Pulverized coal adjustable optimal distribution method based on deep peak regulation state of coal-fired unit

Abstract

The invention relates to a pulverized coal adjustable optimal distribution method based on a deep peak regulation state of a coal-fired unit. The method comprises the steps that pulverized coal conveying parameters, combustion state parameters and equipment abrasion parameters of a combustion system are collected in real time; constructing a multi-objective optimization model based on the parameters, wherein the model takes minimum equipment wear rate, maximum combustion efficiency and pulverized coal distribution balance as optimization objectives; solving the multi-target model through a dynamic iterative algorithm, generating a candidate allocation scheme, and selecting an optimal allocation strategy based on fuzzy logic evaluation; according to the optimal distribution strategy, the opening degree of a valve of a pulverized coal conveying pipeline, the angle of a distributor and an airflow guiding device are dynamically adjusted, and multi-area balanced distribution of pulverized coal in a combustion chamber is achieved; coordinated optimization control between combustion efficiency and equipment wear is achieved by collecting multi-dimensional parameters in real time and establishing a multi-target dynamic optimization model.

Description

Technical Field

[0001] The present invention belongs to the technical field of pulverized coal combustion control, and specifically relates to a method for adjustable and optimized distribution of pulverized coal under the condition of deep peak shaving of coal-fired units. Background Art

[0002] In recent years, with the continuous growth of energy demand, the field of thermal power generation has gradually developed towards large capacity, high parameters and high efficiency, and the pulverized coal combustion technology has also experienced significant progress and optimization. The traditional pulverized coal combustion optimization technology mainly focuses on improving the mixing uniformity of pulverized coal and air and improving the combustion efficiency, such as by optimizing the ratio of primary air to secondary air, improving the design of pulverized coal pipelines, and improving the structure of burners, etc., in order to achieve full combustion of pulverized coal and improve the economic operation of equipment. However, in the actual application process, due to the asymmetry of burner layout, pulverized coal conveying path and combustion chamber structure, the distribution of pulverized coal in the combustion chamber is often uneven, resulting in the emergence of local high-temperature areas and a decrease in combustion efficiency. In addition, the uneven distribution of pulverized coal air flow may also cause serious wear problems in local areas of key equipment, such as conveying pipelines, distributors, and heating surfaces of combustion chambers, shortening the service life of the equipment significantly and increasing the operation and maintenance costs.

[0003] In view of the above problems, the existing pulverized coal distribution methods mainly control the pulverized coal conveying parameters through static or quasi-static manual adjustment methods. This method relies on the experience of operators and off-line measurement data for decision-making, with low adjustment accuracy and unable to perform dynamic adaptive adjustment in real time according to coal quality changes and load fluctuations, making it difficult to achieve the optimization and precise control of pulverized coal distribution. At the same time, most of the traditional optimization methods are single-objective optimizations, such as simply improving combustion efficiency or simply reducing wear, lacking effective coordination of the contradictory relationships between multiple objectives, and it is difficult to take into account the balance of pulverized coal distribution, combustion efficiency and equipment wear control at the same time. Therefore, the existing pulverized coal distribution technology often faces the defects of poor optimization effect, slow response speed and inability to meet the dynamic working conditions of modern large-scale power stations in actual operation.

[0004] To sum up, the existing pulverized coal conveying and combustion technologies generally have problems such as uneven pulverized coal distribution, serious equipment wear and lack of dynamic real-time performance of optimization control means. The method for adjustable and optimized distribution of pulverized coal under the condition of deep peak shaving of coal-fired units proposed by the present invention aims to solve the above problems, and realizes the coordinated optimization control between combustion efficiency and equipment wear by collecting multi-dimensional parameters in real time and establishing a multi-objective dynamic optimization model. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract, and the title, and such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the following technical problems existing in the prior art: the existing coal powder conveying and combustion technologies generally have problems such as uneven coal powder distribution, serious equipment wear, and lack of dynamic real-time optimization control means.

[0007] To solve the above technical problems, the present invention provides the following technical solution: a method for adjustable and optimized distribution of coal powder under the deep peak shaving state of a coal-fired unit, characterized by including:

[0008] Real-time collect the coal powder conveying parameters, combustion state parameters, and equipment wear parameters of the combustion system, and the parameters at least include coal powder particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, air flow velocity, equipment wear monitoring data, and combustion efficiency index;

[0009] Construct a multi-objective optimization model based on the above parameters, and the model takes minimizing the equipment wear rate, maximizing the combustion efficiency, and the balance of coal powder distribution as optimization objectives, and the constraint conditions include the coal powder conveying flow threshold, the safe range of combustion temperature, and the air flow uniformity index;

[0010] Solve the multi-objective model through a dynamic iterative algorithm to generate a candidate distribution plan, and select the optimal distribution strategy based on fuzzy logic evaluation;

[0011] According to the optimal distribution strategy, dynamically adjust the valve opening, distributor angle, and air flow guiding device of the coal powder conveying pipeline to achieve multi-region balanced distribution of coal powder in the combustion chamber.

[0012] As a preferred technical solution of a method for adjustable and optimized distribution of coal powder under the deep peak shaving state of a coal-fired unit,

[0013] The various parameters collected in real time are obtained by a multi-sensor network installed on the coal powder conveying pipeline, in the combustion chamber, and on the key heating surfaces, specifically including:

[0014] Install pressure sensors, flow sensors, and coal powder particle concentration sensors on the coal powder conveying pipeline to obtain pipeline pressure, coal powder conveying flow velocity, and real-time particle distribution information;

[0015] Install temperature sensors and flame detection sensors at multiple positions in the combustion chamber to obtain the temperature field distribution and combustion state;

[0016] Wear monitoring sensors are arranged at key heating surfaces or easily worn parts to obtain real-time change data of the equipment wear rate;

[0017] A combustion efficiency detection unit is arranged on the flue gas exhaust side or other suitable positions to evaluate the combustion efficiency index in real time;

[0018] The data acquisition frequency can be dynamically adjusted according to production requirements to ensure the accuracy and real-time nature of the data.

[0019] As a preferred technical solution of a pulverized coal adjustable optimization distribution method under the deep peak shaving state of a coal-fired unit,

[0020] Constructing a multi-objective optimization model based on the parameters includes,

[0021] According to the obtained real-time data, define the mathematical representation functions of the equipment wear rate, combustion efficiency, and pulverized coal distribution balance;

[0022] Establish the following multi-objective optimization model:

[0023] Minimize: F(X) = α·W(X) - β·E(X) - γ·U(X)

[0024] Among them, α, β, and γ are weight coefficients respectively, determined according to production requirements and operating conditions. Let W(X) be the equipment wear rate function, E(X) be the combustion efficiency function, and U(X) be the pulverized coal distribution balance function.

[0025] As a preferred technical solution of a pulverized coal adjustable optimization distribution method under the deep peak shaving state of a coal-fired unit,

[0026] The specific constraint content of the constraint conditions includes,

[0027] Pulverized coal conveying flow threshold, combustion temperature safety range, air flow uniformity index, equipment force limitation, operation execution delay.

[0028] As a preferred technical solution of a pulverized coal adjustable optimization distribution method under the deep peak shaving state of a coal-fired unit,

[0029] The dynamic iterative algorithm includes,

[0030] Based on the foregoing multi-objective optimization model, read the parameters collected in real time, set the initial solution, and specify the number of iterations;

[0031] In each iteration, use the multi-objective optimization algorithm to perform crossover, mutation, and fitness evaluation on the candidate solutions; among them, the fitness function is comprehensively given by the equipment wear rate, combustion efficiency, and distribution balance

[0032] Revise or discard the candidate solutions that do not meet the constraint conditions (such as pulverized coal flow rate, temperature limit, air flow uniformity, etc.), and re-evaluate the fitness after the revision;

[0033] When the iteration reaches the preset number of times, output the set of candidate allocation schemes obtained in the current iteration.

[0034] As a preferred technical solution of a pulverized coal adjustable optimization allocation method based on the deep peak shaving state of a coal-fired unit,

[0035] The candidate allocation scheme includes,

[0036] For multiple pulverized coal conveying pipelines, give the pulverized coal flow rate or valve opening parameters corresponding to each pipeline;

[0037] Set the air duct air flow distribution scheme, including the air supply volume and the matching ratio of secondary air to primary air;

[0038] For different regions in the combustion chamber, give the corresponding pulverized coal and air supply distribution;

[0039] Optimize the air supply angle at the position where the wear rate is monitored to be relatively high.

[0040] As a preferred technical solution of a pulverized coal adjustable optimization allocation method based on the deep peak shaving state of a coal-fired unit,

[0041] The optimal allocation strategy includes,

[0042] For the set of candidate allocation schemes output by the dynamic iterative algorithm, according to the fuzzy logic evaluation rules, perform multi-factor scoring on the equipment wear rate, combustion efficiency, allocation balance, and operability;

[0043] Based on the preset weights, select the candidate scheme with the highest fuzzy evaluation score as the optimal allocation strategy;

[0044] After implementing the optimal allocation strategy, monitor the operation effect of the system in real time, and input the monitoring data into the fuzzy logic evaluation module. If the evaluation result deviates from the expected target, trigger a new round of iterative optimization or fine-tune the strategy;

[0045] When the optimal allocation strategy needs to balance between low wear rate and high combustion efficiency, coordinate the conflicting goals through hierarchical priorities, and finally output a comprehensive optimal scheme that takes into account both equipment life and combustion performance.

[0046] As a preferred technical solution of a pulverized coal adjustable optimization allocation method based on the deep peak shaving state of a coal-fired unit,

[0047] The specific contents of dynamically adjusting the valve opening of the pulverized coal conveying pipeline, the angle of the distributor, and the air flow guiding device include,

[0048] According to the coal powder flow ratio of each pipeline determined by the optimal allocation strategy, the valve actuator is instructed to increase or decrease the opening, and the adjustment range needs to be combined with the upper limit of the actuator's response and the minimum step size;

[0049] The angle of the guide vanes or rotatable distribution components inside the coal powder distributor can be adjusted to change the flow direction and distribution path of the coal powder; when the wear rate is detected to be high at a certain location, the angle of the distributor can be adjusted appropriately to divert the coal powder to the channel with less wear.

[0050] Beneficial effects of the present invention: Step S1, by real-time collection of the coal powder delivery parameters, combustion state parameters and equipment wear parameters of the combustion system, including coal powder particle distribution, delivery pipeline pressure, combustion chamber temperature field distribution, air flow velocity, equipment wear monitoring data and combustion efficiency index, can comprehensively and accurately grasp the current operating state of the combustion system and form a complete data closed-loop feedback system. Through the real-time monitoring and recording of the above data, the data information foundation necessary for precise optimization control is provided, the reliability and accuracy of subsequent optimization decisions are guaranteed, and finally the beneficial effect of quickly responding to actual working condition changes and avoiding misadjustment or loss of control caused by data lag is achieved.

[0051] Step S2, by constructing a multi-objective optimization model based on the parameters collected in step S1, the model takes minimizing the equipment wear rate, maximizing the combustion efficiency and the balance of coal powder distribution as the optimization objectives, and sets multi-dimensional constraints such as the coal powder delivery flow threshold, the combustion temperature safety range and the airflow uniformity index, so as to achieve a quantitative description and effective trade-off of the system operation objectives. By clarifying the target relationship and boundary constraints between equipment wear and combustion efficiency, extreme working conditions are prevented during the optimization process, and the situation of simply pursuing the improvement of combustion efficiency at the expense of equipment safety is avoided, and finally the beneficial effect of taking into account the three aspects of equipment protection, combustion efficiency improvement and coal powder distribution balance is achieved.

[0052] Step S3, by using a dynamic iterative algorithm to solve the multi-objective optimization model constructed in step S2, generate several candidate allocation schemes, and combine the fuzzy logic evaluation method to screen the optimal allocation strategy, which can effectively overcome the shortcomings of the traditional static optimization strategy of being single and rigid, and ensure the dynamic adaptability of the selected allocation scheme to the actual complex combustion conditions. Specifically, through the continuous optimization of the dynamic iterative algorithm and the multi-dimensional evaluation process of fuzzy logic, it is ensured that the final selected scheme achieves a comprehensive balance between equipment wear rate, coal powder distribution balance and combustion efficiency, realizes the adaptive adjustment of the optimization scheme to real-time changing conditions, and effectively improves the control accuracy and robustness of the combustion system to complex load changes.

[0053] Step S4: According to the optimal allocation strategy obtained in Step S3, dynamically adjust the valve opening degree of the pulverized coal conveying pipeline, the angle of the distributor, and the air flow guiding device to achieve the balanced distribution of pulverized coal in multiple regions of the combustion chamber, effectively solving the problems of local high temperature and severe equipment wear caused by uneven distribution of pulverized coal in the combustion chamber. By specifically implementing the precise dynamic control of the pulverized coal flow rate and the air flow path, not only the refined control of the combustion process is achieved, the wear degree of the key parts of the combustion equipment is reduced, the service life of the equipment is extended, but also the significant improvement of the combustion efficiency is realized, thus achieving the beneficial effects of reducing the equipment maintenance cost, improving the operation economy and safety of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification.

[0057] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0058] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not all refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0059] Furthermore, the present invention is described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual production.

[0060] Embodiment 1

[0061] Refer toFigure 1 , This embodiment provides a method for optimizing the adjustable distribution of pulverized coal under the deep peak shaving state of a coal-fired unit, which specifically includes the following steps:

[0062] S1. Real-time collect the pulverized coal conveying parameters, combustion state parameters, and equipment wear parameters of the combustion system. The parameters at least include pulverized coal particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, air flow velocity, equipment wear monitoring data, and combustion efficiency index. Among them, it should be noted in this step that:

[0063] S1.1 Through a multi-sensor network arranged in the pulverized coal conveying pipeline, combustion chamber, and key heating surfaces, obtain the following real-time data:

[0064] (1) Pulverized coal particle distribution: Detected by a particle concentration sensor installed in the pulverized coal conveying pipeline, denoted as C(t), which is used to characterize the pulverized coal particle concentration and distribution at the cross-section flowing through the pipeline per unit time;

[0065] (2) Conveying pipeline pressure: Measured by a pressure sensor installed on the outer wall or interface of the pulverized coal conveying pipeline, denoted as P(t), which is used to monitor the dynamic change of the pulverized coal conveying pressure in the combustion system;

[0066] (3) Combustion chamber temperature field distribution: Obtained by arranging temperature sensors at multiple key positions in the combustion chamber, denoted as T(t, x, y, z), and the local temperature values can be recorded according to the three-dimensional coordinates (x, y, z) of the combustion chamber;

[0067] (4) Air flow velocity: Detected by a flow velocity sensor installed in the combustion chamber and / or conveying pipeline, denoted as V(t), which is used to characterize the real-time flow velocity of the pulverized coal air flow and the supply air flow;

[0068] (5) Equipment wear monitoring data: Detected by wear sensors arranged on key heating surfaces or easily worn parts, denoted as W(t), which is used to reflect the change amount of the equipment wear rate over time;

[0069] (6) Combustion efficiency index: Measured by a combustion efficiency detection unit on the flue gas side or other suitable positions, denoted as E(t), which is used to evaluate the real-time efficiency level of the combustion process under different loads and working conditions.

[0070] S1.2 To meet the dynamic working condition requirements, the preset data acquisition frequency f(t) can be automatically adjusted according to production and operation needs, so as to realize the real-time tracking and high-frequency monitoring of the key parameters of the combustion system;

[0071] S1.3 Construct the above parameters into a multi-dimensional data vector X(t), where

[0072] X(t)=[C(t),P(t),T(t,x,y,z),V(t),W(t),E(t)]

[0073] Among them, C(t) represents the distribution of coal powder particles; P(t) represents the pressure of the transportation pipeline; T(t,x,y,z) represents the temperature field distribution of the combustion chamber; V(t) represents the air flow velocity; W(t) represents the equipment wear monitoring data; E(t) represents the combustion efficiency index; t represents the acquisition time, and (x,y,z) is the internal coordinate of the combustion chamber.

[0074] S1.4 sends the collected multidimensional data vector X(t) to the data processing and monitoring platform, and preliminarily screens and cleans the real-time data according to the pre-set threshold, abnormality judgment rules and data integrity verification process to eliminate obviously erroneous or invalid sensor data;

[0075] S1.5 inputs the screened and cleaned multidimensional data into the modeling and optimization units relied upon in subsequent steps, so as to achieve multi-objective analysis and real-time optimization of coal powder distribution and anti-wear control, laying a data foundation.

[0076] S2. Construct a multi-objective optimization model based on the parameters, wherein the optimization objectives of the model are to minimize equipment wear rate, maximize combustion efficiency and pulverized coal distribution balance, and the constraints include pulverized coal flow rate threshold, combustion temperature safety range and airflow uniformity index. Among them, what needs to be explained in this step is:

[0077] Based on the real-time data obtained, define the mathematical characterization function of equipment wear rate, combustion efficiency and coal powder distribution balance;

[0078] The following multi-objective optimization model is established:

[0079] Minimize:F(X)=α·W(X)-β·E(X)-γ·U(X)

[0080] Among them, α, β, and γ are weight coefficients, which are determined according to production demand and operating conditions. Let W(X) be the equipment wear rate function, E(X) be the combustion efficiency function, and U(X) be the coal powder distribution balance function.

[0081] Constructing a multi-objective optimization model based on the parameters specifically includes:

[0082] S2.1 defines the following three optimization objective functions based on the real-time data obtained in step S1:

[0083] Equipment wear rate function W(X), used to quantify the wear of key heating surfaces or wear-prone parts;

[0084] The combustion efficiency function E(X), which is used to characterize the degree of full combustion of pulverized coal per unit time and can usually be comprehensively calculated in combination with thermal efficiency, flue gas components or flame detection indicators;

[0085] The pulverized coal distribution uniformity function U(X), which is used to measure whether the pulverized coal distribution in each area of the combustion chamber is uniform and can be defined according to the pulverized coal flow deviation in the pipeline channel or the consistency of the temperature field distribution.

[0086] S2.2 Combining the above three objective functions, set the weight coefficients α, β, γ (all non-negative real numbers, and α + β + γ = 1 or can be scaled according to technical requirements under a certain working condition), which respectively reflect the priorities of different optimization objectives in the overall strategy. Based on the multi-objective optimization idea, the comprehensive problem of "minimizing the equipment wear rate W(X)", "maximizing the combustion efficiency E(X)" and "pulverized coal distribution uniformity U(X)" is transformed into the following multi-objective optimization model:

[0087] Minimize: F(X) = α·W(X) - β·E(X) - γ·U(X)

[0088] Among them, W(X) is the equipment wear rate function, and the smaller the better;

[0089] E(X) is the combustion efficiency function, and the larger the better, so it appears in the overall objective function in the form of "-β·E(X)";

[0090] U(X) is the pulverized coal distribution uniformity function, and the larger the value, the more uniform the distribution; it appears in the objective function in the form of "-γ·U(X)" to achieve the maximization of the distribution uniformity;

[0091] X represents the decision variable to be optimized (including valve opening, pulverized coal flow distribution, air volume and wind speed allocation, etc.), which comprehensively reflects various adjustable parameters of the combustion system regulation.

[0092] S2.3 While constructing the objective function, the following constraint conditions need to be clarified (meeting the requirements of combustion safety and actual working conditions):

[0093] Pulverized coal conveying flow threshold constraint:

[0094] Q min ≤Q pipe，i (t)≤Q max ,

[0095] Among them, Q pipe，i (t) is the actual flow of the i-th pulverized coal conveying pipeline at time t; Q min and Q max are the minimum and maximum flow thresholds set according to the combustion working conditions respectively;

[0096] 2) Combustion temperature safety range constraint:

[0097] T min ≤T(x, y, z, t)≤T max

[0098] where T(x, y, z, t) represents the temperature value of the combustion chamber at spatial coordinates (x, y, z) and time t; T min and T max are the upper and lower bounds of the safe operating range of the combustion chamber;

[0099] 3) Airflow uniformity index constraint:

[0100] ΔV(t)≤δ

[0101] where ΔV(t) represents the maximum difference in the airflow velocity within the combustion chamber or the conveying pipeline at the same time t; δ is the allowable velocity uniformity threshold;

[0102] 4) Equipment stress limitation:

[0103] |F mechanical，j (t)|≤F limit

[0104] where F mechanical，j (t) is the actual load or stress value borne by a certain key heat - receiving surface or mechanical component j at time t; F limit is the safety design range;

[0105] 5) Operation execution delay:

[0106] Δt exec ≤τ allow

[0107] where Δt exec represents the time difference from the issuance of the adjustment command to the execution in place of the valve opening, the distributor angle, and the airflow guiding device; τ allow is the allowable operation delay limit.

[0108] S2.4 In summary, the following multi - objective optimization model is finally formed:

[0109] Minimize: F(X) = α·W(X)-β·E(X)-γ·U(X),

[0110] subject to:

[0111] Q min ≤Q pipe，i (t)≤Q max ,

[0112] T min ≤T(x, y, z, t)≤Tmax ,

[0113] ΔV(t) ≤ δ,

[0114] |F mechanical,j (t)| ≤ F limit ,

[0115] Δt exec ≤ τ allow ,

[0116] X ∈ Ω

[0117] where Ω represents the set of all feasible decision spaces, including the value ranges of adjustable parameters such as valve opening, pulverized coal flow ratio, air supply volume, and distributor angle.

[0118] S2.5 Use the above multi-objective optimization model as the core mathematical framework for combustion control and equipment anti-abrasion, providing the objective function and constraint conditions for the dynamic iterative algorithm or other optimization strategies (such as evolutionary algorithms, fuzzy logic decision-making, etc.) adopted in step S3, to achieve real-time and comprehensive optimal regulation of the combustion system.

[0119] S3. Solve the multi-objective model through a dynamic iterative algorithm to generate candidate allocation schemes, and select the optimal allocation strategy based on fuzzy logic evaluation. It should be noted in this step that:

[0120] S3.1 Initialization and data acquisition

[0121] (1) Read the relevant objective function and constraint conditions from the multi-objective optimization model established in step S2;

[0122] (2) Obtain the parameters (such as pulverized coal particle distribution, temperature field distribution, equipment wear rate, combustion efficiency index, etc.) collected in real time in step S1, and input them into the optimization module of this step;

[0123] (3) Set the initial solution X (0) and other initial conditions required by the algorithm, including the upper limit of the number of iterations N max , population size (or the number of candidate solutions) P, crossover rate, mutation rate, etc., and the specific values can be set according to production requirements or experience.

[0124] S3.2 Generation of candidate solutions by iteration

[0125] (1) Crossover and mutation of candidate solutions:

[0126] Use a multi-objective optimization algorithm (such as genetic algorithm, particle swarm algorithm, or other evolutionary algorithms) to perform crossover operations on the candidate solutions in the current population to generate new offspring solutions;

[0127] Mutate some of the offspring solutions, changing adjustable parameters such as valve opening, pulverized coal flow distribution, or air duct air flow distribution to enhance the diversity of the search;

[0128] (2) Fitness evaluation:

[0129] Based on the equipment wear rate function W(X), combustion efficiency function E(X), and pulverized coal distribution uniformity function U(X), conduct a comprehensive evaluation according to the following fitness function:

[0130] Fitness(X) = α·W(X) - β·E(X) - γ·U(X)

[0131] where α, β, and γ are the weight coefficients from step S2;

[0132] The lower the fitness value (i.e., the smaller the value of α·W(X) - β·E(X) - γ·U(X)), the better the comprehensive performance of the candidate solution in terms of wear rate, combustion efficiency, and distribution uniformity.

[0133] (3) Modify or discard solutions that do not meet the constraints:

[0134] If the candidate solution violates the constraint conditions set in step S2 (such as pulverized coal flow threshold, temperature range, air flow uniformity index, equipment stress limit, etc.), then modify the relevant candidate solution;

[0135] After modification, evaluate its fitness again; if it cannot be modified or still seriously violates the constraints after modification, then discard this candidate solution.

[0136] S3.3 Iteration termination judgment and candidate allocation scheme output

[0137] (1) Repeat the above "crossover - mutation - correction - fitness evaluation" loop until the preset number of iterations N max or the convergence criterion is met;

[0138] (2) Collect a set of feasible and fitness - well - performing solutions obtained in the current iteration as the "candidate allocation scheme set".

[0139] (3) Among them, the candidate allocation scheme at least includes:

[0140] The pulverized coal flow or valve opening parameters corresponding to multiple pulverized coal conveying pipelines;

[0141] The air duct air flow distribution scheme (including the air supply volume and the cooperation ratio of secondary air to primary air);

[0142] The distribution of pulverized coal and air supply in different regions of the combustion chamber, and conduct differential regulation for local high - temperature or uneven regions;

[0143] For the parts with a relatively high wear rate detected, corresponding optimization measures for the air supply angle or pulverized coal flow direction are given to reduce the wear at that place.

[0144] S3.4 Fuzzy logic evaluation and selection of the optimal allocation strategy

[0145] For each scheme in the set of candidate allocation schemes, score the equipment wear rate, combustion efficiency, allocation balance, and operability respectively;

[0146] Set up a fuzzy membership function or a fuzzy rule base to comprehensively score in dimensions such as "low wear rate", "high efficiency", "high degree of allocation uniformity", and "operability convenience";

[0147] Determine the scheme with the highest fuzzy evaluation score as the optimal allocation strategy X * ;

[0148] The expression form of the optimal allocation strategy can be written as:

[0149]

[0150] Among them, represents the optimal pulverized coal flow rate (or valve opening) of the m-th pipeline, represents the air volume distribution parameter of the air duct, represents the adjustable point parameters such as the angle of the distributor;

[0151] (3) Strategy adjustment and feedback:

[0152] After implementing the optimal allocation strategy, if the real-time monitoring data shows deviation or non-compliance, trigger a new round of iterative optimization or fine-tune the strategy;

[0153] When it is necessary to balance between low wear rate and high combustion efficiency, coordinate the conflicting objectives through the priority stratification method, and finally output the optimal balance scheme with the best comprehensive performance.

[0154] S4. According to the optimal allocation strategy, dynamically adjust the valve opening, distributor angle, and air flow guiding device of the pulverized coal conveying pipeline to achieve multi-region balanced distribution of pulverized coal in the combustion chamber. Among them, it should be noted in this step that it specifically includes the following content,

[0155] S4.1 Valve opening adjustment:

[0156] (1) Read the pulverized coal flow ratio or valve opening parameters of each pipeline in the optimal allocation strategy output in step S3;

[0157] (2) Calculate the amplitude Δθ of the execution instruction valve , and the upper limit of the response θ of the actuator needs to be considered max and the minimum step size θ min ;

[0158] (3) Issue an adjustment command to increase or decrease the opening degree, and monitor whether the real-time response conforms to the expected value; if there is a deviation, perform secondary fine-tuning.

[0159] (4) Record the final valve opening degree θ valve,i (t), and incorporate it into the subsequent monitoring and feedback loop to dynamically update the pulverized coal flow rate in the pipeline.

[0160] S4.2 Distributor Angle Adjustment:

[0161] (1) According to the set angle φ of the pulverized coal distributor in the optimal distribution strategy * , make corresponding adjustments to the internal guide vanes or rotatable distribution components of the distributor;

[0162] (2) When it is monitored that the wear rate is relatively high in a certain distribution channel or a certain heating surface, preferentially adjust the distributor angle φ adjust to change the pulverized coal flow direction and distribution path;

[0163] (3) Through appropriate shunting, "guide" the pulverized coal to the channels with relatively less wear or lower temperature to ensure that the load in the local high-wear area is reduced;

[0164] (4) If obvious fluctuations in the local temperature distribution are caused after the angle adjustment, further optimize and fine-tune in combination with the real-time feedback data of S1 and S2.

[0165] S4.3 Airflow Guide Device Regulation:

[0166] (1) In combination with the actual working conditions of the combustion chamber, make a coordinated regulation of the ratio of primary air to secondary air, the air supply volume and the flow rate;

[0167] (2) Change the adjustment position δ of the airflow guide device (such as air damper, guide vane, etc.) air , so that it cooperates with the distributor angle adjustment;

[0168] (3) If local high temperature or uneven distribution areas are found, the air supply volume can be increased or decreased to improve the mixing uniformity of pulverized coal and air, and avoid local overheating or local oxygen enrichment;

[0169] (4) Record the real-time parameter δ of the airflow guide air (t) and transmit it back to the upper monitoring system to form a closed-loop regulation.

[0170] S4.4 Monitoring and Dynamic Feedback:

[0171] (1) Periodically monitor all adjusted execution parameters (valve opening degree, distributor angle, airflow guide device position, etc.);

[0172] (2) If the real-time monitoring data (such as pulverized coal flow rate, temperature field distribution, wear rate, etc.) significantly deviates from the expected range, a new round of multi-objective optimization iteration can be triggered (linked with S3), or local correction can be made within the allowable range;

[0173] (3) When the wear rate in a specific area decreases and there is no significant loss in combustion efficiency, the current adjustment plan can be maintained; if a decrease in combustion efficiency is observed, the distributor or air flow distribution parameters need to be appropriately adjusted back to balance efficiency and wear prevention.

[0174] The dynamic adjustment rule of the valve opening degree is that the control period of the actuator can be set according to actual production requirements, such as updating the command every 5 seconds or 10 seconds; if Δθ valve exceeds the hardware limit θ max , it is restricted to θ max and the operator is prompted or the optimization is iterated again; if Δθ valve < θ min , it means that the current adjustment amount is too small and has limited impact on the flow rate, which can be temporarily ignored or accumulated and executed together in the next cycle.

[0175] The adjustment logic of the distributor angle is as follows:

[0176] Multiple distributors in the combustion chamber are controlled in zones, and the distributor angle φ can be set separately. For example:

[0177] φ = [φ1, φ2,..., φ n

[0178] to correspond to the pulverized coal flow directions in different areas;

[0179] When it is monitored that a certain part W high (has a high wear rate), the corresponding φ in this area is preferentially fine-tuned i to direct the pulverized coal or air flow away from the high-wear area;

[0180] A more optimal distribution path can be sought by iteratively searching for the φ value (similar to the microcirculation of the S3 algorithm).

[0181] The coordinated control method of the air flow guiding device lies in:

[0182] During the thermal power generation process, appropriately adjusting the ratio of primary air to secondary air can significantly improve the pulverized coal combustion efficiency and temperature distribution;

[0183] If the adjustment of the valve or distributor this time causes too large a change in the temperature field of the combustion chamber, compensation is made by increasing or decreasing the air supply volume or changing the position of the guiding device δ air .

[0184] ​Keep the air volume adjustment range moderate. For example, the ratio of primary air to secondary air is controlled within the range of [1:1.2, 1:2.0] (specifically determined according to the boiler design) to avoid combustion instability caused by large fluctuations.

[0185] The present invention saves various collected data to form a data record and uses the data record for closed-loop control, which is specifically reflected in:

[0186] The final states of all execution commands (valve opening θ valve , distributor angle φ, air flow orientation δ air ) must be transmitted back to the central control system in real time;

[0187] Compare with the pulverized coal particle concentration, temperature field, wear rate, and combustion efficiency data collected in step S1 to verify the actual execution effect;

[0188] If it is detected that higher wear still occurs in some parts or the combustion efficiency drops significantly, new iterative optimization or manual intervention can be started on the premise of meeting safety constraints until the goal of balancing anti-wear and efficiency is achieved.

[0189] During the adjustment process, both the safety of the equipment (anti-wear) and the combustion efficiency and environmental protection requirements (such as NOx emissions) need to be considered. Therefore, it is necessary to gradually verify and iterate in actual projects;

[0190] For different coal types or load conditions, the adjustment strategies of valve opening and distributor angle can be adjusted accordingly to achieve adaptive expansion.

[0191] It should be understood that in the development process of any actual implementation, such as in any engineering or design project, a large number of specific implementation decisions can be made. Such development efforts may be complex and time-consuming, but for those ordinary technical personnel who benefit from this disclosure, without excessive experimentation, the development efforts will be a routine work of design, manufacturing, and production.

[0192] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for adjustable optimization distribution of pulverized coal under the deep peak shaving state of a coal-fired unit, characterized in that: Including, Real-time collecting pulverized coal conveying parameters, combustion state parameters and equipment wear parameters of the combustion system, and the parameters at least include pulverized coal particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, air flow velocity, equipment wear monitoring data and combustion efficiency index; Constructing a multi-objective optimization model based on the parameters, and the model takes minimizing the equipment wear rate, maximizing the combustion efficiency and the pulverized coal distribution balance as optimization objectives, and the constraint conditions include the pulverized coal conveying flow threshold, the combustion temperature safety range and the air flow uniformity index; Solving the multi-objective model through a dynamic iterative algorithm, generating a candidate allocation scheme, and selecting the optimal allocation strategy based on fuzzy logic evaluation; According to the optimal allocation strategy, dynamically adjusting the valve opening degree, the distributor angle and the air flow guiding device of the pulverized coal conveying pipeline to realize the multi-region balanced distribution of pulverized coal in the combustion chamber.

2. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 1, wherein Including, All kinds of parameters collected in real time are obtained by a multi-sensor network installed on the pulverized coal conveying pipeline, in the combustion chamber and on the key heating surfaces, and specifically include: Arranging pressure sensors, flow sensors and pulverized coal particle concentration sensors on the pulverized coal conveying pipeline to obtain pipeline pressure, pulverized coal conveying flow velocity and real-time particle distribution information; Arranging temperature sensors and flame detection sensors at multiple positions in the combustion chamber to obtain the temperature field distribution and the combustion state; Arranging wear monitoring sensors on the key heating surfaces or easily worn parts to obtain the real-time change data of the equipment wear rate; Arranging a combustion efficiency detection unit on the smoke exhaust side or other suitable positions to evaluate the combustion efficiency index in real time.

3. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 2, wherein: Constructing a multi-objective optimization model based on the parameters includes, According to the real-time data obtained, defining the mathematical characterization functions of the equipment wear rate, the combustion efficiency and the pulverized coal distribution balance; Establishing the following multi-objective optimization model: Minimize: F(X) = α·W(X) - β·E(X) - γ·U(X) Wherein, α, β, and γ are respectively weight coefficients, which are determined according to production requirements and operating conditions. Let W(X) be the equipment wear rate function, E(X) be the combustion efficiency function, and U(X) be the pulverized coal distribution balance function.

4. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 3, characterized in that: The specific constraint content of the constraint conditions includes, Pulverized coal conveying flow threshold, combustion temperature safety range, air flow uniformity index, equipment force limitation, operation execution delay.

5. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 4, characterized in that: The dynamic iterative algorithm includes, Based on the foregoing multi-objective optimization model, reading the parameters collected in real time, setting the initial solution, and specifying the number of iterations; In each iteration, using a multi-objective optimization algorithm to perform crossover, mutation and fitness evaluation on the candidate solutions; among them, the fitness function is comprehensively given by the equipment wear rate, the combustion efficiency and the distribution balance; Correcting or discarding the candidate solutions that do not meet the constraint conditions, and re-evaluating the fitness after correction; When the iteration reaches the preset number of times, outputting the set of candidate allocation schemes obtained by the current iteration.

6. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 5, wherein: The candidate allocation scheme includes, For multiple pulverized coal conveying pipelines, giving the corresponding pulverized coal flow or valve opening degree parameters for each pipeline; Setting the air duct air flow distribution scheme, including the air supply volume and the matching ratio of the secondary air and the primary air; For different regions in the combustion chamber, giving the corresponding pulverized coal and air supply distribution. Optimize the air supply angle at the position where a high wear rate is detected.

7. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 6, characterized in that: The optimal allocation strategy includes For the set of candidate allocation schemes output by the dynamic iterative algorithm, according to the fuzzy logic evaluation rules, conduct multi-factor scoring on the equipment wear rate, combustion efficiency, allocation balance, and operability; Based on the preset weights, select the candidate scheme with the highest fuzzy evaluation score as the optimal allocation strategy; After implementing the optimal allocation strategy, monitor the operation effect of the system in real time, and input the monitoring data into the fuzzy logic evaluation module. If the evaluation result deviates from the expected target, trigger a new round of iterative optimization or make fine adjustments to the strategy; When the optimal allocation strategy needs to balance between a low wear rate and high combustion efficiency, coordinate the conflicting objectives through hierarchical priorities, and finally output a comprehensive optimal scheme that takes into account both the equipment life and combustion performance.

8. The method for adjustable optimization allocation of pulverized coal under the deep peak shaving state of a coal-fired unit according to claim 7, characterized in that: The specific content of dynamically adjusting the valve opening of the pulverized coal conveying pipeline, the angle of the distributor, and the air flow guiding device includes According to the pulverized coal flow ratio of each pipeline determined by the optimal allocation strategy, send an instruction to increase or decrease the opening of the valve actuator, and the adjustment range needs to be combined with the response upper limit and the minimum step size of the actuator; Adjust the angle of the internal guide vane or the rotatable distribution component of the pulverized coal distributor to change the pulverized coal flow direction and distribution path; when a high wear rate is detected at a certain location, adjust the distributor angle to divert the pulverized coal to the channel with less wear.

9. The method for optimizing the adjustable allocation of pulverized coal under the deep peak shaving state of a coal-fired unit according to claim 8, wherein: The solution with the highest fuzzy evaluation score is defined as the optimal allocation strategy X * ; The expression form of the optimal allocation strategy is written as: Among them, represents the optimal pulverized coal flow rate of the m-th pipeline, represents the air volume distribution parameter of the air duct, represents the distributor angle.

10. The pulverized coal adjustable optimization distribution method based on the deep peak shaving state of a coal-fired unit according to claim 9, characterized in that: The adjustment of the valve opening includes Read the valve opening parameters of each pipeline in the optimal allocation strategy output in step S3; Calculate the amplitude Δθ for executing the instruction valve , the response upper limit θ of the actuator needs to be considered max and the minimum step size θ min ; Send an adjustment command to increase or decrease the opening, and monitor whether the real-time response conforms to the expected value; If there is a deviation, make secondary fine adjustments; Record the final valve opening θ valve，i (t), and incorporate it into the subsequent monitoring and feedback loop to dynamically update the pulverized coal flow rate in the pipeline.

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