CN-121980999-A - Collaborative optimization arrangement method and system for flue flow measurement points of thermal power generating unit

CN121980999ACN 121980999 ACN121980999 ACN 121980999ACN-121980999-A

Abstract

The invention provides a collaborative optimization arrangement method and a collaborative optimization arrangement system for flue flow measurement points of a thermal power generating unit, which belong to the field of thermal power generation carbon metering. And analyzing the multi-condition flow field sample set by using a K-means clustering algorithm, dividing different categories according to the flue gas flow velocity, outputting the clustering center coordinates and the sample number of each category, and calculating the section average flow velocity and the flue gas volume flow by area weighted average. The relative deviation between the reconstructed flow and the actual flow meets the measurement requirement as a standard, and the number of the required minimum measuring points can be determined, and the positions of the minimum measuring points correspond to the cluster center coordinates. The method can automatically and efficiently identify the optimal measuring point combination which is representative in the whole working condition range and can ensure the measurement accuracy based on limited simulation or historical flow field data, and provides reliable data support for carbon measurement.

Inventors

QIAO BOTAO
SUN GUOLEI
Lu Hangke
ZHAO YUANCAI
WANG HUIQING
WANG YUAN
FENG BIN
WANG ZHAO
XU YUANGANG
MENG YONG
WANG WENLONG

Assignees

西安热工研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20260122

Claims (10)

1. The collaborative optimization arrangement method for the flue flow measurement points of the thermal power generating unit is characterized by comprising the following steps of: Acquiring unified optimal monitoring sections of a plurality of typical load working conditions; Dispersing the unified optimal monitoring section into M grid points, and mapping scattered data stored in the grid points in the unified optimal monitoring section onto a regular grid to obtain a multi-condition flow field sample set; Performing unsupervised clustering on the multi-working-condition flow field sample set based on a K-means clustering algorithm to obtain T clustering centers, wherein each clustering center represents a typical flow field mode; and searching an optimal and fixed measuring point position combination by taking the T clustering centers as an optimization target, so that under the measuring point position combination, the error of reconstructing the section flue gas volume flow by using the measuring values of the measuring points is minimum under all typical working conditions.
2. The thermal power generating unit flue flow measurement point collaborative optimization arrangement method according to claim 1, wherein obtaining a unified optimal monitoring section of a plurality of typical load conditions comprises: Acquiring complete three-dimensional flow field data under N typical load conditions through CFD simulation; determining the area where the monitoring section is located in the three-dimensional flow field, and determining a candidate monitoring section in the flue at intervals of a set distance along the flow direction of the flue gas; calculating the relative standard deviation and the weighted average of the relative standard deviation of the smoke flow velocity values under N typical load working conditions; The candidate section with the smallest weighted average of the relative standard deviation is used as the unified optimal monitoring section of N typical load working conditions.
3. The thermal power generating unit flue flow measurement point collaborative optimization arrangement method according to claim 2, wherein obtaining complete three-dimensional flow field data under N typical load conditions through CFD simulation comprises: Establishing an accurate three-dimensional geometric model and a corresponding CFD simulation model according to the actual size of a flue, and setting boundary conditions of N typical load working conditions according to unit design parameters and operation data; And carrying out high-fidelity CFD simulation by adopting a turbulence model to obtain complete three-dimensional flow field data under N typical load working conditions.
4. The collaborative optimization arrangement method for flue flow measurement points of a thermal power generating unit according to claim 1, wherein the unified optimal monitoring section is discretized into M grid points, the Data structure of the ith grid point is defined as a tuple of Data i =(X i ,Y i ), wherein X i =[x i ,y i is a two-dimensional coordinate vector representing the position of the grid point, Y i =[v i,1 ,v i,2 ,…,v i,N is an N-dimensional target vector, and the Data set is represented as d= { (X i ,Y i ) }, wherein i=1, 2, and M; Carrying out standardization treatment on the coordinates [ x, Y ] to enable the average value to be 0 and the standard deviation to be 1, respectively carrying out Min-Max scaling on the target value Y of each working condition, and linearly transforming the target value of each working condition to a fixed interval; when the divided mesh is an unstructured mesh, scatter data stored in mesh points in a unified best monitoring section is mapped onto a regular mesh to form a dataset D '= { (X i ',Y i ') }.
5. The collaborative optimization arrangement method for flue flow measurement points of a thermal power generating unit according to claim 1, wherein unsupervised clustering is performed on the multi-working-condition flow field sample set based on a K-means clustering algorithm to obtain T cluster centers, and each cluster center represents a typical flow field pattern and comprises: Extracting a speed value from each coordinate point, and converting a one-dimensional speed array formed by the speed values into a format suitable for a K-means clustering method; determining the clustering quantity t according to the deviation between the measured value and the actual value of the average flow velocity of the flue gas at the unified optimal monitoring section; Randomly initializing t cluster centers, calculating Euclidean distance from each flow field sample to each cluster center, and distributing the cluster centers to clusters closest to the cluster centers; And mapping the cluster labels back to the original coordinate points, and outputting final T cluster centers, wherein each cluster center represents a typical flow field mode.
6. The method for collaborative optimization arrangement of flue flow measurement points of a thermal power generating unit according to claim 1, wherein the method is characterized in that the method for searching an optimal and fixed measurement point position combination by taking the T clustering centers as optimization targets, so that under the measurement point position combination, the error of reconstructing the section flue gas volume flow by using the measurement values of the measurement points is minimum under all typical working conditions, and comprises the following steps: constructing an objective function for the T flow field patterns, the objective function representing the relative deviation between the reconstructed cross-sectional flue gas volumetric flow Q t and the actual cross-sectional flue gas volumetric flow Q r under N typical load conditions, L (T) being a function of the number of cluster centers T: wherein w i is a weight coefficient, and is set according to the number of operating hours of each working condition year or the contribution degree of the annual carbon emission total; The cross-section flue gas volume flow Q t is calculated by the average speed u i of T clustering centers obtained in the clustering step and the number of sample points T i contained in each category: Where K is the number of sample points in the multi-working fluid field sample set D', To measure the area of the cross section; calculating the value of an objective function L (t), wherein the t value when L (t) meets the measurement error is the number of flow velocity measurement points, and the coordinates corresponding to the t clustering centers are the positions of the flow velocity measurement points.
7. The collaborative optimization arrangement method for flue flow measurement points of a thermal power generating unit according to claim 1, wherein the average speed u i of t clustering centers is calculated according to dynamic pressure p d,i and static pressure p p,i at each measuring point, and the dynamic pressure p d,i and the static pressure p p,i at each measuring point are obtained by measuring three-dimensional pitot tubes: Where k i is the tachometer tube speed coefficient, ρ i is the actual air flow density, ρ θ i is the air flow density under standard conditions, and p a is atmospheric pressure.
8. The collaborative optimization arrangement system for the flue flow measurement points of the thermal power generating unit is characterized by comprising a CFD simulation module, a flow field mode analysis module, a collaborative optimization calculation module, a result output and configuration module and a carbon metering calculation module; The CFD simulation module is used for acquiring unified optimal monitoring sections of a plurality of typical load working conditions, dispersing the unified optimal monitoring sections into M grid points, and mapping scattered points stored in the grid points in the unified optimal monitoring sections onto a regular grid to obtain a multi-working-condition flow field sample set; The flow field pattern analysis module is internally provided with a K-means clustering algorithm and is used for carrying out clustering analysis on the multi-working condition flow field sample set to identify t typical flow field patterns and clustering centers thereof; The collaborative optimization calculation module is used for taking T clustering centers as input, and solving an optimal measuring point combination T by an optimization algorithm, so that under the measuring point position combination T, the error of reconstructing the section flue gas volume flow by utilizing the measuring value of the measuring point is minimum under all typical working conditions; the result output and configuration module is used for outputting the space coordinates of the optimal measuring point combination and guiding the installation and configuration of the actual sensor; The carbon metering calculation module is used for calculating the real-time carbon emission according to the real-time flow velocity data from the fixed measuring point combination T and the smoke composition data.
9. A computer device, characterized by comprising a processor and a memory, wherein the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, and the processor can realize the collaborative optimization arrangement method of the flue flow measurement points of the thermal power generating unit according to any one of claims 1-7 when executing part or all of the computer executable program.
10. A computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the method for collaborative optimization arrangement of flue flow measurement points of a thermal power generating unit according to any one of claims 1-7 can be realized.

Description

Collaborative optimization arrangement method and system for flue flow measurement points of thermal power generating unit Technical Field The invention belongs to the field of industrial process measurement technology and carbon emission monitoring of thermal power plants, in particular relates to a method and a system for collaborative optimization arrangement of flow measurement points of a flue of a thermal power unit, and particularly relates to a method and a system for optimization arrangement of a flow measurement device, which are suitable for a complex flue in front of a chimney, can cover various operation loads of the unit and aim to meet the accurate metering requirement of carbon emission. Background With the deep advancement of the aim of reducing carbon emission, unprecedented high requirements are put forward on accurate monitoring, reporting and checking of carbon emission data in the thermal power industry. The flue gas flow is one of key parameters for calculating the carbon emission of the coal-fired unit, and the measurement accuracy directly determines the reliability of the carbon emission accounting result. At present, the carbon emission accounting in China mainly adopts a material balance algorithm based on the fuel coal consumption, but as the carbon market is established, a direct measurement method based on a continuous flue gas monitoring system has become an important supplement and future trend due to the advantages of high timeliness, real-time verification and the like. Factors influencing the continuous monitoring precision of the carbon emission of the thermal power generating unit mainly comprise the concentration of CO 2, the flow rate of flue gas, the humidity, the temperature, the pressure and the like. The flue gas flow is a core factor influencing the continuous monitoring precision of carbon emission, and a main flue in front of a chimney of the thermal power generating unit is a key position for flue gas flow measurement. However, the flow field environment is extremely complex, the flue section is huge (tens or even hundreds square meters), the flow velocity field is extremely unevenly distributed, and the flow velocity field is severely changed along with the load change of the unit. In addition, the thermal power generating unit in China basically completes ultra-low emission reconstruction, the chimney inlet flue is generally compact, and a continuous monitoring system (CEMS) of the flue gas installed in the chimney inlet flue is generally difficult to meet the technical index of 'front 4 and rear 2' or air flow distribution relative standard deviation less than 0.15 required by the standard DL/T2376-2021. Currently, to solve this problem, a grid method (multipoint measurement) or a single-point or few-point measurement method using a fixed cross section is generally employed. The former has high measuring cost and is difficult to monitor on line for a long time, while the latter can realize on line monitoring, the measuring point position is fixed, once the flow field is changed due to the change of the unit load, the original measuring point position is possibly not representative any more, and the measuring precision is rapidly reduced. The prior art lacks a method capable of adapting to the change of the multi-load working condition of the unit and automatically and optimally selecting the most representative measuring point position. The patent with the publication number CN115900839A provides a flow calculation method and a flow calculation device of a matrix type flue gas flowmeter based on flow field simulation, which are characterized in that the flow calculation method and the flow calculation device of the matrix type flue gas flowmeter based on flow field simulation are adopted, flow field numerical simulation is carried out by acquiring flue design parameters, multi-point measurement positions are designed, different flue gas flows are simulated, a multiple linear regression is utilized to calculate flow calculation coefficients, and then the flue gas flows are calculated, the problem that accurate speed field coefficients and the flue gas flows are difficult to obtain due to uneven distribution of speed fields in a pipeline is solved, but the influence of the actual structure (such as a circle, a rectangle, an elbow and a variable diameter) of the flue on the air flow distribution is focused on, the non-uniformity of the flow field is covered by increasing the number of measuring points, and proper weight is given to each point through regression calculation. Therefore, an innovative method for realizing high-precision and low-cost flow monitoring by comprehensively considering the flow field characteristics of the thermal power generating unit in the full load range and cooperatively optimizing and arranging a small number of key measurement points is urgently needed. Disclosure of Invention The invention aims to overcome the defects of the pri