Method for analyzing corrosion problem of Texaco gasification device

Abstract

The invention provides a Texaco gasification device corrosion problem analysis method, which comprises the following steps: selecting key monitoring points to monitor the corrosion condition of a target device, installing intelligent sensors at the points, collecting operation data in real time and transmitting the operation data to a central processing unit; analyzing the data, identifying an area possibly having a corrosion risk and an abnormal change trend, and determining a suspected corrosion area; carrying out deep detection on the areas to obtain detection data; detection data, equipment operation data and maintenance records are encrypted and stored, so that the authenticity, non-tampering and traceability of the data are ensured, and a reliable basis is provided for subsequent analysis and decision making; and in combination with an intelligent manufacturing technology, operation parameters and maintenance plans of the device are optimized according to a corrosion analysis result, and accurate analysis and effective prevention and control of corrosion problems are realized. By means of the scheme, accurate analysis and effective prevention and control of the corrosion problem of the Texaco gasification device are achieved, and the overall equipment management level and safety are improved.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent monitoring, and in particular to a method for analyzing corrosion problems of a Texaco gasification device. Background Art

[0002] The Texaco gasification unit is a gasification unit mainly used for efficient and clean conversion of coal. It uses a water-coal slurry pressurized gasification process to convert coal into synthesis gas (mainly carbon monoxide and hydrogen), which is then used to produce synthetic ammonia, methanol and other chemicals or generate electricity. At present, the analysis plan for the corrosion problem of the Texaco gasification unit has problems such as inaccurate data, low management level, and low data security. Summary of the invention

[0003] Based on the above problems, the present invention proposes a method for analyzing the corrosion problem of Texaco gasification device. Through the scheme of the present invention, accurate analysis and effective prevention and control of the corrosion problem of Texaco gasification device are achieved, thereby improving the overall equipment management level and safety.

[0004] In view of this, one aspect of the present invention provides a method for analyzing corrosion problems of a Texaco gasification unit, comprising: Select key monitoring points for monitoring the corrosion condition of the target device, and install corresponding intelligent sensors at the key monitoring points; The intelligent sensor collects the operation data of each key monitoring point of the target device in real time, and transmits the operation data to a central processing unit; Use artificial intelligence algorithms to analyze and process the collected operating data, compare it with the preset normal operating data range, identify areas with possible corrosion risks and abnormal data change trends, and obtain suspected corrosion areas; For suspected corrosion areas, use intelligent detection technology to detect and obtain detection data; Encrypt and store test data, equipment operation data, and maintenance records to ensure data authenticity, non-tamperability, and traceability, providing a reliable basis for subsequent analysis and decision-making; Combined with intelligent manufacturing technology, the operating parameters and maintenance plans of the device are optimized according to the corrosion analysis results, so as to achieve accurate analysis and effective prevention and control of corrosion problems in the target device.

[0005] Optionally, the step of selecting key monitoring points for monitoring the corrosion condition of the target device and installing corresponding intelligent sensors at the key monitoring points includes: Acquire device 3D data of the target device, and establish a device 3D model of the target device according to the device 3D data; Acquire corrosion data of similar products of the target device, and establish a corrosion state prediction model of the target device according to the corrosion data; According to the three-dimensional model of the device and the corrosion state prediction model, key monitoring points for monitoring the corrosion condition of the target device are selected, and corresponding intelligent sensors are installed at the key monitoring points in combination with sensor attribute data.

[0006] Optionally, the step of acquiring corrosion data of similar products of the target device and establishing a corrosion state prediction model of the target device according to the corrosion data includes: Determine the scope of similar products, including products that are identical to the target device and products that are similar to the target device in terms of materials, structure, working environment, and usage conditions; Collecting corrosion data of similar products, wherein the corrosion data includes corrosion rate, corrosion type, environmental factors and maintenance records; Organize and analyze the collected corrosion data, identify the key factors affecting the corrosion status, and perform data cleaning to remove outliers and noise; Based on the sorted corrosion data, a corrosion state prediction model is established using statistical analysis methods or machine learning algorithms. Specifically, the correlation between process parameters and corrosion rate is analyzed, a corrosion reaction kinetics model is established, and the corrosion damage evolution equation is constructed in combination with material science theory to verify the physical rationality of the model, thereby obtaining a corrosion mechanism model; long short-term memory networks are used to process time series data, convolutional neural networks are used to extract spatial features, and attention mechanisms are combined to improve prediction accuracy in order to train deep learning models; Integrate the mechanism model with the deep learning model, use the ensemble learning method to improve the prediction stability, and use the Bayesian optimization algorithm to adjust the model hyperparameters; Set the model update cycle to achieve online learning; The corrosion state prediction model is verified, the accuracy of the model is evaluated using historical data, and the model is adjusted and optimized based on the evaluation results.

[0007] Optionally, the step of selecting key monitoring points for monitoring the corrosion condition of the target device according to the device three-dimensional model and the corrosion state prediction model, and installing corresponding intelligent sensors at the key monitoring points in combination with sensor attribute data includes: Analyzing the three-dimensional model of the device to identify a first area that may have a corrosion risk; In combination with the corrosion state prediction model, the corrosion risk level of each first area is evaluated to determine the key monitoring points that need to be monitored; Acquiring sensor attribute data related to the key monitoring point; According to the corrosion type and monitoring requirements, select the intelligent sensors corresponding to the key monitoring points, and determine the installation location and method of the sensors; Install the selected smart sensors at the mentioned key monitoring points, ensuring that the installation of the sensors complies with technical specifications and can effectively monitor corrosion.

[0008] Optionally, the step of using an artificial intelligence algorithm to analyze and process the collected operating data, compare it with a preset normal operating data range, identify areas with possible corrosion risks and abnormal data change trends, and obtain suspected corrosion areas includes: Performing noise reduction and standardization processing on the operation data, decomposing the time series data by wavelet transform, extracting time domain features and frequency domain features, and constructing a multi-dimensional feature vector; Establish a multi-level anomaly detection model, where the first layer uses statistical process control methods to establish parameter baseline ranges in combination with the preset normal operating data range, the second layer uses the isolation forest algorithm to identify abnormal data points, and the third layer uses autoencoders to reconstruct data and detect abnormal patterns; Establish an anomaly scoring mechanism in the multi-level anomaly detection model to output multiple layers of detection results; Obtaining anomaly detection results according to the multidimensional feature vector and the multi-level anomaly detection model; Long short-term memory networks are used to analyze time series correlations, graph neural networks are used to model the relationships between equipment components, and a hybrid reasoning model is built in combination with an expert rule system. Multiple models are integrated to obtain a deep learning model. Combining the deep learning model with real corrosion data, a risk assessment model based on fuzzy logic is established to define multi-level risk thresholds and warning levels; Calculate the corrosion risk data of each key monitoring point according to the abnormal detection result and the risk assessment model, and generate a heat map of the risk area; Establish a time series prediction model, analyze the impact of parameter changes on corrosion, predict the evolution trend of key parameters, and evaluate corrosion development data; According to the corrosion risk data and the corrosion development data, data analysis and comparison are performed to obtain suspected corrosion areas and generate corresponding reports.

[0009] Optionally, the step of detecting the suspected corrosion area using intelligent detection technology to obtain detection data includes: Extracting location features from suspected corrosion areas; Select the appropriate intelligent detection method based on the location characteristics of the suspected corrosion area, establish the location point detection priority, plan the detection path, formulate the detection quality control standard, and obtain the detection plan; According to the inspection plan, use pulsed eddy current testing equipment to inspect pipelines or equipment, automatically identify and locate corrosion defects, and record wall thickness change data; According to the inspection plan, use ultrasonic testing equipment to scan suspected corrosion areas, set the inspection frequency and waveform to ensure that tiny cracks and internal structural damage can be detected, and record the inspection results; According to the detection plan, use the radiographic detection equipment to perform imaging analysis on the internal structure of the device, set the imaging parameters to ensure that the image resolution reaches the preset value, record the imaging results and analyze the integrity of the internal structure; According to the inspection plan, use camera equipment to collect image data of suspected corrosion areas; Comprehensively analyze all the above test data to form a test analysis report, output the specific location, type and severity of the corrosion defects.

[0010] Optionally, the steps of encrypting and storing the detection data, equipment operation data, and maintenance records to ensure the authenticity, non-tamperability, and traceability of the data and provide a reliable basis for subsequent analysis and decision-making include: Collect data to be stored, including test data, equipment operation data and maintenance records, and ensure the integrity and accuracy of the data; Use the Advanced Encryption Standard algorithm, select the key length, and generate encryption keys based on security requirements; Divide the data to be encrypted into data blocks of fixed size and encrypt each data block; Use appropriate encryption modes for encryption operations to ensure the security and efficiency of the encryption process; The encrypted data is stored in the blockchain system, and the distributed ledger technology of the blockchain is used to achieve data sharing and synchronization.

[0011] Optionally, the step of combining intelligent manufacturing technology to optimize the operating parameters and maintenance plan of the device according to the corrosion analysis results to achieve accurate analysis and effective prevention and control of corrosion problems of the target device includes: Collect corrosion analysis results of target devices and identify corrosion risk areas and their influencing factors; Establish a relationship model between device operating parameters and corrosion risk, and determine the impact of key operating parameters on the corrosion process; Monitor the operating status of the device in real time through the automatic control system and obtain the current operating parameter data; Automatically adjust the operating parameters of the device according to the corrosion analysis results and current operating parameter data; Develop and optimize maintenance plans, combine corrosion analysis results, and determine maintenance priorities and frequencies; Use robotics technology to perform equipment maintenance and overhaul, automate routine maintenance tasks, and improve maintenance efficiency and safety; Evaluate the optimized operating parameters and maintenance plans, continuously monitor their effects, and make dynamic adjustments based on feedback.

[0012] Optionally, the key monitoring points include: high-pressure flash condenser acid gas outlet pipeline, vacuum condenser acid gas outlet pipeline, deaerator top acid gas outlet pipeline, washing tower top crude synthesis gas outlet pipeline, coal mill discharge pump outlet pipeline, coal slurry feed pump outlet pipeline, high-pressure ash water pump outlet pipeline, low-pressure ash water pump outlet pipeline, high-pressure flash separator bottom outlet pipeline, high-pressure flash tank bottom outlet pipeline, low-pressure flash tank bottom outlet pipeline, vacuum flash tank bottom outlet pipeline, gasifier black water outlet pipeline, washing tower to high-pressure flash tank pipeline, quenching water pump outlet pipeline, lock hopper circulating water pump outlet pipeline, lock hopper top pressure relief line outlet pipeline, and gasifier bottom to lock hopper inlet pipeline.

[0013] Optionally, the specific monitoring requirements of the key monitoring points are as follows: For acid gas related pipelines such as high pressure flash condenser acid gas outlet pipeline, vacuum condenser acid gas outlet pipeline, deaerator top acid gas outlet pipeline, monitor the changes in acid gas components and the changes in the wall thickness of the condensation part to determine the first corrosion data such as wet hydrogen sulfide damage, hydrochloric acid corrosion, acidic acid water corrosion, and carbon dioxide corrosion; For the crude syngas outlet pipeline at the top of the scrubber, monitor the changes in the crude syngas composition and wall thickness to determine the secondary corrosion data such as ammonium chloride corrosion and chloride stress corrosion cracking; For the outlet pipeline of the coal mill discharge pump and the outlet pipeline of the coal slurry feed pump, monitor the change of the wall thickness of the coal mill feed system to determine the erosion and corrosion data; For the high-pressure ash water pump outlet pipeline, the low-pressure ash water pump outlet pipeline, the high-pressure flash separator bottom outlet pipeline, the high-pressure flash tank bottom outlet pipeline, the low-pressure flash tank bottom outlet pipeline, the vacuum flash tank bottom outlet pipeline, the gasifier black water outlet pipeline, the washing tower to the high-pressure flash tank pipeline, the quenching water pump outlet pipeline, the lock bucket circulating water pump outlet pipeline, the lock bucket top pressure relief line outlet pipeline, and the gasifier bottom to the lock bucket inlet pipeline, monitor the wall thickness changes of the corresponding systems to determine the third corrosion data such as alkaline acidic water corrosion, ammonium chloride corrosion, erosion corrosion, chloride stress corrosion cracking, and carbonate stress corrosion cracking.

[0014] By adopting the technical solution of the present invention, the method for analyzing corrosion problems of Texaco gasification devices includes: selecting key monitoring points for monitoring the corrosion conditions of the target device, and installing corresponding intelligent sensors at the key monitoring points; collecting the operating data of each of the key monitoring points of the target device in real time through the intelligent sensors, and transmitting the operating data to a central processing unit; using an artificial intelligence algorithm to analyze and process the collected operating data, comparing it with a preset normal operating data range, identifying areas where corrosion risks may exist and abnormal data change trends, and obtaining suspected corrosion areas; using intelligent detection technology to detect suspected corrosion areas and obtain detection data; encrypting and storing the detection data, equipment operation data, and maintenance records to ensure the authenticity, non-tamperability, and traceability of the data, and provide a reliable basis for subsequent analysis and decision-making; combining intelligent manufacturing technology, optimizing the operating parameters and maintenance plan of the device according to the corrosion analysis results, and realizing accurate analysis and effective prevention and control of corrosion problems of the target device. The scheme of the present invention can collect the operation data of the target device in real time by installing intelligent sensors at key monitoring points, so that the understanding of the equipment status is more timely and accurate, which is helpful to quickly identify potential corrosion risks; the collected operation data is analyzed by using artificial intelligence algorithms, which can effectively identify deviations from the normal operating data range, which not only improves the efficiency of data processing, but also can accurately locate areas where corrosion risks may exist and abnormal data change trends, thereby realizing early warning of corrosion problems; for suspected corrosion areas, in-depth detection is carried out using intelligent detection technology to obtain more detailed detection data, and this accurate detection capability provides a scientific basis for subsequent maintenance decisions, ensuring the pertinence and effectiveness of maintenance work; the detection data, equipment operation data and maintenance records are encrypted and stored to ensure the authenticity, non-tamperability and traceability of the data, which not only protects the core data assets of the enterprise, but also provides a reliable basis for subsequent analysis and decision-making; combined with the corrosion analysis results, the operation parameters and maintenance plans of the device can be optimized, which not only improves the operation efficiency of the equipment, but also effectively reduces the risks brought by corrosion and prolongs the service life of the equipment; through the comprehensive application of the above measures, accurate analysis and effective prevention and control of corrosion problems of the target device are achieved, and the overall equipment management level and safety are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flowchart of a method for analyzing corrosion problems in a Texaco gasification unit provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] Refer to the following Figure 1 A method for analyzing corrosion problems in a Texaco gasification unit according to some embodiments of the present invention will be described.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a method for analyzing corrosion problems of a Texaco gasification device, comprising: Select key monitoring points for monitoring the corrosion condition of the target device, and install corresponding intelligent sensors at the key monitoring points; The operation data of each key monitoring point of the target device (including but not limited to temperature, pressure, medium composition, flow rate, image and other data) is collected in real time through the intelligent sensor, and the operation data is transmitted to the central processing unit (the intelligent sensor includes a temperature sensor, a pressure sensor, a chemical composition sensor, a flow sensor, etc., which are respectively installed in the key parts of the Texaco gasification device, such as the gasification furnace, the pipeline, the heat exchanger, etc., to fully obtain the device operation status information); Use artificial intelligence algorithms to analyze and process the collected operation data, compare with the preset normal operation data range, identify areas with possible corrosion risks (i.e. areas where regional data meets the preset first condition) and abnormal data change trends, and obtain suspected corrosion areas (the artificial intelligence algorithms include neural network algorithms, deep learning algorithms, etc., which can accurately identify corrosion-related abnormal data patterns and predict corrosion development trends through learning and training of a large amount of historical data); For suspected corrosion areas, use intelligent detection technology to detect (such as pulsed eddy current detection, ultrasonic detection, X-ray detection and other methods to determine the wall thickness change of equipment or pipelines, internal structure damage, etc.; the pulsed eddy current detection equipment used in the intelligent detection technology can accurately measure the wall thickness of the pipe, with a detection accuracy of ±0.1mm, and has the function of automatically identifying and locating corrosion defects; ultrasonic detection equipment can detect tiny cracks and internal structure damage, with the minimum detectable crack length of 0.5mm; X-ray detection equipment can perform imaging analysis on the internal structure of the device, with an image resolution of 0.1mm), and obtain detection data; Encrypt and store detection data, equipment operation data, and maintenance records to ensure data authenticity, immutability, and traceability, providing a reliable basis for subsequent analysis and decision-making (the encryption algorithm used in the blockchain technology is the Advanced Encryption Standard (AES) algorithm, which encrypts data to ensure data security during transmission and storage; at the same time, data sharing and synchronization are achieved through distributed ledger technology to ensure data consistency and integrity); Combined with intelligent manufacturing technology, the operating parameters and maintenance plans of the device are optimized according to the corrosion analysis results, so as to achieve accurate analysis and effective prevention and control of corrosion problems in the target device (the intelligent manufacturing technology can achieve real-time adjustment of the operating parameters of the Texaco gasification device through an automated control system, such as automatically adjusting the medium flow rate, temperature and other parameters according to the corrosion risk to slow down the corrosion process; at the same time, robot technology is used to carry out equipment maintenance and repair work to improve maintenance efficiency and safety).

[0022] By adopting the technical solution of this embodiment, by installing intelligent sensors at key monitoring points, the operating data of the target device can be collected in real time, so that the understanding of the equipment status is more timely and accurate, which helps to quickly identify potential corrosion risks; the collected operating data is analyzed by using artificial intelligence algorithms, which can effectively identify deviations from the normal operating data range, which not only improves the efficiency of data processing, but also can accurately locate areas where corrosion risks may exist and abnormal data change trends, thereby achieving early warning of corrosion problems; for suspected corrosion areas, in-depth detection using intelligent detection technology can obtain more detailed detection data. This accurate detection capability provides a scientific basis for subsequent maintenance decisions and ensures the pertinence and effectiveness of maintenance work; the detection data, equipment operation data and maintenance records are encrypted and stored to ensure the authenticity, non-tamperability and traceability of the data, which not only protects the core data assets of the enterprise, but also provides a reliable basis for subsequent analysis and decision-making; combined with the corrosion analysis results, the operating parameters and maintenance plans of the device can be optimized, which not only improves the operating efficiency of the equipment, but also effectively reduces the risks brought by corrosion and extends the service life of the equipment; through the comprehensive application of the above measures, accurate analysis and effective prevention and control of corrosion problems of the target device are achieved, and the overall equipment management level and safety are improved.

[0023] In some possible implementations of the present invention, the step of selecting key monitoring points for monitoring the corrosion condition of the target device and installing corresponding intelligent sensors at the key monitoring points includes: Acquire device 3D data of the target device, and establish a device 3D model of the target device according to the device 3D data; In this step, the target device is scanned in all directions by a three-dimensional laser scanner to obtain device point cloud data; the device point cloud data is preprocessed (including: removing noise points by a spatial filtering algorithm; simplifying the point cloud by a voxel grid downsampling method; and registering multi-site cloud data by an iterative closest point algorithm); based on the preprocessed point cloud data, a regional growing algorithm is used to segment the point cloud to separate different components of the target device; for the segmented point clouds of each component, geometric feature parameters (including but not limited to flatness, roundness, cylindricity, etc.) are extracted, standard geometric bodies (including but not limited to planes, cylinders, spheres, cones, etc.) are identified, and surface equations are fitted; the identified geometric bodies and fitted surfaces are combined with the engineering design specifications of the Texaco gasification device, Construct a parametric three-dimensional solid model; verify the accuracy of the established three-dimensional solid model (including: comparing the model with the original point cloud data; calculating the key dimensional error, the error should be controlled within ±0.5mm; verifying the geometric characteristic parameters of key components); using the feature layered storage method, establish a multi-level device information model containing the following information (including: geometric feature layer (storing device shape, size and other geometric information), material feature layer (recording the material information of each component), process feature layer (including process parameters, medium flow direction and other information), detection feature layer (recording historical detection data and detection point location information)); regularly conduct tracking scans to obtain real-time deformation data of the equipment; compare and analyze the deformation data with the benchmark model; update the geometric parameters and detection data in the three-dimensional model. Through this step, the high-precision data obtained by three-dimensional scanning technology can accurately reflect the actual shape and size of the target device and reduce human errors; through the processing and analysis of three-dimensional data, the geometric characteristics and surface state of the target device can be fully obtained, providing basic data for subsequent corrosion monitoring and analysis; through the verification and optimization of the three-dimensional model, the accuracy and practicality of the model are ensured, thereby improving the effectiveness of subsequent analysis and monitoring.

[0024] Acquire corrosion data of similar products of the target device, and establish a corrosion state prediction model of the target device according to the corrosion data; According to the three-dimensional model of the device and the corrosion state prediction model, key monitoring points for monitoring the corrosion condition of the target device are selected, and intelligent sensors corresponding to the corrosion type are installed at the key monitoring points in combination with sensor attribute data.

[0025] In some possible implementations of the present invention, the step of obtaining corrosion data of similar products of the target device and establishing a corrosion state prediction model of the target device according to the corrosion data includes: Determine the scope of similar products, including products that are identical to the target device and products that are similar to the target device in terms of materials, structure, working environment, and usage conditions; Collect corrosion data of similar products, including corrosion rate, corrosion type, environmental factors (such as temperature, humidity, medium composition, etc.) and maintenance records; Organize and analyze the collected corrosion data, identify the key factors affecting the corrosion status, and perform data cleaning to remove outliers and noise; Based on the sorted corrosion data, a corrosion state prediction model is established using statistical analysis methods or machine learning algorithms (the model can describe the relationship between corrosion rate and environmental factors). Specifically, the correlation between process parameters and corrosion rate is analyzed, a corrosion reaction kinetics model is established, and the corrosion damage evolution equation is constructed in combination with material science theory to verify the physical rationality of the model, thereby obtaining a corrosion mechanism model; a long short-term memory network (LSTM) is used to process time series data, a convolutional neural network (CNN) is used to extract spatial features, and the attention mechanism is combined to improve the prediction accuracy in order to train a deep learning model; Integrate the mechanism model with the deep learning model, use the ensemble learning method to improve the prediction stability, and use the Bayesian optimization algorithm to adjust the model hyperparameters; Set the model update cycle to achieve online learning; The corrosion state prediction model is verified, the accuracy of the model is evaluated using historical data, and the model is adjusted and optimized based on the evaluation results.

[0026] In this embodiment, by acquiring corrosion data of similar products, a data-based analysis basis can be provided for the corrosion state of the target device, thereby enhancing the scientific nature of decision-making; the established corrosion state prediction model can effectively predict the corrosion behavior of the target device under specific environmental conditions, and help formulate corresponding maintenance and protection measures; through accurate prediction of the corrosion state, potential corrosion risks can be identified in advance, thereby reducing the probability of equipment failure and safety accidents.

[0027] In some possible implementations of the present invention, the step of selecting key monitoring points for monitoring the corrosion condition of the target device according to the three-dimensional model of the device and the corrosion state prediction model, and installing corresponding intelligent sensors at the key monitoring points in combination with sensor attribute data, includes: Analyzing the three-dimensional model of the device to identify a first area that may have a corrosion risk; In this step, the three-dimensional model of the device is first analyzed to identify the first areas that may have corrosion risks, such as high stress areas, fluid flow areas, and temperature change areas. These areas are then further analyzed to ensure accurate detection while saving resources.

[0028] In combination with the corrosion state prediction model, the corrosion risk level of each first area is evaluated to determine the key monitoring points that need to be monitored; In this step, risk areas are identified based on the three-dimensional model of the device and the corrosion status prediction model. The predicted corrosion risk level is marked in the three-dimensional model, the geometric features of the high corrosion risk area are identified, the key parts are determined in combination with the process flow, and the priority ranking of the risk areas is established.

[0029] Acquire sensor attribute data related to the key monitoring points (including information such as sensor type, measurement range, accuracy and response time); According to the corrosion type and monitoring requirements, select the intelligent sensors corresponding to the key monitoring points, and determine the installation location and method of the sensors; In this step, the intelligent sensor matching process includes: selecting the corresponding detection principle according to the corrosion type, considering the impact of environmental conditions on the sensor, evaluating the measurement range and accuracy requirements of the sensor, and analyzing the interference effect between sensors. Determining the monitoring point layout plan includes: using the grid division method to spatially discretize the high-risk area, determining the stress concentration location based on finite element analysis, determining the medium residence area combined with flow field analysis, considering the detection accessibility to screen the monitoring points, optimizing the spatial distribution of the monitoring points, and achieving effective coverage of the risk area.

[0030] Install the selected smart sensors at the mentioned key monitoring points, ensuring that the installation of the sensors complies with technical specifications and can effectively monitor corrosion.

[0031] In this embodiment, by scientifically selecting key monitoring points, accurate monitoring of the corrosion condition of the target device can be achieved, thereby improving the effectiveness and pertinence of the monitoring; by combining the three-dimensional model and the corrosion state prediction model, potential corrosion risk areas can be better identified and protective measures can be taken in advance; by selecting appropriate sensors based on sensor attribute data, the flexibility and adaptability of the monitoring system can be ensured, and the accuracy of data collection can be improved; by real-time monitoring of the corrosion condition, problems can be discovered and maintained in a timely manner, thereby reducing equipment failure rates and improving overall operating efficiency.

[0032] In some possible implementations of the present invention, the step of using an artificial intelligence algorithm to analyze and process the collected operating data, comparing it with a preset normal operating data range, identifying areas with possible corrosion risks and abnormal data change trends, and obtaining suspected corrosion areas includes: Denoising and standardizing the operating data, decomposing the time series data by wavelet transform, extracting time domain features (mean, variance, peak, etc.) and frequency domain features, and constructing a multidimensional feature vector (including process parameters, material properties, environmental factors, etc.); Establish a multi-level anomaly detection model, where the first layer uses statistical process control methods to establish parameter baseline ranges in combination with the preset normal operating data range, the second layer uses the isolation forest algorithm to identify abnormal data points, and the third layer uses autoencoders to reconstruct data and detect abnormal patterns; Establish an anomaly scoring mechanism in the multi-level anomaly detection model to output multiple layers of detection results; Obtaining anomaly detection results according to the multidimensional feature vector and the multi-level anomaly detection model; Long short-term memory network (LSTM) is used to analyze time series correlation, graph neural network (GNN) is used to model the relationship between equipment components, and a hybrid reasoning model is built in combination with an expert rule system. Multiple models are integrated to obtain a deep learning model. Combining the deep learning model with real corrosion data (including equipment attributes of real corrosion conditions, corrosion area data, corrosion process development data, equipment environment data, etc.), a risk assessment model based on fuzzy logic is established to define multi-level risk thresholds and warning levels; Calculate the corrosion risk data of each key monitoring point according to the abnormal detection result and the risk assessment model, and generate a heat map of the risk area; Establish a time series prediction model, analyze the impact of parameter changes on corrosion, predict the evolution trend of key parameters, and evaluate corrosion development data; According to the corrosion risk data and the corrosion development data, data analysis and comparison are performed to obtain suspected corrosion areas (i.e., areas where regional data meets a preset first condition, where the first condition may be set first image data or first material composition data, etc.), and generate a corresponding report.

[0033] In this embodiment, the use of artificial intelligence algorithms can quickly and accurately identify potential corrosion risk areas, thereby improving the timeliness and accuracy of monitoring; by learning from a large amount of historical data, abnormal data patterns related to corrosion can be effectively identified, enhancing the ability to predict the development of corrosion; the analysis results provide data support for subsequent maintenance and management decisions, helping to formulate more scientific maintenance strategies; by identifying corrosion risks in advance, equipment failures and downtime can be reduced, thereby reducing maintenance costs and improving the overall reliability of equipment.

[0034] In some possible implementations of the present invention, the step of detecting the suspected corrosion area using intelligent detection technology to obtain detection data includes: Extracting location features from suspected corrosion areas; Select the appropriate intelligent detection method according to the location characteristics of the suspected corrosion area, establish the location point detection priority, plan the detection path, formulate the detection quality control standard (intelligent detection methods include pulsed eddy current detection, ultrasonic detection and radiographic detection, to ensure that the selected technology can meet the detection requirements), and obtain the detection plan; According to the inspection plan, use pulsed eddy current testing equipment to inspect the pipeline or equipment (set the inspection parameters to ensure that the inspection accuracy reaches ±0.1mm), automatically identify and locate corrosion defects, and record wall thickness change data; According to the inspection plan, use ultrasonic testing equipment to scan the suspected corrosion area, set the inspection frequency and waveform to ensure that tiny cracks and internal structural damage can be detected (such as ensuring that the minimum detectable crack length is 0.5mm), and record the inspection results; According to the detection plan, use the radiographic detection equipment to perform imaging analysis on the internal structure of the device, set the imaging parameters to ensure that the image resolution reaches the preset value (such as 0.1mm), record the imaging results and analyze the integrity of the internal structure; According to the inspection plan, use camera equipment to collect image data of suspected corrosion areas; Comprehensively analyze all the above test data to form a test analysis report, output the specific location, type and severity of the corrosion defects.

[0035] In this step, a multi-source inspection data registration method is established, a Bayesian fusion algorithm is used to generate a comprehensive inspection report, and the reliability of the inspection results is evaluated. It also includes: extracting defect feature parameters, establishing a defect classification model, estimating the degree of defect damage, and providing repair suggestions.

[0036] In this embodiment, multiple intelligent detection technologies such as pulsed eddy current detection, ultrasonic detection and X-ray detection are used to accurately measure the thickness of the pipe wall and the damage of the internal structure, thereby ensuring the accuracy of the detection results. By combining different detection methods, the status of suspected corrosion areas can be comprehensively evaluated and potential safety hazards can be discovered in a timely manner. The automatic recognition and positioning functions of the pulsed eddy current detection equipment improve the detection efficiency and reduce the need for manual intervention.

[0037] In some possible implementations of the present invention, the steps of encrypting and storing the detection data, equipment operation data, and maintenance records to ensure the authenticity, non-tamperability, and traceability of the data and provide a reliable basis for subsequent analysis and decision-making include: Collect data to be stored, including test data, equipment operation data and maintenance records, and ensure the integrity and accuracy of the data; Use the Advanced Encryption Standard (AES) algorithm, select the key length (128 bits, 192 bits, or 256 bits), and generate encryption keys based on security requirements; Divide the data to be encrypted into fixed-size data blocks (128 bits) and encrypt each data block; Use appropriate encryption modes (such as CBC or GCM) for encryption operations to ensure the security and efficiency of the encryption process; The encrypted data is stored in the blockchain system, and the distributed ledger technology of the blockchain is used to achieve data sharing and synchronization (the authenticity, immutability and traceability of the data are ensured through blockchain technology, and the modification and access history of each data is recorded).

[0038] In this embodiment, the construction of the blockchain system includes: designing a data block structure, using the SHA-256 hash algorithm to generate a block link, using the Merkle tree structure to organize data, and implementing smart contracts to manage data access rights; using a multi-node storage architecture to implement data sharding storage, establishing a data synchronization mechanism between nodes, and achieving load balancing; establishing a data integrity verification method, implementing data version control, recording data operation logs, and establishing a data traceability mechanism. This embodiment also includes: implementing regular data backup, establishing a multi-level backup strategy, designing a data recovery process, and conducting regular recovery drills; establishing a role-based access control system, implementing multi-factor identity authentication, recording access operation logs, and conducting regular security audits.

[0039] In this embodiment, the AES algorithm is used to encrypt the data to ensure the security of the data during transmission and storage, and to prevent data leakage and unauthorized access. The characteristics of blockchain technology are used to ensure that the stored data is authentic and reliable, and any modification to the data can be tracked and verified. Distributed ledger technology is used to achieve data sharing and synchronization, and to ensure the consistency and integrity of data between different nodes.

[0040] In some possible implementations of the present invention, the steps of combining intelligent manufacturing technology, optimizing the operating parameters and maintenance plan of the device according to the corrosion analysis results, and realizing accurate analysis and effective prevention and control of the corrosion problems of the target device include: Collect corrosion analysis results of target devices and identify corrosion risk areas and their influencing factors; Establish a relationship model between device operating parameters and corrosion risk, and determine the impact of key operating parameters (such as medium flow rate, temperature, pressure, etc.) on the corrosion process; Monitor the operating status of the device in real time through the automatic control system and obtain the current operating parameter data; Automatically adjust the device's operating parameters based on corrosion analysis results and current operating parameter data (if an increased corrosion risk is detected, automatically adjust the medium flow rate and temperature to slow down the corrosion process); Develop and optimize maintenance plans, combine corrosion analysis results, and determine maintenance priorities and frequencies; Use robotics technology to perform equipment maintenance and overhaul, automate routine maintenance tasks, and improve maintenance efficiency and safety; Evaluate the optimized operating parameters and maintenance plans, continuously monitor their effects, and make dynamic adjustments based on feedback.

[0041] In this embodiment, by combining the corrosion analysis results and intelligent manufacturing technology, it is possible to accurately analyze and effectively prevent and control the corrosion problems of the target device, thereby reducing the risks brought by corrosion; the application of the automated control system enables the device operating parameters to be adjusted in real time, quickly respond to changes in corrosion risks, and slow down the corrosion process; the use of robotics technology for equipment maintenance can reduce manual intervention, improve maintenance efficiency and safety, and reduce labor costs.

[0042] In some possible embodiments of the present invention, the key monitoring points include: a high-pressure flash condenser acid gas outlet pipeline, a vacuum condenser acid gas outlet pipeline, a deaerator top acid gas outlet pipeline, a washing tower top crude synthesis gas outlet pipeline, a coal mill discharge pump outlet pipeline, a coal slurry feed pump outlet pipeline, a high-pressure ash water pump outlet pipeline, a low-pressure ash water pump outlet pipeline, a high-pressure flash separator bottom outlet pipeline, a high-pressure flash tank bottom outlet pipeline, a low-pressure flash tank bottom outlet pipeline, a vacuum flash tank bottom outlet pipeline, a gasifier black water outlet pipeline, a washing tower to a high-pressure flash tank pipeline, a quenching water pump outlet pipeline, a lock bucket circulating water pump outlet pipeline, a lock bucket top pressure relief line outlet pipeline, and a gasifier bottom to a lock bucket inlet pipeline (according to the corrosion mechanism of different monitoring points, select a suitable monitoring system, such as a corrosion probe, online thickness measurement, etc., to monitor the corrosion conditions of each point in real time, and feed the monitoring data back to the intelligent monitoring system).

[0043] In some possible implementations of the present invention, the specific monitoring requirements of the key monitoring points are as follows: For acid gas related pipelines such as the high-pressure flash condenser acid gas outlet pipeline, vacuum condenser acid gas outlet pipeline, and deaerator top acid gas outlet pipeline, monitor the changes in their acid gas components and the changes in the wall thickness of the condensation part to determine the first corrosion data such as wet hydrogen sulfide damage, hydrochloric acid corrosion, acidic acid water corrosion, and carbon dioxide corrosion (the operating conditions are 130°C, 0.2Mpa; 40°C, -0.06MPa; 40°C, -0.08MPa; 120°C, 0.15MPa, etc., and select the appropriate monitoring system and monitoring frequency according to the corrosion mechanism and operating conditions to ensure timely detection of corrosion problems); For the crude syngas outlet pipeline at the top of the scrubber, monitor the changes in the crude syngas composition and wall thickness to determine the secondary corrosion data such as ammonium chloride corrosion and chloride stress corrosion cracking (the operating conditions are 240°C and 6.0MPa, and real-time monitoring is carried out through online thickness measurement and other methods to provide data support for the operation of the device); For the outlet pipeline of the coal mill discharge pump and the outlet pipeline of the coal slurry feed pump, monitor the change of the wall thickness of the coal mill feed system to determine the erosion and corrosion data (the operating conditions are 50°C, 6.5MPa; 50°C, 1.0MPa, respectively. Choose the appropriate monitoring method according to the actual situation to ensure the stable operation of the system); For the high-pressure ash water pump outlet pipeline, the low-pressure ash water pump outlet pipeline, the high-pressure flash separator bottom outlet pipeline, the high-pressure flash tank bottom outlet pipeline, the low-pressure flash tank bottom outlet pipeline, the vacuum flash tank bottom outlet pipeline, the gasifier black water outlet pipeline, the washing tower to the high-pressure flash tank pipeline, the quenching water pump outlet pipeline, the lock bucket circulating water pump outlet pipeline, the lock bucket top pressure relief line outlet pipeline, and the gasifier bottom to the lock bucket inlet pipeline, the wall thickness changes of the corresponding systems are monitored to determine the third corrosion data such as alkaline acid water corrosion, ammonium chloride corrosion, erosion corrosion, chloride stress corrosion cracking, and carbonate stress corrosion cracking (according to different operating conditions, such as temperature, pressure, etc., use corresponding monitoring equipment and technology to achieve a comprehensive grasp of the corrosion status of each system).

[0044] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0045] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0046] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0047] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0048] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0049] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0050] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0051] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0052] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A method for analyzing corrosion problems in a Texaco gasification unit, characterized in that: include: Select key monitoring points for monitoring the corrosion condition of the target device, and install corresponding intelligent sensors at the key monitoring points; The intelligent sensor collects the operation data of each key monitoring point of the target device in real time, and transmits the operation data to a central processing unit; Use artificial intelligence algorithms to analyze and process the collected operating data, compare it with the preset normal operating data range, identify areas with possible corrosion risks and abnormal data change trends, and obtain suspected corrosion areas; For suspected corrosion areas, use intelligent detection technology to detect and obtain detection data; Encrypt and store test data, equipment operation data, and maintenance records to ensure data authenticity, non-tamperability, and traceability, providing a reliable basis for subsequent analysis and decision-making; Combined with intelligent manufacturing technology, the operating parameters and maintenance plans of the device are optimized according to the corrosion analysis results, so as to achieve accurate analysis and effective prevention and control of corrosion problems in the target device.

2. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 1, characterized in that: The step of selecting key monitoring points for monitoring the corrosion condition of the target device and installing corresponding intelligent sensors at the key monitoring points includes: Acquire device 3D data of the target device, and establish a device 3D model of the target device according to the device 3D data; Acquire corrosion data of similar products of the target device, and establish a corrosion state prediction model of the target device according to the corrosion data; According to the three-dimensional model of the device and the corrosion state prediction model, key monitoring points for monitoring the corrosion condition of the target device are selected, and corresponding intelligent sensors are installed at the key monitoring points in combination with sensor attribute data.

3. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 2, characterized in that: The step of obtaining corrosion data of similar products of the target device and establishing a corrosion state prediction model of the target device according to the corrosion data includes: Determine the scope of similar products, including products that are identical to the target device and products that are similar to the target device in terms of materials, structure, working environment, and usage conditions; Collecting corrosion data of similar products, wherein the corrosion data includes corrosion rate, corrosion type, environmental factors and maintenance records; Organize and analyze the collected corrosion data, identify the key factors affecting the corrosion status, and perform data cleaning to remove outliers and noise; Based on the sorted corrosion data, a corrosion state prediction model is established using statistical analysis methods or machine learning algorithms. Specifically, the correlation between process parameters and corrosion rate is analyzed, a corrosion reaction kinetics model is established, and the corrosion damage evolution equation is constructed in combination with material science theory to verify the physical rationality of the model, thereby obtaining a corrosion mechanism model; long short-term memory networks are used to process time series data, convolutional neural networks are used to extract spatial features, and attention mechanisms are combined to improve prediction accuracy in order to train deep learning models; Integrate the mechanism model with the deep learning model, use the ensemble learning method to improve the prediction stability, and use the Bayesian optimization algorithm to adjust the model hyperparameters; Set the model update cycle to achieve online learning; The corrosion state prediction model is verified, the accuracy of the model is evaluated using historical data, and the model is adjusted and optimized based on the evaluation results.

4. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 3, characterized in that: The step of selecting key monitoring points for monitoring the corrosion condition of the target device according to the device three-dimensional model and the corrosion state prediction model, and installing corresponding intelligent sensors at the key monitoring points in combination with sensor attribute data, comprises: Analyzing the three-dimensional model of the device to identify a first area that may have a corrosion risk; In combination with the corrosion state prediction model, the corrosion risk level of each first area is evaluated to determine the key monitoring points that need to be monitored; Acquiring sensor attribute data related to the key monitoring point; According to the corrosion type and monitoring requirements, select the intelligent sensors corresponding to the key monitoring points, and determine the installation location and method of the sensors; Install the selected smart sensors at the mentioned key monitoring points, ensuring that the installation of the sensors complies with technical specifications and can effectively monitor corrosion.

5. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 4, characterized in that: The step of using an artificial intelligence algorithm to analyze and process the collected operation data, compare the preset normal operation data range, identify areas with possible corrosion risks and abnormal data change trends, and obtain suspected corrosion areas includes: Performing noise reduction and standardization processing on the operation data, decomposing the time series data by wavelet transform, extracting time domain features and frequency domain features, and constructing a multi-dimensional feature vector; Establish a multi-level anomaly detection model, where the first layer uses statistical process control methods to establish parameter baseline ranges in combination with the preset normal operating data range, the second layer uses the isolation forest algorithm to identify abnormal data points, and the third layer uses autoencoders to reconstruct data and detect abnormal patterns; Establish an anomaly scoring mechanism in the multi-level anomaly detection model to output multiple layers of detection results; Obtaining anomaly detection results according to the multidimensional feature vector and the multi-level anomaly detection model; Long short-term memory networks are used to analyze time series correlations, graph neural networks are used to model the relationships between equipment components, and a hybrid reasoning model is built in combination with an expert rule system. Multiple models are integrated to obtain a deep learning model. Combining the deep learning model with real corrosion data, a risk assessment model based on fuzzy logic is established to define multi-level risk thresholds and warning levels; Calculate the corrosion risk data of each key monitoring point according to the abnormal detection result and the risk assessment model, and generate a heat map of the risk area; Establish a time series prediction model, analyze the impact of parameter changes on corrosion, predict the evolution trend of key parameters, and evaluate corrosion development data; According to the corrosion risk data and the corrosion development data, data analysis and comparison are performed to obtain suspected corrosion areas and generate corresponding reports.

6. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 5, characterized in that: The step of using intelligent detection technology to detect suspected corrosion areas and obtain detection data includes: Extracting location features from suspected corrosion areas; Select the appropriate intelligent detection method based on the location characteristics of the suspected corrosion area, establish the location point detection priority, plan the detection path, formulate the detection quality control standard, and obtain the detection plan; According to the inspection plan, use pulsed eddy current testing equipment to inspect pipelines or equipment, automatically identify and locate corrosion defects, and record wall thickness change data; According to the inspection plan, use ultrasonic testing equipment to scan suspected corrosion areas, set the inspection frequency and waveform to ensure that tiny cracks and internal structural damage can be detected, and record the inspection results; According to the detection plan, use the radiographic detection equipment to perform imaging analysis on the internal structure of the device, set the imaging parameters to ensure that the image resolution reaches the preset value, record the imaging results and analyze the integrity of the internal structure; According to the inspection plan, use camera equipment to collect image data of suspected corrosion areas; Comprehensively analyze all the above test data to form a test analysis report, output the specific location, type and severity of the corrosion defects.

7. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 6, characterized in that: The steps of encrypting and storing the detection data, equipment operation data, and maintenance records to ensure the authenticity, non-tamperability, and traceability of the data and provide a reliable basis for subsequent analysis and decision-making include: Collect data to be stored, including test data, equipment operation data and maintenance records, and ensure the integrity and accuracy of the data; Use the Advanced Encryption Standard algorithm, select the key length, and generate encryption keys based on security requirements; Divide the data to be encrypted into data blocks of fixed size and encrypt each data block; Use appropriate encryption modes for encryption operations to ensure the security and efficiency of the encryption process; The encrypted data is stored in the blockchain system, and the distributed ledger technology of the blockchain is used to achieve data sharing and synchronization.

8. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 7, characterized in that: The steps of combining intelligent manufacturing technology, optimizing the operating parameters and maintenance plan of the device according to the corrosion analysis results, and realizing accurate analysis and effective prevention and control of corrosion problems of the target device include: Collect corrosion analysis results of target devices and identify corrosion risk areas and their influencing factors; Establish a relationship model between device operating parameters and corrosion risk, and determine the impact of key operating parameters on the corrosion process; Monitor the operating status of the device in real time through the automatic control system and obtain the current operating parameter data; Automatically adjust the operating parameters of the device according to the corrosion analysis results and current operating parameter data; Develop and optimize maintenance plans, combine corrosion analysis results, and determine maintenance priorities and frequencies; Use robotics technology to perform equipment maintenance and overhaul, automate routine maintenance tasks, and improve maintenance efficiency and safety; Evaluate the optimized operating parameters and maintenance plans, continuously monitor their effects, and make dynamic adjustments based on feedback.

9. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 8, characterized in that: The key monitoring points include: high-pressure flash condenser acid gas outlet pipeline, vacuum condenser acid gas outlet pipeline, deaerator top acid gas outlet pipeline, washing tower top crude synthesis gas outlet pipeline, coal mill discharge pump outlet pipeline, coal slurry feed pump outlet pipeline, high-pressure ash water pump outlet pipeline, low-pressure ash water pump outlet pipeline, high-pressure flash separator bottom outlet pipeline, high-pressure flash tank bottom outlet pipeline, low-pressure flash tank bottom outlet pipeline, vacuum flash tank bottom outlet pipeline, gasifier black water outlet pipeline, washing tower to high-pressure flash tank pipeline, quenching water pump outlet pipeline, lock hopper circulating water pump outlet pipeline, lock hopper top pressure relief line outlet pipeline, and gasifier bottom to lock hopper inlet pipeline.

10. The method for analyzing corrosion problems of Texaco gasification equipment according to claim 9, characterized in that: The specific monitoring requirements for the key monitoring points are as follows: For acid gas related pipelines such as high pressure flash condenser acid gas outlet pipeline, vacuum condenser acid gas outlet pipeline, deaerator top acid gas outlet pipeline, monitor the changes in acid gas components and the changes in the wall thickness of the condensation part to determine the first corrosion data such as wet hydrogen sulfide damage, hydrochloric acid corrosion, acidic acid water corrosion, and carbon dioxide corrosion; For the crude syngas outlet pipeline at the top of the scrubber, monitor the changes in the crude syngas composition and wall thickness to determine the secondary corrosion data such as ammonium chloride corrosion and chloride stress corrosion cracking; For the outlet pipeline of the coal mill discharge pump and the outlet pipeline of the coal slurry feed pump, monitor the change of the wall thickness of the coal mill feed system to determine the erosion and corrosion data; For the high-pressure ash water pump outlet pipeline, the low-pressure ash water pump outlet pipeline, the high-pressure flash separator bottom outlet pipeline, the high-pressure flash tank bottom outlet pipeline, the low-pressure flash tank bottom outlet pipeline, the vacuum flash tank bottom outlet pipeline, the gasifier black water outlet pipeline, the washing tower to the high-pressure flash tank pipeline, the quenching water pump outlet pipeline, the lock bucket circulating water pump outlet pipeline, the lock bucket top pressure relief line outlet pipeline, and the gasifier bottom to the lock bucket inlet pipeline, monitor the wall thickness changes of the corresponding systems to determine the third corrosion data such as alkaline acidic water corrosion, ammonium chloride corrosion, erosion corrosion, chloride stress corrosion cracking, and carbonate stress corrosion cracking.

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