Potential distribution simulation method for cathode protection system under complex corrosion condition

Abstract

The invention provides a method for simulating potential distribution of a cathode protection system under a complex corrosion condition, which belongs to the technical field of cathode protection and comprises the following steps of: acquiring polarization data of cathode metal under different dissolved oxygen, pH, temperature and stress conditions by using a workstation; carrying out machine learning on the polarization data to obtain a multi-dimensional polarization database of polarization behaviors of the protected metal and dissolved oxygen, pH, temperature and stress; and establishing a geometric model according to the cathode protection system, taking the multi-dimensional polarization database as an electrode boundary condition of the model, and simulating potential distribution of the cathode protection system through simulation software. The method solves the problem that an existing cathode protection numerical simulation model does not consider the influence of the complex corrosion environment on the cathode potential distribution, can accurately predict the potential distribution of the cathode surface and the evolution process thereof, and can provide reference for the protection and monitoring of a cathode protection system in the complex corrosion environment. And the corrosion risk of the cathode protection device is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cathodic protection, and in particular to a method for simulating potential distribution of a cathodic protection system under complex corrosion conditions. Background Art

[0002] Cathodic protection has been widely used in the field of corrosion and protection. Using numerical simulation to simulate the potential distribution of cathodic protection devices is an effective means of monitoring cathodic protection systems and also provides important reference opinions for the design of cathodic protection devices. Traditional simulation methods often use the polarization behavior of metals under single, stable conditions (Tafel equation, BVE equation and dynamic potential scanning curve) as boundary conditions for solution. However, in actual cathodic protection systems, the service life is as long as 30 years, and the corrosion environment during this period is constantly changing over time. The traditional boundary condition setting method is contrary to the actual situation, which leads to inaccurate prediction results when facing more complex corrosion environments. Summary of the Invention

[0003] In response to the technical issues mentioned in the background technology, a method for simulating the potential distribution of a cathodic protection system under complex corrosion conditions is provided. This invention uses machine learning to analyze the polarization data of metals under different corrosion environments to determine the relationship between the metal's "current density, potential, and environmental factors." This multidimensional polarization data is then used as boundary conditions for numerical simulation using simulation software. The simulation results accurately reflect the potential distribution of the cathodic protection device, providing a more accurate data model for the design and monitoring of cathodic protection systems. This also promotes the digitalization of cathodic protection devices.

[0004] The technical means adopted in the present invention are as follows:

[0005] A method for simulating the potential distribution of a cathodic protection system under complex corrosion conditions comprises the following steps:

[0006] S1. Use the workstation to collect polarization data of cathode metal under different dissolved oxygen, temperature, pH and stress conditions;

[0007] S2. Performing machine learning on the polarization data obtained in S1 to obtain a multidimensional polarization database of the polarization behavior of the protected metal and dissolved oxygen, temperature, and pressure;

[0008] S3. Establish a geometric model based on the cathodic protection system, use the multidimensional polarization database obtained in S2 as the electrode boundary conditions of the model, and simulate the potential distribution of the cathodic protection system through simulation software.

[0009] Furthermore, the machine learning in step S2 includes: prediction of metal polarization behavior by BP artificial neural network.

[0010] Furthermore, the polarization data includes: current density, potential, dissolved oxygen concentration pH, temperature and stress.

[0011] Furthermore, the step S2 includes the following steps:

[0012] S21: preprocessing the polarization data; normalizing the polarization data to [-1, 1];

[0013] S22: Train the artificial neural network using the Neural Network toolkit in Matlab; select trainlm as the training function, learngdm as the learning function, tansig as the transfer function, and purelin as the output function.

[0014] Furthermore, the normalized formula is:

[0015]

[0016] Among them, x i Represents the normalized data; A s Indicates the actual value of the data; A min 、A max Represents the minimum and maximum values ​​of the data respectively.

[0017] Furthermore, the boundary condition setting in S3 includes:

[0018] When setting the local current density expressions of cathode and anode:

[0019]

[0020] Among them, i a represents the local current density at the anode; f a Represents multi-dimensional polarization data of the anode metal; represents the anode electrode potential; represents dissolved oxygen concentration; T represents temperature; p represents pressure; i c represents the local current density at the cathode; f c Representing multi-dimensional polarization data of cathode metal; represents the cathode electrode potential; pH represents the acidity or alkalinity of the solution.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] The present invention solves the problem that the existing cathodic protection numerical simulation model does not consider the influence of complex corrosion environment on the cathode potential distribution. It accurately predicts the potential distribution and its evolution process on the cathode surface, and can provide a reference for the protection and monitoring of the cathodic protection system in complex corrosion environment, greatly reducing the corrosion risk of the cathodic protection device. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0024] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0025] Figure 2 Polarization curves of B30 in different dissolved oxygen concentrations according to an embodiment of the present invention are shown in Figures 1 and 2. (a) shows the polarization curve when the dissolved oxygen concentration is 0-1.8 mg / L; (b) shows the polarization curve when the dissolved oxygen concentration is 2.0-3.3 mg / L; (c) shows the polarization curve when the dissolved oxygen concentration is 3.8-5.8 mg / L; and (d) shows the polarization curve when the dissolved oxygen concentration is 6.3-8.0 mg / L.

[0026] Figure 3 Figure 2 is the relationship between the polarization behavior of the metals of the present invention and the dissolved oxygen concentration. (a) is the relationship between the limiting diffusion current density and the dissolved oxygen concentration; (b) is the relationship between the hydrogen evolution potential and the dissolved oxygen concentration.

[0027] Figure 4 This is the multi-dimensional polarization data of Example B30 of the present invention.

[0028] Figure 5 An experimental device for an embodiment of the present invention;

[0029] Figure 6 This is a geometric model established based on the experimental device in an embodiment of the present invention.

[0030] Figure 7 This is the mesh division of the geometric model of the embodiment of the present invention.

[0031] Figure 8 The relationship between the potential distribution on the inner wall of the heat transfer tube and time obtained in an embodiment of the present invention.

[0032] Figure 9 The relationship between the dissolved oxygen concentration distribution in the heat transfer tube and time obtained in an embodiment of the present invention.

[0033] Figure 10Comparison of simulation results and experimental results for the present invention. (a) compares the experimental results and simulation results after the device has been running for 0.2 hours; (b) compares the experimental results and simulation results after the device has been running for 4 hours; (c) compares the experimental results and simulation results after the device has been running for 12 hours; and (d) compares the experimental results and simulation results after the device has been running for 24 hours.

[0034] Among them, 1 is the sacrificial anode Zn block; 2 is seawater; 3 is the Ag / AgCl reference electrode; 4 is the copper wire connecting the cathode and anode; 5 is the cathode B30 heat transfer tube. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] like Figure 1 As shown, the present invention provides a method for simulating the potential distribution of a cathodic protection system under complex corrosion conditions, comprising the following steps:

[0038] S1. Use a workstation to collect polarization data of cathode metal under different dissolved oxygen, temperature, pH and stress conditions; the polarization data includes: current density, potential, dissolved oxygen concentration pH, temperature and stress.

[0039] S2. Perform machine learning on the polarization data obtained in S1 to obtain a multidimensional polarization database of the polarization behavior of the protected metal and dissolved oxygen, temperature, and pressure. The machine learning in step S2 includes: predicting the polarization behavior of the metal using a BP artificial neural network. S2 includes the following steps:

[0040] S21: Preprocessing the polarization data; normalizing the polarization data to [-1, 1]. The normalization formula is:

[0041]

[0042] Among them, x i Represents the normalized data; A s Indicates the actual value of the data; A min 、A max Represents the minimum and maximum values ​​of the data respectively.

[0043] S22: Train the artificial neural network using the Neural Network toolkit in Matlab; select trainlm as the training function, learngdm as the learning function, tansig as the transfer function, and purelin as the output function.

[0044] S3. Establish a geometric model based on the cathodic protection system, and use the multi-dimensional polarization database obtained in S2 as the electrode boundary conditions of the model, and simulate the potential distribution of the cathodic protection system through simulation software. The boundary condition settings in S3 include:

[0045] When setting the local current density expressions of cathode and anode:

[0046]

[0047] Among them, i a represents the local current density at the anode; f a Represents multi-dimensional polarization data of the anode metal; represents the anode electrode potential; represents dissolved oxygen concentration; T represents temperature; p represents pressure; i c represents the local current density at the cathode; f c Representing multi-dimensional polarization data of cathode metal; represents the cathode electrode potential; pH represents the acidity or alkalinity of the solution.

[0048] Example:

[0049] In this embodiment, the potential distribution of the heat transfer tube cathode protection system is simulated. The simulation process is shown in the flow chart. Figure 1 shown.

[0050] S1: To measure the polarization curve of heat transfer tube material B30 under different oxygen concentrations, such as Figure 2 .

[0051] S2: To obtain the relationship between the polarization behavior of metals and dissolved oxygen concentration through artificial neural networks, such as Figure 3, and obtained the multi-dimensional polarization data of the polarization behavior of B30 with respect to dissolved oxygen concentration, such as Figure 4 .

[0052] The specific technical steps of step S3 are as follows:

[0053] The geometric parameters of the cathodic protection experimental device were measured, and a geometric model was constructed based on the experimental device, such as Figure 5 , Figure 6 .

[0054] (2) Divide the geometric model surface and area, and set the model parameters as shown in Table 1. The specific technical steps are as follows:

[0055] Divide the geometric model into regions and surfaces according to the actual experimental device. Set the parameters in the model as shown in Table 1:

[0056] Table 1 Parameters

[0057] name symbol Numerical Anode equilibrium potential <![CDATA[E eq_Zn ]]> -1.100V cathode equilibrium potential <![CDATA[E eq_B30 ]]> -0.189V Electrolyte conductivity Sigma 4.900S / m Initial dissolved oxygen concentration C 8.0mg / L Dissolved oxygen diffusion coefficient <![CDATA[D c ]]> <![CDATA[1.9×10 -9 m 2 / s]]>

[0058] In this embodiment, the cathode protection potential distribution of the heat transfer tube is simulated. Step S3 sets the tertiary current distribution physical field, sets the boundary conditions, and performs mesh division. The specific technical steps are as follows:

[0059] 1. Select the tertiary current distribution (supporting electrolyte) physical field and set the diffusion coefficient of dissolved oxygen to D C , the electrolyte conductivity is sigma.

[0060] 2. Set the initial value of oxygen concentration to c, the initial value of electrolyte potential to 0.6V, and the initial value of potential to 0V.

[0061] 3. Add electrode reaction 1 on the surface of electrode Zn and set the equilibrium potential to E eq_Zn , the local current density expression is defined as:

[0062]

[0063] Among them, i a represents the local current density of the anode Zn; f a represents the anodic polarization curve of Zn; It represents the opposite of the electrolyte potential, that is, the potential of the electrode surface.

[0064] 4. Add electrode reaction 1 on the surface of electrode B30 and set the equilibrium potential to E eq_B30 , the local current density expression is defined as:

[0065]

[0066] Among them, f c1represents the interpolation function of the B30 polarization surface, such as Figure 4 ; c represents the dissolved oxygen concentration in seawater; f1 represents the limiting diffusion current density of dissolved oxygen as a function of the dissolved oxygen concentration, such as Figure 3 .

[0067] Adding electrode reaction 2 on the surface of electrode B30, the local current density expression is defined as:

[0068]

[0069] Where H represents a step function. When its independent variable is not greater than 0, the function takes 0, and when its independent variable is greater than 0, the function takes 1. f2 represents the function of the hydrogen evolution potential of B30 in the polarization process with respect to the dissolved oxygen concentration, such as Figure 3 .

[0070] 5. Select a triangular mesh and divide it according to the physical field. The mesh size should be finer, such as Figure 7 shown.

[0071] (5) Calculation and solution are performed to obtain the simulation results of the cathodic protection potential distribution of the B30 heat transfer tube. The specific technical steps are as follows:

[0072] 1. Add a transient study with current distribution initialization.

[0073] 2. Set the output time step of the transient study to (0, 0.1, 24) h.

[0074] 3. Calculate the change of electrode potential distribution on the surface of B30 heat transfer tube over time, such as Figure 8 .

[0075] 4. Calculate the change of dissolved oxygen concentration in the heat transfer tube over time, such as Figure 10 .

[0076] 5. The calculated electrode potential distribution is compared with the experimental results, and the agreement is high, reaching 92.4%. Figure 10 .

[0077] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0078] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0080] 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, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0081] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating the potential distribution of a cathodic protection system under complex corrosion conditions, characterized in that: The following steps are involved: S1. Use the workstation to collect polarization data of cathode metal under different dissolved oxygen, temperature, pH and stress conditions; S2. Performing machine learning on the polarization data obtained in S1 to obtain a multidimensional polarization database of the polarization behavior of the protected metal and dissolved oxygen, temperature, and pressure; S3. Establish a geometric model based on the cathodic protection system, use the multidimensional polarization database obtained in S2 as the electrode boundary conditions of the model, and simulate the potential distribution of the cathodic protection system through simulation software.

2. The method for simulating potential distribution of a cathodic protection system under complex corrosion conditions according to claim 1, characterized in that: The machine learning in step S2 includes: prediction of metal polarization behavior by BP artificial neural network.

3. The method for simulating potential distribution of a cathodic protection system under complex corrosion conditions according to claim 1, characterized in that: The polarization data include: current density, potential, dissolved oxygen concentration pH, temperature and stress.

4. The method for simulating potential distribution of a cathodic protection system under complex corrosion conditions according to claim 1, characterized in that: The S2 comprises the following steps: S21: preprocessing the polarization data; normalizing the polarization data to [-1, 1]; S22: Train the artificial neural network using the NeuralNetwork toolkit in Matlab; select trainlm as the training function, learngdm as the learning function, tansig as the transfer function, and purelin as the output function.

5. The method for simulating potential distribution of a cathodic protection system under complex corrosion conditions according to claim 4, characterized in that: The normalized formula is: Among them, x i Represents the normalized data; A s Indicates the actual value of the data; A min 、A max Represents the minimum and maximum values ​​of the data respectively.

6. The method for simulating potential distribution of a cathodic protection system under complex corrosion conditions according to claim 1, wherein the boundary condition setting in S3 comprises: When setting the local current density expressions of cathode and anode: Among them, i a represents the local current density at the anode; f a Represents multi-dimensional polarization data of the anode metal; represents the anode electrode potential; represents dissolved oxygen concentration; T represents temperature; p represents pressure; i c represents the local current density at the cathode; f c Representing multi-dimensional polarization data of cathode metal; represents the cathode electrode potential; pH represents the acidity or alkalinity of the solution.

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