Method and system for predicting service life of pipes under environmental corrosion

Abstract

The present invention provides a method and system for predicting the service life of pipes under the influence of environmental factors, relating to the technical field of pipeline material corrosion and protection. The method obtains environmental factors such as water flow rate, pH value, and temperature, analyzes the interactions of these factors, generates a standard corrosion rate, collects corrosion rate data for each experimental point, compares the data with the standard corrosion rate, records the data of experimental points that exceed the standard corrosion rate, constructs a quadratic polynomial regression equation related to the corrosion rate, calculates the statistical quantity F value and the probability quantity P value in the equation, determines whether the regression equation is significant, predicts the corrosion rate under current environmental conditions, and calculates the remaining service life of the pipe based on the predicted corrosion rate and the initial thickness of the pipe. By obtaining environmental factors, analyzing interactions, collecting experimental data, and constructing a regression equation, the present invention achieves the prediction of the service life of pipes under environmental factor corrosion.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline material corrosion and protection, and in particular to a method and system for predicting the service life of a pipe under the action of environmental factor corrosion. Background Art

[0002] Pipeline systems are crucial in fields such as oil, natural gas, chemicals, and municipal water supply. Accurate life prediction can prevent catastrophic failures and avoid environmental pollution and economic losses caused by pipeline failure. By predicting the service life of pipes, pipeline maintenance plans can be optimized, unnecessary maintenance costs can be reduced, and resource waste caused by excessive maintenance can be avoided.

[0003] In the prior art, publication number CN110749500A discloses a method for predicting the service life of in-service buried pipes, which comprises preparing a notched specimen; performing a full-notch creep test on the prepared notched specimen; obtaining a corresponding relationship curve of the crack opening displacement △COD of the notched specimen-test time T based on the recorded full-notch creep test data and calculating the crack growth rate v; combining a stress intensity factor model with the crack growth rate v to calculate material parameters A and m; measuring the defect depth h of the notched specimen, and substituting the defect depth h, the material parameters A and m into a formula to obtain the corresponding relationship between the critical crack depth h and the service life T of the polyethylene pipe.

[0004] Insufficient existing technology:

[0005] The existing technology requires the production of notched specimens of specific shapes and sizes, which requires professional processing equipment and technicians, increasing the complexity of the preparation work. The test itself needs to be carried out under specific environmental conditions, usually including high temperature, high pressure and other conditions. The experimental equipment is complex and expensive, and requires strict operating procedures and continuous monitoring, resulting in high experimental complexity and poor real-time performance.

[0006] Existing technologies mainly focus on crack propagation rate, with less attention paid to other failure modes such as corrosion and wear. This fails to fully reflect the service life of pipes in complex environments. Environmental parameters such as temperature, water flow rate, and pH value have a significant impact on the corrosion rate of pipes. Insufficient consideration of environmental factors may result in inaccurate prediction results.

[0007] Therefore, it is necessary to provide a method and system for predicting the service life of pipes under the corrosion of environmental factors to solve the above problems.

[0008] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0009] The purpose of the present invention is to provide a method and system for predicting the service life of pipes under the corrosion effect of environmental factors, so as to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for predicting the service life of a pipe under the action of environmental corrosion factors, comprising the following steps:

[0012] Step 1: Obtain environmental factors that affect pipe corrosion, including water flow rate, pH value and temperature;

[0013] Step 2: Analyze the interaction of each factor, calculate the corrosion rates of the three pipe materials under the interaction of water flow rate, pH value, and temperature, and synthesize the calculated corrosion rates of the three pipe materials to generate a standard corrosion rate;

[0014] Step 3: Determine different levels of water flow rate, pH value and temperature, select combined experimental points, collect corrosion rate data for each experimental point, compare the corrosion rate data for each experimental point with the standard corrosion rate, and collect data for experimental points that exceed the standard corrosion rate;

[0015] Step 4: Using the response surface analysis method, a quadratic polynomial regression equation related to the corrosion rate is constructed based on the collected experimental point data, a variance analysis is performed on the regression equation, and the statistical quantity F value and the probability quantity P value in the equation are calculated to determine whether the regression equation is significant;

[0016] Step 5: Monitor the environmental factors in the actual operating environment in real time and input the real-time monitored environmental factors into the constructed regression equation to predict the corrosion rate of the pipe under the current environmental conditions. Based on the predicted corrosion rate and the initial thickness of the pipe, calculate the remaining service life of the pipe.

[0017] Furthermore, the environmental factors affecting pipe corrosion are obtained based on the following method:

[0018] The water flow rate is obtained by adjusting the water flow rate using a flow meter, and measuring the weight loss and surface morphology changes of the pipe within a specific time to observe the effect of water flow rate on corrosion; the pH is obtained by changing the pH value of the water sample by adding acid or alkali, and corrosion tests are carried out under different pH conditions, combined with electrochemical tests to evaluate the corrosion behavior; the temperature is obtained by setting different temperature conditions in a constant temperature water bath or heating equipment for testing. The higher the temperature, the faster the corrosion rate.

[0019] Furthermore, the interaction of each factor was analyzed, and the corrosion rate of the pipe under the interactive fusion conditions of water flow rate, pH value, and temperature was calculated. The three calculated corrosion rates of the pipe were integrated to generate a standard corrosion rate. The method based on this is:

[0020] According to the influence of the interaction between water flow rate and pH value on the corrosion rate, a three-dimensional coordinate graph is established. The horizontal axis represents pH value, the vertical axis represents water flow rate, and the vertical axis represents corrosion rate. When the water flow rate is controlled to be constant, the pH value is changed; when the pH value is controlled to be constant, the water flow rate is changed. In comparison, the surface fluctuation amplitude in the pH direction is larger. Only considering the effect of these two factors, the optimal process combination is determined, and the corrosion rate under the interaction of water flow rate and pH value is calculated. The formula is as follows:

[0021]

[0022] Among them, R pv It represents the corrosion rate under the interaction of water flow rate and pH value, W represents the mass of the corroded material in the time period t, A represents the corroded surface area of ​​the pipe, ρ represents the material density of the pipe, and R pv Where a1 represents the weight of the water flow rate affecting the corrosion rate, a2 represents the weight of the pH value affecting the corrosion rate, a3 represents the weight of the interaction between the water flow rate and the pH value affecting the corrosion rate, and a3>a1>a2, v represents the water flow rate;

[0023] Similarly, the remaining two corrosion rates are analyzed and calculated using the method to obtain R pT 、R vT ;

[0024] Among them, R pT Indicates the corrosion rate under the interaction of pH value and temperature, in R pT In the formula, b1 represents the weight of the effect of pH value on corrosion rate, b2 represents the weight of the effect of temperature on corrosion rate, and b3 represents the weight of the interaction between pH value and temperature on corrosion rate. vT In the formula, c1 represents the weight of the water flow rate affecting the corrosion rate, c2 represents the weight of the temperature affecting the corrosion rate, c3 represents the weight of the interaction between water flow rate and temperature affecting the corrosion rate, and R vT It represents the corrosion rate under the interaction of water flow rate and temperature, T represents temperature, and the weight relationship is b3>b1>b2, c3>c1>c2.

[0025] The standard corrosion rate is generated based on the formula:

[0026] R bz =w1*R pv +w2*R pT +w3*RvT

[0027] Among them, R bz represents the standard corrosion rate, w1, w2, and w3 represent the weights corresponding to the three different corrosion rates, and w1+w2+w3=1.

[0028] Furthermore, the corrosion rate data of each experimental point is collected, and the corrosion rate data of each experimental point is compared with the standard corrosion rate, and the data of the experimental points exceeding the standard corrosion rate are collected, according to the method:

[0029] Select different levels of water flow rate, pH value and temperature, including high, medium and low levels, determine the combined experimental points, and use the central composite design method to record water flow rate, pH value and temperature as three experimental factors. The experimental combination of the three factors includes three types of experimental points. The first type is 2 k A full factorial design with 2 points, considering high and low level combinations of each factor, 3 =8 experimental points; the second is the axial point factor design, in which additional level values ​​are selected on the axis of each factor. Each factor will have two additional experimental points, for a total of 2k = 6 axial points; the third is the center point factor design, in which the experiment is repeated when all factors are at the middle level. In this experiment, there are 3 center points, and the corrosion rate corresponding to each point is recorded;

[0030] Here, k represents the number of experimental factors.

[0031] Furthermore, the corrosion rate data of each experimental point is compared with the standard corrosion rate, and the data of the experimental points exceeding the standard corrosion rate are collected. The logic is as follows:

[0032]

[0033] Among them, Q represents the logical value for judging whether the experimental data exceeds the standard corrosion rate. When Q=1, it means that the experimental data exceeds the standard corrosion rate and needs to be recorded; when Q=0, it means that the experimental data does not exceed the standard corrosion rate and does not need to be recorded.

[0034] Furthermore, a quadratic polynomial regression equation related to the corrosion rate was constructed based on the following formula:

[0035] y=β0+β1x1+β2x1+β3x3+β 12 x1x2+β 13 x1x3+β 23 x2x3+β 11 x1 2 +β 22 x2 2 +β 33 x32 +∈

[0036] Among them, x1, x2, and x3 represent the independent variables defined in the regression equation, namely water flow rate, pH value, and temperature, respectively; y represents the response variable, namely the corrosion rate; β0 is the intercept term, which represents the expected value of the response variable when all independent variables are zero, and it represents the baseline level of the equation; β1, β2, and β3 are the linear coefficients and represent the change in corrosion rate y for each unit increase in water flow rate x1, pH value x2, and temperature x3; β 12 , β 13 , β 23 is the interaction coefficient, which represents the effect of the interaction between the two variables on the corrosion rate y; β 11 , β 22 , β 33 is the quadratic term coefficient, which represents the influence of the square term of a single independent variable on the corrosion rate y; ∈ represents the error term.

[0037] Furthermore, variance analysis was performed to calculate the statistical quantity F value and the probability quantity P value in the equation, according to the method:

[0038] The variance analysis is performed by using the regression sum of squares and the residual sum of squares of the regression equation, combined with the degrees of freedom of the regression equation, to calculate the regression mean square and the residual mean square of the regression equation. The formula is:

[0039]

[0040] Among them, SSR and SSE represent the regression square sum and residual square sum in the regression equation respectively, y j represents the j-th observed value of the response variable, represents the mean of the response variable, represents the predicted value of the j-th response variable observation, df reg 、df res They represent the regression degrees of freedom and residual degrees of freedom in the regression equation, q represents the number of independent variables in the regression equation, n represents the number of observation points, MSR and MSE represent the regression mean square and residual mean square in the regression equation, respectively;

[0041] The F-value statistic is defined as the ratio of the regression mean square to the residual mean square, and the formula is:

[0042]

[0043] P=1-F

[0044] Among them, F represents the statistic in the regression equation, and P represents the probability in the regression equation. Furthermore, the method for judging whether the regression equation is significant is based on:

[0045] The probability value P is expressed as the difference between the overall probability and the probability of the statistic. The obtained P value is compared with the probability value of the significant level to determine whether the regression equation is significant. The logic is as follows:

[0046]

[0047] Among them, U represents the logical value for judging whether the regression equation is significant. When U = 1, the probability value P is less than or equal to the significant level probability value δ, and the regression equation is considered to be significant; when U = 0, the probability value P is greater than the significant level probability value δ, and the regression equation is considered to be not significant.

[0048] Furthermore, the remaining service life of the pipe is calculated based on the predicted corrosion rate and the initial thickness of the pipe according to the following formula:

[0049]

[0050] Among them, L represents the remaining service life of the pipe, T0, T f They represent the initial thickness and final allowable thickness of the pipe respectively, ed It represents the corrosion rate obtained by inputting the real-time monitored water flow rate, pH value and temperature into the regression equation.

[0051] The present invention further provides a system for predicting the service life of a pipe under the action of environmental corrosion factors. The system is used to execute the above-mentioned method for predicting the service life of a pipe under the action of environmental corrosion factors, and comprises:

[0052] An environmental factor acquisition module, which is used to obtain environmental factors that affect pipe corrosion, including water flow rate, pH value and temperature;

[0053] A factor interaction analysis module is used to analyze the interaction of various factors, calculate the corrosion rates of three types of pipes under the interaction of water flow rate, pH value, and temperature, and synthesize the calculated corrosion rates of the three types of pipes to generate a standard corrosion rate;

[0054] An experimental point selection module, the experimental point selection module is used to determine different level intervals of water flow rate, pH value and temperature, select combined experimental points, collect corrosion rate data of each experimental point, compare the corrosion rate data of each experimental point with the standard corrosion rate, and collect data of experimental points that exceed the standard corrosion rate;

[0055] A response surface model construction and analysis module is used to use a response surface analysis method to construct a quadratic polynomial regression equation related to the corrosion rate based on the collected experimental point data, perform variance analysis on the regression equation, calculate the statistical quantity F value and probability quantity P value in the equation, and determine whether the regression equation is significant;

[0056] The corrosion rate prediction and life assessment module is used to monitor the environmental factors in the actual operating environment in real time, and input the real-time monitored environmental factors into the constructed regression equation to predict the corrosion rate of the pipe under the current environmental conditions. Based on the predicted corrosion rate and the initial thickness of the pipe, the remaining service life of the pipe is calculated.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention realizes real-time monitoring of multiple environmental factors such as water flow rate, pH value and temperature by introducing an environmental factor acquisition module, which greatly simplifies the complexity of the experiment and improves the experimental efficiency. Compared with the existing technology, there is no need to make specific notch specimens and conduct time-consuming full-notch creep tests. Using single-factor analysis and response surface analysis methods, single factors can be systematically changed and multi-factor interactions can be analyzed to ensure the simplicity of experimental design and operation. At the same time, through automated data processing and analysis, the experimental point selection module and the response surface model construction and analysis module quickly generate regression equations related to corrosion rate, which greatly shortens the time for data analysis and result acquisition, realizes instant corrosion rate prediction, and greatly improves the real-time performance of the system;

[0059] In order to solve the problems of insufficient consideration of environmental factors and strong model dependence, the present invention comprehensively considers multiple environmental factors such as water flow rate, pH value and temperature, and comprehensively generates standard corrosion rates through a factor interaction analysis module, so that the model can more accurately reflect the comprehensive impact of different environmental factors on the corrosion rate. The response surface analysis method constructs a quadratic polynomial regression equation related to the corrosion rate and performs variance analysis to ensure the statistical significance of the regression equation and improve the robustness of the prediction model. In addition, by real-time monitoring of environmental factors and continuous updating of regression equations, the present invention realizes dynamic adjustment and optimization of the prediction model, effectively reduces the impact of material parameter uncertainty on the prediction results, and significantly improves the accuracy and reliability of the prediction, thereby more accurately evaluating the remaining service life of the pipe;

[0060] The present invention realizes the prediction of the service life of pipes under the corrosion of environmental factors through the collection of environmental factors, response surface analysis, establishment of regression equations, corrosion rate prediction and life assessment methods, and improves the robustness and accuracy of the prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the overall method of the present invention.

[0062] Figure 2 Schematic diagram of the response surface experiment and operation results of the present invention.

[0063] Figure 3 Schematic diagram of variance analysis of the response surface fitting regression equation of the present invention.

[0064] Figure 4 Schematic diagram of the effect of the interaction between water flow rate and pH value on corrosion rate in the present invention.

[0065] Figure 5 Schematic diagram of the effect of the interaction between water flow rate and temperature on the corrosion rate of the present invention.

[0066] Figure 6 Schematic diagram of the effect of the interaction between pH value and temperature on the corrosion rate of the present invention.

[0067] Figure 7 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0069] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0070] Example:

[0071] See also Figure 1-6 , the present invention provides a technical solution:

[0072] A method for predicting the service life of a pipe under the action of environmental corrosion factors, comprising the following steps:

[0073] Step 1: Obtain environmental factors that affect pipe corrosion, including water flow rate, pH value and temperature;

[0074] Step 2: Analyze the interaction of each factor, calculate the corrosion rates of the three pipe materials under the interaction of water flow rate, pH value, and temperature, and synthesize the calculated corrosion rates of the three pipe materials to generate a standard corrosion rate;

[0075] Step 3: Determine different levels of water flow rate, pH value and temperature, select combined experimental points, collect corrosion rate data for each experimental point, compare the corrosion rate data for each experimental point with the standard corrosion rate, and collect data for experimental points that exceed the standard corrosion rate;

[0076] Step 4: Using the response surface analysis method, a quadratic polynomial regression equation related to the corrosion rate is constructed based on the collected experimental point data, a variance analysis is performed on the regression equation, and the statistical quantity F value and the probability quantity P value in the equation are calculated to determine whether the regression equation is significant;

[0077] Step 5: Monitor the environmental factors in the actual operating environment in real time and input the real-time monitored environmental factors into the constructed regression equation to predict the corrosion rate of the pipe under the current environmental conditions. Based on the predicted corrosion rate and the initial thickness of the pipe, calculate the remaining service life of the pipe.

[0078] It should be noted that the single-factor analysis method is used to screen the minimum value of the corrosion rate to determine the optimal operating range of each environmental factor, clarify the specific impact of each factor, optimize the parameter settings, and make the system operate under the best conditions, thereby minimizing the corrosion rate and extending the service life of the pipe.

[0079] Therefore, it is necessary to obtain the environmental factors that affect pipe corrosion, based on the following methods:

[0080] The water flow rate is obtained by adjusting the water flow rate using a flow meter, and measuring the weight loss and surface morphology changes of the pipe within a specific time to observe the effect of water flow rate on corrosion; the pH is obtained by changing the pH value of the water sample by adding acid or alkali, and corrosion tests are carried out under different pH conditions, combined with electrochemical tests to evaluate the corrosion behavior; the temperature is obtained by setting different temperature conditions in a constant temperature water bath or heating equipment for testing. The higher the temperature, the faster the corrosion rate.

[0081] It should be noted that, from Figure 4- The diagram of the interaction between flow rate and pH value on corrosion rate shows that when the flow rate is constant, the corrosion rate first decreases and then increases with the increase of pH. Similarly, when the pH remains unchanged, the corrosion rate also decreases and then increases with the increase of flow rate. However, the fluctuation amplitude of the pH direction curve is larger, indicating that the flow rate has a more significant impact on the results. The optimal process combination considering only the two factors is: flow rate of 1.32m / s-1.64m / s, pH of 8.8-9.2 level combination; Figure 5 - The schematic diagram of the interaction between flow rate and temperature on the corrosion rate shows that the image surface spans a large area and the interaction contour lines are significantly elliptical, indicating that the interaction between the two has a significant effect on the corrosion rate. The corrosion rate decreases first and then increases with the increase of temperature and flow rate. When the flow rate is 1.32m / s-1.64m / s and the temperature is in the horizontal range of 24℃-26℃, the corrosion rate is the highest. Figure 6 The schematic diagram of the interaction between pH and temperature on corrosion rate shows that the P value of BC in the image is less than 0.05, indicating that the interaction between the two has a significant impact on the corrosion rate. When the pH is in the 8.8-9.2 level range and the temperature is around 24℃-26℃, the corrosion rate is low. This condition should be controlled to optimize the process parameters. By analyzing the impact of the interaction between water flow rate and pH value on the corrosion rate, a three-dimensional coordinate diagram is established and a formula is used to calculate the corrosion rate. The key to this process is to fully consider the interactive effects of environmental factors to determine the optimal process combination and achieve more accurate corrosion prediction.

[0082] Therefore, it is necessary to analyze the interaction of various factors, calculate the corrosion rate of pipes under the conditions of water flow rate, pH value and temperature interaction, and synthesize the three calculated corrosion rates of pipes to generate a standard corrosion rate. The method is as follows:

[0083] According to the influence of the interaction between water flow rate and pH value on the corrosion rate, a three-dimensional coordinate graph is established. The horizontal axis represents pH value, the vertical axis represents water flow rate, and the vertical axis represents corrosion rate. When the water flow rate is controlled to be constant, the pH value is changed; when the pH value is controlled to be constant, the water flow rate is changed. In comparison, the surface fluctuation amplitude in the pH direction is larger. Only considering the effect of these two factors, the optimal process combination is determined, and the corrosion rate under the interaction of water flow rate and pH value is calculated. The formula is as follows:

[0084]

[0085] Among them, R pv It represents the corrosion rate under the interaction of water flow rate and pH value, W represents the mass of the corroded material in the time period t, A represents the corroded surface area of ​​the pipe, ρ represents the material density of the pipe, and R pvIn the formula, a1 represents the weight of the water flow rate affecting the corrosion rate, a2 represents the weight of the pH value affecting the corrosion rate, a3 represents the weight of the interaction between the water flow rate and the pH value affecting the corrosion rate, and a3>a1>a2, v represents the water flow rate; in the above formula, the weight is set to a3>a1>a2 because the interaction between the water flow rate and the pH value has the most significant effect on the corrosion rate, because it includes not only the effects of the individual water flow rate and pH value on corrosion, but also the complex effects of their interaction. High water flow rate may intensify the turbulence of the fluid and the disturbance of the boundary layer, making it easier for the corrosive medium to contact the metal surface. This effect will be enhanced at different pH values. , so the weight ratio of a3 is set to the largest; the water flow rate directly affects the corrosion rate, because high flow rate can increase the friction and physical scouring of the metal surface. This physical scouring can remove corrosion products or protective oxide layers, exposing new metal surfaces to the corrosive medium, thereby accelerating the corrosion process, so the weight ratio of a1 is set to the second largest; finally, the pH value affects the chemical properties of the corrosion environment. For example, an acidic environment usually accelerates the corrosion rate of most metals, while an alkaline environment may form a protective passivation layer and slow down the corrosion rate. The influence of the pH value is mainly on the chemical properties, and has little effect on physical scouring and fluid dynamics, so a2 is set to the smallest weight ratio.

[0086] Similarly, the remaining two corrosion rates are analyzed and calculated using the method to obtain R pT 、R vT ;

[0087] Among them, R pT Indicates the corrosion rate under the interaction of pH value and temperature, in R pT In the formula, b1 represents the weight of the effect of pH value on corrosion rate, b2 represents the weight of the effect of temperature on corrosion rate, and b3 represents the weight of the interaction between pH value and temperature on corrosion rate. vT In the formula, c1 represents the weight of the water flow rate affecting the corrosion rate, c2 represents the weight of the temperature affecting the corrosion rate, c3 represents the weight of the interaction between water flow rate and temperature affecting the corrosion rate, and R vTrepresents the corrosion rate under the interaction of water flow rate and temperature, where T represents temperature. The weights are in the order b3 > b1 > b2, and c3 > c1 > c2. In the weighting order b3 > b1 > b2, the interaction between temperature and pH significantly affects chemical reaction rates. Increasing temperature accelerates chemical reaction rates, while changes in pH affect the direction and speed of reactions. Therefore, b3 is given the largest weight. pH directly affects the acidity and alkalinity of the corrosive environment. Low pH environments generally accelerate metal corrosion, while high pH environments may slow it down. Therefore, pH has a significant impact on the corrosion rate, but not as much as its interaction with temperature. Therefore, b1 has a smaller weight. Increasing temperature generally accelerates corrosion reactions because higher temperatures increase the kinetic energy of reactants, accelerating reactions. However, the effect of temperature on the corrosion rate may be limited in some environments, particularly in the presence of a passivation layer. Therefore, b2 has the smallest weight. In the weighting order c3 > c1 > c2, the interaction between water flow rate and temperature affects both physical erosion and chemical reaction rates. High flow rate will increase the physical scouring effect, while increased temperature will accelerate the chemical reaction rate. The interaction between the two will significantly change the corrosion rate, so c3 has the largest weight proportion; water flow rate directly affects the corrosion rate. High flow rate will increase the friction and physical scouring effect on the metal surface, remove corrosion products or protective layers, and thus accelerate the corrosion process, but it is not as significant as its interaction with temperature, so c1 has a smaller weight proportion; increased temperature usually accelerates the corrosion reaction rate because higher temperature increases the kinetic energy of the reactants and promotes the reaction, but the temperature effect alone may be limited under certain conditions, especially when the flow rate is low, the impact of temperature is not as significant as the flow rate, so c2 has the smallest weight proportion.

[0088] The standard corrosion rate is generated based on the formula:

[0089] R bz =w1*R pv +w2*R pT +w3*R vT

[0090] Among them, R bz represents the standard corrosion rate, w1, w2, and w3 represent the weights corresponding to the three different corrosion rates, and w1+w2+w3=1. The relationship between the three weights is w1>w2>w3. The maximum weight w1 is assigned to the corrosion rate R under the interaction of water flow rate and pH value. pv This is because the interaction between water flow rate and pH value has the most significant effect on the corrosion rate, which includes both physical scouring effect and the influence of chemical environment changes; secondly, w2 is allocated to the corrosion rate R under the interaction between pH value and temperature. pRThis is because the interaction between temperature and pH significantly affects the chemical reaction rate, but the temperature effect alone may be limited by the environment and the presence of the passivation layer, so w1>w2; the smallest weight w3 is assigned to R vT This is because although the interaction between water flow rate and temperature has a significant impact on corrosion, in actual applications, the effect of temperature on corrosion rate is limited to the working conditions of lower flow rate, so w1>w2>w3.

[0091] It should be noted that by selecting different levels of water flow rate, pH value and temperature, the water flow rate (V) is set to low (V∈[0.5,0.75]m / s), medium (V∈[0.75,1.25]m / s) and high (V∈[1.25,1.5]m / s), the pH value (PH) is set to low (PH∈[5.0,6.0]), medium (PH∈[6.0,8.0]) and high (PH∈[8.0,9.0]), and the temperature (T) is set to three levels: low (T∈[25,37.5]℃), medium (T∈[37.5,62.5]℃) and high (T∈[62.5,75]℃), and the central composite design method is used to conduct comprehensive full factorial design, axial factorial design and central factorial design, and record the corrosion rate data of each experimental point. This step is to systematically cover the experimental conditions and ensure the comprehensiveness and accuracy of the data, so as to deeply analyze the influence of each factor and its interaction on the corrosion rate. The corrosion rate of the experimental point is compared with the standard corrosion rate, and the experimental data exceeding the standard corrosion rate is identified, which provides a reliable data basis for establishing an accurate corrosion prediction model and optimizing process parameters, ensuring the best performance and longest service life of the pipe in actual application.

[0092] Therefore, it is necessary to collect the corrosion rate data of each experimental point, compare the corrosion rate data of each experimental point with the standard corrosion rate, and collect the data of the experimental points that exceed the standard corrosion rate. The method is as follows:

[0093] Select different levels of water flow rate, pH value and temperature, including high, medium and low levels, determine the combined experimental points, and use the central composite design method to record water flow rate, pH value and temperature as three experimental factors. The experimental combination of the three factors includes three types of experimental points. The first type is 2 k A full factorial design with 2 points, considering high and low level combinations of each factor, 3 =8 experimental points; the second is the axial point factor design, in which additional level values ​​are selected on the axis of each factor. Each factor will have two additional experimental points, for a total of 2k = 6 axial points; the third is the center point factor design, in which the experiment is repeated when all factors are at the middle level. In this experiment, there are 3 center points, and the corrosion rate corresponding to each point is recorded;

[0094] Here, k represents the number of experimental factors.

[0095] It should be noted that the corrosion rate data of each experimental point is compared with the standard corrosion rate, and a logical formula is used to determine whether the data should be recorded. It effectively screens out key data that exceeds the standard corrosion rate, focuses on problem areas, improves data processing efficiency, provides a basis for optimizing process parameters, and supports scientific decision-making. Through this process, the pertinence and practicality of the analysis results are ensured, providing solid scientific support for effectively controlling corrosion and extending equipment service life.

[0096] Therefore, it is necessary to compare the corrosion rate data of each experimental point with the standard corrosion rate and collect the data of the experimental points that exceed the standard corrosion rate. The logic is as follows:

[0097]

[0098] Among them, Q represents the logical value for judging whether the experimental data exceeds the standard corrosion rate. When Q=1, it means that the experimental data exceeds the standard corrosion rate and needs to be recorded; when Q=0, it means that the experimental data does not exceed the standard corrosion rate and does not need to be recorded.

[0099] It should be noted that the construction of a quadratic polynomial regression equation accurately captures the complex influence of water flow rate, pH value, and temperature on corrosion rate, including primary, interactive, and secondary effects. This regression equation quantifies the impact of each independent variable and their combination on corrosion rate, providing a scientific prediction model to guide the optimization and control of parameters in actual projects, ultimately helping to improve the corrosion resistance of equipment and extend its service life.

[0100] Therefore, it is necessary to construct a quadratic polynomial regression equation related to the corrosion rate, based on the formula:

[0101] y=β0+β1x1+β2x1+β3x3+β 12 x1x2+β 13 x1x3+β 23 x2x3+β 11 x1 2 +β 22 x2 2 +β 33 x3 2 +∈

[0102] Among them, x1, x2, and x3 represent the independent variables defined in the regression equation, namely water flow rate, pH value, and temperature, respectively; y represents the response variable, namely the corrosion rate; β0 is the intercept term, which represents the expected value of the response variable when all independent variables are zero, and it represents the baseline level of the equation; β1, β2, and β3 are the linear coefficients and represent the change in corrosion rate y for each unit increase in water flow rate x1, pH value x2, and temperature x3; β 12 , β 13 , β 23 is the interaction coefficient, which represents the effect of the interaction between the two variables on the corrosion rate y; β 11 , β 22 , β 33 is the quadratic term coefficient, which represents the influence of the square term of a single independent variable on the corrosion rate y; ∈ represents the error term.

[0103] It's important to note that performing ANOVA and calculating the F-value and P-value statistics in the regression equation are crucial because they help assess the model's significance and explanatory power. By analyzing the regression sum of squares and the residual sum of squares, and calculating the regression degrees of freedom and residual degrees of freedom, we can quantify whether the model effectively captures the relationship between the independent and response variables. Ultimately, this analysis provides a scientific basis for the model's applicability and predictive power, guiding parameter optimization and control in actual engineering projects and ensuring the reliability and effectiveness of the predictive model in practice.

[0104] Therefore, it is necessary to perform variance analysis to calculate the statistical value F and the probability value P in the equation, based on the following method:

[0105] The variance analysis is performed by using the regression sum of squares and the residual sum of squares of the regression equation, combined with the degrees of freedom of the regression equation, to calculate the regression mean square and the residual mean square of the regression equation. The formula is:

[0106]

[0107] Among them, SSR and SSE represent the regression square sum and residual square sum in the regression equation respectively, y j represents the j-th observed value of the response variable, represents the mean of the response variable, represents the predicted value of the j-th response variable observation, df reg 、df resRepresent the regression degrees of freedom and residual degrees of freedom in the regression equation respectively, q represents the number of independent variables in the regression equation, n represents the number of observation points, MSR and MSE represent the regression mean square and residual mean square in the regression equation respectively; in the above formula, SSR measures the gap between the model predicted value and the mean of the response variable, which reflects the variance explained by the model and represents the explanatory power of the independent variable on the response variable. A larger SSR indicates that the model can better explain the changes in the response variable; SSE measures the gap between the actual observation value and the model predicted value, which reflects the variance that the model cannot explain, that is, the sum of the errors. A smaller SSE indicates that the model has a higher fitting accuracy and a smaller error. The regression degrees of freedom df reg It refers to the number of observations used to estimate the parameters in the regression equation. For a regression model containing q independent variables, the degrees of freedom are the number of independent variables; the residual degrees of freedom df res It is the remaining degrees of freedom after deducting the degrees of freedom used to estimate model parameters from the number of observations. It represents the amount of independent information used to estimate the residuals. The regression mean square (MSR) is the result of dividing the regression sum of squares by the regression degrees of freedom. It represents the amount of variance that each regression parameter can explain and is an indicator after SSR standardization. The MSE is the result of dividing the residual sum of squares by the residual degrees of freedom. It represents the average error contained in each observation and is an indicator after SSE standardization.

[0108] The F-value statistic is defined as the ratio of the regression mean square to the residual mean square, and the formula is:

[0109]

[0110] P=1-F

[0111] Where F represents the statistic in the regression equation, and P represents the probability in the regression equation. F is used to test the overall significance of the regression model. A larger F value indicates a more significant effect of the independent variable on the response variable. By comparing the F value with the critical value, the validity of the model can be determined.

[0112] It should be noted that determining the significance of the regression equation is a key step in evaluating the validity and reliability of the model. It ensures that the impact of the independent variable on the response variable is statistically significant. By calculating and comparing the probability value P with the significance level probability value δ, the explanatory power and prediction accuracy of the model are improved.

[0113] Therefore, it is necessary to determine whether the regression equation is significant, and the method is based on:

[0114] The probability value P is expressed as the difference between the overall probability and the probability of the statistic. The obtained P value is compared with the probability value of the significant level to determine whether the regression equation is significant. The logic is as follows:

[0115]

[0116] Here, U represents the logical value used to determine whether the regression equation is significant. When U = 1, the probability P value is less than or equal to the significance level probability value δ, and the regression equation is considered significant. When U = 0, the probability P value is greater than the significance level probability value δ, and the regression equation is considered not significant. In the above logical formula, the P value represents the complement of this probability, that is, the part outside the F value. Obviously, the smaller the P value, the larger the overall probability F value of the statistic. δ is a pre-set threshold, usually 0.05 or 0.01, indicating the degree to which we accept the occurrence of low-probability events.

[0117] It should be noted that the corrosion rate is predicted by combining the initial thickness and the final allowable thickness of the pipe.

[0118] Therefore, it is necessary to calculate the remaining service life of the pipe based on the predicted corrosion rate and the initial thickness of the pipe. The formula is:

[0119]

[0120] Among them, L represents the remaining service life of the pipe, T0, T f They represent the initial thickness and final allowable thickness of the pipe respectively, ed It represents the corrosion rate obtained by inputting the real-time monitored water flow rate, pH value and temperature into the regression equation.

[0121] See also Figure 7 The present invention further provides a system for predicting the service life of a pipe under the action of environmental factors. The system is used to execute the above-mentioned method for predicting the service life of a pipe under the action of environmental factors, and comprises:

[0122] An environmental factor acquisition module, which is used to obtain environmental factors that affect pipe corrosion, including water flow rate, pH value and temperature;

[0123] A factor interaction analysis module is used to analyze the interaction of various factors, calculate the corrosion rates of three types of pipes under the interaction of water flow rate, pH value, and temperature, and synthesize the calculated corrosion rates of the three types of pipes to generate a standard corrosion rate;

[0124] An experimental point selection module, the experimental point selection module is used to determine different level intervals of water flow rate, pH value and temperature, select combined experimental points, collect corrosion rate data of each experimental point, compare the corrosion rate data of each experimental point with the standard corrosion rate, and collect data of experimental points that exceed the standard corrosion rate;

[0125] A response surface model construction and analysis module is used to use a response surface analysis method to construct a quadratic polynomial regression equation related to the corrosion rate based on the collected experimental point data, perform variance analysis on the regression equation, calculate the statistical quantity F value and probability quantity P value in the equation, and determine whether the regression equation is significant;

[0126] The corrosion rate prediction and life assessment module is used to monitor the environmental factors in the actual operating environment in real time, and input the real-time monitored environmental factors into the constructed regression equation to predict the corrosion rate of the pipe under the current environmental conditions. Based on the predicted corrosion rate and the initial thickness of the pipe, the remaining service life of the pipe is calculated.

[0127] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset factors in the formulas are set by technicians in this field according to actual conditions.

[0128] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0129] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0130] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for predicting the service life of a pipe under the action of environmental factors, characterized in that: The specific steps include: Step 1: Obtain environmental factors that affect pipe corrosion, including water flow rate, pH value and temperature; Step 2: Analyze the interaction of each factor, calculate the corrosion rates of the three pipe materials under the interaction of water flow rate, pH value, and temperature, and synthesize the calculated corrosion rates of the three pipe materials to generate a standard corrosion rate; Step 3: Determine different levels of water flow rate, pH value and temperature, select combined experimental points, collect corrosion rate data for each experimental point, compare the corrosion rate data for each experimental point with the standard corrosion rate, and collect data for experimental points that exceed the standard corrosion rate; Step 4: Using the response surface analysis method, a quadratic polynomial regression equation related to the corrosion rate is constructed based on the collected experimental point data, a variance analysis is performed on the regression equation, and the statistical quantity F value and the probability quantity P value in the equation are calculated to determine whether the regression equation is significant; Step 5: Monitor environmental factors in the actual operating environment in real time and input the real-time monitored environmental factors into the constructed regression equation to predict the corrosion rate of the pipe under the current environmental conditions. Based on the predicted corrosion rate and the initial thickness of the pipe, calculate the remaining service life of the pipe; Collect corrosion rate data for each experimental point, compare the corrosion rate data for each experimental point with a standard corrosion rate, and collect data for experimental points that exceed the standard corrosion rate, according to the following method: Select different levels of water flow rate, pH value and temperature, including high, medium and low levels, determine the combined experimental points, and use the central composite design method to record water flow rate, pH value and temperature as three experimental factors. The experimental combination of the three factors includes three types of experimental points. The first type is 2 k A full factorial design with 2 points, considering high and low level combinations of each factor, 3 =8 experimental points; the second is the axial point factor design, in which additional level values ​​are selected on the axis of each factor. Each factor will have two additional experimental points, for a total of 2k = 6 axial points; the third is the center point factor design, in which the experiment is repeated when all factors are at the middle level. In this experiment, there are 3 center points, and the corrosion rate corresponding to each point is recorded; Among them, k represents the number of experimental factors and its value is 3; A quadratic polynomial regression equation related to the corrosion rate was constructed based on the formula: y=β0+β1x1+β2x1+β3x3+β 12 x1x2+b 13 x1x3+b 23 x2x3+b 11 x1 2 +b 22 x2 2 +b 33 x3 2 +∈ Among them, x1, x2, and x3 represent water flow rate, pH value, and temperature, respectively, which are independent variables in the regression equation, and y represents the response variable, that is, the corrosion rate; β0 is the intercept term, which represents the expected value of the response variable when all independent variables are zero, and it represents the baseline level of the equation; β1, β2, and β3 are the linear coefficients and represent the change in corrosion rate y for each unit increase in water flow rate x1, pH value x2, and temperature x3; β 12 , β 13 , β 23 is the interaction coefficient, which represents the effect of the interaction between the two variables on the corrosion rate y; β 11 , β 22 , β 33 is the quadratic term coefficient, which represents the influence of the square term of a single independent variable on the corrosion rate y; ∈ represents the error term.

2. The method for predicting the service life of a pipe under the action of environmental factors according to claim 1, characterized in that: The method for obtaining environmental factors affecting pipe corrosion is based on: The water flow rate is obtained by adjusting the water flow rate using a flow meter, and measuring the weight loss and surface morphology changes of the pipe within a specific time to observe the effect of water flow rate on corrosion; the pH is obtained by changing the pH value of the water sample by adding acid or alkali, and corrosion tests are carried out under different pH conditions, combined with electrochemical tests to evaluate the corrosion behavior; the temperature is obtained by setting different temperature conditions in a constant temperature water bath or heating equipment for testing. The higher the temperature, the faster the corrosion rate.

3. The method for predicting the service life of a pipe under the action of environmental corrosion factors according to claim 1, characterized in that: The interaction of each factor was analyzed, and the corrosion rate of the pipe under the conditions of water flow rate, pH value and temperature interaction was calculated. The three calculated corrosion rates of the pipe were integrated to generate a standard corrosion rate. The method is as follows: According to the influence of the interaction between water flow rate and pH value on the corrosion rate, a three-dimensional coordinate graph is established. The horizontal axis represents pH value, the vertical axis represents water flow rate, and the vertical axis represents corrosion rate. When the water flow rate is controlled to be constant, the pH value is changed; when the pH value is controlled to be constant, the water flow rate is changed. In comparison, the surface fluctuation amplitude in the pH direction is larger. Only considering the effect of these two factors, the optimal process combination is determined, and the corrosion rate under the interaction of water flow rate and pH value is calculated. The formula is as follows: Among them, R pv It represents the corrosion rate under the interaction of water flow rate and pH value, W represents the mass of the corroded material in the time period t, A represents the corroded surface area of ​​the pipe, ρ represents the material density of the pipe, and R pv Where a1 represents the weight of the water flow rate affecting the corrosion rate, a2 represents the weight of the pH value affecting the corrosion rate, a3 represents the weight of the interaction between the water flow rate and the pH value affecting the corrosion rate, and a3>a1>a2, v represents the water flow rate; Similarly, the remaining two corrosion rates are analyzed and calculated using the method to obtain R pT 、R vT ; Among them, R pT Indicates the corrosion rate under the interaction of pH value and temperature, in R pT In the formula, b1 represents the weight of the effect of pH value on corrosion rate, b2 represents the weight of the effect of temperature on corrosion rate, and b3 represents the weight of the interaction between pH value and temperature on corrosion rate. vT In the formula, c1 represents the weight of the water flow rate affecting the corrosion rate, c2 represents the weight of the temperature affecting the corrosion rate, c3 represents the weight of the interaction between water flow rate and temperature affecting the corrosion rate, and R vT It represents the corrosion rate under the interaction of water flow rate and temperature, T represents temperature, and the weight relationship is b3>b1>b2, c3>c1>c2; The standard corrosion rate is generated based on the formula: R bz =w1*R pv +w2*R pT +w3*R vT Among them, R bz represents the standard corrosion rate, w1, w2, and w3 represent the weights corresponding to the three different corrosion rates, and w1+w2+w3=1.

4. The method for predicting the service life of a pipe under the action of environmental corrosion according to claim 3, characterized in that: Compare the corrosion rate data of each experimental point with the standard corrosion rate, and collect the data of the experimental points that exceed the standard corrosion rate. The logic is as follows: Among them, Q represents the logical value for judging whether the experimental data exceeds the standard corrosion rate. When Q=1, it means that the experimental data exceeds the standard corrosion rate and needs to be recorded; when Q=0, it means that the experimental data does not exceed the standard corrosion rate and does not need to be recorded.

5. The method for predicting the service life of a pipe under the action of environmental corrosion factors according to claim 1, characterized in that: Perform variance analysis to calculate the statistical value F and the probability value P in the equation according to the following method: Perform variance analysis on the equation, calculate the regression sum of squares and residual sum of squares in the equation, and combine the degrees of freedom of the regression equation to calculate the regression mean square and residual mean square of the regression equation. The formula is: Among them, SSR and SSE represent the regression square sum and residual square sum in the regression equation respectively, y j represents the j-th response variable observation, represents the mean of the response variable, represents the predicted value of the j-th response variable observation, which is obtained from the regression equation, df reg 、df res They represent the regression degrees of freedom and residual degrees of freedom in the regression equation, q represents the number of independent variables in the regression equation and is 3, n represents the number of observation points, MSR and MSE represent the regression mean square and residual mean square in the regression equation, respectively; The F-value statistic is defined as the ratio of the regression mean square to the residual mean square, and the formula is: P=1-F Among them, F represents the statistic in the regression equation, and P represents the probability in the regression equation.

6. The method for predicting the service life of a pipe under the action of environmental corrosion factors according to claim 5, characterized in that: The method for judging whether the regression equation is significant is as follows: Compare the obtained P value with the probability value of the significance level to determine whether the regression equation is significant. The logic is as follows: Among them, U represents the logical value for judging whether the regression equation is significant. When U = 1, the probability value P is less than or equal to the significant level probability value δ, and the regression equation is considered to be significant; when U = 0, the probability value P is greater than the significant level probability value δ, and the regression equation is considered to be not significant.

7. The method for predicting the service life of a pipe under the action of environmental corrosion factors according to claim 1, characterized in that: Based on the predicted corrosion rate and the initial thickness of the pipe, the remaining service life of the pipe is calculated according to the following formula: Among them, L represents the remaining service life of the pipe, T0, T f They represent the initial thickness and final allowable thickness of the pipe respectively, ed It represents the corrosion rate obtained by inputting the real-time monitored water flow rate, pH value and temperature into the regression equation.

8. A system for predicting the service life of a pipe under the influence of environmental factors, the system being configured to execute the method for predicting the service life of a pipe under the influence of environmental factors according to any one of claims 1 to 7, comprising: An environmental factor acquisition module, which is used to obtain environmental factors that affect pipe corrosion, including water flow rate, pH value and temperature; A factor interaction analysis module is used to analyze the interaction of various factors, calculate the corrosion rates of three types of pipes under the interaction of water flow rate, pH value, and temperature, and synthesize the calculated corrosion rates of the three types of pipes to generate a standard corrosion rate; An experimental point selection module, the experimental point selection module is used to determine different level intervals of water flow rate, pH value and temperature, select combined experimental points, collect corrosion rate data of each experimental point, compare the corrosion rate data of each experimental point with the standard corrosion rate, and collect data of experimental points that exceed the standard corrosion rate; A response surface model construction and analysis module is used to use a response surface analysis method to construct a quadratic polynomial regression equation related to the corrosion rate based on the collected experimental point data, perform variance analysis on the regression equation, calculate the statistical quantity F value and probability quantity P value in the equation, and determine whether the regression equation is significant; The corrosion rate prediction and life assessment module is used to monitor the environmental factors in the actual operating environment in real time, and input the real-time monitored environmental factors into the constructed regression equation to predict the corrosion rate of the pipe under the current environmental conditions. Based on the predicted corrosion rate and the initial thickness of the pipe, the remaining service life of the pipe is calculated.

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