Air pre-heater blockage degree online monitoring and early warning method and system

Abstract

The invention discloses an air pre-heater blockage degree online monitoring and early warning method and system, and relates to the technical field of air pre-heater fault diagnosis, and the method comprises the steps: obtaining unit data and environment data, and calculating a through-flow coefficient based on the unit data; judging the through-flow coefficient, and formulating an online monitoring and early warning strategy; and constructing a self-adaptive threshold model based on the environmental data, and optimizing the online monitoring and early warning strategy to realize online monitoring and early warning of the air pre-heater blockage degree. According to the method, the real-time value of the through-flow coefficient is calculated and compared with the alarm value, so that the real-time performance of early warning is effectively improved, operation personnel of a power plant can take measures to dredge the air pre-heater when the air pre-heater is slightly blocked, the power consumption rate of a fan of a unit is effectively reduced, non-stop of the unit caused by serious blockage of the air pre-heater is prevented, safe operation of the unit is guaranteed, and the working efficiency is improved. And unit economy is improved, and energy conservation and consumption reduction are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of air preheater fault diagnosis, in particular to an air preheater blockage degree online monitoring and early warning method and system. Background Art

[0002] With the rapid development of various undertakings in our country, people's living standards are improving, and electricity consumption is rising. Driven by the national "carbon peak and carbon neutrality" strategy, the thermal power industry is facing unprecedented pressure to save energy and reduce emissions. As a complex energy production system, thermal power plants have a large number of equipment and a complex system structure. Problems in any link may have a significant impact on the economy of the unit, and thus affect the realization of the energy conservation and emission reduction goals of thermal power plants. In recent years, affected by the rising prices of thermal coal, many thermal power plants have chosen to purchase a large amount of lignite for blending to reduce costs. However, lignite has the characteristics of high moisture and high ash content, which poses serious challenges to the wind and smoke systems of existing thermal power units. For example, the fly ash mixture in the flue gas sticks to ammonium bisulfate and is easily attached and accumulated on the inner wall of the air preheater, causing blockage of the air preheater, thereby affecting the output and efficiency of the unit. Therefore, real-time monitoring and early warning of the degree of air preheater blockage has become a technical problem that needs to be urgently solved in the thermal power industry.

[0003] However, the common solutions currently available have many shortcomings. First, traditional monitoring methods mainly rely on regular maintenance and manual inspections, which not only increase the maintenance costs of thermal power companies, but also bring additional workloads to maintenance personnel. More importantly, this passive monitoring method is difficult to detect and deal with blockage problems in a timely manner, and often cannot be detected until the blockage is serious, which undoubtedly poses a hidden danger to the safe operation of the unit. Secondly, with the rapid development of clean energy, the operation mode of thermal power units is undergoing profound changes. The load of the units fluctuates frequently, and the operating conditions are difficult to remain stable for a long time. Various operating parameters are always in dynamic change. This complex and changeable operating environment greatly increases the difficulty of accurately judging the degree of air preheater blockage. In most cases, judgments still rely on the experience of operators, and it is difficult to ensure accuracy and timeliness. In addition, although the existing online monitoring technology can collect data in real time, it often lacks consideration of environmental factors, resulting in deviations between the monitoring results and the actual situation. Summary of the invention

[0004] In view of the fact that the existing technology relies on regular maintenance and manual inspections when diagnosing air preheater faults, which not only increases the maintenance cost of thermal power companies, but also brings additional workload to maintenance personnel, makes it difficult to ensure accuracy and timeliness, and lacks consideration of environmental factors, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a method for realizing real-time early warning of the blockage degree of the air preheater by calculating the real-time value of the flow coefficient and comparing it with the alarm value without making major modifications to any equipment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an online monitoring and early warning method for the blockage degree of an air preheater, which includes acquiring unit data and environmental data, and calculating the flow coefficient based on the unit data; judging the flow coefficient and formulating an online monitoring and early warning strategy; constructing an adaptive threshold model based on the environmental data, optimizing the online monitoring and early warning strategy, and realizing online monitoring and early warning of the blockage degree of the air preheater.

[0008] As a preferred scheme of the online monitoring and early warning method for the blockage degree of the air preheater described in the present invention, the unit data includes the total air supply volume of the unit, the total coal volume of the unit, the stone coal volume of the unit, the ash content of the coal entering the furnace, the air supply volume of the denitrification dilution fan, the ammonia injection volume of the denitrification system, the air preheater outlet oxygen volume, the air preheater inlet oxygen volume, the air preheater flue gas side outlet pressure and the air preheater flue gas side inlet pressure.

[0009] As a preferred solution of the method for online monitoring and early warning of the blockage degree of the air preheater of the present invention, the calculation of the flow coefficient includes the following steps: calculating the total mass of the flue gas at the flue gas side inlet of the air preheater based on the unit data, and the specific formula is as follows:

[0010] Q 入口 =Q 送风量 +(Q 煤量 -Q 石子煤量 )×(1-Aad%)+Q 稀释风量 +Q 氨

[0011] Among them, Q 入口 Q is the total mass of flue gas at the flue gas inlet of the air preheater; 送风量 is the total air supply volume of the unit; Q 煤量 is the total coal quantity of the unit; Q 石子煤量 is the amount of stone coal in the unit; Aad% is the ash content of the coal entering the furnace; Q 稀释风量 The air supply volume of the denitrification dilution fan; Q 氨 is the amount of ammonia sprayed in the denitrification system; the air leakage rate of the air preheater is calculated according to the empirical formula, and the specific formula is as follows:

[0012] L=(a 出口 -a 入口 )÷(21-a 入口 )×90%

[0013] Wherein, L is the air leakage rate of the air preheater; a 出口 is the oxygen content at the outlet of air preheater; a 入口 It is the oxygen content at the air preheater inlet.

[0014] As a preferred solution of the method for online monitoring and early warning of the blockage degree of the air preheater of the present invention, the amount of flue gas flowing through the air preheater is calculated based on the total mass of flue gas at the flue gas side inlet of the air preheater and the air leakage rate of the air preheater. The calculation formula of the amount of flue gas flowing through the air preheater is as follows:

[0015] Q=Q 入口 ×(1+L)

[0016] Wherein, Q is the flue gas volume flowing through the air preheater; Q 入口 is the total mass of flue gas at the inlet of the flue gas side of the air preheater; L is the air leakage rate of the air preheater; the flue gas side differential pressure of the air preheater is calculated according to the flue gas side outlet pressure of the air preheater and the flue gas side inlet pressure of the air preheater, and the calculation formula of the flue gas side differential pressure of the air preheater is as follows:

[0017] ΔP=P 出 -P 入

[0018] Among them, ΔP is the differential pressure on the flue gas side of the air preheater; P 出 is the flue gas outlet pressure of the air preheater; P 入 is the inlet pressure on the flue gas side of the air preheater; based on the relationship between the flue gas volume flowing through the air preheater and the differential pressure on the flue gas side of the air preheater, the corresponding flow coefficient is obtained, and the calculation formula of the flow coefficient is as follows:

[0019]

[0020] Among them, Q is the flue gas volume flowing through the air preheater; ΔP is the differential pressure on the flue gas side of the air preheater; and C is the flow coefficient.

[0021] As a preferred scheme of the online monitoring and early warning method for the blockage degree of the air preheater described in the present invention, the online monitoring and early warning strategy includes: if the flow coefficient C is less than the alarm value, the number of times the flow coefficient C is less than the alarm value within the T0 time is determined; if the number of times the flow coefficient C is less than the alarm value within the T0 time is greater than T1% of the number of times the flow coefficient C is compared with the alarm value, it is determined that the air preheater is blocked, an alarm is issued and the unit operating personnel are notified to take soot blowing measures to clear the air preheater; if the number of times the flow coefficient C is less than the alarm value within the T0 time is ≤ T1% of the number of times the flow coefficient C is compared with the alarm value, it is determined that the air preheater is not blocked, the unit data is re-collected, the flow coefficient C is calculated and a judgment is made; if the flow coefficient C is ≥ the alarm value, it is determined that the air preheater is not blocked and monitoring continues.

[0022] As a preferred solution of the online monitoring and early warning method for the blockage degree of the air preheater described in the present invention, wherein: the environmental data includes seasonal factors and coal quality factors; the seasonal factors include spring, summer, autumn and winter; the coal quality factors are composed of the ash content of the coal entering the furnace; the construction of the adaptive threshold model includes the following steps: dividing based on the seasonal factors and setting corresponding seasonal weights; introducing a smoothing factor based on the change in smoothing time to adjust the time fluctuation; considering the influence of seasonal factors in the entire time range, combining the smoothing factor, the seasonal factor and the seasonal weight to obtain the influence of seasonal factors on the air preheater; based on the ash distribution, the ash content is used to represent the influence of the coal quality factors on the air preheater; integrating the ash distribution to obtain the cumulative influence of the coal quality factors and calculating the average value to obtain the influence of the coal quality factors on the air preheater; based on the alarm value, combining the influence of the seasonal factors on the air preheater with the influence of the coal quality factors on the air preheater to construct an adaptive threshold model.

[0023] As a preferred solution of the method for online monitoring and early warning of the blockage degree of the air preheater of the present invention, the calculation formula of the influence of the seasonal factors on the air preheater is as follows:

[0024]

[0025] Where S is the impact of seasonal factors on the air preheater; T is the total end time; N is the number of seasonal factors; w i is the weight of the i-th season; A i is the amplitude of the ith seasonal factor; T i is the end time of the i-th seasonal factor; t is the time; φ i is the i-th seasonal phase; B i is the reference value of the i-th seasonal factor; α is the adjustment parameter of the smoothing factor; the calculation formula for the influence of the coal quality factor on the air preheater is as follows:

[0026]

[0027] Among them, P is the influence of coal quality factors on air preheater; Ash max is the maximum value of ash content; C ash is the coefficient of ash content; Aad% is the ash content of the coal entering the furnace; k ash To adjust the parameters.

[0028] On the second aspect, in order to further solve the safety problems existing in the air preheater fault diagnosis, the embodiment of the present invention provides an online monitoring and early warning system for the blockage degree of the air preheater, which includes: a flow coefficient module, which is used to collect unit data, seasonal factors and coal quality factors, and use the unit data to calculate the flow coefficient; an early warning strategy module, which is used to determine whether blockage occurs according to the flow coefficient and formulate an online monitoring and early warning strategy; a model optimization module, which is used to construct an adaptive threshold model based on seasonal factors and coal quality factors, and optimize the online monitoring and early warning strategy.

[0029] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for online monitoring and early warning of the degree of air preheater blockage as described in the first aspect of the present invention.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of an online monitoring and early warning method for the degree of blockage of an air preheater as described in the first aspect of the present invention is implemented.

[0031] The beneficial effects of the present invention are as follows: the present invention effectively improves the real-time nature of the early warning by calculating the real-time value of the flow coefficient and comparing it with the alarm value, so that the power plant operating personnel can take measures to clear the air preheater when the air preheater is slightly blocked, effectively reduce the fan power consumption rate of the unit, prevent the unit from being shut down due to serious blockage of the air preheater, ensure the safe operation of the unit, improve the economy of the unit, and save energy and reduce consumption; it will not affect the performance of the thermal power unit, and does not require major modifications to any equipment. It only needs to have sufficient parameter measurement points to provide real-time early warning of the blockage degree of the air preheater. It is suitable for most of the existing air preheaters of power plants, and has strong applicability and promotion; by constructing an adaptive threshold model based on seasonal factors and coal quality factors, it can better adapt to environmental changes and changes in fuel characteristics on a time scale, thereby improving the practicality and reliability of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0033] Figure 1 This is a flow chart for online monitoring and early warning of the blockage degree of the hollow preheater in Example 1.

[0034] Figure 2 This is a flow chart of the materials flowing through the air preheater in Example 1. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0038] Example 1

[0039] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides an online monitoring and early warning method for the blockage degree of an air preheater, comprising the following steps:

[0040] S1: Obtain unit data and environmental data, and calculate the flow coefficient based on the unit data.

[0041] Specifically, Figure 1 The figure shows the implementation process of online monitoring and early warning of the blockage degree of the air preheater. The flow coefficient is calculated by obtaining the unit data, and the flow coefficient is judged according to the formulated online monitoring and early warning strategy. The corresponding measures are collected based on the judgment results. At the same time, an adaptive threshold model is constructed based on the obtained seasonal factors and coal quality factors, and the alarm value of the online monitoring and early warning strategy is optimized, thereby improving the accuracy of the early warning and realizing online monitoring and early warning of the blockage degree of the air preheater.

[0042] Preferably, Figure 2 It shows a flow chart of materials flowing through the air preheater. The unit data include the total air supply volume of the unit, the total coal volume of the unit, the stone coal volume of the unit, the ash content of the coal entering the furnace, the air supply volume of the denitrification dilution fan, the ammonia injection volume of the denitrification system, the oxygen volume at the outlet of the air preheater, the oxygen volume at the inlet of the air preheater, the outlet pressure of the flue gas side of the air preheater and the inlet pressure of the flue gas side of the air preheater.

[0043] Preferably, the environmental data include seasonal factors and coal quality factors.

[0044] Furthermore, the seasonal factors include spring, summer, autumn and winter.

[0045] Furthermore, the coal quality factor is composed of the ash content of the coal entering the furnace.

[0046] Specifically, the calculation of the flow coefficient includes the following steps: Calculate the total mass of flue gas at the flue gas side inlet of the air preheater based on the unit data, and the specific formula is as follows:

[0047] Q 入口 =Q 送风量 +(Q 煤量 -Q 石子煤量 )×(1-Aad%)+Q 稀释风量 +Q 氨

[0048] Among them, Q 入口 Q is the total mass of flue gas at the flue gas inlet of the air preheater; 送风量 is the total air supply volume of the unit; Q 煤量 is the total coal quantity of the unit; Q 石子煤量 is the amount of stone coal in the unit; Aad% is the ash content of the coal entering the furnace; Q 稀释风量 The air supply volume of the denitrification dilution fan; Q 氨 It is the amount of ammonia sprayed in the denitrification system.

[0049] Taking advantage of the opportunity of shutdown for maintenance, an oxygen measurement point is added to the flue gas outlet of the air preheater, and the air leakage rate of the air preheater is calculated according to the empirical formula. The specific formula is as follows:

[0050] L=(a 出口 -a 入口 )÷(21-a 入口 )×90%

[0051] Wherein, L is the air leakage rate of the air preheater; a 出口 is the oxygen content at the outlet of air preheater; a 入口 It is the oxygen content at the air preheater inlet.

[0052] The amount of flue gas flowing through the air preheater is calculated based on the total mass of flue gas at the flue gas side inlet of the air preheater and the air leakage rate of the air preheater. The calculation formula for the amount of flue gas flowing through the air preheater is as follows:

[0053] Q=Q 入口 ×(1+L)

[0054] Wherein, Q is the flue gas volume flowing through the air preheater; Q 入口 is the total mass of flue gas at the flue gas side inlet of the air preheater; L is the air leakage rate of the air preheater.

[0055] The flue gas side differential pressure of the air preheater is calculated according to the flue gas side outlet pressure of the air preheater and the flue gas side inlet pressure of the air preheater. The calculation formula of the flue gas side differential pressure of the air preheater is as follows:

[0056] ΔP=P 出 -P 入

[0057] Among them, ΔP is the differential pressure on the flue gas side of the air preheater; P 出 is the flue gas outlet pressure of the air preheater; P 入 It is the inlet pressure of flue gas side of air preheater.

[0058] Based on the analysis of the relationship between the flue gas volume flowing through the air preheater and the differential pressure on the flue gas side of the air preheater, the corresponding flow coefficient is obtained. The calculation formula of the flow coefficient is as follows:

[0059]

[0060] Among them, Q is the flue gas volume flowing through the air preheater; ΔP is the differential pressure on the flue gas side of the air preheater; and C is the flow coefficient.

[0061] Preferably, the accuracy of the flow coefficient calculation is ensured by comprehensively considering the total air supply volume, coal volume, ash content, and oxygen content and pressure at the inlet and outlet of the air preheater of the unit; by combining the air supply volume of the denitrification dilution fan and the ammonia injection volume of the denitrification system, the complex process flow of modern power plants is fully considered, which can more accurately reflect the actual operating status of the air preheater, reduce the misjudgment caused by the fluctuation of a single parameter, improve the reliability of monitoring and the accuracy of early warning, provide reliable basic data for subsequent blockage judgment, and help to promptly discover the trend of air preheater performance degradation.

[0062] S2: Determine the flow coefficient and formulate an online monitoring and early warning strategy.

[0063] Preferably, the online monitoring and early warning strategy includes: if the flow coefficient C is less than the alarm value, then the number of times the flow coefficient C is less than the alarm value within the T0 time is determined; if the number of times the flow coefficient C is less than the alarm value within the T0 time is greater than 90% of the number of times the flow coefficient C is compared with the alarm value, then it is determined that the air preheater is blocked, an alarm is issued and the unit operating personnel are notified to take soot blowing measures to clear the air preheater.

[0064] If the number of times the flow coefficient C is less than the alarm value within the T0 time period is less than or equal to 90% of the number of times the flow coefficient C is compared with the alarm value, it is determined that the air preheater is not blocked, and the unit data is collected again, the flow coefficient C is calculated, and a judgment is made.

[0065] If the flow coefficient C ≥ alarm value, it is determined that the air preheater is not blocked and monitoring continues.

[0066] Specifically, in order to determine the alarm value of the flow coefficient, the operating data of the air preheater is collected and analyzed over a long period of time, especially the changing trend of the flow coefficient. The initial alarm value is determined according to the blockage degree of the air preheater and the corresponding treatment measures, and the alarm value is continuously adjusted in subsequent optimization.

[0067] Preferably, the concepts of time window and alarm threshold are introduced to determine whether blockage occurs by judging the frequency of the flow coefficient being less than the alarm value within a specific time. This can effectively balance sensitivity and reliability, avoid misjudgment due to instantaneous fluctuations, and ensure that real blockage problems can be discovered and handled in a timely manner, thereby improving the practicality and credibility of the early warning system.

[0068] S3: Build an adaptive threshold model based on environmental data, optimize the online monitoring and early warning strategy, and realize online monitoring and early warning of the air preheater blockage degree.

[0069] Preferably, constructing the adaptive threshold model includes the following steps: based on seasonal factors, a year is divided into spring, summer, autumn and winter, and corresponding seasonal weights are set. The specific formula of seasonal factors is as follows:

[0070]

[0071] Among them, Season(t) is the seasonal factor; A is the amplitude of the seasonal factor, indicating the amplitude of seasonal changes; T is the seasonal cycle; φ is the seasonal phase, indicating the starting point of seasonal changes; B is the baseline value of the seasonal factor, indicating the constant part of seasonal influence; t is time.

[0072] Based on the change in smoothing time, a smoothing factor is introduced to adjust the fluctuation of time. The specific formula is as follows:

[0073]

[0074] Among them, α is the adjustment parameter of the smoothing factor.

[0075] Considering the influence of seasonal factors in the entire time range [0, T], the smoothing factor, seasonal factors and seasonal weights are combined to obtain the influence of seasonal factors on the air preheater. The specific formula is as follows:

[0076]

[0077] Where S is the impact of seasonal factors on the air preheater; T is the total end time; N is the number of seasonal factors; w i is the weight of the i-th season; i (t) is the i-th seasonal factor, including spring, summer, autumn and winter; α is the adjustment parameter of the smoothing factor.

[0078] Specifically, the calculation formula for the influence of seasonal factors on the air preheater is as follows:

[0079]

[0080] Where S is the impact of seasonal factors on the air preheater; T is the total end time; N is the number of seasonal factors; w i is the weight of the i-th season; A i is the amplitude of the i-th seasonal factor, indicating the magnitude of the i-th seasonal change; T i is the end time of the i-th seasonal factor; t is the time; φ i is the i-th seasonal phase, indicating the starting point of the i-th seasonal change; B i is the baseline value of the i-th seasonal factor, indicating the constant part of the i-th seasonal influence; α is the adjustment parameter of the smoothing factor.

[0081] Preferably, by introducing seasonal cycle functions and smoothing factors, the model can capture the impact of seasonal changes on the performance of the air preheater, significantly improving the adaptability and accuracy of the early warning system, and can automatically adjust the alarm threshold according to different seasons, reducing false alarms or missed alarms caused by changes in seasonal factors, so that the early warning system can maintain efficient operation under different operating conditions throughout the year; through the application of smoothing factors, the model can better handle the gradual effects of seasonal transitions, avoiding the mutation problems that may occur in traditional models when seasons change, and improving the stability and accuracy of the model throughout the year.

[0082] Based on the ash distribution, the ash content is used to express the influence of coal quality factors on the air preheater. The specific formula of ash distribution is as follows:

[0083]

[0084] Where Ash is the ash distribution; C ash is the coefficient of ash content, which represents the constant of ash content on coal quality; Aad% is the ash content of coal entering the furnace; k ash To adjust the parameters to control the nonlinear degree of ash influence.

[0085] Ash distribution from 0 to the maximum ash Ash max Perform integration processing to obtain the cumulative impact of coal quality factors and calculate the average value, so that the impact of coal quality factors on the air preheater is more representative. The impact of coal quality factors on the air preheater is obtained. The specific formula is as follows:

[0086]

[0087] Among them, P is the influence of coal quality factors on air preheater; Ash max is the maximum value of ash content; Ash is the ash content distribution.

[0088] Specifically, the calculation formula for the influence of coal quality factors on air preheater is as follows:

[0089]

[0090] Among them, P is the influence of coal quality factors on air preheater; Ash max is the maximum value of ash content; C ash is the coefficient of ash content, which represents the constant of ash content on coal quality; Aad% is the ash content of coal entering the furnace; k ash To adjust the parameters to control the nonlinear degree of ash influence.

[0091] Preferably, by introducing the ash distribution function and integral processing, the coal quality factors are refined and modeled, which can more accurately reflect the impact of different coal qualities on the air preheater performance, improve the accuracy of early warning under different coal conditions, and is beneficial to the fuel optimization and equipment management of the power plant.

[0092] Based on the alarm value, the influence of seasonal factors on the air preheater and the influence of coal quality factors on the air preheater are combined, and the exponential function is used to process the dynamic changes of the influence of seasonal factors on the air preheater, so that the alarm value is more sensitive to the changes of seasonal factors, and an adaptive threshold model is constructed.

[0093] Specifically, the calculation formula of the adaptive threshold model is as follows:

[0094] C e =C0·e S ·P

[0095] Among them, C e is the optimized alarm value; C0 is the alarm value, that is, the alarm value in the initial online monitoring and early warning strategy; S is the impact of seasonal factors on the air preheater; P is the impact of coal quality factors on the air preheater.

[0096] Preferably, the alarm value is optimized by utilizing seasonal factors and coal quality factors, an adaptive threshold model is constructed, and the alarm threshold is automatically adjusted according to seasonal changes and coal quality changes, so that the early warning system can maintain high efficiency and accuracy under different operating conditions throughout the year, thereby improving the adaptability and practicality of the system, and thus being able to cope with the complex and changeable actual operating environment; by considering coal quality factors, that is, the influence of coal ash content on the air preheater, the model is closer to the actual operating conditions.

[0097] The present embodiment also provides an online monitoring and early warning system for the blockage degree of an air preheater, including: a flow coefficient module, which is used to collect unit data, seasonal factors and coal quality factors, and use the unit data to calculate the flow coefficient; an early warning strategy module, which is used to determine whether blockage occurs based on the flow coefficient and formulate an online monitoring and early warning strategy; a model optimization module, which is used to construct an adaptive threshold model based on seasonal factors and coal quality factors, and optimize the online monitoring and early warning strategy.

[0098] This embodiment also provides a computer device, which is suitable for an online monitoring and early warning method for the blockage degree of an air preheater, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement an online monitoring and early warning method for the blockage degree of an air preheater as proposed in the above embodiment.

[0099] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0100] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements an online monitoring and early warning method for the blockage degree of an air preheater as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0101] In summary, the present invention effectively improves the real-time nature of early warning by calculating the real-time value of the flow coefficient and comparing it with the alarm value, so that the power plant operating personnel can take measures to clear the air preheater when the air preheater is slightly blocked, effectively reduce the fan power consumption rate of the unit, prevent the unit from being shut down due to serious blockage of the air preheater, ensure the safe operation of the unit, improve the economy of the unit, and save energy and reduce consumption; it will not affect the performance of the thermal power unit, and does not require major modifications to any equipment. It only needs to have enough parameter measurement points to provide real-time early warning of the blockage degree of the air preheater. It is suitable for most of the existing air preheaters in power plants, and has strong applicability and popularization; by constructing an adaptive threshold model based on seasonal factors and coal quality factors, it can better adapt to environmental changes and changes in fuel characteristics on a time scale, thereby improving the practicability and reliability of early warning.

[0102] Example 2

[0103] Referring to Tables 1 to 3, which are the second embodiment of the present invention, this embodiment is different from the first embodiment in that, in order to verify its beneficial effects, experimental comparison data between the present invention and the prior art are provided.

[0104] In this example, the air leakage rate of the air preheater is calculated by using the historical data of the units of a thermal power plant, and compared with the air leakage rate in the design parameters of the air preheater to determine whether the air leakage rate of the air preheater has increased, so as to take the opportunity of the unit shutdown for maintenance to repair, clean and dredge the air preheater, and take the opportunity of shutdown for maintenance to install an oxygen measurement point at the flue gas outlet of the air preheater to calculate the air leakage rate of the air preheater; at the same time, by obtaining the key parameters of the unit in real time, and using the method proposed in the present invention, after the unit is started and operated, the stable operating conditions under different loads are selected to calculate the flow coefficient C under the corresponding operating conditions. The specific data are shown in Table 1, where the initial alarm value of the flow coefficient is set to 0.85, and the time window is one hour.

[0105] Table 1 Flow coefficient data table

[0106] Experimental subjects Air preheater differential pressure (Pa) Ash content of coal entering furnace (%) Flow coefficient 1 1050 18.5 0.94 2 1080 19.2 0.93 3 1120 19.8 0.91 4 1150 20.3 0.83 ... ... ... ... n 1230 21.5 0.86

[0107] In view of the fact that the load of thermal power units is currently greatly affected by the output of new energy load, the load of thermal power units changes frequently and at a fast rate. Important parameters such as air supply volume, coal volume and oxygen volume are always in the process of change. The flow coefficient C calculated thereby is accidental. In order to reduce the interference of accidental false alarms to the unit operating personnel, the number of alarms when the flow coefficient C is less than the alarm value and the number of comparisons between the flow coefficient C and the alarm value are counted respectively. If the number of alarms within one hour is greater than 90% of the comparison times, it is determined that the air preheater is blocked and an alarm is issued.

[0108] Based on the alarm value of the above-mentioned flow coefficient, an adaptive threshold model is constructed by considering seasonal factors and coal quality factors to optimize the alarm value. Table 2 shows the optimized alarm value, where the weight of spring is 0.2, the weight of summer is 0.3, the weight of autumn is 0.2, and the weight of winter is 0.3.

[0109] Table 2 Optimized alarm value data table

[0110] Experimental subjects Seasonal factors Coal quality factors (%) Optimized alarm value 1 0.28 18.5 0.87 2 0.29 19.2 0.86 3 0.28 19.8 0.86 4 0.29 20.3 0.85 ... ... ... ... n 0.29 21.5 0.86

[0111] It can be seen from the above table that the present invention optimizes the alarm value by using the flow coefficient to determine whether blockage occurs and taking into account seasonal factors and coal quality factors, thereby effectively improving the accuracy and applicability of the early warning and being able to better adapt to environmental changes.

[0112] Based on the method proposed in the present invention, experiments were conducted in the same environment using the traditional fixed threshold method and the simple adaptive method, and the experimental data of the three methods were compared. The specific data are shown in Table 3.

[0113] Table 3 Experimental data comparison table

[0114] Experimental subjects Early warning accuracy (%) Missing rate (%) Method of the present invention 95.8 3.2 Traditional fixed threshold method 82.3 12.5 Simple Adaptive Method 88.7 7.9

[0115] Therefore, it can be seen from the above comparison table that the present invention improves the accuracy and real-time performance of the early warning, can effectively prevent the unit from being shut down due to severe blockage of the air preheater, ensures the safe operation of the unit, and does not require major modifications to any equipment, and is applicable to most of the existing air preheaters.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An online monitoring and early warning method for air preheater blockage degree, characterized by: include: Acquiring unit data and environmental data, and calculating a flow coefficient based on the unit data; Determine the flow coefficient and formulate an online monitoring and early warning strategy; An adaptive threshold model is constructed based on the environmental data, and the online monitoring and early warning strategy is optimized to achieve online monitoring and early warning of the air preheater blockage degree.

2. The method for online monitoring and early warning of air preheater blockage degree according to claim 1, characterized in that: The unit data include the total air supply volume of the unit, the total coal volume of the unit, the stone coal volume of the unit, the ash content of the coal entering the furnace, the air supply volume of the denitrification dilution fan, the ammonia injection volume of the denitrification system, the oxygen volume at the air preheater outlet, the oxygen volume at the air preheater inlet, the air preheater flue gas side outlet pressure and the air preheater flue gas side inlet pressure.

3. The method for online monitoring and early warning of air preheater blockage degree according to claim 2 is characterized in that: The calculation of the flow coefficient comprises the following steps: The total mass of flue gas at the flue gas inlet of the air preheater is calculated based on the unit data. The specific formula is as follows: Q 入口 =Q 送风量 +(Q 煤量 -Q 石子煤量 )×(1-Aad%)+Q 稀释风量 +Q 氨 Among them, Q 入口 Q is the total mass of flue gas at the flue gas inlet of the air preheater; 送风量 is the total air supply volume of the unit; Q 煤量 is the total coal quantity of the unit; Q 石子煤量 is the amount of stone coal in the unit; Aad% is the ash content of the coal entering the furnace; Q 稀释风量 The air supply volume of the denitrification dilution fan; Q 氨 The amount of ammonia sprayed for the denitrification system; The air leakage rate of the air preheater is calculated according to the empirical formula. The specific formula is as follows: L=(a 出口 -a 入口 )÷(21-a 入口 )×90% Wherein, L is the air leakage rate of the air preheater; a 出口 is the oxygen content at the outlet of air preheater; a 入口 It is the oxygen content at the air preheater inlet.

4. The method for online monitoring and early warning of air preheater blockage degree according to claim 3 is characterized in that: The amount of flue gas flowing through the air preheater is calculated based on the total mass of flue gas at the flue gas side inlet of the air preheater and the air leakage rate of the air preheater. The calculation formula for the amount of flue gas flowing through the air preheater is as follows: Q=Q 入口 ×(1+L) Wherein, Q is the flue gas volume flowing through the air preheater; Q 入口 is the total mass of flue gas at the flue gas inlet of the air preheater; L is the air leakage rate of the air preheater; The flue gas side differential pressure of the air preheater is calculated according to the flue gas side outlet pressure of the air preheater and the flue gas side inlet pressure of the air preheater. The calculation formula of the flue gas side differential pressure of the air preheater is as follows: ΔP=P 出 -P 入 Among them, ΔP is the differential pressure on the flue gas side of the air preheater; P 出 is the flue gas outlet pressure of the air preheater; P 入 is the inlet pressure on the flue gas side of the air preheater; Based on the relationship between the flue gas volume flowing through the air preheater and the flue gas side differential pressure of the air preheater, the corresponding flow coefficient is obtained. The calculation formula of the flow coefficient is as follows: Among them, Q is the flue gas volume flowing through the air preheater; ΔP is the differential pressure on the flue gas side of the air preheater; and C is the flow coefficient.

5. The method for online monitoring and early warning of air preheater blockage degree according to claim 4 is characterized in that: The online monitoring and early warning strategy includes: If the flow coefficient C is less than the alarm value, the number of times the flow coefficient C is less than the alarm value within the T0 time is determined. If the number of times the flow coefficient C is less than the alarm value within the T0 time is greater than T1% of the number of times the flow coefficient C is compared with the alarm value, it is determined that the air preheater is blocked, an alarm is issued, and the unit operating personnel are notified to take soot blowing measures to dredge the air preheater; If the number of times the flow coefficient C is less than the alarm value within T0 is less than T1% of the number of times the flow coefficient C is compared with the alarm value, it is determined that the air preheater is not blocked, and the unit data is collected again, the flow coefficient C is calculated and judged; If the flow coefficient C ≥ alarm value, it is determined that the air preheater is not blocked and monitoring continues.

6. The method for online monitoring and early warning of air preheater blockage degree according to claim 5, characterized in that: The environmental data include seasonal factors and coal quality factors; The seasonal factors include spring, summer, autumn and winter; The coal quality factor is composed of the ash content of the coal entering the furnace; The construction of the adaptive threshold model comprises the following steps: Divide based on the seasonal factors and set corresponding seasonal weights; Based on the change in smoothing time, a smoothing factor is introduced to adjust the fluctuation of time; Considering the influence of seasonal factors in the entire time range, the smoothing factor, the seasonal factors and the seasonal weight are combined to obtain the influence of seasonal factors on the air preheater; Based on the ash distribution, the ash content is used to represent the influence of the coal quality factors on the air preheater; Integrating the ash distribution to obtain the cumulative impact of coal quality factors and calculating the average value to obtain the impact of coal quality factors on the air preheater; Based on the alarm value, the influence of the seasonal factors on the air preheater and the influence of the coal quality factors on the air preheater are combined to construct an adaptive threshold model.

7. The method for online monitoring and early warning of air preheater blockage degree according to claim 6, characterized in that: The calculation formula for the influence of seasonal factors on the air preheater is as follows: Where S is the impact of seasonal factors on the air preheater; T is the total end time; N is the number of seasonal factors; w i is the weight of the i-th season; A i is the amplitude of the ith seasonal factor; T i is the end time of the i-th seasonal factor; t is the time; φ i is the i-th seasonal phase; B i is the benchmark value of the ith seasonal factor; α is the adjustment parameter of the smoothing factor; The calculation formula for the influence of the coal quality factors on the air preheater is as follows: Among them, P is the influence of coal quality factors on air preheater; Ash max is the maximum value of ash content; C ash is the coefficient of ash content; Aad% is the ash content of the coal entering the furnace; k ash To adjust the parameters.

8. An air preheater blockage degree online monitoring and early warning system, based on an air preheater blockage degree online monitoring and early warning method according to any one of claims 1 to 7, characterized in that: include, The flow coefficient module is used to collect unit data, seasonal factors and coal quality factors, and calculate the flow coefficient using the unit data; The early warning strategy module is used to determine whether a blockage occurs based on the flow coefficient and formulate an online monitoring early warning strategy; The model optimization module is used to build an adaptive threshold model based on seasonal factors and coal quality factors to optimize the online monitoring and early warning strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of an online monitoring and early warning method for the blockage degree of an air preheater as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an online monitoring and early warning method for the blockage degree of an air preheater as described in any one of claims 1 to 7 are implemented.

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