Boiler soot blowing parameter fine regulation and control method and system combining fuel characteristics and real-time working conditions

Abstract

The invention discloses a boiler soot blowing parameter fine regulation and control method and system combining fuel characteristics and real-time working conditions, and belongs to the technical field of boiler intelligent control. According to the method, fuel moisture content and ash fusion point difference data are collected, a fuel characteristic database is combined for matching analysis, and quantitative evaluation indexes of fuel combustion characteristics are generated; on the basis of the index and the real-time working condition data, utilizing a machine learning model to determine an accumulated dust cleaning priority ranking sequence; and when the triggering condition is met, fusing the multi-source data to generate a soot blowing parameter preliminary scheme, and performing parameter correction in combination with the fuel moisture influence factor and the ash melting point difference correction term to obtain a final configuration scheme. According to the method, soot blowing control is converted from passive response to active pre-judgment and closed-loop optimization, the refinement and intelligence level of soot blowing operation is improved, soot blowing parameter configuration is more accurate, the cleaning effect is better, and the heat transfer efficiency and operation stability of the boiler are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent boiler control technology, and in particular to a method and system for fine-tuning boiler soot blowing parameters by combining fuel characteristics and real-time operating conditions. Background Technology

[0002] Boiler soot blowing systems are a crucial component in maintaining efficient boiler operation in thermal power generation and industrial production, directly impacting energy utilization efficiency and equipment safety and stability. With the diversification of energy structures and increasingly stringent environmental requirements, boiler operation needs to adapt to changes in different fuel characteristics and complex operating conditions to ensure combustion efficiency and equipment lifespan. The role of the soot blowing system is to remove accumulated ash from the boiler's heating surfaces, maintaining heat transfer efficiency and preventing high-temperature corrosion. Traditional soot blowing control methods often struggle to achieve precise and efficient operation when dealing with varying fuels and dynamic operating conditions, revealing significant shortcomings. This not only increases energy consumption but can also lead to equipment wear or efficiency reduction due to improper soot blowing. Therefore, researching a refined control method that combines fuel characteristics with real-time operating conditions has become a key direction for improving boiler operating efficiency and stability.

[0003] In existing technologies, boiler soot blowing system control methods typically rely on fixed parameters or experience-based settings, making it difficult to adapt to dynamic changes in fuel type and operating conditions. For example, existing systems often use a uniform soot blowing frequency and intensity for different fuels based on their calorific value, moisture content, or ash melting point, ignoring the influence of fuel characteristics on ash accumulation. This approach can easily lead to insufficient or excessive soot blowing when faced with frequent fuel type changes or fluctuations in operating conditions. For instance, when using high-moisture fuels, the ash may be more viscous, but existing methods struggle to adjust soot blowing parameters based on this characteristic, resulting in incomplete ash removal and reduced heat transfer efficiency. Furthermore, real-time operating conditions such as changes in furnace temperature or ash thickness on heated surfaces are not adequately considered in the control process, leading to a mismatch between soot blowing timing and intensity, further exacerbating energy consumption and equipment wear.

[0004] In this field, the core technical challenge lies primarily in the impact of diverse fuel characteristics on soot blowing parameter settings. The calorific value, moisture content, and ash melting point of different fuels directly determine the physical properties of the ash, such as its viscosity or hardness. For example, fuels with high ash melting points may form a harder ash layer, and existing systems struggle to dynamically adjust soot blowing parameters based on these characteristics, resulting in poor cleaning performance. This difference in fuel characteristics presents a technical challenge: how to achieve dynamic parameter matching under real-time operating conditions. During boiler operation, fluctuations in furnace temperature and ash thickness over the heated area alter the rate and characteristics of ash formation. However, current technologies lack the means to combine this dynamic information with fuel characteristics, making it difficult to select appropriate soot blowing timing and intensity under different operating conditions. Summary of the Invention

[0005] The technical problem to be solved by this invention is: to address the problem of inaccurate soot blowing parameter configuration and poor cleaning effect in boiler ash cleaning due to the complex coupling of fuel moisture content, ash melting point difference and real-time operating conditions, and to provide a method and system for fine control of boiler soot blowing parameters that combines fuel characteristics and real-time operating conditions.

[0006] The objective of this invention is achieved as follows: a method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions, comprising the following steps: S1. Collect data on the moisture content and ash fusion point difference of boiler fuel, and perform matching analysis in conjunction with a preset fuel characteristic database to generate quantitative evaluation indicators of fuel combustion characteristics. S2. Based on quantitative evaluation indicators and combined with real-time boiler operating data, a machine learning model is used to analyze furnace temperature fluctuations and ash thickness on the heating surface to determine the priority ranking sequence for ash cleaning. S3. When the ash cleaning priority sorting sequence meets the preset triggering conditions, the fuel characteristics and real-time operating data are integrated to generate a preliminary scheme of soot blowing operation parameters, the preliminary scheme including soot blowing frequency and soot blowing intensity. S4. Based on the fuel moisture influence factor and ash melting point difference correction term, the parameters of the preliminary scheme are corrected to generate the final soot blowing operation parameter configuration scheme. S5. Based on historical heat transfer efficiency data, determine the timing range for soot blowing based on the final configuration scheme, and perform the soot blowing operation.

[0007] In S1, quantitative evaluation indicators for fuel combustion characteristics are generated, including: The collected data on fuel moisture content and ash melting point difference were denoised and standardized. Similarity is calculated by feature matching between the processed data and the fuel characteristic database. When the similarity is higher than a preset threshold, the combustion characteristic category of the fuel is determined, and the combustion efficiency is predicted based on the category to generate a quantitative evaluation index.

[0008] In S2, the priority sequence for ash removal is determined, including: The data on furnace temperature fluctuation and ash thickness on the heated surface were denoised and normalized. Based on feature matching between the processed operating condition data and the ash accumulation characteristic database, the current ash accumulation status category is determined. Based on boiler operating parameters, the ash accumulation status categories are grouped and sorted to generate an ash accumulation cleaning priority ranking sequence.

[0009] In S3, a preliminary scheme for generating soot blowing operation parameters includes: A data fusion algorithm is used to integrate fuel characteristics and real-time operating data to generate initial soot blowing parameters based on soot blowing frequency and intensity. The initial soot blowing parameters are optimized based on historical operating data and dynamically adjusted in conjunction with equipment operating constraints to generate a preliminary plan.

[0010] In S4, the parameters of the preliminary scheme are adjusted based on the fuel moisture influence factor and the ash melting point difference correction term, including: Determine whether the preliminary plan meets the parameter adjustment conditions; If satisfied, the parameter adjustment range is calculated based on the ash melting point difference correction term, and the stability of the parameter configuration is evaluated in conjunction with the fuel moisture impact factor. If the stability index is lower than the preset threshold, the adjustment value is recalculated based on the fuel moisture influence factor and the ash melting point difference correction term to generate the final soot blowing operation parameter configuration scheme.

[0011] In S5, based on historical heat transfer efficiency data, the timing range for soot blowing based on the final configuration scheme is determined, including: By combining historical heat transfer efficiency data, efficiency trend curves are obtained through time series analysis; when the slope of the curve is lower than a preset threshold, candidate operation opportunities are generated; combined with operating parameters, a decision tree algorithm is used to determine priority opportunities, and based on equipment state constraints, an optimized set of operation opportunities is formed; heat transfer efficiency changes are predicted, the final execution range is determined, and soot blowing control instructions are generated.

[0012] Following S5, it also includes extracting time windows and determining trigger conditions based on the soot blowing execution timing range, generating a cleaning effect verification instruction sequence; collecting real-time feedback data of the soot blowing process through sensors, performing classification analysis, and judging whether the cleaning effect meets the standard; combining time series trends and outlier clustering to extract effect evaluation indicators, generating process control adjustment parameters, which are used to dynamically update the operation timing range and optimize the steps of subsequent soot blowing instructions.

[0013] It also includes the following steps: if the cleaning effect does not achieve the expected heat transfer efficiency, classify and identify the deviation type of the feedback data, determine the initial parameter update value based on the preset deviation-parameter mapping relationship; when there is a conflict, prioritize the parameters based on the historical update effect and generate optimized parameter iterative update values; adjust the control parameters and generate a new instruction sequence; if the result is still not up to standard after simulation verification, use the gradient boosting algorithm for secondary analysis, output further update values, and form the final control parameter configuration.

[0014] It also includes iteratively updating parameter values, calculating thermal efficiency and ash accumulation based on real-time operating data, and judging the operating status; if the operating status deviates from the optimization target, it uses historical data to predict the ash accumulation trend through support vector machine, and uses gradient descent algorithm to update control parameters; the rule engine generates operation instructions containing start-up time and soot blowing area, drives the automation system to adjust nozzle angle and steam pressure configuration, executes soot blowing and verifies the effect, and realizes closed-loop optimization steps; real-time operating data includes temperature, pressure and flow rate.

[0015] A refined control system for boiler soot blowing parameters is provided to implement the refined control method for boiler soot blowing parameters that combines fuel characteristics and real-time operating conditions. The system includes: The data acquisition module is used to collect real-time operating data on boiler fuel moisture content, ash melting point difference, furnace temperature, and ash thickness on the heating surface. The fuel characteristic evaluation module is used to perform matching analysis between the collected data and the preset fuel characteristic database to generate quantitative evaluation indicators of fuel combustion characteristics. The dust accumulation priority determination module is used to determine the priority ranking sequence of dust accumulation cleaning by combining the quantitative evaluation indicators and real-time operating data. The soot blowing parameter generation module is used to generate a preliminary plan of soot blowing operation parameters by integrating fuel characteristics and operating condition data when the priority triggering conditions are met. The parameter correction module is used to adjust the preliminary scheme according to the fuel moisture influence factor and ash melting point difference correction item to generate the final soot blowing operation parameter configuration scheme. The execution timing decision module is used to determine the execution timing range of soot blowing operations by combining historical heat transfer efficiency data; The effect monitoring and feedback module is used to monitor the feedback data of the soot blowing process, analyze the reasons for the deviation when the heat transfer efficiency does not meet expectations, and generate iterative update values ​​for parameters. Each module is implemented by the processor calling program instructions stored in memory, forming a closed-loop control system.

[0016] The present invention, which adopts the above technical solution, has the following prominent features compared with the prior art: This invention addresses the problem of inaccurate soot blowing parameter configuration and poor cleaning effect in boiler ash removal due to the complex coupling of fuel moisture content, ash melting point difference, and real-time operating data. It collects fuel moisture content and ash melting point difference data through sensors, performs matching analysis with a pre-set fuel characteristic database, generates quantitative evaluation indicators of fuel combustion characteristics, and uses a machine learning model to process real-time operating data such as furnace temperature fluctuations and ash thickness on the heating surface to determine the priority ranking sequence for ash removal. When the priority is higher than a threshold, this invention integrates operating data and fuel characteristics to generate a preliminary soot blowing parameter scheme, and introduces a moisture influence factor and ash melting point difference correction term to optimize the parameters. It also combines historical heat transfer efficiency data to determine the operation timing range and generates a cleaning effect verification instruction sequence. If the feedback data does not meet expectations, this invention analyzes the cause of the deviation through a classification algorithm, iteratively updates the parameters, and regenerates the soot blowing scheme for the next cycle. This invention achieves dynamic optimization of soot blowing parameters and continuous improvement of cleaning effect, resulting in more accurate soot blowing parameter configuration, better cleaning effect, and improved boiler heat transfer efficiency and operational stability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0018] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0021] Example 1: See Figure 1 A method for finely controlling boiler soot blowing parameters by combining fuel characteristics and real-time operating conditions includes the following steps: S1. Collect data on the moisture content and ash fusion point difference of boiler fuel, and perform matching analysis in conjunction with a preset fuel characteristic database to generate quantitative evaluation indicators of fuel combustion characteristics. S2. Based on quantitative evaluation indicators and combined with real-time boiler operating data, a machine learning model is used to analyze furnace temperature fluctuations and ash thickness on the heating surface to determine the priority ranking sequence for ash cleaning. S3. When the ash cleaning priority sorting sequence meets the preset triggering conditions, the fuel characteristics and real-time operating data are integrated to generate a preliminary scheme of soot blowing operation parameters, the preliminary scheme including soot blowing frequency and soot blowing intensity. S4. Based on the fuel moisture influence factor and ash melting point difference correction term, the parameters of the preliminary scheme are corrected to generate the final soot blowing operation parameter configuration scheme. S5. Based on historical heat transfer efficiency data, determine the timing range for soot blowing based on the final configuration scheme, and perform the soot blowing operation.

[0022] This invention collects data on fuel moisture content and ash melting point difference, and performs matching analysis with a pre-set fuel characteristic database to generate quantitative evaluation indicators for fuel combustion characteristics. This allows for the prediction of ash accumulation trends under different fuel conditions, enabling control to be initiated before ash accumulation severely affects heat transfer efficiency. Based on the quantitative evaluation indicators of fuel characteristics and real-time operating data, such as furnace temperature fluctuations and ash thickness on the heating surface, a priority ranking sequence for ash cleaning is generated. This prioritizes cleaning areas with high ash accumulation risk, optimizes the spatiotemporal allocation of soot blowing resources, and avoids ineffective or inefficient soot blowing operations. Based on the generated preliminary soot blowing scheme, fuel moisture influence factors and ash melting point difference correction terms are introduced for parameter correction, dynamically adjusting the soot blowing frequency and intensity. This effectively addresses harsh fuel conditions such as high moisture content and low ash melting point, preventing efficiency loss due to insufficient soot blowing or wear on the heating surface due to excessive soot blowing, significantly improving the system's operational safety and stability. By combining historical heat transfer efficiency data, the timing range for soot blowing operations is scientifically determined, avoiding soot blowing under high boiler load, variable operating conditions, or unstable states, reducing disturbances to the main system operation and improving operational safety. At the same time, by blowing at opportune times, the cleaning effect can be maximized, the effective cleaning cycle can be extended, and the number of times to blow dust can be reduced.

[0023] In S1, data related to the moisture content and ash melting point difference of boiler fuel are collected by sensor devices, and matched and analyzed with a preset fuel characteristic database to obtain quantitative evaluation indicators of fuel combustion characteristics.

[0024] Specifically, the moisture content and ash fusion point difference of boiler fuel are collected in real time using sensor devices and stored as a raw dataset. If the raw dataset is complete and conforms to a preset format, data preprocessing techniques are used to denoise and standardize the moisture content and ash fusion point difference to obtain a processed dataset. Based on the processed dataset, feature matching is performed using a preset fuel characteristic database to calculate the similarity between the moisture content and ash fusion point difference and the fuel characteristics in the database, obtaining matching results. If the similarity of the matching results is higher than a preset threshold, the k-nearest neighbor algorithm is used to classify the matching results and determine the combustion characteristic category of the fuel. Based on the combustion characteristic category, a linear regression algorithm is used to predict the combustion efficiency of the fuel during boiler operation, obtaining a quantitative index. By comparing the quantitative index with a preset combustion characteristic threshold, it is determined whether the fuel meets the boiler operation requirements, obtaining an operation status assessment result. Based on the operation status assessment result, fuel adjustment parameters are generated and output to the boiler control system to complete the combustion characteristic optimization.

[0025] In scenarios involving real-time acquisition of boiler fuel data, sensor devices measure the fuel's moisture content and ash fusion point difference using an infrared moisture analyzer and a thermogravimetric analyzer, respectively. Assume a given dataset contains 100 data sets, each including moisture content and ash fusion point difference. This data is stored in JSON format, including timestamps and sensor numbers, ensuring data integrity and traceability. If data is missing or misformatted, the system automatically removes abnormal data.

[0026] In one embodiment, data preprocessing employs a moving average filtering method to denoise the moisture content and ash melting point difference, thereby reducing the impact of sensor noise.

[0027] For example, the average of every 5 data sets is taken to smooth out fluctuations in moisture content. Next, Z-score standardization transforms the data into a distribution with a mean of 0 and a standard deviation of 1, facilitating subsequent analysis. This processing makes the dataset more suitable for feature matching and reduces the interference of noise on the results.

[0028] Specifically, feature matching is performed in conjunction with a pre-set fuel characteristic database. The database contains various fuel types, such as lignite and bituminous coal, with typical moisture content and ash fusion point difference ranges labeled for each fuel.

[0029] For example, lignite has a moisture content range of 15%-25% and an ash fusion point difference of 100-200℃. The system calculates the Euclidean distance between the collected data and the fuel characteristics in the database to obtain a similarity score. If the similarity score is higher than a threshold, such as 0.8, a match is considered successful. This step ensures that fuel characteristics are accurately identified, providing a reliable basis for subsequent classification.

[0030] The k-nearest neighbor algorithm is used to classify fuel combustion characteristics. Assuming the matching results indicate the fuel is similar to bituminous coal, the system selects k=5 and classifies it based on the combustion characteristics of bituminous coal in the database, such as calorific value and volatile matter, determining it to be in the "high volatile matter, medium calorific value" category. This step improves classification accuracy through multi-dimensional feature analysis, providing accurate input for combustion efficiency prediction.

[0031] Combustion efficiency is predicted using a linear regression algorithm. The model is trained based on historical data, with input variables including moisture content, ash melting point difference, and combustion characteristic category. The output is the combustion efficiency, such as 85%.

[0032] The model predicts a combustion efficiency of 82% for a certain fuel, which is lower than expected. The system compares this to a preset threshold and determines that the fuel does not meet operational requirements. This step quantifies the fuel's performance, providing a basis for optimization.

[0033] If the moisture content is too high, the system recommends increasing the pre-drying time or adjusting the feed ratio. These parameters are output to the boiler control system to automatically adjust the combustion conditions.

[0034] Preferably, this optimization can improve combustion efficiency by 2%-5%, reduce energy consumption and emissions, and enhance boiler operational stability.

[0035] The advantage of the above method lies in achieving precise control through data-driven approaches. Real-time data acquisition ensures data timeliness, preprocessing improves data quality, feature matching and classification enhance fuel identification accuracy, and prediction and optimization directly improve operational efficiency. This multi-stage collaborative approach forms a closed-loop control system, providing efficient support for boiler operation.

[0036] In S2, based on the quantitative evaluation index of the fuel combustion characteristics, a machine learning model is used to process the real-time operating data of the boiler, including furnace temperature fluctuations and ash thickness on the heating surface, to determine the priority ranking sequence for ash cleaning.

[0037] Specifically, furnace temperature fluctuation and ash thickness data on the heating surface are acquired from the boiler operating environment and stored as an initial operating condition dataset. If the initial operating condition dataset is complete and meets the preset format requirements, data preprocessing techniques are used to denoise and normalize the furnace temperature fluctuation and ash thickness on the heating surface to obtain a standardized operating condition dataset. Based on the standardized operating condition dataset, feature matching is performed using a preset ash accumulation characteristic database to calculate the similarity between the furnace temperature fluctuation and ash thickness on the heating surface and the ash accumulation characteristics in the database, obtaining the matching results. If the similarity of the matching results is higher than a preset threshold, a support vector machine algorithm is used to classify the matching results and determine the ash cleaning priority category. Based on the ash cleaning priority category, a clustering analysis algorithm is used to group the priority categories, obtaining the ash cleaning priority grouping results. Based on the ash cleaning priority grouping results and combined with boiler operating parameters, a sorting algorithm is used to generate an ash cleaning priority ranking sequence. Based on the ash cleaning priority ranking sequence, ash cleaning control parameters are generated and output to the boiler control system to determine the ash cleaning execution order.

[0038] In step S3, when the ash removal priority ranking sequence is higher than a preset threshold, the control system integrates operating condition data and fuel characteristics to generate a preliminary plan for soot blowing operation parameters, including soot blowing frequency and intensity. If the ash removal priority sequence is higher than the preset threshold, operating condition data and fuel characteristics are acquired, and an initial soot blowing operation parameter is generated using a data fusion algorithm.

[0039] By analyzing initial soot blowing operation parameters and combining them with historical operating data, a support vector machine algorithm is used to optimize the parameter scheme and determine the optimized soot blowing frequency and intensity. If the optimized soot blowing frequency and intensity meet the equipment operating constraints, the parameter scheme is adjusted based on real-time operating data to obtain dynamically adjusted operating parameters. The dynamically adjusted operating parameters are verified by the control system, and a logistic regression algorithm is used to determine the applicability of the parameter scheme, obtaining verification results. If the verification results meet the preset performance indicators, a preliminary scheme is generated based on the verification results to determine the final soot blowing frequency and intensity. The final soot blowing operation parameters are sent to the execution equipment through the control system to obtain execution feedback data and determine whether the execution effect meets expectations. Based on the execution feedback data, the historical records of operating data and fuel characteristics are updated to obtain the latest data fusion basis.

[0040] In S4, the preliminary scheme of soot blowing operation parameters is adopted, the fuel moisture influence factor and ash melting point difference correction term are input, it is determined whether to adjust the parameters, and the final soot blowing operation parameter configuration scheme is obtained.

[0041] Specifically, a preliminary soot blowing operation parameter scheme is obtained. Fuel moisture influencing factors and ash melting point difference correction terms are extracted from the database to generate an input dataset. Using this input dataset, a support vector machine algorithm is employed to classify the fuel moisture influencing factors and ash melting point difference correction terms, determining whether parameter adjustment conditions are triggered, and obtaining the classification results. If the classification results indicate parameter adjustment, a linear regression algorithm is used to predict the parameter adjustment magnitude based on the ash melting point difference correction term, generating an adjustment value. Based on the adjustment value, the preliminary soot blowing operation parameter scheme is updated, generating a first soot blowing operation parameter scheme. Using the first soot blowing operation parameter scheme, combined with the fuel moisture influencing factors, a decision tree algorithm is used to evaluate the stability of the parameter configuration, obtaining a stability index. If the stability index is lower than a preset threshold, the adjustment value is recalculated based on the fuel moisture influencing factors and ash melting point difference correction terms, generating a second soot blowing operation parameter scheme. The consistency of the parameter configuration is verified using the second soot blowing operation parameter scheme, determining the final soot blowing operation parameter configuration scheme.

[0042] In this embodiment, the fuel moisture content influence factor is a correction parameter used to characterize the degree of influence of fuel moisture content on ash accumulation characteristics and soot blowing effect. It is obtained based on historical operating data or experimental calibration and is used to dynamically adjust soot blowing operation parameters. During experimental calibration, soot blowing tests are conducted under fuels with different moisture contents, and the soot blowing effect, such as pressure difference changes and heat transfer efficiency recovery rate, is recorded to establish a moisture content influence factor database. When fitting historical data, based on long-term power plant operating data, regression analysis or machine learning models, such as linear regression or random forest, are used to fit the influence weight of moisture on the soot blowing effect.

[0043] In S5, based on the final soot blowing operation parameter configuration scheme and combined with historical heat transfer efficiency data, the timing range for soot blowing is determined.

[0044] Specifically, historical heat transfer efficiency data is acquired, and time-series data is extracted from equipment operation logs and stored as a structured dataset. Time-series analysis is performed using a moving average algorithm to process the structured dataset, resulting in an efficiency trend curve. Based on the efficiency trend curve, if the slope is below a preset threshold, it is identified as a potential opportunity for soot blowing, generating a candidate operation opportunity list. From the candidate operation opportunity list, combined with operating parameters, a decision tree algorithm is used to determine the priority of the soot blowing operation, resulting in an optimized operation opportunity set. For the optimized operation opportunity set, equipment status data is acquired; if the equipment status meets preset conditions, a soot blowing operation parameter configuration scheme is generated. Based on the soot blowing operation parameter configuration scheme, a linear regression algorithm is used to predict the heat transfer efficiency change within the execution range, determining the final execution range. Based on the final execution range, a control strategy is generated, and the execution instructions for the soot blowing operation are output.

[0045] Following S5, steps are also included to generate a sequence of instructions for verifying the cleaning effect by controlling the timing of soot blowing and to monitor the feedback data of the soot blowing process.

[0046] Specifically, the operation timing range is determined by extracting a time window from preset equipment operating parameters to identify the triggering conditions for the soot blowing process. If the triggering conditions are met, a verification instruction sequence is generated, and a preset instruction template is used to obtain soot blowing control instructions. Feedback data during the soot blowing process is collected by equipment sensors and stored as time-series data to obtain real-time monitoring data. Support vector machine (SVM) algorithms are used to classify the real-time monitoring data to determine whether the cleaning effect reaches a preset threshold. Based on the classification results, the time-series trend of the feedback data is analyzed to obtain performance evaluation indicators. Outliers are extracted from the performance evaluation indicators, and K-means clustering is used to divide the data into clusters to determine process control adjustment parameters. By adjusting the parameters, the operation timing range is updated, a new verification instruction sequence is generated, and optimized soot blowing control instructions are obtained.

[0047] Furthermore, it also includes a step of generating parameter iterative update values ​​if the feedback data of the cleaning effect verification instruction sequence does not reach the expected heat transfer efficiency.

[0048] If the feedback data does not achieve the expected heat transfer efficiency, key indicators from the feedback data are obtained, and the data is classified using a support vector machine algorithm to obtain the classification results of the deviation causes. Based on the classification results, corresponding parameter adjustment strategies are extracted from a pre-established deviation cause database to determine preliminary parameter update values. If the preliminary parameter update values ​​conflict with historical parameter update records, the effects of historical parameter update values ​​are compared, and the conflicting parameters are prioritized using a random forest algorithm to obtain optimized parameter update values. Based on the optimized parameter update values, the control parameters in the instruction sequence are adjusted to generate a new instruction sequence. The new instruction sequence is simulated to obtain simulated feedback data, and it is determined whether the simulated feedback data achieves the expected heat transfer efficiency. If the simulated feedback data does not achieve the expected heat transfer efficiency, a gradient boosting algorithm is used for secondary analysis of the deviation causes to obtain further iterative parameter update values. Based on the further iterative parameter update values, the instruction sequence is updated to generate the final control parameter configuration.

[0049] Furthermore, it also includes the steps of reprocessing the boiler's real-time operating data based on the iteratively updated parameter values ​​to determine the soot blowing operation parameter configuration scheme for the next cycle.

[0050] Specifically, real-time data, including temperature, pressure, and flow information, is acquired from the boiler equipment using sensor acquisition technology to obtain operating condition data. Based on this data, operating status indicators, including thermal efficiency and ash accumulation level, are calculated and compared using preset thresholds to determine the operating status. If the operating status deviates from the optimization target, a support vector machine algorithm is used based on historical data to predict the ash accumulation trend, yielding a prediction result. Based on the prediction result, control parameters, including soot blowing frequency and duration, are iteratively updated using a gradient descent algorithm to obtain updated parameters. These updated parameters are used to generate soot blowing operation instructions, including start time and soot blowing area, which are processed using a rule engine to determine the operation instructions. Based on the operation instructions, the boiler soot blowing equipment configuration is adjusted, including nozzle angle and steam pressure, using an automated control system to obtain a configuration scheme. The soot blowing operation is executed according to the configuration scheme, and real-time data is collected after execution. Data verification technology is used to determine the execution effect.

[0051] Example 2: A refined control system for boiler soot blowing parameters is provided to implement the refined control method for boiler soot blowing parameters that combines fuel characteristics and real-time operating conditions. The system includes: The data acquisition module is used to collect real-time operating data on boiler fuel moisture content, ash melting point difference, furnace temperature, and ash thickness on the heating surface. The fuel characteristic evaluation module is used to perform matching analysis between the collected data and the preset fuel characteristic database to generate quantitative evaluation indicators of fuel combustion characteristics. The dust accumulation priority determination module is used to determine the priority ranking sequence of dust accumulation cleaning by combining the quantitative evaluation indicators and real-time operating data. The soot blowing parameter generation module is used to generate a preliminary plan of soot blowing operation parameters by integrating fuel characteristics and operating condition data when the priority triggering conditions are met. The parameter correction module is used to adjust the preliminary scheme according to the fuel moisture influence factor and ash melting point difference correction item to generate the final soot blowing operation parameter configuration scheme. The execution timing decision module is used to determine the execution timing range of soot blowing operations by combining historical heat transfer efficiency data; The effect monitoring and feedback module is used to monitor the feedback data of the soot blowing process, analyze the reasons for the deviation when the heat transfer efficiency does not meet expectations, and generate iterative update values ​​for parameters. Each module is implemented by the processor calling program instructions stored in memory, forming a closed-loop control system.

[0052] This system is deployed on an industrial server in the boiler control station, communicating with the DCS system, sensor network, and soot blowing actuators via Ethernet. Each functional module runs as software on an embedded processor, sharing the same database. The quantitative indicators output by the fuel characteristic evaluation module serve as input to the priority determination module. Iterative parameter updates can back-optimize the evaluation model parameters, enabling the system to learn itself. While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the invention. Modifications and variations made by those skilled in the art in accordance with the spirit of the invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions, characterized by: Includes the following steps: S1. Collect data on the moisture content and ash fusion point difference of boiler fuel, and perform matching analysis in conjunction with a preset fuel characteristic database to generate quantitative evaluation indicators of fuel combustion characteristics. S2. Based on quantitative evaluation indicators and combined with real-time boiler operating data, a machine learning model is used to analyze furnace temperature fluctuations and ash thickness on the heating surface to determine the priority ranking sequence for ash cleaning. S3. When the ash cleaning priority sorting sequence meets the preset triggering conditions, the fuel characteristics and real-time operating data are integrated to generate a preliminary scheme of soot blowing operation parameters, the preliminary scheme including soot blowing frequency and soot blowing intensity. S4. Based on the fuel moisture influence factor and ash melting point difference correction term, the parameters of the preliminary scheme are corrected to generate the final soot blowing operation parameter configuration scheme. S5. Based on historical heat transfer efficiency data, determine the timing range for soot blowing based on the final configuration scheme, and perform the soot blowing operation.

2. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 1, characterized in that: In S1, quantitative evaluation indicators for fuel combustion characteristics are generated, including: The collected data on fuel moisture content and ash melting point difference were denoised and standardized. Similarity is calculated by feature matching between the processed data and the fuel characteristic database. When the similarity is higher than a preset threshold, the combustion characteristic category of the fuel is determined, and the combustion efficiency is predicted based on the category to generate a quantitative evaluation index.

3. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 1, characterized in that: In S2, the priority sequence for ash removal is determined, including: The data on furnace temperature fluctuation and ash thickness on the heated surface were denoised and normalized. Based on feature matching between the processed operating condition data and the ash accumulation characteristic database, the current ash accumulation status category is determined. Based on boiler operating parameters, the ash accumulation status categories are grouped and sorted to generate an ash accumulation cleaning priority ranking sequence.

4. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 1, characterized in that: In S3, a preliminary scheme for generating soot blowing operation parameters includes: A data fusion algorithm is used to integrate fuel characteristics and real-time operating data to generate initial soot blowing parameters based on soot blowing frequency and intensity. The initial soot blowing parameters are optimized based on historical operating data and dynamically adjusted in conjunction with equipment operating constraints to generate a preliminary plan.

5. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 1, characterized in that: In S4, the parameters of the preliminary scheme are adjusted based on the fuel moisture influence factor and the ash melting point difference correction term, including: Determine whether the preliminary plan meets the parameter adjustment conditions; If satisfied, the parameter adjustment range is calculated based on the ash melting point difference correction term, and the stability of the parameter configuration is evaluated in conjunction with the fuel moisture impact factor. If the stability index is lower than the preset threshold, the adjustment value is recalculated based on the fuel moisture influence factor and the ash melting point difference correction term to generate the final soot blowing operation parameter configuration scheme.

6. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 1, characterized in that: In S5, based on historical heat transfer efficiency data, the timing range for soot blowing based on the final configuration scheme is determined, including: By combining historical heat transfer efficiency data, efficiency trend curves are obtained through time series analysis; when the slope of the curve is lower than a preset threshold, candidate operation opportunities are generated; combined with operating parameters, a decision tree algorithm is used to determine priority opportunities, and based on equipment state constraints, an optimized set of operation opportunities is formed; heat transfer efficiency changes are predicted, the final execution range is determined, and soot blowing control instructions are generated.

7. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 1, characterized in that: Following S5, it also includes extracting time windows and determining trigger conditions based on the soot blowing execution timing range, generating a cleaning effect verification instruction sequence; collecting real-time feedback data of the soot blowing process through sensors, performing classification analysis, and judging whether the cleaning effect meets the standard; combining time series trends and outlier clustering to extract effect evaluation indicators, generating process control adjustment parameters, which are used to dynamically update the operation timing range and optimize the steps of subsequent soot blowing instructions.

8. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 7, characterized in that: It also includes the following steps: if the cleaning effect does not achieve the expected heat transfer efficiency, classify and identify the deviation type of the feedback data, determine the initial parameter update value based on the preset deviation-parameter mapping relationship; when there is a conflict, prioritize the parameters based on the historical update effect and generate optimized parameter iterative update values; adjust the control parameters and generate a new instruction sequence; if the result is still not up to standard after simulation verification, use the gradient boosting algorithm for secondary analysis, output further update values, and form the final control parameter configuration.

9. The method for refined control of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions according to claim 8, characterized in that, It also includes calculating thermal efficiency and ash accumulation based on parameter iterative update values ​​and real-time operating data, and judging the operating status; If the operating status deviates from the optimization target, historical data is used to predict the ash accumulation trend through support vector machine, and the control parameters are updated using gradient descent algorithm; the rule engine generates operation instructions containing start time and soot blowing area, drives the automation system to adjust nozzle angle and steam pressure configuration, execute soot blowing and verify the effect, and realize the closed-loop optimization steps; the real-time operating data includes temperature, pressure and flow rate.

10. A refined control system for boiler soot blowing parameters, characterized in that, The system for implementing the refined control method of boiler soot blowing parameters combining fuel characteristics and real-time operating conditions as described in any one of claims 1 to 9 includes: The data acquisition module is used to collect real-time operating data on boiler fuel moisture content, ash melting point difference, furnace temperature, and ash thickness on the heating surface. The fuel characteristic evaluation module is used to perform matching analysis between the collected data and the preset fuel characteristic database to generate quantitative evaluation indicators of fuel combustion characteristics. The dust accumulation priority determination module is used to determine the priority ranking sequence of dust accumulation cleaning by combining the quantitative evaluation indicators and real-time operating data. The soot blowing parameter generation module is used to generate a preliminary plan of soot blowing operation parameters by integrating fuel characteristics and operating condition data when the priority triggering conditions are met. The parameter correction module is used to adjust the preliminary scheme according to the fuel moisture influence factor and ash melting point difference correction item to generate the final soot blowing operation parameter configuration scheme. The execution timing decision module is used to determine the execution timing range of soot blowing operations by combining historical heat transfer efficiency data; The effect monitoring and feedback module is used to monitor the feedback data of the soot blowing process, analyze the reasons for the deviation when the heat transfer efficiency does not meet expectations, and generate iterative update values ​​for parameters. Each module is implemented by the processor calling program instructions stored in memory, forming a closed-loop control system.

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