Entrained-flow bed gasifier burner life prediction method and electronic equipment

Abstract

The invention discloses a method for predicting the service life of a burner of an entrained-flow bed gasifier and electronic equipment. The entrained-flow bed gasifier burner life prediction method comprises the following steps: constructing a flow field network structure; establishing a reaction mechanism network model, and performing simulation by adopting the reaction mechanism network model to obtain simulation data of the burner root parameters of the burner; acquiring training data of other multi-modal characteristic parameters of the burner; screening the multi-modal characteristic parameters, and taking the screened multi-modal characteristic parameters and the burner root parameters as optimal multi-modal characteristic parameters; constructing a service life prediction model of the entrained-flow gasifier burner, and training the service life prediction model of the entrained-flow gasifier burner; and inputting the current data of the optimal multi-mode characteristic parameters into the entrained-flow bed gasifier burner life prediction model to obtain the predicted residual life of the entrained-flow bed gasifier burner. According to the method, key parameters are complemented through a mechanism network, the problem of prediction failure caused by a temperature measurement blind area is solved, and meanwhile, the service life of the burner is accurately predicted by constructing a multi-dimensional feature system.

Description

Technical Field

[0001] This invention relates to the technical field of gasifiers, and in particular to a method for predicting the burner life of an entrained gasifier, electronic equipment, storage medium, and computer program product. Background Technology

[0002] As a core piece of equipment in coal chemical industry, clean energy conversion, and industrial gasification, fluidized bed gasifiers convert carbon-based raw materials such as coal and biomass into syngas (H2 / CO) through high-temperature and high-pressure reactions. They are widely used in power generation, chemical synthesis, and hydrogen production. The burner, as a core component of the gasifier, is responsible for mixing, atomizing, and igniting fuel and oxidant; its service life directly determines the gasifier's operating efficiency and stability. However, burners are exposed to high temperatures, high pressures, corrosive media, and complex thermal stress environments for extended periods. This makes them prone to failure due to thermal fatigue, wear, burner coil damage, external oxygen nozzle cracking, and coil corrosion. These failures can lead to unplanned downtime and safety accidents, posing a serious threat to production economy and safety.

[0003] Currently, existing burner life management methods have significant limitations: they rely on fixed-cycle maintenance and manual experience judgment, lack real-time monitoring methods, and have significant prediction lag; the analysis of operating parameters is singular (such as focusing only on temperature or pressure thresholds) and does not deeply integrate the complex coupling relationship of multi-dimensional data such as flow rate, oxygen-coal ratio, gasification temperature, syngas composition, and load fluctuations; existing physical models or statistical methods have large prediction errors in dynamic operating conditions and nonlinear variable interaction scenarios, while conservative maintenance strategies are prone to excessive replacement costs or sudden failure risks.

[0004] In recent years, machine learning technology has demonstrated advantages in nonlinear modeling for industrial equipment condition prediction. However, no publicly available prediction framework specifically designed for burners in fluidized bed gasifiers has yet emerged. Existing research largely focuses on predicting the remaining life of general equipment or analyzing single sensor signals (such as acoustic emission fault diagnosis), lacking in-depth integrated modeling of the specific process parameters of gasifiers and burner degradation mechanisms.

[0005] Machine learning models have very strict requirements for datasets. However, due to the extreme environment of high temperature (1300-1600℃), high pressure (3-8MPa), and strong corrosion (sulfidation, oxidation, slag erosion) inside the gasifier, many sensors inside the furnace are prone to damage during long-term operation. Although these components do not directly affect the operation of the gasifier, the lack of their values ​​will lead to the loss of input parameters for the burner machine learning model. When these parameters have a significant impact on the results, it will cause the model to fail.

[0006] For example, the Chinese patent "A Method and System for Recommending Measures Based on the Fusion of Data-Driven and Mechanistic Models" (application number: 202410891739.X) focuses on the basic application and construction methods of data and mechanistic models in industrial scenarios. However, it lacks solutions and operational methods for specific problems. In the case of burner life prediction, this solution cannot solve important issues such as the acquisition and preprocessing of multi-source data, multi-modal data processing, difficulties in acquiring key model input parameters, multi-scale model construction and coupling, industrial deployment of models, and online updates.

[0007] Additionally, a Chinese patent, "A Modeling Method and Apparatus for Pulverized Coal Gasification Furnace Based on a CFD-Based Reduced-Order Model" (application number: 202411886161.5), proposes a method for constructing a regional network model based on a Shell pulverized coal gasification furnace. This method can establish a reduced-order network model of the pulverized coal gasification furnace, improving computational efficiency. However, this solution aims to address the problems of computational complexity and construction difficulty associated with traditional models. Regarding burner life prediction, while this solution can obtain a mechanistic model and determine the gasification device, specifically the process simulation results of the Shell pulverized coal gasification furnace, it cannot predict burner life. Summary of the Invention

[0008] Therefore, it is necessary to address the technical problem of burner life prediction failure caused by temperature measurement blind spots due to the lack of existing technologies based on burner root parameters, and to provide a method, electronic equipment, storage medium, and computer program product for predicting the burner life of a fluidized bed gasifier.

[0009] This invention provides a method for predicting the burner life of a fluidized bed gasifier, comprising:

[0010] Analyze the flow field inside the fluidized bed gasifier and construct the flow field network structure;

[0011] Based on the aforementioned flow field network structure, a reaction mechanism network model is established, and simulation data of the burner root parameters are obtained using the aforementioned reaction mechanism network model.

[0012] Obtain training data for other multimodal feature parameters of the burner;

[0013] The multimodal characteristic parameters are screened to obtain the screened multimodal characteristic parameters of the gasifier burner. The screened multimodal characteristic parameters and the burner root parameters are taken as the optimal multimodal characteristic parameters.

[0014] A burner life prediction model for a fluidized bed gasifier is constructed. Based on the training data of the optimal multimodal characteristic parameters, the burner life prediction model for the fluidized bed gasifier is trained. The burner life prediction model for the fluidized bed gasifier predicts the predicted remaining life of the burner based on the data of the optimal multimodal characteristic parameters.

[0015] By inputting the current data of the optimal multimodal characteristic parameters into the gasification furnace burner life prediction model, the predicted remaining life of the gasification furnace burner is obtained.

[0016] Furthermore, based on the flow field network structure, a reaction mechanism network model is established, and simulation data of the burner root parameters are obtained using the reaction mechanism network model, including:

[0017] For multiple flow field components in the aforementioned flow field network structure, corresponding reaction sub-models are established;

[0018] Multiple reaction sub-models are connected together to form a reaction mechanism network model;

[0019] The network model of the reaction mechanism is simulated to obtain simulation data of temperature parameters and product indicators within a preset range centered on the burner, wherein the temperature parameters and product indicators are the parameters at the root of the burner.

[0020] Furthermore, the burner life prediction model for the fluidized bed gasifier includes:

[0021] The system includes a neural network branch, a time-series sensor data processing branch, a semantic feature branch, a cross-attention mechanism module, a weighting module, and a burner remaining life prediction module, among which:

[0022] The neural network branch takes into input static or quasi-static feature parameters from the optimal multimodal feature parameters and outputs a static feature vector;

[0023] The timing sensing data processing branch takes into input timing sensing data and simulation data of burner root parameters, and outputs timing feature vectors.

[0024] The semantic feature branch takes the maintained text as input and outputs a semantic feature vector.

[0025] The output of the time-series sensing data processing branch serves as the query value input to the cross-attention mechanism module, the output of the semantic feature branch serves as the keywords and key values ​​input to the cross-attention mechanism module, and the cross-attention mechanism module outputs a context feature vector.

[0026] The weighting module takes the working condition context as input and outputs the weights of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module.

[0027] The outputs of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module are multiplied by their corresponding weights and used as a fusion feature vector. This vector is then input into the burner remaining life prediction module, which outputs the predicted remaining life of the fluidized bed gasifier burner.

[0028] Furthermore, the gasification furnace burner life prediction model further includes: a maintenance type classification module and a degradation stage classification module, wherein:

[0029] The maintenance type classification module takes a fused feature vector as input and outputs a maintenance type label.

[0030] The degradation stage classification module takes a fused feature vector as input and outputs a degradation stage label.

[0031] Furthermore, it also includes:

[0032] When the update conditions are met, the burner remaining life prediction module, the maintenance type classification module, and the degradation stage classification module are retrained.

[0033] Furthermore, the conditions for satisfying the update include:

[0034] If the prediction error of the gasifier burner life prediction model exceeds a set threshold, then the update condition is deemed met; or

[0035] If the clustering results of the fluidized bed gasifier's operating conditions change, then the update condition is deemed met; or

[0036] If the difference between the current data distribution and the training data distribution of the input fluidized bed gasifier burner life prediction model is greater than the difference threshold, then the update condition is determined to be met.

[0037] Furthermore, the conditions for satisfying the update include:

[0038] Set a confidence level and calculate the upper and lower limits of the confidence interval corresponding to the current burner's running time.

[0039] Based on the predicted remaining life output from the fluidized bed gasifier burner life prediction model, the burner health index is calculated as follows: Among them, HI is the health index of the burner, and RUL is the health index of the burner. pred To predict remaining lifetime, RUL design The burner's designed lifespan;

[0040] If the burner health index falls outside the confidence interval, then the update condition is met.

[0041] This invention provides an electronic device, comprising:

[0042] At least one processor; and,

[0043] A memory communicatively connected to at least one of the processors; wherein,

[0044] The memory stores instructions that can be executed by at least one of the processors to enable at least one of the processors to perform the fluidized bed gasifier burner life prediction method as described above.

[0045] The present invention provides a storage medium that stores computer instructions, which, when executed by a computer, are used to perform all the steps of the fluidized bed gasifier burner life prediction method as described above.

[0046] This invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the aforementioned method for predicting the burner life of a fluidized bed gasifier.

[0047] This invention uses a mechanistic network model for simulation to complete the burner root parameters. Simultaneously, it acquires other multimodal feature parameters, filters them, and uses the filtered multimodal feature parameters along with the burner root parameters as the optimal multimodal feature parameters. A burner life prediction model for a fluidized bed gasifier is then constructed, and the predicted remaining life of the burner is predicted based on the data from the optimal multimodal feature parameters. This invention completes key parameters such as burner root temperature and local thermal stress through a mechanistic network, improving the model's input dimension coverage by 70%, solving the prediction failure problem caused by temperature measurement blind spots. Furthermore, by constructing a multidimensional feature system, it can accurately predict burner life, significantly reducing unplanned downtime losses and extending the effective life of the burner. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating a method for predicting the burner life of a fluidized bed gasifier according to an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating a method for predicting the burner life of a fluidized bed gasifier according to another embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of the burner life prediction model for an airflow gasifier according to an embodiment of the present invention;

[0051] Figure 4 A flowchart illustrating a preferred embodiment of the present invention for predicting the burner life of a fluidized bed gasifier.

[0052] Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to the present invention. Detailed Implementation

[0053] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, while the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0054] like Figure 1 The diagram shown is a flowchart of a method for predicting the burner life of a fluidized bed gasifier according to an embodiment of the present invention, including:

[0055] Step S101: Analyze the flow field inside the fluidized bed gasifier and construct the flow field network structure;

[0056] Step S102: Based on the flow field network structure, establish a reaction mechanism network model, and use the reaction mechanism network model to perform simulation to obtain simulation data of the burner root parameters.

[0057] Step S103: Obtain training data for other multimodal feature parameters of the burner;

[0058] Step S104: The multimodal characteristic parameters are screened to obtain the screened multimodal characteristic parameters of the gasifier burner. The screened multimodal characteristic parameters and the burner root parameters are taken as the optimal multimodal characteristic parameters.

[0059] Step S105: Construct a gas flow gasifier burner life prediction model. Based on the training data of the optimal multimodal characteristic parameters, train the gas flow gasifier burner life prediction model. The gas flow gasifier burner life prediction model predicts the predicted remaining life of the gas flow gasifier burner according to the data of the optimal multimodal characteristic parameters.

[0060] Step S106: Input the current data of the optimal multimodal characteristic parameters into the gasification furnace burner life prediction model to obtain the predicted remaining life of the gasification furnace burner.

[0061] Specifically, the present invention can be applied to electronic devices with processing capabilities, such as computers.

[0062] First, step S101 is executed to analyze the flow field inside the fluidized bed gasifier and construct the flow field network structure.

[0063] Specifically, the application scenario and gasification technology are determined, and fluid computing software such as Computational Fluid Dynamics (CFD) is used to analyze the flow field inside the furnace. The flow field network structure and characteristic parameters such as the fluid recirculation ratio between each grid are determined by using the simple two-dimensional flow field distribution characteristics.

[0064] Then, step S102 is executed, based on the flow field network structure, a reaction mechanism network model is established, and the reaction mechanism network model is used for simulation to obtain simulation data of the burner root parameters.

[0065] Specifically, the reaction mechanism network model is used for simulation. The burner distribution position can be determined based on the gasifier structure size. Process parameters such as furnace temperature and product indicators near the burner are extracted as simulation data for burner root parameters.

[0066] Then, step S103 is executed to obtain training data for other multimodal characteristic parameters of the gasifier burner.

[0067] Other multimodal characteristic parameters of the fluidized bed gasifier burner refer to characteristic parameters of various modes other than the aforementioned burner root parameters. Specifically, multi-source data acquisition and characterization of the fluidized bed gasifier burner are carried out. Data from the entire operating cycle of the fluidized bed gasifier burner is collected, including medium parameters (composition and hardness of carbon-containing feedstock), process parameters (oxygen-to-coal ratio, load rate, feed rate), burner characteristic parameters (material, structural dimensions, etc.), dynamic sensor data (burner root temperature distribution, vibration spectrum), reaction mechanism network model parameters (temperature distribution inside the gasifier, gasification product indicators, etc.), and historical maintenance records (burner replacement cycle, repair measures).

[0068] Then, step S104 is executed to screen the multimodal characteristic parameters to obtain the screened multimodal characteristic parameters of the gasifier burner. The screened multimodal characteristic parameters and the burner root parameters are taken as the optimal multimodal characteristic parameters.

[0069] Specifically, the screening and optimization of burner degradation-sensitive features are carried out. First, all available data are preprocessed and organized. After collecting and organizing relevant databases, Spearman's rank correlation analysis and expert experience bases (such as prior knowledge of corrosion mechanisms) are used to eliminate redundant parameters (such as the weak influence of ambient temperature and humidity on high-temperature burners) and narrow down the range of prediction indicators. Finally, an improved genetic algorithm (with constrained population initialization) is applied, with prediction error and computational cost as dual objective functions, to screen high-contribution feature combinations (such as hot spot area change rate, load rate, feed flow rate, etc.).

[0070] Finally, the filtered multimodal feature parameters and the burner root parameters are used as the optimal multimodal feature parameters.

[0071] Then, step S105 is executed to construct a gas flow gasifier burner life prediction model. Based on the training data of the optimal multimodal characteristic parameters, the gas flow gasifier burner life prediction model is trained. The gas flow gasifier burner life prediction model predicts the remaining life of the gas flow gasifier burner according to the data of the optimal multimodal characteristic parameters.

[0072] Specifically, a machine learning model architecture was used to design and train a burner life prediction model for a fluidized bed gasifier.

[0073] Finally, step S106 is executed, inputting the current data of the optimal multimodal characteristic parameters into the gasification furnace burner life prediction model to obtain the predicted remaining life of the gasification furnace burner.

[0074] Specifically, the current data of the actual optimal multimodal characteristic parameters are obtained and input into the gas flow gasifier burner life prediction model to obtain the predicted remaining life of the gas flow gasifier burner output by the gas flow gasifier burner life prediction model.

[0075] In some embodiments, the method further includes recommending maintenance measures based on the prediction results.

[0076] Maintenance measures include, but are not limited to, periodic welding repairs and ceramic coating repairs.

[0077] This invention uses a mechanistic network model for simulation to complete the burner root parameters. Simultaneously, it acquires other multimodal feature parameters, filters them, and uses the filtered multimodal feature parameters along with the burner root parameters as the optimal multimodal feature parameters. A burner life prediction model for a fluidized bed gasifier is then constructed, and the predicted remaining life of the burner is predicted based on the data from the optimal multimodal feature parameters. This invention completes key parameters such as burner root temperature and local thermal stress through a mechanistic network, improving the model's input dimension coverage by 70%, solving the prediction failure problem caused by temperature measurement blind spots. Furthermore, by constructing a multidimensional feature system, it can accurately predict burner life, significantly reducing unplanned downtime losses and extending the effective life of the burner.

[0078] like Figure 2 The diagram shown is a flowchart of a method for predicting the burner life of a fluidized bed gasifier according to another embodiment of the present invention, including:

[0079] Step S201: Analyze the flow field inside the fluidized bed gasifier and construct the flow field network structure.

[0080] Step S202: Establish corresponding reaction sub-models for multiple flow field components in the flow field network structure;

[0081] Multiple reaction sub-models are connected together to form a reaction mechanism network model;

[0082] The network model of the reaction mechanism is simulated to obtain simulation data of temperature parameters and product indicators within a preset range centered on the burner, wherein the temperature parameters and product indicators are the parameters at the root of the burner.

[0083] Step S203: Obtain training data for other multimodal feature parameters of the burner.

[0084] Step S204: The multimodal characteristic parameters are screened to obtain the screened multimodal characteristic parameters of the gasifier burner. The screened multimodal characteristic parameters and the burner root parameters are taken as the optimal multimodal characteristic parameters.

[0085] Step S205: Construct a gasification furnace burner life prediction model. Based on the training data of the optimal multimodal characteristic parameters, train the gasification furnace burner life prediction model. The gasification furnace burner life prediction model predicts the predicted remaining life of the gasification furnace burner according to the data of the optimal multimodal characteristic parameters. The gasification furnace burner life prediction model includes:

[0086] The system includes a neural network branch, a time-series sensor data processing branch, a semantic feature branch, a cross-attention mechanism module, a weighting module, and a burner remaining life prediction module, among which:

[0087] The neural network branch takes into input static or quasi-static feature parameters from the optimal multimodal feature parameters and outputs a static feature vector;

[0088] The timing sensing data processing branch takes into input timing sensing data and simulation data of burner root parameters, and outputs timing feature vectors.

[0089] The semantic feature branch takes the maintained text as input and outputs a semantic feature vector.

[0090] The output of the time-series sensing data processing branch serves as the query value input to the cross-attention mechanism module, the output of the semantic feature branch serves as the keywords and key values ​​input to the cross-attention mechanism module, and the cross-attention mechanism module outputs a context feature vector.

[0091] The weighting module takes the working condition context as input and outputs the weights of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module.

[0092] The outputs of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module are multiplied by their corresponding weights and used as a fusion feature vector. This vector is then input into the burner remaining life prediction module, which outputs the predicted remaining life of the fluidized bed gasifier burner.

[0093] Step S206: Input the current data of the optimal multimodal characteristic parameters into the gasification furnace burner life prediction model to obtain the predicted remaining life of the gasification furnace burner.

[0094] Step S207: When the update conditions are met, the burner remaining life prediction module, the maintenance type classification module, and the degradation stage classification module are retrained.

[0095] This embodiment addresses several technical challenges in the burner life management of fluidized bed gasifiers, namely insufficient multi-source data fusion, poor industrial data quality, poor adaptability to dynamic operating conditions, and non-intelligent maintenance decisions. It proposes solutions to address issues such as data silos and modal fragmentation, contradictions between static models and dynamic operating conditions, and excessive reliance on human experience in maintenance decisions. The goal is to achieve more comprehensive data fusion, more flexible adaptability to operating conditions, and more intelligent maintenance decisions.

[0096] This invention mainly solves the following problems:

[0097] Multimodal data collaborative modeling is insufficient. Traditional burner life prediction relies on single-type data (such as temperature thresholds or vibration spectra), neglecting the deep coupling relationships between multimodal information such as process parameters (oxygen-to-coal ratio, load rate), coal characteristics (sulfur content, hardness), and maintenance records. For example, coal type switching may cause abrupt changes in thermal stress distribution, but existing models do not incorporate this parameter into the feature system, resulting in significant errors in degradation rate estimation. This embodiment proposes multimodal data-driven feature engineering, which constructs thermal-fluid coupling parameters and uses an improved genetic algorithm to screen cross-modal key variables (such as the "high-sulfur coal-coating wear correlation factor") to achieve full-dimensional data fusion of process, materials, and environment, fundamentally improving prediction accuracy and the model's broad applicability.

[0098] The quality of industrial operation data is poor. The fluidized bed gasifier operates under constant high temperature, high pressure, strong convection, and strong corrosion, making process sensors (such as thermocouples) prone to damage. During long-term operation, accurate process parameters cannot be obtained, significantly impacting model predictions. Furthermore, the temperature measured by conventional thermocouples may not accurately reflect the temperature near the burner in various process technologies, failing to fully account for the influence of this input on the results. Therefore, for specific fluidized bed gasification technologies, a customized reaction mechanism network model should be constructed to accurately and efficiently provide the furnace temperature field. This model can provide a suitable temperature around the burner for the burner life prediction model, significantly improving prediction accuracy and enhancing the correlation between process operation methods and burner life, thus providing a reasonable process operation plan for extending burner life.

[0099] The model's generalization ability is weak under dynamic operating conditions. Frequent coal type switching and load fluctuations lead to significant differences in burner degradation patterns, while static models (such as offline-trained LSTMs) struggle to adapt to real-time changes in operating conditions. For example, the burner corrosion rate is three times higher during lignite gasification than during bituminous coal gasification, but traditional methods cannot automatically adjust the prediction threshold. This embodiment introduces an unsupervised operating condition fingerprinting module (t-SNE clustering) and a streaming incremental learning framework (Flink windowed local update) to achieve adaptive inference for new coal types and load scenarios (model switching latency < 5 minutes), avoiding the cost of manual calibration.

[0100] The maintenance decisions are disconnected from lifespan and rely on experience. Traditional maintenance strategies depend on fixed cycles or manual experience, failing to quantify the actual lifespan gains of measures such as re-welding and coating repair. This embodiment quantifies the contribution of measures through maintenance effect weights (e.g., a descaling operation increases the HI value by 15%) and establishes a closed-loop feedback mechanism: when the actual maintenance effect deviates significantly from the prediction, model retraining is triggered, and the optimized strategy is automatically updated to the decision base, forming an intelligent closed loop of "prediction-maintenance-verification-iteration".

[0101] Specifically, step S201 is executed first to analyze the flow field inside the fluidized bed gasifier and construct the flow field network structure.

[0102] Specifically, flow field analysis was performed on the fluidized bed gasifier. The application scenario and gasification technology were determined, and fluid dynamics software such as CFD was used to analyze the flow field inside the furnace. The flow field network structure and characteristic parameters such as the fluid recirculation ratio between each grid were determined using the simplified two-dimensional flow field distribution characteristics.

[0103] Then, step S202 is executed to establish corresponding reaction sub-models for multiple flow field components in the flow field network structure;

[0104] Multiple reaction sub-models are connected together to form a reaction mechanism network model;

[0105] The network model of the reaction mechanism is simulated to obtain simulation data of temperature parameters and product indicators within a preset range centered on the burner, wherein the temperature parameters and product indicators are the parameters at the root of the burner.

[0106] Specifically, a gasifier mechanism prediction model is constructed, and characteristic parameters near the burner are obtained. Based on the flow field analysis of a specific fluidized bed gasifier in step S201, a flow field network is constructed, dividing the network into multiple flow field parts. For each flow field part, a corresponding reaction sub-model is established. For example, in a flow field with a high concentration of gasifying agent (O2), the combustion reaction of the gasification feedstock is dominant, forming a combustion module; in a high-temperature, strongly mixed flow field region, combustion and gasification reactions coexist, forming a fully mixed combustion gasification module, and so on. After constructing the reaction mechanism network model, the accuracy of the model is proven to be higher than 90% based on the verification of gasification index parameters (product composition, flow rate, furnace temperature measurable by thermocouples, etc.). At this point, the temperature at all locations within the gasifier can be predicted using the calculated values ​​from the reaction mechanism network model. The burner distribution location can be determined based on the gasifier's structural dimensions. Process parameters such as furnace temperature and product indicators are used as burner root parameters. The process parameters (model predicted values / simulation values) such as furnace temperature and product indicators near the burners are extracted as simulation data for burner root parameters.

[0107] Then, step S203 is executed to obtain training data for other multimodal feature parameters of the burner.

[0108] Specifically, multi-source data acquisition and characterization of the burners in the fluidized bed gasifier were conducted. Data from the entire operating cycle of the fluidized bed gasifier burners was collected, including media parameters (composition and hardness of the carbon-containing feedstock), process parameters (oxygen-to-coal ratio, load rate, feed velocity), burner characteristic parameters (material, structural dimensions, etc.), dynamic sensor data (temperature distribution at the burner root, vibration spectrum), reaction mechanism network model parameters (temperature distribution within the gasifier, gasification product indicators, etc.), and historical maintenance records (burner replacement cycle, repair measures).

[0109] Then, step S204 is executed to screen the multimodal characteristic parameters to obtain the screened multimodal characteristic parameters of the gasifier burner. The screened multimodal characteristic parameters and the burner root parameters are used as the optimal multimodal characteristic parameters.

[0110] Specifically, the sensitive characteristics of burner degradation are screened and optimized. First, all available data is preprocessed and organized. For example, for discrete data such as medium parameters and burner characteristic parameters, laboratory test results are imported into the burner life prediction model database by establishing data interfaces. The data to be stored includes, but is not limited to, industrial analysis of carbon-containing materials, particle size distribution, hardness, sulfur content, burner material, burner channel diameter, and burner nozzle angle. For process parameters, dynamic sensor data, and environmental parameters, real-time data collected through sensors and a DCS system is transmitted to the burner life prediction model database. The data to be stored includes, but is not limited to, oxygen-to-coal ratio, load rate, and feed flow rate (for dry pulverized coal gasification technology, this involves solid flow rate and feed...). The data includes gas flow rate, coal-water slurry flow rate and concentration, oxygen flow rate, gasifier temperature, syngas composition, burner cooling water flow rate, burner temperature distribution, vibration frequency, etc. For multimodal data such as historical maintenance records, NLP technology (such as BERT fine-tuning) can be used to extract key events from the work order log (such as "2023-05-12 burner A repair welding, duration 120min") and encode them into semantic vectors; or the quantitative weight of maintenance type (cleaning, coating repair) on the life repair effect can be defined (such as repair welding can restore 10% of life, repair coating can restore 30%), and a maintenance effect matrix based on time decay can be constructed (such as: maintenance effect decreases exponentially with time). After collecting and organizing the relevant databases, Spearman's rank correlation analysis and expert experience base (such as prior knowledge of corrosion mechanisms) were used to eliminate redundant parameters (such as the weak influence of ambient temperature and humidity on high-temperature burners) and narrow down the range of prediction indicators. Finally, an improved genetic algorithm (with constrained population initialization) was applied to select high-contribution feature combinations (such as hot spot area change rate, load rate, feed flow rate, etc.) with prediction error and computational cost as dual objective functions.

[0111] An improved genetic algorithm (with constrained population initialization) encodes multimodal features (the parameters mentioned above) into chromosomes, where each gene represents a feature variable and its weight. The algorithm uses prediction error and feature set simplicity (number of features) as dual objective functions, calculating fitness values ​​through weighted summation. A tournament selection strategy is employed to retain high-fitness individuals. Offspring are generated through single-point crossover operations to enhance the diversity of feature combinations. The algorithm iterates until the fitness values ​​converge or the maximum number of generations is reached, outputting the optimal feature subset to complete the selection of high-contribution feature combinations (such as hotspot area change rate, load rate, feed flow rate, etc.).

[0112] In some embodiments, the improved genetic algorithm includes:

[0113] Multimodal features are encoded as chromosomes, with each gene representing a feature variable and its weight;

[0114] Initialize the first generation population, which consists of multiple chromosomes;

[0115] Iteratively execute the following operations:

[0116] Using prediction error and feature set simplicity as dual objective functions, the fitness value of each chromosome is calculated by weighted summation;

[0117] If the fitness value converges or reaches the maximum number of generations, the optimal feature subset is output as the optimal multimodal feature parameters of the gasifier burner and the iteration ends; otherwise, a chromosome is selected from the population based on fitness as the chromosome to be operated.

[0118] A progeny population is generated by performing a single-point crossover operation on the chromosome to be operated on.

[0119] Perform the next iteration on the offspring population.

[0120] Specifically, the multimodal features (the parameters mentioned above) are encoded as chromosomes, with each gene representing a feature variable and its weight. A chromosome is a combination of a candidate subset of features and the importance of each feature. The "feature variable" indicates which features were selected for this chromosome; the "weight" indicates the relative importance of these features within this chromosome. Encoding the weights into the genes allows the genetic algorithm to optimize simultaneously in both "feature selection" and "weight adjustment," improving prediction accuracy while maintaining feature simplicity.

[0121] Then, using prediction error and feature set simplicity (number of features) as dual objective functions, the fitness value is calculated by weighted summation; a tournament selection strategy is used to retain individuals with high fitness; and offspring are generated through single-point crossover operations to enhance the diversity of feature combinations. The iteration continues until the fitness value converges or reaches the maximum number of generations, at which point the optimal feature subset is output, completing the selection of high-contribution feature combinations (such as hot spot area change rate, load rate, feed flow rate, etc.).

[0122] For the i-th chromosome, its fitness F i Calculate using the following weighted summation formula:

[0123]

[0124] In the formula, E i : Normalized prediction error (mean squared error or MAE, normalized to [0,1]) of the feature subset corresponding to the i-th chromosome on the validation set; S i S: The normalized value of the number of selected features on this chromosome. i = (number of selected features) / (total number of features), also in [0,1]; w1, w2: user-preset weight coefficients, satisfying w1+w2=1 (e.g. w1=0.7, w2=0.3).

[0125] Then, step S205 is executed to construct a gasification furnace burner life prediction model. Based on the training data of the optimal multimodal characteristic parameters, the gasification furnace burner life prediction model is trained. The gasification furnace burner life prediction model predicts the predicted remaining life of the gasification furnace burner according to the data of the optimal multimodal characteristic parameters. The gasification furnace burner life prediction model includes:

[0126] The system includes a neural network branch, a time-series sensor data processing branch, a semantic feature branch, a cross-attention mechanism module, a weighting module, and a burner remaining life prediction module, among which:

[0127] The neural network branch takes into input static or quasi-static feature parameters from the optimal multimodal feature parameters and outputs a static feature vector;

[0128] The timing sensing data processing branch takes into input timing sensing data and simulation data of burner root parameters, and outputs timing feature vectors.

[0129] The semantic feature branch takes the maintained text as input and outputs a semantic feature vector.

[0130] The output of the time-series sensing data processing branch serves as the query value input to the cross-attention mechanism module, the output of the semantic feature branch serves as the keywords and key values ​​input to the cross-attention mechanism module, and the cross-attention mechanism module outputs a context feature vector.

[0131] The weighting module takes the working condition context as input and outputs the weights of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module.

[0132] The outputs of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module are multiplied by their corresponding weights and used as a fusion feature vector. This vector is then input into the burner remaining life prediction module, which outputs the predicted remaining life of the fluidized bed gasifier burner.

[0133] Specifically, this involves designing and training a machine learning model architecture. To achieve accurate prediction of the burner life of a fluidized bed gasifier, this embodiment constructs a multimodal fusion deep learning architecture. This architecture consists of multiple sub-models working collaboratively to process different types of input data, and achieves joint training and optimization through shared underlying feature representations and a multi-task learning mechanism. Details are as follows:

[0134] 1. Model Structure and Functional Division

[0135] This architecture includes the following core sub-models, each of which processes data of a specific modality and outputs intermediate feature vectors for subsequent fusion and prediction:

[0136] 1) Neural network branch, preferably using the DNN (Deep Neural Network) branch:

[0137] Inputs include static or quasi-static characteristics such as process parameters (e.g., oxygen-to-coal ratio, load rate), coal characteristics (e.g., sulfur content, ash content), and burner structural parameters (e.g., nozzle diameter);

[0138] Output: A static feature vector of fixed dimensions (e.g., 128-dimensional) used to characterize the impact of process and material properties on burner degradation. For example, it includes elements such as gasifier oxygen-to-coal ratio, load rate, coal ash content, coal sulfur content, and burner nozzle size, forming a feature vector, which is normalized to a static feature vector [0.67, 0.12, -0.05, -0.12, 0.03…].

[0139] 2) The timing sensor data processing branch is preferably an LSTM (Long Short-Term Memory) network or a Transformer branch:

[0140] Input: Time-series sensor data (such as temperature, vibration, cooling water flow rate, etc.) and simulation data of the reaction mechanism network model (such as temperature, temperature gradient, gas composition, etc. near the gasifier burner);

[0141] Output: A time-series feature vector (e.g., 128-dimensional) used to capture the dynamic evolution trend during burner degradation. For example, time-series data measured by instruments during the process, including elements such as gasifier temperature, burner vibration frequency, burner cooling water flow rate, and gasification product flow rate, form a feature vector, which, after normalization, becomes a time-series feature vector [0.82, -0.24, 0.17, 0.21, ...].

[0142] The time-series sensing data comprises the time-series sensing data from other multimodal characteristic parameters, while the burner root parameters (such as temperature distribution, thermal stress, and local gas composition) are continuous time-series data generated through dynamic simulation using a reaction mechanism network model. These parameters change in real time with the gasifier's operating conditions (load fluctuations, coal type switching, oxygen flow rate changes, etc.), exhibiting significant temporal correlation. This data is used to compensate for input loss or inaccuracies caused by sensor failure under high-temperature conditions or the absence of sensors at specific locations. It is input together with the measured time-series data into the time-series sensing data processing branch (preferably the LSTM branch) to construct a complete spatiotemporal evolution characteristic of burner degradation.

[0143] 3) Semantic feature branch, preferably a BERT fine-tuned model (text encoder):

[0144] Input: Maintenance record text (e.g., "2023-05-12 Burner A repair welding");

[0145] Output: Semantic feature vector (e.g., 128-dimensional), used to encode the type, frequency, and impact on lifespan of maintenance events. For example, maintenance record text, parking record text, etc., extracting maintenance methods, repair duration, post-repair status, etc., and using BERT to translate them into vector encoding, normalized into semantic feature vectors [0.45, 0.03, 0.60, ...].

[0146] 4) Cross-Attention Mechanism Module:

[0147] Input: Temporal features from LSTM / Transformer are used as the Query, and the semantic features maintained by BERT output are used as the Key / Value;

[0148] Output: The fused context feature vector is used to capture the correlation of "performing a certain type of maintenance operation in a specific degradation stage". For example, under the trend of "gasifier temperature rise", the comprehensive impact of the maintenance action "coating repair" on the remaining life is obtained, and attention weights are obtained. The attention weights are used to perform weighted summation on each semantic to form a new multidimensional vector, which is normalized to the context feature vector [0.71, 0.09, -0.18, ...].

[0149] 5) For the weight module, a learnable gating network is preferred:

[0150] Input: Current operating context (e.g., coal type, load level);

[0151] Output: Weight coefficients for each modality branch (e.g., DNN weight 0.3, LSTM weight 0.5, BERT weight 0.2), used to dynamically adjust the contribution of each modality in the final prediction.

[0152] 6) Burner Remaining Life Prediction Module (RUL), which is a regression task.

[0153] The input to the burner remaining life prediction module is the fused multimodal feature vector (which is formed by weighting and concatenating the outputs of the branch weighting module). Specifically, the static feature vector of the neural network branch, the temporal feature vector output by the temporal sensing data processing branch, the semantic feature vector output by the semantic feature branch, and the context feature vector output by the cross attention mechanism module are multiplied by the corresponding weights output by the weighting module and concatenated into a large feature vector, which is used as the fused feature vector.

[0154] Output of the burner remaining life prediction module: Burner remaining life (RUL), in hours.

[0155] In one embodiment, the gasification burner life prediction model further includes: a maintenance type classification module and a degradation stage classification module, wherein:

[0156] The maintenance type classification module takes a fused feature vector as input and outputs a maintenance type label.

[0157] The degradation stage classification module takes a fused feature vector as input and outputs a degradation stage label.

[0158] Specifically, a multi-task learning framework and sharing mechanism are adopted.

[0159] This embodiment adopts a multi-task learning framework, where the main task and auxiliary tasks share the underlying feature representation, as detailed below:

[0160] Main task: Burner Remaining Life Prediction Module, used for burner remaining life prediction (RUL), is a regression task;

[0161] First auxiliary task: Maintenance type classification module, used for maintenance type classification (such as cleaning, welding repair, coating repair, etc.), which is a multi-classification task;

[0162] The second auxiliary task is the degradation stage classification module, which is used to classify degradation stages (such as normal period, warning period, and critical period), and is a multi-classification task.

[0163] The meaning of sharing the underlying feature representation is that the feature vectors output by branches such as the neural network branch (DNN), the time-series sensing data processing branch (LSTM), and the semantic feature branch (BERT) are fused and used as common inputs for the lifetime prediction module, the maintenance type classification module, and the degradation stage classification module, thereby avoiding redundant modeling and improving training efficiency and generalization ability.

[0164] Input-output relationship and lifetime prediction mechanism:

[0165] Input to the lifetime prediction model: the fused multimodal feature vector (composed of the branch outputs weighted by a Gating Network);

[0166] Output of the life prediction model: Burner Remaining Life (RUL), in hours;

[0167] The input to the auxiliary task is also the fused feature vector;

[0168] Output of auxiliary tasks: maintain type label or degradation stage label.

[0169] The classification results of the auxiliary task are not used as input for lifetime prediction, but rather share feature representations with the main task. Joint training enhances the model's understanding of degradation patterns, thereby improving the prediction accuracy of the main task.

[0170] like Figure 3 The model architecture shown is the preferred embodiment of the present invention, including: a neural network branch 31 (preferably a DNN branch), a time-series sensing data processing branch 32 (preferably an LSTM / Transformer branch), a semantic feature branch 33 (preferably a BERT fine-tuning model), a cross-attention mechanism module 34, a weight module 35 (preferably a learnable gated network model), a lifetime prediction model 36, a maintenance type classification model 37, and a degradation stage classification model 38.

[0171] Loss function design and model update mechanism:

[0172] To coordinate multi-task training and prevent a single modality or task from dominating model updates, this embodiment designs the following loss function:

[0173] Main task loss: L MsE Specifically, it is the mean squared error loss (MSE), used for regression lifetime prediction;

[0174] First auxiliary mission loss: L CE1 Specifically, it is cross-entropy loss, used to maintain type classification tasks;

[0175] Second auxiliary mission loss: L CE2 Specifically, it is cross-entropy loss, used for classification tasks in the degradation stage;

[0176] Modality Balance Loss: L balance Specifically, L2 norm constraints are applied to the feature vectors output by each modal branch to prevent a particular modal feature from dominating the fusion process;

[0177] Total loss function:

[0178] L total =λ1L MSE +λ2L CE1 +λ3L CE2 +λ4L balance

[0179] Among them, λ1, λ2, λ3 and λ4 are adjustable weight coefficients used to balance the contributions of each task and the regularization term.

[0180] Model training and update methods:

[0181] Training method: End-to-end training is adopted, and all sub-modules / branches, including: neural network branch (DNN), temporal sensing data processing branch (LSTM), semantic feature branch (BERT), cross-attention mechanism branch / module (Cross-Attention) and weight module (Gating Network, etc.), participate in backpropagation to uniformly optimize the total loss function.

[0182] To prevent single-modality dominance in updates: Modality equalization loss and gating network are used to dynamically adjust the weights of each modality to ensure stable learning of the model under multimodal inputs;

[0183] Model update mechanism: During the online deployment phase, incremental learning is used to fine-tune the model locally (e.g., updating only the fully connected layers) to adapt to new working conditions.

[0184] This embodiment uses cross-modal feature engineering and cross-attention mechanism to quantify the coupled impact of coal type, process, and maintenance events on burner life, achieving a breakthrough in prediction accuracy. At the same time, it can classify maintenance type and degradation stage while predicting burner life.

[0185] Then, step S206 is executed, inputting the current data of the optimal multimodal characteristic parameters into the gasification furnace burner life prediction model to obtain the predicted remaining life of the gasification furnace burner.

[0186] When the update conditions are met, step S207 is executed, in which the burner remaining life prediction module, the maintenance type classification module, and the degradation stage classification module are retrained.

[0187] Specifically, dynamic operating condition adaptation and online updates are performed on the burner life prediction model. Unsupervised clustering (such as t-SNE) is used to identify coal type switching or load fluctuation scenarios, and the model threshold is adjusted in real time to improve the model's robustness. Based on the Flink stream processing engine, real-time data is divided into time windows to trigger local retraining (updating only the fully connected layer parameters) to reduce the risk of model drift.

[0188] In some embodiments, based on the predicted remaining lifespan of the gasifier burner, the burner health index of the gasifier burner is calculated, and whether an early warning is triggered is determined based on the burner health index and the burner health index threshold (HI threshold).

[0189] Specifically, the burner health index is calculated as follows: Among them, HI is the health index of the burner, and RUL is the health index of the burner. pred To predict remaining lifetime, RUL design This refers to the design life of the burner.

[0190] Then, based on the burner health index and the burner health index threshold (HI threshold), it is determined whether to trigger an alert.

[0191] The burner health index (HI) is used to classify the degradation stages of burners (such as normal period, warning period, and critical period).

[0192] Among them, the real-time adjustment of model thresholds includes: HI threshold, used to trigger different levels of early warning (e.g., HI<0.7 triggers an early warning, HI<0.4 triggers a critical alarm); and confidence interval threshold, used to determine whether the current prediction result is reliable (e.g., when the confidence interval width exceeds the set threshold, the model is updated).

[0193] The specific adjustment method is as follows: when the system detects changes in operating conditions (such as coal type switching or load fluctuations) through unsupervised clustering (such as t-SNE), it will dynamically adjust the HI threshold under the current operating condition based on the HI distribution under the same operating conditions in historical data.

[0194] Specifically, when t-SNE detects a new operating condition (such as high-sulfur coal), the system does not immediately retrain the model. Instead, it determines (1) the historical HI distribution of the same operating condition from the historical database; and (2) adjusts the "early warning / alarm threshold" using the statistical values ​​of these historical HIs (such as the 5th percentile) (for example, adjusting the HI alarm line from 0.7 to 0.6). For example, under high-sulfur coal operating conditions, the burner degrades faster, and the HI will be adjusted accordingly (such as from 0.7 to 0.6) to adapt to the new degradation rate.

[0195] In one embodiment, satisfying the update condition includes:

[0196] If the prediction error of the gasifier burner life prediction model exceeds a set threshold, then the update condition is deemed met; or

[0197] If the clustering results of the fluidized bed gasifier's operating conditions change, then the update condition is deemed met; or

[0198] If the difference between the current data distribution and the training data distribution of the input fluidized bed gasifier burner life prediction model is greater than the difference threshold, then the update condition is determined to be met.

[0199] Specifically, based on the Flink stream processing engine, real-time data is divided into time windows, and the following metrics are continuously monitored:

[0200] 1) Prediction error exceeds a set threshold (e.g., MAE > 10%);

[0201] 2) Changes in the clustering results under different operating conditions (e.g., coal type switching);

[0202] 3) The distribution of the new data differs significantly from that of the training data (judged by KL divergence or PSI index).

[0203] Local retraining is triggered when any of the above conditions are met.

[0204] The scope of local retraining is limited to the parameters of the fully connected layers after the fusion layer, namely the burner remaining life prediction module, the maintenance type classification module, and the degradation stage classification module; the backbone feature extraction networks such as the neural network branch (DNN), the temporal sensing data processing branch (LSTM), the semantic feature branch (BERT), the cross-attention mechanism branch / module (Cross-Attention), and the weight module (Gating Network) are not updated to save computational resources.

[0205] In one embodiment, satisfying the update condition includes:

[0206] Set a confidence level and calculate the upper and lower limits of the confidence interval corresponding to the current burner's running time.

[0207] Based on the predicted remaining life output from the fluidized bed gasifier burner life prediction model, the burner health index is calculated as follows: Among them, HI is the health index of the burner, and RUL is the health index of the burner. pred To predict remaining lifetime, RUL design The burner's designed lifespan;

[0208] If the burner health index falls outside the confidence interval, then the update condition is met.

[0209] Specifically, the Health Index (HI) of a blower is defined as follows:

[0210]

[0211] Among them, RUL pred The model predicts the remaining lifetime (in hours); RUL design This represents the burner's designed lifespan (e.g., 2000 hours). When HI is 1, the burner is in brand new condition; when HI is 0, the burner's lifespan is exhausted and it needs to be replaced immediately; when HI is in the range of 0 to 1, it indicates the degree of burner degradation, with smaller values ​​indicating more severe degradation.

[0212] Based on the RUL (Remaining Life Stage) division from the model output (e.g., Normal Period → Warning Period → Critical Period), the confidence interval can be calibrated using a Weibull distribution model. The Weibull distribution is a statistical model commonly used in lifespan analysis, suitable for describing the failure time distribution of burners. Its probability density function is:

[0213]

[0214] Where k is the shape parameter; λ is the size parameter; and t is the burner running time. Confidence interval calibration procedure:

[0215] Step 1: Based on historical burner failure data, fit a Weibull distribution and estimate the parameters k and λ;

[0216] Step 2: Calculate the survival probability P (T>t) based on the current burner running time t;

[0217] Step 3: Set a confidence level (e.g., 95%), and use the same historical failure samples to estimate the parameter uncertainty of the Weibull parameter (k, λ) through maximum likelihood or Bayesian methods, and then find the confidence interval of P (T>t) to obtain the upper and lower limits of the confidence interval corresponding to the confidence level of the current burner's running time.

[0218] Step 4: Compare the predicted RUL of the model with the confidence interval of the Weibull distribution. If the predicted value falls outside the confidence interval, trigger model retraining or issue an alarm.

[0219] This embodiment supplements key parameters such as burner root temperature and local thermal stress through a mechanistic network, increasing the model input dimension coverage by 70% and solving the prediction failure problem caused by temperature measurement blind spots. Simultaneously, by constructing a multi-dimensional feature system, it achieves a significant improvement in multi-modal data fusion and analysis efficiency. Through constructing a multi-dimensional feature system, designing an adaptive hybrid model architecture, and introducing a dynamic feedback mechanism, it achieves accurate capture and dynamic adaptation of burner degradation patterns. The implementation of this technology will significantly reduce unplanned downtime losses (estimated to be over 20%), extend the effective lifespan of burners (15%-30%), and provide key technical support for the transformation of gasification units from "preventive maintenance" to "predictive maintenance."

[0220] like Figure 4 The flowchart of the burner life prediction method based on machine learning, which is the preferred embodiment of the present invention, includes:

[0221] Step S401, flow field analysis inside the fluidized bed gasifier;

[0222] Step S402: Construct a reaction mechanism network model;

[0223] Step S403: Multi-source data acquisition and characterization construction.

[0224] Step S404: Screening and optimization of degradation-sensitive features.

[0225] Step S405, Machine learning model architecture design and training.

[0226] Step S406, Dynamic operating condition adaptation and online update.

[0227] Step S407, Lifetime-Maintenance Mapping and Early Warning Decision.

[0228] Specifically, the burner health index (HI) is defined, and the remaining lifespan stages (such as normal period → warning period → critical period) are divided based on the RUL output of the model. The confidence interval is calibrated by combining the Weibull distribution model. The maintenance strategy library is matched, and maintenance measures (such as periodic welding repair and ceramic coating repair) are recommended based on the prediction results. The effect of the measures on the lifespan gain (such as HI recovery of 15% after welding repair) is quantified.

[0229] Step S408, System Verification and Industrial Deployment.

[0230] Specifically, an offline testing platform was established to compare the prediction errors of traditional thresholding methods and ARIMA time series models (e.g., a 22% reduction in MAE, R...). 2 Upgraded to version 0.93); Deploy an edge-cloud collaborative architecture: The lightweight model (TensorFlow Lite) at the edge achieves second-level inference, while the global model is simultaneously optimized and updated in the cloud.

[0231] Step S409: During maintenance, determine the accuracy of the model. If it is accurate, proceed to step S410; otherwise, proceed to step S405.

[0232] Specifically, the accuracy of the model is assessed during maintenance. During shutdown maintenance, the burner condition is evaluated by experts. Based on the guidance of engineers and manufacturers, the burner condition is determined and compared with the model's prediction results. If the model deviates significantly from the actual situation, proceed to step S405 to retrain the model; if the model deviates slightly from the actual situation, proceed to step S410.

[0233] Step S410: Continue online application.

[0234] Specifically, the burner life prediction model for the fluidized bed gasifier is continuously applied online to predict the entire life cycle of the burner, ensuring the safe operation of the unit.

[0235] This embodiment constructs a reaction mechanism network model for a specific gasifier, providing long-term, customized, and high-precision results of temperature distribution and thermal stress around the burner, ensuring that the burner life prediction model has no blind spots in the acquisition of process input parameters; by combining the reaction mechanism network model with a multimodal, multi-scale machine learning model, a highly specialized burner life prediction model is constructed; a maintenance-life gain closed loop is formed: the strategy library is dynamically optimized based on historical maintenance effects, promoting the transformation from experience-based decision-making to intelligent decision-making.

[0236] This embodiment achieves a significant improvement in the efficiency of multimodal data fusion and analysis. Through cross-modal feature engineering and cross-attention mechanisms, it quantifies the coupled impact of coal type, process, and maintenance events on burner life, achieving a breakthrough in prediction accuracy.

[0237] This embodiment has the advantage of dynamic working condition adaptability. It employs an unsupervised clustering + online fine-tuning mechanism to achieve automatic coal type identification. Based on coal quality parameters and historical working condition fingerprint matching, the dynamic switching time of model parameters is less than 2 minutes. Incremental learning improves efficiency: triggering local retraining (updating only the fully connected layers) reduces model update time from 2 hours to 10 minutes, and can be completed at the edge.

[0238] This embodiment optimizes the closed loop of maintenance-life gain. It constructs a maintenance strategy library based on quantified effects, enabling suggestion generation: matching maintenance types with predicted RUL (Remaining Lifetime) and maintenance resources, such as triggering coating repair or replacement suggestions for "Lifetime ≤ 50 hours"; strategy closed-loop correction: automatically updating the maintenance effect matrix through model-actual deviation comparison in the seven-step process (e.g., correcting burner degradation coefficient under low load). The optimized strategy library reduces maintenance costs by 17%.

[0239] This embodiment optimizes resource efficiency for industrial deployment. Through an edge-cloud collaborative architecture and lightweight model compression, it achieves edge inference latency of <100ms (75% reduction in TensorFlow Lite model size); cloud training and optimization are triggered on demand (e.g., when deviations exceed thresholds or new coal types are added to the database), with computing power consumption only 40% of similar systems.

[0240] Application example:

[0241] Prediction and maintenance optimization of burner life in coal-water slurry gasifiers

[0242] 1. Implementation Environment

[0243] Equipment: AP coal-water slurry gasifier (capacity 2000 tons / day), equipped with a three-channel oxygen-coal burner, made of Inconel 625 alloy.

[0244] Data source:

[0245] Process parameters: oxygen-to-coal ratio (0.8-1.2), furnace temperature (1300-1500℃), furnace pressure (6-6.5MPa). After long-term operation, 50% of the furnace thermocouples are damaged, and the reading is 1700 (i.e., overheating damage, which has no guiding significance).

[0246] Sensor data: burner cooling water temperature (temperature sensor), vibration spectrum (accelerometer, sampling rate 1kHz);

[0247] Coal type data: Hebi bituminous coal (ash content 12.8%, sulfur content 0.9%), Shenmu lignite (ash content 25.3%, sulfur content 2.1%); coal-water slurry concentration is 55%–65% (mass concentration);

[0248] Maintenance records: Work order log for the past six months (18 backflushing for desiccant removal, 3 coating repairs).

[0249] 2. Construction of Reaction Mechanism Network Model

[0250] The AP coal-water slurry gasifier employs a top-mounted, bottom-spraying single-nozzle design. The burner structure utilizes a three-channel design: a central channel for central oxygen, an outer ring channel for outer ring oxygen, and an inner ring channel for coal-water slurry. The coal-water slurry is injected into the gasifier's combustion chamber via a jet structure, forming a jet zone where intense combustion and gasification reactions occur at high temperatures. Part of the jet diffuses outwards to the inner wall of the gasifier, forming a recirculation zone where the recirculated gas and solids react around the burner. The main jet gradually forms a laminar flow zone, where turbulence is weak, oxygen is gradually depleted, and the reaction is primarily characterized by gasification and gentle homogeneous reactions. The temperature distribution near the burner in both the jet and recirculation zones significantly impacts burner degradation.

[0251] After obtaining the flow field distribution inside the gasifier using fluid calculation software, different reaction modules of process simulation software are used to calculate each reaction zone. Then, each reaction zone is connected by a flow path to form a reaction mechanism network model.

[0252] Based on the model results, key temperature parameters around the burner, TS (jet zone temperature) and TH (recirculation zone temperature), as well as product index results, such as syngas composition and flow rate, are obtained.

[0253] 3. Data Acquisition and Feature Construction

[0254] Multi-source data integration:

[0255] Construct a burner degradation dataset covering 4 categories of features (a total of 126 features):

[0256] Process characteristics: oxygen-to-coal ratio fluctuation entropy, load rate standard deviation, and burner temperature calculated by the reaction mechanism model, etc.

[0257] Thermodynamic characteristics: vaporization temperature gradient (ΔT / Δt), hot spot area growth rate (based on infrared image analysis), etc.

[0258] Corrosion characteristics: sulfur content × cumulative corrosion over operating time (calculated based on H2S concentration kinetic model), etc.

[0259] Maintenance features: backflush frequency and HI recovery rate (initially set at 10%, dynamically calibrated via step seven), etc.

[0260] Redundant feature removal: 15 redundant features (such as ambient humidity and nitrogen flow rate) were removed using Spearman correlation coefficient (threshold > 0.9), and 111 core features were retained.

[0261] 4. Model Training and Deployment

[0262] Hybrid model architecture:

[0263] Static branch: Processing technology and coal type data using a fully connected network (3 layers, 256 nodes);

[0264] Timing branch: Bidirectional LSTM (time step = 24h) is used to process temperature vibration timing;

[0265] Maintenance branch: BERT fine-tuning model coding work order log (e.g., generating a 128-dimensional vector for "2023-09-01 Burner B coating crack repair");

[0266] Cross-attention mechanism: Using time-series branches as queries and maintaining branches as keys / values, the necessity weight of descaling during high-temperature periods is captured.

[0267] Training data: Based on 6 months of historical data (normal to degradation stage), the data is divided into training set (70%), validation set (15%), and test set (15%).

[0268] 5. Dynamic operating condition adaptation

[0269] Lignite switching scenario test:

[0270] Operating condition identification: t-SNE clustering detected a shift in coal type characteristics (Shenmu lignite entering the warehouse), triggering threshold adjustment (the weight of corrosion rate prediction was increased by 30%);

[0271] Incremental update: The parameters of the fully connected layer are updated based on the Flink window (new data every 24 hours), and the model iteration cycle is shortened from 72 hours to 4 hours.

[0272] 6. Lifespan prediction and maintenance effectiveness

[0273] Health Index (HI) Output: The remaining life prediction error in the first month of deployment is ±4% (the actual replacement cycle is 2180h, and the model prediction is 2090-2270h);

[0274] Maintenance strategy matching:

[0275] Lifespan ≤ 500h: Trigger repair welding or coating repair operation (HI rises 12%-18% after execution);

[0276] Lifespan ≤200h: It is recommended to replace the burner and propose a maintenance plan for the replacement burner to avoid unplanned downtime.

[0277] 6. Verification and Calibration

[0278] Offline test results:

[0279] Comparison Methods MAE (hours) R2 Maintenance cost reduction Traditional threshold method 320 0.52 This invention 40 0.95 40％

[0280] 7. Post-maintenance feedback and optimization

[0281] Typical Case:

[0282] The model predicts that burner A has less than 100 hours of remaining life. The burner will be replaced without shutting down the furnace. The replacement burner will be subjected to flaw detection analysis, which will confirm that the nozzle has cracked and the coil has corroded. Deviation analysis shows that the model prediction is accurate.

[0283] Incorporating actual inspection and maintenance results as a reward mechanism into the burner life prediction model enhances the model's robustness.

[0284] 8. Benefits of Industrial Deployment

[0285] Cost savings: Two fewer unplanned downtimes per year (saving approximately ¥1.2 million per downtime);

[0286] Efficiency Improvement: Perform timely and efficient maintenance based on model alerts;

[0287] Enhanced safety: By combining the reaction mechanism network model, blind spots in key parameters are avoided, reducing the risk of sudden burner failure by more than 97%.

[0288] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0289] like Figure 5 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0290] At least one processor 501; and,

[0291] A memory 502 is communicatively connected to at least one of the processors 501; wherein,

[0292] The memory 502 stores instructions that can be executed by at least one of the processors to enable the at least one of the processors to perform the fluidized bed gasifier burner life prediction method as described above.

[0293] Figure 5 Take a processor 501 as an example.

[0294] The electronic device may also include an input device 503 and a display device 504.

[0295] The processor 501, memory 502, input device 503 and display device 504 can be connected by a bus or other means. The figure shows an example of connection by bus.

[0296] The memory 502, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the fluidized bed gasifier burner life prediction method in the embodiments of this application, for example, Figure 1 , Figure 2 The method flow is shown. The processor 501 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 502, thereby realizing the burner life prediction method for the fluidized bed gasifier in the above embodiment.

[0297] Memory 502 may include a program storage area and a data storage area. The program storage area may store an operating system and an application program required for at least one function. The data storage area may store data created based on the use of the fluidized bed gasifier burner life prediction method. Furthermore, memory 502 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 502 may optionally include memory remotely located relative to processor 501, which can be connected via a network to the apparatus performing the fluidized bed gasifier burner life prediction method. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0298] The input device 503 can receive user clicks and generate signal inputs related to user settings and function control of the fluidized bed gasifier burner life prediction method. The display device 504 may include a display screen or other display equipment.

[0299] When one or more modules are stored in the memory 502 and are run by one or more processors 501, the burner life prediction method of the fluidized bed gasifier in any of the above method embodiments is executed.

[0300] This invention uses a mechanistic network model for simulation to complete the burner root parameters. Simultaneously, it acquires other multimodal feature parameters, filters them, and uses the filtered multimodal feature parameters along with the burner root parameters as the optimal multimodal feature parameters. A burner life prediction model for a fluidized bed gasifier is then constructed, and the predicted remaining life of the burner is predicted based on the data from the optimal multimodal feature parameters. This invention completes key parameters such as burner root temperature and local thermal stress through a mechanistic network, improving the model's input dimension coverage by 70%, solving the prediction failure problem caused by temperature measurement blind spots. Furthermore, by constructing a multidimensional feature system, it can accurately predict burner life, significantly reducing unplanned downtime losses and extending the effective life of the burner.

[0301] One embodiment of the present invention provides a storage medium that stores computer instructions, which, when executed by a computer, are used to perform all the steps of the fluidized bed gasifier burner life prediction method as described above.

[0302] In the context of this disclosure, a storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The storage medium can be a machine-readable signal medium or a machine-readable storage medium. Optionally, the storage medium can be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0303] One embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the aforementioned method for predicting the burner life of a fluidized bed gasifier.

[0304] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for predicting the burner life of a fluidized bed gasifier, characterized in that, include: Analyze the flow field inside the fluidized bed gasifier and construct the flow field network structure; Based on the aforementioned flow field network structure, a reaction mechanism network model is established, and simulation data of the burner root parameters are obtained using the aforementioned reaction mechanism network model. Obtain training data for other multimodal feature parameters of the burner; The multimodal characteristic parameters are screened to obtain the screened multimodal characteristic parameters of the gasifier burner. The screened multimodal characteristic parameters and the burner root parameters are taken as the optimal multimodal characteristic parameters. A burner life prediction model for a fluidized bed gasifier is constructed. Based on the training data of the optimal multimodal characteristic parameters, the burner life prediction model for the fluidized bed gasifier is trained. The burner life prediction model for the fluidized bed gasifier predicts the predicted remaining life of the burner based on the data of the optimal multimodal characteristic parameters. By inputting the current data of the optimal multimodal characteristic parameters into the gasification furnace burner life prediction model, the predicted remaining life of the gasification furnace burner is obtained.

2. The method for predicting the burner life of a fluidized bed gasifier according to claim 1, characterized in that, Based on the flow field network structure, a reaction mechanism network model is established, and simulation data of the burner root parameters are obtained using the reaction mechanism network model, including: For multiple flow field components in the aforementioned flow field network structure, corresponding reaction sub-models are established; Multiple reaction sub-models are connected together to form a reaction mechanism network model; The network model of the reaction mechanism is simulated to obtain simulation data of temperature parameters and product indicators within a preset range centered on the burner, wherein the temperature parameters and product indicators are the parameters at the root of the burner.

3. The method for predicting the burner life of a fluidized bed gasifier according to claim 1, characterized in that, The burner life prediction model for the fluidized bed gasifier includes: The system includes a neural network branch, a time-series sensor data processing branch, a semantic feature branch, a cross-attention mechanism module, a weighting module, and a burner remaining life prediction module, among which: The neural network branch takes into input static or quasi-static feature parameters from the optimal multimodal feature parameters and outputs a static feature vector; The timing sensing data processing branch takes into input timing sensing data and simulation data of burner root parameters, and outputs timing feature vectors. The semantic feature branch takes the maintained text as input and outputs a semantic feature vector. The output of the time-series sensing data processing branch serves as the query value input to the cross-attention mechanism module, the output of the semantic feature branch serves as the keywords and key values ​​input to the cross-attention mechanism module, and the cross-attention mechanism module outputs a context feature vector. The weighting module takes the working condition context as input and outputs the weights of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module. The outputs of the neural network branch, the time-series sensing data processing branch, the semantic feature branch, and the cross-attention mechanism module are multiplied by their corresponding weights and used as a fusion feature vector. This vector is then input into the burner remaining life prediction module, which outputs the predicted remaining life of the fluidized bed gasifier burner.

4. The method for predicting the burner life of a fluidized bed gasifier according to claim 3, characterized in that, The gasifier burner life prediction model further includes: a maintenance type classification module and a degradation stage classification module, wherein: The maintenance type classification module takes a fused feature vector as input and outputs a maintenance type label. The degradation stage classification module takes a fused feature vector as input and outputs a degradation stage label.

5. The method for predicting the burner life of a fluidized bed gasifier according to claim 4, characterized in that, Also includes: When the update conditions are met, the burner remaining life prediction module, the maintenance type classification module, and the degradation stage classification module are retrained.

6. The method for predicting the burner life of a fluidized bed gasifier according to claim 5, characterized in that, The conditions for satisfying the update include: If the prediction error of the gasifier burner life prediction model exceeds a set threshold, then the update condition is deemed met; or If the clustering results of the fluidized bed gasifier's operating conditions change, then the update condition is deemed met; or If the difference between the current data distribution and the training data distribution of the input fluidized bed gasifier burner life prediction model is greater than the difference threshold, then the update condition is determined to be met.

7. The method for predicting the burner life of a fluidized bed gasifier according to claim 5, characterized in that, The conditions for satisfying the update include: Set a confidence level and calculate the upper and lower limits of the confidence interval corresponding to the current burner's running time. Based on the predicted remaining life output from the fluidized bed gasifier burner life prediction model, the burner health index is calculated as follows: Among them, HI is the health index of the burner, and RUL is the health index of the burner. pred To predict remaining lifetime, RUL design The burner's designed lifespan; If the burner health index falls outside the confidence interval, then the update condition is met.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, which enable the at least one processor to perform the burner life prediction method for a fluidized bed gasifier as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium stores computer instructions, which, when executed by the computer, are used to perform all the steps of the fluidized bed gasifier burner life prediction method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the burner life prediction method for a fluidized bed gasifier as described in any one of claims 1 to 7.

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