Air pre-heater maintenance heat exchange optimization method and system based on fin sequential arrangement

Abstract

The invention relates to the technical field of heat exchange equipment, and discloses an air pre-heater maintenance heat exchange optimization method and system based on fin sequential arrangement, and the method comprises the steps: firstly obtaining a fin arrangement structure of a target air pre-heater, recognizing sequential distribution points, analyzing the heat exchange data of the sequential distribution points, and generating a heat exchange efficiency channel; monitoring a dust deposition queue in the channel, analyzing a thermal resistance distribution demand, generating a heat flow fluctuation curve, calculating an airflow scouring value based on a thermal resistance extreme value curve point, and reconstructing a maintenance optimization process in combination with a dust deposition degree; analyzing a key job queue, and calculating a channel blocking ratio through a job energy efficiency index; and finally, according to the blocking ratio, a flue gas flowing channel is reconstructed, vortex node heat exchange data is collected, and a heat exchange optimization report is formulated. The heat exchange performance of the air pre-heater can be improved.

Description

TECHNICAL FIELD

[0001] The application relates to a maintenance and heat exchange optimization method and system for an air preheater based on fin arrangement in sequence, and belongs to the technical field of heat exchange equipment. BACKGROUND

[0002] The air preheater is a heat exchange device for heating air required for combustion by using the waste heat of exhaust smoke of a boiler or the like, and the performance of the air preheater directly affects the energy utilization rate of a system.

[0003] At present, maintenance and heat exchange optimization of the air preheater mainly rely on two methods: one is traditional manual maintenance, which checks the fin blockage or corrosion by disassembling and checking during shutdown, but the efficiency is low and the structure is prone to damage due to repeated disassembly and assembly; and the other is uniform fin design based on fixed spacing, which simplifies the manufacturing process, but it is difficult to balance the high heat exchange efficiency and maintenance channel demand, especially when the dust deposition is serious, which is prone to form local flow resistance and aggravate the problem of uneven heat exchange. Therefore, a maintenance and heat exchange optimization method for the air preheater based on fin arrangement in sequence is needed to improve the heat exchange performance of the air preheater. SUMMARY

[0004] The application provides a maintenance and heat exchange optimization method and system for an air preheater based on fin arrangement in sequence, which mainly aims to improve the heat exchange performance of the air preheater.

[0005] To achieve the above-mentioned purpose, the application provides a maintenance and heat exchange optimization method for an air preheater based on fin arrangement in sequence, which comprises the following steps:

[0006] Obtain the fin arrangement structure corresponding to the target air preheater, identify the distribution points in sequence in the fin arrangement structure, analyze the point heat exchange data corresponding to the distribution points in sequence, generate the heat exchange efficiency channel corresponding to the distribution points in sequence based on the point heat exchange data, and obtain the heat exchange efficiency channel corresponding to the distribution points in sequence.

[0007] Monitor the dust deposition queue in the heat exchange efficiency channel, analyze the thermal resistance distribution demand corresponding to the dust deposition queue, query the heat exchange attenuation index corresponding to the thermal resistance distribution demand, and generate the heat flow fluctuation curve corresponding to the heat exchange attenuation index.

[0008] Identify the thermal resistance extreme point in the heat flow fluctuation curve, calculate the airflow scouring value corresponding to the heat exchange efficiency channel based on the thermal resistance extreme point, and reconstruct the maintenance optimization process corresponding to the target air preheater in combination with the airflow scouring value and the fin dust accumulation degree in the target air preheater.

[0009] Analyze the key operation queue in the maintenance optimization process, query the operation energy efficiency index corresponding to the key operation queue, and calculate the channel blockage ratio value corresponding to the heat exchange efficiency channel based on the operation energy efficiency index.

[0010] Based on the channel blockage ratio, the flue gas flow passage corresponding to the target air preheater is reconstructed, a vortex node in the flue gas flow passage is identified, and node heat exchange data corresponding to the vortex node is collected. Based on the node heat exchange data, a heat exchange optimization report corresponding to the target air preheater is formulated.

[0011] Optionally, the generating of the heat exchange efficiency channel of the serially distributed node based on the point heat exchange data comprises:

[0012] Analyzing the temperature gradient distribution in the point heat exchange data;

[0013] According to the temperature gradient distribution, a heat flow abnormal area in the serially distributed node is identified;

[0014] Mapping a heat transfer path corresponding to the heat flow abnormal area;

[0015] Extracting a key heat exchange node in the heat transfer path;

[0016] Based on the key heat exchange node, a heat exchange efficiency channel of the serially distributed node is generated.

[0017] Optionally, the monitoring of the ash deposition queue in the heat exchange efficiency channel comprises:

[0018] Inquiring an ash deposition distribution in the heat exchange efficiency channel;

[0019] Extracting a deposition distribution feature corresponding to the ash deposition distribution;

[0020] Based on the deposition distribution feature, a deposition accumulation area in the heat exchange efficiency channel is determined;

[0021] Dividing a core area unit corresponding to the deposition accumulation area;

[0022] Monitoring an ash deposition queue in the core area unit.

[0023] Optionally, the generating of the heat flow fluctuation curve corresponding to the heat exchange attenuation index comprises:

[0024] Analyzing heat flow index data corresponding to the heat exchange attenuation index;

[0025] Extracting an attenuation fluctuation feature in the heat flow index data;

[0026] Based on the attenuation fluctuation feature, a heat flow time-varying sequence corresponding to the heat flow index data is constructed;

[0027] Setting a fluctuation threshold corresponding to the heat flow time-varying sequence;

[0028] Generate a heat flow fluctuation curve corresponding to the heat exchange attenuation index based on the fluctuation threshold.

[0029] Optionally, the calculation of the air flow scouring value corresponding to the heat exchange efficiency channel based on the thermal resistance extreme value point comprises:

[0030] The air flow scouring value corresponding to the heat exchange efficiency channel is calculated using the following formula:

[0031]

[0032] Wherein, v crit represents the air flow scouring value corresponding to the heat exchange efficiency channel, p represents the fluid density flowing through the air preheater, n represents the total number of thermal resistance extreme value points, i represents the number index corresponding to the thermal resistance extreme value point, m i represents the friction coefficient between the dust deposition and the fin surface corresponding to the i-th thermal resistance extreme value point, d i represents the dust deposition thickness corresponding to the i-th thermal resistance extreme value point, g represents the dust deposition value, t max represents the maximum time value from the initial operation of the air preheater to the current time, R(t) represents the thermal resistance value corresponding to time t, A i represents the effective action area of the fin corresponding to the i-th thermal resistance extreme value point, k represents the dust adhesion strength, and C represents the thermal resistance change rate corresponding to the i-th thermal resistance extreme value point.

[0033] Optionally, the reconstruction of the maintenance optimization process corresponding to the target air preheater by combining the air flow scouring value and the fin dust deposition degree comprises:

[0034] Determine the dust deposition influence data of each region of the target air preheater based on the air flow scouring value and the fin dust deposition degree.

[0035] Divide the dust deposition level corresponding to the dust deposition influence data;

[0036] Query the maintenance priority corresponding to the dust deposition level division;

[0037] According to the maintenance priority parameter, the regions of the target air preheater are sorted to obtain a partitioned maintenance sequence;

[0038] Reconstruct the maintenance optimization process corresponding to the target air preheater based on the partitioned maintenance sequence.

[0039] Optionally, the analysis of the key operation queue in the maintenance optimization process comprises:

[0040] Parse the operation time axis corresponding to the maintenance optimization process;

[0041] Determine a key job interval in the maintenance optimization process based on the job timeline;

[0042] Analyze a high-priority task in the key job interval;

[0043] Identify a task job feature corresponding to the high-priority task;

[0044] Analyze a key job queue in the maintenance optimization process based on the task job feature.

[0045] Optionally, based on the job energy efficiency indicator, the channel blockage ratio value corresponding to the heat exchange efficiency channel is calculated, comprising:

[0046] The channel blockage ratio value corresponding to the heat exchange efficiency channel is calculated by the following formula:

[0047]

[0048] Where θ represents the channel blockage ratio value corresponding to the heat exchange efficiency channel, m represents the number of monitoring segments divided in the heat exchange efficiency channel, j represents the number index of the monitoring segment, ΔPS j represents the current pressure loss value corresponding to the jth monitoring segment, ΔPS j,0 represents the initial pressure loss value corresponding to the jth monitoring segment, CT j represents the job processing time corresponding to the jth monitoring segment, ZT j represents the standard job time corresponding to the jth monitoring segment, Nh j represents the job energy consumption indicator of the jth monitoring segment.

[0049] Optionally, based on the channel blockage ratio value, the flue gas flow path corresponding to the target air preheater is reconstructed, comprising:

[0050] Parse the blockage state data corresponding to the channel blockage ratio value;

[0051] Analyze the blockage influence characteristics corresponding to the blockage state data;

[0052] Based on the blockage influence characteristics, filter the key blockage section corresponding to the flue of the target air preheater;

[0053] Determine the key flow node corresponding to the key blockage section;

[0054] Based on the key flow node, reconstruct the flue gas flow path corresponding to the target air preheater.

[0055] In order to solve the above problems, the present application also provides an air preheater maintenance and heat exchange optimization system based on fin column arrangement, which comprises:

[0056] The channel generation module is configured to acquire a fin arrangement structure corresponding to a target air preheater, identify sequentially distributed point positions in the fin arrangement structure, analyze point heat exchange data corresponding to the sequentially distributed point positions, generate heat exchange efficiency channels corresponding to the sequentially distributed point positions based on the point heat exchange data, and generate the heat exchange efficiency channels corresponding to the sequentially distributed point positions.

[0057] The curve generation module is configured to monitor a soot deposition queue in the heat exchange efficiency channel, analyze heat resistance distribution requirements corresponding to the soot deposition queue, query heat exchange attenuation indexes corresponding to the heat resistance distribution requirements, generate a heat flow fluctuation curve corresponding to the heat exchange attenuation indexes, and generate the heat flow fluctuation curve corresponding to the heat exchange attenuation indexes.

[0058] The flow reconstruction module is configured to identify a heat resistance extreme value point in the heat flow fluctuation curve, calculate a gas flow scouring value corresponding to the heat exchange efficiency channel based on the heat resistance extreme value point, combine the gas flow scouring value and a fin dust accumulation degree in the target air preheater, and reconstruct a maintenance optimization flow corresponding to the target air preheater.

[0059] The ratio calculation module is configured to analyze a key operation queue in the maintenance optimization flow, query an operation energy efficiency index corresponding to the key operation queue, calculate a channel blockage ratio value corresponding to the heat exchange efficiency channel based on the operation energy efficiency index, and calculate the channel blockage ratio value corresponding to the heat exchange efficiency channel.

[0060] The report development module is configured to reconstruct a flue gas flow path corresponding to the target air preheater based on the channel blockage ratio value, identify a vortex node in the flue gas flow path, collect node heat exchange data corresponding to the vortex node, and develop a heat exchange optimization report corresponding to the target air preheater based on the node heat exchange data.

[0061] Compared with the problems described in the background art, the present application can accurately position the point distribution by acquiring the fin arrangement structure corresponding to the target air preheater, lay the foundation for analyzing the heat exchange data of the point, generating efficient heat exchange channels, and further realize the accurate monitoring of the ash deposition and thermal resistance distribution, effectively improve the heat exchange performance and maintenance efficiency of the air preheater. The present application can master the distribution and development trend of the ash in the efficient heat exchange area in real time by monitoring the ash deposition queue in the heat exchange efficiency channel, accurately position the position of the thermal resistance increment caused by the ash, and provide data support for quantifying the heat exchange efficiency decay degree. At the same time, it can predict the maintenance demand in advance according to the ash deposition rule, realize the change from passive maintenance to active prevention, guarantee the stability and reliability of the heat exchange performance of the air preheater. Further, the present application can accurately position the key position and time node of the abnormal increase of thermal resistance in the air preheater by identifying the thermal resistance extreme point in the thermal flow fluctuation curve, and determine the critical point of the sudden drop of heat transfer efficiency caused by ash deposition, and provide accurate target for formulating targeted ash removal strategy and maintenance scheme. Further, the present application can accurately focus on the link which plays a core role in the performance recovery of the air preheater by analyzing the key operation queue in the maintenance optimization process, and determine the operation logic and resource investment focus. And by optimizing the operation sequence and coordination mechanism, the overall maintenance efficiency is improved, the stable and efficient operation of the air preheater is guaranteed. Finally, based on the channel blockage ratio, the flue gas flow path corresponding to the target air preheater is reconstructed, which can accurately identify the serious blockage area, optimize the path layout, and improve the flue gas flow efficiency. According to the blockage degree, the air flow distribution can be dynamically adjusted to reduce the system energy consumption and guarantee the heat exchange performance of the air preheater. Therefore, the air preheater maintenance and heat exchange optimization method and system based on fin arrangement provided by the embodiment of the present application can improve the heat exchange performance of the air preheater. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flowchart of an air preheater maintenance and heat exchange optimization method based on fin arrangement provided by an embodiment of the present application is shown in the figure.

[0063] Figure 2 An architecture diagram of a maintenance optimization flow in the air preheater maintenance and heat exchange optimization method based on fin arrangement provided by an embodiment of the present application is shown in the figure.

[0064] Figure 3 A module diagram of the air preheater maintenance and heat exchange optimization system based on fin arrangement provided by an embodiment of the present application is shown in the figure.

[0065] The purpose of the present application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0066] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0067] The embodiment of the present application provides a kind of based on fin order arrangement's air preheater maintenance heat exchange optimization method.The execution subject of the kind of based on fin order arrangement's air preheater maintenance heat exchange optimization method includes but is not limited to server, terminal and at least one of the electronic equipment that can be configured to execute the method provided in the embodiment of the present application, etc..In other words, the kind of based on fin order arrangement's air preheater maintenance heat exchange optimization method can be executed by software or hardware installed in terminal equipment or server equipment.The server includes but is not limited to: single server, server cluster, cloud server or cloud server cluster, etc.

[0068] Embodiment 1:

[0069] Referring to Figure 1 As shown in the flowchart of the kind of based on fin order arrangement's air preheater maintenance heat exchange optimization method provided in an embodiment of the present application.In this embodiment, the kind of based on fin order arrangement's air preheater maintenance heat exchange optimization method includes:

[0070] S1, the fin arrangement structure corresponding to target air preheater is acquired, the order distribution point in the fin arrangement structure is identified, and the point heat exchange data corresponding to the order distribution point is analyzed, and the heat exchange efficiency channel corresponding to the order distribution point is generated based on the point heat exchange data.

[0071] The present application can accurately position the order distribution point by acquiring the fin arrangement structure corresponding to the target air preheater, lay the foundation for analyzing point heat exchange data and generating efficient heat exchange channel, and further realize accurate monitoring of dust deposition and thermal resistance distribution, effectively improve the air preheater heat exchange performance and maintenance efficiency.

[0072] The target air preheater refers to a specific air preheater in an actual application scenario that needs to be repaired and optimized for heat exchange, for example, a boiler air preheater in a large coal-fired power plant that has been running for many years and has a decreased heat exchange efficiency, or an industrial kiln air preheater in chemical production that is seriously affected by ash accumulation, which can be used as a target air preheater, and a series of optimization work can be carried out to improve its energy utilization efficiency and working performance; the fin arrangement structure refers to the arrangement, distribution mode and related parameter combination of the fins in the target air preheater. For example, the fin arrangement structure is the arrangement of the fins in a certain type of power plant air preheater, that is, the fins are arranged in a matrix form with a horizontal spacing of 20 mm and a vertical spacing of 30 mm. This structure parameter setting and arrangement rule together constitutes the fin arrangement structure, which has an important influence on the heat exchange efficiency and airflow resistance of the air preheater. Alternatively, the fin arrangement structure corresponding to the target air preheater can be obtained by using a three-dimensional modeling software, such as SolidWorks, to build a parametric model of the air preheater heat exchange unit, thereby obtaining the fin arrangement structure.

[0073] Further, the present application can accurately grasp the heat transfer characteristics and fluid flow state of each point by identifying the in-line distribution points in the fin arrangement structure and analyzing the point heat exchange data corresponding to the in-line distribution points, providing data support for constructing efficient heat exchange channels, and timely discovering weak heat exchange areas and abnormal heat resistance points, laying a foundation for subsequent ash accumulation monitoring, maintenance strategy formulation and flue gas passage optimization.

[0074] The in-line distribution points refer to specific position points in the fin arrangement structure that are aligned in a row-column rule and arranged periodically. In the air preheater, the fins are usually arranged in a row-column rule with a horizontal spacing and a vertical spacing, forming a matrix-like structure. The intersection position of each fin in the row-column is the in-line distribution point. For example, the fins of a certain air preheater are arranged in a grid pattern with a horizontal spacing of 50 mm and a vertical spacing of 40 mm. The intersection position of each row of fins and each column of fins is the in-line distribution point. The regular distribution of these points is the basis for analyzing heat exchange performance. The point heat exchange data refers to a set of related parameters reflecting the heat exchange characteristics at each in-line distribution point, including the flue gas temperature, air temperature, heat flux density, surface heat transfer coefficient, etc. at the point, which is used to quantify the heat exchange efficiency of the point. For example, the flue gas temperature at a certain in-line distribution point is measured to be 300°C, the air temperature is 150°C, the heat flux density is 2000 W / m 2The data collectively constitute heat exchange data of the point, and by analyzing the data of different points, the heat exchange uniformity and weak links of the air preheater can be determined, and optionally, the identification of the in-line distribution point in the fin arrangement structure can be realized by a grid division tool, such as using ANSYS Meshing to perform structured grid division on the fin model and mark the node position, so as to obtain the in-line distribution point; the analysis of the point heat exchange data corresponding to the in-line distribution point can be realized by infrared thermal imaging technology, such as using a FLIR thermal imager to collect the surface temperature distribution of the fin in actual operation, so as to obtain the point heat exchange data.

[0075] Further, based on the point heat exchange data, the heat exchange efficiency channel corresponding to the in-line distribution node is generated, the high-efficiency heat exchange path in the air preheater can be intuitively presented, the heat flow dense area and the heat exchange dead zone can be identified, and the visual basis for optimizing the flue gas flow path is provided; at the same time, the coupling relationship between fluid resistance and heat transfer can be analyzed through the channel distribution, so as to help to formulate targeted measures for ash deposition prevention and structure improvement.

[0076] The heat exchange efficiency channel refers to a visual channel formed by connecting and integrating regions or paths with high heat exchange efficiency according to the point heat exchange data of the in-line distribution node, for example, by analyzing the heat flow density, temperature and other data of each point, a plurality of in-line distribution nodes with high heat flow density and good heat exchange effect are connected to form a “channel” penetrating through the inside of the air preheater, and the channel is the heat exchange efficiency channel.

[0077] As an embodiment of the present application, the generation of the heat exchange efficiency channel corresponding to the in-line distribution node based on the point heat exchange data comprises: analyzing the temperature gradient distribution in the point heat exchange data; identifying the heat flow abnormal area in the in-line distribution node according to the temperature gradient distribution; mapping the heat transfer path corresponding to the heat flow abnormal area; extracting the key heat exchange node in the heat transfer path; and generating the heat exchange efficiency channel of the in-line distribution node based on the key heat exchange node.

[0078] Among them, the temperature gradient distribution refers to the rate and direction of temperature change between different serial distribution points in the air preheater, which reflects the trend of heat transfer and is obtained by calculating the temperature difference and change direction of adjacent points. For example, in a certain air preheater, the temperatures of three laterally adjacent serial distribution points are 180°C, 160°C, and 140°C, respectively. The temperature gradually decreases from left to right, and the temperature difference between each two points is 20°C. This temperature decrease rate and direction constitute the temperature gradient distribution of the area, which can be used to judge the severity of heat transfer; the abnormal heat flow area refers to the area in the air preheater where the heat flow transfer deviates significantly from the normal state due to factors such as dust accumulation, structural defects or uneven fluid distribution. For example, when a part of the fin is seriously dusty, The temperature in this area is significantly lower than that of the surrounding points, and the heat flux density drops significantly. This area is the abnormal heat flow area; the heat transfer path refers to the specific route through which heat is transferred from the high-temperature side (such as flue gas) to the low-temperature side (such as air) in the air preheater. It consists of multiple serially distributed points and the heat transfer process connecting these points. For example, in the air preheater, heat is first transferred from the high-temperature flue gas to the surface of the fin in contact with it, and then conducted to the other side through the fins, and finally transferred to the low-temperature air. The route formed by this series of transfer processes is the heat transfer path, and its smoothness directly affects the heat exchange effect; the key heat exchange node refers to the serially distributed points in the heat transfer path that play a decisive role in the heat transfer efficiency and the overall heat exchange performance. These nodes are usually located at locations where heat flow is concentrated or thermal resistance is large. For example, on the heat transfer path of an air preheater, there is a point at the corner of the fin. Since the fluid flow rate slows down here, heat exchange is insufficient and thermal resistance increases. This point is the key heat exchange node. Improving the heat exchange conditions of this node can effectively improve the overall heat exchange efficiency.

[0079] Furthermore, the analysis of the temperature gradient distribution in the point heat exchange data can be achieved through a numerical difference algorithm, such as: using MATLAB based on the five-point difference method to calculate the temperature change rate of each node, thereby obtaining the temperature gradient distribution; the identification of the heat flow abnormal area in the serially distributed nodes can be achieved through a cluster analysis algorithm, such as: using the K-means method to separate the abnormal clusters of the node heat flow data, thereby obtaining the heat flow abnormal area; the mapping of the heat transfer path corresponding to the heat flow abnormal area can be achieved through a graph network analysis method, such as: using the NetworkX library to construct a node thermal resistance network and solve the shortest heat transfer path, thereby obtaining the heat transfer path; the extraction of the key heat exchange nodes in the heat transfer path can be achieved through a centrality measurement algorithm, such as: using Gephi software to calculate the path node betweenness centrality and sort it, thereby obtaining the key heat exchange nodes; the generation of the heat exchange efficiency channel of the serially distributed nodes can be achieved through a multi-objective optimization algorithm, such as: using the NSGA-II genetic algorithm to solve the Pareto front with minimum thermal resistance and maximum flow, thereby obtaining the heat exchange efficiency channel.

[0080] S2, monitor the ash deposition queue in the heat exchange efficiency channel, analyze the thermal resistance distribution demand corresponding to the ash deposition queue, query the heat exchange attenuation index corresponding to the thermal resistance distribution demand, and generate a heat flow fluctuation curve corresponding to the heat exchange attenuation index.

[0081] By monitoring the ash deposition queue in the heat exchange efficiency channel, the distribution and development trend of the ash in the high-efficiency heat exchange region can be grasped in real time, the position of the thermal resistance increment caused by the ash can be accurately positioned, data support for quantifying the heat exchange efficiency attenuation degree can be provided, and the maintenance demand can be predicted in advance according to the ash deposition law, so that the change from passive maintenance to active prevention is realized, and the stability and reliability of the air preheater heat exchange performance are ensured.

[0082] The ash deposition queue refers to sequence data formed by the ash thickness, mass and other parameters changing with time in a core region unit. For example, the ash thickness is 3mm on the first day, 3.5mm on the second day, and 4mm on the third day. The ash thickness data at different time points are arranged in sequence to form the ash deposition queue of the core region unit, which can be used to predict the ash growth trend and evaluate the maintenance demand.

[0083] As an embodiment of the present application, the monitoring of the ash deposition queue in the heat exchange efficiency channel comprises: querying the ash deposition distribution in the heat exchange efficiency channel; extracting the deposition distribution characteristics corresponding to the ash deposition distribution; determining the deposition accumulation region in the heat exchange efficiency channel based on the deposition distribution characteristics; dividing the core region unit corresponding to the deposition accumulation region; and monitoring the ash deposition queue in the core region unit.

[0084] The ash deposition distribution refers to the spatial distribution state of the ash in the heat exchange efficiency channel, covering information such as the thickness and coverage area of the ash at different positions, for example, in the heat exchange efficiency channel of a certain air preheater, the ash thickness near the inlet is 5 mm, the ash thickness in the middle section is only 2 mm, and there is no obvious ash at the outlet. The difference in the amount of ash along the different positions in the channel is the ash deposition distribution, which directly reflects the spatial difference of the ash in the channel. The deposition distribution characteristics refer to the representative rules and characteristics extracted from the ash deposition distribution, including the thickness variation trend and distribution form of the ash, for example, through analysis, it is found that the ash in a certain heat exchange efficiency channel presents a distribution form of “thick at both ends and thin in the middle”, and the thickness of the ash gradually decreases along the direction of the flue gas flow. The regular form and variation trend are the deposition distribution characteristics. The deposition accumulation area refers to a specific area in the heat exchange efficiency channel, where the thickness of the ash is significantly higher than that of the surrounding area, and the ash is concentrated and accumulated. In actual air preheaters, due to the influence of fluid flow characteristics or fin structure, some parts are prone to form vortexes, resulting in a large amount of accumulated ash. For example, at the corner of the channel, the ash thickness reaches 8 mm, which is much higher than the 2-3 mm of the surrounding area. The corner is the deposition accumulation area, which is a key position affecting the heat exchange performance. The core area unit refers to the smallest area unit with similar deposition characteristics obtained by further subdividing the deposition accumulation area, for example, a larger deposition accumulation area is divided into several small areas according to factors such as ash thickness and distribution uniformity, and the ash conditions in each small area are similar. These small areas are the core area units.

[0085] Further, the query of the ash deposition distribution in the heat exchange efficiency channel can be realized by a discrete phase model simulation method, such as using ANSYS Fluent to set the discrete phase boundary condition to simulate the particle deposition process, thereby obtaining the ash deposition distribution. The extraction of the deposition distribution characteristics corresponding to the ash deposition distribution can be realized by a fractal dimension calculation method, such as using the ImageJ software box counting method to analyze the self-similarity characteristics of the deposition pattern, thereby obtaining the deposition distribution characteristics. The determination of the deposition accumulation area in the heat exchange efficiency channel can be realized by a density clustering algorithm, such as applying the DBSCAN method to identify high-density areas of the deposition point cloud data, thereby obtaining the deposition accumulation area. The division of the core area unit corresponding to the deposition accumulation area can be realized by a Voronoi diagram segmentation method, such as using the QGIS software to generate the Thiessen polygon of the deposition center to divide the unit, thereby obtaining the core area unit. The monitoring of the ash deposition queue in the core area unit can be realized by a time series data analysis method, such as using the Pandas library to perform sliding window statistics on the multi-period deposition thickness data, thereby obtaining the ash deposition queue.

[0086] The application can accurately quantify the degree of hindering of heat transfer by the ash by analyzing the heat resistance distribution requirement corresponding to the ash deposition queue and querying the heat exchange attenuation index corresponding to the heat resistance distribution requirement, thereby providing data support for evaluating the performance degradation of the air preheater; meanwhile, the energy efficiency critical point of the heat exchange system can be predicted according to the attenuation index, thereby effectively avoiding the problem of sudden drop of heat exchange efficiency caused by accumulation of ash.

[0087] The heat resistance distribution requirement refers to the distribution state and corresponding quantitative requirement of the thermal resistance in different regions of the air preheater caused by deposition of the ash. The ash will increase the resistance of the heat transfer path, and different deposition thicknesses correspond to different thermal resistances. For example, in a certain heat exchange efficiency channel, the thermal resistance is about 0.2 m2·K / W when the ash thickness in the core region is 5 mm, and the thermal resistance is 0.35 m2·K / W when the ash thickness is 8 mm. The difference and specific value requirement of the thermal resistance in different regions are the heat resistance distribution requirement. The heat exchange attenuation index refers to a quantitative parameter or standard representing the degree of decrease of the heat exchange efficiency of the air preheater caused by factors such as the ash. The heat exchange attenuation index is usually established based on the corresponding relationship between the thermal resistance and the heat exchange efficiency. For example, when the thermal resistance of a certain air preheater increases from 0.1 m2·K / W to 0.2 m2·K / W, the heat exchange efficiency decreases from 85% to 70%. Therefore, “the heat exchange efficiency decreases by 15% for every increase of 0.1 m2·K / W of the thermal resistance” is a heat exchange attenuation index. Optionally, the analysis of the heat resistance distribution requirement corresponding to the ash deposition queue can be realized by a heat network modeling method, such as using the Thermal Desktop software to construct an equivalent thermal resistance network model of the deposition layer, thereby obtaining the heat resistance distribution requirement. The query of the heat exchange attenuation index corresponding to the heat resistance distribution requirement can be realized by a heat transfer efficiency evaluation method, such as applying the HTRI Xchanger Suite to calculate the attenuation rate of the heat transfer coefficient to the fouling coefficient, thereby obtaining the heat exchange attenuation index.

[0088] Further, the application can present the dynamic change trend of the heat flow density with the development of the ash in a visualized manner by generating the heat flow fluctuation curve corresponding to the heat exchange attenuation index, thereby intuitively displaying the time sequence characteristics of the heat exchange efficiency attenuation, facilitating the identification of the critical point and periodic law of the heat flow abnormal fluctuation, and improving the stability of the operation of the heat exchange system.

[0089] The heat flow fluctuation curve refers to a curve drawn with time as the horizontal axis and heat flow index data as the vertical axis, which intuitively displays the fluctuation of the heat flow with time. For example, based on the heat flow density data of a certain air preheater for 30 consecutive days, a curve gradually decreasing with time is drawn, and the process of the heat flow density decreasing from 2200 W / m 2 to 1700 W / m 2 can be clearly seen in the curve. The curve is the heat flow fluctuation curve, which is used to visually present the heat exchange attenuation trend.

[0090] As an embodiment of the present application, the generating the heat flow fluctuation curve corresponding to the heat exchange attenuation index comprises: analyzing the heat flow index data corresponding to the heat exchange attenuation index; extracting the attenuation fluctuation characteristics in the heat flow index data; constructing the heat flow time-varying sequence corresponding to the heat flow index data based on the attenuation fluctuation characteristics; setting the fluctuation threshold corresponding to the heat flow time-varying sequence; and generating the heat flow fluctuation curve corresponding to the heat exchange attenuation index based on the fluctuation threshold.

[0091] The heat flow index data refers to a set of various quantified parameters reflecting the heat flow transfer state in the air preheater, including heat flow density, temperature difference, heat transfer coefficient, etc. For example, during the operation of an air preheater, the heat flow densities measured at different times are 1800 W / m 2 , 1650 W / m 2 , and 1500 W / m 2 . These data representing heat flow intensity constitute the heat flow index data. The attenuation fluctuation characteristics refer to the fluctuation rules and characteristics related to heat exchange efficiency attenuation extracted from the heat flow index data, such as fluctuation amplitude, frequency, trend, etc. For example, it is found through analysis that the heat flow density of an air preheater increases periodically with the increase of ash accumulation, with a fluctuation amplitude of 10% every 10 days. This "periodic attenuation with increasing amplitude" is the attenuation fluctuation characteristics. The heat flow time-varying sequence refers to a dynamic data sequence formed by arranging the heat flow index data in chronological order, which is used to reflect the change process of heat flow over time. For example, the heat flow density data monitored every day of an air preheater (e.g., 2000 W / m 2 on the first day, 1950 W / m 2 on the second day,..., and 1800 W / m 2 on the tenth day) is arranged in chronological order to form a time-varying sequence reflecting the heat flow attenuation process, which intuitively displays the evolution trend of heat flow over time. The fluctuation threshold refers to a critical value set for judging whether the heat flow fluctuation is abnormal, which is used to define the range of normal fluctuation and abnormal attenuation. For example, according to the design parameters and historical data of the air preheater, a heat flow density fluctuation amplitude exceeding 15% of the initial value is set as abnormal. This "15%" is the fluctuation threshold. When the actual fluctuation exceeds this value, a warning is triggered, prompting the need for ash cleaning and maintenance.

[0092] Further, the analysis of the heat exchange attenuation index corresponding heat flow index data can be achieved by a thermodynamic parameter inversion method, such as: using the FLUENT post-processing module to extract the wall heat flow density distribution cloud data, so as to obtain the heat flow index data; the extraction of the attenuation fluctuation characteristics in the heat flow index data can be achieved by a wavelet transform algorithm, such as: applying the cwt function of MATLAB for multi-scale time-frequency feature decomposition, so as to obtain the attenuation fluctuation characteristics; the construction of the heat flow time-varying sequence corresponding to the heat flow index data can be achieved by a time series interpolation method, such as: using the resample function of Pandas for equal-interval resampling of discrete data, so as to obtain the heat flow time-varying sequence; the setting of the fluctuation threshold corresponding to the heat flow time-varying sequence can be achieved by an extreme value statistical analysis method, such as: using Gumbel distribution fitting historical data to determine the 99% quantile threshold, so as to obtain the fluctuation threshold; the generation of the heat flow fluctuation curve corresponding to the heat exchange attenuation index can be achieved by a Bayesian probability modeling, such as: constructing a Gaussian process regression prediction confidence interval curve by PyMC3, so as to obtain the heat flow fluctuation curve.

[0093] S3, identifying the thermal resistance extreme point in the heat flow fluctuation curve, based on the thermal resistance extreme point, calculating the airflow scouring value corresponding to the heat exchange efficiency channel, combining the airflow scouring value with the dust accumulation degree of the target air preheater, and reconstructing the maintenance optimization process corresponding to the target air preheater.

[0094] By identifying the thermal resistance extreme point in the heat flow fluctuation curve, the application can accurately locate the key position and time node of the abnormal increase of thermal resistance in the air preheater, and clearly determine the critical point of the sudden drop of heat transfer efficiency caused by dust deposition, so as to provide accurate target points for formulating targeted dust removal strategies and maintenance schemes.

[0095] The thermal resistance extreme point refers to a curve feature point in the heat flow fluctuation curve, which is caused by factors such as dust deposition, and the thermal resistance has a significant mutation (maximum value or minimum value). This point usually corresponds to a sudden drop or rise of heat flow density, reflects the sharp change of heat exchange efficiency, for example, the heat flow fluctuation curve of a certain air preheater at the 25th day of operation, the heat flow density suddenly drops from 1800W / m 2 to 1200W / m 2 , and the curve forms an obvious downward inflection point at this point, which is the thermal resistance extreme point, indicating that the thermal resistance increases sharply due to the sudden increase of dust thickness, and the heat exchange efficiency decreases significantly. Optionally, the identification of the thermal resistance extreme point in the heat flow fluctuation curve can be achieved by a curvature analysis method, such as: using the diff function of MATLAB to calculate the second derivative of the curve to determine the inflection point position, so as to obtain the thermal resistance extreme point.

[0096] Further, based on the thermal resistance extreme value point, the air flow scouring value corresponding to the heat exchange efficiency channel is calculated, the critical air flow parameter required for removing the accumulated dust is accurately determined, a quantitative basis for formulating an efficient dust removal strategy is provided, energy waste caused by insufficient air flow energy leading to incomplete dust removal or excessive energy is avoided, and the continuous smoothness of the air preheater heat exchange channel is ensured.

[0097] The air flow scouring value refers to the critical scouring speed required for removing the accumulated dust in the air preheater heat exchange channel and restoring the heat exchange efficiency. When the air flow speed reaches this value, the adhesion and friction between the accumulated dust and the fins can be overcome, the accumulated dust is separated from the fin surface, and the heat exchange channel is ensured to be smooth.

[0098] As an embodiment of the present application, based on the thermal resistance extreme value point, the air flow scouring value corresponding to the heat exchange efficiency channel is calculated, including:

[0099] The air flow scouring value corresponding to the heat exchange efficiency channel is calculated by the following formula:

[0100]

[0101] Wherein, v crit represents the air flow scouring value corresponding to the heat exchange efficiency channel, p represents the fluid density flowing through the air preheater, n represents the total number of the thermal resistance extreme value points, i represents the number index corresponding to the thermal resistance extreme value point, m i represents the friction coefficient between the accumulated dust and the fin surface corresponding to the i th thermal resistance extreme value point, d i represents the accumulated dust thickness corresponding to the i th thermal resistance extreme value point, g represents the accumulated dust deposition value, t max represents the maximum time value from the initial operation of the air preheater to the current time, R(t) represents the thermal resistance value corresponding to the time t, A i represents the effective fin area corresponding to the i th thermal resistance extreme value point, k represents the accumulated dust adhesion strength, and C represents the thermal resistance change rate corresponding to the i th thermal resistance extreme value point.

[0102] In detail, the friction coefficient refers to the friction characteristic parameter of the relative sliding between the ash particles and the fin surface at the i-th thermal resistance extreme value point, which reflects the friction resistance between the ash (such as soot, impurity mixture) and the fin material (metal, etc.), and is affected by the shape of the ash particles, the surface roughness of the fin, etc.; the ash thickness refers to the vertical thickness of the ash deposited on the fin surface at the position corresponding to the i-th thermal resistance extreme value point, and the greater the ash thickness, the higher the thermal resistance, which requires a higher airflow scouring force for removal, and is an intuitive indicator for judging the degree of ash blockage; the ash deposition value refers to the characteristic parameter of the natural sedimentation of the ash particles in the air preheater due to gravity, which reflects the sedimentation trend of the ash affected by gravity, and assists in judging the stability of the accumulation of the ash on the fin surface, which covers the ash deposition mass of a unit area (10 cm x 10 cm detection area) under standard working conditions (flue gas dust content ≤ 50 mg / m 3 ) for 24 hours of continuous operation, which is determined by weighing method (accuracy ± 0.01 g), and the measurement process includes: 1. Pre-clean the detection area and weigh (W0) 2. Collect the ash after 24 hours of operation with an anti-static brush 3. Weigh (W1) using a METTLER AE200 electronic balance 4. Calculate: g = (W1-W0) / 0.01m 2 ; the maximum time value refers to the total length of time from the start of the operation of the air preheater to the current time, which is used to define the time range (i.e. the integral interval [0, t max ]) for calculating the thermal resistance, and reflects the cumulative effect of the ash over time, and the longer the time, the longer the correlation between the ash and the thermal resistance needs to be supported by long-term data; the thermal resistance value refers to the heat transfer resistance of the heat exchange channel of the air preheater due to factors such as ash and structure, and the greater the thermal resistance, the lower the heat exchange efficiency; the effective fin area refers to the actual area of the fin at the position corresponding to the i-th thermal resistance extreme value point, which participates in heat exchange and is in contact with the ash, which is not the total area of the fin, and the ash-free and invalid shielding area needs to be deducted, and the greater the area, the wider the influence range of the ash on the heat exchange; the ash adhesion strength refers to the quantitative parameter of the adhesion force between the ash particles and the fin surface, which reflects the firmness of the ash on the fin, and is affected by factors such as ash composition (such as sulfur content, water content) and fin surface energy, and the higher the adhesion strength, the greater the airflow scouring force required; the thermal resistance change rate refers to the change rate of the thermal resistance value over time at the i-th thermal resistance extreme value point, which reflects the degree of thermal resistance mutation, and the greater the thermal resistance change rate, the more intense the processes of ash deposition and shedding, and the airflow scouring strategy needs to be matched accordingly.

[0103] Specifically, the measurement standard for the parameter k of the above formula can adopt the ASTM D5350-2018 standard test:

[0104] 1. Use TA.XTplus texture analyzer, 50mm diameter head;

[0105] 2. Peel off the ash layer at a speed of 0.5mm / s;

[0106] 3. Take the average of the peak force (measured k = 0.36 ± 0.05 N / mm 2 )

[0107] Formula verification data:

[0108] Dust thickness g (mm) Measured critical wind speed (m / s) Calculated value (m / s) Error rate ... ... ... ... 1.0 8.2 8.5 3.7％ 2.0 12.1 11.7 3.7％

[0109] Further, the present application can accurately match the soot cleaning operation intensity by combining the airflow scouring value with the fin dust accumulation degree in the target air preheater, avoid excessive or insufficient maintenance, dynamically adjust the maintenance node according to the dust accumulation and airflow parameters, improve the heat exchange efficiency of the air preheater, and ensure long-term stable operation of the equipment.

[0110] The fin dust accumulation degree refers to a quantitative index of the pollution state of the fin surface of the air preheater due to the deposition of smoke dust, impurities, etc., covering the dust thickness, distribution uniformity, dust composition, and the influence degree on the fin heat exchange performance. For example, during the operation cycle of the boiler, fly ash particles generated by combustion adhere to the fins. After detection, the fin dust thickness in a certain area reaches 2 mm, and the thermal resistance increases by 30% due to dust coverage compared to the clean state, directly reflecting that the fin dust in this position is serious and affects the heat exchange efficiency. The maintenance optimization process refers to the air preheater maintenance operation specification reconstructed according to the partition maintenance sequence, covering maintenance preparation, different regional maintenance operation steps, quality inspection, and post-maintenance connection, etc., replacing the original process. For example, the new process stipulates that the soot cleaning in high-priority areas is completed first according to the sequence, then detection is carried out, and then low-priority area operation is carried out, and the time and technical standards of each link are clearly defined to improve maintenance effect and equipment reliability.

[0111] As an embodiment of the present application, the combination of the airflow scouring value and the fin dust accumulation degree in the target air preheater to reconstruct the corresponding maintenance optimization process of the target air preheater includes: determining the dust influence data of each region of the target air preheater based on the airflow scouring value and the fin dust accumulation degree; dividing the dust level corresponding to the dust influence data; querying the maintenance priority corresponding to the dust level division; partitioning and sorting each region of the target air preheater according to the maintenance priority parameter to obtain a partition maintenance sequence; and reconstructing the corresponding maintenance optimization process of the target air preheater based on the partition maintenance sequence.

[0112] The dust accumulation influence data refers to a quantitative information set that comprehensively reflects the influence of dust accumulation on the heat exchange and operation of the air preheater, covering the change of thermal resistance caused by dust accumulation, the correlation degree of the required airflow scouring value, etc. For example, through calculation, the dust accumulation in a certain area increases the thermal resistance by 20%, and the required airflow scouring value is 30 Pa higher than that in the clean state. These data collectively constitute the dust accumulation influence data of the area. The dust accumulation level refers to the classification and definition of the harm degree of dust accumulation in different areas of the air preheater based on the dust accumulation influence data. According to the influence of dust accumulation on the heat exchange efficiency and the stability of equipment operation, it is divided into levels such as light, medium, and heavy. For example, if the dust accumulation increases the thermal resistance by 5%-15%, it corresponds to the light level; if it increases by 15%-30%, it corresponds to the medium level, which is used to intuitively distinguish the harm degree of dust accumulation. The maintenance priority refers to the standard of the maintenance sequence of different dust accumulation areas determined based on the dust accumulation level and the overall operation demand of the equipment. The areas with greater influence on the performance and safety of the air preheater are given priority in maintenance. For example, the area with heavy dust accumulation is given the highest maintenance priority because it may cause equipment failure. The area with light dust accumulation has a relatively low priority to ensure that resources are reasonably allocated to critical parts.

[0113] Further, the determination of the dust accumulation influence data of each area of the target air preheater can be achieved by a thermal imager temperature field analysis method, such as using a FLIR T1020 infrared thermal imager to obtain the temperature abnormal distribution of the dust accumulation area, thereby obtaining the dust accumulation influence data. The division of the dust accumulation level corresponding to the dust accumulation influence data can be achieved by a fuzzy clustering algorithm, such as using the fcm function of MATLAB to perform fuzzy classification on multi-dimensional influence parameters, thereby obtaining the dust accumulation level. The query of the maintenance priority corresponding to the division of the dust accumulation level can be achieved by Bayesian network reasoning, such as constructing a maintenance decision network using GeNIe software to perform probability reasoning, thereby obtaining the maintenance priority. The partitioning and sorting of each area of the target air preheater according to the maintenance priority parameter can be achieved by a topological sorting algorithm, such as using the NetworkX library to construct a maintenance dependency relationship graph to generate a topological sequence, thereby obtaining the partitioning and sorting sequence. The reconstruction of the maintenance optimization process corresponding to the target air preheater can be achieved by a reinforcement learning method, such as constructing a DQN agent based on TensorFlow to autonomously explore the optimal maintenance strategy, thereby obtaining the maintenance optimization process.

[0114] Specifically, to further intuitively understand the execution logic and data flow relationship corresponding to the maintenance optimization process in the present scheme, please refer to Figure 2The maintenance optimization flowchart is shown, and the Figure 2 As the core process framework of the air preheater operation and maintenance system, the complete link from the basic data input to the optimization process output is clearly presented: the input layer focuses on the key parameters of the air preheater operation (air flow scouring value, fin dust accumulation degree), which is the basis for subsequent analysis; the processing layer converts the raw data into executable maintenance strategies through the ladder logic of "determining dust accumulation influence data, dividing dust accumulation levels, querying maintenance priority, partitioning and sorting, and reconstructing the process"; the output layer takes the "maintenance optimization process" as the final result. It should be noted that the correlation of each link in the flowchart is essentially an abstract refinement of the air preheater dust-maintenance logic. In actual scenarios, the complexity of parameter calculation (such as the dynamic correlation of air flow scouring value and dust accumulation degree) and the diversity of link adaptation (different dust accumulation levels correspond to differentiated maintenance priority rules) are much more complex than the diagram presents. This architecture only provides a simple display of the core logic and provides a direct reference for understanding the systematic approach to air preheater maintenance optimization.

[0115] S4, analyze the key job queue in the maintenance optimization process, query the job energy efficiency index corresponding to the key job queue, and calculate the channel blockage ratio value corresponding to the heat exchange efficiency channel based on the job energy efficiency index.

[0116] By analyzing the key job queue in the maintenance optimization process, the present application can accurately focus on the key link that plays a core role in restoring the performance of the air preheater, clearly define the job sequence and resource input priorities, and improve the overall efficiency of maintenance through optimizing the job order and coordination mechanism to ensure stable and efficient operation of the air preheater.

[0117] The key job queue refers to a job sequence formed by arranging the key tasks in the maintenance optimization process in logical order according to task characteristics (such as priority, time dependency, resource demand, etc.). For example, the key job queue for a certain air preheater maintenance is: ① high-pressure cleaning of high-priority areas → ② heat resistance detection → ③ structure adjustment of low-efficiency areas. This queue ensures efficient connection of maintenance jobs through task characteristic correlation.

[0118] As an embodiment of the present application, the analysis of the key job queue in the maintenance optimization process includes: analyzing the job time axis corresponding to the maintenance optimization process; determining the key job interval in the maintenance optimization process based on the job time axis; analyzing high-priority tasks in the key job interval; identifying the task job characteristics corresponding to the high-priority tasks; and analyzing the key job queue in the maintenance optimization process based on the task job characteristics.

[0119] The job time axis refers to a visual time axis formed by arranging each job in the maintenance optimization process in chronological order, covering job start and end times, task time connection relationships, etc. For example, the job time axis of a certain air preheater maintenance process shows that the first to second hours are for cleaning equipment debugging, the second to fifth hours are for high-priority area cleaning, and the fifth to sixth hours are for heat exchange efficiency detection. The time axis clearly shows the time nodes and sequence of each job. The key job interval refers to a time period in the job time axis that has a decisive effect on the overall efficiency and quality of the maintenance optimization process, usually including high-priority tasks or non-parallel key links. For example, in air preheater maintenance, if the high-priority area cleaning interval from the second to fifth hours is delayed, the subsequent detection and debugging links will be delayed as a whole, and this interval is the key job interval that affects maintenance progress. The high-priority task refers to a job task in the maintenance optimization process that is given a higher processing order because it has a significant impact on air preheater performance recovery and safe operation. For example, for a heavily dusted and heat resistance extreme value outstanding fin area, its cleaning job is classified as a high-priority task and needs to be executed before maintenance work in a lightly dusted area. The task job characteristics refer to the attributes and characteristics exhibited by high-priority tasks during execution, including job duration, technical difficulty, resource requirements (such as equipment, manpower), and dependency relationships with other tasks. For example, a certain high-priority cleaning task has the following job characteristics: high-pressure airflow equipment is required (resource requirement), it takes 3 hours (job duration), and it needs to be completed before the detection task (dependency relationship).

[0120] Further, the analysis of the job time axis corresponding to the maintenance optimization process can be achieved by the critical path network analysis method, such as calculating the earliest and latest start times of each process by Primavera P6 to obtain the job time axis. The determination of the key job interval in the maintenance optimization process can be achieved by the buffer management analysis method, such as identifying the resource constraint interval using the Goldratt critical chain project management method to obtain the key job interval. The analysis of high-priority tasks in the key job interval can be achieved by the weight scoring matrix, such as constructing a multi-dimensional scoring table including duration, resource consumption, and risk coefficient to obtain high-priority tasks. The identification of task job characteristics corresponding to the high-priority tasks can be achieved by association rule mining, such as using the Apriori algorithm to find high-frequency co-occurring task characteristic combinations to obtain task job characteristics. The analysis of the key job queue in the maintenance optimization process can be achieved by the dynamic programming method, such as using the JuMP package of the Julia language to solve the optimal task scheduling scheme to obtain the key job queue.

[0121] The application can accurately quantify the resource utilization rate and time efficiency of the maintenance work by querying the work energy efficiency index corresponding to the key work queue, can analyze the synergistic effect of the work sequence based on the index correlation, can predict the actual contribution of high-priority tasks to the performance recovery of the air preheater, can help dynamically adjust the maintenance strategy, and can improve the accuracy of air preheater operation and maintenance.

[0122] The work energy efficiency index is a quantitative parameter for measuring the execution efficiency and resource utilization effect of the key work queue in the maintenance optimization process, and includes unit time dust removal amount, energy consumption ratio, task punctuality completion rate, etc. For example, the work energy efficiency index of the high-pressure dust removal work in the maintenance of an air preheater is: 0.8 kg / m2 per hour (unit time dust removal amount), 15 kW·h / kg (energy consumption ratio), and the actual work time deviation from the plan is less than or equal to 5% (punctuality completion rate). The economy and efficiency of the work can be evaluated through these indexes. Optionally, the query of the work energy efficiency index corresponding to the key work queue can be realized by the data envelopment analysis method, such as using DEAP software to calculate the input-output efficiency value of each maintenance task, so as to obtain the work energy efficiency index.

[0123] Further, based on the work energy efficiency index, the channel blockage ratio value corresponding to the heat exchange efficiency channel is calculated, the influence degree of dust accumulation on the channel flow capacity is accurately quantified, the quantitative correlation between the blockage degree and the maintenance work is established through the dust removal efficiency and the energy consumption ratio, direct data support is provided for evaluating the heat exchange efficiency decay, the forward-looking and accuracy of the air preheater operation and maintenance are improved, and the long-term efficient operation of the heat exchange system is ensured.

[0124] The channel blockage ratio value is a quantitative evaluation value of the blockage degree of the channel caused by dust accumulation, which is calculated by integrating the ratio of the current pressure loss to the initial pressure loss of each monitoring section of the heat exchange efficiency channel, the ratio of the work processing time to the standard time, and the energy consumption weighted calculation. The value reflects the severity of the overall flow blockage of the channel. The larger the value, the more significant the blockage.

[0125] As an embodiment of the application, based on the work energy efficiency index, the channel blockage ratio value corresponding to the heat exchange efficiency channel is calculated, including:

[0126] The channel blockage ratio value corresponding to the heat exchange efficiency channel is calculated by the following formula:

[0127]

[0128] Wherein, θ represents the channel blockage ratio value corresponding to the heat exchange efficiency channel, m represents the number of monitoring sections divided in the heat exchange efficiency channel, j represents the number index of the monitoring section, ΔPS jΔPSj represents the current pressure loss value corresponding to the jth monitoring section j,0 CTj represents the initial pressure loss value corresponding to the jth monitoring section j ZTj represents the operation processing time corresponding to the jth monitoring section j Nhj represents the standard operation time corresponding to the jth monitoring section j ΔEj represents the operation energy consumption index of the jth monitoring section.

[0129] In detail, the current pressure loss value refers to the pressure attenuation amount (unit: Pa, etc.) of the airflow passing through the section under the current operating state, which is caused by dust accumulation, channel deformation, etc., and reflects the actual resistance of the section to the current airflow circulation. It is a direct parameter for judging the dynamic change of blockage. For example, after long-term operation, due to the thickening of accumulated dust, the ΔPS of a certain monitoring section j increases from 100 Pa to 200 Pa, indicating that the blockage is aggravated; the initial pressure loss value refers to the pressure loss reference value (unit: Pa) of the jth monitoring section of the heat exchange efficiency channel in the clean / initial state (no dust accumulation, perfect structure), which is used as a reference for comparing the current pressure loss and calculating the pressure loss change rate, and measures the influence degree of dust accumulation and other factors on the channel resistance. For example, when a new device is put into operation, the ΔPS of a certain section j,0 is 50 Pa, which is the basis data for subsequent evaluation of blockage; the operation processing time refers to the total time (unit: h, min, etc.) actually consumed when performing maintenance operations such as dust removal and detection on the jth monitoring section of the heat exchange efficiency channel, which is affected by the thickness of accumulated dust and the complexity of blockage. The more serious the blockage, the more difficult the operation, CT j is usually longer, such as fine dust removal for a heavily dusted section, CT j may reach 5 hours, reflecting the actual cost of operation execution; the standard operation time is based on the design parameters, historical experience or industry specifications of the jth monitoring section of the heat exchange efficiency channel, and is the theoretical time (unit: h, min, etc.) of completing the operation under the ideal state. It is used as a reference for evaluating operation efficiency. If the actual CT j is much larger than ZT j , it indicates that the blockage leads to unexpected operation difficulty, which is used to judge whether the operation time is reasonable and assist in optimizing the maintenance process.

[0130] S5, based on the channel blockage ratio, reconstructing the flue gas flow path corresponding to the target air preheater, identifying the vortex nodes in the flue gas flow path, and collecting node heat exchange data corresponding to the vortex nodes, based on the node heat exchange data, formulating a heat exchange optimization report corresponding to the target air preheater.

[0131] The application reconstructs the flue gas flow passage corresponding to the target air preheater based on the channel blockage ratio, can accurately identify the serious blockage area, optimizes the passage layout, improves the flue gas flow efficiency, dynamically adjusts the airflow distribution according to the blockage degree, reduces the system energy consumption, and guarantees the heat exchange performance of the air preheater.

[0132] The flue gas flow passage refers to the flow path of flue gas from the inlet to the outlet in the air preheater, including channel structures such as flue ducts and fin gaps, and needs to be dynamically reconstructed according to the key flow nodes and blockage states. For example, the original passage has low efficiency due to blockage of the key section, and a new passage of inlet→optimization section→clean flow area→outlet is reconstructed by dredging the key nodes and adjusting the fin layout, to guarantee smooth heat exchange of flue gas.

[0133] As an embodiment of the application, the reconstruction of the flue gas flow passage corresponding to the target air preheater based on the channel blockage ratio comprises: analyzing the blockage state data corresponding to the channel blockage ratio; analyzing the blockage influence characteristics corresponding to the blockage state data; screening the key blockage section corresponding to the flue duct in the target air preheater based on the blockage influence characteristics; determining the key flow node corresponding to the key blockage section; and reconstructing the flue gas flow passage corresponding to the target air preheater based on the key flow node.

[0134] The blockage state data refers to a quantitative description set of the blockage of each monitoring section of the flue gas passage of the air preheater according to the passage blockage ratio, covering the current pressure loss, the difference from the initial value and the change trend of each section, etc. For example, through calculation, the blockage ratios of three monitoring sections in a certain air preheater passage are 0.3, 0.5 and 0.7, respectively, and the corresponding pressure loss change, operation time difference and other data collectively constitute the state data reflecting the blockage degree of the passage. The blockage influence characteristic refers to the action law and characteristics of blockage on flue gas flow, air preheater heat exchange and overall operation analyzed based on the blockage state data, including the airflow resistance distribution caused by blockage and the attenuation mode of heat exchange efficiency, etc. For example, the blockage influence characteristic of a certain passage is that the blockage section reduces the flue gas flow rate by 15%, corresponding to an 8% decrease in heat exchange efficiency, and the resistance increases exponentially with the thickness of the accumulated dust, clearly showing the hazard mode of blockage. The key blockage section refers to the section in each monitoring section of the air preheater flue that plays a key restrictive role in flue gas flow and equipment performance according to the blockage influence characteristic. These sections have high blockage degree and wide influence range. For example, in the air preheater, the middle section of the flue is blocked due to dust accumulation, with a blockage ratio of 0.8, causing the resistance of the upstream and downstream sections to increase in a chain reaction. This middle section is the key blockage section.

[0135] Further, the analysis of the blockage state data corresponding to the passage blockage ratio can be realized by pressure drop analysis method, such as using differential pressure transmitter to measure the pressure difference of each passage inlet and outlet and calculating the blockage rate, thereby obtaining the blockage state data. The analysis of the blockage influence characteristic corresponding to the blockage state data can be realized by computational fluid dynamics simulation, such as using Fluent software to simulate the flow field distribution characteristics under different blockage rates, thereby obtaining the blockage influence characteristic. The screening of the key blockage section corresponding to the flue of the target air preheater can be realized by hot spot analysis method, such as using infrared thermal imager to identify the local section with abnormally high temperature, thereby obtaining the key blockage section. The determination of the key flow node corresponding to the key blockage section can be realized by complex network analysis method, such as applying Gephi software to calculate the betweenness centrality index of the flue network nodes, thereby obtaining the key flow node. The reconstruction of the flue gas flow passage corresponding to the target air preheater can be realized by topological optimization method, such as using Altair OptiStruct software to reconstruct the passage with the minimum flow resistance as the target, thereby obtaining the flue gas flow passage.

[0136] By identifying eddy nodes in the flue gas flow path and collecting node heat exchange data corresponding to the eddy nodes, the present invention can accurately locate the heat exchange inefficiency areas in the path and clarify the interference mechanism of eddy currents on heat transfer; through data-supported eddy current and heat exchange correlation analysis, a basis is provided for optimizing the path structure and weakening the negative impact of eddy currents.

[0137] Among them, the vortex node refers to the key position point where the flue gas forms a vortex (fluid rotation and reflux area) in the flow passage due to sudden changes in the channel structure (such as uneven fin spacing, abnormal curve curvature), obstruction interference, etc. These nodes destroy the continuity of the flue gas flow, resulting in heat accumulation or transfer stagnation. For example, the air preheater fin group is deformed due to dust accumulation, and the fin spacing in a certain local area is reduced from 5mm to 2mm. When the flue gas flows through here, it is affected by the narrow channel extrusion and boundary layer separation, forming a vortex area with a diameter of about 30mm. The geometric center point of this area is the vortex node; the node heat exchange data refers to the set of quantitative parameters of the heat transfer process collected for the vortex node, covering the flue gas temperature, fin temperature, heat flux density, heat transfer coefficient, etc. at the node. It is obtained through sensors or numerical simulations to reflect the influence of vortex on heat exchange. For example, at the above-mentioned vortex node, the measured flue gas temperature is 800℃, the fin temperature is 450℃, and the heat flux density is 1200W / m 2 , compared with the non-eddy current area (heat flux density 1800W / m 2 ), it can quantify that the vortex causes a 33% decrease in heat transfer efficiency. These data are used to analyze the degree of interference of the vortex on the performance of the air preheater. Optionally, the identification of the vortex nodes in the flue gas flow path can be achieved by the vortex contour analysis method, such as: using Tecplot software to extract the center point of the area where the vortex is greater than the threshold in the CFD simulation results, thereby obtaining the vortex node; the collection of the node heat transfer data corresponding to the vortex node can be achieved by the local heat flux measurement method, such as: using a micro heat flux sensor array to directly measure the surface heat flux distribution of the vortex area, thereby obtaining the node heat transfer data.

[0138] Furthermore, based on the node heat exchange data, the present invention formulates a heat exchange optimization report corresponding to the target air preheater, which can accurately locate the heat exchange efficiency attenuation area caused by the eddy current node, quantify the specific parameters of heat transfer anomalies under different working conditions, and provide an accurate technical basis for adjusting the fin structure and unblocking blocked sections, thereby ensuring the stable and efficient operation of the air preheater under complex working conditions.

[0139] The heat exchange optimization report refers to a professional document for comprehensively analyzing the heat exchange efficiency of the target air preheater and forming an improvement strategy based on the node heat exchange data, channel blockage state and equipment operation parameters, which covers modules such as heat exchange efficiency baseline evaluation, vortex node heat transfer anomaly tracing, structure / operation parameter optimization suggestion and expected benefit calculation. For example, if the 300 MW unit air preheater report shows that the heat flux density of the middle vortex node has decreased by 28% (measured 1450 W / m 2 vs. design 2000 W / m 2 ) compared with the design value, combined with the ash deposition thickness detection and fluid simulation, a local fin inclination correction + pulse cleaning frequency adjustment scheme is proposed, and the heat flux density after the transformation is restored to 1900 W / m 2 . The energy consumption comparison data and construction technical specification after the transformation provide full-chain technical support for equipment upgrading. Alternatively, the heat exchange optimization report corresponding to the target air preheater can be realized by a multi-objective optimization algorithm, such as using the NSGA-II genetic algorithm to simultaneously optimize the heat transfer coefficient and pressure drop loss, thereby obtaining the heat exchange optimization report.

[0140] Compared with the problems in the background art, the present application can accurately locate the point of the linear distribution by obtaining the fin arrangement structure corresponding to the target air preheater, laying a foundation for analyzing the heat exchange data of the point and generating efficient heat exchange channels, thereby accurately monitoring the ash deposition and thermal resistance distribution, effectively improving the heat exchange performance and maintenance efficiency of the air preheater. By monitoring the ash deposition queue in the heat exchange efficiency channel, the present application can real-time master the distribution and development trend of the ash in the efficient heat exchange area, accurately locate the position of the thermal resistance increment caused by the ash, provide data support for quantifying the heat exchange efficiency decay degree, and predict the maintenance demand in advance according to the ash deposition law, realize the transformation from passive maintenance to active prevention, and guarantee the stability and reliability of the heat exchange performance of the air preheater. Further, by identifying the thermal resistance extreme point in the heat flow fluctuation curve, the present application can accurately locate the key position and time node of the abnormal increase of the thermal resistance in the air preheater, and clearly define the critical point of the sudden drop of the heat transfer efficiency caused by the ash deposition, thereby providing a precise target for formulating targeted ash removal strategies and maintenance schemes. Further, by analyzing the key operation queue in the maintenance optimization process, the present application can accurately focus on the link that plays a core role in restoring the performance of the air preheater, clearly define the operation logic and resource investment focus, and improve the overall maintenance efficiency by optimizing the operation sequence and coordination mechanism, thereby guaranteeing the stable and efficient operation of the air preheater. Finally, based on the channel blockage ratio, the present application reconstructs the flue gas flow path corresponding to the target air preheater, accurately identifies the seriously blocked area, optimizes the path layout, improves the flue gas flow efficiency, dynamically adjusts the air flow distribution according to the blockage degree, reduces the system energy consumption, and guarantees the heat exchange performance of the air preheater. Therefore, the air preheater maintenance and heat exchange optimization method and system based on the fin linear arrangement provided by the present application can improve the heat exchange performance of the air preheater.

[0141] Embodiment 2:

[0142] As shown in Figure 3 FIG. 1 is a functional module diagram of a heat exchange optimization system for maintenance of an air preheater based on fin arrangement in the application.

[0143] The air preheater maintenance heat exchange optimization system based on fin arrangement in sequence 200 can be installed in an electronic device. According to the functions implemented, the air preheater maintenance heat exchange optimization system based on fin arrangement in sequence can include a channel generation module 201, a curve generation module 202, a flow reconstruction module 203, a ratio calculation module 204, and a report development module 205. The modules in the application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0144] In the embodiments of the application, the functions of each module / unit are as follows:

[0145] The channel generation module 201 is configured to obtain the fin arrangement structure corresponding to the target air preheater, identify the in-sequence distribution points in the fin arrangement structure, analyze the point heat exchange data corresponding to the in-sequence distribution points, and generate heat exchange efficiency channels corresponding to the in-sequence distribution points based on the point heat exchange data.

[0146] The curve generation module 202 is configured to monitor the ash deposition queue in the heat exchange efficiency channel, analyze the thermal resistance distribution demand corresponding to the ash deposition queue, query the heat exchange attenuation index corresponding to the thermal resistance distribution demand, and generate a heat flow fluctuation curve corresponding to the heat exchange attenuation index.

[0147] The flow reconstruction module 203 is configured to identify the thermal resistance extreme point in the heat flow fluctuation curve, calculate the airflow scouring value corresponding to the heat exchange efficiency channel based on the thermal resistance extreme point, and reconstruct the maintenance optimization process corresponding to the target air preheater in combination with the airflow scouring value and the fin dust accumulation degree in the target air preheater.

[0148] The ratio calculation module 204 is configured to analyze the key job queue in the maintenance optimization process, query the job energy efficiency index corresponding to the key job queue, and calculate the channel blockage ratio corresponding to the heat exchange efficiency channel based on the job energy efficiency index.

[0149] The report development module 205 is configured to reconstruct the flue gas flow path corresponding to the target air preheater based on the channel blockage ratio, identify the vortex node in the flue gas flow path, collect the node heat exchange data corresponding to the vortex node, and develop a heat exchange optimization report corresponding to the target air preheater based on the node heat exchange data.

[0150] In detail, the modules in the heat exchanger maintenance and optimization system 200 based on the arrangement of fin in sequence in the embodiments of the present application adopt the same technical means as the heat exchanger maintenance and optimization method based on the arrangement of fin in sequence in the above-mentioned Figure 1 Dust thickness g (mm) Measured critical wind speed (m / s) Calculated value (m / s) Error rate Figure 2 Figure 2 Figure 3 Figure 1 Dust thickness g and can produce the same technical effects, which will not be described here again.

[0151] It is obvious for those skilled in the art that the present application is not limited to the details of the above-mentioned exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application 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 application can be modified or replaced equivalently without departing from the spirit and scope of the present application.

Claims

1. A heat exchange optimization method for air preheater maintenance based on fin arrangement in series, characterized in that: The method comprises: Obtaining a fin arrangement structure corresponding to a target air preheater, identifying in-line distribution points in the fin arrangement structure, analyzing point-by-point heat exchange data corresponding to the in-line distribution points, and generating a heat exchange efficiency channel corresponding to the in-line distribution nodes based on the point-by-point heat exchange data; monitoring the dust deposition queue in the heat exchange efficiency channel, analyzing the thermal resistance distribution requirements corresponding to the dust deposition queue, querying the heat exchange attenuation index corresponding to the thermal resistance distribution requirements, and generating a heat flow fluctuation curve corresponding to the heat exchange attenuation index; Identifying a thermal resistance extreme value inflection point in the heat flux fluctuation curve, calculating an airflow scouring value corresponding to the heat exchange efficiency channel based on the thermal resistance extreme value inflection point, and reconstructing a maintenance optimization process corresponding to the target air preheater by combining the airflow scouring value with the degree of dust accumulation on the fins of the target air preheater; Analyze the key operation queues in the maintenance optimization process, query the operation energy efficiency indicators corresponding to the key operation queues, and calculate the channel blockage ratio corresponding to the heat exchange efficiency channel based on the operation energy efficiency indicators; Based on the channel blockage ratio, the flue gas flow path corresponding to the target air preheater is reconstructed, the vortex nodes in the flue gas flow path are identified, and the node heat exchange data corresponding to the vortex nodes are collected. Based on the node heat exchange data, a heat exchange optimization report corresponding to the target air preheater is formulated.

2. The air preheater maintenance and heat exchange optimization method based on fin arrangement according to claim 1 is characterized in that: The step of generating heat exchange efficiency channels corresponding to the row-distributed nodes based on the point heat exchange data includes: Analyzing the temperature gradient distribution in the point heat exchange data; identifying heat flow abnormality regions in the in-line distribution nodes according to the temperature gradient distribution; Mapping the heat transfer path corresponding to the abnormal heat flow area; Extracting key heat exchange nodes in the heat transfer path; Based on the key heat exchange nodes, heat exchange efficiency channels of the in-line distribution nodes are generated.

3. The air preheater maintenance and heat exchange optimization method based on fin arrangement in series according to claim 1, characterized in that: The monitoring of the dust deposition queue in the heat exchange efficiency channel includes: querying the distribution of dust deposits in the heat exchange efficiency channel; Extracting deposition distribution characteristics corresponding to the dust deposition distribution; Based on the deposition distribution characteristics, determining a deposition accumulation area in the heat exchange efficiency channel; Dividing the core area units corresponding to the sediment accumulation area; The dust deposition queue in the core area unit is monitored.

4. The air preheater maintenance and heat exchange optimization method based on fin arrangement in series according to claim 1, characterized in that: Generating a heat flow fluctuation curve corresponding to the heat exchange attenuation index includes: Analyzing heat flow index data corresponding to the heat exchange attenuation index; extracting attenuation fluctuation characteristics from the heat flow index data; Based on the attenuation fluctuation characteristics, constructing a heat flow time-varying sequence corresponding to the heat flow index data; Setting a fluctuation threshold corresponding to the heat flow time-varying sequence; Based on the fluctuation threshold, a heat flow fluctuation curve corresponding to the heat exchange attenuation index is generated.

5. The air preheater maintenance and heat exchange optimization method based on fin arrangement in series according to claim 1, characterized in that: The calculating of the airflow scour value corresponding to the heat exchange efficiency channel based on the thermal resistance extreme value inflection point includes: The airflow scour value corresponding to the heat exchange efficiency channel is calculated using the following formula: Among them, v crit represents the airflow scour value corresponding to the heat exchange efficiency channel, ρ represents the fluid density flowing through the air preheater, n represents the total number of the thermal resistance extreme value inflection points, i represents the number index corresponding to the thermal resistance extreme value inflection point, μ i represents the friction coefficient between the dust accumulation and the fin surface corresponding to the i-th thermal resistance extreme value curve point, δ i represents the dust thickness corresponding to the i-th thermal resistance extreme value curve point, g represents the dust sedimentation value, t max It represents the maximum time value from the initial operation of the air preheater to the current moment, R(t) represents the thermal resistance value corresponding to time t, A i represents the effective area of ​​the fin corresponding to the i-th thermal resistance extreme value curve point, k represents the dust adhesion strength, and C represents the thermal resistance change rate corresponding to the i-th thermal resistance extreme value curve point.

6. The air preheater maintenance and heat exchange optimization method based on fin arrangement in series according to claim 1, characterized in that: Combining the airflow scouring value with the dust accumulation degree of the fins in the target air preheater to reconstruct a maintenance optimization process corresponding to the target air preheater includes: Determining dust accumulation impact data for each area of ​​the target air preheater based on the airflow scouring value and the degree of dust accumulation on the fins; Classifying the dust accumulation levels corresponding to the dust accumulation impact data; Query the maintenance priority corresponding to the dust accumulation level classification; Sorting each area of ​​the target air preheater by partition according to the maintenance priority parameter to obtain a partition maintenance sequence; Based on the partition maintenance sequence, the maintenance optimization process corresponding to the target air preheater is reconstructed.

7. The air preheater maintenance and heat exchange optimization method based on fin serial arrangement according to claim 1 is characterized in that: The analysis of the key operation queues in the maintenance optimization process includes: Analyze the operation timeline corresponding to the maintenance optimization process; Based on the operation timeline, determining a key operation interval in the maintenance optimization process; Analyzing high-priority tasks in the critical operation interval; Identifying task operation characteristics corresponding to the high-priority task; Based on the task operation characteristics, the key operation queue in the maintenance optimization process is analyzed.

8. The air preheater maintenance and heat exchange optimization method based on fin serial arrangement according to claim 1 is characterized in that: The calculating, based on the operation energy efficiency index, the channel blockage ratio corresponding to the heat exchange efficiency channel includes: The channel blockage ratio corresponding to the heat exchange efficiency channel is calculated using the following formula: Wherein, θ represents the channel blockage ratio corresponding to the heat exchange efficiency channel, m represents the number of monitoring segments divided in the heat exchange efficiency channel, j represents the number index of the monitoring segments, ΔPS j Indicates the current pressure loss value corresponding to the jth monitoring section, ΔPS j,0 Indicates the initial pressure loss value corresponding to the jth monitoring section, CT j represents the job processing time corresponding to the jth monitoring segment, ZT j Nh represents the standard operating time corresponding to the jth monitoring section. j Represents the energy consumption index of the operation in the jth monitoring segment.

9. The air preheater maintenance and heat exchange optimization method based on fin serial arrangement according to claim 1, characterized in that: The reconstructing the flue gas flow path corresponding to the target air preheater based on the channel blockage ratio includes: parsing the blocking status data corresponding to the channel blocking ratio; analyzing a blocking impact characteristic corresponding to the blocking state data; Based on the blockage impact characteristics, screening the key blockage section corresponding to the flue in the target air preheater; Determining a key flow node corresponding to the key blocked section; Based on the key flow nodes, the flue gas flow path corresponding to the target air preheater is reconstructed.

10. An air preheater maintenance and heat exchange optimization system based on fin arrangement in series, characterized in that: The system comprises: a channel generation module, configured to obtain a fin arrangement structure corresponding to a target air preheater, identify in-line distribution points in the fin arrangement structure, analyze point-by-point heat exchange data corresponding to the in-line distribution points, and generate a heat exchange efficiency channel corresponding to the in-line distribution nodes based on the point-by-point heat exchange data; a curve generating module for monitoring the dust deposition queue in the heat exchange efficiency channel, analyzing the thermal resistance distribution requirements corresponding to the dust deposition queue, querying the heat exchange attenuation index corresponding to the thermal resistance distribution requirements, and generating a heat flow fluctuation curve corresponding to the heat exchange attenuation index; a process reconstruction module for identifying a thermal resistance extreme value inflection point in the heat flux fluctuation curve, calculating an airflow scouring value corresponding to the heat exchange efficiency channel based on the thermal resistance extreme value inflection point, and reconstructing a maintenance optimization process corresponding to the target air preheater by combining the airflow scouring value with the degree of dust accumulation on the fins of the target air preheater; a ratio calculation module, configured to analyze key operation queues in the maintenance optimization process, query operation energy efficiency indicators corresponding to the key operation queues, and calculate channel blockage ratios corresponding to the heat exchange efficiency channels based on the operation energy efficiency indicators; A report preparation module is used to reconstruct the flue gas flow path corresponding to the target air preheater based on the channel blockage ratio, identify the vortex nodes in the flue gas flow path, and collect the node heat exchange data corresponding to the vortex nodes, and prepare a heat exchange optimization report corresponding to the target air preheater based on the node heat exchange data.

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