original hypothesis H is rejected 0 Step seven is carried out, wherein the independent variable influence is obvious; if there is only one t j Failed test, test
Corresponding z ij With any one of the other four z ik If the absolute value of the correlation coefficient between (k 1,2.. 5; k ≠ j) is greater than 0.7, the model will be used
Item elimination, step seven is carried out; otherwise, expanding the sample data and returning to the step two;
step seven: determining a corrosion and scaling rate prediction equation;
the corrosion rate prediction equation is formula (12);
in the formula: y is 1 Predicting the corrosion rate;
regression coefficient beta for corrosion rate j An estimated value of (d); x 1 、X 2 、X 3 The first, second and third main control factors are respectively;
the fouling rate prediction equation is formula (13);
in the formula: y is 2 Predicting the scaling rate;
regression coefficient beta for fouling rate j An estimated value of (d); x 1 、X 2 、X 3 The first, second and third main control factors are respectively.
2. The method for predicting the corrosion and scaling rate considering the multi-factor coupling effect as claimed in claim 1, wherein in the second step, the variation range of the main control factors is determined, the lower limit of the value of each main control factor is 0.5-0.8 times of the parameter data of the normal operation condition of the work area, and the upper limit of the value of each main control factor is 1.2-2 times of the parameter data of the ultimate operation condition of the work area.
3. The method of claim 1, wherein the coupling effect is considered to be multifactorialThe corrosion and scaling rate prediction method is characterized in that the corrosion rate and scaling rate calculation method in the second step is specifically as follows: orthogonal design n groups of experiments, before the experiments, 4 metal hanging pieces of the ith group are weighed and m is recorded 1id (d is 1,2,3,4), after the experiment, the i-th group of 4 metal hanging pieces are dried by cold air, weighed and recorded 2id (d ═ 1,2,3,4), and then the i-th group of 4 metal coupons were weighed after descaling and m was recorded 3id (d ═ 1,2,3, 4); the corrosion rate v of each coupon of the set is obtained from equation (14) 1id (d is 1,2,3,4), and the same set of 4 coupons are etched at a rate v 1id (d is 1,2,3,4) and the average value is recorded as the corrosion rate x of the corresponding group number i4 (ii) a Equation (15) yields the fouling rate v for each coupon of the set 2id (d ═ 1,2,3,4), fouling rates v for 4 coupons in the same group were determined 2id (d ═ 1,2,3,4) fouling rates x averaged for the corresponding groups i5 ；
In the formula: i is the ith group in n groups of experiments; rho is the density of the metal pendant, g/cm 3 (ii) a A is the surface area of the metal pendant in cm 2 (ii) a t is experimental time, h; m is a unit of 1id Is the initial mass of the metal hanging sheet, mg; m is 2id The mass of the metal hanging piece after the experiment is finished and dried by cold air is mg; m is 3id Mg is the mass of the metal hanging sheet after the rust and scale removal treatment; v. of 1id (d ═ 1,2,3,4) for 4 coupon corrosion rates, mm/a, respectively; v. of 2id (d is 1,2,3,4) is the fouling rate of 4 metal hanger plates, mg/m 2 /h。
4. The method according to claim 1, wherein the sample data is expanded in the fourth, sixth and seventh steps by reducing the horizontal spacing of the main control factors and adding the experimental data set during orthogonal test design.
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