Pressure-bearing equipment pitting corrosion damage risk quantitative evaluation method, equipment, medium and product

Abstract

The invention discloses a pressure-bearing equipment pitting corrosion damage risk quantitative evaluation method, equipment, a medium and a product, and relates to the field of petrochemical engineering pressure-bearing equipment safety, and the method comprises the steps: determining the damage form of the equipment; calculating a local / uniform thinning factor according to the damage form; according to the local / uniform thinning factor, the general condition factor, the mechanical factor and the process factor, calculating to obtain an equipment correction coefficient; according to the equipment correction coefficient, the management system evaluation coefficient, the average failure probability of the similar equipment and the standard exceeding defect influence coefficient, the pitting corrosion and pit corrosion failure possibility is obtained through calculation; calculating a leakage result area; according to the leakage consequence area, the pitting corrosion and pit corrosion failure consequence is determined; according to the pitting corrosion and pitting corrosion failure possibility and the pitting corrosion and pitting corrosion failure consequence, establishing a two-dimensional risk matrix graph; the two-dimensional risk matrix graph is used for real-time quantitative evaluation of pitting corrosion and pit corrosion risks. The method can effectively guide pitting corrosion and pit corrosion risk prevention and control in the petrochemical and chemical industry.

Description

Technical Field

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

[0002] The corrosion mechanism of pitting or pitting often exists in the pressure-bearing equipment of petrochemical and chlor-alkali chemical industries. For example, the anode electrolyzer, (desalted) brine pipeline and chlorine gas heat exchanger in chlor-alkali chemical plants are prone to pitting pits caused by crevice corrosion. The 300 series and 400 series stainless steel in petrochemical plants are prone to pitting caused by the presence of corrosive ions such as chloride ions, and even penetrating pitting may occur. The location and morphology of local damage caused by pitting are uncertain and dispersed, and the expansion rate of pitting is difficult to quantitatively measure, so it is difficult to quantitatively assess the risk of pitting damage.

[0003] Relevant pitting damage evaluation methods at home and abroad, such as those in GB / T 18590-2001 and ASTM G46-94 (2018) and other standards, can quantitatively obtain information such as the degree of pitting damage and metal damage sensitivity through standard chart methods, statistical methods, and mechanical property verification tests, thereby predicting material life. The above single evaluation method is not enough to accurately describe the pitting situation. The combination of multiple methods increases the difficulty and workload of the evaluation. There is no clear and unified evaluation standard for the quantitative classification of pitting damage, and it cannot provide information on the pitting expansion rate, so it is impossible to quantitatively evaluate the risk of pitting damage. Summary of the invention

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

[0005] To achieve the above object, the present invention provides the following solutions:

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

[0007] Determine the damage form of the equipment; the damage form includes: uniform damage and local damage.

[0008] Based on the damage form, the local / uniform thinning factors are calculated.

[0009] The equipment correction factor is calculated 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 API 581-2016 or GB / T 26610.4-2022 standards.

[0010] The possibility of pitting failure is calculated based on the equipment correction coefficient, the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of excessive defects; the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of excessive defects 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] According to the leakage consequence area, the pitting corrosion failure consequence is determined; the pitting corrosion failure consequence includes: a quantitative evaluation of the failure consequence of static data and a quantitative evaluation of the failure consequence of dynamic data.

[0013] A two-dimensional risk matrix diagram is established 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 assessment of pitting corrosion risk.

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

[0015] Determine the damage stage of the equipment; the damage stage includes: pitting corrosion initiation stage, pitting corrosion development stage and pitting corrosion expansion stage.

[0016] According to the damage stage, the damage form of the equipment is determined.

[0017] Optionally, when the damage form is uniform damage, calculating the local / uniform thinning factor according to the damage form specifically includes:

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

[0019] Optionally, when the damage form is local damage, calculating the local / uniform thinning factor according to the damage form specifically includes:

[0020] Based on the pit assessment method and the fitness for use evaluation method, the critical wall thickness value for failure is calculated respectively, and the maximum value of the critical wall thickness for failure is recorded.

[0021] The strength correction factor is determined according to the pitting corrosion local damage expansion 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 expansion rate prediction model is used to predict the expansion 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] According to the strength correction factor, a local thinning factor is determined.

[0023] Optionally, when the leakage consequence area is a failure consequence area of ​​static data, determining the pitting failure consequence according to the leakage consequence area specifically includes:

[0024] According to API 581-2016 or GB / T 26610.5-2022 standards, the quantitative evaluation of failure consequences of static data is calculated.

[0025] Optionally, when the leakage consequence area is a failure consequence area of ​​dynamic data, determining the pitting failure consequence according to the leakage consequence area specifically includes:

[0026] The evolution law of leakage hole size over time is deduced based on the prediction model of local damage expansion rate of pitting corrosion.

[0027] According to the evolution law, the real-time predicted leakage area size is determined.

[0028] According to the real-time predicted leakage area size, a quantitative evaluation of failure consequences of dynamic data is calculated.

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

[0030]

[0031] Wherein, Δx is the expansion rate of the pitting group on the metal surface in the circumferential direction, that is, the expansion rate of the pit radius, mm / a; Δy is the local damage expansion rate of the pitting group in the direction perpendicular to the metal surface, that is, the expansion rate of the pit depth, mm / a; c is the corrosive ion concentration, mol / L; T is the test temperature, °C; pH is the acidity and alkalinity of the corrosive solution; and f(·) is the pitting 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 any of the above-described methods for quantitatively assessing pitting damage risk of pressure-bearing equipment.

[0033] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for quantitatively assessing pitting damage risks of pressure-bearing equipment.

[0034] In a fourth aspect, the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for quantitatively assessing pitting damage risks of pressure-bearing equipment.

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

[0036] The present invention provides a method, equipment, medium and product for quantitative assessment of pitting damage risk of pressure equipment. By determining the damage form of the equipment, the local thinning factor or the uniform thinning factor can be calculated respectively. According to the local thinning factor or the uniform thinning factor, the general condition factor, the mechanical factor and the process factor, the equipment correction factor can be calculated. Then, according to the equipment correction factor, the management system evaluation factor, the average failure probability of similar equipment and the influence coefficient of the defect exceeding the standard, the possibility of pitting failure can be calculated; in addition, according to the calculated leakage consequence area, the consequences of pitting failure can be determined; finally, according to the pitting failure possibility and the pitting failure consequences, a two-dimensional risk matrix diagram is established, which can realize the real-time quantitative assessment of pitting risk. The present invention quantitatively evaluates the possibility of equipment failure caused by pitting and the consequences of failure, and proposes a method for quantitative assessment of local damage rate, state expansion and failure risk considering pitting, which solves the problem that the risk of pitting damage is difficult to quantitatively assess and deduce under corrosive environment, and provides support for the setting of safety boundaries and risk prevention and control of pitting damage of pressure equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 This is a diagram of the application environment of a method for quantitatively assessing pitting damage risk of pressure-bearing equipment in one embodiment of the present invention.

[0039] Figure 2 A schematic flow chart of a method for quantitatively assessing pitting damage risk of pressure-bearing equipment provided in one embodiment of the present invention.

[0040] Figure 3A schematic flow chart of a method for quantitatively assessing pitting damage failure risk of petrochemical and chemical pressure equipment provided in one embodiment of the present invention.

[0041] Figure 4 A diagram showing the division of pitting areas on the sample surface provided by an embodiment of the present invention.

[0042] Figure 5 An unbalanced matrix diagram provided by an embodiment of the present invention.

[0043] Figure 6 A balance matrix diagram provided by an embodiment of the present invention.

[0044] Figure 7 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] This invention, for the first time, targets the field of petrochemicals and pitting corrosion in pressure vessels and industrial pipelines, and proposes a method for quantitatively evaluating the local damage rate, state extension, and failure risk of pitting corrosion. It solves the problem that the risk of pitting corrosion damage in a corrosive environment is difficult to quantitatively evaluate and deduce, and provides support for the setting of safety boundaries and risk prevention and control of pitting corrosion damage in pressure equipment.

[0048] The method for quantitatively assessing pitting damage risk of pressure-bearing equipment provided by the embodiment of the present invention can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the damage form of the equipment to the server 104. After the server 104 receives the damage form of the equipment, for the damage form of the equipment, the server 104 calculates the local / uniform thinning factor according to the damage form; according to the local / uniform thinning factor, the general condition factor, the mechanical factor and the process factor, the equipment correction factor is calculated; the general condition factor, the mechanical factor and the process factor are obtained according to API 581-2016 or GB / T26610.4-2022 standards; according to the equipment correction factor, the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of excessive defects, the possibility of pitting failure is calculated; the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of excessive defects are calculated according to API 581-2016 or GB / T 26610.4-2022 standard; 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; according to the leakage consequence area, determine the pitting failure consequence; the pitting failure consequence includes: the quantitative evaluation of the failure consequence of static data and the quantitative evaluation of the failure consequence of dynamic data; according to the pitting failure possibility and the pitting failure consequence, establish a two-dimensional risk matrix diagram; the two-dimensional risk matrix diagram is used for real-time quantitative evaluation of pitting risk. The server 104 can feed back the obtained two-dimensional risk matrix diagram to the terminal 102. In addition, in some embodiments, the method for quantitative evaluation of pitting damage risk of pressure-bearing equipment can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform risk quantitative evaluation on the damage form of the equipment, or the server 104 can obtain the damage form of the equipment from the data storage system and perform risk quantitative evaluation on the damage form of the equipment.

[0049] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones and tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0050] In an exemplary embodiment, Figure 2As shown, a method for quantitatively assessing the risk of pitting damage of pressure-bearing equipment is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present invention, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S1 to S7. Among them:

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

[0052] S2: Calculate the local / uniform thinning factor according to the damage form.

[0053] S3: The equipment correction factor is calculated based on 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 API 581-2016 or GB / T26610.4-2022 standards.

[0054] S4: The possibility of pitting failure is calculated based on the equipment correction coefficient, the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of excessive defects; the management system evaluation coefficient, the average failure probability of similar equipment and the influence coefficient of excessive defects 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 failure consequence according to the leakage consequence area; the pitting failure consequence includes: a quantitative evaluation of the failure consequence of static data and a quantitative evaluation of the failure consequence of dynamic data.

[0057] S7: Establish a two-dimensional risk matrix diagram according to the pitting failure possibility and the pitting failure consequence; the two-dimensional risk matrix diagram is used for real-time quantitative assessment of pitting risk.

[0058] By implementing the above-mentioned steps S1 to S7, the present invention quantitatively evaluates the possibility of equipment failure caused by pitting corrosion and the consequences of failure, and proposes a method for quantitatively evaluating the local damage rate, state extension and failure risk of pitting corrosion, which solves the problem that the risk of pitting corrosion damage in a corrosive environment is difficult to quantitatively evaluate and deduce, and provides support for the setting of safety boundaries and risk prevention and control of pitting corrosion damage in pressure-bearing equipment.

[0059] In another exemplary embodiment of the present invention, in step S1, it 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 form of the device according to the damage stage.

[0062] like Figure 3 As shown, the present invention conducts quantitative assessment from two aspects: the possibility of equipment failure caused by pitting and the consequences of failure. A two-dimensional risk matrix is ​​established based on the possibility of failure and the consequences of failure, and the risk levels are divided into high risk, medium-high risk, medium risk, and low risk. Among them, in terms of the possibility of pitting failure, it is necessary to identify the dominant mechanism, identify the boundaries and divide the boundaries. The concept of pitting set is newly proposed. For the pitting cluster area within a unit area, a circular area containing the pitting cluster area is drawn. When the number of pitting on the "cross" intersection line in the inner diameter direction of the circle reaches a certain number, it is considered to be a local area with severe damage (see Figure 4 ), the severity of corrosion is evaluated by considering the number of pitting in the pitting set divided by the size of the divided circular area, that is, the density of pitting; a new pitting rate engineering prediction model and a pitting set expansion rate prediction model (pitting local damage expansion rate prediction model) are proposed to predict the maximum local corrosion position and the damage morphology expansion rate respectively, and deduce the possibility of pitting failure in the future operation cycle; in terms of the consequences of pitting failure, different leakage models are selected according to the type of medium. The leakage consequence area calculation based on static data needs to be combined with the failure frequency and leakage model for theoretical calculation. The leakage consequence area calculation based on dynamic data needs to be combined with the pitting pitting set expansion rate and leakage rate prediction model to calculate. The consequences of pitting failure are quantitatively evaluated by calculating the medium area consequences after failure (leakage toxicity consequences, combustion and explosion consequences) and economic consequences.

[0063] Among them, the expression of the engineering prediction model of pitting corrosion rate is:

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

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

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

[0067]

[0068] Among them, Δx is the expansion rate of the pitting group on the metal surface in the circumferential direction, that is, the expansion rate of the pit radius, mm / a; Δy is the local damage expansion rate of the pitting group in the direction perpendicular to the metal surface, that is, the expansion rate of the pit depth, mm / a; c is the corrosive ion concentration, mol / L; T is the test temperature, ℃; pH is the acidity and alkalinity of the corrosive solution; f(·) is the pitting damage rate function; Δx and Δy need to be calibrated according to the material through experiments.

[0069] In another exemplary embodiment of the present invention, when the damage form is uniform damage, 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 form is local damage, in step S2, the steps specifically include:

[0072] A1: Based on the pit assessment method GB 19624-2004 and the compliance assessment 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 for failure.

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

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

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

[0076] According to API 581-2016 or GB / T 26610.5-2022 standards, the quantitative evaluation of failure consequences of static data is calculated.

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

[0078] B1: The evolution of leakage hole size over time is deduced based on the prediction model of local damage expansion rate of pitting corrosion.

[0079] B2: Determine the real-time predicted leakage area size according to the evolution law.

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

[0081] The method for quantitatively assessing the risk of pitting damage to pressure equipment of the present invention is divided into two categories, one is a static assessment based on historical detection data, and the other is a dynamic assessment based on real-time data. For the static risk quantitative assessment, the input data is valid data under a stable state without adjustment of the process and equipment during a maintenance period, and the input parameters include at least the original wall thickness, the wall thickness detection data of the last shutdown, the type and concentration of the corrosive medium, temperature, pressure, material, etc. For the dynamic risk quantitative assessment, the input data is valid data monitored in real time within 1 to 24 hours and the structural data that does not change during a single maintenance period, and the input parameters include at least the original wall thickness, real-time wall thickness detection data, the type and concentration of the corrosive medium, temperature, pressure, material and size parameters, etc.

[0082] In another exemplary embodiment of the present invention, the pitting failure possibility is obtained by multiplying the management system evaluation coefficient, the average failure probability of similar equipment, the equipment correction coefficient, and the excess defect influence coefficient, and its size is divided into five levels of 1, 2, 3, 4, and 5; wherein, the equipment correction coefficient includes 4 factors, namely, local / uniform thinning factor, general condition factor, mechanical factor, and process factor, and the size of the equipment correction coefficient is obtained by multiplying these 4 factors. The management system evaluation system, the average failure probability of similar equipment, the excess defect influence coefficient, the general condition factor, the mechanical factor, and the process factor calculation method can directly refer to API 581-2016 or GB / T 26610.4-2022 standards, and the local / uniform thinning factor needs to select the calculation model according to the damage form. Among them, the pitting thinning severity index ar / t (calculated by the time a, pitting 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 needs to be calculated based on real-time monitoring data combined with pitting corrosion prediction models.

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

[0084] When calculating the local thinning factor according to the engineering prediction model of pitting thinning rate, it is necessary to introduce a strength correction factor to consider the failure boundary. The recommended calculation method is to combine the pitting assessment GB 19624-2004 and the use-conformity evaluation method GB35013-2018 or API RP 579-1-2021, and take the maximum value of the critical wall thickness of failure calculated by the two. For the evolution of the local thinning factor over time, the local damage propagation rate prediction model of pitting is introduced to deduce the change law of the severity index of pitting thinning in real time, so as to deduce the nonlinear change law of the local thinning factor by combining the strength theory calculation method, so as to more effectively predict the damage change law. Based on the pitting assessment GB 19624-2004 and the use-conformity evaluation method GB35013-2018 or API RP 579-1-2021 standard method, the theoretical value is calculated or a large amount of industrial test data to determine the strength correction factor, and the local thinning factor value is corrected by the strength correction factor.

[0085] In another exemplary embodiment of the present invention, the size of the pitting failure consequence is calculated by calculating the leakage consequence area, predicting the toxic hazard, explosion hazard, environmental cleanup cost, and economic loss after failure, and its size is divided into five levels: A, B, C, D, and E; wherein, the quantitative evaluation of the failure consequence based on static data can be directly calculated with reference to API 581-2016 or GB / T26610.5-2022 standards. For the calculation of the dynamic failure consequence area, it is necessary to deduce the evolution law of the leakage hole size over time through the pitting set expansion rate model, select different leakage models according to the type of medium to calculate the leakage rate in real time, use the real-time leakage rate as the input condition of the diffusion model, and predict the leakage area size in real time; the real-time leakage area size is used as the input condition for the dynamic evaluation of the area consequences (toxic consequences, combustion and explosion consequences) and economic consequences after failure, thereby realizing the dynamic quantitative evaluation of the pitting failure consequences. Among them, the economic consequences can refer to the economic consequence algorithm in API 581-2016 or GB / T26610.5-2022 standards to calculate the series of economic losses caused by equipment failure, and classify the economic consequences 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 failure can be expressed by a two-dimensional risk matrix diagram, with the ordinate representing the possibility level of pitting failure and the abscissa representing the consequence level of pitting failure. The risk matrix diagram can refer to the GB / T26610.1-2022 or API 581-2016 standard to select the non-equilibrium matrix diagram and the equilibrium matrix diagram for risk grading, and divide the pitting risk into four levels, namely high risk, medium-high risk, medium risk and low risk.

[0087] The present invention solves the problem of the difficulty in quantitatively assessing and deducing the risk of pitting corrosion damage in corrosive environments through a quantitative assessment method for the risk of local damage failure caused by pitting corrosion of petrochemical and chemical pressure-bearing equipment, and provides support for the setting of safety boundaries and risk prevention and control of pitting corrosion damage of pressure-bearing equipment.

[0088] The present invention also provides an application scenario, which applies the above-mentioned method for quantitative assessment of pitting damage risk of pressure equipment. Specifically: The method for quantitative assessment of pitting damage risk of pressure equipment provided in this embodiment can be applied in the quantitative assessment of risk of petrochemical and chemical pressure equipment. The risk quantitative assessment scenario of petrochemical and chemical pressure equipment includes a damage form determination link, a pitting failure possibility calculation link, a pitting failure consequence calculation link and a quantitative assessment link; after determining the damage form of the equipment, the thinning factor is calculated according to the damage form, and then the equipment correction factor can be calculated according to the thinning factor, and the pitting failure possibility can be calculated according to the equipment correction factor, the management system evaluation coefficient, the average failure probability of similar equipment and the excess defect influence coefficient; in addition, the pitting failure consequence can be determined by calculating the leakage consequence area; finally, a quantitative assessment can be achieved based on the pitting failure possibility and the pitting failure consequence.

[0089] In an exemplary embodiment, this implementation case is for a certain chlor-alkali chemical plant anode brine pipeline, and a pitting corrosion failure risk quantitative assessment is performed based on real-time monitoring data. The material is TA2 titanium, and the medium is a sodium chloride solution containing wet chlorine gas (the main medium is Cl2, Cl - , ClO - , ClO 3- 、Na + etc.), where Cl - The concentration is 190-220g / 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, when the corrosion temperature exceeds the critical temperature of about 70°C, pitting corrosion caused by crevice corrosion occurs in this service environment. The pitting corrosion continues to expand to form larger corrosion pits, resulting in pitting corrosion, and the damage form is local thinning. For the pitting pitting cluster area per unit area, a circular area containing the pitting pitting cluster area is drawn. When the number of pitting pits on the "cross" intersection line in the inner diameter direction of the circle reaches a certain number (for example, more than 10), it is considered to be a severely damaged local area (see Figure 4), calculate the number of pitting in different pitting sets divided by the size of the divided circular area, and select the density of pitting to evaluate the severity of corrosion; select the engineering prediction model for pitting rate (see formula 1), based on this prediction model, combine the actual monitoring data to predict the damage rate in real time, and calculate the severity index of pitting thinning. Consider the effectiveness of detection and the number of detections with the highest category effectiveness to determine the local pitting thinning factor. Based on the theoretical value calculated based on the pitting assessment GB 19624-2004 and the fitness for use evaluation method GB 35013-2018 or a large amount of industrial test data to determine the ratio of the intended service time Tn and the remaining life TSL, and determine the strength correction factor value (1 to 500) according to the ratio of the intended service time and the remaining life, and use this value to correct the local thinning factor. Among them, the remaining life needs to be predicted by the pitting local damage propagation rate prediction model (see Formula 2) to predict the future morphology evolution and the change of the pitting thinning severity index, and is estimated based on the appropriate evaluation method GB 35013-2018 or mature engineering experience.

[0091] According to the local pitting thinning factor, combined with the calculation method of general condition factor, mechanical factor and process factor in GB / T 26610.1-2022 standard, the equipment correction factor is calculated. Then, the GB / T26610.1-2022 standard method is further used to consider the management system evaluation coefficient, the average failure probability of similar equipment, the equipment correction factor, and the influence coefficient of excessive defects, so as to predict the possibility of pitting failure, and the evaluation level is 3.

[0092] According to the type and stock of hazardous media and combined with monitoring data, the pitting set expansion rate model is used to predict the equivalent pore diameter 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 size. The GB / T 26610.1-2022 standard method is used to evaluate the area consequences and economic consequences after damage and leakage, so as to predict the current corrosion failure consequences, and the evaluation level is C.

[0093] Based on the real-time pitting corrosion damage failure possibility and consequence level, a two-dimensional non-equilibrium matrix diagram is drawn, such as Figure 5 As shown, the ordinate is the possibility of pitting failure, and the abscissa is the consequence of pitting failure; according to the standard type, the non-equilibrium matrix diagram and the equilibrium matrix diagram are selected as shown in Figure 6 As shown, a real-time quantitative assessment of pitting corrosion risk is conducted, and the risk level is medium risk.

[0094] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the damage form of the device. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for quantitatively assessing the risk of pitting damage of pressure-bearing equipment is implemented.

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

[0096] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above-mentioned method embodiments are implemented when the processor executes the computer program.

[0097] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0098] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[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, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0101] The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0102] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, 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] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for quantitatively assessing pitting damage risk of pressure-bearing equipment, characterized in that: The method for quantitatively assessing pitting damage risk of pressure-bearing equipment includes: Determine the damage form of the equipment; the damage form includes: uniform damage and local damage; According to the damage form, calculate the local / uniform thinning factor; The equipment correction factor is calculated 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 API 581-2016 or GB / T 26610.4-2022 standards; The possibility of pitting failure is calculated according to the equipment correction factor, the management system evaluation factor, the average failure probability of similar equipment and the influence coefficient of excessive defects; the management system evaluation factor, the average failure probability of similar equipment and the influence coefficient of excessive defects are obtained according to API 581-2016 or GB / T26610.4-2022 standards; 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; Determine the pitting corrosion failure consequence according to the leakage consequence area; the pitting corrosion failure consequence includes: a quantitative evaluation of the failure consequence of static data and a quantitative evaluation of the failure consequence of dynamic data; A two-dimensional risk matrix diagram is established 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 assessment of pitting corrosion risk.

2. The method for quantitatively assessing pitting damage risk of pressure-bearing equipment according to claim 1, characterized in that: The determination of the damage form of the equipment specifically includes: Determine the damage stage of the equipment; the damage stage includes: pitting corrosion initiation stage, pitting corrosion development stage and pitting corrosion expansion stage; According to the damage stage, the damage form of the equipment is determined.

3. The method for quantitatively assessing pitting damage risk of pressure-bearing equipment according to claim 1, characterized in that: When the damage form is uniform damage, the local / uniform thinning factor is calculated according to the damage form, specifically including: Determine the uniform thinning factor according to API 581-2016 or GB / T 26610.4-2022 standards.

4. The method for quantitatively assessing pitting damage risk of pressure-bearing equipment according to claim 3 is characterized in that: When the damage form is local damage, the local / uniform thinning factor is calculated according to the damage form, specifically including: Based on the pit assessment method and the fitness for use assessment method, the critical wall thickness value of failure is calculated respectively, and the maximum value of the critical wall thickness value of failure is recorded; Determine the strength correction factor according to the local damage expansion rate prediction model of pitting, the pitting thinning severity index and the maximum value of the critical wall thickness for failure; the local damage expansion rate prediction model of pitting is used to predict the expansion rate of local damage morphology over time; the pitting thinning severity index is calculated according to time, pitting rate and thickness data; According to the strength correction factor, a local thinning factor is determined.

5. The method for quantitatively assessing pitting damage risk of pressure-bearing equipment according to claim 1, characterized in that: When the leakage consequence area is the failure consequence area of ​​static data, the pitting failure consequence is determined according to the leakage consequence area, specifically including: According to API 581-2016 or GB / T 26610.5-2022 standards, the quantitative evaluation of failure consequences of static data is calculated.

6. The method for quantitatively assessing pitting damage risk of pressure-bearing equipment according to claim 5, characterized in that: When the leakage consequence area is the failure consequence area of ​​dynamic data, the pitting failure consequence is determined according to the leakage consequence area, specifically including: The evolution of leakage hole size over time is deduced based on the prediction model of local damage expansion rate of pitting corrosion; According to the evolution law, determining the real-time predicted leakage area size; According to the real-time predicted leakage area size, a quantitative evaluation of failure consequences of dynamic data is calculated.

7. The method for quantitatively assessing pitting damage risk of pressure-bearing equipment according to claim 4 or 6, characterized in that: The expression of the prediction model for the local damage expansion rate of pitting corrosion is: Wherein, Δx is the expansion rate of the pitting group on the metal surface in the circumferential direction, that is, the expansion rate of the pit radius, mm / a; Δy is the local damage expansion rate of the pitting group in the direction perpendicular to the metal surface, that is, the expansion rate of the pit depth, mm / a; c is the corrosive ion concentration, mol / L; T is the test temperature, °C; pH is the acidity and alkalinity of the corrosive solution; and f(·) is the pitting damage rate function.

8. 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 quantitatively assessing the risk of pitting damage to pressure equipment as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quantitatively assessing pitting damage risk of pressure-bearing equipment described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for quantitatively assessing pitting damage risk of pressure-bearing equipment described in any one of claims 1 to 7 is implemented.

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