Soot blowing optimization method for intelligent circulating fluidized bed boiler

Abstract

The invention relates to a soot blowing optimization method for an intelligent circulating fluidized bed boiler, belongs to the technical field of boiler equipment and intelligent control, and is suitable for efficient operation and maintenance of circulating fluidized bed (CFB) boilers in the industries of electric power, chemical engineering, metallurgy and the like. The method aims at solving the technical problems that real-time sensing of the soot deposition state is insufficient, and soot blowing strategy hysteresis is high. The method comprises the steps of multi-dimensional data real-time acquisition and fusion, dust deposition state intelligent prediction and decision making, closed-loop feedback optimization and adaptive adjustment, and system integration and intelligent operation and maintenance. A high-precision sensor network (including hearth temperature, flue gas parameters, heating surface temperature gradient and the like) is deployed in a key area of a boiler, an infrared thermal imaging or image recognition technology is combined to monitor an ash deposition state, and an ash deposition prediction model is constructed through a machine learning algorithm (such as LSTM, random forest and CNN). Based on real-time data and model analysis, the soot blowing frequency, strength and medium parameters are dynamically adjusted through fuzzy control or a self-adaptive PID algorithm, and accurate soot blowing is achieved.

Description

I. Technical Field

[0001] The present invention relates to a method for optimizing soot blowing of an intelligent circulating fluidized bed boiler, belonging to the technical field of boiler equipment, and specifically to a method for optimizing the soot blowing process by means of intelligent and automated means in combination with artificial intelligence (AI) and machine learning (ML) technologies. The present invention is particularly applicable to the soot blowing operation of a circulating fluidized bed boiler (CFB boiler), and can effectively solve the problem of ash fouling on the heating surface of the boiler, improve the thermal efficiency of the boiler, reduce energy consumption, and extend the service life of the equipment. II. Background Art

[0002] A circulating fluidized bed boiler (CFB boiler) is an efficient and low-pollution combustion device, which is widely used in industries such as electric power, chemical industry, and metallurgy. Its working principle is to make fuel particles circulate in the furnace through a high-speed air flow to achieve efficient combustion and low pollutant emissions. However, during the operation of the boiler, the problem of ash fouling on the heating surface is a common and difficult problem. Ash fouling will significantly reduce the heat transfer efficiency of the boiler, increase the flue gas temperature, lead to an increase in energy consumption, and may even cause equipment failures in severe cases, affecting the safe operation of the boiler.

[0003] Traditional soot blowing methods mainly rely on fixed time intervals or the experience judgment of operators. For example, the soot blower is started for soot blowing at regular intervals, or the need for soot blowing operation is judged according to the observation of the operator. However, the disadvantage of this method is the lack of scientificity and flexibility. The soot blowing method with a fixed time interval may result in too high or too low soot blowing frequency: too high soot blowing frequency will waste energy and increase equipment wear; too low soot blowing frequency may lead to serious ash fouling and affect the boiler efficiency. In addition, the method relying on manual experience judgment is easily affected by subjective factors and it is difficult to achieve the optimal soot blowing effect.

[0004] In recent years, with the rapid development of artificial intelligence and machine learning technologies, intelligent and automated boiler operation optimization methods have gradually become a research hotspot. Some studies have proposed soot blowing optimization methods based on sensor monitoring and data analysis. For example, by monitoring parameters such as the flue gas temperature and pressure of the boiler, the degree of ash fouling is judged and the soot blowing strategy is adjusted. However, these methods usually rely on simple threshold judgment or static models and are difficult to cope with the complex dynamic changes during the operation of the boiler. Therefore, it is of great significance to develop an intelligent optimization method that can real-time monitor the operation state of the boiler and dynamically adjust the soot blowing strategy by combining artificial intelligence and machine learning technologies.

[0005] The following is an introduction to the principle of the circulating fluidized bed boiler:

[0006] The flow state of the solid particle bed in a circulating fluidized bed is also different at different gas flow velocities. As the gas flow velocity increases, the solid particles exhibit solid bed, bubbling fluidized bed, turbulent fluidized bed, and pneumatic conveying states respectively. The riser section of the circulating fluidized bed usually operates in the fast fluidized bed state. The formation of the hydrodynamic characteristics of the fast fluidized bed is crucial for the circulating fluidized bed. At this time, the solid fuel is fluidized by the gas flow with a velocity greater than the terminal velocity of a single fuel particle and moves up and down in the form of particle clusters, resulting in a high degree of backmixing. The particle clusters move in all directions and continuously form and disintegrate. In this fluid state, the gas flow can also carry a certain number of large particles, although their terminal velocity is much greater than the cross-sectional average gas velocity. In this gas-solid operation mode, there is a large gas-solid two-phase velocity difference, that is, the relative velocity. The circulating fluidized bed consists of a fast fluidized bed (riser section), a gas-solid fuel separation device, and a solid fuel return device. III. Summary of the Invention

[0007] The object of the present invention is to provide an intelligent sootblowing optimization method for a circulating fluidized bed boiler, which dynamically adjusts the sootblowing strategy by real-time monitoring of the boiler operation status and combining artificial intelligence and machine learning technologies to achieve efficient, energy-saving, and safe sootblowing operations. The present invention can not only significantly improve the thermal efficiency of the boiler, but also reduce energy consumption, extend the service life of the equipment, and reduce the risk of equipment failure.

[0008] IV. Technical Solution

[0009] The technical solution of the present invention mainly includes the following steps:

[0010] 1. Data Acquisition and Monitoring

[0011] Sensors are installed at key positions of the boiler to collect boiler operation data in real time. These data include but are not limited to:

[0012] (1) Flue gas temperature: reflecting the boiler combustion efficiency and the ash fouling condition of the heating surface.

[0013] (2) Flue gas pressure: used to judge the flue gas resistance and the degree of ash fouling.

[0014] (3) Flue gas flow rate: reflecting the boiler load and combustion status.

[0015] (4) Heating surface temperature: directly reflecting the ash fouling condition of the heating surface.

[0016] (5) Boiler load: used to adjust the sootblowing strategy to adapt to different operation states.

[0017] (6) Combustion efficiency: used to evaluate the overall operation condition of the boiler.

[0018] These data are transmitted to the central control system through the data acquisition system, providing a basis for subsequent data analysis and optimization.

[0019] 2. Data Analysis and Modeling

[0020] Use machine learning algorithms to analyze the collected data and establish a boiler fouling prediction model. The specific steps are as follows:

[0021] (1) Data preprocessing: Clean and normalize the collected raw data to remove noise and outliers.

[0022] (2) Feature extraction: Extract fouling-related features from the preprocessed data, such as the change rate of flue gas temperature, the gradient of heating surface temperature, etc.

[0023] (3) Model training: Use machine learning algorithms (such as support vector machines, random forests, neural networks, etc.) to train historical data and establish a fouling prediction model. This model can predict the fouling degree of each part of the boiler according to real-time data.

[0024] (4) Model validation: Evaluate the prediction accuracy of the model through methods such as cross-validation to ensure the reliability of the model.

[0025] 3. Optimization of Soot Blowing Strategy

[0026] Based on the fouling prediction model and combined with the boiler operation status, use an optimization algorithm to dynamically generate the optimal soot blowing strategy. The specific steps are as follows:

[0027] (1) Definition of objective function: Define the objective function of the optimization problem with the maximization of boiler thermal efficiency and the minimization of energy consumption as the goals.

[0028] (2) Setting of constraint conditions: Consider the safety of boiler operation and equipment limitations, and set constraint conditions, such as the upper limit of soot blowing frequency, the range of soot blowing intensity, etc.

[0029] (3) Selection of optimization algorithm: Use machine learning algorithms (such as LSTM, random forest, CNN) to solve the optimization problem and generate the optimal soot blowing strategy. This strategy includes the soot blowing timing, soot blowing intensity, and soot blowing sequence, etc. After soot blowing, secondary evaluation of the heating surface cleanliness is carried out through sensor and image recognition technologies, the soot blowing effect is quantified, and a reinforcement learning algorithm (such as Q-learning) is used to iteratively optimize the strategy to form a closed-loop control process of "monitoring - prediction - soot blowing - evaluation - correction".

[0030] (4) Strategy adjustment: Dynamically adjust the soot blowing strategy according to the changes in the boiler operation status to ensure its adaptability and effectiveness.

[0031] 4. Intelligent Control and Execution

[0032] Automatically execute the optimized soot blowing strategy through the control system. The specific steps are as follows:

[0033] (1) Control instruction generation: Generate control instructions according to the soot blowing strategy, including the start and stop times of the soot blowers, the flow rate and pressure of the soot blowing medium (such as steam or compressed air), etc.

[0034] (2) Execution and monitoring: Execute the soot blowing operation through the control system and monitor the soot blowing process in real time to ensure the safety and effectiveness of the operation.

[0035] (3) Abnormal handling: During the soot blowing process, if abnormal situations (such as soot blower failures or unsatisfactory soot blowing effects) are detected, immediately stop the operation and issue an alarm.

[0036] 5. Effect evaluation and feedback

[0037] After the soot blowing operation is completed, evaluate the soot blowing effect by monitoring the boiler operation data. The specific steps are as follows:

[0038] (1) Effect evaluation: Compare the boiler operation data before and after soot blowing to evaluate the soot blowing effect. For example, judge whether the fouling is effectively removed by comparing the changes in flue gas temperature and heating surface temperature.

[0039] (2) Feedback and optimization: Feed back the evaluation results to the fouling prediction model and optimization algorithm, adjust the model parameters and optimization strategy, and achieve closed-loop optimization.

[0040] The advantages are as follows:

[0041] (1) High efficiency and energy saving: By intelligently optimizing the soot blowing strategy, reduce unnecessary soot blowing operations and lower energy consumption.

[0042] (2) Prolong equipment life: Avoid excessive or insufficient soot blowing, reduce equipment wear, and extend the service life of the boiler.

[0043] (3) Improve safety: Monitor and dynamically adjust the soot blowing operation in real time to reduce the risk of equipment failures caused by fouling.

[0044] (4) High degree of automation: Reduce manual intervention and improve operation efficiency.

[0045] (5) Strong adaptability: Can dynamically adjust the soot blowing strategy according to the changes in the boiler operation status and adapt to different working conditions. V. Description of the drawings

[0046] Figure 1 : Schematic diagram of a circulating fluidized bed boiler.

[0047] Figure 2 : Schematic diagram of the boiler combustion system

[0048] Figure 3 : Schematic diagram of the circulating fluidization principle

[0049] Figure 4 : Schematic diagram of the coal feeding system.

[0050] Figure 5 : Schematic diagram of boiler soot blowing monitoring.

[0051] For details, see the attached drawings in the specification. VI. Specific implementation manners

[0052] 1. Data acquisition and monitoring

[0053] Install sensors at key positions of the boiler to collect data such as flue gas temperature, pressure, flow rate, and heating surface temperature in real time, and transmit the data to the central control system through the data acquisition system. The installation positions of the sensors include but are not limited to the furnace outlet, superheater, economizer, and air preheater, etc.

[0054] 2. Data analysis and modeling

[0055] Utilize historical data and real-time data, and adopt machine learning algorithms to train the fouling prediction model. For example, use neural network algorithms to model the relationship between flue gas temperature and heating surface temperature to predict the fouling degree. The training data of the model should include boiler operation data under different working conditions to ensure its generalization ability.

[0056] 3. Optimization of soot blowing strategy

[0057] Based on the fouling prediction results and combined with the boiler operation status, adopt optimization algorithms to generate the optimal soot blowing strategy. For example, when it is predicted that the fouling at a certain part is serious, give priority to soot blowing at that part. The parameter settings of the optimization algorithm should be adjusted according to the specific situation of the boiler to ensure the effectiveness of the strategy.

[0058] 4. Intelligent control and execution

[0059] Automatically execute the optimized soot blowing strategy through the control system. The hardware of the control system includes PLC (programmable logic controller) and soot blowers, and the software includes control algorithms and human-machine interfaces. During the execution process, monitor the soot blowing operation in real time to ensure its safety and effectiveness.

[0060] 5. Effect evaluation and feedback

[0061] After the soot blowing operation is completed, evaluate the soot blowing effect by monitoring the boiler operation data. For example, judge whether the fouling has been effectively removed by comparing the change in flue gas temperature before and after soot blowing. The evaluation results are fed back to the optimization model to adjust the model parameters to achieve closed-loop optimization.

[0062] VII. Conclusion

[0063] By combining artificial intelligence and machine learning technologies, the present invention optimizes the soot blowing process of circulating fluidized bed boilers, achieving efficient, energy-saving, and safe boiler operation, with broad application prospects and significant economic benefits. The present invention can not only improve the thermal efficiency of boilers, but also reduce energy consumption, extend the service life of equipment, reduce the risk of equipment failure, and provide a new solution for the intelligentization and automation of boiler operation.

Claims

1. An intelligent soot blowing optimization method for a circulating fluidized bed boiler, characterized in that It includes the following steps: (1) Data acquisition: Real-time acquisition of boiler operation parameters through high-precision sensors, including but not limited to temperature, pressure, flue gas composition, flow parameters, and boiler load, etc.; (2) Ash fouling detection: Use advanced image processing technology (such as deep learning image recognition algorithm) or high-resolution thermal imaging technology to detect the ash fouling condition of the boiler heating surface, and achieve accurate positioning of the ash fouling area and quantitative evaluation of the ash fouling degree; (3) Intelligent analysis: Analyze the impact of ash fouling on boiler efficiency based on advanced machine learning algorithms (including but not limited to neural network, support vector machine, random forest, and deep learning algorithms such as convolutional neural network CNN, long short-term memory network LSTM), and predict the potential impact of ash fouling on boiler thermal efficiency, energy consumption, and emission performance; (4) Optimization control: According to the results of intelligent analysis, dynamically adjust the soot blowing strategy, including the start time, operation duration, soot blowing medium pressure, and soot blowing path of the soot blower, to achieve the optimal soot blowing effect; (5) Feedback mechanism: Through real-time monitoring of the boiler operation state after soot blowing, use artificial intelligence algorithm to evaluate the soot blowing effect, and continuously optimize the soot blowing strategy and the parameters of the machine learning model according to the feedback results, form a closed-loop control system, and continuously improve the soot blowing efficiency and boiler operation performance.

2. The method according to claim 1, characterized in that In the data acquisition step, the sensors are arranged at key positions of the boiler to ensure that the collected data can comprehensively reflect the operation state of the boiler.

3. The method according to claim 1, characterized in that In the ash fouling detection step, the image processing technology uses a deep learning algorithm to identify the ash fouling area and improve the accuracy and robustness of detection.

4. The method according to claim 1, characterized in that In the intelligent analysis step, the LSTM algorithm is used to process time series data, predict the change trend of boiler efficiency over time, and provide a scientific basis for the adjustment of the soot blowing strategy.

5. The method according to claim 1, characterized in that, In the optimization control step, combined with the actual operation conditions and ash fouling situation of the boiler, use the reinforcement learning algorithm to dynamically adjust the soot blowing strategy to achieve intelligent and adaptive soot blowing control.

6. The method according to claim 1, characterized in that, In the feedback mechanism, use the AI algorithm to evaluate the soot blowing effect in real time, and adjust the parameters of the machine learning model, such as weights, biases, etc., according to the evaluation results to improve the prediction accuracy and generalization ability of the model.

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