Method, device, medium and product for quantitatively evaluating risk of pitting damage of pressure equipment

Abstract

The application discloses a kind of pressure-bearing equipment pitting damage risk quantitative evaluation method, equipment, medium and product, is relevant to petrochemical chemical pressure-bearing equipment safety field, which comprises: determining the damage form of equipment;According to the damage form, calculate local / uniform thinning factor;According to the local / uniform thinning factor, general condition factor, mechanical factor and process factor, the equipment correction coefficient is calculated;According to the equipment correction coefficient, management system evaluation coefficient, similar equipment average failure probability and over-standard defect influence coefficient, the pitting pit corrosion failure possibility is calculated;Leakage consequence area is calculated;According to the leakage consequence area, determine pitting pit corrosion failure consequence;According to the pitting pit corrosion failure possibility and the pitting pit corrosion failure consequence, establish two-dimensional risk matrix diagram;The two-dimensional risk matrix diagram is used for pitting pit corrosion risk real-time quantitative evaluation.The application can effectively guide petrochemical industry pitting pit corrosion risk prevention and control.

Description

Technical Field

[0001] This invention relates to the field of safety of pressure-bearing equipment in petrochemical and chemical industries, and in particular to a method, equipment, medium, and product for quantitatively assessing the risk of pitting damage to pressure-bearing equipment. Background Technology

[0002] Pitting corrosion is a common corrosion mechanism in pressure-bearing equipment in petrochemical and chlor-alkali chemical plants. For example, in chlor-alkali chemical plants, anodic electrolytic cells, (desalinated) brine pipelines, and chlorine heat exchangers are prone to pitting corrosion caused by crevice corrosion. In petrochemical plants, 300 series and 400 series stainless steel are susceptible to pitting corrosion caused by the presence of corrosive ions such as chloride ions, and may even develop penetrating pits. The location and morphology of local damage caused by pitting corrosion are uncertain and dispersed, and the rate of pitting corrosion propagation is difficult to measure quantitatively, making it difficult to quantitatively assess the damage risk of pitting corrosion.

[0003] Domestic and international methods for evaluating pitting corrosion damage, such as those in GB / T 18590-2001 and ASTM G46-94 (2018), can quantitatively obtain information on the degree of pitting corrosion damage and metal damage sensitivity through standard graphical methods, statistical methods, and mechanical property verification tests, thereby predicting material life. However, a single evaluation method is insufficient to accurately describe the condition of pitting. Using multiple methods in combination increases the difficulty and workload of evaluation. Furthermore, there is no clear and unified evaluation standard for the quantitative grading of pitting corrosion damage, and no information on the pitting corrosion propagation rate can be provided. Therefore, it is impossible to quantitatively assess the risk of pitting corrosion damage. Summary of the Invention

[0004] The purpose of this invention is to provide a method, equipment, medium, and product for quantitatively assessing the risk of pitting corrosion damage in pressure equipment, which can effectively guide the prevention and control of pitting corrosion risks in the petrochemical industry.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] In a first aspect, the present invention provides a method for quantitatively assessing the risk of pitting damage to pressure-bearing equipment, the method comprising:

[0007] Determine the damage type of the equipment; the damage type includes: uniform damage and localized damage.

[0008] Calculate the local / uniform thinning factor based on the damage type.

[0009] The equipment correction coefficient is calculated based on the local / uniform thinning factor, general condition factor, mechanical factor, and process factor; the general condition factor, mechanical factor, and process factor are obtained according to API 581-2016 or GB / T 26610.4-2022 standards.

[0010] The probability of pitting corrosion failure is calculated based on the equipment correction coefficient, management system evaluation coefficient, average failure probability of similar equipment, and influence coefficient of defects exceeding the standard. The management system evaluation coefficient, average failure probability of similar equipment, and influence coefficient of defects exceeding the standard are obtained according to API 581-2016 or GB / T26610.4-2022 standards.

[0011] Calculate the leakage consequence area; the leakage consequence area includes: the failure consequence area of ​​static data and the failure consequence area of ​​dynamic data.

[0012] Based on the area of ​​the leakage consequences, the pitting corrosion failure consequences are determined; the pitting corrosion failure consequences include: quantitative assessment of failure consequences based on static data and quantitative assessment of failure consequences based on dynamic data.

[0013] A two-dimensional risk matrix is ​​established based on the probability of pitting corrosion failure and the consequences of pitting corrosion failure; the two-dimensional risk matrix is ​​used for real-time quantitative assessment of pitting corrosion risk.

[0014] Optionally, determining the damage mode of the device specifically includes:

[0015] The damage stages of the equipment are determined; the damage stages include: pitting corrosion initiation stage, pitting corrosion development stage, and pitting corrosion expansion stage.

[0016] Based on the damage stage, the damage type of the equipment is determined.

[0017] Optionally, when the damage type is uniform damage, a local / uniform thinning factor is calculated based on the damage type, specifically including:

[0018] Determine the uniform thinning factor according to API 581-2016 or GB / T 26610.4-2022 standards.

[0019] Optionally, when the damage type is localized, a local / uniform thinning factor is calculated based on the damage type, specifically including:

[0020] Based on the pitting assessment method and the usability evaluation method, the critical wall thickness value for failure was calculated and the maximum value of the critical wall thickness value for failure was recorded.

[0021] The strength correction factor is determined based on the pitting corrosion local damage propagation rate prediction model, the pitting corrosion thinning severity index, and the maximum value of the critical wall thickness for failure. The pitting corrosion local damage propagation rate prediction model is used to predict the propagation rate of local damage morphology over time. The pitting corrosion thinning severity index is calculated based on time, pitting corrosion rate, and thickness data.

[0022] Based on the intensity correction factor, the local thinning factor is determined.

[0023] Optionally, when the leakage consequence area is the failure consequence area of ​​static data, the pitting corrosion failure consequence is determined based on the leakage consequence area, specifically including:

[0024] The quantitative assessment of failure consequences of static data is calculated according to API 581-2016 or GB / T 26610.5-2022 standards.

[0025] Optionally, when the leakage consequence area is the failure consequence area of ​​dynamic data, the pitting corrosion failure consequence is determined based on the leakage consequence area, specifically including:

[0026] The evolution of leak hole size over time was deduced based on the prediction model of the local damage propagation rate of pitting corrosion.

[0027] Based on the aforementioned evolutionary pattern, the size of the leak area can be predicted in real time.

[0028] Based on the real-time predicted leakage area, a quantitative assessment of the failure consequences of dynamic data is calculated.

[0029] Optionally, the expression for the prediction model of the local damage propagation rate of pitting corrosion is:

[0030]

[0031] Where Δx is the circumferential expansion rate of the pitting corrosion on the metal surface, i.e., the expansion rate of the pit radius, mm / a; Δy is the local damage expansion rate of the pitting corrosion in the direction perpendicular to the metal surface, i.e., the expansion rate of the pit depth, mm / a; c is the concentration of corrosive ions, mol / L; T is the test temperature, ℃; pH is the acidity or alkalinity of the corrosive solution; f(·) is the pitting corrosion damage rate function.

[0032] In a second aspect, the present invention provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for quantitative assessment of pitting damage risk of pressure equipment as described above.

[0033] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for quantitatively assessing the pitting damage risk of pressure equipment as described above.

[0034] Fourthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method for quantitatively assessing the pitting damage risk of pressure equipment as described above.

[0035] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0036] This invention provides a method, equipment, medium, and product for quantitatively assessing the risk of pitting corrosion damage in pressure equipment. By determining the damage form of the equipment, a local thinning factor or a uniform thinning factor can be calculated. Based on the local or uniform thinning factor, general condition factors, mechanical factors, and process factors, an equipment correction coefficient can be calculated. Furthermore, based on the equipment correction coefficient, management system evaluation coefficient, average failure probability of similar equipment, and the influence coefficient of excessive defects, the probability of pitting corrosion failure can be calculated. Additionally, based on the calculated leakage consequence area, the consequences of pitting corrosion failure can be determined. Finally, based on the probability and consequences of pitting corrosion failure, a two-dimensional risk matrix is ​​established, enabling real-time quantitative assessment of pitting corrosion risk. This invention quantitatively assesses both the probability and consequences of pitting corrosion causing equipment failure, proposing a method that considers the local damage rate, state expansion, and quantitative assessment of failure risk. It solves the problem of difficulty in quantitatively assessing and extrapolating the risk of pitting corrosion damage in corrosive environments, providing support for setting safety boundaries and preventing risks associated with pitting corrosion damage in pressure equipment. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an application environment diagram of a method for quantitatively assessing the pitting damage risk of pressure equipment according to an embodiment of the present invention.

[0039] Figure 2 This is a flowchart illustrating a method for quantitatively assessing the pitting damage risk of pressure equipment, as provided in an embodiment of the present invention.

[0040] Figure 3This is a schematic diagram of a method for quantitatively assessing the risk of pitting corrosion damage failure in petrochemical and chemical pressure equipment, provided in an embodiment of the present invention.

[0041] Figure 4 This is a diagram showing the division of pitting corrosion areas on the sample surface according to an embodiment of the present invention.

[0042] Figure 5 This is an unbalanced matrix diagram provided in one embodiment of the present invention.

[0043] Figure 6 This is a balance matrix diagram provided in one embodiment of the present invention.

[0044] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] This invention is the first to propose a method for quantitatively assessing the local damage rate, state propagation, and failure risk of pitting and cratering in pressure vessels and industrial pipelines, specifically targeting the petrochemical industry. This method addresses the challenge of quantifying and extrapolating the damage risk of pitting and cratering in corrosive environments, and provides support for setting safety boundaries and preventing risks associated with pitting and cratering damage in pressure equipment.

[0048] The method for quantitatively assessing the risk of pitting damage in pressure-bearing equipment provided in this invention can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the damage form of the device to server 104. After receiving the damage form, server 104 calculates a local / uniform thinning factor based on the damage form; and calculates a device correction coefficient based on the local / uniform thinning factor, general condition factor, mechanical factor, and process factor; the general condition factor, mechanical factor, and process factor are obtained according to API 581-2016 or GB / T26610.4-2022 standards; and calculates the pitting corrosion failure probability based on the device correction coefficient, management system evaluation coefficient, average failure probability of similar equipment, and excess defect influence coefficient; the management system evaluation coefficient, average failure probability of similar equipment, and excess defect influence coefficient are obtained according to API 581-2016 or GB / T The method is based on standard 26610.4-2022; the leakage consequence area is calculated; the leakage consequence area includes: the failure consequence area of ​​static data and the failure consequence area of ​​dynamic data; based on the leakage consequence area, the pitting corrosion failure consequence is determined; the pitting corrosion failure consequence includes: quantitative assessment of failure consequence of static data and quantitative assessment of failure consequence of dynamic data; based on the pitting corrosion failure probability and the pitting corrosion failure consequence, a two-dimensional risk matrix is ​​established; the two-dimensional risk matrix is ​​used for real-time quantitative assessment of pitting corrosion risk. Server 104 can feed back the obtained two-dimensional risk matrix to terminal 102. Furthermore, in some embodiments, the method for quantitatively assessing the risk of pitting corrosion damage to pressure equipment can also be implemented separately by server 104 or terminal 102. For example, terminal 102 can directly perform risk quantitative assessment of the damage form of the equipment, or server 104 can obtain the damage form of the equipment from the data storage system and perform risk quantitative assessment of the damage form of the equipment.

[0049] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones and tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0050] In one exemplary embodiment, such as Figure 2As shown, a method for quantitatively assessing the risk of pitting corrosion damage in pressure-bearing equipment is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment of the invention, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S1 to S7. Wherein:

[0051] S1: Determine the damage type of the equipment; the damage type includes: uniform damage and local damage.

[0052] S2: Calculate the local / uniform thinning factor based on the damage type.

[0053] S3: Calculate the equipment correction coefficient based on the local / uniform thinning factor, general condition factor, mechanical factor, and process factor; the general condition factor, mechanical factor, and process factor are obtained according to API 581-2016 or GB / T26610.4-2022 standards.

[0054] S4: The probability of pitting corrosion failure is calculated based on the equipment correction coefficient, management system evaluation coefficient, average failure probability of similar equipment, and influence coefficient of defects exceeding the standard; the management system evaluation coefficient, average failure probability of similar equipment, and influence coefficient of defects exceeding the standard are obtained according to API 581-2016 or GB / T26610.4-2022 standards.

[0055] S5: Calculate the leakage consequence area; the leakage consequence area includes: the failure consequence area of ​​static data and the failure consequence area of ​​dynamic data.

[0056] S6: Determine the pitting corrosion failure consequences based on the area of ​​the leakage consequences; the pitting corrosion failure consequences include: quantitative assessment of failure consequences based on static data and quantitative assessment of failure consequences based on dynamic data.

[0057] S7: Based on the probability of pitting corrosion failure and the consequences of pitting corrosion failure, establish a two-dimensional risk matrix diagram; the two-dimensional risk matrix diagram is used for real-time quantitative assessment of pitting corrosion risk.

[0058] By implementing steps S1 to S7 above, this invention quantifies the possibility and consequences of pitting corrosion causing equipment failure. It proposes a method that considers the local damage rate, state expansion, and failure risk quantification of pitting corrosion, solving the problem of difficulty in quantifying and extrapolating the risk of pitting corrosion damage in corrosive environments. This provides support for setting safety boundaries and risk prevention and control of pitting corrosion damage in pressure equipment.

[0059] In another exemplary embodiment of the present invention, step S1 specifically includes:

[0060] S101: Determine the damage stage of the equipment; the damage stage includes: pitting corrosion initiation stage, pitting corrosion development stage, and pitting corrosion expansion stage.

[0061] S102: Determine the damage type of the equipment based on the damage stage.

[0062] like Figure 3 As shown, this invention quantitatively assesses the likelihood and consequences of pitting corrosion causing equipment failure. A two-dimensional risk matrix is ​​established based on the likelihood and consequences, classifying risk levels as high, medium-high, medium, and low. Regarding the likelihood of pitting corrosion failure, the dominant mechanism needs to be identified, boundaries identified, and demarcated. A new concept of pitting corrosion set is proposed. For the accumulation area of ​​pitting corrosion per unit area, a circular region containing the pitting corrosion accumulation area is drawn. A certain number of pitting corrosion points on the "cross" lines along the inner diameter of the circle are considered areas of severe local damage (see...). Figure 4 This paper proposes a new method to evaluate the severity of corrosion by dividing the number of pits within a pitting set by the size of the circular area, i.e., the density of pitting corrosion. A new engineering prediction model for pitting corrosion rate and a prediction model for pitting set expansion rate (a local damage expansion rate prediction model for pitting corrosion) are proposed to predict the location of maximum local corrosion and the rate of damage morphology expansion, respectively, thus extrapolating the likelihood of pitting corrosion failure within future operating cycles. Regarding the consequences of pitting corrosion failure, different leakage models are selected based on the type of medium. The calculation of the leakage consequence area based on static data requires theoretical calculation in conjunction with the failure frequency and leakage model. The calculation of the leakage consequence area based on dynamic data requires calculation in conjunction with the pitting corrosion set expansion rate and leakage rate prediction model. The magnitude of the consequences of pitting corrosion failure is quantitatively assessed by calculating the medium area consequences (leakage toxicity consequences, combustion and explosion consequences), economic consequences, etc., after failure.

[0063] The expression for the engineering prediction model of pitting corrosion rate is as follows:

[0064] r = f(c,T,pH) (1);

[0065] In the formula, r is the pitting corrosion rate (mm / a); c is the concentration of corrosive ions (mol / L); T is the test temperature (°C); pH is the acidity or alkalinity of the corrosive solution; and f(·) is the pitting corrosion damage rate function.

[0066] The expression for the prediction model of the local damage propagation rate of pitting corrosion is:

[0067]

[0068] Wherein, Δx is the circumferential expansion rate of the pitting corrosion on the metal surface, i.e., the expansion rate of the pit radius, mm / a; Δy is the local damage expansion rate of the pitting corrosion in the direction perpendicular to the metal surface, i.e., the expansion rate of the pit depth, mm / a; c is the concentration of corrosive ions, mol / L; T is the test temperature, ℃; pH is the acidity or alkalinity of the corrosive solution; f(·) is the pitting corrosion damage rate function; Δx and Δy need to be calibrated experimentally according to the material.

[0069] In another exemplary embodiment of the present invention, when the damage is uniform, step S2 specifically includes:

[0070] Determine the uniform thinning factor according to API 581-2016 or GB / T 26610.4-2022 standards.

[0071] In another exemplary embodiment of the present invention, when the damage is localized, step S2 specifically includes:

[0072] A1: Based on the pitting assessment method GB 19624-2004 and the applicable evaluation method GB 35013-2018 or APIRP 579-1-2021, calculate the critical wall thickness value for failure and record the maximum value of the critical wall thickness value for failure.

[0073] A2: Determine the strength correction factor based on the pitting corrosion local damage propagation rate prediction model, the pitting corrosion thinning severity index, and the maximum value of the critical wall thickness for failure; the pitting corrosion local damage propagation rate prediction model is used to predict the local damage morphology propagation rate over time; the pitting corrosion thinning severity index is calculated based on time, pitting corrosion rate, and thickness data.

[0074] A3: Determine the local thinning factor based on the intensity correction factor.

[0075] In another exemplary embodiment of the present invention, when the leakage consequence area is the failure consequence area of ​​static data, step S6 specifically includes:

[0076] The quantitative assessment of failure consequences of static data is calculated according to API 581-2016 or GB / T 26610.5-2022 standards.

[0077] In another exemplary embodiment of the present invention, when the leakage consequence area is the failure consequence area of ​​dynamic data, step S6 specifically includes:

[0078] B1: Based on the prediction model of the local damage propagation rate of pitting corrosion, the evolution law of leakage hole size over time is deduced.

[0079] B2: Based on the aforementioned evolutionary pattern, determine the real-time predicted leakage area size.

[0080] B3: Based on the real-time predicted leakage area, a quantitative assessment of the failure consequences of dynamic data is calculated.

[0081] The present invention provides a method for quantitatively assessing the risk of pitting corrosion damage in pressure equipment, which is divided into two categories: static assessment based on historical detection data and dynamic assessment based on real-time data. For static risk quantification assessment, the input data consists of valid data under stable conditions with no adjustments to the process and equipment during a maintenance period. Input parameters include at least the original wall thickness, wall thickness detection data from the last shutdown, type and concentration of the corrosive medium, temperature, pressure, and material. For dynamic risk quantification assessment, the input data consists of valid real-time monitoring data within 1 to 24 hours and structural data remaining unchanged during a single maintenance period. Input parameters include at least the original wall thickness, real-time wall thickness detection data, type and concentration of the corrosive medium, temperature, pressure, material, and dimensional parameters.

[0082] In another exemplary embodiment of the present invention, the probability of pitting corrosion failure is obtained by multiplying the management system evaluation coefficient, the average failure probability of similar equipment, the equipment correction coefficient, and the influence coefficient of out-of-standard defects, with a magnitude of 1, 2, 3, 4, and 5. The equipment correction coefficient comprises four factors: a local / uniform thinning factor, a general condition factor, a mechanical factor, and a process factor, and the magnitude of the equipment correction coefficient is obtained by multiplying these four factors. The calculation methods for the management system evaluation coefficient, the average failure probability of similar equipment, the influence coefficient of out-of-standard defects, the general condition factor, the mechanical factor, and the process factor can directly refer to API 581-2016 or GB / T 26610.4-2022 standards, while the calculation model for the local / uniform thinning factor needs to be selected based on the damage form. The pitting corrosion thinning severity index ar / t (calculated using time a, pitting corrosion rate r, and thickness data t) is introduced to describe the wall thickness loss rate caused by thinning. Static risk assessment of pitting corrosion can be roughly calculated using historical wall thickness detection data, or it can be predicted based on historical data and pitting corrosion prediction models; while dynamic risk assessment requires calculation based on real-time monitoring data and combined with pitting corrosion prediction models.

[0083] For pitting corrosion systems in petrochemical and chemical industries, an engineering prediction model for pitting corrosion rate based on a large number of field experiments is adopted to improve the prediction accuracy. The input parameters of this model are all parameters directly collected from engineering, including at least the type and concentration of corrosive media, temperature, material and size parameters.

[0084] When calculating the local thinning factor based on the engineering prediction model of pitting corrosion thinning rate, a strength correction factor needs to be introduced to consider the failure boundary. The recommended calculation method is a combination of the pitting assessment standard GB 19624-2004 and the applicable evaluation method GB35013-2018 or API RP 579-1-2021, taking the maximum value of the critical wall thickness calculated by both. For the evolution of the local thinning factor over time, a pitting corrosion local damage propagation rate prediction model is introduced to extrapolate the severity exponential change of pitting corrosion thinning in real time. This, combined with strength theory calculation methods, extrapolates the nonlinear change law of the local thinning factor, thus more effectively predicting damage change patterns. The strength correction factor is determined by calculating theoretical values ​​or using extensive industrial testing data based on the pitting assessment standard GB 19624-2004 and the applicable evaluation method GB35013-2018 or API RP 579-1-2021. This strength correction factor is then used to correct the local thinning factor value.

[0085] In another exemplary embodiment of the present invention, the magnitude of pitting corrosion failure consequences is determined by calculating the leakage consequence area to predict post-failure toxicity hazards, combustion and explosion hazards, environmental cleanup costs, and economic losses. The magnitude is categorized into five levels: A, B, C, D, and E. For the quantitative assessment of failure consequences based on static data, calculations can be performed directly with reference to API 581-2016 or GB / T26610.5-2022 standards. For the calculation of dynamic failure consequence area, the evolution of leak hole size over time needs to be deduced using a pitting corrosion set expansion rate model. Different leakage models are selected based on the medium type to calculate the leakage rate in real time. The real-time leakage rate is used as input to the diffusion model to predict the leakage area size in real time. The real-time leakage area size serves as input for the dynamic assessment of post-failure area consequences (toxicity consequences, combustion and explosion consequences), economic consequences, etc., thereby achieving dynamic quantitative assessment of pitting corrosion failure consequences. The economic consequences can be calculated by referring to the economic consequences algorithm in API 581-2016 or GB / T26610.5-2022 standards to account for a series of economic losses caused by equipment failure. The economic consequences are classified into levels based on the local socio-economic level at the time and the average annual profit of the factory in recent years.

[0086] The risk of pitting corrosion failure can be represented by a two-dimensional risk matrix diagram, with the vertical axis representing the probability level of pitting corrosion failure and the horizontal axis representing the consequence level of pitting corrosion failure. This risk matrix diagram can be used to classify risks by selecting non-equilibrium matrix diagrams and balanced matrix diagrams respectively, referring to GB / T26610.1-2022 or API 581-2016 standards, dividing the risk of pitting corrosion into four levels: high risk, medium-high risk, medium risk, and low risk.

[0087] This invention solves the problem of difficulty in quantifying and extrapolating the risk of pitting corrosion damage in corrosive environments by providing a quantitative assessment method for the risk of pitting corrosion failure in petrochemical and chemical pressure equipment. It also provides support for setting safety boundaries and preventing risks associated with pitting corrosion damage in pressure equipment.

[0088] This invention also provides an application scenario in which the aforementioned method for quantitatively assessing the risk of pitting corrosion damage in pressure equipment is applied. Specifically, the method for quantitatively assessing the risk of pitting corrosion damage in pressure equipment provided in this embodiment can be applied to the quantitative assessment of risks in petrochemical and chemical pressure equipment. The quantitative assessment scenario for petrochemical and chemical pressure equipment includes a damage form determination step, a pitting corrosion failure probability calculation step, a pitting corrosion failure consequence calculation step, and a quantitative assessment step. After determining the damage form of the equipment, a thinning factor is calculated based on the damage form, and then an equipment correction coefficient can be calculated based on the thinning factor. Based on the equipment correction coefficient, management system evaluation coefficient, average failure probability of similar equipment, and influence coefficient of excessive defects, the probability of pitting corrosion failure can be calculated. In addition, by calculating the leakage consequence area, the consequences of pitting corrosion failure can be determined. Finally, a quantitative assessment can be achieved based on the probability of pitting corrosion failure and the consequences of pitting corrosion failure.

[0089] In one exemplary embodiment, this implementation case focuses on the anode brine pipeline of a chlor-alkali chemical plant. Based on real-time monitoring data, a quantitative assessment of pitting corrosion failure risk is conducted. The material is TA2 titanium, and the medium is a sodium chloride solution containing moist chlorine gas (the main medium being Cl2 and Cl). - ,ClO - ,ClO 3- Na + (etc.), of which Cl - The concentration is 190-220 g / L, the pH is 2-2.5; the chlorine concentration is saturated, and the temperature is 80-90℃.

[0090] Based on previous experimental research and literature data, the corrosion temperature exceeds the critical temperature by approximately 70°C. Under this service environment, crevice corrosion leads to pitting corrosion, which continuously expands to form larger corrosion pits, resulting in pitting erosion. The damage form is localized thinning. For the accumulation area of ​​pitting erosion per unit area, a circular area containing the pitting erosion accumulation area is drawn. Areas with a certain number of pits (e.g., more than 10) along the "cross" direction of the inner diameter of the circle are considered to be areas of severe localized damage (see...). Figure 4The number of pits within different pitting corrosion sets is calculated by dividing by the area of ​​the divided circles. The density of pitting corrosion is selected to evaluate the severity of corrosion. An engineering prediction model for pitting corrosion rate is selected (see Equation 1). Based on this prediction model and actual monitoring data, the damage rate is predicted in real time, and the severity index of pitting corrosion thinning is calculated. The local pitting corrosion thinning factor is determined by considering the effectiveness of the detection and the number of detections for the highest category of effectiveness. The ratio of the intended service time Tn to the remaining service life TSL is determined by calculating theoretical values ​​or a large amount of industrial testing data based on the pitting assessment standard GB 19624-2004 and the evaluation method for suitability for use GB 35013-2018. The strength correction factor (1-500) is determined according to the ratio of the intended service time to the remaining service life, and this value is used to correct the local thinning factor. The remaining life needs to be estimated based on the prediction model of the local damage propagation rate of pitting corrosion (see Equation 2) to predict the future morphological evolution and the change of the pitting corrosion thinning severity index, and based on the evaluation method GB 35013-2018 or mature engineering experience.

[0091] Based on the local pitting corrosion thinning factor, and combined with the calculation methods for general condition factors, mechanical factors, and process factors in the GB / T 26610.1-2022 standard, the equipment correction coefficient is calculated. Then, the GB / T26610.1-2022 standard method is further used to consider the influence of management system evaluation coefficient, average failure probability of similar equipment, equipment correction coefficient, and excess defect influence coefficient, thereby predicting the possibility of pitting corrosion failure, with an assessment level of 3.

[0092] Based on the type and quantity of hazardous media, combined with monitoring data, the pitting corrosion set propagation rate model is used to predict the equivalent diameter of the hole at failure in real time. The small hole Gaussian leakage rate model or the modified Gaussian leakage rate model is selected to calculate the current leakage area. The GB / T 26610.1-2022 standard method is used to evaluate the area consequences and economic consequences after the damage leakage, thereby predicting the current corrosion failure consequences. The evaluation level is C.

[0093] Based on the probability and severity of pitting corrosion damage failure in real time, a two-dimensional non-equilibrium matrix diagram is plotted, such as... Figure 5 As shown, the vertical axis represents the probability of pitting corrosion failure, and the horizontal axis represents the consequences of pitting corrosion failure; based on the standard type, non-equilibrium matrix diagrams and equilibrium matrix diagrams are selected as follows: Figure 6 As shown, a real-time quantitative assessment of pitting corrosion risk was conducted, and the risk level was determined to be medium risk.

[0094] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores the damage patterns of the device. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for quantitatively assessing the pitting damage risk of pressure-bearing equipment.

[0095] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0096] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.

[0097] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0098] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0100] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided by this 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0101] The databases involved in the various embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the various embodiments provided by this invention may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0102] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for quantitatively evaluating the risk of pitting corrosion damage of a pressure-bearing equipment, characterized by, The method comprises the following steps: determining the damage form of the equipment; the damage form includes uniform damage and local damage; calculating the local / uniform thinning factor according to the damage form; calculating the equipment correction coefficient according to the local / uniform thinning factor, the general condition factor, the mechanical factor and the process factor; the general condition factor, the mechanical factor and the process factor are obtained according to the API 581-2016 or GB / T 26610.4-2022 standard; calculating the pitting corrosion failure possibility according to the equipment correction coefficient, the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of exceeding defect; the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of exceeding defect are obtained according to the API 581-2016 or GB / T 26610.4-2022 standard; calculating the leakage consequence area; the leakage consequence area includes the failure consequence area of static data and the failure consequence area of dynamic data; determining the pitting corrosion failure consequence according to the leakage consequence area; the pitting corrosion failure consequence includes the quantitative evaluation of failure consequence of static data and the quantitative evaluation of failure consequence of dynamic data; establishing a two-dimensional risk matrix diagram according to the pitting corrosion failure possibility and the pitting corrosion failure consequence; the two-dimensional risk matrix diagram is used for real-time quantitative evaluation of pitting corrosion risk; when the damage form is uniform damage, calculating the local / uniform thinning factor according to the damage form, specifically including: determining the uniform thinning factor according to the API 581-2016 or GB / T 26610.4-2022 standard; when the damage form is local damage, calculating the local / uniform thinning factor according to the damage form, specifically including: calculating the failure critical wall thickness value respectively based on the pit evaluation method and the as-used evaluation method, and recording the maximum value of the failure critical wall thickness value; determining the strength correction factor according to the pitting corrosion local damage expansion rate prediction model, the pitting corrosion thinning severity index and the maximum value of the failure critical wall thickness value; the pitting corrosion local damage expansion rate prediction model is used to predict the expansion rate of local damage morphology with time; the pitting corrosion thinning severity index is calculated according to time, pitting corrosion rate and thickness data; determining the local thinning factor according to the strength correction factor; the expression of the pitting corrosion local damage expansion rate prediction model is: where Δx is the pitting set expansion rate in the circumferential direction of the metal surface, i.e. the pit radius expansion rate, mm / a; Δy is the local damage expansion rate in the direction perpendicular to the metal surface, i.e. the pit depth expansion rate, mm / a; c is the concentration of erosive ions, mol / L; T is the test temperature, ℃; pH is the acidity and alkalinity of the corrosive solution; f(·) is the pitting corrosion damage rate function.

2. The method for quantitatively evaluating a risk of a pitting corrosion damage of a pressure-bearing equipment according to claim 1, characterized by, The method comprises the following steps: determining the damage form of the equipment; the damage form includes uniform damage and local damage; calculating the local / uniform thinning factor according to the damage form; calculating the equipment correction coefficient according to the local / uniform thinning factor, the general condition factor, the mechanical factor and the process factor; the general condition factor, the mechanical factor and the process factor are obtained according to the API 581-2016 or GB / T 26610.4-2022 standard; calculating the pitting corrosion failure possibility according to the equipment correction coefficient, the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of exceeding defect; the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of exceeding defect are obtained according to the API 581-2016 or GB / T 26610.4-2022 standard; calculating the leakage consequence area; the leakage consequence area includes the failure consequence area of static data and the failure consequence area of dynamic data; determining the pitting corrosion failure consequence according to the leakage consequence area; the pitting corrosion failure consequence includes the quantitative evaluation of failure consequence of static data and the quantitative evaluation of failure consequence of dynamic data; establishing a two-dimensional risk matrix diagram according to the pitting corrosion failure possibility and the pitting corrosion failure consequence; the two-dimensional risk matrix diagram is used for real-time quantitative evaluation of pitting corrosion risk; when the damage form is uniform damage, calculating the local / uniform thinning factor according to the damage form, specifically including: determining the uniform thinning factor according to the API 581-2016 or GB / T 26610.4-2022 standard; when the damage form is local damage, calculating the local / uniform thinning factor according to the damage form, specifically including: calculating the failure critical wall thickness value respectively based on the pit evaluation method and the as-used evaluation method, and recording the maximum value of the failure critical wall thickness value; determining the strength correction factor according to the pitting corrosion local damage expansion rate prediction model, the pitting corrosion thinning severity index and the maximum value of the failure critical wall thickness value; the pitting corrosion local damage expansion rate prediction model is used to predict the expansion rate of local damage morphology with time; the pitting corrosion thinning severity index is calculated according to time, pitting corrosion rate and thickness data; determining the local thinning factor according to the strength correction factor; the expression of the pitting corrosion local damage expansion rate prediction model is: where Δx is the pitting set expansion rate in the circumferential direction of the metal surface, i.e. the pit radius expansion rate, mm / a; Δy is the local damage expansion rate in the direction perpendicular to the metal surface, i.e. the pit depth expansion rate, mm / a; c is the concentration of erosive ions, mol / L; T is the test temperature, ℃; pH is the acidity and alkalinity of the corrosive solution; f(·) is the pitting corrosion damage rate function. The method comprises the following steps: determining the damage form of the equipment; the damage form includes uniform damage and local damage; According to the damage stage, a damage form of the equipment is determined.

3. The method for quantitatively evaluating a risk of a pitting corrosion damage of a pressure-bearing equipment according to claim 1, characterized by, When the leakage consequence area is a failure consequence area of static data, a pitting corrosion failure consequence is determined according to the leakage consequence area, specifically including: According to the API 581-2016 or GB / T 26610.5-2022 standard, a failure consequence quantitative evaluation of the static data is calculated.

4. The method for quantitatively evaluating a risk of a pitting corrosion damage of a pressure-bearing equipment according to claim 3, characterized by, When the leakage consequence area is a failure consequence area of dynamic data, a pitting corrosion failure consequence is determined according to the leakage consequence area, specifically including: According to a pitting corrosion pit corrosion local damage expansion rate prediction model, an evolution law of a leakage hole size over time is deduced; According to the evolution law, a real-time predicted leakage area size is determined; According to the real-time predicted leakage area size, a failure consequence quantitative evaluation of the dynamic data is calculated.

5. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the pressure equipment pitting corrosion damage risk quantitative evaluation method of any one of claims 1-4.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the pressure equipment pitting corrosion damage risk quantitative evaluation method of any one of claims 1-4.

7. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the pressure equipment pitting corrosion damage risk quantitative evaluation method of any one of claims 1-4.

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