Methods, equipment, media and products for quantitative assessment of erosion damage risk in pressure equipment

Abstract

This invention discloses a method, equipment, medium, and product for quantitatively assessing the risk of erosion damage to pressure equipment, relating to the safety of pressure equipment in the petrochemical and chemical industries. The method includes: determining the damage form of the equipment; calculating a local / uniform thinning factor based on the damage form; calculating an equipment correction coefficient based on the local / uniform thinning factor, a general condition factor, a mechanical factor, and a process factor; calculating the probability of erosion corrosion failure based on the equipment correction coefficient, a management system evaluation coefficient, the average failure probability of similar equipment, and an impact coefficient for defects exceeding standards; calculating the leakage consequence area; determining the consequences of erosion corrosion failure based on the leakage consequence area; and establishing a two-dimensional risk matrix based on the probability of erosion corrosion failure and the consequences of erosion corrosion failure. The two-dimensional risk matrix is ​​used for real-time quantitative assessment of erosion risk. This invention can effectively guide the prevention and control of erosion corrosion risks in the petrochemical and chemical industries.

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 erosion damage to pressure-bearing equipment. Background Technology

[0002] In petrochemical and chemical pressure equipment, there is often a multiphase flow erosion and corrosion coupling mechanism. For example, in the black ash water system of a coal gasification unit, acidic water and coal powder erosion occur, and in the quenching system of a methanol-to-olefins unit, acidic water and solid particles such as catalysts and metal fragments cause local damage with uncertainty, making it difficult to quantitatively assess the risk of erosion and corrosion coupling damage.

[0003] Currently, there is little research on risk assessment methods for erosion-corrosion coupled damage, both domestically and internationally, and existing methods are not applicable to the petrochemical and chemical industries. Most relevant risk assessment methods abroad originate from the oil and gas extraction and transportation sector, and largely establish erosion-corrosion coupled risk assessment methods by studying erosion-corrosion damage in simple sand and water environments. A typical example is the semi-quantitative erosion damage assessment method in the DNV GL-RP-O501 standard, "Managing sand production and erosion," which classifies the probability of erosion failure into six levels based on fluid flow velocity and assesses the acceptability of risk by combining the object's daily management level and different limiting requirements, thus classifying it into high-risk, medium-risk, and low-risk categories. The API 581 standard, "Risk-Based Inspection," includes a method for quantitative assessment of localized damage, but it lacks a predictive model for the coupling effect of erosion and corrosion, as well as an assessment algorithm based on damage morphology. The Lloyd's Register API assessment software also has an erosion-corrosion damage module, but it is mostly applicable to risk prediction within liquefied natural gas extraction and transportation systems, and it is difficult to examine the coupled damage patterns of corrosion and erosion. In China, the assessment of erosion and corrosion damage risks remains at the level of qualitative prediction, and is mostly based on the summary of field experience. 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 erosion damage to pressure equipment, which can effectively guide the prevention and control of erosion 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 erosion damage to pressure 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 scouring 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 consequences of erosion corrosion failure are determined; the consequences of erosion corrosion failure 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 erosion failure and the consequences of erosion failure; the two-dimensional risk matrix is ​​used for real-time quantitative assessment of erosion risk.

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

[0015] The effective area of ​​the equipment is determined; the effective area includes: the corrosion-dominant area, the scouring-dominant area, and the scouring-corrosion synergistic area.

[0016] Based on the affected area, 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 whether the damaged structure is a special structure to obtain the first judgment result.

[0019] If the first judgment result is negative, then the uniform thinning factor is determined according to API 581-2016 or GB / T 26610.4-2022 standards.

[0020] If the first judgment result is yes, then the structural correction factor is calculated based on the radius of curvature of the elbow.

[0021] The uniform thinning factor is calculated based on the structure correction factor.

[0022] Optionally, when the damage type is localized, a local / uniform thinning factor is calculated based on the damage type, specifically including:

[0023] Determine whether the damaged structure is a special structure to obtain a second judgment result.

[0024] If the second judgment result is negative, the critical wall thickness value for failure is calculated based on the pitting assessment method and the applicable evaluation method, and the maximum value of the critical wall thickness value for failure is recorded.

[0025] The strength correction factor is determined based on the scour corrosion local damage propagation rate prediction model, the scour corrosion thinning severity index, and the maximum value of the critical wall thickness for failure. The scour corrosion local damage propagation rate prediction model is used to predict the propagation rate of local damage morphology over time. The scour corrosion thinning severity index is calculated based on time, scour corrosion rate, and thickness data.

[0026] Based on the intensity correction factor, the local thinning factor is determined.

[0027] If the second judgment result is yes, then the structural correction factor is calculated based on the curvature radius of the elbow.

[0028] Based on the pitting assessment method and the usability evaluation method, the critical wall thickness value for failure is calculated and the maximum value of the critical wall thickness value for failure is recorded.

[0029] The strength correction factor is determined based on the prediction model of the local damage propagation rate of erosion corrosion, the severity index of erosion corrosion thinning, and the maximum value of the critical wall thickness for failure.

[0030] The local thinning factor is determined based on the intensity correction factor and the structure correction factor.

[0031] Optionally, when the leakage consequence area is the failure consequence area of ​​static data, the erosion corrosion failure consequence is determined based on the leakage consequence area, specifically including:

[0032] The quantitative assessment of failure consequences of static data is calculated according to API 581-2016 or GB / T 26610.5-2022 standards.

[0033] Optionally, when the leakage consequence area is the failure consequence area of ​​dynamic data, the erosion corrosion failure consequence is determined based on the leakage consequence area, specifically including:

[0034] The evolution of the leak hole size over time was deduced based on the prediction model of the local damage propagation rate of erosion corrosion.

[0035] Based on the aforementioned evolutionary pattern, the size of the leak area can be predicted in real time.

[0036] Based on the real-time predicted leakage area, a quantitative assessment of the failure consequences of dynamic data is calculated.

[0037] Optionally, the expression for the prediction model of the local damage propagation rate of erosion corrosion is:

[0038]

[0039] Where Δx is the local damage propagation rate along the main flow direction, mm / a; Δy is the local damage propagation rate circumferentially along the main flow direction, mm / a; Δz is the local damage propagation rate perpendicular to the main flow direction, mm / a; v is the particle impact velocity, m / s; ε s ε is the solid volume fraction; cor is the volume fraction of the corrosive medium; f(·) is the thinning damage propagation function.

[0040] 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 erosion damage risk of pressure equipment as described above.

[0041] 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 risk of erosion damage to pressure equipment as described above.

[0042] 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 risk of erosion damage to pressure equipment as described above.

[0043] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0044] This invention provides a method, equipment, medium, and product for quantitatively assessing the risk of erosion damage to 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 erosion corrosion failure can be calculated. Additionally, based on the calculated leakage consequence area, the consequences of erosion corrosion failure can be determined. Finally, based on the probability and consequences of erosion corrosion failure, a two-dimensional risk matrix is ​​established, enabling real-time quantitative assessment of erosion risk. This invention quantitatively assesses both the probability and consequences of equipment failure caused by erosion corrosion. It proposes a method for quantitatively assessing the local damage rate, state expansion, and failure risk, considering the competition and synergistic acceleration between particle erosion and corrosion. This solves the problem of difficulty in quantitatively assessing and extrapolating the risk of coupled erosion corrosion damage in complex environments, providing support for setting safety boundaries and preventing risks associated with coupled erosion corrosion damage to pressure equipment. Attached Figure Description

[0045] 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.

[0046] Figure 1 This is an application environment diagram of a method for quantitatively assessing the risk of erosion damage to pressure equipment according to an embodiment of the present invention.

[0047] Figure 2 This is a flowchart illustrating a method for quantitatively assessing the risk of erosion damage to pressure equipment, as provided in an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of a method for quantitatively assessing the risk of erosion corrosion damage failure in petrochemical and chemical pressure equipment, provided in an embodiment of the present invention.

[0049] Figure 4 This is an unbalanced matrix diagram provided in one embodiment of the present invention.

[0050] Figure 5 This is a balance matrix diagram provided in one embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0052] 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.

[0053] 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.

[0054] This invention is the first of its kind in the petrochemical industry to address the multiphase corrosion and erosion coupling effects in complex pressure vessels and industrial pipelines. It proposes a method for quantitatively assessing local damage rates, state propagation, and failure risks by considering the competition and synergistic acceleration between particle erosion and corrosion. This solves the problem of difficulty in quantifying and extrapolating the risk of erosion-corrosion coupling damage in complex environments, and provides support for setting safety boundaries and risk prevention and control of erosion-corrosion coupling damage in pressure equipment.

[0055] The method for quantitatively assessing the risk of erosion damage to pressure 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 erosion 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 leakage consequence area is calculated according to standard 26610.4-2022. This leakage consequence area includes both static and dynamic data areas. Based on the leakage consequence area, the erosion corrosion failure consequence is determined. This erosion corrosion failure consequence includes both static and dynamic data quantitative assessments. A two-dimensional risk matrix is ​​established based on the erosion corrosion failure probability and the erosion corrosion failure consequence. This two-dimensional risk matrix is ​​used for real-time quantitative assessment of erosion 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 erosion damage risk of 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 equipment's damage form, or server 104 can obtain the equipment's damage form from the data storage system and perform risk quantitative assessment of the equipment's damage form.

[0056] 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.

[0057] In one exemplary embodiment, such as Figure 2As shown, a method for quantitatively assessing the risk of erosion damage to pressure 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:

[0058] S1: Determine the damage type of the equipment; the damage type includes: uniform damage and local damage.

[0059] S2: Calculate the local / uniform thinning factor based on the damage type.

[0060] 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.

[0061] S4: The probability of failure due to scouring corrosion 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.

[0062] 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.

[0063] S6: Determine the erosion corrosion failure consequences based on the leakage consequence area; the erosion corrosion failure consequences include: quantitative assessment of failure consequences based on static data and quantitative assessment of failure consequences based on dynamic data.

[0064] S7: Based on the probability of erosion corrosion failure and the consequences of erosion corrosion failure, a two-dimensional risk matrix diagram is established; the two-dimensional risk matrix diagram is used for real-time quantitative assessment of erosion risk.

[0065] By implementing steps S1 to S7 above, this invention provides a quantitative assessment of the possibility and consequences of equipment failure caused by scouring corrosion. It considers the competition and synergistic acceleration of local damage rates, state expansion, and failure risk quantification methods between particle scouring and corrosion competition and synergistic acceleration. This solves the problem of difficulty in quantifying and extrapolating the risk of coupled scouring corrosion damage in complex environments, and provides support for setting safety boundaries and preventing risks associated with coupled scouring corrosion damage in pressure equipment.

[0066] In another exemplary embodiment of the present invention, step S1 specifically includes:

[0067] S101: Determine the effective area of ​​the equipment; the effective area includes: corrosion-dominant area, scouring-dominant area, and scouring-corrosion synergistic area.

[0068] S102: Determine the damage type of the equipment based on the affected area.

[0069] like Figure 3 As shown, this invention provides a quantitative assessment of the likelihood and consequences of equipment failure caused by erosion corrosion. A two-dimensional risk matrix is ​​established based on the likelihood and consequences of failure, classifying risk levels as high, medium-high, medium, and low. Regarding the likelihood of erosion corrosion failure, the dominant mechanism needs to be identified, boundaries identified, and demarcated. A new method is proposed: an engineering prediction model for erosion corrosion rate and a prediction model for the local damage propagation rate of erosion corrosion are used to predict the maximum erosion location and the damage morphology propagation rate, respectively, thus extrapolating the likelihood of erosion corrosion failure within future operating cycles. Regarding the consequences of erosion 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 prediction model for the local damage propagation rate of erosion corrosion. The magnitude of the consequences of erosion corrosion failure is quantitatively assessed by calculating the medium area consequences (leakage toxicity consequences, combustion and explosion consequences), economic consequences, etc., after failure.

[0070] The expression for the engineering prediction model of erosion corrosion rate is as follows:

[0071]

[0072] In the formula, R erosion The erosion weight loss rate of the sample is expressed in kg·m. -2 ·s -1 K is the material constant, with units of (m·s). -1 ) n1 f(α) is a function of the impact angle; for ductile materials, see formula (2), or it can be determined experimentally; ε pack For close-packed volume fraction, spherical particles typically have a volume fraction of 0.63; ε s d represents the solid volume fraction; p ε represents the particle size; cor R is the volume fraction of the corrosive medium; v is the particle impact velocity, m / s; n1 is the velocity factor under erosion corrosion, which is generally between 2 and 4 for simple erosion, and needs to be experimentally calibrated for the coupled effect of erosion and corrosion; n2 is the corrosion factor, describing the synergistic acceleration effect of the coupled effect of erosion and corrosion, which is generally n2≤0, and needs to be experimentally calibrated according to the corrosion mechanism; cρs is the radius of curvature of the bend, m; D is the equivalent diameter of the erosion zone, m; ρs is the particle density, kg / m³. 3 ;ρ face The density of the metal wall is kg / m³. 3 α is the structure correction factor.

[0073] The expression for the prediction model of the local damage propagation rate of erosion corrosion is as follows:

[0074]

[0075] Where Δx is the local damage propagation rate along the main flow direction, mm / a; Δy is the local damage propagation rate circumferentially along the main flow direction, mm / a; Δz is the local damage propagation rate perpendicular to the main flow direction, mm / a; v is the particle impact velocity, m / s; ε s ε is the solid volume fraction; cor Δx represents the volume fraction of the corrosive medium; f(·) is the thinning damage propagation function; Δx, Δy, and Δz need to be determined experimentally based on the material.

[0076] In another exemplary embodiment of the present invention, when the damage is uniform, step S2 specifically includes:

[0077] A1: Determine whether the damaged structure is a special structure to obtain the first judgment result.

[0078] A2: If the first judgment result is negative, then determine the uniform thinning factor according to API 581-2016 or GB / T 26610.4-2022 standards.

[0079] A3: If the first judgment result is yes, then calculate the structural correction factor based on the radius of curvature of the elbow.

[0080] A4: The uniform thinning factor is calculated based on the structure correction factor.

[0081] In another exemplary embodiment of the present invention, when the damage is localized, step S2 specifically includes:

[0082] B1: Determine whether the damaged structure is a special structure to obtain the second judgment result.

[0083] B2: If the second judgment result is negative, the critical wall thickness value for failure is calculated based on the pit assessment method GB 19624-2004 and the applicable evaluation method GB 35013-2018 or API RP 579-1-2021, and the maximum value of the critical wall thickness value for failure is recorded.

[0084] B3: Determine the strength correction factor based on the scouring corrosion local damage propagation rate prediction model, the scouring corrosion thinning severity index, and the maximum value of the failure critical wall thickness; the scouring corrosion local damage propagation rate prediction model is used to predict the local damage morphology propagation rate over time; the scouring corrosion thinning severity index is calculated based on time, scouring corrosion rate, and thickness data.

[0085] B4: Determine the local thinning factor based on the intensity correction factor.

[0086] B5: If the second judgment result is yes, then calculate the structural correction factor based on the curvature radius of the elbow.

[0087] B6: 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.

[0088] B7: Determine the strength correction factor based on the prediction model of the local damage propagation rate of erosion corrosion, the severity index of erosion corrosion thinning, and the maximum value of the critical wall thickness for failure.

[0089] B8: Determine the local thinning factor based on the strength correction factor and the structure correction factor.

[0090] 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:

[0091] The quantitative assessment of failure consequences of static data is calculated according to API 581-2016 or GB / T 26610.5-2022 standards.

[0092] 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:

[0093] C1: Based on the prediction model of the local damage propagation rate of erosion corrosion, the evolution law of the leakage hole size over time is deduced.

[0094] C2: Based on the aforementioned evolutionary pattern, determine the real-time predicted leakage area size.

[0095] C3: Based on the real-time predicted leakage area, a quantitative assessment of the failure consequences of dynamic data is calculated.

[0096] The present invention provides a method for quantitatively assessing the risk of erosion damage to pressure equipment, which is divided into two categories: static assessment based on historical monitoring data and dynamic assessment based on real-time data. For static risk 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 monitoring data from the last shutdown, type and concentration of the corrosive medium, temperature, pressure, flow rate, continuous fluid density and vortex, particle / droplet discrete phase volume fraction and hardness, and hardness of the inner wall material. For dynamic risk assessment, the input data consists of valid data monitored in real-time over 1–24 hours and structural data remaining unchanged within a single maintenance period. Input parameters include at least the original wall thickness, real-time wall thickness monitoring data, type and concentration of the corrosive medium, temperature, pressure, flow rate, continuous fluid density and vortex, particle / droplet discrete phase volume fraction and hardness, as well as the hardness of the inner wall material, local structural types (such as elbows, tees, reducers), and dimensional parameters.

[0097] In another exemplary embodiment of the present invention, the probability of erosion 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 excessive defects. The magnitude of this coefficient is divided into five levels: 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. 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 excessive defects, the general condition factor, the mechanical factor, and the process factor can be directly referenced to API 581-2016 or GB / T 26610.4-2022 standards. The local / uniform thinning factor requires selection of a calculation model based on the damage form. For special structures (such as elbows, tees, and reducers), a structural correction factor needs to be added to correct for the erosion corrosion rate. The erosion corrosion thinning severity index ar / t (calculated using time a, erosion corrosion rate r, and thickness data t) is introduced to describe the wall thickness loss rate caused by thinning. The severity index of erosion corrosion thinning needs to consider the combined effects of corrosion and erosion. Static risk assessment can be roughly calculated using historical wall thickness detection data, or it can be predicted based on historical data and an engineering prediction model of erosion corrosion rate. This model can be derived from theoretical / empirical / semi-empirical models in literature / corrosion databases and corrected by actual equipment monitoring data. Dynamic risk assessment, on the other hand, needs to be calculated based on real-time monitoring data and combined with an engineering prediction model of erosion corrosion rate.

[0098] For acidic water erosion corrosion systems in petrochemical and chemical industries, an engineering prediction model for erosion corrosion rate based on extensive field experiments is adopted to improve prediction accuracy. The input parameters of this model are all parameters directly collected from engineering, including at least the original wall thickness, real-time wall thickness detection data, type and concentration of corrosive medium, temperature, pressure, flow rate, continuous fluid density and rotation, particle / droplet discrete phase volume fraction and hardness, as well as the hardness of the inner wall material, local structural types (such as elbows, tees, reducers) and dimensional parameters.

[0099] When calculating the local thinning factor based on the engineering prediction model of erosion corrosion thinning rate, a strength correction factor needs to be introduced to consider the failure boundary. This embodiment combines the methods based on pit assessment GB 19624-2004 and the applicable evaluation method GB 35013-2018 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, an erosion corrosion local damage propagation rate prediction model is introduced to extrapolate the severity index change of erosion 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 based on theoretical values ​​calculated using pit assessment GB 19624-2004 and applicable evaluation methods GB 35013-2018 or API RP 579-1-2021, or a large amount of industrial testing data. This strength correction factor is then used to correct the value of the local thinning factor.

[0100] In another exemplary embodiment of the present invention, the magnitude of the failure consequences of erosion corrosion is determined by calculating the leakage consequence area, predicting the toxic hazards, combustion and explosion hazards, environmental cleanup costs, and economic losses after failure, and the magnitude is divided into five levels: A, B, C, D, and E. The quantitative assessment of failure consequences based on static data can be directly calculated using API 581-2016 or GB / T26610.5-2022 standards. For the calculation of dynamic failure consequence area, the evolution of the leak hole size over time needs to be deduced using a erosion corrosion local damage propagation rate prediction model. Different leakage models are selected according to the type of medium to calculate the leakage rate in real time. The real-time leakage rate is used as the input condition for the diffusion model to predict the leakage area size in real time. The real-time leakage area size serves as the input condition for the dynamic assessment of the area consequences (toxic consequences, combustion and explosion consequences), economic consequences, etc., after failure, thereby achieving a dynamic quantitative assessment of the failure consequences of erosion corrosion. 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.

[0101] The risk of erosion corrosion failure can be represented by a two-dimensional risk matrix diagram, with the vertical axis representing the probability level of erosion corrosion failure and the horizontal axis representing the consequence level of erosion corrosion failure. This risk matrix diagram can be used to classify risks by selecting non-equilibrium matrix diagrams and equilibrium matrix diagrams respectively, referring to GB / T26610.1-2022 or API 581-2016 standards, dividing erosion corrosion risk into four levels: high risk, medium-high risk, medium risk, and low risk.

[0102] This invention solves the problem of difficulty in quantifying and extrapolating the risk of coupled scour corrosion damage in complex environments by providing a quantitative assessment method for the risk of localized damage failure due to scour corrosion in petrochemical and chemical pressure equipment. It also provides support for setting safety boundaries and preventing risks associated with coupled scour corrosion damage in pressure equipment.

[0103] This invention also provides an application scenario in which the aforementioned method for quantitatively assessing the risk of erosion damage to pressure equipment is applied. Specifically, the method for quantitatively assessing the risk of erosion damage to 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 the risk of petrochemical and chemical pressure equipment includes a damage form determination step, a scour corrosion failure probability calculation step, a scour 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. Then, an equipment correction coefficient can be calculated based on the thinning factor. 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 probability of scour corrosion failure can be calculated. In addition, by calculating the leakage consequence area, the consequences of scour corrosion failure can be determined. Finally, a quantitative assessment can be achieved based on the probability of scour corrosion failure and the consequences of scour corrosion failure.

[0104] In an exemplary embodiment, this implementation case focuses on the black water pipeline of a coal-water slurry gasification unit. Based on real-time monitoring data, a quantitative assessment of the risk of scouring corrosion failure is conducted. The material is 20 steel (SA106 Gr.B), and the phase is a two-phase liquid-solid system. The particulate phase is coal slag, and the liquid phase is alkaline acidic water (the main media are NH4HS, Cl-, OH-, HCO3-, etc.). The concentration of NH4HS is 0.1%, and the pH is 10. The flow rate is between 10 and 20 m / s, and the solid content is between 0.5% and 5%.

[0105] Based on previous experimental studies and literature data, the erosion rate exceeds the critical rate of 6 m / s. Under this service environment, the erosion and corrosion are synergistic, and the damage mode is local thinning. An engineering prediction model for the erosion corrosion rate is selected (see Formula 5); among which, the prediction model for the erosion corrosion rate is modified for elbows, tees, and reducers respectively (see Formula 3).

[0106]

[0107] Based on an engineering prediction model for erosion corrosion rate, and combined with actual monitoring data, damage rate is predicted in real time, and the severity index of erosion corrosion thinning is calculated. The local erosion corrosion thinning factor is determined by considering detection effectiveness and the number of tests for the highest level of effectiveness. The intended service life T is determined by calculating theoretical values ​​or using extensive industrial testing data based on pitting assessment method GB 19624-2004 and conformity evaluation method GB 35013-2018. n and remaining lifespan T SL The ratio of the planned service time to the remaining service life is used to determine the strength correction factor (1-500), which is then used to correct the local thinning factor. The remaining service life needs to be predicted using the erosion local damage propagation rate model (see Equation 6 below) to forecast future morphological evolution and changes in the erosion thinning severity index, and estimated based on the applicable evaluation method GB 35013-2018 or mature engineering experience.

[0108]

[0109] In the formula, since the concentration of alkaline acidic water is low, its influence on the damage propagation rate is small, so ε is ignored here. cor The impact.

[0110] Based on the localized erosion 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 probability of erosion failure, with an assessment level of 3.

[0111] Based on the type and quantity of hazardous media, and combined with monitoring data, the equivalent diameter of the orifice at failure is predicted in real time using the local damage propagation rate model of erosion. The size of the current leakage area is calculated by selecting the small-hole Gaussian leakage rate model or the modified Gaussian leakage rate model. The area consequences and economic consequences after the damage leakage are evaluated using the standard method of GB / T 26610.1-2022, thereby predicting the consequences of the current erosion failure. The evaluation level is C.

[0112] Based on the real-time failure probability and consequence level of erosion damage, a two-dimensional non-equilibrium matrix diagram is plotted, such as... Figure 4 As shown, the vertical axis represents the probability of erosion corrosion failure, and the horizontal axis represents the consequences of erosion corrosion failure; based on the standard type, non-equilibrium matrix diagrams and equilibrium matrix diagrams are selected as follows: Figure 5 As shown, a real-time quantitative assessment of erosion risk was conducted, and the risk level was determined to be medium risk.

[0113] 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 6 As 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 risk of erosion damage to pressure equipment.

[0114] Those skilled in the art will understand that Figure 6 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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 assessing the risk of erosion damage to pressure equipment, characterized in that, The method for quantitatively assessing the risk of erosion damage to pressure equipment includes: Determine the damage type of the equipment; the damage type includes: uniform damage and localized damage; Calculate the local / uniform thinning factor based on the damage type; 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. The probability of scouring 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 / T 26610.4-2022 standards. 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; The consequences of erosion corrosion failure are determined based on the area of ​​the leakage consequences; the consequences of erosion corrosion failure include: quantitative assessment of failure consequences based on static data and quantitative assessment of failure consequences based on dynamic data. A two-dimensional risk matrix is ​​established based on the probability of erosion failure and the consequences of erosion failure; the two-dimensional risk matrix is ​​used for real-time quantitative assessment of erosion risk. When the leakage consequence area is the failure consequence area of ​​static data, the erosion corrosion failure consequence is determined based on the leakage consequence area, specifically including: According to API 581-2016 or GB / T 26610.5-2022 standards, the quantitative assessment of the failure consequences of static data is calculated. When the leakage consequence area is the failure consequence area of ​​dynamic data, the erosion corrosion failure consequence is determined based on the leakage consequence area, specifically including: The evolution of the leak hole size over time was deduced based on the prediction model of the local damage propagation rate of erosion corrosion. Based on the aforementioned evolutionary pattern, the real-time predicted leakage area size is determined; Based on the real-time predicted leakage area, a quantitative assessment of the failure consequences of dynamic data is calculated. The expression for the prediction model of the local damage propagation rate of erosion corrosion is as follows: Where Δx is the local damage propagation rate along the main flow direction, mm / a; Δy is the local damage propagation rate circumferentially along the main flow direction, mm / a; Δz is the local damage propagation rate perpendicular to the main flow direction, mm / a; v is the particle impact velocity, m / s; ε s ε is the solid volume fraction; cor is the volume fraction of the corrosive medium; f(·) is the thinning damage propagation function.

2. The method for quantitatively assessing the risk of erosion damage to pressure equipment according to claim 1, characterized in that, The determination of the damage mode of the equipment specifically includes: Determine the effective zone of the equipment; the effective zone includes: a corrosion-dominant zone, a scouring-dominant zone, and a scouring-corrosion synergistic zone. Based on the affected area, the damage type of the equipment is determined.

3. The method for quantitatively assessing the risk of erosion damage to pressure equipment according to claim 1, characterized in that, When the damage type is uniform, the local / uniform thinning factor is calculated based on the damage type, specifically including: Determine whether the damaged structure is a special structure to obtain the first judgment result; If the first determination result is negative, then proceed according to API 581-2016 or GB / T Standard 26610.4-2022 determines the uniform thinning factor; If the first judgment result is yes, then calculate the structural correction factor based on the radius of curvature of the elbow; The uniform thinning factor is calculated based on the structure correction factor.

4. The method for quantitatively assessing the risk of erosion damage to pressure equipment according to claim 3, characterized in that, When the damage type is localized, a local / uniform thinning factor is calculated based on the damage type, specifically including: Determine whether the damaged structure is a special structure to obtain a second judgment result; If the second judgment result is negative, the critical wall thickness value for failure is calculated based on the pitting assessment method and the usability evaluation method, and the maximum value of the critical wall thickness value for failure is recorded. The strength correction factor is determined based on the scouring corrosion local damage propagation rate prediction model, the scouring corrosion thinning severity index, and the maximum value of the failure critical wall thickness; the scouring corrosion local damage propagation rate prediction model is used to predict the local damage morphology propagation rate over time; the scouring corrosion thinning severity index is calculated based on time, scouring corrosion rate, and thickness data. Based on the intensity correction factor, determine the local thinning factor; If the second judgment result is yes, then calculate the structural correction factor based on the radius of curvature of the elbow; Based on the pitting assessment method and the usability evaluation method, the critical wall thickness value for failure is calculated and the maximum value of the critical wall thickness value for failure is recorded. The strength correction factor is determined based on the prediction model of the local damage propagation rate of erosion corrosion, the severity index of erosion corrosion thinning, and the maximum value of the critical wall thickness for failure. The local thinning factor is determined based on the intensity correction factor and the structure correction factor.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for quantitatively assessing the risk of erosion damage to pressure equipment as described in any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for quantitatively assessing the risk of erosion damage to pressure equipment as described in any one of claims 1-4.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for quantitatively assessing the risk of erosion damage to pressure equipment as described in any one of claims 1-4.

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