Method for evaluating residual service life of high-corrosion pipeline

Abstract

The invention relates to a method for evaluating the residual service life of a high-corrosion pipeline. The method comprises the following steps: data acquisition: deploying a multi-film sensor network, and acquiring temperature distribution, dynamic pressure value, F <-> concentration, wall thickness reduction, local stress distribution, corrosion rate, material strain deformation, environment humidity, pH value change and Cl <-> concentration in real time; calculating the annual corrosion rate: based on an improved Arrhenius-Butler corrosion kinetic equation, embedding an F-concentration gradient model, and adopting a KPCA-FA-ELM combined algorithm to obtain an annual corrosion rate calculation equation, and calculating the residual service life of the pipeline: calculating the service life of the pipeline by utilizing the annual corrosion rate, and calculating the residual service life of the pipeline. And the remaining service life of the pipeline is equal to (the initial thickness of the pipeline-the minimum allowable wall thickness) / Rcorr * the safety coefficient-the service life of the pipeline. The method is advanced in technology, practical in engineering and dynamic in adaptability, and a reusable technical framework is provided for service life evaluation and risk management of the high-corrosion industrial pipeline.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pipeline safety evaluation, and particularly relates to a method for evaluating the residual service life of a highly corrosive pipeline. BACKGROUND

[0002] In recent years, the proportion of domestic dangerous chemicals in various aspects of industrial production is increasing, and the continuous iteration and update of products are the inevitable way to survive and develop for enterprises. New materials and new technologies are gradually put into industrial production, which makes the accidents caused by dangerous chemical production increasingly complex and variable, and various raw materials gradually improve the material quality of the chemical pipeline.

[0003] The service life of fluorine chemical pipeline is usually shortened by 30% to 50% compared with conventional pipelines due to strong corrosive medium (such as stress corrosion cracking caused by fluoride ion penetration) and high temperature and high pressure working conditions. Corrosion is an important reason for the failure of fluorine chemical pipeline. After the fluorine chemical pipeline corrodes, the pressure-bearing capacity of the pipeline will be reduced, which may cause pipeline leakage, leading to fire, explosion or poisoning accidents, and even causing domino effect, which poses a serious threat to the life and property safety of the surrounding people. Therefore, how to accurately evaluate the corrosion rate of the fluorine chemical pipeline, predict the corrosion depth of the pipeline, and evaluate the corrosion residual life of the pipeline has become a problem that needs to be solved in the current safety evaluation of fluorine chemical pipeline.

[0004] The traditional evaluation method (such as ultrasonic thickness measurement and manual inspection) has problems such as data dispersion and prediction lag, and it is difficult to realize real-time dynamic evaluation. At present, a certain data distribution model is used to study the corrosion of materials under different conditions, and the unified data distribution model cannot simultaneously fit different detection data of different pipelines, and considering the particularity and complexity of fluorine chemical pipeline, the existing pipeline corrosion prediction method is obviously not suitable. In addition, the particularity of fluorine chemical pipeline materials (such as Hastelloy alloy and PTFE lining) requires customized modeling, and the existing standards (such as ASME B31G) have poor applicability in fluorine environment. SUMMARY

[0005] In order to solve the above problems, the application provides a method for evaluating the residual service life of a highly corrosive pipeline.

[0006] The technical scheme of the method for evaluating the residual service life of a highly corrosive pipeline provided by the application is as follows:

[0007] The method for evaluating the residual service life of a highly corrosive pipeline comprises the following steps:

[0008] Data collection: Deploy multi-membrane sensor network, real-time collection of temperature distribution, dynamic pressure value, F-concentration, wall thickness reduction, local stress distribution, corrosion rate, material strain deformation, environmental humidity, pH value change, Cl-concentration;

[0009] Annual corrosion rate calculation: Based on the improved Arrhenius-Butler corrosion kinetics equation, embedded F-concentration gradient model, KPCA-FA-ELM combined algorithm is used to obtain the annual corrosion rate calculation equation,

[0010]

[0011] Wherein, 3.2×10-8 is the pre-exponential factor, representing the basic corrosion rate under standard conditions; CF represents the power function relationship between corrosion rate and corrosion medium concentration CF, CF is the F-concentration; represents the inhibitory effect of temperature on reaction rate; represents the F-concentration gradient; ELM(v,σ,pH) represents the correction function of comprehensive environmental parameters on corrosion rate;

[0012] Pipeline remaining service life calculation: Use annual corrosion rate to calculate pipeline service life,

[0013] Pipeline remaining service life=(pipe initial thickness-minimum allowable wall thickness) / Rcorr×safety factor-pipeline used years.

[0014] Further, the calculation formula of the minimum allowable wall thickness is:

[0015]

[0016] Wherein, P is the design pressure, d0 is the outer diameter of the pipeline (mm), s is the allowable stress of the material, e is the weld coefficient, w is the weld strength reduction coefficient, and y is the design factor.

[0017] Further, every two years, the pipeline wall thickness is detected by ultrasonic wave to verify whether the calculated annual corrosion rate is accurate; if the calculated annual corrosion rate exceeds the set value, the pipeline wall thickness is detected by ultrasonic wave every year to verify whether the calculated annual corrosion rate is accurate.

[0018] Further, optical fiber grating sensors are arranged every 20 meters along the pipeline, quantum dot pressure arrays are installed at the welds, F-concentration signals are processed by wavelet denoising, and ELM network is trained using 5 years of historical data, and the number of hidden layer nodes is determined by FA optimization.

[0019] Further, the integrated AI control center compares the obtained remaining service life of the pipeline with the set pipeline service life set value, and feeds back to the relevant responsible department when the pipeline service life approaches the set value, so that the relevant responsible department can replace or take remedial measures in time to avoid accidents.

[0020] Further, the multi-membrane sensor network adopts a multi-source data fusion technology, and performs real-time synchronous calibration on temperature distribution, dynamic pressure value and pH value change through Kalman filtering and adaptive weighting algorithm, and establishes a synergistic model of F-concentration and Cl-concentration to calculate a composite ion corrosion index.

[0021] Further, in the KPCA-FA-ELM combined algorithm, a dynamic parameter optimization strategy is adopted: in the ELM network training stage, the kernel function parameters and the hidden layer node weights of the kernel principal component analysis are simultaneously optimized through the FA algorithm, and the iteration number is dynamically controlled by the historical error threshold of the corrosion rate.

[0022] Further, the safety factor is dynamically adjusted according to the real-time risk level of the pipeline service environment: when the environmental humidity is greater than 80% and the pH value is less than 4, the safety factor increases by 0.2; if the local stress distribution exceeds 70% of the material yield strength, the safety factor increases by 0.3.

[0023] The application provides a high-corrosion pipeline residual service life evaluation method, which has the beneficial effects that: the high-corrosion pipeline residual service life evaluation method realizes real-time high-precision monitoring of key parameters such as temperature, pressure, F- / Cl-concentration, wall thickness and stress by deploying a multi-modal sensor network (such as a fiber grating sensor, a quantum dot pressure array and an ion selective electrode), effectively eliminates signal noise by combining a wavelet denoising algorithm to ensure the reliability and anti-interference of data, and dynamically adjusts the detection cycle according to the corrosion rate to balance the monitoring efficiency and safety demand. Secondly, the improved Arrhenius-Butler corrosion kinetics model is scientific, not only embeds an F-concentration gradient correction term to reflect the diffusion effect, but also comprehensively considers the influence of flow rate, stress and pH value through a multi-field correction function, the parameter calibration is clear, and the model has high physical interpretability. In terms of data-driven algorithm, the kernel principal component analysis is used to reduce the dimension of the original data, the principal components retaining 95% information amount are reserved, the extreme learning machine optimized by the firefly algorithm is used to realize high-precision prediction, the corrosion rate is dynamically updated every 10 minutes and the high-risk section is labeled, and the real-time early warning capability is improved. The residual life evaluation strictly follows the ASME B31.3 standard to calculate the minimum allowable wall thickness, introduces a safety factor of 1.5 to conservatively estimate the residual life, and verifies the model deviation through periodic ultrasonic detection, forms a "prediction-verification-response" closed-loop management. The AI control center integrates a three-level risk early warning mechanism and Monte Carlo simulation assisted decision-making, dynamically calibrates parameters and recommends the optimal replacement time window, realizes intelligent control. In practical application, the system successfully early warns 3 high-risk welds in 2023-2025, reduces the maintenance cost by 40%, and in the future, the prediction accuracy and generalization ability can be further improved through digital twin 3D visualization, hydrogen permeation probe expansion and federated learning technology. The application has advanced technology, engineering practicability and dynamic adaptability, and provides a reusable technical framework for the life evaluation and risk management of high-corrosion industrial pipelines. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flow chart of the high-corrosion pipeline residual service life evaluation method of the embodiment of the application;

[0025] Figure 2 is a model diagram of the control center of the high-corrosion pipeline residual service life evaluation method of the embodiment of the application. DETAILED DESCRIPTION

[0026] The application will be further described in detail below in combination with the drawings and specific embodiments:

[0027] The specific embodiment of the high-corrosion pipeline residual service life evaluation method of the application is that a high-pressure pipeline for conveying high-concentration fluorides in a fluorine chemical plant has a design pressure of 6.4 MPa, an outer diameter d0 = 508 mm, an initial wall thickness of 12 mm, and has been operated for 8 years. In order to evaluate the residual life, the following multi-modal sensor network is deployed: temperature and pressure monitoring: optical fiber grating sensors (FBG) are arranged every 20 meters along the pipeline, and temperature distribution (-20-150℃, accuracy ±0.5℃) and dynamic pressure fluctuation (0-10 MPa, resolution 0.1% FS) are collected in real time. Quantum dot pressure array sensors are installed at the welds, with a spatial resolution of 1 cm 2 , which can capture local stress concentration.

[0028] Corrosion medium detection: ion selective electrodes (ISE) are installed on the inner wall of the pipeline, and F-concentration sensors (range 0-500 ppm, accuracy ±2%) are arranged every 5 meters, and Cl-concentration is synchronously collected by electrochemical impedance spectroscopy (EIS) sensors. Wavelet denoising algorithm (Daubechies-5 basis function) is used for signal transmission to eliminate high-frequency noise interference.

[0029] Wall thickness and deformation monitoring: distributed ultrasonic thickness gauge (accuracy ±0.1 mm) is used to automatically scan wall thickness reduction every quarter; strain gauge array (range ±5000με) records material plastic deformation in real time.

[0030] Environmental parameters: temperature and humidity sensors (humidity range 0-100% RH, accuracy ±3%) are deployed on the outer surface of the pipeline, and pH value is monitored online by microfluidic electrochemical cell.

[0031] Data is transmitted to the AI control center through LoRaWAN wireless network, with a sampling frequency of 1 Hz and a historical data storage period of 5 years.

[0032] Based on the improved Arrhenius-Butler corrosion kinetics equation, the corrosion rate equation is established:

[0033]

[0034] In the formula: k0: pre-exponential factor (related to the surface state of the material); Ea: activation energy (about 45 kJ / mol in fluorine environment); CF - : fluoride ion concentration (mol / L); η: overpotential (V); β: symmetry factor.

[0035] For the special nature of fluorine chemical pipeline, the F-concentration gradient diffusion term and multi-field coupling correction are introduced, and the parameter γ = 0.12 is added: gradient sensitivity coefficient (experimentally calibrated).

[0036] Concentration spatial gradient (mol / (L·m)), f(v, σ, pH) = correction function of flow rate, stress, pH.

[0037] The new fitting formula is derived:

[0038]

[0039] where 3.2×10 -8 m / s, representing the base corrosion rate under standard conditions (25℃, 1 atm, F - = 50 ppm). Reflects the nonlinear corrosion acceleration effect when the concentration exceeds the threshold (50 ppm). - C F is the F - concentration; represents the inhibitory effect of temperature on reaction rate; represents the F - concentration gradient; ELM(v, σ, pH) represents the correction function of comprehensive environmental parameters on corrosion rate.

[0040] KPCA is used to reduce the dimensionality of the 10-dimensional original data, retaining 4 principal components (temperature, F - gradient, stress, pH) with cumulative contribution rate > 95%. The ELM network has 4 nodes in the input layer, the number of hidden layer nodes is optimized to 18 by FA, and the output layer is Rcorr. The training set is 5 years of historical data (2018-2022), and the test set error is < 3%. The corrosion rate is updated every 10 minutes, and the AI control center dynamically displays the high-risk sections (such as Rcorr > 0.5 mm / a near the weld).

[0041] The minimum operating wall thickness of the pipeline is calculated according to the formula for the minimum operating wall thickness of the pipeline.

[0042]

[0043] where s = 138 MPa is the allowable stress of the material, e = 0.85 is the weld coefficient, w = 0.9 is the strength reduction factor, and y = 0.72 is the design factor.

[0044] The current average wall thickness t = 8.5 mm, and the safety factor is taken as 1.5 (considering fluctuating load):

[0045]

[0046] The full-pipeline ultrasonic wall thickness scanning is performed every 2 years. The latest detection (2023) shows that the maximum corrosion rate is 0.45 mm / a, with a deviation of <7% from the model prediction value of 0.42 mm / a. If Rcorr>0.5 mm / a in a certain section (e.g., Rcorr=0.53 mm / a in the weld area detected in 2024), the detection period is shortened to 1 year, and local reinforcement measures are initiated.

[0047] The AI control center in this application integrates the following functions: real-time comparison of sensor data and model prediction values, automatic calibration of parameters (such as temperature compensation coefficients). When the remaining life is <3 years or Rcorr increases by >20%, a three-level alarm is triggered: Level 1: SMS notification to inspection personnel; Level 2: generate maintenance work order and push to management system; Level 3: automatically interlock and close upstream valve (manual confirmation required). Based on Monte Carlo simulation, a life distribution curve (confidence level 95%) is generated to recommend the optimal replacement time window.

[0048] The high-corrosion pipeline remaining service life evaluation method of the application realizes real-time high-precision monitoring of key parameters such as temperature, pressure, F- / Cl- concentration, wall thickness, and stress by deploying a multi-modal sensor network (such as fiber Bragg grating sensors, quantum dot pressure arrays, and ion-selective electrodes). The wavelet denoising algorithm effectively eliminates signal noise, ensuring data reliability and anti-interference. At the same time, the detection period is dynamically adjusted according to the corrosion rate, balancing monitoring efficiency and safety requirements. Second, the improved Arrhenius-Butler corrosion kinetics model is scientifically strong. It not only embeds a F-concentration gradient correction term to reflect the diffusion effect, but also integrates the effects of flow rate, stress, and pH through multi-field correction functions. The parameter calibration is clear, and the model has high physical interpretability. In terms of data-driven algorithms, kernel principal component analysis is used to reduce the dimensionality of the original data, retaining 95% of the information content of the principal components. Combined with the extreme learning machine optimized by the firefly algorithm, high-precision prediction is achieved, with dynamic updating of the corrosion rate every 10 minutes and labeling of high-risk sections, improving real-time warning capability. The remaining life evaluation strictly follows the ASME B31.3 standard to calculate the minimum allowable wall thickness, introduces a safety factor of 1.5 to conservatively estimate the remaining life, and verifies the model deviation through regular ultrasonic detection, forming a "prediction-verification-response" closed-loop management. The AI control center integrates a three-level risk warning mechanism and Monte Carlo simulation for decision support, dynamically calibrates parameters, and recommends the optimal replacement time window, achieving intelligent control. In practical applications, the system successfully warned 3 high-risk welds in 2023-2025, reducing maintenance costs by 40%. In the future, through digital twin 3D visualization, hydrogen permeation probe expansion, and federated learning technology, the prediction accuracy and generalization ability can be further improved. This scheme is advanced in technology, practical in engineering, and has dynamic adaptability, providing a reusable technical framework for the life evaluation and risk management of high-corrosion industrial pipelines.

[0049] The above merely preferred embodiments of the present application and are not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing the remaining service life of a highly corrosive pipeline, characterized in that: The following steps are involved: Data collection: Deploy a multi-film sensor network to collect real-time data on temperature distribution, dynamic pressure value, F-concentration, wall thickness reduction, local stress distribution, corrosion rate, material strain, ambient humidity, pH value change, Cl - concentration; Annual corrosion rate calculation: Based on the improved Arrhenius-Butler corrosion kinetics equation, embedded in the F-concentration gradient model, and using the KPCA-FA-ELM combined algorithm, the annual corrosion rate calculation equation is obtained. Among them, 3.2×10 -8 is the pre-exponential factor, which represents the basic corrosion rate under standard conditions; Indicates the corrosion rate and the concentration of the corrosive medium C F The power function relationship, C F is the F-concentration; It indicates the inhibitory effect of temperature on the reaction rate; represents the F-concentration gradient; ELM(v, σ, pH) represents the correction function of comprehensive environmental parameters on corrosion rate; Calculation of remaining service life of pipeline: Calculate the service life of pipeline using annual corrosion rate. Remaining service life of the pipeline = (initial thickness of the pipeline - minimum allowable wall thickness) / R corr ×Safety factor - Age of the pipeline.

2. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: The minimum allowable wall thickness is calculated as follows: Where P is the design pressure, d0 is the outer diameter of the pipe (mm), s is the allowable stress of the material, e is the weld coefficient, w is the weld strength reduction coefficient, and y is the design factor.

3. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: Every two years, the pipe wall thickness is tested by ultrasonic inspection to verify whether the calculated annual corrosion rate is accurate. If the calculated annual corrosion rate exceeds the set value, the pipe wall thickness is tested by ultrasonic inspection every year to verify whether the calculated annual corrosion rate is accurate.

4. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: Fiber Bragg grating sensors were arranged every 20 meters along the pipeline, quantum dot pressure arrays were installed at the welds, F-concentration signals were processed by wavelet denoising, and the ELM network was trained using 5 years of historical data. The number of hidden layer nodes was determined by FA optimization.

5. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: The integrated AI control center compares the assessed remaining service life of the pipeline with the set pipeline service life setting value. When the pipeline service life approaches the set value, feedback is given to the relevant responsible department. The relevant responsible department will replace the pipeline or take remedial measures in time to avoid accidents.

6. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: The multi-membrane state sensor network adopts multi-source data fusion technology, and performs real-time synchronous calibration of temperature distribution, dynamic pressure value and pH value changes through Kalman filtering and adaptive weighting algorithm, and establishes a synergistic effect model of F- concentration and Cl- concentration to calculate the composite ion corrosion index.

7. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: In the KPCA-FA-ELM combination algorithm, a dynamic parameter optimization strategy is adopted: during the ELM network training phase, the kernel function parameters of the kernel principal component analysis and the hidden layer node weights are simultaneously optimized through the FA algorithm, and the number of iterations is dynamically controlled by the historical error threshold of the corrosion rate.

8. The method for evaluating the remaining service life of a highly corrosive pipeline according to claim 1, wherein: The safety factor is dynamically adjusted based on the real-time risk level of the pipeline service environment: when the ambient humidity is >80% and the pH value is <4, the safety factor is increased by 0.2; if the local stress distribution is detected to exceed 70% of the material yield strength, the safety factor is increased by 0.3.

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