CN107506876B - Method for predicting erosion wear weight loss rate of liquid-solid two-phase flow based on multiple regression analysis - Google Patents
Method for predicting erosion wear weight loss rate of liquid-solid two-phase flow based on multiple regression analysis Download PDFInfo
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Abstract
The invention provides a method for predicting the erosion wear weight loss rate of liquid-solid two-phase flow based on multiple regression analysis, which comprises the following steps: an orthogonal test method is applied, three factors of impact speed, slurry temperature and sulfuric acid concentration of a slurry medium and a relation between two levels and weight loss rate are selected to design an orthogonal test scheme; establishing a regression model of the weight loss rate of the sample and interaction factors among the single factors; determining significance parameters influencing the regression model by carrying out significance test on the regression model; and meanwhile, selecting a zero level to compare a theoretical predicted value with a test value, and verifying the accuracy of the regression equation. The regression model established by the invention considers the interaction among the test factors, comprehensively researches all the factors and the synergistic effect thereof, accurately predicts the loss condition of the sample under other conditions within the horizontal range of the test factors, improves the reliability of the regression model, and simultaneously provides a new research method for the liquid-solid two-phase flow erosion wear test.
Description
Technical Field
The invention relates to a prediction method of a material erosion wear weight loss rate, in particular to a prediction method of a liquid-solid two-phase flow erosion wear weight loss rate based on multiple regression analysis, which is mainly applied to the field of material erosion wear test research.
Background
The erosion and abrasion phenomenon of liquid-solid two-phase flow or multiphase flow is commonly existed in fluid mechanical equipment, and is one of important reasons for damaging flow passage components such as slurry pumps, slurry conveying pipelines, valves and the like. Scholars at home and abroad carry out a great deal of research on erosion and wear mechanisms, influence factors and control measures, and research the erosion and wear resistance of various materials by adopting different test equipment according to specific working conditions. Erosive wear of materialsThe quality of the energy is usually measured by weight loss rate. The total weight loss rate comprises the mechanical abrasion action of solid particles on the target material, the electrochemical corrosion action of corrosive media on the target material and the interaction of mutual promotion or inhibition between the solid particles and the target material under the condition of an erosive wear test. However, there are many factors influencing the erosion and wear resistance of the material, and the existing related literature reports mostly focus on the influence of a single factor on the erosion and wear resistance of the material, and the research results of the influence of the interaction between a multi-factor orthogonal test and a consideration factor on the erosion and wear resistance of the material are still deficient. For example, Liuxin Wide, Fang Yi, liquid-solid two-phase flow erosion Corrosion study [ J]The relation between the erosion corrosion weight loss and the erosion speed and the erosion angle is studied in the paper of water conservancy and electric power machinery 1998,5:33-35 respectively, but the influence of the interaction between the erosion speed and the erosion angle on the test result is not involved. Research on erosion and abrasion resistance of high-safety and safety carbon steel stainless steel material by abalone [ J)]The casting technology, 2009,30(1):23-25, the flow passage component material for pump, namely 55# carbon steel and 1Cr18Ni9Ti stainless steel, are subjected to erosion wear tests under the conditions of different erosion speeds, different slurry sand contents and different erosion angles, the erosion failure rule and the microscopic damage mechanism of the material are explored, a single-factor test method is still adopted, and the influence of the interaction among the erosion speed, the sand content and the erosion angles on the test result is not involved. HVOF is sprayed with Cr under different working conditions3C2Influence of the Corrosion erosive wear Properties of the 25% NiCr coating [ J]Weapons materials science and engineering, 2011,34(3):1-5, using a corrosion erosion abrasion tester in water, 5% H2SO4Spraying Cr to supersonic flame in the corrosive erosion medium prepared from 15% brown corundum3C2The corrosion erosion wear performance of the 25 percent NiCr coating under different working conditions is tested and researched, the influence rule of the erosion speed of the medium and the type of the medium on the corrosion erosion rate of the coating is discussed, and the influence of the interaction between the erosion speed and the medium on the test result is not involved. Erosion behavior of square sages, gay, coconut etc. Ni-Cu-P and 316L in high temperature fluid containing hydrochloric acid [ J]Rare Metal materials and engineering, 2012(s2):505-The erosion behavior of the steel in different hydrochloric acid concentrations and different fluid temperatures does not relate to the influence of the interaction between the hydrochloric acid concentration and the fluid temperature on the erosion test results.
The orthogonal experiment method is a method for selecting a proper amount of representative points from a large number of test points, designing an orderly-arranged orthogonal table to arrange tests, and then carrying out statistical analysis on test data, so that the test times can be greatly reduced, the test feasibility cannot be reduced, a regression prediction equation can be established by calculating the test data, and the test results in the test condition range can be subjected to prediction analysis. According to the literature search, the research for analyzing the influence factors of the erosion wear performance of the material by adopting a multi-factor orthogonal test is few, and particularly the influence of the interaction among the multi-factors is not reported. For example, Chenxipeng, Yuli, Li Ainong, etc. Cr25Ni20Stainless steel in H2O2Study of tribological Properties in media [ J]Lubrication and sealing, 2009,34(2):28-30, by orthogonal test design method, the abrasion loss and the rotating speed, the load and the medium H of the test piece are established2O2The regression equation between the volume fractions only considers the influence of a single factor on the abrasion loss, and does not consider the influence of the interaction between the factors on the result of the orthogonal regression statistical analysis. However, the interaction between the factors has a great influence on the statistical analysis and accurate judgment of the test result in many cases, and should not be ignored.
In summary, the existing research methods have the following disadvantages:
(1) the test times are more, the workload is large, and the data information amount is small;
(2) in most cases, only the influence of a single factor on the test result is researched, and the influence of interaction factors among the factors is not considered;
(3) erosion wear under other conditions within the given test level range cannot be predicted.
Disclosure of Invention
In order to overcome the defects of the existing research method for the erosion wear performance of the material, the invention provides a prediction method for the erosion wear weight loss rate of liquid-solid two-phase flow based on multiple regression analysis. The prediction method adopts a multi-factor orthogonal test and considers the influence of the interaction among factors on the erosion and wear performance of the material, thereby solving the defects existing in the existing research method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the method for predicting the erosion wear weight loss rate of the liquid-solid two-phase flow based on the multiple regression analysis comprises the following steps:
(1) sample pretreatment
Cutting a raw material into a rectangular sample, taking one surface of the rectangular sample as an erosion surface, grinding the erosion surface to 1200 meshes by using metallographic abrasive paper, polishing, respectively ultrasonically cleaning by using acetone and alcohol, drying, and weighing the initial weight of the sample;
(2) design of orthogonal experiments
An orthogonal experimental scheme is designed according to the relationship between impact velocity, slurry temperature, sulfuric acid concentration of slurry medium, two levels of the three factors and weight loss rate, and L of the three factors with interaction at two levels is listed8(27) An orthogonal table comprising 8 sets of experiments for 7 factors and 2 levels;
(3) erosion wear test
According to the orthogonal test scheme designed in the step (2), carrying out an erosion wear test by using the sample prepared in the step (1), repeating the test scheme of each group for three times, obtaining weight loss by measuring the weight of the sample before and after erosion wear, taking an average value, and obtaining the weight loss rate of the sample under each group of test conditions by calculation;
(4) regression equation establishment
Establishing a regression model of the sample weight loss rate and each single factor and interaction factor among the factors, and calculating and processing regression coefficients according to the data obtained in the step (3) to obtain a regression equation of the sample weight loss rate and the corresponding coding value of each factor;
(5) significance testing of regression equations
Determining significance parameters influencing the regression model by carrying out significance test on the regression model, selecting a zero level to compare a theoretical predicted value with an actually measured value, further verifying the accuracy of the regression equation, and obtaining the regression equation between the weight loss rate and the factor actual value by substituting an encoding formula into the regression equation;
(6) prediction of erosion wear weight loss rate
And (4) within the test level range, optionally selecting a group of test conditions, and predicting the weight loss rate of the material under other test conditions within the factor level range.
In a preferred embodiment of the present invention, in the step (1), 304 stainless steel having an austenitic matrix structure is used as the test material.
As a preferred technical solution of the present invention, in order to facilitate the conversion of the orthogonal table into the regression orthogonal design table, the step (2) includes encoding each factor of the orthogonal table, and converting the orthogonal table into the regression orthogonal test table represented by the encoding.
As a preferred technical solution of the present invention, an encoding formula for encoding each factor by an orthogonal table is as follows:
Zjdenotes the jth factor, Z0jDenotes the zero level of the j factor, Z2jRepresents the upper level of the j factor, Z1jRepresents the lower level of the j factor.
As a preferred technical scheme of the invention, the 7 factors in the step (2) comprise three single factors of impact speed, slurry temperature and sulfuric acid concentration, an interaction factor of two single factors and an interaction factor of three single factors.
As a preferred technical solution of the present invention, the regression equation established in the step (4) is: if the response variable (weight loss ratio) is represented by Y, the multiple regression equation of the designed orthogonal test in step (2) can be expressed as:
Y＝b0+b1x1+b2x2+b3x3+b4x1x2+b5x2x3+b6x1x3+b7x1x2x3
wherein, b0Is a response variable at the base level, b1、b2、b3Respectively represent three single-factor variables x in step (2)1、x2、x3The influence coefficient of (a); b4、b5、b6Respectively represent variable x1-x2、x2-x3、x1-x3The coefficient of interaction of (a); b7Variable x representing a selected level1-x2-x3The coefficient of interaction between them, where positive values of Y indicate weight loss and negative values indicate weight gain.
Compared with the prior art, the invention has the beneficial effects that:
under the condition of less test times, the method can accurately predict the total weight loss rate of the material under other conditions in the test range by establishing the multiple regression equation of the total weight loss rate of the material erosive wear, the slurry impact speed, the slurry temperature, the sulfuric acid concentration in the slurry medium, the pairwise interaction factor and the three-factor interaction factor. According to the prediction method provided by the invention, the influence rule of the interaction among factors on the erosion wear performance of the material can be obtained without carrying out pure corrosion and pure wear tests, and the defects in the existing research method are overcome.
Drawings
FIG. 1 is a flow chart of a prediction method according to the present invention.
Detailed Description
The invention provides a method for predicting the erosion wear weight loss rate of liquid-solid two-phase flow based on multivariate regression analysis, which specifically comprises the following steps:
(1) for the preparation and pretreatment of the test material, a material with better corrosion resistance can be selected as the test material, for example, 304 stainless steel is adopted as the test material in the embodiment of the invention. Before the test, the test material is pretreated, specifically: cutting a raw material into a rectangular sample, taking one surface of the rectangular sample as an erosion surface, grinding the erosion surface to 1200 meshes by using metallographic abrasive paper, polishing, respectively ultrasonically cleaning by using acetone and alcohol, drying, and weighing the initial weight of the sample.
(2) Orthogonal test design, wherein an orthogonal test method is used, three factors of the impact velocity of slurry, the temperature of the slurry and the sulfuric acid concentration of a slurry medium and the relation between two levels and the weight loss rate are selected to design an orthogonal test scheme, and L of three factors and two levels with interaction is listed8(27) An orthogonal table comprising 8 sets of tests at 2 levels and 7 factors, the 7 factors comprising three single factors of impact velocity, slurry temperature, sulfuric acid concentration and interaction factors of two to two single factors and interaction factors of three single factors. As an embodiment of the present invention, in order to facilitate the conversion of the orthogonal table into the regression orthogonal design table, the step (2) further includes encoding the orthogonal table, and converting the orthogonal table into a regression orthogonal test table expressed by codes, where the encoding formula is:
Zjdenotes the jth factor, Z0jDenotes the zero level of the j factor, Z2jRepresents the upper level of the j factor, Z1jRepresents the lower level of the j factor.
(3) And (3) carrying out erosion wear test, namely carrying out the erosion wear test by using the sample prepared in the step (1) according to the orthogonal test scheme designed in the step (2), repeating the test scheme of each group for three times, obtaining weight loss by measuring the weight of the sample before and after erosion wear, averaging, and obtaining the weight loss ratio of the sample under each group of test conditions by calculation. As one embodiment of the invention, the invention adopts a rotary liquid-solid two-phase flow erosive wear test.
(4) Establishing a regression model of the sample weight loss rate, each single factor and interaction factors among the factors, specifically comprising the following steps: let us consider p variables x1,x2,···,xpThe influence of a response variable y, y and x are established1,x2,···,xpThe linear regression equation of (1) is given by:
y＝β0+β1x1+β2x2+…+βpxp+ε
where ε is the experimental error and ε -N (0, σ) is assumed2). As an embodiment of the present invention, if the response variable (weight loss ratio) is represented by Y, the multiple regression equation of the designed orthogonal test in step (2) may be represented as:
Y＝b0+b1x1+b2x2+b3x3+b4x1x2+b5x2x3+b6x1x3+b7x1x2x3
wherein, b0Is a response variable at the base level, b1、b2、b3Respectively represent three single-factor variables x in step (2)1、x2、x3The influence coefficient of (a); b4、b5、b6Respectively represent variable x1-x2、x2-x3、x1-x3The coefficient of interaction of (a); b7Variable x representing a selected level1-x2-x3The coefficient of interaction between them, where positive values of Y indicate weight loss and negative values indicate weight gain.
After the regression equation is established, calculating and processing the regression coefficient according to the data obtained in the step (3), specifically, if x is usedijRepresenting the coded value, x, of the jth variable in the ith experimentijAnd taking the value as 1 or-1, and selecting a two-level orthogonal table with the number of columns not less than p to perform experimental design under the condition of p variables. And if the total number of tests is N, the result of the ith test is as follows:
yi＝β0+β1xi1+β2xi2+…+βpxip+εi,i＝1,2,…,N.
note betajIs estimated as
The following can be obtained:
bj＝Bj/N,j＝0,1,…,p.
wherein:
calculation of regression coefficients:
the sum of squares of the variables and their degrees of freedom are:
Sj＝Bj×bj,j＝1,2,…,p,fj＝1
the sum of the squares of the sums:
regression sum of squares:
SR＝S1+…+Sp,fR＝p
sum of squares of residuals:
Se＝ST-SR,fe＝N-1-p
and obtaining a regression equation of the weight loss rate of the sample and the corresponding coding value of each factor according to the processed data.
(5) And (5) carrying out significance test on the regression equation, and determining significance parameters influencing the regression model.
And (3) checking a regression equation:
and (3) checking the regression coefficient:
and selecting a zero level to compare theoretical predicted values with measured values, further verifying the accuracy of the regression equation, and substituting the coding formula into the regression equation to obtain the regression equation between the weight loss rate and the actual value of the factor.
(6) Within the test level range, a set of test conditions is optional. And predicting the weight loss rate of the material under other test conditions within the range of each factor level.
The present invention will be described in further detail with reference to specific examples.
Example 1
The method for predicting the erosion wear weight loss rate of the liquid-solid two-phase flow based on the multiple regression analysis in the embodiment specifically comprises the following steps:
the method comprises the following steps: preparation and pretreatment of test materials
In this example, 304 stainless steel having good corrosion resistance, which is commercially available, was used as a test material, and the matrix structure was austenite. 304 stainless steel is widely applied to industries such as ships, chemical engineering and the like due to excellent corrosion resistance.
In this embodiment, a rotary erosion and wear tester is used to simulate the erosion and wear effect of the acidic slurry on the surface of the sample. The raw material is subjected to warp cutting to obtain a sample with the size of 10mm multiplied by 20mm, wherein one erosion surface with the size of 10mm multiplied by 10mm is gradually ground to 1200 meshes through metallographic abrasive paper, polished, cleaned through an ultrasonic cleaner by using acetone and alcohol, and after drying treatment, the initial weight of the sample is weighed by using an analytical balance with the precision of 0.1 mg. The total volume of the test slurry is 8L, wherein the content of 20-40 mesh quartz sand sold in the market accounts for 5% of the total mass.
Step two: regression orthogonal experimental design
According to actual needs, the test factors are determined to be the impact speed A, the slurry temperature B and the sulfuric acid concentration C of a slurry medium, for convenience of coding, the factors are respectively taken as two levels 1 and 2, and interaction factors among the factors are considered, wherein A multiplied by B represents the impact speed and the slurry temperature interaction factor, A multiplied by C represents the impact speed and the sulfuric acid concentration interaction factor, B multiplied by C represents the slurry temperature and the sulfuric acid concentration interaction factor, and A multiplied by B multiplied by C represents the three-factor interaction factor. The impact speed is determined by adjusting the rotating speed of the motor, namely the rotating speed of the motorThe average is 800r/min and 1200r/min respectively. The temperature of the slurry is regulated by a device temperature control system, and the two levels of temperature are respectively 25 ℃ and 45 ℃. The concentration of sulfuric acid in the slurry was 0.25mol/L and 0.5mol/L, respectively, from 98% analytically pure H2SO4And tap water. The test factor levels are shown in table 1.
TABLE 1 test factor level table
Selection of orthogonal tables and determination of test protocols: firstly, selecting an orthogonal table according to the number of levels, wherein the item is two levels, and selecting a 2-level table; secondly, the minimum number of columns is determined according to the number of the factors and the number of the interactions (when the number of the orthogonal tables is selected, each interaction should be regarded as a factor), the item has three single factors and four interaction factors, and therefore L is finally determined and selected8(27) And (4) an orthogonal table. Due to the presence of the interaction factor between the factors,
the arrangement of columns is based on L with interaction list8(27) And (4) an orthogonal table. The orthogonal test protocol is shown in table 2.
TABLE 2 orthogonal test protocol
In order to convert the orthogonal test table and the regression orthogonal design table, each factor needs to be coded, specifically, ZjDenotes the jth factor, Z2jRepresents the upper level of the j factor, Z1jRepresenting the lower level of the j factor, Z0jRepresenting the zero level of the j factor, the coding formula is obtained as follows:
zero level
Horizontal interval
Coding formula
Thus, a factor code table is obtained as shown in table 3.
TABLE 3 factor code table
And transforming the two horizontal orthogonal tables into a regression orthogonal design table. Tabular transformation principle 1 → 1, 2 → -1. In the table, 1 represents the upper level and-1 represents the lower level. Orthogonal table L8(27) After conversion, a regression orthogonal test code table was obtained as shown in table 4.
TABLE 4 orthogonal table after conversion
In order to establish the regression equation, each factor is defined by variable xjInstead, each factor corresponds to a variable, as shown in table 5.
TABLE 5 factor-to-variable correspondence table
The final experimental protocol was obtained after introducing the structural matrix, as shown in table 6.
TABLE 6 regression orthogonal test Table
Step three: erosion wear test
And (3) carrying out the test by combining the test methods described in the first step and the second step according to a regression orthogonal test scheme. And repeating the test of each group for three times respectively, obtaining the weight loss by measuring the weight of the sample before and after erosion and abrasion, taking an average value, and finally obtaining the weight loss rate of the material under the test condition of each group by calculation. The results of the tests of each group are shown in Table 7.
TABLE 7 weight loss ratio in orthogonal regression test
Step four: establishment of regression equation
The test results were subjected to data processing according to the calculation method in the embodiment to obtain regression calculation results of the orthogonal test data, as shown in table 8.
TABLE 8 results of regression orthogonal design calculations
The sum of the overall squares is calculated as follows:
will SjThe last two least significant bits are combined as the error term SeI.e. Se＝0.49,fe＝2。
Regression sum of squares:
SR＝S1+S2+S3+S23+S123＝9343.10,fR＝5
establishing weight loss ratio and coding value xjRegression equation (1):
Y＝67.01+3.94x1+29.73x2+14.15x3+8.26x2x3-0.56x1x2x3 (1)
step five: significance test
Significance testing of the regression equation:
from this the equation can be derived.
The regression coefficients were examined as follows:
looking up F table to obtain F0.1(1,2)＝8.53,F0.05(1,2)＝18.5,F0.01(1,2)＝98.5，
F1＝S1/0.245＝124.03÷0.245＝506.24>F0.01(1,2)，
F2＝S2/0.245＝7068.61÷0.245＝28851.47>F0.01(1,2)，
F3＝S3/0.245＝1601.78÷0.245＝6537.88>F0.01(1,2)，
F23＝S23/0.245＝546.15÷0.245＝2229.18>F0.01(1,2)，
F123＝S123/0.245＝2.53÷0.245＝10.33>F0.1(1,2)＝8.53，
Thus, factor x1,x2,x3,x2x3The height is significant; factor x1x2x3Generally significant.
In order to facilitate the application of the regression equation, the coding formula is brought back to the regression equation to obtain the weight loss ratio Y and each factor ZjThe regression equation of (1).
The coding formula is as follows:
in the formula:
Zj-the jth factor;
Z2j-upper level of the j factor;
Z1j-lower level of factor j;
Z0j-zero level of the j factor;
the coding formula of each factor is obtained as shown in table 9:
TABLE 9 coding formula for each factor
Obtaining the weight loss ratio Y and each factor ZjRegression equation (2):
Y＝16.935-0.0097Z1-0.345Z2-196.48Z3+0.00084Z1Z2+0.0784Z1Z3+8.848Z2Z3-0.00224Z1Z2Z3 (2)
wherein Z is1Represents the impact speed A, r/min; z2Represents the slurry temperature B, DEG C; z3Represents the sulfuric acid concentration C, mol/L.
Step six: prediction of erosion wear weight loss rate
And selecting a certain test condition in the range of the upper level and the lower level to verify the regression equation. For example, taking the zero level in Table 9 allows calculation of the values for each test factor, as shown in Table 10.
TABLE 10 zero level test factor values
And (4) carrying out erosion wear test on the zero level according to the method in the step 3 to obtain a test result. Accordingly, the values of the factors in the regression equation are shown in table 10, and the theoretical values of the weight loss rate of the material under the zero level condition can be obtained according to the formula (2), and the results are shown in table 11.
TABLE 11 comparison of zero level test results with theoretical values
From the comparison of the test value and the theoretical value under the zero level condition in table 11, it can be seen that the relative error between the theoretical value obtained by the regression equation and the test result is only 0.51%, further proving the reliability of the regression equation. Therefore, the regression equation obtained by the regression orthogonal test design method can be well applied to the erosion wear test of the material to predict the weight loss rate. The method provides a new research method for the research of the material erosion wear test.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. The method for predicting the erosion wear weight loss rate of the liquid-solid two-phase flow based on the multiple regression analysis is characterized by comprising the following steps of:
(1) sample pretreatment;
cutting a raw material into a rectangular sample, taking one surface of the rectangular sample as an erosion surface, grinding the erosion surface to 1200 meshes by using metallographic abrasive paper, polishing, respectively ultrasonically cleaning by using acetone and alcohol, drying, and weighing the initial weight of the sample;
(2) designing an orthogonal test;
an orthogonal experimental scheme is designed according to the relationship between impact velocity, slurry temperature, sulfuric acid concentration of slurry medium, two levels of the three factors and weight loss rate, and L of the three factors with interaction at two levels is listed8(27) An orthogonal table, and encoding the orthogonal table by an encoding formula to convert the orthogonal table into a regression orthogonal test table expressed by codes, wherein the orthogonal table comprises 8 sets of tests of 7 factors and 2 levels,
the coding formula is as follows:
Zjdenotes the jth factor, Z0jDenotes the zero level of the j factor, Z2jRepresents the upper level of the j factor, Z1jRepresents the lower level of the j factor;
(3) erosion wear test;
according to the orthogonal test scheme designed in the step (2), carrying out an erosion wear test by using the sample prepared in the step (1), repeating the test scheme of each group for three times, obtaining weight loss by measuring the weight of the sample before and after erosion wear, taking an average value, and obtaining the weight loss rate of the sample under each group of test conditions by calculation;
(4) establishing a regression equation;
establishing a regression model of the sample weight loss rate and each single factor and interaction factor among the factors, and calculating and processing regression coefficients according to the data obtained in the step (3) to obtain a regression equation of the sample weight loss rate and the corresponding coding value of each factor;
the regression equation is:
Y＝b0+b1x1+b2x2+b3x3+b4x1x2+b5x2x3+b6x1x3+b7x1x2x3
b0is a response variable at the base level, b1、b2、b3Respectively represent three single-factor variables x in step (2)1、x2、x3The influence coefficient of (a); b4、b5、b6Respectively represent variable x1-x2、x2-x3、x1-x3The coefficient of interaction of (a); b7Variable x representing a selected level1-x2-x3The interaction coefficient between them, wherein, the positive value of Y represents weight loss, and the negative value represents weight gain;
the regression coefficient is:
wherein the content of the first and second substances,
n is the total number of tests, xijCode values representing the jth variable in the ith trial, j being 0, 1 … …, p; p represents the number of variables; y isiThe result of the ith test is shown;
(5) carrying out significance test on the regression equation and the regression coefficient;
determining significance parameters influencing the regression model by carrying out significance test on the regression model, selecting a zero level to compare a theoretical predicted value with an actually measured value, further verifying the accuracy of the regression equation, and obtaining the regression equation between the weight loss rate and the factor actual value by substituting an encoding formula into the regression equation;
(6) predicting erosion wear weight loss rate;
and (4) within the test level range, optionally selecting a group of test conditions, and predicting the weight loss rate of the material under other test conditions within the factor level range.
2. The method for predicting the erosion wear weight loss rate of the liquid-solid two-phase flow based on the multiple regression analysis according to claim 1, wherein the sample in the step (1) adopts 304 stainless steel with an austenitic matrix as a test material.
3. The method for predicting the erosion wear weight loss rate of the liquid-solid two-phase flow based on the multiple regression analysis according to claim 1, wherein the 7 factors in the step (2) comprise three single factors of impact velocity, slurry temperature and sulfuric acid concentration, and interaction factors of two single factors and interaction factors of three single factors.
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