Intelligent anti-ash-deposition self-cleaning system for flue of low-temperature economizer of coal-fired power plant

Abstract

The invention relates to the technical field of automatic control, and discloses an intelligent ash deposition prevention self-cleaning system for a flue of a low-temperature economizer of a coal-fired power plant. According to the system, data such as temperature, humidity and flue gas components in a flue are acquired in real time through the data acquisition module, the dust deposition condition and cleaning requirements of the flue are accurately evaluated, the omission phenomenon caused by misjudgment in the manual cleaning process is avoided, the data processing and analysis module improves the data processing precision, and the data processing and analysis efficiency is improved. The system can also monitor the running state of the flue in real time, so that a cleaning instruction is automatically sent out when necessary, the timeliness and effectiveness of cleaning work are ensured, the cleaning execution module can efficiently remove accumulated dust while reducing the temperature of the flue through a mode of combining high-pressure airflow, a vibration device and a water spraying system, the time needed by cleaning is remarkably shortened, and the cleaning efficiency is improved. And the monitoring feedback module evaluates the cleaning effect, it is ensured that each time of cleaning can reach the preset standard, and the intelligent level and safety of flue cleaning are further improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to an intelligent anti-ash deposition low-temperature economizer flue self-cleaning system for a coal-fired power plant. Background Art

[0002] During the operation of coal-fired power plants, the problem of ash accumulation in the flue is an important factor that cannot be ignored. It directly affects the overall efficiency and emission standards of the equipment. The accumulation of ash in the flue will lead to a significant decrease in heat exchange efficiency, thereby reducing fuel utilization and increasing the operating costs of the power plant. In addition, ash accumulation may also cause equipment failure, especially in high temperature and high pressure environments. The deposition of ash will cause additional wear and tear on equipment such as pipes and valves, shortening their service life and increasing the frequency of maintenance and replacement. This will undoubtedly increase the maintenance cost of the power plant and affect the economic benefits.

[0003] More importantly, the ash accumulation problem in the flue may also have a negative impact on the overall safety and operational stability of the power plant. Under certain conditions, the accumulated ash may cause safety accidents such as fire or explosion, posing serious safety hazards to equipment and personnel. Therefore, regular cleaning of the flue can effectively improve the utilization rate of thermal energy and reduce the emission of harmful substances, thereby complying with the requirements of national environmental protection policies and helping to achieve sustainable development goals. This not only helps to improve the economic benefits of the power plant, but also has a positive impact on environmental protection.

[0004] Traditional flue cleaning methods usually rely on manual labor, which is inefficient and often requires a lot of time and human resources. At the same time, omissions or incompleteness may occur during the manual cleaning process, resulting in unsatisfactory cleaning results. In addition, manual cleaning in an environment of high temperature and harmful gases poses certain safety risks. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an intelligent anti-ash accumulation low-temperature economizer flue self-cleaning system for coal-fired power plants. Through the data acquisition module, multiple key data such as temperature, humidity, and flue gas composition in the flue are obtained in real time. The system can accurately assess the ash accumulation and cleaning needs of the flue, avoiding omissions or incompleteness caused by misjudgment during manual cleaning. The intelligent algorithm of the data processing and analysis module not only improves the accuracy of data processing, but also can monitor the operating status of the flue in real time, thereby automatically issuing cleaning instructions when necessary to ensure the timeliness and effectiveness of the cleaning work. The cleaning execution module can efficiently remove ash while reducing the flue temperature through a combination of high-pressure airflow, a vibration device and a water spray system, significantly reducing the time and manpower required for cleaning. The monitoring feedback module continuously evaluates the cleaning effect to ensure that each cleaning meets the preset standards, further improving the intelligence level and safety of the system. This intelligent self-cleaning system not only reduces the safety risks brought by manual cleaning, but also helps power plants achieve both economic benefits and environmental protection goals by improving thermal energy utilization and reducing harmful emissions.

[0007] (2) Technical solution

[0008] To achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent anti-ash deposition low-temperature economizer flue self-cleaning system for coal-fired power plants, comprising a data acquisition module, a data processing and analysis module, a control instruction generation module, a cleaning execution module, and a monitoring feedback module;

[0009] The data acquisition module acquires the temperature data, humidity data, flue gas composition data, flow data and pressure data in the flue in real time through sensors, and transmits the data to the data processing and analysis module through a wireless network;

[0010] The data processing and analysis module is used to perform preliminary denoising and remove outliers on the data transmitted by the data acquisition module, calculate the flue gas flow rate, flue temperature gradient, flue ash accumulation, ash accumulation rate in the flue, and flue fouling degree, and transmit them to the control instruction generation module;

[0011] The control instruction generation module analyzes whether the flue needs to be cleaned based on the calculated values transmitted by the data processing and analysis module, and generates a flue cleaning instruction when flue cleaning is required and sends it to the cleaning execution module;

[0012] When receiving a flue cleaning instruction, the cleaning execution module performs a flue self-cleaning operation by emitting high-pressure airflow, activating a vibration device, and using a water spray system to reduce the flue temperature, and calculates the flue resistance change rate after the cleaning is completed to evaluate the flue cleaning effect;

[0013] The monitoring feedback module monitors the flue cleaning effect, and when the flue cleaning effect does not meet the system preset standard, it sends a signal to the cleaning execution module to clean the flue again.

[0014] Preferably, the data denoising formula is as follows:

[0015]

[0016] In the formula, D n represents the average value of the denoised data, N represents the total number of original data, and D i Represents the i-th data point in the original data sequence, where i represents the index subscript.

[0017] Preferably, the formula for removing outliers from the data is as follows:

[0018]

[0019] In the formula, D clean represents the data retained after cleaning, D i represents the i-th data point in the original data sequence, D m represents the mean of the original data, D std It represents the standard deviation of the original data, and k represents the outlier detection threshold, which is between 2 and 3.

[0020] Preferably, the formula for calculating the flue gas flow rate is as follows:

[0021]

[0022] In the formula, v represents the flue gas flow velocity, Q represents the volume flow rate of flue gas, and A represents the cross-sectional area of the flue.

[0023] Preferably, the formula for calculating the flue temperature gradient is as follows:

[0024] ΔT=T in -T out

[0025] In the formula, ΔT represents the flue temperature gradient, T in Indicates the flue inlet temperature, T out Indicates the flue outlet temperature.

[0026] Preferably, the formula for calculating the amount of flue ash accumulation is as follows:

[0027] G=C*V*ρ

[0028] In the formula, G represents the amount of ash accumulated in the flue, C represents the ash concentration in the flue gas, V represents the volume of the flue, and ρ represents the density of ash in the flue.

[0029] Preferably, the formula for calculating the ash accumulation rate in the flue is as follows:

[0030]

[0031] In the formula, R represents the ash accumulation rate in the flue, G t2 represents the amount of dust accumulation at time t2, G t1 Indicates the amount of dust accumulation at time t1.

[0032] Preferably, the formula for calculating the degree of flue fouling is as follows:

[0033]

[0034] In the formula, C scale Indicates the degree of flue fouling, G scale Indicates the amount of dust accumulation of scaling substances, G total Indicates the total amount of ash accumulated in the flue.

[0035] Preferably, the formula for calculating the flue resistance change rate is as follows:

[0036]

[0037] In the formula, ΔL represents the change rate of flue resistance, R clean Indicates the flue resistance measured after cleaning, R before Indicates the flue resistance measured before cleaning.

[0038] Preferably, when the monitoring feedback module detects that the flue resistance change rate ΔL is ≥ 10%, it indicates that the cleaning is ineffective, and then sends a re-cleaning signal to the cleaning execution module.

[0039] Compared with the existing technology, the present invention provides an intelligent anti-ash deposition low-temperature economizer flue self-cleaning system for coal-fired power plants, which has the following beneficial effects:

[0040] The present invention obtains multiple key data such as temperature, humidity, flue gas composition, etc. in the flue in real time through the data acquisition module. The system can accurately evaluate the dust accumulation and cleaning needs of the flue, avoiding omissions or incompleteness caused by misjudgment during manual cleaning. The intelligent algorithm of the data processing and analysis module not only improves the accuracy of data processing, but also can monitor the operating status of the flue in real time, thereby automatically issuing cleaning instructions when necessary to ensure the timeliness and effectiveness of the cleaning work. The cleaning execution module can efficiently remove dust while reducing the flue temperature through a combination of high-pressure airflow, vibration device and water spray system, significantly reducing the time and manpower required for cleaning. The monitoring feedback module continuously evaluates the cleaning effect to ensure that each cleaning can meet the preset standards, further improving the intelligence level and safety of the system. This intelligent self-cleaning system not only reduces the safety risks brought by manual cleaning, but also helps power plants achieve both economic benefits and environmental protection goals by improving thermal energy utilization and reducing harmful emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Traditional flue cleaning methods usually rely on manual labor, which has problems such as low cleaning efficiency, high time cost, and difficulty in ensuring cleaning results. Therefore, an intelligent anti-ash accumulation low-temperature economizer flue self-cleaning system for coal-fired power plants is proposed. Figure 1 ,The system includes a data acquisition module, a data processing and analysis module, a control ,instruction generation module, a cleaning execution module and a monitoring feedback ,module;

[0044] The data acquisition module plays a vital role in the intelligent, anti-ash deposition, low-temperature economizer flue self-cleaning system of coal-fired power plants. This module monitors key flue information in real time through a high-precision sensor network. Specifically, the module includes multiple types of sensors, including temperature, humidity, flue gas composition, flow, and pressure. The temperature sensor accurately measures flue gas temperature to ensure efficient and safe combustion. The humidity sensor monitors the water vapor content in the flue gas, helping to determine the likelihood of ash accumulation and scaling.

[0045] For flue gas composition analysis, the module uses a gas analyzer to detect the concentrations of harmful components such as sulfur dioxide, nitrogen oxides, and carbon monoxide in the flue gas. This data not only helps ensure environmental compliance but also provides an important basis for optimizing combustion control. At the same time, the flow sensor accurately measures the flow rate of the flue gas, helping to evaluate the operating efficiency of the entire system and providing real-time feedback on possible resistance changes. The pressure sensor monitors dynamic pressure changes in the flue to obtain real-time data on fluid dynamics, thereby ensuring the stability and safety of system operation.

[0046] All detected data is quickly and stably transmitted to the data processing and analysis module through a reliable wireless network protocol (such as Internet of Things technology) to achieve real-time monitoring of the flue status. This wireless network not only has powerful data transmission capabilities, but also enables remote access to monitoring data, improving the flexibility and maintainability of the system. Through such technical means, the data acquisition module ensures the timeliness, accuracy and completeness of information, providing a solid data foundation for subsequent data analysis, decision-making and cleaning execution;

[0047] The data processing and analysis module plays a core role in the intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system. Its main function is to organize and analyze the data transmitted by the data acquisition module to ensure the accuracy and effectiveness of subsequent control decisions. First, the module performs preliminary denoising and removes outliers on the data. This process is achieved through advanced signal processing algorithms. For example, for temperature data, the sliding average method can be used for denoising. The specific formula is:

[0048]

[0049] In this formula, D n represents the denoised data, D i is the i-th data point in the original data sequence, and N is the window size under consideration. Denoising is crucial to improving data quality. It can effectively reduce data fluctuations caused by external environmental changes, sensor noise, or accidental errors, thereby ensuring more stable and reliable temperature readings. The effective implementation of this process enables subsequent data analysis to better reflect the true flue status, making the decision-making basis more reliable, thereby improving the system's response speed and accuracy to temperature changes, and helping to take cooling or other necessary intervention measures before potential dangers arise;

[0050] After processing the data, the module calculates several key indicators to comprehensively evaluate the internal conditions of the flue. The first one is the flue gas flow rate, which is calculated as follows:

[0051]

[0052] In this formula, Q is the volume flow rate through the flue, and A is the cross-sectional area of the flue. Accurately monitoring the flue gas flow rate not only optimizes the combustion process and ensures smooth gas flow within the flue, but also enables early detection of potential faults caused by increased resistance. For example, a decrease in flow rate may indicate ash accumulation or scaling within the flue. This helps automatically adjust cleaning strategies and combustion parameters, effectively preventing equipment damage and energy waste. By continuously monitoring the flue gas flow rate, the system can achieve real-time feedback and adjustments, ensuring optimal operating conditions and improving overall operational efficiency.

[0053] Secondly, the module calculates the flue temperature gradient using the formula:

[0054] ΔT=T in -T out

[0055] Here, T in and T out Representing the temperatures at the flue inlet and outlet, respectively, temperature gradient monitoring not only helps evaluate thermal efficiency but also brings significant benefits in energy conservation and emission reduction. More specifically, understanding temperature gradient fluctuations can help managers detect heat loss or excessive local cooling, allowing them to take timely measures to maintain the flue temperature within the optimized operating range. For example, if an abnormal temperature gradient is found in a certain area, it may indicate the initial signs of scaling, thus providing an important early warning for production operations.

[0056] In addition, the calculation formula for flue ash accumulation is:

[0057] G=C*V*ρ

[0058] By determining the ash concentration C, volume V, and ash density ρ within the flue, effective ash accumulation assessment can not only prevent equipment aging and reduced energy efficiency due to excessive ash accumulation, but also help formulate appropriate cleaning plans. Excessive ash accumulation not only affects heat exchange efficiency but may also cause equipment failure. Therefore, timely monitoring and calculation of ash accumulation is an important means to ensure equipment safety and extend service life. Adjusting the cleaning frequency based on actual ash accumulation can reduce maintenance costs, minimize downtime, and improve overall production efficiency.

[0059] In order to understand the ash accumulation in real time, the ash accumulation rate is also calculated. The formula is:

[0060]

[0061] Monitoring this rate ensures that cleanup measures are taken promptly when ash begins to accumulate rapidly, avoiding greater operational burdens, wasted time, and economic costs during subsequent cleaning processes. For example, if the ash accumulation rate is found to be significantly increasing within a certain range, a cleaning operation can be initiated to prevent system efficiency decline and equipment wear caused by excessive ash accumulation. In addition, dynamic monitoring of ash accumulation changes can better adapt to changes in energy costs and operating environment, ensuring system operating efficiency under different operating conditions.

[0062] Finally, the degree of scaling is calculated using the formula:

[0063]

[0064] This indicator helps to judge the effectiveness of cleaning and provides data support for future adjustments to cleaning frequency;

[0065] Through these calculations, the data processing and analysis module not only provides multi-dimensional insights into flue gas operation but also enables dynamic adjustments to cleaning strategies, enabling the system to find the optimal balance between high efficiency and energy conservation and emission reduction. This significantly improves the overall operating efficiency and safety of the low-temperature economizers in intelligent, anti-ash coal-fired power plants. The smooth operation of this module ensures the system's intelligence and ultimately enables long-term equipment maintenance and operational optimization.

[0066] The control instruction generation module plays a key role in the flue self-cleaning system of the low-temperature economizer in intelligent anti-ash accumulation coal-fired power plants. Its main function is to comprehensively determine whether the flue needs to be cleaned based on the calculated values transmitted by the data processing and analysis module. Key indicators obtained from the data processing and analysis module include flue ash accumulation, ash accumulation rate, flue gas flow rate, etc. These values help the control instruction generation module to conduct a comprehensive assessment. When any monitored parameter exceeds the preset threshold, the module will generate a flue cleaning instruction and send it to the cleaning execution module. The efficiency of this process depends on advanced data algorithms and decision-making models, which enable the system to respond flexibly to various operating conditions and avoid erroneous decisions caused by human judgment.

[0067] After the cleaning execution module receives the flue cleaning instruction, it immediately initiates a series of self-cleaning operations to enhance the cleaning efficiency of the equipment. The technical means of execution include emitting high-pressure airflow, activating the vibration device, and using a water spray system to reduce the flue temperature. The combination of these means has obvious technical advantages. The high-pressure airflow can effectively combat the accumulated dust and scaling in the flue, achieving preliminary cleaning with mechanical force, while the activation of the vibration device loosens the deposited ash, improving the effect of subsequent cleaning. The water spray system reduces the flue temperature, which not only maintains the working safety of the flue material, but also increases the wetness of dust and ash, making it easier to remove during the cleaning process.

[0068] After the cleaning operation is completed, the cleaning execution module will calculate the flue resistance change rate using the following formula to evaluate the cleaning effect:

[0069]

[0070] In this formula, ΔL represents the rate of change of resistance, R clean is the flue resistance measured after cleaning, and R before The resistance before cleaning. By comparing the change in flue resistance before and after cleaning, the system can determine whether the cleaning effect has reached the expected standard. If the calculated resistance change rate is significantly reduced, the system will consider the cleaning operation to be successful. Conversely, if the resistance change rate does not reach the preset standard, the system can issue a cleaning instruction again based on the monitored value. Such a dynamic feedback mechanism can significantly improve the operating efficiency of the flue, reduce energy consumption, ensure the safe operation of the equipment, and ultimately find the best balance between environmental protection, high efficiency and economy.

[0071] Through this technical approach, the effective collaboration between the control instruction generation module and the cleaning execution module not only improves the intelligence level of the self-cleaning system, but also ensures that coal-fired power plants can maintain efficient cleaning even under complex operating conditions.

[0072] The monitoring and feedback module's main responsibility is to continuously monitor the flue cleaning effect and evaluate whether it meets the system's preset standards. This module relies on real-time data, especially key indicators output by the data processing and analysis module, such as flue ash accumulation, ash accumulation rate, flue gas flow rate, temperature gradient, and flue resistance change rate. The monitoring of these parameters ensures the timeliness and accuracy of the data through regular collection and analysis, thus providing a solid foundation for the evaluation of the cleaning effect. When the monitoring and feedback module finds that the flue resistance change rate is greater than or equal to 10% during the analysis process, it indicates that the previous cleaning operation has failed to effectively remove the ash or other dirt in the flue. At this time, the system will issue an alarm signal indicating that the cleaning is invalid, and promptly remind the cleaning execution module to perform the flue self-cleaning operation again. This mechanism effectively improves the intelligence level of the cleaning process, avoids energy efficiency loss or equipment failure caused by incomplete cleaning, and thus ensures the safe operation of the entire coal-fired power plant in an efficient and stable state. In addition, through this real-time feedback and control method, the system can continuously optimize the cleaning frequency and methods, ultimately achieving the long-term goal of reducing operating costs and improving energy efficiency.

[0073] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system, characterized by: It includes data acquisition module, data processing and analysis module, control instruction generation module, cleaning execution module and monitoring feedback module; The data acquisition module acquires the temperature data, humidity data, flue gas composition data, flow data and pressure data in the flue in real time through sensors, and transmits the data to the data processing and analysis module through a wireless network; The data processing and analysis module is used to perform preliminary denoising and remove outliers on the data transmitted by the data acquisition module, calculate the flue gas flow rate, flue temperature gradient, flue ash accumulation, ash accumulation rate in the flue, and flue fouling degree, and transmit them to the control instruction generation module; The control instruction generation module analyzes whether the flue needs to be cleaned based on the calculated values transmitted by the data processing and analysis module, and generates a flue cleaning instruction when flue cleaning is required and sends it to the cleaning execution module; When receiving a flue cleaning instruction, the cleaning execution module performs a flue self-cleaning operation by emitting high-pressure airflow, activating a vibration device, and using a water spray system to reduce the flue temperature, and calculates the flue resistance change rate after the cleaning is completed to evaluate the flue cleaning effect; The monitoring feedback module monitors the flue cleaning effect, and when the flue cleaning effect does not meet the system preset standard, it sends a signal to the cleaning execution module to clean the flue again.

2. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 1 is characterized by: The formula for data denoising is as follows: In the formula, D n represents the average value of the denoised data, N represents the total number of original data, and D i Represents the i-th data point in the original data sequence, where i represents the index subscript.

3. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 2 is characterized by: The formula for removing outliers from the data is as follows: In the formula, D clean represents the data retained after cleaning, D i represents the i-th data point in the original data sequence, D m represents the mean of the original data, D std It represents the standard deviation of the original data, and k represents the outlier detection threshold, which is between 2 and 3.

4. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 3 is characterized by: The formula for calculating the flue gas flow rate is as follows: In the formula, v represents the flue gas flow velocity, Q represents the volume flow rate of flue gas, and A represents the cross-sectional area of the flue.

5. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 4 is characterized by: The formula for calculating the flue temperature gradient is as follows: ΔT=T in -T out In the formula, ΔT represents the flue temperature gradient, T in Indicates the flue inlet temperature, T out Indicates the flue outlet temperature.

6. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 5, characterized in that: The formula for calculating the amount of ash deposited in the flue is as follows: G=C*V*ρ In the formula, G represents the amount of ash accumulated in the flue, C represents the ash concentration in the flue gas, V represents the volume of the flue, and ρ represents the density of ash in the flue.

7. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 6, characterized in that: The formula for calculating the ash accumulation rate in the flue is as follows: In the formula, R represents the ash accumulation rate in the flue, G t2 represents the amount of dust accumulation at time t2, G t1 Indicates the amount of dust accumulation at time t1.

8. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 7, characterized in that: The formula for calculating the degree of flue fouling is as follows: In the formula, C scale Indicates the degree of flue fouling, G scale Indicates the amount of dust accumulation of scaling substances, G total Indicates the total amount of ash accumulated in the flue.

9. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 8, characterized in that: The formula for calculating the flue resistance change rate is as follows: In the formula, ΔL represents the change rate of flue resistance, R clean Indicates the flue resistance measured after cleaning, R before Indicates the flue resistance measured before cleaning.

10. The intelligent anti-ash deposition coal-fired power plant low-temperature economizer flue self-cleaning system according to claim 9, characterized in that: When the monitoring feedback module detects that the flue resistance change rate ΔL is ≥ 10%, it indicates that the cleaning is ineffective, and then sends a re-cleaning signal to the cleaning execution module.

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