CO2 conveying pipeline corrosion prediction method considering synergistic effect among impurities

Abstract

The invention provides a CO2 conveying pipeline corrosion prediction method considering a synergistic effect among impurities, and relates to the field of corrosion prediction, and the method comprises the steps: building an experiment database comprising an impurity-free basic database, a single impurity database and a different impurity combination database according to the conveying working condition of a carbon dioxide pipeline; based on the impurity-free basic database, processing to obtain a carbon dioxide conveying pipeline corrosion rate basic model only considering working condition parameters; aiming at a single impurity condition and a different impurity combination condition, respectively processing in combination with the experimental database to obtain a single impurity corrosion influence model and an inter-impurity collaborative interaction corrosion influence model; and integrating the corrosion rate basic model, the single impurity corrosion influence model and the inter-impurity collaborative interaction corrosion influence model to obtain an impurity-containing carbon dioxide corrosion prediction model. On the basis of the synergistic effect of the impurities, the change rule of the corrosion severity along with the impurity type, content and impurity combination under different conveying conditions is studied.

Description

Technical Field

[0001] The present invention relates to the technical field of corrosion prediction, and is applied to the prediction of internal corrosion in CO2 transportation pipelines with different phases of impurities. Specifically, it relates to a method for predicting CO2 pipeline corrosion considering the synergistic effect between impurities. Background Art

[0002] CCUS (Carbon Capture, Utilization and Storage) is a key technology to address global climate change. Pipeline transportation is one of the most economical and efficient ways to transport CO2 from the capture site to the utilization site and the storage site. Statistical data from the Pipeline and Hazardous Materials Safety Administration (PHMSA) in the United States shows that 45% of CO2 pipeline failures are due to corrosion. Therefore, accurate prediction of the corrosion rate is very important for ensuring the safety of CO2 transportation pipelines. However, there are many influencing factors for CO2 pipeline corrosion, which poses a challenge to accurately predicting the corrosion rate. Restricted by the CO2 gas source, capture method, and treatment cost, etc., impurities such as H2O, SO2, O2, NO2, and H2S will inevitably exist in CO2. The effects of each impurity on CO2 corrosion are different, and the combined action of multiple impurities makes the pipeline corrosion very complex.

[0003] Aiming at the problems of the existing technology, the present invention provides a method for predicting CO2 pipeline corrosion considering the synergistic effect between impurities. Summary of the Invention

[0004] Aiming at the problems of the current existing technology, the present invention provides a method for predicting CO2 pipeline corrosion considering the synergistic effect between impurities, and the method includes:

[0005] Establish an experimental database including a base database without impurities, a database with a single impurity, and a database with different impurity combinations according to the working conditions of CO2 pipeline transportation;

[0006] Based on the base database without impurities, process to obtain a basic model for the corrosion rate of CO2 transportation pipelines considering only the working condition parameters;

[0007] For the case of a single impurity and different impurity combinations, combine the experimental database and process to obtain a single impurity corrosion influence model and a synergistic interaction corrosion influence model between impurities respectively;

[0008] Integrate the basic corrosion rate model, the single impurity corrosion influence model, and the synergistic interaction corrosion influence model between impurities to obtain a corrosion prediction model for CO2 with impurities.

[0009] According to an embodiment of the present invention, the base database without impurities is established through the following steps:

[0010] Set the experimental conditions according to the actual pipeline transportation conditions of carbon dioxide and determine the working condition parameters, where the working condition parameters include but are not limited to: transportation temperature, transportation pressure, carbon dioxide flow rate, experimental time, and carbon dioxide water content;

[0011] Under the experimental conditions, conduct experiments in a high-temperature and high-pressure reactor, and calculate the corrosion rate of the carbon dioxide transportation pipeline through weight loss analysis after the experiment;

[0012] Set different working condition parameters to conduct multiple experiments, covering gaseous, liquid, and supercritical states, store the working condition parameters and the corresponding corrosion rate of the carbon dioxide transportation pipeline during each experiment, and obtain the base database without impurities.

[0013] According to an embodiment of the present invention, the base database with a single impurity is established through the following steps:

[0014] For a single impurity, on the basis of the base database without impurities, conduct multiple experiments by changing the content of the current impurity, covering gaseous, liquid, and supercritical states, store the working condition parameters, impurity content, and the corresponding corrosion rate of the carbon dioxide transportation pipeline during each experiment, so as to obtain the base database with a single impurity containing each impurity database.

[0015] According to an embodiment of the present invention, the database of different impurity combinations is established through the following steps:

[0016] For any two impurities, on the basis of the base database without impurities, conduct multiple experiments by changing the content of the current two impurities, covering gaseous, liquid, and supercritical states, store the working condition parameters, the content of the two impurities, and the corresponding corrosion rate of the carbon dioxide transportation pipeline during each experiment, and obtain the database with two impurities;

[0017] For any three impurities, on the basis of the base database without impurities, conduct multiple experiments by changing the content of the current three impurities, covering gaseous, liquid, and supercritical states, store the working condition parameters, the content of the three impurities, and the corresponding corrosion rate of the carbon dioxide transportation pipeline during each experiment, and obtain the database with three impurities;

[0018] For any four impurities, on the basis of the base database without impurities, conduct multiple experiments by changing the content of the current four impurities, covering gaseous, liquid, and supercritical states, store the working condition parameters, the content of the four impurities, and the corresponding corrosion rate of the carbon dioxide transportation pipeline during each experiment, and obtain the database with four impurities;

[0019] For any five kinds of impurities, on the basis of the impurity-free basic database, by changing the contents of the current five kinds of impurities, multiple experiments are carried out to cover the gaseous, liquid, and supercritical states, and the working condition parameters, the contents of the five kinds of impurities, and the corresponding corrosion rate of the carbon dioxide pipeline are stored for each experiment to obtain a database containing five kinds of impurities;

[0020] And so on until all combinations of impurities are completed, and all impurity databases are integrated to obtain the different impurity combination databases.

[0021] According to an embodiment of the present invention, the impurities include but are not limited to: SO2, NO2, NO, H2S, O2.

[0022] According to an embodiment of the present invention, the experimental database is obtained through the following steps:

[0023] For the case of incomplete data, fill it with data under similar experimental conditions;

[0024] For the case of duplicate data, perform a deletion operation;

[0025] For outliers, analyze and delete them through the methods of drawing box plots and cluster analysis.

[0026] According to an embodiment of the present invention, the basic corrosion rate model is obtained through the following steps:

[0027] Establish a multiple linear regression fitting equation, and obtain the coefficients of the multiple linear regression fitting equation by fitting the data in the impurity-free basic database to obtain the basic corrosion rate model.

[0028] According to an embodiment of the present invention, the basic corrosion rate model is:

[0029]

[0030] In the formula, V0 is the corrosion rate of the carbon dioxide pipeline when only considering the working condition parameters; p is the transportation pressure, MPa; T is the transportation temperature, °C; t is the experimental time, d; v is the carbon dioxide flow rate, m / s; is the water content of carbon dioxide, ppm; a0, a1, a2, a3, a4, a5 are coefficients.

[0031] According to an embodiment of the present invention, the single-impurity corrosion influence model is obtained through the following steps:

[0032] Compare the single-impurity-containing database and the impurity-free basic database. According to the corrosion rate of the carbon dioxide pipeline without impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the content of the single impurity, analyze the trend of the corrosion influence degree of each impurity alone with the change of the impurity content, and obtain the single-impurity corrosion influence model.

[0033] According to an embodiment of the present invention, the single-impurity corrosion influence model is:

[0034]

[0035] In the formula, b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is present at the impurity content of C i ; C i is the content of the i-th impurity; V 1，i is the corrosion rate of the i-th impurity at the impurity content of C i ; V0 is the corrosion rate of the carbon dioxide pipeline only considering the working conditions parameters.

[0036] According to an embodiment of the present invention, the synergistic interaction corrosion influence model between impurities is obtained through the following steps:

[0037] For the case where two impurities coexist, compare the two-impurity database and the single-impurity-containing database. According to the corrosion rate of the carbon dioxide pipeline with two impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the content of the two impurities, analyze the synergistic interaction trend of the two impurities on corrosion when the two impurities coexist, and obtain the two-impurity corrosion influence model;

[0038] For the case where three impurities coexist, compare the three-impurity database and the single-impurity-containing database. According to the corrosion rate of the carbon dioxide pipeline with three impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the content of the three impurities, analyze the synergistic interaction trend of the three impurities on corrosion when the three impurities coexist, and obtain the three-impurity corrosion influence model;

[0039] For the case where four impurities coexist, compare the four-impurity database and the single-impurity-containing database. According to the corrosion rate of the carbon dioxide pipeline with four impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the content of the four impurities, analyze the synergistic interaction trend of the four impurities on corrosion when the four impurities coexist, and obtain the four-impurity corrosion influence model;

[0040] For the case where five impurities coexist, compare the five-impurity database and the single-impurity-containing database. According to the corrosion rate of the carbon dioxide pipeline when five impurities are present, the corrosion rate of the carbon dioxide pipeline when a single impurity is present, and the contents of the five impurities, analyze the trend of the synergistic interaction of the five impurities on corrosion when the five impurities coexist, and obtain a corrosion influence model for the five impurities;

[0041] And so on, until the corrosion influence models for all impurity combinations are obtained to obtain the synergistic interaction corrosion influence model between the impurities.

[0042] According to an embodiment of the present invention, the corrosion influence model for the two impurities is as follows:

[0043]

[0044] In the formula, γ2 is the synergistic interaction coefficient of the i-th and j-th impurities on corrosion; C i is the content of the i-th impurity; C j is the content of the j-th impurity; V 2,(i,j) is the corrosion rate when the content of the i-th impurity is C i and the content of the j-th impurity is C j ; V0 is the corrosion rate of the carbon dioxide pipeline when only considering the operating parameters; b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is present at the impurity content of C i ; b j is the influence degree coefficient of the corrosion rate when only the j-th impurity is present at the impurity content of C j ;

[0045] The corrosion influence model for the three impurities is as follows:

[0046]

[0047] In the formula, γ3 is the synergistic interaction coefficient of the i-th, j-th, and k-th impurities on corrosion; C k is the content of the k-th impurity; V 3,(i,j,k) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , and the content of the k-th impurity is C k ; b k is the influence degree coefficient of the corrosion rate when only the k-th impurity is present at the impurity content of C k ;

[0048] The corrosion influence model for the four impurities is as follows:

[0049]

[0050] In the formula, γ4 is the coefficient of the synergistic interaction on corrosion among the i-th, j-th, k-th, and l-th impurities; C l is the content of the l-th impurity; V 4,(i,j,k,l) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , the content of the k-th impurity is C k , and the content of the l-th impurity is C l ; b l is the influence degree coefficient on the corrosion rate when only the l-th impurity is present and the impurity content is C l .

[0051] The corrosion influence model of the five impurities is as follows:

[0052]

[0053] In the formula, γ5 is the coefficient of the synergistic interaction on corrosion among the i-th, j-th, k-th, l-th, and m-th impurities; C m is the content of the m-th impurity; V 5,(i,j,k,l,m) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , the content of the k-th impurity is C k , the content of the l-th impurity is C l , and the content of the m-th impurity is C m ; b m is the influence degree coefficient on the corrosion rate when only the m-th impurity is present and the impurity content is C m .

[0054] According to an embodiment of the present invention, the synergistic interaction corrosion influence model among the impurities is as follows:

[0055]

[0056] In the formula, γ n is the coefficient of the synergistic interaction on corrosion among n impurities; V0 is the corrosion rate of the carbon dioxide pipeline when only considering the working conditions parameters; b i is the influence degree coefficient on the corrosion rate when only the i-th impurity is present and the impurity content is C i .

[0057] According to an embodiment of the present invention, the corrosion prediction model of the carbon dioxide containing impurities is as follows:

[0058]

[0059] In the formula, V corris the corrosion prediction rate of carbon dioxide containing impurities; p is the transportation pressure, MPa; T is the transportation temperature, °C; t is the experimental time, which is the transportation time in the actual transportation condition, d; v is the carbon dioxide flow rate, m / s; is the water content of carbon dioxide, ppm; b i is the influence degree coefficient on the corrosion rate when only the i-th impurity is contained and the impurity content is C i ; γ n is the synergistic interaction coefficient between n impurities on corrosion.

[0060] According to another aspect of the present invention, there is also provided a storage medium, which contains a series of instructions for executing the method steps described in any one of the above.

[0061] According to another aspect of the present invention, there is also provided a CO2 transportation pipeline corrosion prediction device considering the synergistic effect between impurities, which executes the method described in any one of the above. The device includes:

[0062] An experimental database module, which establishes an experimental database including a base database without impurities, a database with a single impurity, and a database with different impurity combinations according to the carbon dioxide pipeline transportation conditions;

[0063] A basic model module, which processes the base database without impurities to obtain a basic model of the corrosion rate of the CO2 transportation pipeline considering only the working condition parameters;

[0064] An influence model module, which processes the single impurity situation and different impurity combination situations respectively in combination with the experimental database to obtain a single impurity corrosion influence model and a synergistic interaction corrosion influence model between impurities;

[0065] A corrosion prediction module, which combines the basic corrosion rate model, the single impurity corrosion influence model, and the synergistic interaction corrosion influence model between impurities to obtain a corrosion prediction model of carbon dioxide containing impurities.

[0066] The present invention provides a method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities. Compared with the prior art, it has the following advantages:

[0067] (1) The present invention proposes a research method for systematically studying the synergistic effect between impurities on CO2 corrosion and a more accurate characterization index.

[0068] (2) The present invention can study the variation law of the severity of CO2 corrosion under different transportation conditions with the change of impurity type, content, and impurity combination on the basis of the synergistic effect between impurities, providing theoretical guidance for impurity quality control.

[0069] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the specification, claims and drawings. Description of the Drawings

[0070] The drawings are provided to further understand the present invention and form a part of the specification. They are used in conjunction with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0071] Figure 1 The flowchart showing the steps of a method for predicting corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to an embodiment of the present invention;

[0072] Figure 2 The flowchart showing the steps of establishing a basic database of impurities according to an embodiment of the present invention;

[0073] Figure 3 The flowchart showing the steps of establishing a database of different impurity combinations according to an embodiment of the present invention;

[0074] Figure 4 The flowchart showing the steps of obtaining a synergistic interaction corrosion effect model between impurities according to an embodiment of the present invention;

[0075] Figure 5 The flowchart showing the steps of a method for predicting corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to another embodiment of the present invention.

[0076] In the drawings, the same components are denoted by the same reference numerals. Additionally, the drawings are not drawn to actual scale. Detailed Description of the Embodiments

[0077] To make the objectives, technical solutions and advantages of the present invention clearer, the following further describes the embodiments of the present invention in detail with reference to the drawings.

[0078] Prior arts (Method for establishing CO2 corrosion prediction chart considering multiple factors, 202010028987.3), prior arts (Method for predicting CO2 corrosion rate considering influence of corrosion product film, 202010692956.8), prior arts (Prediction model for CO2 corrosion in oil and gas fields, Journal of Chinese Society for Corrosion and Protection, April 2005), prior arts (Research on CO2 corrosion prediction model for offshore oil and gas fields, Contemporary Chemical Industry, October 2016) have established CO2 corrosion prediction models for their respective oil and gas environments. However, the CO2 corrosion prediction models established by these prior arts all belong to the traditional oil and gas extraction and transportation pipeline environments, and there are essential differences between the corrosion environments and the CO2 transportation environment applicable to the present invention. In the traditional oil and gas extraction and transportation pipeline environments, the solvent is H2O, and the solutes are CO2 and inorganic salts, etc. The solvent in the CO2 transportation environment applicable to the present invention is CO2, and the solutes are impurities such as H2O, SO2, and O2.

[0079] Prior arts (Research on influence of impurities on corrosion of X65 steel in supercritical CO2 transportation pipelines, China University of Petroleum (East China), December 2016) studied the influence of different impurities (H2S, NO2, SO2, O2) on the corrosion of X65 steel under supercritical CO2 transportation phase states, and established a calculation formula for the synergistic interaction coefficient between impurities when the impurity content is 1000 ppm. However, this prior art did not conduct research on the synergistic interaction between impurities under gaseous and liquid CO2 transportation phase states, did not consider the impurity content when establishing the synergistic interaction coefficient between impurities, and was not involved in the research on the construction of CO2 corrosion prediction models.

[0080] Aiming at the above defects of the prior arts, the present invention aims to provide a research method and characterization index for characterizing the synergistic effect of impurities on CO2 corrosion, solve the current situation of incomplete research on impurity synergistic effects and inaccurate characterization indexes, and based on this, a CO2 corrosion prediction model is proposed, which can realize predictions under different working conditions.

[0081] Compared with the prior arts, in addition to the supercritical CO2 transportation phase state, the present invention can also realize the research on the synergistic interaction between impurities and corrosion prediction under gaseous and liquid CO2 transportation phase states; compared with the prior arts, the present invention takes the impurity content into consideration and establishes a calculation formula for the synergistic interaction coefficient of different impurity contents, types, and different impurity combinations; based on the research on the synergistic interaction between impurities, the present invention constructs a CO2 corrosion prediction model on this basis, which can realize predictions under different working conditions.

[0082] Figure 1 The flowchart shows the steps of a CO2 transportation pipeline corrosion prediction method considering the synergistic effect of impurities according to an embodiment of the present invention.

[0083] As Figure 1As shown, in step S101, according to the working conditions of carbon dioxide pipeline transportation, an experimental database is established, which includes a basic database without impurities, a database with a single impurity, and a database with different impurity combinations.

[0084] As Figure 1 shown, in step S102, based on the basic database without impurities, a basic model of the corrosion rate of the carbon dioxide transportation pipeline considering only the working condition parameters is processed.

[0085] As Figure 1 shown, in step S103, for the case of a single impurity and different impurity combinations, combined with the experimental database, a single-impurity corrosion influence model and an inter-impurity synergistic interaction corrosion influence model are respectively processed. In one embodiment, the impurities include but are not limited to: SO2, NO2, NO, H2S, O2.

[0086] As Figure 1 shown, in step S104, a corrosion prediction model of carbon dioxide containing impurities is obtained by integrating the basic corrosion rate model, the single-impurity corrosion influence model, and the inter-impurity synergistic interaction corrosion influence model.

[0087] The present invention is applicable to the field of corrosion prediction in the CO2 transportation link of the CCUS process, especially for carbon dioxide transportation pipelines containing impurities. The present invention provides a research method for characterizing the synergistic effect of impurities on CO2 corrosion and constructs an inter-impurity synergistic interaction coefficient, filling the gap in the corrosion prediction of CO2 transportation working conditions with complex impurities in the prior art, and having good application prospects in the field of CO2 transportation in the CCUS process, especially for carbon dioxide transportation pipelines containing impurities.

[0088] Figure 2 Shows a flowchart of the steps for establishing an impurity basic database according to an embodiment of the present invention.

[0089] As Figure 2 shown, in step S201, experimental conditions are set according to the actual pipeline transportation working conditions of carbon dioxide and the working condition parameters are determined. Among them, the working condition parameters include but are not limited to: transportation temperature T, transportation pressure p, carbon dioxide flow rate v, experimental time t, water content of carbon dioxide

[0090] Specifically, referring to the actual transportation working conditions of the CO2 transportation pipeline, the experimental simulation parameters (working condition parameters) include: transportation temperature, transportation pressure, CO2 flow rate, experimental time, water content in CO2, and the units are: °C, MPa, m / s, d, ppm respectively.

[0091] As Figure 2As shown, in step S202, under experimental conditions, experiments are conducted using a high-temperature and high-pressure autoclave. After the experiments are completed, the corrosion rate of the carbon dioxide pipeline is calculated through weight loss analysis.

[0092] Specifically, set the experimental conditions and conduct experiments using a high-temperature and high-pressure autoclave. After the experiments are completed, remove the carbon dioxide pipeline coupon, calculate the corrosion rate V0 through weight loss analysis, and store the data in accordance with the format. Among them, V0 is the corrosion rate of the carbon dioxide pipeline considering only the operating conditions parameters, that is, without impurities.

[0093] As Figure 2 shown, in step S203, set different operating conditions parameters to conduct multiple experiments, covering gaseous, liquid, and supercritical states. Store the operating conditions parameters and the corresponding corrosion rate of the carbon dioxide pipeline for each experiment to obtain a base database without impurities.

[0094] Specifically, set different (transport temperature, transport pressure, CO2 flow rate, experimental time, water content in CO2) conditions within the actual transport operating conditions range, conduct experiments, and record relevant data to obtain a basic indoor experimental database 0 (DB-0), that is, a base database without impurities.

[0095] In one embodiment, a database containing a single impurity is established through the following steps: For a single impurity, based on the base database without impurities, change the content of the current impurity and conduct multiple experiments, covering gaseous, liquid, and supercritical states. Store the operating conditions parameters, impurity content, and the corresponding corrosion rate of the carbon dioxide pipeline for each experiment to obtain a database containing a single impurity that includes each impurity database.

[0096] Specifically, based on steps S202 and S203, add impurity gases one by one in the order of SO2, NO2, NO, H2S, O2. Set different contents for each impurity, such as 200 ppm, 400 ppm, 600 ppm, 800 ppm... Conduct experiments to construct a database named database 1 (DB-1), that is, a database containing a single impurity. Among them, C i is the content of the i-th impurity, and V1 is the corrosion rate under the influence of a single impurity.

[0097] Figure 3 shows a flowchart of the steps for establishing a database of different impurity combinations according to an embodiment of the present invention.

[0098] As Figure 3As shown in the figure, in step S301, for any two kinds of impurities, based on the impurity-free basic database, by changing the contents of the current two kinds of impurities, multiple experiments are carried out to cover the gaseous, liquid, and supercritical states. The working condition parameters, the contents of the two kinds of impurities, and the corresponding corrosion rate of the carbon dioxide pipeline are stored in each experiment to obtain a database containing two kinds of impurities.

[0099] Specifically, based on steps S202 and S203, impurity gases are added one by one in the order of SO2, NO2, NO, H2S, and O2. Different contents are set for the two impurities in a cross manner to conduct experiments and construct a database, named database 2 (DB-2), that is, a database containing two kinds of impurities. Among them, C i is the content of the i-th kind of impurity, C j is the content of the j-th kind of impurity, and V2 is the corrosion rate affected by the combination of the two impurities.

[0100] As Figure 3 shown in the figure, in step S302, for any three kinds of impurities, based on the impurity-free basic database, by changing the contents of the current three kinds of impurities, multiple experiments are carried out to cover the gaseous, liquid, and supercritical states. The working condition parameters, the contents of the three kinds of impurities, and the corresponding corrosion rate of the carbon dioxide pipeline are stored in each experiment to obtain a database containing three kinds of impurities.

[0101] Specifically, based on steps S202 and S203, impurity gases are added one by one in the order of SO2, NO2, NO, H2S, and O2. Different contents are set for the three impurities in a cross manner to conduct experiments and construct a database, named database 3 (DB-3), that is, a database containing three kinds of impurities. Among them, C i is the content of the i-th kind of impurity, C j is the content of the j-th kind of impurity, C k is the content of the k-th kind of impurity, and V3 is the corrosion rate affected by the combination of the three impurities.

[0102] As Figure 3 shown in the figure, in step S303, for any four kinds of impurities, based on the impurity-free basic database, by changing the contents of the current four kinds of impurities, multiple experiments are carried out to cover the gaseous, liquid, and supercritical states. The working condition parameters, the contents of the four kinds of impurities, and the corresponding corrosion rate of the carbon dioxide pipeline are stored in each experiment to obtain a database containing four kinds of impurities.

[0103] Specifically, on the basis of steps S202 and S203, impurity gases are added one by one in the order of SO2, NO2, NO, H2S, and O2. Different contents are cross-set for the 4 impurities, and experiments are carried out to construct a database, named database 4 (DB-4), that is, a four-impurity database. Among them, C i is the content of the i-th impurity, C j is the content of the j-th impurity, C k is the content of the k-th impurity, C l is the content of the l-th impurity, and V4 is the corrosion rate under the influence of the combination of the four impurities.

[0104] As Figure 3 shown, in step S304, for any five impurities, on the basis of the impurity-free basic database, by changing the contents of the current five impurities, multiple experiments are carried out to cover gaseous, liquid, and supercritical states, and the working condition parameters, the contents of the five impurities, and the corresponding corrosion rate of the carbon dioxide pipeline are stored each time an experiment is conducted, so as to obtain a five-impurity database.

[0105] Specifically, on the basis of steps S202 and S203, five impurity gases are added in the order of SO2, NO2, NO, H2S, and O2. Different contents are cross-set for the 5 impurities, and experiments are carried out to construct a database, named database 5 (DB-5), that is, a five-impurity database. Among them, C i is the content of the i-th impurity, C j is the content of the j-th impurity, C k is the content of the k-th impurity, C l is the content of the l-th impurity, C m is the content of the m-th impurity, and V5 is the corrosion rate under the influence of the combination of the five impurities.

[0106] As Figure 3 shown, in step S305, and so on, until all combinations of impurities are completed, and all impurity databases are integrated to obtain a different impurity combination database.

[0107] In one embodiment, an experimental database is obtained through the following steps: for the case of incomplete data, data under similar experimental conditions are used for filling; for the case of duplicate data, deletion operations are performed; for outliers, they are deleted after analysis by methods such as drawing box plots and cluster analysis. Specifically, for all databases obtained from experiments, outlier identification methods such as boxplot are used to process the databases, and after outliers are found, operations such as deletion, correction, and filling are performed.

[0108] In one embodiment, the basic corrosion rate model is obtained through the following steps: establish a multiple linear regression fitting equation, and obtain the coefficients of the multiple linear regression fitting equation by fitting the data in the impurity-free basic database, so as to obtain the basic corrosion rate model.

[0109] Specifically, the establishment of the basic CO2 corrosion prediction model (without impurities): establish a multiple linear regression fitting formula (as shown in Equation (1)), and obtain a0 to a5 by fitting the data in the DB-0 database, thereby establishing a CO2 basic model that only considers five influencing factors: transportation temperature, transportation pressure, CO2 flow rate, experimental time, and water content in CO2.

[0110] Furthermore, the basic corrosion rate model is:

[0111]

[0112] In the formula, V0 is the corrosion rate of the CO2 transportation pipeline when only considering the working conditions parameters; p is the transportation pressure, MPa; T is the transportation temperature, °C; t is the experimental time, d; v is the CO2 flow rate, m / s; is the water content in CO2, ppm; a0, a1, a2, a3, a4, a5 are coefficients.

[0113] In one embodiment, the single-impurity corrosion influence model is obtained through the following steps: compare the database containing a single impurity and the impurity-free basic database, and respectively analyze the trend of the corrosion influence degree changing with the impurity content when each impurity exists alone according to the corrosion rate of the CO2 transportation pipeline without impurities, the corrosion rate of the CO2 transportation pipeline containing a single impurity, and the content of the single impurity, so as to obtain the single-impurity corrosion influence model.

[0114] Specifically, the characterization of the influence degree of a single impurity on CO2 corrosion: compare the data in the DB-1 database and the DB-0 database, and according to V1, V0, C i Establish the coefficient of the corrosion influence degree changing with the impurity content and impurity type when a single impurity exists one by one according to the data, and the formula is shown in Equation (2).

[0115] Furthermore, the single-impurity corrosion influence model is:

[0116]

[0117] In the formula, b i is the influence degree coefficient on the corrosion rate when only the i-th impurity exists and the impurity content is C i ; C i is the content of the i-th impurity; V 1，i is the i-th impurity at the impurity content of C iThe corrosion rate at this time; V0 is the corrosion rate of the carbon dioxide pipeline considering only the operating conditions.

[0118] Figure 4 The flowchart shows the steps of obtaining the synergistic interaction corrosion influence model of impurities according to an embodiment of the present invention.

[0119] As Figure 4 shown, in step S401, for the case where two impurities coexist, compare the two-impurity database and the single-impurity database, and analyze the synergistic interaction trend of the two impurities on corrosion according to the corrosion rate of the carbon dioxide pipeline when two impurities are present, the corrosion rate of the carbon dioxide pipeline when a single impurity is present, and the contents of the two impurities, to obtain the corrosion influence model of the two impurities.

[0120] Specifically, when two impurities coexist: compare the data in the DB-2 database and the DB-1 database, and according to V2, V1, C i , C j data, establish the formula for the change of the synergistic interaction coefficient between impurities with the impurity content when two impurities coexist through fitting, as shown in Equation (3).

[0121] Furthermore, the corrosion influence model of the two impurities is:

[0122]

[0123] In the formula, γ2 is the synergistic interaction coefficient between the i-th and j-th impurities on corrosion; C i is the content of the i-th impurity; C j is the content of the j-th impurity; V 2,(i,j) is the corrosion rate when the content of the i-th impurity is C i , and the content of the j-th impurity is C j ; V0 is the corrosion rate of the carbon dioxide pipeline considering only the operating conditions; b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is present at the impurity content of C i ; b j is the influence degree coefficient of the corrosion rate when only the j-th impurity is present at the impurity content of C j .

[0124] As Figure 4 shown, in step S402, for the case where three impurities coexist, compare the three-impurity database and the single-impurity database, and analyze the synergistic interaction trend of the three impurities on corrosion according to the corrosion rate of the carbon dioxide pipeline when three impurities are present, the corrosion rate of the carbon dioxide pipeline when a single impurity is present, and the contents of the three impurities, to obtain the corrosion influence model of the three impurities.

[0125] Specifically, when three impurities coexist: By comparing the data in the DB-3 database and the DB-1 database, according to V3, V1, C i , C j , C k The data is used to establish, through fitting, the formula for the change of the synergistic interaction coefficient between impurities with the impurity content when three impurities coexist, as shown in Equation (4).

[0126] Furthermore, the corrosion influence model for the three impurities is as follows:

[0127]

[0128] In the formula, γ3 is the synergistic interaction coefficient between the i-th, j-th, and k-th impurities on corrosion; C k is the content of the k-th impurity; V 3,(i,j,k) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , and the content of the k-th impurity is C k ; b k is the influence degree coefficient of the corrosion rate when only the k-th impurity is present at the impurity content of C k .

[0129] As Figure 4 shown, in step S403, for the case where four impurities coexist, by comparing the four-impurity database and the single-impurity database, and based on the corrosion rate of the carbon dioxide pipeline when four impurities are present, the corrosion rate of the carbon dioxide pipeline when a single impurity is present, and the contents of the four impurities, the trend of the synergistic interaction of the four impurities on corrosion is analyzed to obtain the corrosion influence model for the four impurities.

[0130] Specifically, when four impurities coexist: By comparing the data in the DB-4 database and the DB-1 database, according to V4, V1, C i , C j , C k , C l The data is used to establish, through fitting, the formula for the change of the synergistic interaction coefficient between impurities with the impurity content when four impurities coexist, as shown in Equation (5).

[0131] Furthermore, the corrosion influence model for the four impurities is as follows:

[0132]

[0133] In the formula, γ4 is the synergistic interaction coefficient between the i-th, j-th, k-th, and l-th impurities on corrosion; C l is the content of the l-th impurity; V4,(i,j,k,l) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , the content of the k-th impurity is C k , the content of the l-th impurity is C l ; b l is the influence degree coefficient on the corrosion rate when only the l-th impurity is present and the impurity content is C l .

[0134] As Figure 4 shown, in step S404, for the case where five impurities coexist, by comparing the five-impurity database and the single-impurity database, based on the corrosion rate of the carbon dioxide pipeline when five impurities are present, the corrosion rate of the carbon dioxide pipeline when a single impurity is present, and the contents of the five impurities, the trend of the synergistic interaction of the five impurities on corrosion is analyzed to obtain the corrosion influence model of the five impurities.

[0135] Specifically, when five impurities coexist: By comparing the data in the DB-5 database and the DB-1 database, according to V5, V1, C i , C j , C k , C l , C m data, a formula for the change of the synergistic interaction coefficient between impurities with the impurity content is established by fitting when five impurities coexist, as shown in Equation (6).

[0136] Furthermore, the corrosion influence model of the five impurities is as follows:

[0137]

[0138] In the formula, γ5 is the synergistic interaction coefficient between the i-th, j-th, k-th, l-th, and m-th impurities on corrosion; C m is the content of the m-th impurity; V 5,(i,j,k,l,m) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , the content of the k-th impurity is C k , the content of the l-th impurity is C l , and the content of the m-th impurity is C m ; b m is the influence degree coefficient on the corrosion rate when only the m-th impurity is present and the impurity content is C m .

[0139] As Figure 4 shown, in step S405, and so on, until the corrosion influence models of all impurity combinations are obtained to obtain the synergistic interaction corrosion influence model between impurities.

[0140] In one embodiment, the synergistic interaction corrosion influence model among impurities is as follows:

[0141]

[0142] In the formula, γ n is the synergistic interaction coefficient of corrosion among n impurities; V0 is the corrosion rate of the carbon dioxide pipeline considering only the operating parameters; b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is contained at the impurity content of C i .

[0143] In one embodiment, the corrosion prediction model of carbon dioxide containing impurities under different impurity conditions is as follows:

[0144]

[0145] In the formula, V corr is the corrosion prediction rate of carbon dioxide containing impurities; p is the transportation pressure, MPa; T is the transportation temperature, °C; t is the experimental time, which is the transportation time in the actual transportation condition, d; v is the carbon dioxide flow rate, m / s; is the water content of carbon dioxide, ppm; b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is contained at the impurity content of C i ; γ n is the synergistic interaction coefficient of corrosion among n impurities.

[0146] Figure 5 The flowchart shows the steps of a method for predicting corrosion of a CO2 pipeline considering the synergistic effect among impurities according to another embodiment of the present invention.

[0147] As Figure 5 shown, in one embodiment, under the condition of gaseous CO2 transportation, a corrosion prediction model of carbon dioxide containing impurities is constructed:

[0148] In step S501, experimental method and database establishment: According to the actual transportation conditions of carbon dioxide, experimental conditions are set, and indoor experiments are carried out to establish a basic database without impurities (DB-0), a database containing a single impurity (DB-1), and databases with different impurity combinations (DB-2 to DB-5) respectively.

[0149] In step S502, data cleaning: Data processing operations such as filling the experimental values and deleting outliers are performed on the experimental values.

[0150] In step S503, a basic corrosion prediction formula is established: Multiple linear regression is performed to fit the data in DB-0, and a basic corrosion rate model is established that only considers five influencing factors: transportation temperature, transportation pressure, carbon dioxide flow rate, experimental time, and water content in carbon dioxide.

[0151] Among them, only considering five influencing factors: transportation temperature, transportation pressure, CO2 flow rate, experimental time, and water content in CO2, the multiple linear regression fitting formula is:

[0152]

[0153] In step S504, the influence degree of single impurity on CO2 corrosion is characterized: By comparing the data in DB-1 and DB-0, the change coefficients of the influence degree of corrosion with impurity content and impurity type are established one by one using formula (2).

[0154] In step S505, the synergistic interaction between impurities is characterized: By comparing the data in DB-2 to DB-5 and DB-1, the synergistic interaction coefficients between different impurity combinations are calculated. Among them, the synergistic interaction coefficients of different impurity combinations are calculated using formulas (3) to (6).

[0155] In step S506, a CO2 corrosion prediction model for the impurity-containing condition: By integrating steps S503 - S505, a CO2 corrosion prediction model containing impurities is established.

[0156] In summary, the CO2 corrosion prediction models under different impurity conditions are:

[0157]

[0158] As Figure 5 shown, in one embodiment, under the condition of liquid-phase CO2 transportation, a CO2 corrosion prediction model containing impurities is constructed:

[0159] In step S501, experimental methods and database establishment: According to the actual CO2 transportation conditions, experimental conditions are set, indoor experiments are carried out, and a basic database without impurities (DB-0), a database containing a single impurity (DB-1), and databases with different impurity combinations (DB-2 to DB-5) are established respectively.

[0160] In step S502, data cleaning: Data processing operations such as filling experimental values and deleting outliers are performed.

[0161] In step S503, a basic corrosion prediction formula is established: Multiple linear regression is performed to fit the data in DB-0, and a basic corrosion rate model is established that only considers five influencing factors: transportation temperature, transportation pressure, carbon dioxide flow rate, experimental time, and water content in carbon dioxide.

[0162] Among them, only considering five influencing factors: conveying temperature, conveying pressure, CO2 flow rate, experimental time, and water content in CO2, the multiple linear regression fitting formula is established as follows:

[0163]

[0164] In step S504, the characterization of the influence degree of single impurities on CO2 corrosion: By comparing the data in DB-1 and DB-0, the change coefficients of the corrosion influence degree with impurity content and impurity type are established one by one using formula (2).

[0165] In step S505, the characterization of the synergistic interaction between impurities: By comparing the data in DB-2 to DB-5 and DB-1, the synergistic interaction coefficients between different impurity combinations are calculated. Among them, the synergistic interaction coefficients of different impurity combinations are calculated using formulas (3) to (6).

[0166] In step S506, the CO2 corrosion prediction model for the impurity-containing working condition: Combining steps S503 - S505, a CO2 corrosion prediction model containing impurities is established.

[0167] In summary, the CO2 corrosion prediction models under different impurity working conditions are as follows:

[0168]

[0169] Such as Figure 5 shown, in one embodiment, under the CO2 conveying conditions in the supercritical phase, a CO2 corrosion prediction model containing impurities is constructed:

[0170] In step S501, experimental method and database establishment: According to the actual CO2 conveying working conditions, set the experimental conditions, conduct indoor experiments, and establish a basic database without impurities (DB-0), a database containing single impurities (DB-1), and databases of different impurity combinations (DB-2 to DB-5) respectively.

[0171] In step S502, data cleaning: Perform data processing operations such as filling the experimental values and deleting outliers.

[0172] In step S503, establish a basic corrosion prediction formula: Perform multiple linear regression fitting on the data in DB-0, and establish a basic corrosion rate model considering only five influencing factors: conveying temperature, conveying pressure, CO2 flow rate, experimental time, and water content in CO2.

[0173] Among them, only considering five influencing factors: conveying temperature, conveying pressure, CO2 flow rate, experimental time, and water content in CO2, the multiple linear regression fitting formula is established as follows:

[0174]

[0175] In step S504, the characterization of the influence degree of a single impurity on CO2 corrosion: By comparing the data in DB-1 and DB-0, the change coefficients of the corrosion influence degree with the impurity content and impurity type are established one by one using Equation (2).

[0176] In step S505, the characterization of the synergistic interaction between impurities: By comparing the data in DB-2 to DB-5 and DB-1, the synergistic interaction coefficients between different impurity combinations are calculated. Among them, the synergistic interaction coefficients of different impurity combinations are calculated using Equations (3) to (6).

[0177] In step S506, the CO2 corrosion prediction model for the impurity-containing working condition: By integrating steps S503 - S505, a CO2 corrosion prediction model considering impurities is established.

[0178] In summary, the CO2 corrosion prediction model under different impurity working conditions is as follows:

[0179]

[0180] A CO2 pipeline corrosion prediction method considering the synergistic effect between impurities provided by the present invention can also cooperate with a computer-readable storage medium. A computer program is stored on the storage medium, and the computer program is executed to run a CO2 pipeline corrosion prediction method considering the synergistic effect between impurities. The computer program can run computer instructions, and the computer instructions include computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0181] The computer-readable storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0182] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0183] According to another aspect of the present invention, there is also provided a CO2 pipeline corrosion prediction device considering the synergistic effect between impurities, which executes a CO2 pipeline corrosion prediction method considering the synergistic effect between impurities. The device includes: an experimental database module, a basic model module, an influence model module, and a corrosion prediction module.

[0184] The experimental database module establishes an experimental database including a basic database without impurities, a database with a single impurity, and a database with different impurity combinations according to the working conditions of CO2 pipeline transportation; the basic model module processes the basic database without impurities to obtain a basic model of the corrosion rate of the CO2 pipeline considering only the working condition parameters; the influence model module processes the single impurity case and different impurity combination cases respectively, in combination with the experimental database, to obtain a single impurity corrosion influence model and an inter-impurity synergistic interaction corrosion influence model; the corrosion prediction module synthesizes the basic corrosion rate model, the single impurity corrosion influence model, and the inter-impurity synergistic interaction corrosion influence model to obtain a corrosion prediction model of CO2 with impurities.

[0185] In summary, the present invention provides a CO2 pipeline corrosion prediction method considering the synergistic effect between impurities. Compared with the prior art, it has the following advantages:

[0186] (1) The present invention proposes a research method for systematically studying the synergistic effect of impurities on CO2 corrosion and a more accurate characterization index.

[0187] (2) The present invention can study the variation law of the severity of CO2 corrosion under different transportation conditions with respect to the type, content, and combination of impurities on the basis of the synergistic effect between impurities, providing theoretical guidance for impurity quality control.

[0188] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps, or materials disclosed herein, but should extend to equivalent alternatives of these features understood by those of ordinary skill in the relevant art. It should also be understood that the terms used herein are only for the purpose of describing specific embodiments and do not mean to limit.

[0189] In the description of the present invention, unless otherwise specified, the meaning of "a plurality of" is two or more; the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0190] In the description of the present invention, it should be noted that, unless otherwise clearly defined and limited, the terms "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0191] Certain terms are used throughout this application to refer to particular system components. As those skilled in the art will recognize, the same component may typically be referred to by different names, and thus this application is not intended to distinguish components that differ only in name and not in function. In this application, the terms "comprise", "include", and "have" are used in an open-ended fashion and should thus be interpreted to mean "including but not limited to...". Additionally, the terms "substantially", "essentially", or "approximately" as may be used herein refer to the industry-accepted tolerances for the corresponding terms. The term "coupled" as may be used herein includes direct coupling and indirect coupling via another component, element, circuit, or module, where for indirect coupling, the intervening component, element, circuit, or module does not change the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (such as where one element is inferred to be coupled to another element) includes both direct and indirect coupling between the two elements in the same manner as "coupled".

[0192] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures, or characteristics described in connection with the embodiment are included in at least one embodiment of the present invention. Thus, the phrases "one embodiment" or "embodiment" that appear throughout the specification do not necessarily all refer to the same embodiment.

[0193] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the present invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are chosen and described in order to better illustrate the principles of the present invention and its practical application, and to enable those of ordinary skill in the art to understand the present invention so as to design various embodiments with various modifications suitable for a particular purpose.

[0194] Although the embodiments disclosed in the present invention are as above, the content described is only an embodiment adopted for the convenience of understanding the present invention and is not intended to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains may make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed in the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A corrosion prediction method for CO2 transmission pipelines considering the synergistic effect between impurities, characterized in that, The method includes: Based on the working conditions of carbon dioxide pipeline transportation, establish an experimental database including a base database without impurities, a database with a single impurity, and a database with different impurity combinations; Based on the base database without impurities, process to obtain a basic corrosion rate model of the carbon dioxide transportation pipeline considering only the working condition parameters; For the case of a single impurity and different impurity combinations, combined with the experimental database, respectively process to obtain a single impurity corrosion influence model and an impurity synergistic interaction corrosion influence model; Integrate the basic corrosion rate model, the single impurity corrosion influence model, and the impurity synergistic interaction corrosion influence model to obtain an impurity-containing carbon dioxide corrosion prediction model.

2. The CO2 pipeline corrosion prediction method considering the synergistic effect between impurities according to claim 1, wherein Establish the base database without impurities through the following steps: Set the experimental conditions according to the actual pipeline transportation working conditions of carbon dioxide and determine the working condition parameters, where the working condition parameters include but are not limited to: transportation temperature, transportation pressure, carbon dioxide flow rate, experimental time, carbon dioxide water content; Under the experimental conditions, conduct experiments in a high-temperature and high-pressure autoclave, and calculate the corrosion rate of the carbon dioxide transportation pipeline through weight loss analysis after the experiment; Set different working condition parameters to conduct multiple experiments, covering gaseous, liquid, and supercritical states, and store the working condition parameters and the corresponding corrosion rate of the carbon dioxide transportation pipeline for each experiment to obtain the base database without impurities.

3. The CO2 pipeline corrosion prediction method considering the synergistic effect between impurities according to claim 2, wherein Establish the database with a single impurity through the following steps: For a single impurity, based on the base database without impurities, conduct multiple experiments by changing the content of the current impurity, covering gaseous, liquid, and supercritical states, and store the working condition parameters, impurity content, and the corresponding corrosion rate of the carbon dioxide transportation pipeline for each experiment to obtain the database with a single impurity including each impurity database.

4. The CO2 transportation pipeline corrosion prediction method considering the synergistic effect between impurities according to claim 2, wherein Establish the database with different impurity combinations through the following steps: For any two impurities, based on the base database without impurities, conduct multiple experiments by changing the content of the current two impurities, covering gaseous, liquid, and supercritical states, and store the working condition parameters, the content of the two impurities, and the corresponding corrosion rate of the carbon dioxide transportation pipeline for each experiment to obtain a database with two impurities; For any three impurities, based on the base database without impurities, conduct multiple experiments by changing the content of the current three impurities, covering gaseous, liquid, and supercritical states, and store the working condition parameters, the content of the three impurities, and the corresponding corrosion rate of the carbon dioxide transportation pipeline for each experiment to obtain a database with three impurities; For any four impurities, based on the base database without impurities, conduct multiple experiments by changing the content of the current four impurities, covering gaseous, liquid, and supercritical states, and store the working condition parameters, the content of the four impurities, and the corresponding corrosion rate of the carbon dioxide transportation pipeline for each experiment to obtain a database with four impurities; For any five kinds of impurities, on the basis of the impurity-free basic database, by changing the contents of the current five kinds of impurities, multiple experiments are carried out to cover gaseous, liquid, and supercritical states, and the operating parameters, the contents of the five kinds of impurities, and the corresponding corrosion rates of the carbon dioxide pipeline are stored during each experiment to obtain a database containing five kinds of impurities; And so on, until all combinations of impurities are completed, and all impurity databases are integrated to obtain the different impurity combination databases.

5. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities, as described in any one of claims 1-4, characterized in that, Impurities include but are not limited to: SO2, NO2, NO, H2S, O2.

6. A corrosion prediction method for a CO2 transportation pipeline considering the synergistic effect between impurities according to any one of claims 1-5, characterized in that, The experimental database is obtained through the following steps: For the case of incomplete data, fill it with data under similar experimental conditions; For the case of duplicate data, perform a deletion operation; For outliers, delete them after analysis by drawing box plots and clustering analysis methods.

7. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to any one of claims 2-6, characterized in that, The basic corrosion rate model is obtained through the following steps: Establish a multiple linear regression fitting equation, and obtain the coefficients of the multiple linear regression fitting equation by fitting the data in the impurity-free basic database to obtain the basic corrosion rate model.

8. A method for predicting corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to any one of claims 1-7, characterized in that, The basic corrosion rate model is: In the formula, V0 is the corrosion rate of the carbon dioxide pipeline considering only operating parameters; p is the transportation pressure, MPa; T is the transportation temperature, °C; t is the experimental time, d; v is the carbon dioxide flow rate, m / s; is the water content of carbon dioxide, ppm; a0, a1, a2, a3, a4, and a5 are coefficients.

9. A CO2 pipeline corrosion prediction method considering the synergistic effect between impurities according to any one of claims 1-8, characterized in that, The single-impurity corrosion influence model is obtained through the following steps: Compare the database containing a single impurity and the impurity-free basic database, and respectively analyze the trend of the corrosion influence degree changing with the impurity content when each impurity exists alone according to the corrosion rate of the carbon dioxide pipeline without impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the content of the single impurity, to obtain the single-impurity corrosion influence model.

10. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to any one of claims 1-9, characterized in that The single-impurity corrosion influence model is: where b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is contained and the impurity content is C i ; C i is the content of the i-th impurity; V 1，i is the corrosion rate of the i-th impurity when the impurity content is C i ; V0 is the corrosion rate of the carbon dioxide pipeline when only considering the operating conditions.

11. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to any one of claims 4-10, characterized in that, The synergistic interaction corrosion influence model between impurities is obtained through the following steps: For the case where two impurities exist simultaneously, compare the database of the two impurities and the database containing a single impurity, and analyze the synergistic interaction trend of the two impurities on corrosion according to the corrosion rate of the carbon dioxide pipeline with two impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the contents of the two impurities, to obtain the corrosion influence model of the two impurities; For the case where three impurities exist simultaneously, compare the database of the three impurities and the database containing a single impurity, and analyze the synergistic interaction trend of the three impurities on corrosion according to the corrosion rate of the carbon dioxide pipeline with three impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the contents of the three impurities, to obtain the corrosion influence model of the three impurities; For the case where four impurities exist simultaneously, compare the database of the four impurities and the database containing a single impurity, and analyze the synergistic interaction trend of the four impurities on corrosion according to the corrosion rate of the carbon dioxide pipeline with four impurities, the corrosion rate of the carbon dioxide pipeline with a single impurity, and the contents of the four impurities, to obtain the corrosion influence model of the four impurities; For the case where five impurities coexist, compare the five-impurity database and the single-impurity-containing database. According to the corrosion rate of the carbon dioxide pipeline when five impurities are present, the corrosion rate of the carbon dioxide pipeline when a single impurity is present, and the contents of the five impurities, analyze the trend of the synergistic interaction of the five impurities on corrosion when the five impurities coexist, and obtain a corrosion influence model for the five impurities; And so on, until corrosion influence models for all impurity combinations are obtained to obtain the synergistic interaction corrosion influence model between the impurities.

12. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to claim 11, characterized in that The corrosion influence model for the two impurities is: Where γ2 is the coefficient of synergistic interaction on corrosion between the i-th and j-th impurities; C i is the content of the i-th impurity; C j is the content of the j-th impurity; V 2,(i,j) is the corrosion rate when the content of the i-th impurity is C i and the content of the j-th impurity is C j ; V0 is the corrosion rate of the carbon dioxide pipeline considering only the operating parameters; b i is the influence degree coefficient of the corrosion rate when only the i-th impurity is contained at the impurity content of C i ; b j is the influence degree coefficient of the corrosion rate when only the j-th impurity is contained at the impurity content of C j . The corrosion influence model for the three impurities is: where γ3 is the coefficient of the synergistic interaction on corrosion among the i-th, j-th, and k-th impurities; C k is the content of the k-th impurity; V 3,(i,j,k) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , and the content of the k-th impurity is C k ; b k is the influence degree coefficient on the corrosion rate when only the k-th impurity is present at the impurity content of C k . The corrosion influence model for the four impurities is: In the formula, γ4 is the coefficient of the synergistic interaction on corrosion among the i-th, j-th, k-th, and l-th impurities; C l is the content of the l-th impurity; V 4,(i,j,k,l) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , the content of the k-th impurity is C k , and the content of the l-th impurity is C l ; b l is the influence degree coefficient of the corrosion rate when only the l-th impurity is contained and the impurity content is C l . The corrosion influence model for the five impurities is: In the formula, γ5 is the coefficient of the synergistic interaction on corrosion among the i-th, j-th, k-th, l-th, and m-th impurities; C m is the content of the m-th impurity; V 5,(i,j,k,l,m) is the corrosion rate when the content of the i-th impurity is C i , the content of the j-th impurity is C j , the content of the k-th impurity is C k , the content of the l-th impurity is C l , and the content of the m-th impurity is C m ; b m is the influence degree coefficient on the corrosion rate when only the m-th impurity is contained and the impurity content is C m .

13. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to claim 11 or 12, characterized in that, The synergistic interaction corrosion influence model between the impurities is: In the formula, γ n is the coefficient of the synergistic interaction of corrosion among n kinds of impurities; V0 is the corrosion rate of the carbon dioxide pipeline only considering the working conditions parameters; b i is the influence degree coefficient of the corrosion rate when only the i-th kind of impurity is contained at the impurity content of C i ​ 14. A method for predicting the corrosion of a CO2 transportation pipeline considering the synergistic effect between impurities according to any one of claims 1-13, characterized in that, The corrosion prediction model for the carbon dioxide containing impurities is: In the formula, V corr is the corrosion prediction rate of carbon dioxide containing impurities; p is the transportation pressure, MPa; T is the transportation temperature, °C; t is the experimental time, which is the transportation time in actual transportation conditions, d; v is the carbon dioxide flow rate, m / s; is the water content of carbon dioxide, ppm; b i is the influence degree coefficient on the corrosion rate when only the i-th impurity is contained and the impurity content is C i ; γ n is the synergistic interaction coefficient between n impurities on corrosion.

15. A storage medium, characterized in that, It includes a series of instructions for performing the method steps described in any one of claims 1-14.

16. A CO2 transportation pipeline corrosion prediction device considering the synergistic effect between impurities, characterized in that, Execute the method described in any one of claims 1-14, and the device includes: An experimental database module that establishes an experimental database including a base database without impurities, a single-impurity-containing database, and a database of different impurity combinations according to the operating conditions of the carbon dioxide pipeline; A base model module that processes the base database without impurities to obtain a basic model of the corrosion rate of the carbon dioxide pipeline considering only the operating condition parameters; An influence model module that, for the case of a single impurity and different impurity combinations, combines the experimental database and processes to obtain a single-impurity corrosion influence model and a synergistic interaction corrosion influence model between the impurities respectively; A corrosion prediction module that comprehensively combines the basic corrosion rate model, the single-impurity corrosion influence model, and the synergistic interaction corrosion influence model between the impurities to obtain a corrosion prediction model for the carbon dioxide containing impurities.

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