A method and model for predicting corrosion rate of metal materials

Abstract

The present invention belongs to the technical field of atmospheric corrosion evaluation of metal materials, and discloses a method for predicting the corrosion rate of metal materials, including: obtaining the corrosion influence factor of the detection site, the corrosion influence factor includes the atmospheric corrosion humidity of the metal material, and the atmospheric corrosion humidity of the metal material is the ratio of the time when the metal material exceeds the critical corrosion rate to the whole year; according to the corrosion influence factor, predicting the corrosion rate of the metal material through a prediction model. The prediction method of the embodiment of the present invention can more accurately predict the corrosion rate of the metal material. The embodiment of the present invention also discloses a prediction model for the corrosion rate of the metal material.

Description

Technical Field

[0001] The invention relates to the field of atmospheric environment corrosion evaluation of metal materials, in particular to a method for predicting the corrosion rate of metal materials, and also to a model for predicting the corrosion rate of metal materials. Background Art

[0002] At present, the evaluation and prediction of atmospheric corrosion of metal materials mainly rely on the exposure test method. The exposure test lasting up to one year is used to calculate the corrosion rate of the material. This method is time-consuming and cumbersome to operate manually. Exposure tests need to be repeated for different regions. In addition to the above exposure test method, the GB / T 19292 series of standards also provides a dose response function method to calculate the first-year corrosion rate of metal materials (zinc, copper, aluminum, steel). This method is based on the atmospheric corrosion factors (including time, temperature and humidity, wetting time, SO 2 , Cl - The formula for calculating the amount of material corrosion is:

[0003] C=A1[SO2] B1 exp{c1(Rh)+g1(T)}+D1Cl E1 exp{m1(Rh)+k1(T)}

[0004] Where C is the first-year corrosion rate of the metal; [SO2] is the annual average SO2 deposition rate; Cl is the annual average Cl - deposition rate; T is the annual average temperature; Rh is the annual average relative humidity, and the rest are constants.

[0005] The standard mainly relies on empirical power exponential relationships to predict long-term corrosion of metal materials.

[0006] D=At n

[0007] Where, t is the exposure time (years); A is the corrosion loss in the first year; n is a constant, whose value is generally less than 1; and D is the corrosion depth.

[0008] Based on the thin liquid film corrosion theory, atmospheric corrosion of metal materials is mainly electrochemical corrosion (more than 95%). Thin liquid film is one of the main factors for corrosion of metal materials, and it synergistically affects the occurrence of corrosion with other environmental pollutants. -The environmental parameters for establishing the dose response function are T (temperature) and Rh (relative humidity). The standard intends to use temperature and humidity to characterize the wetting of the material surface and the formation of a thin liquid film. Although temperature and humidity can reflect the wetting of the material surface and the formation of a thin liquid film to a certain extent, there is no necessary correspondence, especially on the surface of materials that have been corroded or contaminated. Therefore, the dose response function established in the national standard also has the problem of inaccuracy.

[0009] Based on current standards, the use of empirical power exponential relationships to predict the corrosion rate of metal materials also has the problem of large errors.

[0010] Therefore, how to provide an effective method for predicting the corrosion rate of metal materials is a problem that needs to be solved urgently. Summary of the invention

[0011] The embodiments of the present invention provide a method and model for predicting the corrosion rate of metal materials to solve the problem of inaccurate prediction of the corrosion rate of metal materials in the prior art. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0012] According to a first aspect of an embodiment of the present invention, a method for predicting the corrosion rate of a metal material is provided.

[0013] In one embodiment, the method for predicting the corrosion rate of a metal material includes:

[0014] Obtain the corrosion influencing factors of the inspection site, which include the atmospheric corrosion humidity of metal materials. The atmospheric corrosion humidity of metal materials is the ratio of the time when the metal material exceeds the critical corrosion rate to the whole year;

[0015] According to the corrosion influencing factors, the corrosion rate of metal materials is predicted by the prediction model.

[0016] Optionally, the corrosion influencing factors also include the annual average SO2 concentration, the annual average Cl - Deposition rate.

[0017] Optionally, the prediction model is based on the annual average SO2 concentration, annual average Cl - The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of the metal materials under test. The formula is as follows:

[0018] γ=A[SO2] B [Hd]+D[Cl - ] E [Hd]

[0019] Where: γ represents the corrosion rate of metal materials;

[0020] [SO2] represents the annual average SO2 concentration;

[0021] [Cl - ] represents the annual average Cl - Deposition rate;

[0022] [Hd] represents the atmospheric corrosion humidity of metal materials;

[0023] A, B, D, and E are constants.

[0024] Optionally, the corrosion influencing factor is obtained through actual measurement.

[0025] Optionally, the corrosion influencing factor is obtained through a time series analysis prediction model.

[0026] Optionally, the corrosion influencing factor is obtained by prediction through any one of an autoregressive model, a chaotic time series prediction model, and a neural network prediction model.

[0027] Optionally, the corrosion influencing factor is obtained by the following autoregressive model:

[0028]

[0029] Among them, c is a constant term; is a constant coefficient; ε t is a random disturbance term.

[0030] Optionally, the atmospheric corrosion humidity of the metal material is obtained by the following steps:

[0031] Step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample under different relative humidity, and the point at which the corrosion rate changes fastest is the critical corrosion rate;

[0032] Step (2), converting the corrosion rate of the metal material sample into a corrosion current per unit area;

[0033] Step (3), using the same metal material as the sample as the anode of the galvanic sensor, and calibrating the galvanic sensor so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment;

[0034] Step (4), placing the galvanic sensor at the detection site, reading the current value at fixed time intervals, and calculating the proportion of time that the current value is greater than the corrosion current value corresponding to the critical corrosion rate in the whole year, which is the atmospheric corrosion humidity of the metal material at the detection site.

[0035] According to a second aspect of an embodiment of the present invention, a metal material corrosion rate prediction model is provided.

[0036] In one embodiment, the metal material corrosion rate prediction model includes:

[0037] According to the annual average SO2 concentration, annual average Cl - The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of the metal materials under test. The formula is as follows:

[0038] γ=A[SO2] B [Hd]+D[Cl - ] E [Hd]

[0039] Where: γ represents the corrosion rate of metal materials;

[0040] [SO2] represents the annual average SO2 concentration;

[0041] [Cl - ] represents the annual average Cl - Deposition rate;

[0042] [Hd] represents the atmospheric corrosion humidity of metal materials;

[0043] A, B, D, and E are constants.

[0044] Optionally, the corrosion influencing factor is obtained through actual measurement.

[0045] Optionally, the corrosion influencing factor is obtained through a time series analysis prediction model.

[0046] Optionally, the corrosion influencing factor is obtained by prediction through any one of an autoregressive model, a chaotic time series prediction model, and a neural network prediction model.

[0047] Optionally, the corrosion influencing factor is obtained by the following autoregressive model:

[0048]

[0049] Among them, c is a constant term; is a constant coefficient; ε t is a random disturbance term.

[0050] Optionally, the atmospheric corrosion humidity of the metal material is obtained by the following steps:

[0051] Step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample under different relative humidity, and the point at which the corrosion rate changes fastest is the critical corrosion rate;

[0052] Step (2), converting the corrosion rate of the metal material sample into a corrosion current per unit area;

[0053] Step (3), using the same metal material as the sample as the anode of the galvanic sensor, and calibrating the galvanic sensor so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment;

[0054] Step (4), placing the galvanic sensor at the detection site, reading the current value at fixed time intervals, and calculating the proportion of time that the current value is greater than the corrosion current value corresponding to the critical corrosion rate in the whole year, which is the atmospheric corrosion humidity of the metal material at the detection site.

[0055] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0056] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0057] According to a fourth aspect of an embodiment of the present invention, a storage medium is provided.

[0058] In some embodiments, the storage medium stores a computer program thereon, and the computer program implements the steps of the above method when executed by a processor.

[0059] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0060] The corrosion humidity of metal materials characterizes the corrosion caused by wetting during the corrosion process of metal materials. Compared with the single temperature and humidity defined by the wet time, it can more accurately reflect the real corrosion process of metal materials, is applicable to different regions, and has a stronger correlation with the atmospheric corrosion of metal materials.

[0061] The prediction method of the embodiment of the present invention can more accurately predict the corrosion rate of metal materials.

[0062] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0064] Figure 1 is a flow chart of a method for predicting the corrosion rate of a metal material according to an exemplary embodiment;

[0065] Figure 2 is a flow chart of a method for testing atmospheric corrosion humidity of metal materials according to an exemplary embodiment;

[0066] Figure 3 is a schematic structural diagram of a galvanic sensor according to an exemplary embodiment;

[0067] Figure 4 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0068] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0069] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0070] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0071] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0072] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0073] Figure 1 An embodiment of the method for predicting the corrosion rate of metal materials of the present invention is shown.

[0074] In this embodiment, the method for predicting the corrosion rate of a metal material comprises the following steps:

[0075] Step 100, obtaining the corrosion influencing factor of the detection site, wherein the corrosion influencing factor includes the atmospheric corrosion humidity of the metal material;

[0076] Step 200: predicting the corrosion rate of the metal material by using a prediction model according to the corrosion influencing factors.

[0077] In order to accurately characterize the corrosion of metal materials caused by wetting in the atmosphere, the present invention adopts the atmospheric corrosion humidity of metal materials. The corrosion rate of metal materials in the atmospheric environment increases with the increase of humidity. The humidity point where the corrosion rate increases significantly is the critical point, and it is considered that the surface of the metal material has been wetted at this humidity point. The corrosion rate generated at this point is the critical corrosion rate. The ratio of the time that the metal material exceeds the critical corrosion rate to the time of the whole year is the corrosion humidity of the metal material. The corrosion caused by wetting in the corrosion process of the metal material is characterized by the corrosion humidity of the metal material. Compared with the single temperature and humidity defined by the wet time, it can more accurately reflect the real corrosion process of the metal material, and is applicable to different regions. It has a stronger correlation with the atmospheric corrosion of the metal material. Therefore, the prediction method of the embodiment of the present invention can more accurately predict the corrosion rate of the metal material.

[0078] In one embodiment, the corrosion influencing factors also include the annual average SO2 concentration, the annual average Cl - Deposition rate. In this embodiment, the method for predicting the corrosion rate of metal materials includes the following steps:

[0079] Step S100, obtaining the annual average SO2 concentration and annual average Cl - Deposition rate and atmospheric corrosion humidity of metallic materials;

[0080] Step S200, based on the annual average SO2 concentration, annual average Cl -The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of metal materials. The corrosion rate prediction formula of metal materials is as follows:

[0081] γ=A[SO2] B [Hd]+D[Cl - ] E [Hd] (1)

[0082] Where: γ represents the corrosion rate of metal materials, unit: μm / a;

[0083] [SO2] represents the annual average SO2 concentration, in μg / m 3 ;

[0084] [Cl - ] represents the annual average Cl - Sedimentation rate, unit: mg / (m 2 .d);

[0085] [Hd] represents the atmospheric corrosion humidity of metal materials, %;

[0086] A, B, D, and E are constants, where A and D are in the range of (0-1), and B and E are greater than 0.

[0087] A specific embodiment of the metal material corrosion rate prediction formula is given below.

[0088] The corrosion data of 28 testing stations in Shandong are known, including the corrosion rate of metal material zinc, atmospheric corrosion humidity, annual average SO2 concentration, annual average Cl - The deposition rate data is shown in Table 1 below.

[0089] Table 1 Corrosion data of 28 testing stations in Shandong

[0090]

[0091]

[0092] Based on formula 1 and the above data, the prediction formula for zinc corrosion rate of metal materials is:

[0093] γ=0.0131[SO2] 0.25 [Hd]+0.0164[Cl - ] 0.15 [Hd]

[0094] Where: γ represents the zinc corrosion rate of metal materials, unit: μm / a;

[0095] [SO2] represents the annual average SO2 concentration, in μg / m 3 ;

[0096] [Cl- ] represents the annual average Cl - Sedimentation rate, unit: mg / (m 2 .d);

[0097] [Hd] represents the atmospheric corrosion humidity of metal material zinc, %;

[0098] N=28, R 2 =0.89, N is the number of samples, R 2 It represents the coefficient of determination, which characterizes the goodness of fit.

[0099] Optionally, the corrosion influencing factor is obtained through actual measurement. For example, a detection device is installed at the site, and the corrosion influencing factor is obtained after a set detection period. For example, according to the requirements of GB / T 19292.3, a sulfur dioxide and chloride ion detection device is installed at the exposure corrosion test station to obtain the annual average SO2 concentration and annual average Cl - For the data of deposition rate, the detection cycle is usually set to 1 year or more. For another example, a galvanic sensor is set at the detection site, connected to the tester and storage device, and the test interval is set to 10 minutes. After 1 year of testing, the ratio of the time when the metal material exceeds the critical corrosion rate to the whole year is calculated, which is the corrosion humidity of the metal material at the detection site.

[0100] With the development of corrosion process, the data of humidity detection by galvanic corrosion sensors are also changing year by year. SO2 and Cl in the atmospheric environment - The content of SO2 is also changing year by year. Based on the annual average SO2 concentration, annual average Cl - The change of deposition rate and atmospheric corrosion humidity of metal materials is used to predict the progress of metal material corrosion using the prediction model shown in formula (1). The long-term prediction of the above parameters can be achieved using time series analysis prediction models such as autoregressive model, chaotic time series prediction model, neural network prediction model, etc.

[0101] Optionally, the above average annual SO2 concentration and average annual Cl - The deposition rate and atmospheric corrosion humidity of metal materials are obtained through the following autoregressive model based on historical data:

[0102]

[0103] Among them, c is a constant term; is a constant coefficient; ε t is the random disturbance (noise) term.

[0104] The following is a time series analysis prediction model for the annual average SO2 concentration and annual average Cl - A specific embodiment of predicting the deposition rate and atmospheric corrosion humidity of metal materials.

[0105] In this embodiment, the corrosion data of a testing station in Shandong for nearly 10 years is shown in Table 2.

[0106] Table 2 Corrosion data of the inspection site in the past 10 years

[0107]

[0108] According to the average annual SO2 concentration and annual Cl - The deposition rate and humidity data of zinc atmospheric corrosion are used to predict the average annual SO2 concentration of 12.5 μg / m 3 , annual average Cl - The deposition rate is 4.2 mg / (m 2 .d), the atmospheric corrosion humidity of zinc is 20.1%. Then, the annual average SO2 concentration, annual average Cl - Substitute the deposition rate and humidity of zinc atmospheric corrosion into the prediction formula of zinc corrosion rate of metal materials. The prediction formula is as follows:

[0109] γ=0.0131[SO2] 0.25 [Hd]+0.0164[Cl - ] 0.15 [Hd]

[0110] The zinc corrosion rate forecast for this site over the next year is:

[0111] γ=0.0131[12.5] 0.25 [20.1]+0.0164[4.2] 0.15 [20.1]=0.9039μm / a

[0112] Figure 2 An embodiment of the metal material atmospheric corrosion humidity testing method of the present invention is shown.

[0113] In this embodiment, the method for testing atmospheric corrosion humidity of metal materials includes the following steps:

[0114] Step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample under different relative humidity, taking the point where the corrosion rate changes fastest as the critical point, and the corrosion rate at this point is the critical corrosion rate;

[0115] Step (2), converting the corrosion rate of the metal material sample into a corrosion current per unit area;

[0116] Step (3), using the same metal material as the sample as the anode of the galvanic sensor, using an inert conductive material as the cathode of the galvanic sensor, and calibrating the galvanic sensor at different relative humidities so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment;

[0117] Step (4), placing the galvanic sensor at the detection site, reading the current value at fixed time intervals, and calculating the proportion of time that the recorded current value is greater than the corrosion current value corresponding to the critical corrosion rate in the whole year, which is the atmospheric corrosion humidity of the metal material at the detection site.

[0118] The above embodiment characterizes the corrosion caused by wetting in the corrosion process of metal materials through humidity. Compared with the single temperature and humidity defined by the wet time, it can more accurately reflect the real corrosion process of metal materials and is applicable to different regions. Humidity is more strongly correlated with the atmospheric corrosion of metal materials.

[0119] In one embodiment, step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample, specifically comprises:

[0120] Step (11), in a laboratory environment, setting the relative humidity to a fixed value, and calculating the corrosion rate of the metal material sample under the humidity condition;

[0121] Step (12), setting the relative humidity to increase successively to obtain the corrosion rate of the metal material sample at different relative humidities.

[0122] Optionally, step (11) specifically includes: in a laboratory environment, using an alternating humidity chamber to conduct a corrosion test on a metal material sample, setting the relative humidity of the alternating humidity chamber to a fixed value, after a set corrosion time, removing corrosion products according to GB / T 16545, and calculating the corrosion rate under the humidity conditions.

[0123] Optionally, a corrosion rate and relative humidity curve is drawn, and the point where the first-order derivative of the corrosion rate is the maximum, that is, the point where the corrosion rate changes fastest, is taken as the critical point. The corrosion rate at this point is the critical corrosion rate.

[0124] In one embodiment, step (2), the step of converting the corrosion rate of the metal material sample into the corrosion current per unit area, specifically comprises: according to Faraday's law, converting the corrosion rate of the metal material sample into the corrosion molar amount, then converting it into the amount of electrons gained or lost, and finally converting it into the corrosion current per unit area, and the calculation formula is:

[0125] Where, M is the corrosion amount, unit is g;

[0126] I is the corrosion current density, unit is A / cm2 ;

[0127] t is the detection time of the corrosion amount M, in seconds;

[0128] n is the molar mass of the metal material to be tested;

[0129] a is the common valence state of the anode metal;

[0130] S is the surface area of ​​the metal sample to be tested, cm 2 .

[0131] Optionally, the calculation formula of the above embodiment is used to convert the critical corrosion rate of the metal material into corrosion current to obtain the corrosion current value per unit area of ​​the critical corrosion rate.

[0132] In one embodiment, in step (3), the same metal material as the sample in steps (1) to (2) is used as the anode of the galvanic sensor, and a conductive material that does not corrode significantly in the atmosphere is used as the cathode of the galvanic sensor. The galvanic sensor is calibrated so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment.

[0133] The galvanic sensor connects the anode and cathode to the two ends of an ammeter with zero internal resistance through leads. When a thin liquid film exists between the anode and cathode, that is, when the surface is wetted, the galvanic sensor can detect the current in the conductive loop between the anode and cathode. Since the cathode is an inert material and does not corrode, the detected current is the corrosion current generated by the corrosion of the anode metal, which is then converted into the corrosion current per unit area of ​​the anode metal. By calibrating the galvanic sensor, the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current of the metal material sample in the laboratory environment.

[0134] In one embodiment, the step of calibrating the galvanic sensor adjusts the gap between the anode and cathode of the galvanic sensor and the insulation resistance of the insulating material to ensure that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment.

[0135] By adjusting the gap between the positive and negative electrodes of the galvanic sensor and the insulation resistance of the insulating material, the corrosion current per unit area of ​​the anode metal of the galvanic sensor is ensured to be consistent with the corrosion current per unit area of ​​the metal material sample in the laboratory environment, thereby achieving the sensitivity and accuracy of the sensor. In the above calibration steps, the relative humidity of the alternating heat and humidity chamber is set to increase successively, and the corrosion rate of the metal material at different relative humidities is obtained. At the same time, the corrosion current is detected at low humidity, and the accuracy of the current value detected at each humidity is ensured.

[0136] In one embodiment, the galvanic sensor uses the metal material of the sample to be detected as the anode, and uses a conductive material that does not corrode significantly in the atmosphere, i.e., an inert conductive material, as the cathode, such as platinum, gold, graphite, etc. The anode and cathode materials of the galvanic sensor are insulated by insulating materials. Optionally, the anode and cathode of the galvanic sensor adopt an upper and lower laminated structure. Optionally, the anode and cathode of the galvanic sensor adopt a comb structure or a cross structure.

[0137] Figure 3 One embodiment of a galvanic sensor is shown.

[0138] In this embodiment, the galvanic sensor includes an anode metal 10 and a cathode metal 20, and the anode and cathode adopt a mesh structure of upper and lower laminates, an insulating layer 30 is arranged between the anode and cathode, and a lead 40 connects the anode and cathode to the two ends of an ammeter with zero internal resistance. Optionally, the insulating material is epoxy resin, and the gap between the anode and cathode is adjusted by a brushing process. Optionally, the insulation resistance is adjusted by adding nano-carbon powder. Optionally, the anode metal, cathode metal, insulating layer, and lead of the galvanic sensor are fixed by an epoxy resin layer 50.

[0139] In one embodiment, in step (4), a galvanic sensor is placed at a testing site, and the current value is read at a fixed time interval, wherein the time interval for reading the current value is less than or equal to 30 minutes. After one year of testing, the proportion of time during which the recorded current value is greater than the critical corrosion rate current value is calculated, which is the humidity of the anode metal material used at the testing site.

[0140] A specific embodiment of the method for testing atmospheric corrosion humidity of metal materials of the present invention is given below.

[0141] Step (1), in a laboratory environment, using an alternating humidity chamber to conduct corrosion testing of metal material zinc, the sample size is 100mm×150mm×3mm. Under a constant temperature of 25°C, the relative humidity of the test chamber is set to 50%, 60%, 70%, 80%, 90%, and 95% in sequence, and the corrosion test is carried out for 1000h, 800h, 600h, 400h, 400h, and 400h, respectively. After removing the corrosion products according to GB / T16545, the corrosion rate under the humidity conditions is calculated, and the annual corrosion rates of zinc under relative humidity of 50%, 60%, 70%, 80%, 90%, and 95% are obtained as follows: 0.00106μm / a, 0.00178μm / a, 0.02320μm / a, 0.1856μm / a, 1.4848μm / a, and 4.4543μm / a.

[0142] The above data were plotted into a curve, and after slope calculation, it was found that at 80% humidity, the corrosion rate changed fastest, and the annual corrosion rate of 0.1856 μm / a was the critical point.

[0143] Step (2), in step (1), at a relative humidity of 80%, the time is 400h, and the corrosion weight loss is 1.815mg, according to the above formula:

[0144] Where, M is the corrosion amount, unit is g;

[0145] I is the corrosion current density, unit is A / cm 2 ;

[0146] t is the detection time of the corrosion amount M, in seconds;

[0147] n is the molar mass of the metal material to be tested;

[0148] a is the common valence state of the anode metal, which is 2 for zinc;

[0149] S is the surface area of ​​the zinc sample, cm 2 ;

[0150] After calculation, the corrosion current density I is 12.47nA / cm 2 .

[0151] Step (3), the galvanic sensor uses metal zinc as the anode and metal platinum as the cathode, using Figure 2 In the structure shown, the anode and cathode areas are both 10 cm 2 Epoxy resin glue is used as the insulating material. The gap between the anode and cathode is adjusted by the brushing process, and the insulation resistance is adjusted by adding nano-carbon powder. After the galvanic sensor is connected to a picoammeter, it is tested in an alternating heat and humidity chamber so that the current value detected by the galvanic sensor meets the current value in the above-mentioned metal zinc sample test process. After adjustment, when the gap of the galvanic sensor is controlled at 10-30μm and the insulation resistance is controlled at 1-3KΩ, the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the actual sample. At 80% humidity, the current obtained by the galvanic sensor is 12nA.

[0152] Step (4), connect the above galvanic sensor to the tester and storage device, place it in Jinan, and set the test interval to 10 minutes. After 1 year of testing, the percentage of corrosion current values ​​greater than 12nA in the whole year was statistically calculated, and the result was 0.2352, that is, the humidity of atmospheric corrosion of metal zinc in Jinan area was 0.2352.

[0153] The above-mentioned atmospheric corrosion humidity test method for metal materials is based on the principle of atmospheric corrosion of metal materials. It characterizes the corrosion caused by wetting in the corrosion process of metal materials through humidity. Wetting will cause a surge in corrosion rate. The critical corrosion rate is used to define the time when the metal material produces effective wetting. Compared with the single temperature and humidity defined by the wet time, it can more accurately reflect the actual corrosion process of the metal material and is applicable to different regions. The humidity is more strongly correlated with the atmospheric corrosion of metal materials.

[0154] The humidity galvanic sensor designed by the present invention uses the same metal as the sample being tested as the anode, so that the changes such as hydrophobicity or hydrophilicity caused by the corrosion of the anode metal in the atmosphere are accurately reflected in the test results. Using an inert conductive material as the cathode avoids the influence of the corrosion of the cathode material on the test results. The time when the sensor detects that the current is greater than the critical corrosion current can accurately represent the surface wetting time of the anode metal material in the atmospheric corrosion process, which is more accurate than the wet time in the current standard for the characterization of metal surface wetting.

[0155] In one embodiment, a metal material corrosion rate prediction model is also provided, wherein the prediction model is based on the annual average SO2 concentration, the annual average Cl - The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of the metal materials under test. The formula is as follows:

[0156] γ=A[SO2] B [Hd]+D[Cl - ] E [Hd]

[0157] Where: γ represents the corrosion rate of metal materials;

[0158] [SO2] represents the annual average SO2 concentration;

[0159] [Cl - ] represents the annual average Cl - Deposition rate;

[0160] [Hd] represents the atmospheric corrosion humidity of metal materials;

[0161] A, B, D, and E are constants.

[0162] The working principle of the metal material corrosion rate prediction model is the same as the working principle of the above-mentioned metal material corrosion rate prediction method, which will not be repeated here.

[0163] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above algorithm embodiment are implemented.

[0164] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above algorithm embodiment when executing the computer program.

[0166] In one embodiment, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above algorithm embodiment are implemented.

[0167] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0168] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for predicting the corrosion rate of metal materials, characterized in that: include: Obtain the corrosion influencing factors of the detection site, which include atmospheric corrosion humidity of metal materials, annual average SO2 concentration, annual average Cl - Deposition rate, atmospheric corrosion humidity of metal materials is the ratio of the time when the metal material exceeds the critical corrosion rate to the whole year; The prediction model is based on the annual average SO2 concentration, annual average Cl - The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of the metal materials under test. The formula is as follows: γ=A[SO2] B [Hd]+D[Cl - ] E [Hd] Where: γ represents the corrosion rate of metal materials; [SO2] represents the annual average SO2 concentration; [Cl - ] represents the annual average Cl - Deposition rate; [Hd] represents the atmospheric corrosion humidity of metal materials; A, B, D, and E are constants.

2. A method for predicting the corrosion rate of a metal material according to claim 1, characterized in that: The corrosion influencing factor is obtained through actual measurement.

3. A method for predicting the corrosion rate of a metal material according to claim 1, characterized in that: The corrosion influencing factors are obtained through a time series analysis prediction model.

4. A method for predicting the corrosion rate of a metal material according to claim 3, characterized in that: The corrosion influencing factor is obtained by prediction through any one of an autoregressive model, a chaotic time series prediction model, and a neural network prediction model.

5. A method for predicting the corrosion rate of a metal material according to claim 4, characterized in that: The corrosion influencing factor is obtained through the following autoregressive model: Among them, c is a constant term; is a constant coefficient; ε t is a random disturbance term.

6. A method for predicting the corrosion rate of a metal material according to claim 1, characterized in that: The atmospheric corrosion humidity of the metal material is obtained by the following steps: Step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample under different relative humidity, and the point at which the corrosion rate changes fastest is the critical corrosion rate; Step (2), converting the corrosion rate of the metal material sample into a corrosion current per unit area; Step (3), using the same metal material as the sample as the anode of the galvanic sensor, and calibrating the galvanic sensor so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment; Step (4), placing the galvanic sensor at the detection site, reading the current value at fixed time intervals, and calculating the proportion of time that the current value is greater than the corrosion current value corresponding to the critical corrosion rate in the whole year, which is the atmospheric corrosion humidity of the metal material at the detection site.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, a method for predicting the corrosion rate of a metal material is implemented, the method comprising: Obtain the corrosion influencing factors of the detection site, which include atmospheric corrosion humidity of metal materials, annual average SO2 concentration, annual average Cl - Deposition rate, atmospheric corrosion humidity of metal materials is the ratio of the time when the metal material exceeds the critical corrosion rate to the whole year; The prediction model is based on the annual average SO2 concentration, annual average Cl - The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of the metal materials under test. The formula is as follows: γ=A[SO2] B [Hd]+D[Cl - ] E [Hd] Where: γ represents the corrosion rate of metal materials; [SO2] represents the annual average SO2 concentration; [Cl - ] represents the average annual Cl- deposition rate; [Hd] represents the atmospheric corrosion humidity of metal materials; A, B, D, and E are constants.

8. A computer device as claimed in claim 7, characterized in that: The corrosion influencing factor is obtained through actual measurement.

9. A computer device as claimed in claim 7, characterized in that: The corrosion influencing factors are obtained through a time series analysis prediction model.

10. A computer device as claimed in claim 9, characterized in that: The corrosion influencing factor is obtained by prediction through any one of an autoregressive model, a chaotic time series prediction model, and a neural network prediction model.

11. A computer device as claimed in claim 10, characterized in that: The corrosion influencing factor is obtained through the following autoregressive model: Among them, c is a constant term; is a constant coefficient; ε t is a random disturbance term.

12. A computer device as claimed in claim 7, characterized in that: The atmospheric corrosion humidity of the metal material is obtained by the following steps: Step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample under different relative humidity, and the point at which the corrosion rate changes fastest is the critical corrosion rate; Step (2), converting the corrosion rate of the metal material sample into a corrosion current per unit area; Step (3), using the same metal material as the sample as the anode of the galvanic sensor, and calibrating the galvanic sensor so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment; Step (4), placing the galvanic sensor at the detection site, reading the current value at fixed time intervals, and calculating the proportion of time that the current value is greater than the corrosion current value corresponding to the critical corrosion rate in the whole year, which is the atmospheric corrosion humidity of the metal material at the detection site.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for predicting the corrosion rate of a metal material are implemented, and the method comprises: Obtain the corrosion influencing factors of the detection site, which include atmospheric corrosion humidity of metal materials, annual average SO2 concentration, annual average Cl - Deposition rate, atmospheric corrosion humidity of metal materials is the ratio of the time when the metal material exceeds the critical corrosion rate to the whole year; The prediction model is based on the annual average SO2 concentration, annual average Cl - The deposition rate and atmospheric corrosion humidity of metal materials are used to predict the corrosion rate of the metal materials under test. The formula is as follows: γ=A[SO2] B [Hd]+D[Cl - ] E [Hd] Where: γ represents the corrosion rate of metal materials; [SO2] represents the annual average SO2 concentration; [Cl - ] represents the annual average Cl - Deposition rate; [Hd] represents the atmospheric corrosion humidity of metal materials; A, B, D, and E are constants.

14. A storage medium according to claim 13, characterized in that: The corrosion influencing factor is obtained through actual measurement.

15. A storage medium according to claim 13, characterized in that: The corrosion influencing factors are obtained through a time series analysis prediction model.

16. A storage medium according to claim 15, characterized in that: The corrosion influencing factor is obtained by prediction through any one of an autoregressive model, a chaotic time series prediction model, and a neural network prediction model.

17. A storage medium according to claim 16, characterized in that: The corrosion influencing factor is obtained through the following autoregressive model: Among them, c is a constant term; is a constant coefficient; ε t is a random disturbance term.

18. A storage medium according to claim 13, characterized in that: The atmospheric corrosion humidity of the metal material is obtained by the following steps: Step (1), in a laboratory environment, obtaining the corrosion rate of a metal material sample under different relative humidity, and the point at which the corrosion rate changes fastest is the critical corrosion rate; Step (2), converting the corrosion rate of the metal material sample into a corrosion current per unit area; Step (3), using the same metal material as the sample as the anode of the galvanic sensor, and calibrating the galvanic sensor so that the corrosion current per unit area of ​​the anode metal of the galvanic sensor is consistent with the corrosion current per unit area of ​​the metal material sample in a laboratory environment; Step (4), placing the galvanic sensor at the detection site, reading the current value at fixed time intervals, and calculating the proportion of time that the current value is greater than the corrosion current value corresponding to the critical corrosion rate in the whole year, which is the atmospheric corrosion humidity of the metal material at the detection site.

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