A method and device for monitoring corrosion of water-vapor pipelines in a supercritical unit

Abstract

The present invention provides a method and a monitoring device for corrosion monitoring of water-vapor pipelines in a supercritical unit, which relates to the technical field of pipeline corrosion monitoring. The present invention trains a corrosion model through the historical working conditions and corrosion amounts of the water-vapor pipelines, inputs the real-time working condition data of the water-vapor pipelines to be monitored into the corrosion model to obtain the predicted corrosion amount, obtains the water quality data at the inlet and outlet of the pipeline, forms a water quality difference and generates a water quality score and the current corrosion amount, obtains the initial vibration data and the current vibration data of each single-section pipeline, forms the vibration change amount of each single-section pipeline and the standard deviation of the vibration change amounts of all pipelines, sets a threshold value for the standard deviation of the vibration change amounts, sets corrosion thresholds I and II, and analyzes the corrosion situation in combination with the standard deviation of the vibration change amounts of all pipelines. The present invention realizes the analysis of the corrosion conditions of all individual pipelines and provides a basis for specific pipeline replacement.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline corrosion monitoring, and particularly to a corrosion monitoring method and monitoring device for water-vapor pipelines of a supercritical unit. Background Art

[0002] In the industrial field, the safe operation of water-vapor pipelines is crucial. However, during long-term use, water-vapor pipelines are faced with multiple impacts such as corrosion and vibration. Traditional monitoring means often rely on manual inspections and regular detections, and potential corrosion problems and equipment abnormalities cannot be detected in a timely manner. This not only increases safety hazards but also causes economic losses. Existing technologies have deficiencies in data collection, real-time monitoring, and corrosion prediction capabilities. Especially when dealing with complex variables and dynamic changes, it is often difficult to form an effective basis for evaluation and decision-making, resulting in low efficiency in pipeline maintenance management and an increased risk of production stoppage.

[0003] In the prior art, the publication number CN117969388A discloses a corrosion monitoring system for water-vapor pipelines of a supercritical unit, including an inductance probe module, a data acquisition and conversion module, and a data analysis and processing module; the inductance probe module is connected to the data acquisition and conversion module, and the data acquisition and conversion module is connected to the data analysis and processing module; two inductance probe modules are respectively installed at the measuring points behind the secondary valves of the boiler feed water sampling pipe and the superheated steam sampling pipe to collect the corrosion information of the pipeline; the data acquisition and conversion module is used to convert the corrosion information collected by the inductance probe into a digital signal and then transmit it to the data analysis and processing module; the data analysis and processing module is used to calculate the corrosion situation of the water-vapor pipelines of the supercritical unit.

[0004] According to the disclosed technical document, although the monitoring of water-vapor pipelines is achieved, the method of using an inductance probe module in the bypass pipeline has inaccurate results because, affected by the fluid model, the corrosion effects at different positions of the pipeline are different, and the inductance probe module installed in the bypass pipeline cannot directly reflect the most real corrosion effect of the pipeline, and errors are likely to occur.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a corrosion monitoring method and monitoring device for water-vapor pipelines of a supercritical unit to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A corrosion monitoring method for water-vapor pipelines of a supercritical unit, the specific steps include:

[0009] Obtain the historical operating condition data and corrosion amount of the scrapped water-vapor pipeline. The operating condition data includes the number of times the water-vapor pipeline is transported, the maximum pressure, the highest temperature, the maximum flow rate, and the working duration, and summarize the historical operating conditions into a historical working data set. The corrosion amount corresponding to the water-vapor pipeline is used as the label of the historical working data set;

[0010] Construct a model based on a convolutional neural network. Use the working data set as the input feature and the corrosion amount corresponding to the water-vapor pipeline as the target variable to train the model with a convolutional neural network. After the training is completed, output the corrosion model. Real-time collect the operating condition data of the water-vapor pipeline to be monitored to construct a real-time working data set, and input it into the corrosion model to obtain the predicted corrosion amount of the water-vapor pipeline;

[0011] Obtain the water quality data of the current water inlet and outlet of the water-vapor pipeline to be monitored. The water quality data includes conductivity, ferrous ion concentration, and dissolved solids, and obtain the water quality difference to generate a single corrosion score, and obtain the current corrosion amount;

[0012] Obtain the current corrosion amount through the predicted corrosion amount and the single corrosion score. The basis formula is as follows:

[0013]

[0014] Among them, is the current corrosion amount, is the single corrosion score, is the predicted corrosion amount;

[0015] Obtain the initial vibration data and the current vibration data of each single-section pipeline. The initial vibration data and the current vibration data are the vibration amplitudes of the single-section pipeline. Obtain the vibration change amount of each single-section pipeline, obtain the standard deviation of the vibration change amounts of all single-section pipelines, and set the standard deviation threshold of the vibration change amount;

[0016] Set corrosion threshold Ⅰ and corrosion threshold Ⅱ, respectively judge the relationship between the current corrosion amount and corrosion threshold Ⅰ and corrosion threshold Ⅱ, and combine the standard deviation of the vibration change amount of the single-section pipeline and the standard deviation threshold of the vibration change amount for analysis to judge the corrosion situation of the water-vapor pipeline to be monitored.

[0017] Furthermore, obtain the historical operating condition data of the scrapped water-vapor pipeline through the work log. The historical operating condition data includes the number of times the water-vapor pipeline is transported, and the maximum pressure, the highest temperature, the maximum flow rate, and the working duration during each transportation. Obtain the corrosion amount of the water-vapor pipeline through the maintenance log, summarize the historical operating condition data into a historical working data set, and use the corrosion amount as the label.

[0018] Further, input the working data set into the convolutional neural network model. The convolutional neural network consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. Set the activation function of the convolutional layer according to the following formula:

[0019]

[0020]

[0021] Where, is the rectified linear unit activation function, is the input of the neuron, is the th weight of the input, is the th activation value of the input, is the bias term;

[0022] Set the number of nodes in the input layer to 7, the number of neurons in the fully connected layer to 32, the initial learning rate of the neural network to 0.001, and the output layer to 1 neuron;

[0023] Calibrate the trained model as the corrosion model, and the output result is the predicted corrosion amount;

[0024] Deploy the corrosion model, collect the working condition data of the water vapor pipeline to be monitored in real time to construct a real-time working data set, map the real-time working data set to the historical working data set and input it into the corrosion model to obtain the current predicted corrosion amount of the water vapor pipeline.

[0025] Further, obtain the water quality data of the current inlet and outlet of the water vapor pipeline to be monitored. The water quality data includes conductivity, ferrous ion concentration, and solid dissolved amount, and obtain the water quality difference between the inlet and outlet according to the following formula:

[0026]

[0027]

[0028]

[0029] Where, is the conductivity difference, is the inlet conductivity, is the outlet conductivity, is the ion concentration difference, is the inlet ion concentration, is the outlet ion concentration, is the solid dissolved amount difference, is the inlet solid dissolved amount, is the outlet solid dissolved amount;

[0030] Generate a single - time corrosion score based on the water quality difference between the inlet and the outlet, and the formula is as follows:

[0031]

[0032] Wherein, is the single - time corrosion score, is the conductivity difference, is the ion concentration difference, is the solid dissolution amount difference, , , are the scoring coefficients respectively, .

[0033] Furthermore, vibration collectors are arranged on each single - section pipeline of the water - vapor pipeline to be monitored, and the initial vibration data and the current vibration data of each single - section pipeline are obtained respectively. The initial vibration data and the current vibration data record the vibration amplitude of the single - section pipeline, and the vibration change amount of each single - section pipeline is obtained respectively. The formula is as follows:

[0034]

[0035] Wherein, is the vibration change amount of the th section of the pipeline, is the current vibration data of the th section of the pipeline, is the initial vibration data of the th section of the pipeline, is the pipeline number retrieval variable, , , is the number of pipelines;

[0036] Obtain the standard deviation of the vibration change amounts of all single - section pipelines. The formula is as follows:

[0037]

[0038] Wherein, is the standard deviation of the vibration change amounts of the single - section pipelines, is the vibration change amount of the th section of the pipeline, is the average value of the vibration change amounts of all pipelines, is the pipeline number retrieval variable, , , is the number of pipelines;

[0039] Set the standard deviation threshold of the vibration change amount.

[0040] Furthermore, corrosion thresholds I and II are respectively set, where corrosion threshold I is less than corrosion threshold II. Analyze the current corrosion amount and the standard deviation of the vibration change amount of a single-section pipeline. The analysis logic is as follows:

[0041] When the current corrosion amount is less than corrosion threshold I, minor corrosion has occurred in the water-vapor pipeline to be monitored, but it does not affect operation, and the single-section pipeline is not replaced;

[0042] When the current corrosion amount is greater than corrosion threshold I, judge the standard deviation of the vibration change amount of the single-section pipeline. If the standard deviation of the vibration change amount of the single-section pipeline is less than the standard deviation threshold, corrosion has occurred in all pipelines, and the overall corrosion has reached a certain level. However, due to the combined effect of all pipelines, each single-section pipeline has not reached the corrosion scrapping effect, so no replacement is carried out. If the standard deviation of the vibration change amount of the single-section pipeline is greater than the standard deviation threshold, it means that serious corrosion has occurred in individual pipelines, and the individual pipelines are replaced. The replacement logic is as follows:

[0043] Judge the pipeline with the largest vibration change amount, replace the vibration change amount of the pipeline with the largest vibration change amount with 0, and obtain the standard deviation of the vibration change amount of the single-section pipeline again. If the standard deviation of the vibration change amount of the single-section pipeline is still greater than the standard deviation threshold, then replace the vibration change amounts of the pipelines with the largest and second largest vibration change amounts with 0 at the same time, and obtain the standard deviation of the vibration change amount of the single-section pipeline again, and so on, until the standard deviation of the vibration change amount of the single-section pipeline is less than the standard deviation threshold, and the single-section pipelines participating in the vibration change amount replacement are replaced;

[0044] When the current corrosion amount is greater than corrosion threshold I and the standard deviation of the vibration change amount of the single-section pipeline is always less than the standard deviation threshold, judge whether the current corrosion amount is greater than corrosion threshold II. If the current corrosion amount is less than corrosion threshold II, no pipeline replacement is carried out. If the current corrosion amount is greater than corrosion threshold II, all pipelines are replaced.

[0045] A corrosion monitoring device for the water-vapor pipeline of a supercritical unit, which is used to execute the above-mentioned corrosion monitoring method for the water-vapor pipeline of a supercritical unit, includes:

[0046] A working condition data acquisition module, which is used to acquire the historical working condition data of the scrapped water-vapor pipeline. The historical working condition data includes the number of times the water-vapor pipeline is transported, the maximum pressure, the highest temperature, the maximum flow rate, and the working duration, and summarize them into a historical working data set. Obtain the corrosion amount of the pipeline in the historical working condition summary and use it as the label of the working data set, and obtain the real-time working data of the water-vapor pipeline to be monitored to form a real-time working data set and map it to the historical working data set;

[0047] A model construction module, which is used to input the historical working data set into a convolutional neural network model for training to obtain a corrosion model and output a predicted corrosion amount;

[0048] A water quality data acquisition module, which is used to acquire the water quality data of the current inlet and outlet of the water vapor pipeline to be monitored. The water quality data includes conductivity, divalent iron ion concentration, and solid dissolved amount, and acquires the water quality difference between the inlet and outlet and the current corrosion amount;

[0049] The current corrosion amount is obtained by predicting the corrosion amount and the single - time corrosion score. The basis formula is as follows:

[0050]

[0051] Wherein, is the current corrosion amount, is the single - time corrosion score, is the predicted corrosion amount;

[0052] A vibration data acquisition module, which is used to acquire the initial vibration data and the current vibration data of each single - section pipeline. The initial vibration data and the current vibration data are the vibration amplitudes of the single - section pipeline, generate the vibration change amount of each single - section pipeline and the standard deviation of the vibration change amounts of all single - section pipelines, and set the standard deviation threshold;

[0053] A single - section pipeline replacement selection module, which is used to set corrosion threshold Ⅰ and corrosion threshold Ⅱ, respectively judge the relationship between the current corrosion amount and corrosion threshold Ⅰ, corrosion threshold Ⅱ, and analyze by combining the standard deviation of the vibration change amounts of the single - section pipeline and the vibration change amount standard deviation threshold to judge the corrosion situation of the water vapor pipeline to be monitored.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] The present invention obtains a corrosion model according to the historical working condition data and corrosion amount of the water vapor pipeline, forms a predicted corrosion amount according to the working condition data of the actual water vapor pipeline, obtains the overall corrosion condition of the water vapor pipeline according to the water quality data of the inlet and outlet, respectively obtains the vibration change amount of each single - section pipeline and the standard deviation of the vibration change amounts of all single - section pipelines through the vibration data of each single - section pipeline, and obtains the corrosion situation of the monitored water vapor pipeline through analysis. The present invention obtains the real - time corrosion condition of each single - section pipeline through the overall corrosion condition and the vibration condition of each single - section pipeline, improving the accuracy and effectiveness of the corrosion of the water vapor pipeline. Brief Description of the Drawings

[0056] Figure 1 is the overall method flow schematic diagram of the present invention;

[0057] Figure 2 is the module structure schematic diagram of the detection device of the present invention. Detailed Embodiment

[0058] To make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the following further elaborates on the present invention in detail with reference to specific embodiments.

[0059] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second", and similar terms used in the present invention do not denote any sequence, quantity, or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0060] Embodiment:

[0061] Please refer to Figure 1 , the present invention provides a technical solution:

[0062] A method for monitoring corrosion of water-vapor pipelines in a supercritical unit, the specific steps including:

[0063] Step 1: Obtain the historical operating conditions data and corrosion amount of the scrapped water-vapor pipeline. The operating conditions data includes the number of times of water-vapor pipeline transportation, the maximum pressure, the highest temperature, the maximum flow rate, and the working duration, and summarize the historical operating conditions into a historical working data set, and use the corrosion amount corresponding to the water-vapor pipeline as the label of the historical working data set;

[0064] The said Step 1 includes the following contents:

[0065] Step 101: Obtain the historical operating conditions data of the scrapped water-vapor pipeline through the work log. The historical operating conditions data includes the number of times of transportation of the water-vapor pipeline, as well as the maximum pressure, the highest temperature, the maximum flow rate, and the working duration during each transportation. Obtain the corrosion amount of the water-vapor pipeline through the maintenance log, summarize the historical operating conditions data into a historical working data set, and use the corrosion amount as the label.

[0066] By collecting historical operating condition data, a comprehensive data foundation has been established. This process not only ensures the integrity and accuracy of the data but also provides a reliable basis for subsequent analysis and model training. As the number of times of water vapor pipeline transportation increases, the corrosion amount of the water vapor pipeline will become larger. Similarly, the maximum pressure, the highest temperature, the maximum flow rate, and the working duration will all increase the corrosion amount of the water vapor pipeline. By obtaining the corrosion amount of the water vapor pipeline through the maintenance log, the most real corrosion condition of the water vapor pipeline can be obtained, improving the data quality, and ultimately laying a solid foundation for subsequent decision-making and ensuring that the subsequent model can operate effectively in the real environment.

[0067] Step 2: Construct a model based on a convolutional neural network. Use the working data set as the input feature and the corresponding corrosion amount of the water vapor pipeline as the target variable to train the model with a convolutional neural network. After the training is completed, output the corrosion model. Real-time collect the operating condition data of the water vapor pipeline to be monitored to construct a real-time working data set, and input it into the corrosion model to obtain the predicted corrosion amount of the water vapor pipeline;

[0068] The said Step 2 includes the following contents:

[0069] Step 201: Input the working data set into the convolutional neural network model. The convolutional neural network consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. Set the activation function of the convolutional layer, and the formula is as follows:

[0070]

[0071]

[0072] Where, is the rectified linear unit activation function, is the input of the neuron, is the weight of the th input, is the activation value of the th input,

[0073] Set the number of nodes in the input layer to 7, the number of neurons in the fully connected layer to 32, the initial neural network learning rate to 0.001, and the output layer to 1 neuron;

[0074] Calibrate the trained model as the corrosion model, and the output result is the predicted corrosion amount.

[0075] Data processing is carried out using a convolutional neural network (CNN), enabling the model to recognize complex patterns and relationships. CNN performs excellently in processing multi-dimensional data, capable of automatically extracting data features and reducing the need for manual feature selection. Through this step, the accuracy of predicting the corrosion amount is significantly improved, and the hidden correlations in the data can be explored more deeply. This efficient model training enables the system to maintain a high prediction ability in the face of different operating conditions and environmental changes, thus enhancing the assessment ability of pipeline corrosion risk.

[0076] Step 202: Deploy the corrosion model, collect the working condition data of the water vapor pipeline to be monitored in real time to construct a real-time working data set, map the real-time working data set to the historical working data set and input it into the corrosion model to obtain the current predicted corrosion amount of the water vapor pipeline.

[0077] Step 3: Obtain the water quality data of the current inlet and outlet of the water vapor pipeline to be monitored, where the water quality data includes conductivity, divalent iron ion concentration and solid dissolved amount, obtain the water quality difference, generate a single corrosion score, and obtain the current corrosion amount;

[0078] The said Step 3 includes the following contents:

[0079] Step 301: Obtain the water quality data of the current inlet and outlet of the water vapor pipeline to be monitored, where the water quality data includes conductivity, divalent iron ion concentration and solid dissolved amount, and obtain the water quality difference between the inlet and outlet. The formula is as follows:

[0080]

[0081]

[0082]

[0083] Among them, is the conductivity difference, is the inlet conductivity, is the outlet conductivity, is the ion concentration difference, is the inlet ion concentration, is the outlet ion concentration, is the solid dissolved amount difference, is the inlet solid dissolved amount, is the outlet solid dissolved amount;

[0084] Among them, as the anti-corrosion layer inside the water vapor pipeline is damaged, it will definitely cause some soluble substances in the anti-corrosion layer to dissolve in the water vapor. The soluble substances will cause changes in the conductivity and ion concentration of the water vapor. The most important one is the concentration of divalent iron ions. By comparing the water quality difference between the inlet and the outlet, the corrosion condition inside the current pipeline can be judged. If there is no corrosion in the water vapor pipeline, then the water quality at the inlet and the outlet will not change. When corrosion has occurred, due to the high-temperature and high-pressure environment, the corrosion rate will increase exponentially, and then each water quality difference will be different and also show an exponentially increasing trend. The current corrosion state can be reflected by each specific water quality difference.

[0085] Generate a single corrosion score based on the water quality difference between the inlet and the outlet. The formula is as follows:

[0086]

[0087] Among them, is the single corrosion score, is the conductivity difference, is the ion concentration difference, is the solid dissolution amount difference, 、 、 are the scoring coefficients respectively, .

[0088] Step 302: Obtain the current corrosion amount through the predicted corrosion amount and the single corrosion score. The formula is as follows:

[0089]

[0090] Among them, is the current corrosion amount, is the single corrosion score, is the predicted corrosion amount.

[0091] Among the three factors in the single - time corrosion score, conductivity can most directly reflect the change in water quality. Therefore, the coefficient of the conductivity difference is greater than the other two. Also, the difference in ion concentration is easier to detect and reflected by data compared to the difference in solid dissolution. So, the coefficient of the ion - concentration difference is greater than that of the solid - dissolution difference. When the conductivity difference, ion - concentration difference, and solid - dissolution difference are all 0, it indicates that the pipeline has hardly corroded, and the single - time corrosion score has no corrective effect on predicting the corrosion amount. However, as the conductivity difference, ion - concentration difference, and solid - dissolution difference increase, it shows that the water - vapor pipeline has started to corrode, and the single - time corrosion score also gradually increases, and the corrective effect on predicting the corrosion amount becomes greater and greater. By increasing the correction coefficient, the effect of the accelerating corrosion rate of the water - vapor pipeline is offset, thereby obtaining a more accurate current corrosion amount.

[0092] Comprehensively analyzing water - quality data and considering multiple influencing factors such as conductivity, ferrous - ion concentration, and solid dissolution can comprehensively evaluate the impact of water quality on pipeline corrosion. This step not only provides a scientific basis for the generated single - time corrosion score but also deepens the understanding of the relationship between water - quality changes and corrosion risks. Through multi - dimensional analysis, key indicators of water - quality changes can be identified.

[0093] Step 4: Obtain the initial vibration data and the current vibration data of each single - section pipeline. The initial vibration data and the current vibration data are the vibration amplitudes of the single - section pipeline. Obtain the vibration change amount of each single - section pipeline, obtain the standard deviation of the vibration change amounts of all single - section pipelines, and set a threshold for the standard deviation of the vibration change amounts.

[0094] The content of Step 4 is as follows:

[0095] Arrange vibration collectors on each single - section pipeline of the water - vapor pipeline to be monitored, and respectively obtain the initial vibration data and the current vibration data of each single - section pipeline. The initial vibration data and the current vibration data record the vibration amplitudes of the single - section pipeline. Respectively obtain the vibration change amount of each single - section pipeline, and the formula is as follows:

[0096]

[0097] Among them, is the vibration change amount of the th section of the pipeline, is the current vibration data of the th section of the pipeline, is the initial vibration data of the th section of the pipeline, is the pipeline - number retrieval variable, , , is the number of pipelines;

[0098] The standard deviation of the vibration variation of all single-section pipelines is obtained according to the following formula:

[0099]

[0100] in, is the standard deviation of the vibration variation of a single pipe section, For the The vibration change of the pipe section, is the average value of the vibration variation of all pipes, retrieves the variable for the pipe number, , , is the number of pipelines;

[0101] Set the standard deviation threshold of vibration variation.

[0102] Among them, the water vapor pipeline will vibrate when transporting the medium, and this vibration is mainly caused by its own state. Therefore, the vibration state of each single section of the pipeline will change after corrosion, and the vibration change of each single section of the pipeline reflects this vibration change from the perspective of amplitude. At the same time, because the water vapor pipeline is connected by multiple single pipelines, but the fluid model analysis process of the internal medium is very complicated, the corrosion state of each single pipeline cannot be directly analyzed. The standard deviation of the vibration change of the single section of the pipeline is used to judge. When the standard deviation of the vibration change of the single section of the pipeline is less than the standard deviation threshold of the vibration change, it means that all pipelines have undergone corrosion to a similar degree. When the standard deviation of the vibration change of the single section of the pipeline is greater than the standard deviation threshold of the vibration change, it means that some single sections of the pipeline have undergone severe corrosion, while other pipelines have not undergone too severe corrosion. The specific corrosion state of each single section of the pipeline is analyzed again by the current corrosion amount.

[0103] Vibration monitoring can be used to quantify the structural health of the pipeline and calculate the standard deviation of the vibration variation to detect potential problems in a timely manner. Vibration monitoring technology can capture abnormal conditions that may occur during the operation of the pipeline and provide an objective and quantifiable basis for detection. The introduction of this strategy has significantly improved the reliability and efficiency of fault detection. Compared with traditional visual inspections, it can detect potential structural damage earlier, providing valuable time for subsequent repairs and replacements, and reducing economic losses caused by sudden failures.

[0104] Step 5: Set corrosion threshold I and corrosion threshold II, respectively determine the relationship between the current corrosion amount and corrosion threshold I and corrosion threshold II, and analyze the standard deviation of the vibration change of a single section of the pipeline and the standard deviation threshold of the vibration change to determine the corrosion condition of the water vapor pipeline to be monitored.

[0105] The step 5 includes the following contents:

[0106] Corrosion threshold I and corrosion threshold II are set respectively, where corrosion threshold I is smaller than corrosion threshold II. The current corrosion amount and the standard deviation of the vibration change of a single section of pipeline are analyzed. The analysis logic is as follows:

[0107] When the current corrosion amount is less than the corrosion threshold I, the monitored water vapor pipeline has slight corrosion, but it does not affect the operation, and the single section of the pipeline does not need to be replaced;

[0108] When the current corrosion amount is greater than the corrosion threshold I, the standard deviation of the vibration change of a single section of the pipeline is determined. If the standard deviation of the vibration change of a single section of the pipeline is less than the standard deviation threshold, all pipelines are corroded, and the overall corrosion has reached a certain level. However, due to the combined effect of all pipelines, each single section of the pipeline has not reached the corrosion scrap effect, and it will not be replaced. If the standard deviation of the vibration change of a single section of the pipeline is greater than the standard deviation threshold, it means that the individual pipeline is severely corroded, and the individual pipeline is replaced. The replacement logic is as follows:

[0109] Determine the pipeline with the largest vibration change, replace the vibration change of the pipeline with the largest vibration change with 0, and obtain the standard deviation of the vibration change of the single-section pipeline again. If the standard deviation of the vibration change of the single-section pipeline is still greater than the standard deviation threshold, replace the vibration change of the pipeline with the largest and second largest vibration change with 0 at the same time, and obtain the standard deviation of the vibration change of the single-section pipeline again, and so on, until the standard deviation of the vibration change of the single-section pipeline is less than the standard deviation threshold, and the single-section pipeline involved in the vibration change replacement is replaced;

[0110] When the current corrosion amount is greater than the corrosion threshold I and the standard deviation of the vibration change of a single pipeline section is always less than the standard deviation threshold, it is determined whether the current corrosion amount is greater than the corrosion threshold II. If the current corrosion amount is less than the corrosion threshold II, the pipeline will not be replaced. If the current corrosion amount is greater than the corrosion threshold II, all pipelines will be replaced.

[0111] Set the corrosion threshold and the standard deviation threshold of the vibration change to achieve maintenance decisions based on actual working conditions. Through this analysis, it is possible to formulate corresponding maintenance strategies based on the actual operating conditions to avoid unnecessary maintenance and waste of resources. When necessary, ensure that appropriate replacement measures are taken in time to maximize the operating efficiency and safety of the pipeline. This data-driven decision-making method not only improves economic benefits, but also provides a strong guarantee for the long-term safe operation of the pipeline, and ultimately forms an efficient and intelligent pipeline management system.

[0112] See also Figure 2 The present invention further provides a supercritical unit steam pipeline corrosion monitoring device, the device is used to perform the above-mentioned supercritical unit steam pipeline corrosion monitoring method, comprising:

[0113] The working condition data acquisition module is used to acquire the historical working condition data of the scrapped water-vapor pipeline. The historical working condition data includes the number of times of water-vapor pipeline transportation, the maximum pressure, the highest temperature, the maximum flow rate, and the working duration, which are summarized into a historical working data set. The corrosion amount of the pipeline in the historical working condition summary is obtained and used as the label of the working data set. The real-time working data of the water-vapor pipeline to be monitored is acquired to form a real-time working data set and mapped to the historical working data set;

[0114] The model construction module is used to input the historical working data set into a convolutional neural network model for training to obtain a corrosion model and output the predicted corrosion amount;

[0115] The water quality data acquisition module is used to acquire the water quality data of the current inlet and outlet of the water-vapor pipeline to be monitored. The water quality data includes the conductivity, the concentration of divalent iron ions, and the amount of dissolved solids, and the water quality difference between the inlet and the outlet is acquired to obtain the current corrosion amount;

[0116] The current corrosion amount is obtained through the predicted corrosion amount and the single corrosion score. The basis formula is as follows:

[0117]

[0118] Among them, is the current corrosion amount, is the single corrosion score, is the predicted corrosion amount;

[0119] The vibration data acquisition module is used to acquire the initial vibration data and the current vibration data of each single-section pipeline. The initial vibration data and the current vibration data are the vibration amplitudes of the single-section pipeline, generate the vibration change amount of each single-section pipeline and the standard deviation of the vibration change amounts of all single-section pipelines, and set the standard deviation threshold;

[0120] The single-section pipeline replacement selection module is used to set a corrosion threshold Ⅰ and a corrosion threshold Ⅱ, respectively judge the relationship between the current corrosion amount and the corrosion threshold Ⅰ and the corrosion threshold Ⅱ, and analyze in combination with the standard deviation of the vibration change amount of the single-section pipeline and the standard deviation threshold of the vibration change amount to judge the corrosion situation of the water-vapor pipeline to be monitored.

[0121] All the above formulas are dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0122] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A method for monitoring corrosion of water vapor pipelines in a supercritical unit, characterized in that: The specific steps include: Obtain the historical working condition data and corrosion amount of the scrapped water vapor pipeline, wherein the working condition data includes the number of water vapor pipeline transportation, maximum pressure, maximum temperature, maximum flow rate and working time, and summarize the historical working conditions into a historical working data set, and the corresponding corrosion amount of the water vapor pipeline is used as a label of the historical working data set; Construct a model based on convolutional neural network, use the working data set as input feature, and the corresponding corrosion amount of water vapor pipeline as target variable to train the model. After training, output the corrosion model, collect the working condition data of the water vapor pipeline to be monitored in real time to construct a real-time working data set, input it into the corrosion model, and obtain the predicted corrosion amount of the water vapor pipeline. Obtain water quality data of the current water inlet and outlet of the water vapor pipeline to be monitored, the water quality data including conductivity, divalent iron ion concentration and dissolved solids, obtain water quality difference, generate a single corrosion score, and obtain the current corrosion amount; The current corrosion amount is obtained by predicting the corrosion amount and the single corrosion score, and the formula is as follows: ， in, is the current corrosion amount, To score a single corrosion, To predict the amount of corrosion; Obtaining initial vibration data and current vibration data of each single-section pipeline, wherein the initial vibration data and current vibration data are the vibration amplitude of the single-section pipeline, obtaining the vibration variation of each single-section pipeline, obtaining the standard deviation of the vibration variation of all single-section pipelines, and setting a threshold value of the standard deviation of the vibration variation; Set corrosion threshold I and corrosion threshold II to determine the relationship between the current corrosion amount and corrosion threshold I and corrosion threshold II respectively. Combine the standard deviation of the vibration change of a single section of pipeline with the standard deviation threshold of the vibration change to determine the corrosion condition of the water vapor pipeline to be monitored.

2. A method for monitoring corrosion of water vapor pipelines of a supercritical unit according to claim 1, characterized in that: The historical working condition data of the scrapped water vapor pipeline is obtained through the work log, and the historical working condition data includes the number of transportation times of the water vapor pipeline, and the maximum pressure, maximum temperature, maximum flow rate and working time during each transportation. The corrosion amount of the water vapor pipeline is obtained through the maintenance log, and the historical working condition data is summarized into a historical working data set, and the corrosion amount is used as a label.

3. A method for monitoring corrosion of water vapor pipelines of a supercritical unit according to claim 2, characterized in that: The working data set is input into the convolutional neural network model. The convolutional neural network consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. The activation function of the convolutional layer is set according to the following formula: ， ， in, is the rectified linear unit activation function, is the input of the neuron, For the The weight of the input, For the The activation value of the input, is the bias term; Set the number of input layer nodes to 7, the number of fully connected layer neurons to 32, the initial neural network learning rate to 0.001, and the output layer to 1 neuron; The trained model is calibrated as a corrosion model, and the output result is the predicted corrosion amount; The corrosion model is deployed, and the operating data of the water vapor pipeline to be monitored is collected in real time to build a real-time working data set. The real-time working data set is mapped to the historical working data set and input into the corrosion model to obtain the current predicted corrosion amount of the water vapor pipeline.

4. A method for monitoring corrosion of water vapor pipelines of a supercritical unit according to claim 3, characterized in that: Obtain the water quality data of the current water inlet and outlet of the water vapor pipeline to be monitored, the water quality data including conductivity, divalent iron ion concentration and solid dissolved content, and obtain the water quality difference between the water inlet and the water outlet, based on the following formula: ， ， ， in, is the conductivity difference, is the inlet conductivity, is the outlet conductivity, is the ion concentration difference, is the ion concentration at the water inlet, is the outlet ion concentration, is the difference in solid dissolution, is the amount of dissolved solids at the water inlet, is the amount of dissolved solids at the outlet; A single corrosion score is generated based on the difference in water quality between the inlet and outlet, based on the following formula: ， in, To score a single corrosion, is the conductivity difference, is the ion concentration difference, is the difference in solid dissolution, , , are the scoring coefficients, .

5. A method for monitoring corrosion of water vapor pipelines of a supercritical unit according to claim 4, characterized in that: A vibration collector is arranged in each single section of the water vapor pipeline to be monitored, and the initial vibration data and the current vibration data of each single section of the pipeline are obtained respectively. The initial vibration data and the current vibration data record the vibration amplitude of the single section of the pipeline, and the vibration change of each single section of the pipeline is obtained respectively, according to the following formula: ， in, For the The vibration change of the pipe section, For the The vibration data of the pipeline. For the Initial vibration data of the pipeline, Retrieve variable for pipe number, , , is the number of pipelines; The standard deviation of the vibration variation of all single-section pipelines is obtained according to the following formula: ， in, is the standard deviation of the vibration variation of a single pipe section, For the The vibration change of the pipe section, is the average value of the vibration variation of all pipelines, Retrieve variable for pipe number, , , is the number of pipelines; Set the standard deviation threshold of vibration variation.

6. A method for monitoring corrosion of water vapor pipelines of a supercritical unit according to claim 5, characterized in that: Corrosion threshold I and corrosion threshold II are set respectively, where corrosion threshold I is smaller than corrosion threshold II. The current corrosion amount and the standard deviation of the vibration change of a single section of pipeline are analyzed. The analysis logic is as follows: When the current corrosion amount is less than the corrosion threshold I, the monitored water vapor pipeline has slight corrosion, but it does not affect the operation, and the single section of the pipeline does not need to be replaced; When the current corrosion amount is greater than the corrosion threshold I, the standard deviation of the vibration change of a single section of the pipeline is determined. If the standard deviation of the vibration change of a single section of the pipeline is less than the standard deviation threshold, all pipelines are corroded, and the overall corrosion has reached a certain level. However, due to the combined effect of all pipelines, each single section of the pipeline has not reached the corrosion scrap effect, and it will not be replaced. If the standard deviation of the vibration change of a single section of the pipeline is greater than the standard deviation threshold, it means that the individual pipeline is severely corroded, and the individual pipeline is replaced. The replacement logic is as follows: Determine the pipeline with the largest vibration change, replace the vibration change of the pipeline with the largest vibration change with 0, and obtain the standard deviation of the vibration change of the single-section pipeline again. If the standard deviation of the vibration change of the single-section pipeline is still greater than the standard deviation threshold, replace the vibration change of the pipeline with the largest and second largest vibration change with 0 at the same time, and obtain the standard deviation of the vibration change of the single-section pipeline again, and so on, until the standard deviation of the vibration change of the single-section pipeline is less than the standard deviation threshold, and the single-section pipeline involved in the vibration change replacement is replaced; When the current corrosion amount is greater than the corrosion threshold I and the standard deviation of the vibration change of a single pipeline section is always less than the standard deviation threshold, it is determined whether the current corrosion amount is greater than the corrosion threshold II. If the current corrosion amount is less than the corrosion threshold II, the pipeline will not be replaced. If the current corrosion amount is greater than the corrosion threshold II, all pipelines will be replaced.

7. A supercritical unit steam pipeline corrosion monitoring device, the device is used to perform a supercritical unit steam pipeline corrosion monitoring method according to any one of claims 1 to 6, characterized in that: include: The working condition data acquisition module is used to obtain the historical working condition data of the scrapped water vapor pipeline, wherein the historical working condition data includes the number of water vapor pipeline transportation, the maximum pressure, the maximum temperature, the maximum flow rate and the working time, and summarizes them into a historical working data set, obtains the corrosion amount of the pipeline in the historical working condition data set and uses it as a label of the working data set, obtains the real-time working data of the water vapor pipeline to be monitored to form a real-time working data set and maps it to the historical working data set; A model building module is used to input the historical working data set into the convolutional neural network model for training, obtain the corrosion model, and output the predicted corrosion amount; A water quality data acquisition module is used to obtain the water quality data of the current water inlet and outlet of the water vapor pipeline to be monitored, wherein the water quality data includes conductivity, divalent iron ion concentration and solid dissolved content, and obtain the water quality difference between the water inlet and the water outlet and obtain the current corrosion amount; The current corrosion amount is obtained by predicting the corrosion amount and the single corrosion score, and the formula is as follows: ， in, is the current corrosion amount, To score a single corrosion, To predict the amount of corrosion; A vibration data acquisition module is used to acquire the initial vibration data and current vibration data of each single-section pipeline, wherein the initial vibration data and current vibration data are the vibration amplitude of the single-section pipeline, generate the vibration variation of each single-section pipeline and the standard deviation of the vibration variation of all single-section pipelines, and set the standard deviation threshold; The single-section pipeline replacement selection module is used to set corrosion threshold I and corrosion threshold II, respectively determine the relationship between the current corrosion amount and corrosion threshold I and corrosion threshold II, and analyze the standard deviation of the vibration change of the single-section pipeline and the standard deviation threshold of the vibration change to determine the corrosion condition of the water vapor pipeline to be monitored.

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