A model-based stack soot analysis method and apparatus

Abstract

The present application relates to the technical field of flue cleaning, and provides a chimney soot analysis method and device based on a model.The method comprises: establishing a gas continuous phase model of a chimney, monitoring the gas flow rate at a preset position of the chimney, and analyzing the gas flow rate at each position in the chimney according to the gas flow rate at the preset position and the gas continuous phase model; establishing a soot deposition model of the chimney wall surface, substituting the gas flow rate at each position in the chimney into the soot deposition model, calculating the soot deposition model, obtaining the soot growth law at each position in the chimney, and determining the chimney cleaning period according to the soot growth law.The present application can analyze the soot growth from multiple aspects such as the gas flow rate and soot deposition law by establishing the gas continuous phase model and the soot deposition model, thereby providing a basis for determining the chimney cleaning period and enabling management personnel to determine a suitable cleaning period.

Description

Technical Field

[0001] The present invention relates to the technical field of flue cleaning, and in particular to a chimney smoke analysis method and device based on a model. Background Art

[0002] In the power generation system of a thermal power plant, coal combustion plays a crucial role as the core energy conversion link. Coal undergoes a complex combustion reaction within the furnace, starting with the rapid evaporation of water, followed by the precipitation and intense combustion of volatile components at high temperatures, and finally, the continuous combustion of fixed carbon, releasing a large amount of heat energy. However, due to various factors such as coal quality and combustion conditions, various types of soot impurities are easily generated during this process. These soot impurities include incompletely burned carbon particles, metal oxides, and mineral particles.

[0003] When the hot air flow generated by high-temperature combustion carries these smoke impurities and is discharged through the chimney, some of the smoke impurities are easily attached to the inner wall of the chimney due to gravity and the adsorption effect of the inner wall of the chimney, thus forming soot.

[0004] As soot accumulates on the chimney's inner walls, the chimney's effective ventilation cross-sectional area decreases, hindering gas exhaust and causing increased pressure inside the chimney. This affects the complete combustion of coal, reduces energy efficiency, and even affects the operational stability of the entire power generation system. Due to incomplete combustion and poor smoke exhaust, power generation equipment may experience frequent load fluctuations. Long-term operation also accelerates equipment wear, increases maintenance costs, and ultimately significantly reduces the efficiency of the entire power generation system. Therefore, regular cleaning of the chimney's inner walls is necessary.

[0005] In the prior art, due to the lack of an effective method for analyzing the growth patterns of soot, it is difficult to determine the exact period for chimney cleaning. Therefore, chimney cleaning mainly relies on the experience and judgment of managers, which places higher demands on the management personnel's ability. In addition, in order to ensure the safety of the operation, it is often necessary to stop the combustion when cleaning chimney soot. If the chimney soot is cleaned too frequently, it is easy to affect the progress of power generation. If the interval between chimney soot cleaning is too long, it is easy for the flue to be blocked due to excessive soot.

[0006] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in this technical field. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a chimney smoke analysis method and device based on a model.

[0008] The present invention adopts the following technical solutions:

[0009] In a first aspect, a model-based chimney smoke analysis method includes:

[0010] Establishing a gas continuous phase model of the chimney, monitoring the gas flow rate at a preset position of the chimney, and analyzing and obtaining the gas flow rate at each position in the chimney based on the gas flow rate at the preset position and the gas continuous phase model;

[0011] A soot deposition model for the chimney wall is established. The gas flow rate at each location in the chimney is substituted into the soot deposition model, and the soot deposition model is calculated to obtain the soot growth pattern at each location in the chimney. The chimney cleaning cycle is determined based on the soot growth pattern.

[0012] Preferably, the gas continuous phase model includes a first continuity equation and a first momentum conservation equation;

[0013] The first continuity equation is ;

[0014] The first momentum conservation equation is ;

[0015] in, is the turbulent pulsation kinetic energy, is the velocity component of the gas in the i direction, is the spatial coordinate in the i-th direction, is the velocity component of the gas in the j direction, is the spatial coordinate in the jth direction, is the pressure of the gas, is the density of the gas, is the dynamic viscosity coefficient of the gas.

[0016] Preferably, the turbulent pulsation kinetic energy and dissipation rate having a first relation and a second relation;

[0017] The first relationship is ;

[0018] The second relationship is ;

[0019] G is the rate of generation of turbulent kinetic energy, , For time, 、 、 and All are preset coefficients.

[0020] Preferably, the soot deposition model includes a second continuity equation and a second momentum conservation equation;

[0021] The second continuity equation is ;

[0022] The second momentum conservation equation is ;in, is the soot density, is the surface gradient operator, is the airflow velocity on the outer surface of the soot, is the mass source of soot deposited on the unit wall surface, is the thickness of the soot in the dynamic level, t is the time, is the heat flow per unit volume, is the acceleration due to gravity, is the preset calculation coefficient, For pressure, is the kinematic viscosity of the soot.

[0023] Preferably, a flue gas analyzer is used to monitor the composition of smoke in real time, and a corresponding sliding window period is selected as a standard sliding window within a historical period;

[0024] When the difference between the smoke composition monitored in real time and the smoke composition in the standard sliding window is less than a preset difference, measuring the gas flow rate at the preset position of the chimney and calculating the soot deposition model;

[0025] Otherwise, the measurement of the gas flow rate at the preset position of the chimney and the calculation of the soot deposition model are not performed.

[0026] Preferably, the selecting of a corresponding sliding window period as a standard sliding window within a historical period specifically includes:

[0027] Use Preset Length Perform sliding window sampling and combine the concentration of the smoke component obtained by the i-th measurement in the sampled sliding window to obtain the i-th component vector of the sliding window ;

[0028] Calculate the rate of change of smoke composition of the sliding window ;in, ; is the concentration of the jth smoke component obtained by the i-th measurement within the sliding window; The number of categories of smoke components measured;

[0029] When the smoke component change rate of the corresponding sliding window is less than the preset change rate, the sliding window is regarded as a standard sliding window.

[0030] Preferably, the difference between the smoke components monitored in real time and the smoke components in the standard sliding window is ;

[0031] in, To monitor the concentration of the jth smoke component obtained by the i-th measurement within the sliding window in real time.

[0032] Preferably, the method further comprises:

[0033] Under different gas flow rates, using a camera to capture images of the interior of the chimney, and performing image recognition on the images of the interior of the chimney to determine the soot scaling conditions under different inlet gas flow rates;

[0034] The chimney cleaning cycle is determined based on the growth pattern of soot and the soot flaking situation at the current inlet gas flow rate.

[0035] In a second aspect, the present invention further provides a model-based chimney smoke analysis device for implementing the model-based chimney smoke analysis method described in the first aspect, the device comprising:

[0036] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to perform the model-based chimney smoke analysis method described in the first aspect.

[0037] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0038] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in a memory, and executing any method of the first aspect.

[0039] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer or a processor, causes the computer or the processor to execute any of the methods of the first aspect.

[0040] The present invention establishes a gas continuous phase model and a soot deposition model, thereby being able to analyze the soot growth from multiple aspects such as gas flow rate and soot deposition law, thereby providing a basis for determining the chimney cleaning cycle and enabling managers to determine an appropriate cleaning cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0042] Figure 1 is a schematic diagram of a chimney smoke analysis system provided by an embodiment of the present invention;

[0043] Figure 2 1 is a flow chart of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0045] Figure 4 1 is a flow chart of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0046] Figure 5 is a schematic diagram of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0047] Figure 6 is a schematic diagram of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0048] Figure 7 is a schematic diagram of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0049] Figure 8 is a schematic diagram of a chimney smoke analysis method based on a model provided by an embodiment of the present invention;

[0050] Figure 9 Schematic diagram of the architecture of a model-based chimney smoke analysis device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as meaning open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example" or "some examples" and the like are intended to indicate that the specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0053] In the description of the present invention, the terms "first" and "second" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "multiple" means two or more. In addition, for example, the description may also use the method of adding "A" and "B" at the end to describe the same type of nouns as two independent individuals. In this case, the corresponding features defined as "A" and "B" are only used to distinguish the description purposes of the same type of individuals, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated.

[0054] In the description of the present invention, the expression "A and / or B" (where A and B are used to formally represent specific characteristic contents) is involved, and the corresponding expressions include the following three combinations: only A, only B, and a combination of A and B.

[0055] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and errors associated with measurement of the particular quantity (i.e., limitations of the measurement system).

[0056] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0057] Embodiment 1:

[0058] Due to the lack of an effective method for analyzing soot growth patterns in the prior art, it is difficult to accurately determine the chimney cleaning cycle. Consequently, chimney cleaning relies primarily on the experience and judgment of managers, which places higher demands on their skills. Furthermore, to ensure operational safety, combustion often needs to be stopped during chimney soot cleaning. Too frequent chimney soot cleaning can easily affect power generation progress, while excessive soot cleaning intervals can easily lead to flue blockage. To address this issue, this embodiment provides a model-based chimney soot analysis method that can determine the growth patterns of soot in chimneys, thereby providing a basis for determining the chimney cleaning cycle.

[0059] For ease of understanding, before describing in detail the chimney smoke analysis method based on the model provided in this embodiment, a chimney smoke analysis system provided in this embodiment is also described. The chimney smoke analysis system provided in this embodiment is as follows: Figure 1 As shown, the system includes a gas monitoring module, a flue gas analyzer, a particle camera (i.e., a laser particle size analyzer), a data acquisition device, and a processing and analysis module (i.e., a host PC). The gas detection module includes two pitot tubes and a digital pressure gauge. The two pitot tubes are inserted into the chimney to collect gas inside the chimney and transmit the gas to the digital pressure gauge. The digital pressure gauge measures the pressure difference between the gases collected by the two pitot tubes and transmits the pressure difference to the data acquisition device, which in turn transmits the pressure difference to the processing and analysis module. The flue gas analyzer analyzes the composition of the smoke dust, which is then transmitted to the processing and analysis module via the data acquisition device. The particle camera captures smoke dust images and analyzes the smoke dust images to determine the diameter of the smoke dust. The processing and analysis module calculates the gas flow rate based on the pressure difference and applies the model-based chimney smoke dust analysis method described in this embodiment. That is, the model-based chimney smoke dust analysis method provided in this embodiment is applied to the chimney smoke dust analysis system, specifically, to the processing and analysis module.

[0060] Example 1 of the present invention provides a chimney smoke analysis method based on a model, such as Figure 2 and Figure 3 Shown, including:

[0061] In step 201, a gas continuous phase model of the chimney is established, and the gas flow rate at a preset location in the chimney is monitored. Based on the gas flow rate at the preset location and the gas continuous phase model, the gas flow rate at each location in the chimney is analyzed and obtained. The gas continuous phase model can be understood as a model used to represent the variation pattern of gas flow rate at each location in the chimney. The gas flow rate at the preset location is substituted into the gas continuous phase model to analyze and obtain the gas flow rate at other locations in the chimney. In a specific application scenario, substituting the gas flow rate at the preset location into the gas continuous phase model actually means substituting the gas flow rate at the preset location into the gas continuous phase model to calculate the pressure to which the gas is subjected, thereby determining the gas flow rate at each location based on the pressure to which the gas is subjected and the gas continuous phase model. The gas flow rate at the preset location in the chimney can be measured and monitored using a particle image velocimetry (PIV) system.

[0062] In step 202, a soot deposition model for the chimney wall is established. The gas flow rate at each location within the chimney is substituted into the soot deposition model, and the soot deposition model is calculated to obtain the soot growth pattern at each location within the chimney. Based on the soot growth pattern, the chimney cleaning cycle is determined. The soot deposition model can be understood as a model that represents the relationship between soot growth and gas flow rate on the soot surface. The soot growth pattern can be understood as the deposition of soot on the chimney wall.

[0063] This embodiment establishes a gas continuous phase model and a soot deposition model, thereby being able to analyze the soot growth from multiple aspects such as gas flow rate and soot deposition patterns, thereby providing a basis for determining the chimney cleaning cycle and enabling management personnel to determine an appropriate cleaning cycle.

[0064] The gas continuous phase model includes a first continuity equation and a first momentum conservation equation; the first continuity equation is ; The first momentum conservation equation is ; The flow of the gas is turbulent.

[0065] in, is the turbulent pulsation kinetic energy, is the velocity component of the gas in the i direction, For time, is the spatial coordinate in the i-th direction, is the velocity component of the gas in the jth direction, is the spatial coordinate in the jth direction, i=1, 2, 3 correspond to the x, y, z directions respectively, and j=1, 2, 3 correspond to the x, y, z directions respectively.

[0066] is the pressure of the gas, is the density of the gas, is the dynamic viscosity coefficient of the gas.

[0067] The turbulent pulsation kinetic energy and dissipation rate Has a first relationship and a second relationship; the first relationship is The second relationship is ; G is the generation rate of turbulent kinetic energy, , 、 、 and are all preset coefficients, which are obtained by those skilled in the art based on demand analysis. In an optional embodiment, , , , .

[0068] In a practical application scenario, the soot deposition model includes a second continuity equation and a second momentum conservation equation; the second continuity equation is ; The second momentum conservation equation is ;in, is the soot density, is the surface gradient operator, is the airflow velocity on the outer surface of the soot, is the mass source of soot deposited on the unit wall surface, is the thickness of the soot in the dynamic level, t is the time, is the heat flow per unit volume, is the acceleration due to gravity, is the preset calculation coefficient, For pressure, is the kinematic viscosity of the soot. The soot deposition model is determined by establishing a three-dimensional rectangular coordinate system in the chimney space, that is, the positive directions of the x, y, and z coordinates are the direction of airflow, perpendicular to the wall, and downward along the wall, respectively. Figure 5 As shown in Figure 1, there is a layer of soot on the wall. At this time, the thickness of the soot on the chimney wall is in a dynamic flat state. In addition, this embodiment assumes that the soot is very thin relative to the entire flow channel, which is applicable to boundary layer theory, and there is no heat exchange.

[0069] For the second momentum conservation equation, is the effect of pressure and gravity perpendicular to the wall, is the effect of gravity in the direction parallel to the soot, The influence of gas and soot wall shear force, To collect or separate soot volume force.

[0070] Substituting the gas flow rate at each position in the chimney into the soot deposition model can be understood as substituting the gas flow rate at the corresponding position calculated by the gas continuous phase model into the soot deposition model. Substitute the airflow velocity on the outer surface of the soot at the corresponding position in the soot deposition model , thereby calculating the dynamic flat thickness of the corresponding position , the dynamic flat thickness Integrate to get the soot thickness at a certain position after a period of time , is the area of ​​the corresponding position.

[0071] This implementation is designed for application scenarios where the number of particles in the smoke is small or the gas flow rate is much greater than the velocity of the particles themselves (i.e., the particles have little impact on the overall flow rate of the smoke). In actual use, there may also be situations where the number of particles in the smoke is large or the gas flow rate is not much different from the particle flow rate (i.e., the particles have a greater impact on the overall flow rate of the smoke). To address this scenario, this embodiment also provides a preferred implementation method, namely, substituting the gas flow rate at each position in the chimney into the soot deposition model and calculating the soot deposition model, specifically including:

[0072] A chimney smoke discrete phase model is established, and the smoke discrete phase model, the gas continuous phase model and the soot deposition model are coupled and calculated to obtain the soot growth law; the smoke discrete phase model can be understood as a model for representing the movement trajectory of smoke.

[0073] The smoke discrete phase model is specifically: That is, the smoke in the chimney is regarded as a discrete phase to establish a model. The discrete phase model of smoke is a differential equation of the velocity of the smoke in the spatial direction.

[0074] in, is the drag force per unit mass of smoke, ; is the gas flow rate, is the speed of the smoke, For time, is the density of the gas, is the density of smoke, is the molecular viscosity of the airflow, is the diameter of the smoke, is the relative Reynolds number, , is the drag coefficient, , The external force on the smoke and dust, is the component of gravitational acceleration in the x direction, where the x direction is along the slope of the chimney inner wall, and the y direction is perpendicular to the slope of the chimney inner wall.

[0075] The diameter of the smoke is It is measured using a laser particle size analyzer, such as Figure 4 As shown, specifically including:

[0076] In step 301, a laser particle size analyzer is used to measure the particle size of the smoke at the chimney section, and the average particle size of the smoke is calculated using the smoke volume as a weighting factor. .

[0077] In step 302, the average particle size of the smoke is used as the diameter of the smoke; wherein, is the particle size of the i-th smoke, The coefficient required for smoke volume calculation is: In actual use, the smoke is calculated as a regular sphere. .

[0078] Smoke density The composition of smoke is measured by a flue gas analyzer and then calculated using the composition of smoke.

[0079] The coupled calculation of the soot discrete phase model, the gas continuous phase model, and the soot deposition model can be understood as first coupling the gas continuous phase model and the soot discrete phase model until a stable gas flow rate is achieved, and then introducing the gas flow rate into the soot deposition model for calculation. This coupling manifests itself as the mutual influence between the soot velocity and the gas flow rate, specifically by feeding the momentum, energy, and mass source terms of the soot particles back into the gas continuous phase model, performing multiple iterative calculations until the residual converges and a stable gas flow rate is achieved. In practice, this coupled calculation is performed using computer tools, such as Fluent software.

[0080] In actual use, in order to ensure the stable power generation of the power generation system, it is necessary to add coal regularly. When the coal is first added, the combustion process may not be stable enough, and the chimney deposition calculated during this process is usually not a reference (this is because the combustion conditions may deviate greatly from the established models, mainly because the preset coefficients in the models may not be applicable, mainly: the preset calculation coefficients in the soot deposition model Not applicable), therefore, this embodiment also provides a preferred implementation, specifically including:

[0081] A flue gas analyzer is used to monitor the composition of smoke in real time, and a corresponding sliding window period is selected as a standard sliding window within a historical period. When the difference between the composition of the smoke monitored in real time and the smoke composition in the standard sliding window is less than a preset difference, the gas flow rate at the preset position of the chimney is measured and the soot deposition model is calculated; otherwise, the gas flow rate at the preset position of the chimney is not measured and the soot deposition model is not calculated.

[0082] The step of selecting a corresponding sliding window period as a standard sliding window within a historical period specifically includes:

[0083] Use Preset Length Perform sliding window sampling and combine the concentration of the smoke component obtained by the i-th measurement in the sampled sliding window to obtain the i-th component vector of the sliding window ; Calculate the change rate of smoke composition of sliding window ;in, ; is the concentration of the jth smoke component obtained by the i-th measurement within the sliding window; is the number of categories of smoke components measured, is the preset weight corresponding to the jth smoke component; when the smoke component change rate of the corresponding sliding window is less than the preset change rate, the sliding window is used as a standard sliding window. Refers to a sliding window times measurement.

[0084] The difference between the smoke components monitored in real time and the smoke components in the standard sliding window is ;in, To monitor the concentration of the jth smoke component obtained by the i-th measurement in the sliding window in real time, the length of the real-time monitoring sliding window is also the preset length .

[0085] It can be understood as follows: before analyzing the growth pattern of soot in the chimney, the gas flow rate and smoke composition are monitored for a period of time. This period is the historical period. Then, according to the above-mentioned standard sliding window selection method, one or more standard sliding windows are determined. According to the gas flow rate monitored within the standard sliding window, the preset calculation coefficients in the soot deposition model are determined. In the subsequent real-time monitoring process, the difference between the smoke components in the real-time monitoring sliding window and the smoke components in the standard sliding window is calculated.

[0086] The preset rate of change and the preset degree of difference are both determined by those skilled in the art based on empirical analysis, and the preset rate of change is typically less than the preset degree of difference. When the rate of change of soot composition within a corresponding sliding window is greater than or equal to the preset rate of change, it can be considered that the combustion conditions within that sliding window are significantly variable and unstable. When the rate of change of soot composition within a corresponding sliding window is less than the preset rate of change, combustion within that sliding window is considered stable, and the stable combustion sliding window is used as the standard sliding window for subsequent real-time monitoring. Similarly, when the degree of difference between the corresponding real-time monitoring sliding window and the standard sliding window is greater than or equal to the preset degree of difference, combustion within that real-time monitoring sliding window is considered unstable, and soot deposition analysis is not performed.

[0087] Moreover, in actual use, when the difference between the soot composition in a real-time monitoring sliding window and the soot composition in the corresponding standard sliding window is less than a preset difference, each measurement data obtained in the real-time monitoring sliding window is aligned with the standard sliding window to calculate the soot deposition model, and the data is directly jumped to the end position of the real-time monitoring sliding window to perform sampling of the next real-time monitoring sliding window; otherwise, the real-time monitoring sliding window is sampled according to the preset step size. That is, if the nth real-time monitoring sliding window The difference between the smoke composition of the corresponding standard sliding window is greater than or equal to the preset difference, then the n+1th real-time monitoring sliding window is ; If the nth real-time monitoring sliding window The difference between the smoke composition of the corresponding standard sliding window is less than the preset difference, then the n+1th real-time monitoring sliding window is ,in, Representative Measurement~ times measurement.

[0088] For example, assuming the preset step size is , since entering the real-time monitoring, the first real-time monitoring sliding window is The first measurement~ If the difference between the smoke composition of the first real-time monitoring sliding window and the smoke composition of the corresponding standard sliding window is less than the preset difference, the second real-time monitoring sliding window is If the difference between the smoke composition of the second real-time monitoring sliding window and the smoke composition in the corresponding standard sliding window is greater than or equal to the preset difference, the third real-time monitoring sliding window is .

[0089] When multiple standard sliding windows are selected within the historical period, the corresponding preset calculation coefficients can be obtained by analyzing each standard sliding window separately. In the real-time monitoring process, the difference between the real-time monitoring sliding window and each standard sliding window is calculated, and the standard sliding window with the smallest difference is selected for comparison, that is, the preset calculation coefficient of the standard sliding window with the smallest difference is used. Perform calculations for the soot deposition model.

[0090] Furthermore, considering that soot accumulation may also occur during unstable combustion, this embodiment also provides a preferred implementation method, that is, using a preset compensation value for compensation during the unstable combustion stage, that is, after filtering out the real-time monitoring sliding window whose difference from the standard sliding window is less than the preset difference, for other measurements that are not involved in the calculation of the soot deposition model, the thickness of the soot in the dynamic level is , That is, the preset compensation value is obtained by those skilled in the art based on empirical analysis.

[0091] Considering that the soot may also peel off in actual use, this embodiment also provides another optional implementation method, such as Figure 6 and Figure 7 As shown, the method further includes:

[0092] In step 401, a camera is used to capture images of the interior of the chimney at different gas flow rates, and image recognition is performed on the images of the interior of the chimney to determine the soot flaking conditions at different inlet gas flow rates;

[0093] In step 402, the chimney cleaning cycle is determined based on the growth pattern of soot and the soot flaking condition at the current inlet gas flow rate.

[0094] The camera is a high-speed camera. The chimney cleaning cycle is determined based on the growth pattern of soot and the soot peeling condition at the current inlet gas flow rate. Specifically, the cycle includes:

[0095] The actual deposition rate of soot is obtained by subtracting the rate of soot peeling from the rate of soot growth. Based on the actual deposition rate of soot, the time t at which the thickness of the soot reaches a preset thickness is determined, so that the chimney can be cleaned after time t. The preset thickness is obtained by those skilled in the art based on empirical analysis.

[0096] In a specific application scenario, part of the calculation process in this embodiment is implemented with the assistance of corresponding computer tools, and also needs to be performed in combination with a three-dimensional model of the chimney. In a specific application scenario, this embodiment can be understood as follows:

[0097] A three-dimensional model of the chimney is established using three-dimensional modeling software (such as SolidWorks), and the three-dimensional model is imported into Ansys ICEM CFD software. The three-dimensional model is discretized using Ansys ICEM CFD software to obtain a mesh file, which is then imported into Fluent software to calculate the soot growth law.

[0098] Example 2:

[0099] The present invention is based on the method described in Example 1, combined with specific application scenarios, and uses technical descriptions in related scenarios to illustrate the implementation process of the present invention in characteristic scenarios.

[0100] The model-based chimney dust analysis method provided in this embodiment specifically includes analysis of the mass transfer process in the chimney and analysis of the chimney dust diffusion and sedimentation mechanism.

[0101] The analysis of the mass transfer process in the chimney specifically includes:

[0102] Based on numerical simulation combined with experimental methods, a two-phase three-flow field model of the smoke-airflow separation process is established to analyze the flow characteristics of gas and smoke, correct the distribution law of smoke particle groups, and obtain the critical speed of inertial separation and turbulent effect separation of droplets of different particle sizes, so as to obtain the law of gas-solid two-phase mass transfer process, that is, the law of soot growth. Its technical route is as follows Figure 3 shown.

[0103] This simulation aims to identify sampling points within the horizontal flue that represent the actual flue gas velocity and use these as appropriate locations for velocity measurement. During the simulation, it is assumed that no phase change occurs in the gas. The gas is considered a continuous phase, and the dust a discrete phase, to establish a two-phase flow model for the steam-water separation process.

[0104] a) Gas continuous phase model

[0105] The continuity equation and momentum equation of the gas in the chimney are:

[0106] (1)

[0107] (2)

[0108] The gas flow is turbulent, and its turbulent pulsation kinetic energy k equation and dissipation rate ε equation are:

[0109] (3)

[0110] (4)

[0111] In the above formula, G is the generation rate of turbulent kinetic energy, which is expressed as:

[0112] (5)

[0113] In equations (3) and (4), the coefficients are = 1.44, = 1.92, A = 1.0, B = 1.3.

[0114] b) Soot discrete phase model

[0115] The smoke in the chimney can be regarded as a discrete phase, and the differential equation of the force in the spatial direction is as follows:

[0116] (6)

[0117] Where, is the drag force per unit mass of smoke, where as follows:

[0118] (7)

[0119] in 、 are the velocities of air flow and smoke, respectively. 、 are the densities of airflow and smoke, is the molecular viscosity of the airflow, is the diameter of the soot dust, is the relative Reynolds number, defined as follows:

[0120] (8)

[0121] CD is the drag coefficient, which can be expressed as follows:

[0122] (9)

[0123] The motion trajectory of smoke in the flow field can be obtained by integrating the differential force equation of formula (6).

[0124] Experimental research on single-phase air flow in chimneys was conducted, using particle tracer flow (PIV) technology to study the flow characteristics of air in the chimney flow channel at different flow rates. Using the single variable method, the effects of different chimney structural parameters on air flow were studied, and the effects of structural parameters on performance parameters such as velocity distribution and pressure drop were investigated.

[0125] Study the flow characteristics of smoke in chimneys. By solving the droplet phase model, calculating the inertial force and airflow drag force on the droplets, analyzing the interaction between smoke and gas, and studying the movement of smoke in the flow channel.

[0126] When smoke enters the chimney flow channel, collision and aggregation will occur. By giving a single particle size droplet at the inlet, the volume fraction of droplets of different particle sizes in the chimney is calculated, the collision and aggregation process of smoke in the chimney is studied, and the droplet aggregation characteristics are analyzed.

[0127] By giving the inlet Rosin-Rammler distribution of different particle size smoke groups, the velocity and movement trajectory of different particle size smoke in each section of the chimney are analyzed, and the influence of different particle size smoke and distribution on the two-phase mass transfer process is obtained.

[0128] A laser particle size analyzer was used to measure the particle size of the smoke dust at the chimney inlet, and the average particle size of the droplet group was calculated using the droplet volume as the weight factor. The average particle size of the droplets was defined as follows:

[0129] (10)

[0130] The volume distribution of soot of varying particle sizes was measured experimentally, and the mean particle size and size distribution index of the modified Rosin-Rammler distribution of soot particles were fitted. The frequency distribution of droplet size at the chimney outlet was measured, and the variation of this distribution with flow velocity was studied, analyzing the relationship between soot particle size and separation efficiency. By varying the inlet velocity, the droplet size distribution at the chimney outlet was analyzed, and the critical velocities for inertial and turbulent separation of droplets of varying sizes were determined.

[0131] The analysis of chimney dust diffusion and deposition mechanism includes two parts: numerical simulation and experimental research on chimney dust distribution evolution and spalling mechanism. Based on the Eulerian Wall Film (EWF) model, high-speed cameras and image recognition technology are used to analyze the dust process (including the water splash movement process) under different working conditions, obtain the critical criterion of airflow shearing soot, study the evolution law of soot spalling process and modify the EWF model. The specific technical route is as follows: Figure 7 shown.

[0132] The smoke dust adheres to the wall surface to form smoke scale. A three-dimensional rectangular coordinate system is established, and the positive directions of the x, y, and z coordinates are respectively the direction of airflow (i.e. the direction of the chimney inner wall slope (also called the plate wall)), perpendicular to the plate wall outward, and along the plate wall downward, such as Figure 5 There is a layer of soot on the wall, and the thickness of the soot on the chimney wall is in a dynamic flat state.

[0133] Assuming that the soot is very thin relative to the entire flow channel, the boundary layer theory is applicable, and there is no heat exchange, etc. The continuity equation and momentum conservation equation are:

[0134] (11)

[0135] (12)

[0136] In formula (11) is the soot density, is the surface gradient operator, is the airflow velocity on the outer surface of the soot, is the mass source of soot deposited on the unit wall surface. The terms on the right side of Equation (12) are: the first term describes the effects of pressure and the gravity component perpendicular to the wall, the second term describes the effect of gravity in the direction parallel to the soot, the third term describes the gas-soot wall shear force, and the fourth term describes the volume force that collects or separates the soot.

[0137] By adding mass and momentum source terms to the mass and momentum equations, respectively, the EWF model is coupled with a discrete phase model (DPM) to analyze the process by which discrete soot particles adhere to the wall and form soot scale. Experimental studies of soot scale growth were conducted, revealing patterns of soot scale growth at different locations.

[0138] In actual use, this embodiment also uses a flue gas analyzer to measure the flue gas composition, analyzes the changes in the flue gas composition to determine the stable combustion process and the unstable combustion process, and thus calculates the soot deposition model during the stable combustion process. The specific implementation process has been described in detail in Example 1 and will not be repeated here.

[0139] This embodiment also analyzes and optimizes the flow channel structure in the chimney to determine a suitable flow channel structure, specifically: multi-objective optimization of the flow channel structure parameters in the chimney, and the multi-objective optimization criterion research of the flow channel structure and measuring point position in the chimney includes three parts: first, taking the exhaust efficiency, critical flow velocity and friction coefficient as optimization targets, and the chimney geometric structure parameters as influencing factors, a series of chimney structures are established through the Taguchi method, and their flow and separation characteristics are numerically simulated, and signal-to-noise ratio analysis and grayscale analysis are carried out to determine the influence of geometric parameters on comprehensive performance; secondly, a multi-objective optimization mathematical model is established, and the reliability of the model is verified by regression analysis; finally, the structural optimization research of the model is carried out based on the crossover operator genetic algorithm (Non-Crossover Genetic Algorithm, abbreviated as: NCGA), and the corresponding optimization criteria are established to obtain the chimney flow velocity measurement scheme. The technical route of the optimization criterion research is as follows: Figure 8 shown.

[0140] Example 3:

[0141] like Figure 9 FIG. 1 is a schematic diagram of the architecture of a chimney smoke analysis device based on a model according to an embodiment of the present invention. The chimney smoke analysis device based on a model according to this embodiment includes one or more processors 21 and a memory 22. Figure 9 A processor 21 is taken as an example.

[0142] The processor 21 and the memory 22 may be connected via a bus or other means. Figure 9 The bus connection is taken as an example.

[0143] Memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs and non-volatile computer-executable programs, such as the model-based chimney smoke analysis method in Example 1. Processor 21 executes the model-based chimney smoke analysis method by running the non-volatile software programs and instructions stored in memory 22.

[0144] The memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 22 may optionally include a memory remotely located relative to the processor 21, and such remote memory may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0145] The program instructions / modules are stored in the memory 22 , and when executed by the one or more processors 21 , the model-based chimney smoke analysis method in the above-mentioned embodiment 1 is executed.

[0146] It is worth noting that the information interaction, execution process, etc. between the modules and units within the above-mentioned devices and systems are based on the same concept as the processing method embodiment of the present invention. The specific content can be found in the description of the method embodiment of the present invention and will not be repeated here.

[0147] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a disk or an optical disk, etc.

[0148] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A chimney smoke analysis method based on a model, characterized in that: include: Establishing a gas continuous phase model of the chimney, monitoring the gas flow rate at a preset position of the chimney, and analyzing and obtaining the gas flow rate at each position in the chimney based on the gas flow rate at the preset position and the gas continuous phase model; Establishing a soot deposition model on the chimney wall, substituting the gas flow rate at each location in the chimney into the soot deposition model, and calculating the soot deposition model to obtain the soot growth pattern at each location in the chimney, so as to determine the chimney cleaning cycle based on the soot growth pattern; The gas continuous phase model includes a first continuity equation and a first momentum conservation equation; The first continuity equation is ; The first momentum conservation equation is ; in, is the turbulent pulsation kinetic energy, is the velocity component of the gas in the i direction, is the spatial coordinate in the i-th direction, is the velocity component of the gas in the j direction, is the spatial coordinate in the jth direction, is the pressure of the gas, is the density of the gas, is the dynamic viscosity coefficient of the gas; The soot deposition model includes a second continuity equation and a second momentum conservation equation; The second continuity equation is ; The second momentum conservation equation is ;in, is the soot density, is the surface gradient operator, is the airflow velocity on the outer surface of the soot, is the mass source of soot deposited on the unit wall surface, is the thickness of the soot in the dynamic level, t is the time, is the heat flow per unit volume, is the acceleration due to gravity, is the preset calculation coefficient, For pressure, is the kinematic viscosity of the soot.

2. The chimney smoke analysis method based on the model according to claim 1, characterized in that: Turbulent pulsation kinetic energy and dissipation rate having a first relation and a second relation; The first relationship is ; The second relationship is ; G is the rate of generation of turbulent kinetic energy, , For time, 、 、 and All are preset coefficients.

3. The chimney smoke analysis method based on the model according to claim 1, characterized in that: Use a flue gas analyzer to monitor the composition of smoke in real time, and select the corresponding sliding window period within the historical period as the standard sliding window; When the difference between the smoke composition monitored in real time and the smoke composition in the standard sliding window is less than a preset difference, measuring the gas flow rate at the preset position of the chimney and calculating the soot deposition model; Otherwise, the measurement of the gas flow rate at the preset position of the chimney and the calculation of the soot deposition model are not performed.

4. The chimney smoke analysis method based on the model according to claim 3, characterized in that: The step of selecting a corresponding sliding window period as a standard sliding window within a historical period specifically includes: Use Preset Length Perform sliding window sampling and combine the concentration of the smoke component obtained by the i-th measurement in the sampled sliding window to obtain the i-th component vector of the sliding window ; Calculate the rate of change of smoke composition of the sliding window ;in, ; is the concentration of the jth smoke component obtained by the i-th measurement within the sliding window; is the number of categories of smoke components measured, is the preset weight corresponding to the jth smoke component; When the smoke component change rate of the corresponding sliding window is less than the preset change rate, the sliding window is regarded as a standard sliding window.

5. The chimney smoke analysis method based on the model according to claim 4, characterized in that: The difference between the smoke components monitored in real time and the smoke components in the standard sliding window is ; in, To monitor the concentration of the jth smoke component obtained by the i-th measurement within the sliding window in real time.

6. The chimney smoke analysis method based on a model according to any one of claims 1 to 5, characterized in that: The method also includes: Under different gas flow rates, using a camera to capture images of the interior of the chimney, and performing image recognition on the images of the interior of the chimney to determine the soot scaling conditions under different inlet gas flow rates; The chimney cleaning cycle is determined based on the growth pattern of soot and the soot flaking situation at the current inlet gas flow rate.

7. A non-volatile computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which are executed by one or more processors to implement the model-based chimney smoke analysis method according to any one of claims 1 to 6.

8. A chimney smoke analysis device based on a model, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to perform the model-based chimney smoke analysis method according to any one of claims 1-6.

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