A coal gasification plant full-cycle protection management system fusing multi-source corrosion data

Abstract

The present application relates to the technical field of corrosion monitoring, in particular to a coal gasification device full-cycle protection management system fusing multi-source corrosion data, comprising a multi-source corrosion data acquisition and preprocessing module, an information validity evaluation module, a corrosion mechanism identification module, a mechanism constraint multi-source fusion module, a corrosion state comprehensive evaluation and evolution prediction module, a protection decision and parameter optimization module and a model life cycle management module; by calculating data credibility and information validity weight, multi-source fusion is carried out under the constraint of dominant corrosion mechanism, solving the problem of unstable evaluation caused by data quality and time difference; through the mechanism of double weight and mechanism constraint fusion, the robustness and interpretability of the evaluation are significantly improved; a closed loop of "evaluation-prediction-optimization" is constructed, realizing the change from passive monitoring to active protection; through the life cycle of the management model, the system has self-adaptability, ensuring the continuous reliability under long-term operation and working condition fluctuation.

Description

Technical Field

[0001] This invention relates to the field of corrosion monitoring, specifically to a full-cycle protection management system for coal gasification plants that integrates multi-source corrosion data. Background Technology

[0002] Coal gasification units operate under complex conditions, including high temperature, high pressure, and highly corrosive media. Their reactors, pipelines, and critical connections are susceptible to the cumulative effects of various forms of corrosion, making corrosion failure a significant factor affecting the safe operation and service life of the unit. Therefore, continuous, objective, and accurate assessment of the corrosion status of critical components in coal gasification units, and the implementation of effective protective management based on this assessment, is of significant engineering importance.

[0003] Existing corrosion management methods typically analyze corrosion status based on online monitoring data, periodic inspection results, and manual inspection records. However, most of these methods rely on monitoring data at discrete time points and empirical judgments. Data from different sources vary significantly in terms of acquisition frequency, reliability, and timeliness. There is a lack of a unified data fusion and quality constraint mechanism, making it difficult to form a stable characterization of the overall corrosion status of the target area.

[0004] Meanwhile, the corrosion process in coal gasification units exhibits significant mechanistic diversity and operational condition dependence. Existing technologies generally lack the identification and constraint of the dominant corrosion mechanisms, making it difficult to reflect the impact of different corrosion mechanisms on the evaluation results during multi-source data fusion and feature construction. Furthermore, existing methods typically do not model the time-dependent decay of corrosion data, using historical and current data equally, which can easily reduce the accuracy and stability of the evaluation results.

[0005] In terms of protection decision-making, existing solutions mostly rely on static configuration or manual adjustment based on current assessment results, lacking a parameter optimization mechanism that combines corrosion evolution trends. Furthermore, they lack effective degradation judgment and update management for assessment and prediction models, making it difficult to support the continuous protection management needs under the full-cycle operation conditions of coal gasification units. Summary of the Invention

[0006] This invention provides a full-cycle protection management system for coal gasification plants that integrates multi-source corrosion data. This system enables stable assessment and evolution prediction of the corrosion state of target areas within the coal gasification plant, even when the quality and timeliness of the multi-source corrosion data vary. To achieve the above objective, this invention provides the following technical solution: A full-cycle protection management system for coal gasification plants that integrates multi-source corrosion data includes: The multi-source corrosion data acquisition and preprocessing module collects corrosion data, operating data, and related history data corresponding to the target parts of the coal gasification unit, performs standardized processing on the data, and calculates the credibility weight of various types of data. The information validity assessment module is used to establish an information timeliness decay model for corrosion data from different sources, and to calculate the current information validity weight of the corrosion data based on the information timeliness decay model. The corrosion mechanism identification module combines the corrosion data and operational data to identify the dominant corrosion mechanism type of the target location; The mechanism-constrained multi-source fusion module, under the constraint of the dominant corrosion mechanism type, integrates the information validity weight and the credibility weight, dynamically allocates multi-source data fusion weights according to the mechanism type, and constructs mechanism-constrained fusion features. The corrosion state comprehensive assessment and evolution prediction module performs qualitative and quantitative evaluation of the corrosion state of the target part based on the mechanistic constraint fusion characteristics, outputs comprehensive corrosion state assessment results, and establishes a corrosion evolution model to predict the corrosion development trend in subsequent operating cycles. The protection decision and parameter optimization module, under the constraints of the comprehensive corrosion status assessment results and corrosion evolution prediction results, optimizes the control parameters of the protection measures and outputs the corresponding set of protection parameters. The model lifecycle management module manages the lifecycle of the comprehensive corrosion status assessment model, the corrosion evolution prediction model, and the protection parameter optimization model. Under the constraint of information validity weight, it performs a weighted evaluation of the model prediction error. When the degradation judgment condition is met, the model is determined to be degraded and the model update process is triggered.

[0007] As a preferred embodiment of the present invention, the process for constructing the confidence weight in the multi-source corrosion data acquisition and preprocessing module specifically includes: Obtain corrosion data, operational data, and related historical data corresponding to the target location; The data is standardized to unify its format and units; Based on preset rules, the missing rate, outlier rate, and stability index of each type of data, along with their reliability weights, are calculated.

[0008] As a preferred embodiment of the present invention, the calculation process of the information validity weight in the information validity evaluation module includes: Information decay models were established for corrosion data from different sources. Based on the information time-related decay model, the time-related decay factor of various types of corrosion data at the current evaluation time point relative to the data acquisition time point is calculated, and the current information validity weight of various types of corrosion data is calculated.

[0009] As a preferred technical solution of the present invention, the construction process of the information timeliness decay model in the information validity assessment module specifically includes: Analyze historical data and, based on the source type and characteristics of corrosion data, set corresponding aging attenuation parameters and attenuation function forms for corrosion data from different sources; Using the aforementioned time-decrease parameters and the form of the decay function, a corresponding information time-decrease model is constructed.

[0010] As a preferred embodiment of the present invention, the identification process of the dominant corrosion mechanism type in the corrosion mechanism identification module includes: Extract the characteristic parameter combination from the corrosion data and the operation data; The combination of the feature parameters is matched with a preset corrosion mechanism knowledge base, and the dominant corrosion mechanism type of the target part under the current working conditions is identified based on the matching results.

[0011] As a preferred embodiment of the present invention, the process of constructing the mechanism constraint fusion feature in the mechanism constraint multi-source fusion module includes: Based on the dominant corrosion mechanism type, a fusion weight allocation rule corresponding to the mechanism type is determined from a preset weight allocation strategy library. According to the fusion weight allocation rule, the information validity weight and the credibility weight are combined to dynamically allocate fusion weights for each data source. The multi-source data are weighted and fused using the assigned fusion weights to construct the mechanism-constrained fusion features.

[0012] As a preferred embodiment of the present invention, the corrosion state comprehensive assessment and evolution prediction process in the corrosion state comprehensive assessment and evolution prediction module includes: Based on the aforementioned mechanism-constrained fusion characteristics, a combination of qualitative evaluation models and quantitative calculations is used to evaluate the corrosion status of the target location and generate a comprehensive corrosion status evaluation result. Using the comprehensive corrosion state assessment results as the initial state input, a corrosion evolution model matching the dominant corrosion mechanism type is established; Based on the corrosion evolution model, the corrosion development trend of the target part in subsequent operating cycles is predicted.

[0013] As a preferred embodiment of the present invention, the calculation process of the protection parameter set in the protection decision and parameter optimization module includes: Obtain comprehensive corrosion status assessment results and corrosion evolution prediction results; Under the constraints of the comprehensive corrosion state assessment results and the corrosion evolution prediction results, with the optimization objective of mitigating the current corrosion state and suppressing the predicted corrosion development trend, the control parameters of at least one protective measure are optimized and calculated using an optimization algorithm to generate and output a set of protective parameters.

[0014] As a preferred embodiment of the present invention, the model lifecycle management process in the model lifecycle management module includes: Continuously acquire the prediction results of the comprehensive corrosion state assessment model and the corrosion evolution prediction model, as well as the corresponding actual detection results, and obtain the set of protection parameters output by the protection parameter optimization model; Calculate the error between the predicted result and the actual detection result, and combine it with the information validity weight to determine whether the model degradation judgment condition is met; When the model degradation determination condition is met, the corresponding model is updated.

[0015] As a preferred embodiment of the present invention, the model update process in the model lifecycle management module includes: Based on the latest corrosion data and operational data processed by the multi-source corrosion data acquisition and preprocessing module, the model that was determined to have degraded was retrained to obtain an updated model. Using a validation dataset independent of the retraining process, the updated model is validated to assess its output accuracy in evaluating the corrosion status of the target area, predicting corrosion evolution, and evaluating the effectiveness of the set of protection parameters. When the verification calculation results meet the preset accuracy requirements, the updated model replaces the original model for subsequent system operation.

[0016] The beneficial effects of this invention are: 1. Improved robustness and reliability of multi-source data fusion and evaluation results. Addressing the issues of data heterogeneity, significant differences in quality and timeliness, and unstable fusion mentioned in the background technology, this invention introduces reliability weights and information validity weights, and performs differentiated weighted fusion of data from different sources under the constraint of the dominant corrosion mechanism. This effectively suppresses the interference of abnormal data, low-reliability data, and expired data on the evaluation conclusions, ensuring that the corrosion state evaluation process is consistent with the actual physicochemical mechanism, and that the output results possess both numerical accuracy and engineering interpretability.

[0017] 2. This invention achieves a shift from passive monitoring to proactive protection. Addressing the shortcomings of background technologies, such as the lack of foresight in protection decisions and reliance on experience for parameter configuration, this invention performs a comprehensive evaluation based on mechanistic constraints and integrated characteristics, and establishes a corrosion evolution model matched to the mechanism for trend prediction. Building upon this, the protection decision-making and parameter optimization module performs parameter optimization calculations with the goal of suppressing the predicted trend. This transforms the protection strategy from "post-event handling" or "static configuration" to a data- and model-driven dual-mode of "evaluation-prediction-optimization," significantly improving the accuracy and timeliness of protection.

[0018] 3. Ensure the system's adaptability and continuous reliability during long-term operation. Addressing the issue of the lack of model degradation assessment and updates in the background technology, this invention uses a model lifecycle management module to continuously monitor the core model's performance. When the prediction error meets the degradation conditions under the information validity weight constraint, the model is automatically retrained and validated for updates. This mechanism enables the system to adapt to changes in operating conditions and material aging during long-term operation, ensuring the continuous effectiveness of protection and management capabilities and reducing reliance on external manual intervention. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the data flow structure of the present invention; Figure 2 This is a schematic diagram of the control flow structure of the present invention; Figure 3 This is a schematic diagram of the program flow of Embodiment 2 of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] Example 1: As Figure 1 As shown, this invention provides a full-cycle protection management system for coal gasification plants that integrates multi-source corrosion data, including: The multi-source corrosion data acquisition and preprocessing module collects corrosion data, operational data, and related historical data corresponding to the target parts of the coal gasification unit, performs standardized processing on the above data, and calculates the credibility weight of various types of data.

[0022] Furthermore, the process for constructing the credibility weight in the multi-source corrosion data acquisition and preprocessing module includes: acquiring corrosion data, operational data, and their associated historical data corresponding to the target location; standardizing the above data to unify the data format and dimensions; and calculating the missing rate, anomaly rate, and stability index of each type of data according to preset rules, and then calculating the credibility weight of each type of data.

[0023] Specifically, in this embodiment, key corrosion risk areas in the coal gasification unit are located based on process hazard analysis results and historical failure data, including core components such as the gasifier combustion chamber, syngas cooler, high-temperature and high-pressure pipelines, and scrubbing system.

[0024] Data acquisition for the aforementioned target locations is conducted in parallel, covering four sources: online corrosion monitoring sensors deployed at the target locations, providing real-time signals such as corrosion rate and electrochemical noise; the plant process control system, providing process parameters such as temperature, pressure, flow rate, and concentration of key gas components; regularly executed offline testing programs, generating reports on ultrasonic thickness measurement, endoscopic inspection, and corrosion product analysis; and the enterprise asset management system, providing static and dynamic historical information such as equipment material, maintenance history, and coating condition. These data types exhibit significant heterogeneity in transmission protocols, sampling frequencies, and storage formats.

[0025] The preprocessing unit in the module is responsible for converting multi-source heterogeneous data into a standardized dataset with a unified format and synchronized time. Preprocessing includes three main operations: time alignment, which unifies the time stamps of data from different systems to the same time base; and for non-real-time data, defining its timestamp as the actual moment the data was generated. Field mapping unifies the names of different fields representing the same physical quantity into a standard naming convention within the system. Dimensional normalization transforms all numerical data to a dimensionless or dimensionless scale through linear transformations or standardization algorithms. This process eliminates the heterogeneity of the original data, laying the foundation for subsequent quantitative analysis.

[0026] Subsequently, the quality assessment unit within the module quantifies and scores the performance of each data source category within a defined assessment time window, and calculates its credibility weight. The scoring is based on three computable metrics: The data missing rate m is used to characterize the proportion of missing data points due to communication anomalies or acquisition failures within the evaluation time window, relative to the total number of data points that should be collected within that evaluation time window.

[0027] The data anomaly rate α is used to characterize the proportion of data points that exceed the reasonable process range and physical constraint range within the evaluation time window to the total number of valid data points collected within the evaluation time window. The reasonable process range and physical constraint range are determined by design parameters and historical statistical data.

[0028] The data stability index s is used to characterize the degree of statistical volatility of this type of data sequence within the evaluation time window, and its value increases as the volatility of the data sequence increases.

[0029] In one implementation, the data stability index s can be determined by the ratio of the standard deviation to the corresponding mean of the data sequence within the evaluation time window, i.e.: ; in, To evaluate the standard deviation of the data series within a time window, This corresponds to the mean.

[0030] Combining the above three indicators, the credibility weight is calculated using a pre-defined weighting function. ; in The stability impact coefficient is determined based on historical data statistics. The final output reliability weight is a scalar between 0 and 1; a higher value indicates that the data source is more reliable in the current time period.

[0031] The aforementioned weighted composite function adopts the form of a weighted product of the scores of each indicator, with the stability indicator participating in the calculation in the form of a negative exponent, so as to ensure that abnormal fluctuations have a suppressive effect on credibility.

[0032] The information validity assessment module is used to establish an information timeliness decay model for corrosion data from different sources, and to calculate the current information validity weight of the corrosion data based on the information timeliness decay model. The information validity weight is used to characterize the information validity of a single corrosion data point at the current evaluation time, and the credibility weight is used to characterize the overall reliability of the corresponding data source within the current evaluation period.

[0033] Furthermore, the calculation process for the information validity weight in the information validity assessment module includes: establishing information timeliness decay models for corrosion data from different sources; and calculating the timeliness decay factor of various types of corrosion data at the current assessment time point relative to the data acquisition time point, as well as the current information validity weight, based on the information timeliness decay models.

[0034] Specifically, this module is responsible for characterizing the decay of corrosion monitoring data value over time. Its core is defining an information decay function for a type of corrosion data source. This function maps the length of time since the data was acquired to a decay factor, which represents the relative information value of the data at the current evaluation moment. The decay factor is constrained to the range of 0 to 1, with a factor of 1 at the time of data acquisition, and the factor value monotonically decreases over time.

[0035] Different types of data sources correspond to different decay function forms, determined by their data update patterns and engineering significance. Continuously outputting online monitoring data becomes outdated quickly and is typically modeled using an exponential decay function, with the decay rate controlled by a decay coefficient. In contrast, discrete, long-period offline monitoring data tends to maintain relatively stable information value over a longer period before rapidly declining; for this type of data, the Weibull decay function is more suitable.

[0036] For online data, time decay factor It can be represented as, ; in, This is the time difference between the current time and the data acquisition time. This is the attenuation coefficient.

[0037] For discrete offline detection data with long periods, the Weibull decay function is used for description. This function characterizes the decay pattern of "plateau period" plus "decline period" through scale and shape parameters, and its expression is as follows: ; in, For scale parameters, The shape parameter is defined by analyzing the pattern of historical data value changes over time.

[0038] When the system performs online evaluation, for each piece of corrosion data that is called, this module selects the corresponding decay function according to its data source type and calculates a specific time decay factor based on the length of time the data has been generated.

[0039] in Indicates the first Information decay factor corresponding to the corrosion data.

[0040] To comprehensively weigh the timeliness of all data in this evaluation, the module normalizes the decay factors of all data in the current batch. The normalized result is the information validity weight of each data point. For the i-th data point, its information validity weight is... for: ; The validity weight of this information, along with the credibility weight, will serve as a key input in the subsequent data fusion stage.

[0041] The corrosion mechanism identification module combines the corrosion data with the operational data to identify the dominant corrosion mechanism type of the target area; Furthermore, in the corrosion mechanism identification module, the identification process of the dominant corrosion mechanism type includes: extracting the combination of feature parameters from the corrosion data and the operating data; matching the combination of feature parameters with a preset corrosion mechanism knowledge base; and identifying the dominant corrosion mechanism type of the target part under the current working conditions based on the matching result.

[0042] Specifically, this module's task is to identify the underlying physicochemical causes of observed corrosion phenomena. The module extracts a multidimensional feature vector from preprocessed multi-source data, comprehensively describing the corrosive microenvironment of the target site. Elements of the feature vector include direct environmental parameters such as temperature, pressure, medium pH, partial pressures of hydrogen sulfide and carbon dioxide, chloride ion concentration, and fluid flow rate; material state parameters such as alloy composition, heat treatment status, and service life; and characterization parameters of corrosion behavior such as recent average corrosion rate, corrosion rate trends, and the spectral characteristics of electrochemical signals. All features are standardized after extraction.

[0043] The module includes a built-in corrosion mechanism knowledge base, which is constructed based on corrosion electrochemistry, materials science principles, and numerous field failure cases. The knowledge base defines various corrosion mechanisms that may occur in coal gasification units and establishes corresponding feature space models or discrimination rules for each mechanism.

[0044] These mechanisms include high-temperature oxidation, sulfide stress corrosion, hydrogen-induced cracking, dew point corrosion, chloride corrosion, and erosion.

[0045] The recognition process uses a pattern matching algorithm to calculate the current feature vector. Feature vectors of various mechanistic models in the knowledge base similarity Similarity can be calculated using cosine similarity: ; The matching degree calculation can further consider the correlation between features and assign higher weights to features that contribute significantly to the discrimination of specific mechanisms.

[0046] The module ultimately outputs one or a few mechanism types with the highest matching degree as the dominant corrosion mechanism, along with the corresponding identification confidence level. This identification result is the fundamental basis for selecting subsequent corrosion evolution physical models and determining data fusion strategies.

[0047] The mechanism-constrained multi-source fusion module, under the constraint of the dominant corrosion mechanism type, integrates the information validity weight and the credibility weight, dynamically allocates multi-source data fusion weights according to the mechanism type, and constructs mechanism-constrained fusion features. Furthermore, the process of constructing the mechanism constraint fusion feature in the mechanism constraint multi-source fusion module includes: obtaining the dominant corrosion mechanism type, the information validity weight, and the credibility weight; based on the dominant corrosion mechanism type, determining the fusion weight allocation rule corresponding to the mechanism type from a preset weight allocation strategy library, and dynamically allocating fusion weights for each data source by combining the information validity weight and the credibility weight according to the fusion weight allocation rule; and using the allocated fusion weights to perform weighted fusion of multi-source data to construct the mechanism constraint fusion feature.

[0048] Specifically, data from different sources and at different times are fused into a unified feature representation that reflects both the quality of the data itself and conforms to current understanding of corrosion mechanisms. The module receives three upstream inputs: the dominant corrosion mechanism type, the information validity weight of each data point, and the credibility weight of each data type.

[0049] The weight allocation strategy library is stored in the form of a mapping matrix between mechanism type and the importance of monitoring parameters, and is used to provide corresponding mechanism sensitivity coefficients for different dominant corrosion mechanisms.

[0050] The module internally stores a weight allocation strategy matrix, which defines the importance coefficients of various monitoring parameters for state assessment under different dominant corrosion mechanisms, i.e., mechanism sensitivity coefficients.

[0051] The weight allocation process is a multi-factor decision-making process. First, each data source receives a base weight, which is the product of its information validity weight and the credibility weight of its parent data source, reflecting the data's comprehensive quality in terms of both "timeliness" and "reliability." Then, based on the currently identified dominant corrosion mechanism, the module queries the strategy matrix to obtain the mechanism sensitivity coefficient corresponding to the parameter type represented by that data point. Ultimately, the fusion weight of this data point is equal to the product of its base weight and the mechanism sensitivity coefficient. .

[0052] in, For the first The credibility weight of the data source to which the corrosion data belongs.

[0053] By introducing a mechanism sensitivity coefficient, parameter data that is sensitive to the current active corrosion mechanism can obtain a higher influence weight in the fusion, even if its basic weight is not the highest; conversely, the influence weight of parameter data that is weakly related to the current dominant mechanism will be suppressed accordingly.

[0054] After completing all weight redistribution, the module normalizes all fusion weights: ; Then, the normalized weights are used to perform a weighted linear superposition of the standardized multi-source feature data to generate a fixed-dimensional mechanism-constrained fusion feature vector Z.

[0055] ; in, For the first Standardized feature components corresponding to the corrosion data.

[0056] This feature vector provides a concentrated representation of multi-source information and demonstrates a clear mechanism-oriented approach.

[0057] The corrosion state comprehensive assessment and evolution prediction module performs qualitative and quantitative evaluation of the corrosion state of the target part based on the mechanistic constraint fusion characteristics, outputs comprehensive corrosion state assessment results, and establishes a corrosion evolution model to predict the corrosion development trend in subsequent operating cycles. Furthermore, the corrosion state comprehensive assessment and evolution prediction process of the corrosion state comprehensive assessment and evolution prediction module includes: based on the mechanism constraint fusion characteristics, using a combination of qualitative assessment model and quantitative calculation, evaluating the corrosion state of the target part, and generating a comprehensive corrosion state assessment result; using the comprehensive corrosion state assessment result as the initial state input, establishing a corrosion evolution model that matches the dominant corrosion mechanism type; Based on the identified dominant corrosion mechanism type, the corresponding corrosion evolution model is determined from the preset mechanism type and corrosion evolution model mapping table.

[0058] Based on the corrosion evolution model, the corrosion development trend of the target area in subsequent operating cycles is predicted.

[0059] Specifically, this module diagnoses the current corrosion status based on the fused features and analyzes future trends. The assessment work proceeds in two directions: First, quantitative evaluation will be conducted by fusing feature vectors. By inputting a regression model or physical empirical formula, continuous corrosion state indices can be directly calculated. , For example, using a linear regression model ; in, and These are the model parameters, obtained through training with historical data.

[0060] Second, qualitative assessment involves inputting the same feature vector into a classification model, which outputs a discrete risk level label, such as "Safe," "Caution," "Warning," or "Danger." The system then verifies and integrates these two types of outputs. For example, it maps the calculated quantitative corrosion rate to a standard-defined risk level range, or corrects the quantitative results based on the qualitative risk level, ultimately generating a comprehensive assessment report containing precise numerical values ​​and a clear risk level.

[0061] After completing the current state assessment, the module enters prediction mode. The prediction is based on an evolutionary model that matches the dominant corrosion mechanism. The system model library contains various mathematical models of corrosion development corresponding to different mechanisms, such as linear or exponential growth models for uniform corrosion, statistical extremum models for pitting corrosion development, or power exponential models. Taking the exponential model of uniform corrosion as an example, the change of corrosion depth d with time t can be described as: ; in, This represents the initial corrosion depth. and These are parameters related to materials and the environment.

[0062] The module uses the currently assessed corrosion state as an initial value, combined with expected future operating parameters, or assuming that the operating parameters remain unchanged, and recursively calculates using a selected mechanistic evolution model to simulate the trajectory of corrosion state indicators over a future period. The predicted output includes a corrosion development trend curve and estimated time points for reaching preset safety or maintenance thresholds at various levels, providing clear time window guidance for preventative maintenance.

[0063] The protection decision and parameter optimization module, under the constraints of the comprehensive corrosion status assessment results and corrosion evolution prediction results, optimizes the control parameters of the protection measures and outputs a set of protection parameters. Furthermore, the calculation process of the protection parameter set in the protection decision and parameter optimization module includes: obtaining the comprehensive corrosion state assessment results and corrosion evolution prediction results; under the constraints of the comprehensive corrosion state assessment results and the corrosion evolution prediction results, with the optimization objective of mitigating the current corrosion state and suppressing the predicted corrosion development trend, using an optimization algorithm to optimize the control parameters of at least one protection measure, and generating and outputting the protection parameter set.

[0064] Specifically, this module is the terminal for the system to generate direct operational suggestions. Its decisions are based on the current corrosion severity provided upstream and the trend of its development without intervention. The module includes a pre-set list of available protective measures, such as generating parameters for injecting corrosion inhibitors, adjusting process operating conditions, and generating parameters for online repair operations.

[0065] The core of the module is a mathematical optimization solver. The solver sets the operational parameters of the protective measures as decision variables, constructing an optimization model with the primary objective of controlling corrosion risk and constraints of economic cost and operational feasibility. The primary objective is typically expressed as minimizing the total corrosion increment over the predicted future period or maximizing the remaining safe life of the equipment.

[0066] For example, minimizing the time interval [ , The target is the total corrosion increment. ; The optimization process is a simulation-evaluation-iteration cycle: First, a set of protection parameters is assumed. Then, based on the influence of these parameters on the corrosion rate, the corrosion development curve predicted by the baseline is corrected, and the equipment state trajectory and the corresponding objective function value under the new protection strategy are calculated. Subsequently, the optimization algorithm adjusts the protection parameters and repeats the above simulation evaluation until the parameter combination that makes the objective function optimal under all constraints is found.

[0067] The effectiveness of the final output set of protection parameters is verified through the aforementioned simulation loop.

[0068] The model lifecycle management module manages the lifecycle of the comprehensive corrosion status assessment model, the corrosion evolution prediction model, and the protection parameter optimization model. Under the constraint of information validity weight, it performs a weighted evaluation of the model prediction error. When the degradation judgment condition is met, the model is determined to be degraded and the model update process is triggered.

[0069] Furthermore, in the model lifecycle management module, the model lifecycle management process includes: continuously acquiring the prediction results of the corrosion state comprehensive assessment model and the corrosion evolution prediction model, as well as the corresponding actual detection results, and acquiring the set of protection parameters output by the protection parameter optimization model; calculating the error between the prediction results and the actual detection results, and combining the information validity weight to determine whether the model degradation judgment condition is met. The model degradation determination criteria include the case where the prediction error, after being weighted based on the information validity weight, meets the preset degradation threshold within the preset evaluation time window; When the model degradation determination condition is met, the corresponding model is updated.

[0070] Specifically, this module ensures that the system's core analytical model can adapt to changes in operating conditions and states during long-term operation of the equipment. The module continuously monitors the online performance of the evaluation model, prediction model, and optimization model. The monitoring method involves periodically comparing the model's online output with the actual values ​​obtained through offline high-precision testing or equipment disassembly inspection.

[0071] Considering that these real-value data themselves are time-sensitive and uncertain, information validity weights are used to weight them when calculating model errors, so that recent and reliable validation data have a higher proportion in performance evaluation.

[0072] Calculate the weighted average absolute error: ; The module maintains a performance time series for each model and sets a sliding evaluation window. Within the sliding evaluation window, the module calculates the weighted average prediction error of the model. When a model's error metric exceeds a degradation threshold set based on its historical performance and engineering tolerance multiple times consecutively, the module considers the model's performance. When, that is, the following condition is met: ; The module determines that the model's performance no longer meets the accuracy requirements. After triggering the degradation judgment, the module automatically starts the model update process, using recently accumulated new data samples to retrain the model or recalibrate the parameters.

[0073] The updated model must pass rigorous performance testing on an independent validation dataset to confirm that its accuracy and stability meet deployment standards before it can replace the old online model. This closed-loop management mechanism enables the system to learn and continuously improve, ensuring long-term operational reliability.

[0074] Example 2: To address the problem of delayed corrosion assessment and difficulty in timely guidance of protection decisions in high-temperature components of coal gasification plants under complex operating conditions, this invention provides a full-cycle protection management system for coal gasification plants that integrates multi-source corrosion data. Its program flow structure is as follows: Figure 3 As shown. The specific implementation process of this system is as follows: During continuous operation of the unit, operators select the gasifier combustion chamber as the target monitoring area through the system interface and initiate the full-cycle protection management process. The system then automatically activates and enters working mode, initiating multi-source data acquisition: high-temperature corrosion probes deployed on the combustion chamber wall begin to collect corrosion current and noise signals in real time; the DCS system transmits process parameters such as combustion chamber temperature, pressure, concentration and flow rate of key gases (H2S, CO, O2) in real time; simultaneously, the system automatically retrieves ultrasonic thickness measurement records, endoscopic images and corrosion product analysis reports obtained during the most recent shutdown maintenance, as well as the material, service time and maintenance history of this part in the equipment management system. The system automatically performs time alignment, format unification and dimension normalization on these multi-source heterogeneous data, and calculates their reliability weights based on the missing rate, anomaly rate and sequence stability of each data source.

[0075] Considering the harsh operating conditions of the combustion chamber under high temperature and sulfur-containing atmosphere for extended periods, the system establishes an information time-dependent decay model for corrosion data from different sources: exponential decay is applied to real-time monitoring data, and Weibull decay is applied to offline detection data, thereby calculating the information validity weight of each data point at the current evaluation moment. The system integrates real-time corrosion rate, process gas composition, temperature and pressure, and historical detection results to extract multi-dimensional feature vectors, which are then matched with a built-in corrosion mechanism knowledge base to identify a composite corrosion mechanism under the current operating conditions, dominated by "high-temperature sulfidation corrosion" and accompanied by "erosion."

[0076] Under the constraint of a clearly defined dominant mechanism, the system dynamically integrates multi-source information from online monitoring, process data, and offline detection based on a preset mechanism sensitivity coefficient to generate mechanism constraint fusion features. Based on these features, the system uses a trained evaluation model to output a quantitative assessment of the current corrosion state (such as corrosion rate and remaining wall thickness) and risk level, and calls an evolution model that matches "high-temperature sulfidation corrosion" to predict the corrosion development trend in the future.

[0077] The system takes the current assessment results and predicted trends as input, and optimizes the adjustable protection parameters (such as corrosion inhibitor injection rate and process temperature fine-tuning amount) in the protection decision module with the goal of slowing down corrosion development and extending safe service life, and outputs the recommended combination of protection parameters.

[0078] The system continuously monitors the actual corrosion rate and key process parameters using online probes and compares them with model predictions. If the prediction error continues to exceed a threshold within a certain period, the model lifecycle management module determines that the model performance has degraded, automatically triggering the model update process. The system retrains, evaluates, and predicts the model using recently accumulated data, and replaces the original model after independent verification, ensuring that the system adapts to changes in operating conditions over the long term.

[0079] Through the aforementioned closed-loop operation, the system achieves real-time and accurate assessment of the corrosion status of the combustion chamber, forward-looking prediction of trends, and dynamic optimization of protection parameters without shutting down the system or relying on frequent manual inspections. This significantly improves the initiative and reliability of corrosion management in high-temperature parts of the coal gasification unit.

[0080] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A full-cycle protection management system for a coal gasification unit that integrates multi-source corrosion data, characterized in that, include: The multi-source corrosion data acquisition and preprocessing module collects corrosion data, operating data, and related history data corresponding to the target parts of the coal gasification unit, performs standardized processing on the data, and calculates the credibility weight of various types of data. The information validity assessment module is used to establish an information timeliness decay model for corrosion data from different sources, and to calculate the current information validity weight of the corrosion data based on the information timeliness decay model. The corrosion mechanism identification module combines the corrosion data and operational data to identify the dominant corrosion mechanism type of the target location; The mechanism-constrained multi-source fusion module, under the constraint of the dominant corrosion mechanism type, integrates the information validity weight and the credibility weight, dynamically allocates multi-source data fusion weights according to the mechanism type, and constructs mechanism-constrained fusion features. The corrosion state comprehensive assessment and evolution prediction module performs qualitative and quantitative evaluation of the corrosion state of the target part based on the mechanistic constraint fusion characteristics, outputs comprehensive corrosion state assessment results, and establishes a corrosion evolution model to predict the corrosion development trend in subsequent operating cycles. The protection decision and parameter optimization module, under the constraints of the comprehensive corrosion status assessment results and corrosion evolution prediction results, optimizes the control parameters of the protection measures and outputs the corresponding set of protection parameters. The model lifecycle management module manages the lifecycle of the comprehensive corrosion status assessment model, the corrosion evolution prediction model, and the protection parameter optimization model. Under the constraint of information validity weight, it performs a weighted evaluation of the model prediction error. When the degradation judgment condition is met, the model is determined to be degraded and the model update process is triggered.

2. The full-cycle protection management system for coal gasification equipment integrating multi-source corrosion data as described in claim 1, characterized in that, The specific process for constructing the confidence weight in the multi-source corrosion data acquisition and preprocessing module includes: Obtain corrosion data, operational data, and related historical data corresponding to the target location; The data is standardized to unify its format and units; Based on preset rules, the missing rate, outlier rate, and stability index of each type of data, along with their reliability weights, are calculated.

3. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 1, characterized in that, The calculation process for the information validity weight in the information validity assessment module includes: Information decay models were established for corrosion data from different sources. Based on the information time-related decay model, the time-related decay factor of various types of corrosion data at the current evaluation time point relative to the data acquisition time point is calculated, and the current information validity weight of various types of corrosion data is calculated.

4. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 3, characterized in that, The construction process of the information timeliness decay model in the information effectiveness assessment module specifically includes: Analyze historical data and, based on the source type and characteristics of corrosion data, set corresponding aging attenuation parameters and attenuation function forms for corrosion data from different sources; Using the aforementioned time-decrease parameters and the form of the decay function, a corresponding information time-decrease model is constructed.

5. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 1, characterized in that, The process for identifying the dominant corrosion mechanism type in the corrosion mechanism identification module includes: Extract the characteristic parameter combination from the corrosion data and the operation data; The combination of the feature parameters is matched with a preset corrosion mechanism knowledge base, and the dominant corrosion mechanism type of the target part under the current working conditions is identified based on the matching results.

6. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 1, characterized in that, The construction process of the mechanism-constrained fusion features in the mechanism-constrained multi-source fusion module includes: Based on the dominant corrosion mechanism type, a fusion weight allocation rule corresponding to the mechanism type is determined from a preset weight allocation strategy library. According to the fusion weight allocation rule, the information validity weight and the credibility weight are combined to dynamically allocate fusion weights for each data source. The multi-source data are weighted and fused using the assigned fusion weights to construct the mechanism-constrained fusion features.

7. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 1, characterized in that, The comprehensive assessment and evolution prediction process for corrosion state in the corrosion state comprehensive assessment and evolution prediction module includes: Based on the aforementioned mechanism-constrained fusion characteristics, a combination of qualitative evaluation models and quantitative calculations is used to evaluate the corrosion status of the target location and generate a comprehensive corrosion status evaluation result. Using the comprehensive corrosion state assessment results as the initial state input, a corrosion evolution model matching the dominant corrosion mechanism type is established; Based on the corrosion evolution model, the corrosion development trend of the target part in subsequent operating cycles is predicted.

8. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data according to claim 1, characterized in that, The calculation process of the protection parameter set in the protection decision and parameter optimization module includes: Obtain comprehensive corrosion status assessment results and corrosion evolution prediction results; Under the constraints of the comprehensive corrosion state assessment results and the corrosion evolution prediction results, with the optimization objective of mitigating the current corrosion state and suppressing the predicted corrosion development trend, the control parameters of the protection measures are optimized and calculated using an optimization algorithm to generate and output a set of protection parameters.

9. The full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 1, characterized in that, The model lifecycle management process in the model lifecycle management module includes: Continuously acquire the prediction results of the comprehensive corrosion state assessment model and the corrosion evolution prediction model, as well as the corresponding actual detection results, and obtain the set of protection parameters output by the protection parameter optimization model; Calculate the error between the predicted result and the actual detection result, and combine it with the information validity weight to determine whether the model degradation judgment condition is met; When the model degradation determination condition is met, the corresponding model is updated.

10. A full-cycle protection management system for a coal gasification unit integrating multi-source corrosion data as described in claim 9, characterized in that, The model update process described in the model lifecycle management module includes: Based on the latest corrosion data and operational data processed by the multi-source corrosion data acquisition and preprocessing module, the model that was determined to have degraded was retrained to obtain an updated model. Using a validation dataset independent of the retraining process, the updated model is validated to assess its output accuracy in evaluating the corrosion status of the target area, predicting corrosion evolution, and evaluating the effectiveness of the set of protection parameters. When the verification calculation result meets the preset accuracy requirements, the updated model replaces the original model for subsequent system operation.

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