A data-driven dynamic risk and health degree evaluation method for pressure equipment facilities

Abstract

The application discloses a data-driven dynamic risk and health degree evaluation method for pressure-bearing equipment and facilities, belongs to the technical field of risk evaluation of pressure-bearing equipment and facilities, and establishes a data-driven real-time dynamic risk and health degree evaluation model for petrochemical equipment and facilities, so that the current dynamic risk grade and health degree grade of the pressure-bearing equipment and facilities are calculated in real time. The model comprises a spheroidization mechanism k model, a graphitization mechanism k model, a high-temperature sulfur / naphthenic acid corrosion mechanism k model and an ammonium bisulfide corrosion mechanism k model. The failure possibility is dynamically corrected by introducing a failure possibility influence coefficient, so that the dynamic risk grade is obtained. The application applies the dynamic risk grade change, especially the change from a low-level risk to a high-level risk, so that the system can automatically give the influence factors of the increased failure possibility, and guide the operating personnel to reduce the operation risk of the pressure-bearing equipment and facilities by optimizing operation process parameters and the like, and ensure the operation safety and reliability of the equipment and facilities.

Description

Technical Field

[0001] This invention belongs to the technical field of risk assessment for pressure equipment and facilities, specifically relating to a data-driven method for assessing the dynamic risk and health of pressure equipment and facilities. Background Technology

[0002] Currently, the integrity of pressure equipment and facilities encompasses both management and technical integrity. Specifically, Risk Assessment (RBI) for pressure equipment and facilities aims to optimize inspection strategies and content, reduce operational risks, and improve operational safety and reliability. The risk assessment level primarily guides post-shutdown inspection and maintenance. However, for the daily operation, maintenance, and inspection of pressure equipment and facilities, this static risk level is unrelated to factors such as current operating temperature, operating pressure, operating flow rate, and changes in media composition. Therefore, the static risk level of the RBI assessment provides enhanced and improved guidance for the daily operation of pressure equipment and facilities.

[0003] Dynamic risk calculation for pressure equipment and facilities is the basis for continuous, long-term risk-based inspection (RBI) assessments. In-service pressure equipment and facilities are affected by changes in temperature, pressure, flow rate, and working medium, and their risks have significant dynamic attributes. For example, changes in raw materials processed, operating temperature, operating pressure, composition of the operating medium, and the effectiveness of corrosion and protection will all cause changes in the likelihood of corrosion failure of pressure equipment and facilities.

[0004] Current Risk Bias Assessment (RBI) technology can provide quantifiable risks and inspection plans, laying the foundation for risk control and management. However, during the assessment process, the parameters used in RBI to calculate equipment risk, such as operating pressure, operating temperature, flow rate, media composition, wall thickness, materials, and material composition of pressure equipment, are all design values. Therefore, the calculated risk is a static value. In actual equipment operation, the technical parameters involved in RBI assessment are dynamically changing. If the static risk of RBI is used to characterize the current operational risk of equipment, it will deviate from the actual needs of predictive maintenance and inspection of equipment, making it difficult to guide the daily operation and maintenance of pressure equipment. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a data-driven method for evaluating the dynamic risk and health of pressure equipment and facilities. The equipment risk level obtained using this evaluation method can be used to guide the daily process optimization of pressure equipment and facilities.

[0006] The technical solution of the present invention is as follows:

[0007] A data-driven method for assessing the dynamic risk and health of pressure equipment facilities includes the following steps:

[0008] S1. Real-time acquisition of failure-sensitive characteristic parameter data of pressure-bearing equipment and facilities, including monitoring parameter data corresponding to spheroidization mechanism, graphitization mechanism, high-temperature sulfur / cycloalkane acid corrosion mechanism, and ammonium hydrosulfide corrosion mechanism;

[0009] S2. Calculate the real-time corrosion rate and real-time service life based on the real-time operational failure sensitive characteristic parameter values ​​and the design failure sensitive characteristic parameter values;

[0010] S3. Calculate the real-time dynamic failure probability influencing factor k;

[0011] S4. Calculate the real-time health status based on the real-time dynamic failure probability influence factor k;

[0012] S5. Based on the health assessment criteria, output the current health level of the pressure equipment and facilities in real time;

[0013] S6. Calculate the dynamic failure probability level based on the real-time dynamic failure probability influence factor k and the static RBI assessment failure probability discrimination criterion;

[0014] S7. Based on the static RBI failure consequence level and dynamic failure probability level, construct a dynamic RBI risk discrimination matrix, calculate and output the real-time dynamic risk level.

[0015] Preferably, the formula for calculating the real-time dynamic failure probability influence factor k is:

[0016]

[0017] Among them, P f0 =f(α) 10 ,α 20 ,α 30 ,…,α n0 α represents the probability of failure of petrochemical equipment and facilities under design conditions. i0 (i = 1, 2, ..., n) represent the design parameters of pressure-bearing equipment and facilities; P f = f(α1,α2,α3,…,α) n α represents the probability of failure of petrochemical equipment and facilities under operating conditions. i (i = 1, 2, ..., n) represent the operating parameters of pressure equipment and facilities.

[0018] Preferably, the formula for calculating health is:

[0019]

[0020] Where α represents the magnitude of the real-time dynamic monitoring process parameters, f represents the probability of failure, and k represents the influencing factor of the probability of real-time dynamic failure of the equipment.

[0021] Preferably, the health evaluation criteria are statistically analyzed and defined using the fuzzy comprehensive membership method, dividing health into four health states: excellent, good, acceptable, and unacceptable, with intervals of [1, 0.75), [0.75, 0.5), [0.5, 0.25), and [0.25, 0.05], respectively.

[0022] Preferably, the formula for calculating the real-time dynamic risk level is:

[0023] R(t)=[k·P f (t)]×C(t) (5)

[0024] Where R(t) represents the real-time dynamic risk of pressure-bearing equipment and facilities, k represents the real-time dynamic failure probability influencing factor of pressure-bearing equipment and facilities, and P f C(t) represents the probability of failure of pressure equipment under operating conditions, and C(t) represents the consequences of failure.

[0025] Preferably, based on the monitoring parameter data corresponding to the graphitization mechanism, high-temperature sulfur / cycloalkane acid corrosion mechanism, ammonium hydrosulfide corrosion mechanism, and spheroidization mechanism, k-models for the graphitization mechanism, high-temperature sulfur / cycloalkane acid corrosion mechanism, ammonium hydrosulfide corrosion mechanism, and spheroidization mechanism were constructed respectively.

[0026] Preferably, the content of the k-model for the graphitization mechanism is as follows:

[0027] The formula for calculating the service life T(t) is:

[0028] T(t) = 3.8 × 10 18 ×exp(-0.0797·t)+1.08 (6)

[0029] The formula for calculating the real-time dynamic failure probability influencing factor k corresponding to the graphitization mechanism is as follows:

[0030]

[0031] Where T(t) is the service life, t is the actual operating temperature, and t0 is the design temperature.

[0032] Preferably, the content of the high-temperature sulfur / cycloalkane acid corrosion mechanism k-model is as follows:

[0033] The failure-sensitive characteristic parameters for high-temperature sulfur / naphthenic acid corrosion are operating temperature, sulfur content of the oil, acid value, and H2S content in the gas phase; the corrosion rate fitting formula is:

[0034]

[0035] The formula for calculating the real-time dynamic failure probability influencing factor k corresponding to the high-temperature sulfur / naphthenic acid corrosion mechanism is as follows:

[0036]

[0037] Where f(t) is the corrosion rate, t is the actual operating temperature, and t0 is the design temperature.

[0038] Preferably, the k-model for the corrosion mechanism of ammonium hydrosulfide is as follows:

[0039] The sensitive characteristic parameter of ammonium hydrosulfide corrosion mechanism is cyanide concentration. The applicable material is carbon steel. The operating conditions are: pH > 7, operating temperature (25-65)℃, and NH4HS concentration > 2wt%.

[0040] When the cyanide concentration is greater than 20 ppm, the corrosion rate model is as follows:

[0041] f(α) = 0.01α - 0.15 (10)

[0042] The formula for calculating the real-time dynamic failure probability influencing factor k corresponding to the ammonium hydrosulfide corrosion mechanism is as follows:

[0043]

[0044] Where f(α) is the corrosion rate, α is the cyanide concentration under actual operating conditions, and α0 is the cyanide concentration under design conditions.

[0045] Preferably, the content of the spheroidization mechanism k-model is as follows:

[0046] The spheroidization mechanism parameters are as follows: materials are carbon steel and low alloy steel, including C-0.5Mo, 1Cr-0.5Mo, 1.25Cr-0.5Mo, 2.25Cr-1Mo and 9Cr-1Mo; operating conditions: operating temperature is 440~760℃;

[0047] The relationship between the service life L of pressure equipment and temperature t is expressed as follows:

[0048] L(t) = 2 × 10 14 ×exp(-0.069·t) (12)

[0049] The formula for calculating the real-time dynamic failure probability influence factor k corresponding to the spheroidization mechanism is as follows:

[0050]

[0051] Where L(t) is the operating life, t is the real-time operating temperature, and t0 is the design temperature.

[0052] The beneficial technical effects of this invention are as follows:

[0053] This invention addresses the spheroidization mechanism, graphitization mechanism, high-temperature sulfur / cycloalkane acid corrosion mechanism, and ammonium hydrosulfide corrosion mechanism. A quantitative method is used to calculate the real-time dynamic failure probability influencing factor k. Drawing upon the failure probability level evaluation criteria developed by the original static RBI assessment method and applying the failure consequence level evaluation criteria and risk discrimination matrix, the dynamic risk level of pressure equipment and facilities as failure-sensitive characteristic parameters change can be easily calculated. The dynamic risk level change is related to the corresponding failure-sensitive characteristic parameter change. When the risk level changes from a lower level to a higher level, it indicates a change in operating parameters. Operators can easily optimize operating parameters to keep the pressure equipment and facilities operating within an acceptable risk level range, effectively improving the operational safety and reliability of the pressure equipment and facilities. This invention employs a dynamic RBI assessment method to optimize operating parameters and guide daily operations. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the data-driven real-time dynamic risk and health assessment principle of pressure equipment and facilities according to the present invention.

[0055] Figure 2 This invention provides a real-time dynamic risk level discrimination matrix for petrochemical equipment and facilities.

[0056] Figure 3 This is a comparison chart of the risk levels at reactor operating temperatures of 440℃ (left) and 475℃ (right) in Example 1 of the present invention. Detailed Implementation

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0058] A data-driven method for dynamic risk and health assessment of pressure equipment and facilities is proposed. This method dynamically adjusts the probability of failure by introducing a failure probability influence coefficient, thereby obtaining a dynamic risk level. Applying dynamic risk level changes, especially when the risk level changes from a lower to a higher level, the system can automatically identify the factors influencing the increased probability of failure, guiding operators to reduce the operational risk of pressure equipment and facilities by optimizing operating parameters. Key components of this method include defining the failure probability influence factor k, real-time health assessment of pressure equipment and facilities, real-time dynamic risk assessment of pressure equipment and facilities, and a model for the real-time dynamic failure probability influence factor k of pressure equipment and facilities. These components are analyzed in detail below.

[0059] I. Defining the failure probability influencing factor k

[0060] (1) Analysis of key influencing factors on the possibility of failure of pressure-bearing equipment and facilities

[0061] Different mechanisms correspond to different monitoring parameters, such as operating temperature, operating pressure, operating flow rate, total acid value, crude oil sulfur content, pH value, partial pressure of H2S in gas, partial pressure of CO2 in gas, etc. Correlation analysis is performed on the above parameters to screen out the key influencing factors that can cause changes in the probability of failure of pressure equipment and facilities.

[0062] (2) Define the probability of failure of pressure-bearing equipment and facilities under design conditions.

[0063] With α 10 ,α 20 ,α 30 ,…,α n0 This represents the design temperature, design pressure, design flow rate, and allowable total acid value, crude oil sulfur content, pH value, partial pressure of H2S gas, and partial pressure of CO2 gas for pressure equipment facilities. Under these design conditions, the probability of failure is defined as follows:

[0064] P f0 =f(α) 10 ,α 20 ,α 30 ,…,α n0 (1)

[0065] In the formula, f(·) is related to the corrosion mechanism and damage mechanism of pressure equipment and facilities.

[0066] (3) Define the probability of failure of pressure equipment and facilities under operating conditions.

[0067] Using α1, α2, α3, ..., α n This represents parameters such as operating temperature, operating pressure, operating flow rate, and actual operating conditions, including total acid value, crude oil sulfur content, pH value, partial pressure of H2S gas, and partial pressure of CO2 gas. The probability of failure under these operating conditions is defined as follows:

[0068] P f = f(α1,α2,α3,…,α) n (2)

[0069] In the formula, f(·) is related to the corrosion mechanism and damage mechanism of pressure equipment and facilities.

[0070] (4) Based on different mechanisms, the influencing factor of the real-time dynamic failure probability of pressure-bearing equipment and facilities is determined as follows:

[0071]

[0072] II. Real-time health assessment of pressure equipment and facilities

[0073] The health status of pressure equipment facilities is a normalized metric that measures the health of equipment. It is an assessment of the degree of deviation between the current state and the expected state of the equipment. The normalized metric is generally within the range of (0,1). The expected state refers to the design baseline state of the same equipment or similar equipment under the same operating conditions.

[0074] (1) Health characterization of pressure-bearing equipment and facilities

[0075] This invention utilizes the failure probability influencing factor to characterize the health of pressure-bearing equipment and facilities. If the magnitude of the real-time dynamic monitoring process technology parameter is directly proportional to the failure probability, then the health is expressed as H = 1 / k; if the magnitude of the real-time dynamic monitoring process technology parameter is inversely proportional to the failure probability, then the health is expressed as H = k. The health of pressure-bearing equipment and facilities can be characterized by formula (4).

[0076]

[0077] In the formula, α represents the magnitude of the real-time dynamic monitoring process technology parameter, f represents the magnitude of the failure probability, k represents the influence factor of the real-time dynamic failure probability of pressure equipment and facilities, k∈(0,1), then H∈(0,1).

[0078] (2) Health evaluation criteria for pressure equipment and facilities

[0079] The fuzzy comprehensive membership method was used to statistically analyze and define the health evaluation criteria for pressure equipment and facilities (as shown in Table 1). The health status of pressure equipment and facilities was scored using four indicators: excellent, good, permissible, and unacceptable. The membership function (health function H) was defined as ([1,0.75),[0.75,0.5),[0.5,0.25),[0.25,0)).

[0080] Table 1 Health Evaluation Criteria for Pressure Equipment and Facilities

[0081] health status Health function H excellent H＞0.75 good 0.5＜H≤0.75 allow 0.25＜H≤0.5 Not allowed 0＜H≤0.25

[0082] III. Real-time dynamic risk assessment of pressure equipment and facilities

[0083] (1) Characterization of real-time dynamic risk level of pressure-bearing equipment and facilities

[0084] R(t)=[k·P f (t)]×C(t) (5)

[0085] In the formula, R(t) represents the real-time dynamic risk of the pressure-bearing equipment and facilities, k represents the influencing factor of the real-time dynamic failure probability of the pressure-bearing equipment and facilities, and P f C(t) represents the probability of failure of pressure equipment under operating conditions, and C(t) represents the consequences of failure.

[0086] (2) Criteria for determining the probability level of real-time failure

[0087] The real-time failure probability judgment criterion draws on the static RBI assessment failure probability level judgment criterion, where the original RBI assessment failure probability P f (t) using [k·P f (t)] is used instead. According to [k·P] f The numerical range of [(t)] is used to determine the probability level of real-time failure of equipment and facilities. Table 2 shows the specific criteria for determining the probability level of real-time failure of pressure equipment and facilities.

[0088] Table 2 Criteria for Determining the Probability Level of Real-Time Failure of Pressure Equipment and Facilities

[0089] Failure probability level Failure probability 1 <![CDATA[0.00000<k·P f (t)≤0.00001]]> 2 <![CDATA[0.00001<k·P f (t)≤0.00010]]> 3 <![CDATA[0.00010<k·P f (t)≤0.00100]]> 4 <![CDATA[0.00100<k·P f (t)≤0.01000]]> 5 <![CDATA[0.01000<k·P f (t)≤0.1000]]>

[0090] (3) Real-time failure consequence level discrimination matrix

[0091] The criteria for judging the consequences of real-time failures are based on the criteria for judging the severity of failures in static RBI assessments.

[0092] (4) Dynamic RBI Risk Assessment Matrix

[0093] The dynamic risk assessment matrix for pressure equipment and facilities draws on the static RBI assessment risk assessment matrix. Figure 2 It displays a real-time dynamic risk level assessment matrix for petrochemical equipment and facilities. Figure 2 The horizontal axis represents the failure consequence level, with A to E indicating increasingly higher failure consequence levels; the vertical axis represents the failure probability level, with 1 to 5 indicating increasingly higher failure probability levels; the risk level is divided into four levels: low risk (L), medium risk (M), medium-high risk (MH), and high risk (H).

[0094] IV. k-model of influencing factors on the real-time dynamic failure probability of petrochemical pressure equipment and facilities

[0095] Based on different failure mechanisms in petrochemical pressure equipment and facilities, corresponding k-models were constructed, including the graphitization mechanism k-model, the high-temperature sulfur / cycloalkane acid corrosion mechanism k-model, the ammonium hydrosulfide corrosion mechanism k-model, and the spheroidization mechanism k-model.

[0096] (1) k-model of graphitization mechanism

[0097] The formula for calculating the predicted service life is defined as follows:

[0098] T(t) = 3.8 × 10 18 ×exp(-0.0797·t)+1.08 (6)

[0099] When the material is low-carbon steel or CrMoV, the failure sensitivity characteristic parameter is T, which is the number of operating hours.

[0100] The real-time dynamic failure probability influencing factor corresponding to the graphitization mechanism can be expressed by the following formula:

[0101]

[0102] Where T(t) is the service life, t is the actual operating temperature, and t0 is the design temperature.

[0103] (2) High-temperature sulfur / cycloalkane acid corrosion mechanism k-model

[0104] The failure-sensitive characteristic parameters for high-temperature sulfur / naphthenic acid corrosion are operating temperature, sulfur content of oil, acid value, and H2S content in the gas phase.

[0105] The corrosion rate fitting formula is:

[0106]

[0107] The real-time dynamic failure probability influencing factor corresponding to the high-temperature sulfur / naphthenic acid corrosion mechanism can be expressed by the following formula:

[0108]

[0109] Where f(t) is the corrosion rate, t is the actual operating temperature, and t0 is the design temperature.

[0110] (3) k-model of ammonium hydrosulfide corrosion mechanism

[0111] The sensitive characteristic parameter of ammonium hydrosulfide corrosion mechanism is cyanide concentration. The applicable material is carbon steel. The operating conditions are: pH > 7, operating temperature (25-65)℃, and NH4HS concentration > 2wt%.

[0112] When the cyanide concentration is greater than 20 ppm, the corrosion rate model is as follows:

[0113] f(α) = 0.01α - 0.15 (10)

[0114] The formula for calculating the real-time dynamic failure probability factor k is:

[0115]

[0116] Where f(α) is the corrosion rate, α is the cyanide concentration under actual operating conditions, and α0 is the cyanide concentration under design conditions.

[0117] (4) Spheroidization mechanism k-model

[0118] The spheroidization mechanism parameters are as follows: materials are carbon steel and low alloy steel, including C-0.5Mo, 1Cr-0.5Mo, 1.25Cr-0.5Mo, 2.25Cr-1Mo and 9Cr-1Mo; operating conditions: operating temperature is 440~760℃.

[0119] The relationship between the service life L of pressure equipment and temperature t is expressed as follows:

[0120] L(t) = 2 × 10 14 ×exp(-0.069·t) (12)

[0121] The formula for calculating the real-time dynamic failure probability factor k is:

[0122]

[0123] Where L(t) is the operating life, t is the real-time operating temperature, and t0 is the design temperature.

[0124] The core of a data-driven method for dynamic risk and health assessment of pressure equipment facilities is to construct a data-driven real-time dynamic risk and health assessment model for petrochemical equipment facilities. For example... Figure 1 As shown, the data-driven real-time dynamic risk assessment model for petrochemical equipment and facilities is designed as a "black box." The input parameters of the "black box" are failure-sensitive characteristic parameter data, and the output of the model is the real-time dynamic risk level and health level of the pressure equipment and facilities. The data-driven real-time dynamic risk assessment of pressure equipment and facilities is based on static RBI assessment. The failure-sensitive characteristic parameter design data, the static RBI assessment failure probability level discrimination criteria, and the static RBI failure consequence level constitute the static RBI assessment database, which also becomes the knowledge base for real-time dynamic risk assessment of pressure equipment and facilities.

[0125] The steps for data-driven real-time dynamic risk assessment and health assessment of petrochemical pressure equipment and facilities are as follows:

[0126] (1) Collect failure-sensitive characteristic parameter data.

[0127] The failure sensitivity parameter data of this invention are related to the spheroidization mechanism, graphitization mechanism, high-temperature sulfur / cycloalkane acid corrosion mechanism, and ammonium hydrosulfide corrosion mechanism.

[0128] (2) Based on the real-time operation failure sensitive characteristic parameter value and the design failure sensitive characteristic parameter value, the real-time corrosion rate and real-time service life are characterized.

[0129] The real-time corrosion rate and real-time service life are calculated based on the aforementioned mechanism k-model.

[0130] (3) Calculate the influence factor k of the real-time dynamic failure probability.

[0131] Similarly, the real-time dynamic failure probability influence factor k is calculated according to the corresponding mechanism k model.

[0132] (4) Calculate the real-time health status based on the real-time dynamic failure probability influence factor k.

[0133] (5) Based on the health evaluation criteria, output the current health level of the pressure equipment and facilities in real time.

[0134] (6) Calculate the dynamic failure probability level based on (3) and the static RBI assessment failure probability discrimination criterion.

[0135] (7) Calculate the real-time dynamic risk level based on the dynamic RBI risk discrimination matrix constructed based on the static RBI failure consequence level and the dynamic failure probability level.

[0136] Example 1

[0137] The following uses the "spheroidization mechanism" as an example to illustrate the process of dynamic risk assessment and health assessment in this invention. The failure mechanism of a reactor weld is spheroidization, and its failure-sensitive characteristic parameter is temperature. Temperature changes significantly affect the reactor's service life. The following describes the specific implementation of data-driven real-time dynamic risk assessment and health assessment of the reactor.

[0138] (1) Design conditions of the reactor

[0139] Equipment material: 2.25Cr-1Mo low alloy steel.

[0140] Design temperature: 440℃-450℃

[0141] The (static) RBI assessment of the reactor's corrosion mechanism is: spheroidization;

[0142] (Static) RBI assessment of reactor failure probability is: P f =0.00098, which corresponds to a failure probability level of 3 according to Table 2;

[0143] (Static) RBI assessment of the reactor failure consequences classifies it as Level D;

[0144] according to Figure 2 The real-time dynamic risk level assessment matrix for petrochemical equipment and facilities shows that the risk level of this reactor is classified as medium to high risk (MH) by RBI.

[0145] (3) The normal operating temperature of the reactor is 440℃. During abnormal production, the operating temperature suddenly rises to 475℃. The service life of the reactor at the corresponding operating temperature is calculated using formula (12).

[0146] Estimated service life of the reactor at an operating temperature of 440℃:

[0147] L = 2 × 10 14 ×exp(-0.069·440)=13.057 years

[0148] Estimated service life of the reactor at an operating temperature of 475℃:

[0149] L = 2 × 10 14 ×exp(-0.069·475)=1.1668 years

[0150] The influencing factor of real-time dynamic failure probability corresponding to the spheroidization mechanism is calculated according to formula (13):

[0151]

[0152] The reactor operating temperature increases from 440℃ to 475℃. According to the failure probability calculation method in formula (5), the failure probability is:

[0153] P f =k·P f (t)=11.1904×0.0098=0.1097

[0154] Based on the failure consequence being classified as Level D, referring to Table 2, the reactor failure probability level under the operating condition of 475℃ is Level 5.

[0155] according to Figure 2 The real-time dynamic risk level assessment matrix for petrochemical equipment and facilities shows that the dynamic risk level of this reactor is high according to RBI.

[0156] According to formula (4), the health status H of the reactor is 0.089. Table 1 shows the health status evaluation criteria for pressure equipment and facilities, indicating that the reactor is not allowed to operate at 475℃.

[0157] like Figure 3 As shown, the petrochemical reactor operates under design conditions of 440℃, with an excellent reactor health status and an RBI risk level of MH; at 475℃, it is permissible, but the reactor health status is not permissible, and the real-time dynamic risk level is H. It can be seen that after applying the technical solution of this invention, the reactor risk level is reduced, fully demonstrating the effectiveness of the invention's technical application.

[0158] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A data-driven method for assessing the dynamic risk and health of pressure equipment and facilities, characterized in that, Includes the following steps: S1. Real-time acquisition of failure-sensitive characteristic parameter data of pressure-bearing equipment and facilities, including monitoring parameter data corresponding to spheroidization mechanism, graphitization mechanism, high-temperature sulfur / cycloalkane acid corrosion mechanism, and ammonium hydrosulfide corrosion mechanism; S2. Calculate the real-time corrosion rate and real-time service life based on the real-time operational failure sensitive characteristic parameter values ​​and the design failure sensitive characteristic parameter values; S3. Calculate the real-time dynamic failure probability influence factor k; the formula for calculating the real-time dynamic failure probability influence factor k is: (3) in, To assess the likelihood of failure of petrochemical equipment and facilities under design conditions. Represents the design parameters of pressure-bearing equipment and facilities, i=1,2,…,n; To account for the possibility of failure of petrochemical equipment and facilities under operating conditions. These represent the operating parameters of pressure-bearing equipment and facilities, i=1,2,…,n; S4. Calculate the real-time health status based on the real-time dynamic failure probability influence factor k; S5. Based on the health assessment criteria, output the current health level of the pressure equipment and facilities in real time; S6. Calculate the dynamic failure probability level based on the real-time dynamic failure probability influence factor k and the static RBI assessment failure probability discrimination criterion; S7. Based on the dynamic RBI failure consequence level and dynamic failure probability level, construct a dynamic RBI risk discrimination matrix, calculate and output the real-time dynamic risk level; Based on the monitoring parameter data corresponding to the graphitization mechanism, high-temperature sulfur / naphthenic acid corrosion mechanism, ammonium hydrosulfide corrosion mechanism, and spheroidization mechanism, graphitization mechanisms were constructed respectively. Model, High-Temperature Sulfur / Cyclonal Acid Corrosion Mechanism Model, Ammonium hydrosulfide corrosion mechanism Model, spheroidization mechanism Model; The content of the graphitization mechanism k-model is as follows: Service life The calculation formula is: (6) The formula for calculating the real-time dynamic failure probability influencing factor k corresponding to the graphitization mechanism is as follows: (7) in, t is the service life, t is the actual operating temperature, and t0 is the design temperature. The high-temperature sulfur / cycloalkane corrosion mechanism The model contains the following: The failure-sensitive characteristic parameters for high-temperature sulfur / naphthenic acid corrosion are operating temperature, sulfur content of oil, acid value, and H2S content in the gas phase. The corrosion rate fitting formula is: (8) The formula for calculating the real-time dynamic failure probability influencing factor k corresponding to the high-temperature sulfur / naphthenic acid corrosion mechanism is as follows: (9) in, t is the corrosion rate, t is the actual operating temperature, and t0 is the design temperature. The corrosion mechanism of ammonium hydrosulfide The model contains the following: The sensitive characteristic parameter for the corrosion mechanism of ammonium hydrosulfide is the cyanide concentration. The applicable material is carbon steel, and the operating conditions are: pH > 7, operating temperature (25–65)℃, and NH4HS concentration > 2 wt%. When the cyanide concentration is greater than 20 ppm, the corrosion rate model is as follows: (10) The formula for calculating the real-time dynamic failure probability influencing factor k corresponding to the ammonium hydrosulfide corrosion mechanism is as follows: (11) in, For corrosion rate, This represents the cyanide concentration under actual operating conditions. The cyanide concentration under design conditions; The spheroidization mechanism The model contains the following: The spheroidization mechanism parameters are as follows: materials are carbon steel and low alloy steel, including C-0.5Mo, 1Cr-0.5Mo, 1.25Cr-0.5Mo, 2.25Cr-1Mo and 9Cr-1Mo; operating conditions: operating temperature is 440~760℃; The relationship between the service life L of pressure equipment and temperature t is expressed as follows: (12) The formula for calculating the real-time dynamic failure probability influence factor k corresponding to the spheroidization mechanism is as follows: (13) in, This refers to the service life, where t is the actual operating temperature. Design temperature.

2. The data-driven dynamic risk and health assessment method for pressure-bearing equipment and facilities according to claim 1, characterized in that, The formula for calculating the health status is: (4) in, This indicates the magnitude of real-time dynamic monitoring process parameters. Indicates the probability of failure. This indicates the influencing factor of the possibility of real-time dynamic failure of the equipment.

3. The data-driven dynamic risk and health assessment method for pressure-bearing equipment and facilities according to claim 2, characterized in that, The health evaluation criteria are statistically analyzed and defined using the fuzzy comprehensive membership method, dividing health into four health states: excellent, good, acceptable, and unacceptable, with intervals of [1, 0.75), [0.75, 0.5), [0.5, 0.25), and [0.25, 0.05], respectively.

4. The data-driven dynamic risk and health assessment method for pressure-bearing equipment and facilities according to claim 1, characterized in that, The formula for calculating the real-time dynamic risk level is as follows: (5) in, Real-time dynamic risk assessment for pressure-bearing equipment and facilities. This refers to the influencing factors of the real-time dynamic failure probability of pressure-bearing equipment and facilities. It refers to the possibility of failure of pressure equipment and facilities under operating conditions. This is a consequence of failure.

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