A pulverized coal adjustable optimization distribution method based on a deep peak regulation state of a coal-fired unit

Abstract

The present application relates to a kind of coal powder adjustable optimization distribution method based on the deep peak shaving state of coal-fired unit, including the real-time acquisition of coal powder conveying parameter, combustion state parameter and equipment wear parameter of combustion system;Multi-objective optimization model is constructed based on the parameters, the model minimizes equipment wear rate, maximizes combustion efficiency and coal powder distribution balance as optimization target;Through dynamic iteration algorithm, the multi-objective model is solved, candidate distribution scheme is generated, and optimal distribution strategy is selected based on fuzzy logic evaluation;According to the optimal distribution strategy, the valve opening of coal powder conveying pipeline, distributor angle and airflow guide device are dynamically adjusted, to realize the multi-region balanced distribution of coal powder in combustion chamber;Through real-time acquisition of multi-dimensional parameters and establishment of multi-objective dynamic optimization model, the coordinated optimization control between combustion efficiency and equipment wear is realized.

Description

Technical Field

[0001] This invention belongs to the field of pulverized coal combustion control technology, specifically a method for adjustable and optimized allocation of pulverized coal under deep peak shaving conditions in coal-fired power units. Background Technology

[0002] In recent years, with the continuous growth of energy demand, the thermal power generation sector has gradually developed towards larger capacity, higher parameters, and higher efficiency, and pulverized coal combustion technology has also undergone significant progress and optimization. Traditional pulverized coal combustion optimization technologies mainly focus on improving the uniformity of pulverized coal and air mixing and improving combustion efficiency, such as optimizing the ratio of primary air to secondary air, improving pulverized coal pipeline design, and modifying burner structure, in order to achieve complete pulverized coal combustion and improve equipment operating economy. However, in practical applications, 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, leading to the emergence of local high-temperature areas and a decrease in combustion efficiency. In addition, uneven pulverized coal airflow distribution may also cause severe wear in local areas of key equipment, such as conveying pipelines, distributors, and combustion chamber heating surfaces, significantly shortening equipment lifespan and increasing operating and maintenance costs.

[0003] To address the aforementioned issues, existing pulverized coal distribution methods primarily control pulverized coal conveying parameters through static or quasi-static manual adjustments. These methods rely on operator experience and offline measurement data for decision-making, resulting in low adjustment precision and an inability to dynamically adapt to changes in coal quality and load fluctuations in real time, making it difficult to achieve optimized and precise control of pulverized coal distribution. Furthermore, most traditional optimization methods are single-objective optimizations, such as simply improving combustion efficiency or reducing wear, lacking effective coordination of the conflicting relationships between multiple objectives. This makes it difficult to simultaneously consider pulverized coal distribution balance, combustion efficiency, and equipment wear control. Therefore, existing pulverized coal distribution technologies often suffer from poor optimization effects, slow response speeds, and an inability to meet the dynamic operating conditions of modern large-scale power plants in actual operation.

[0004] In summary, existing pulverized coal conveying and combustion technologies generally suffer from uneven pulverized coal distribution, severe equipment wear, and a lack of dynamic real-time optimization control methods. The adjustable optimization distribution method for pulverized coal based on deep peak shaving of coal-fired units proposed in this invention aims to solve the above problems. By collecting multi-dimensional parameters in real time and establishing a multi-objective dynamic optimization model, it achieves coordinated optimization control between combustion efficiency and equipment wear. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] Given the following technical problems in the existing technology: existing pulverized coal conveying and combustion technologies generally suffer from uneven pulverized coal distribution, severe equipment wear, and a lack of dynamic real-time optimization control methods.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for adjustable and optimized allocation of pulverized coal under deep peak-shaving conditions in coal-fired power units, characterized in that it includes:

[0008] Real-time acquisition of coal powder conveying parameters, combustion status parameters, and equipment wear parameters of the combustion system. The parameters include at least coal powder particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, airflow velocity, equipment wear monitoring data, and combustion efficiency indicators.

[0009] A multi-objective optimization model is constructed based on the parameters. The model aims to minimize equipment wear rate, maximize combustion efficiency and coal powder distribution uniformity. The constraints include coal powder conveying flow rate threshold, safe range of combustion temperature and airflow uniformity index.

[0010] The multi-objective model is solved by a dynamic iterative algorithm to generate candidate allocation schemes, and the optimal allocation strategy is selected based on fuzzy logic evaluation.

[0011] Based on the optimal allocation strategy, the valve opening, distributor angle, and airflow guiding device of the pulverized coal conveying pipeline are dynamically adjusted to achieve balanced multi-zone distribution of pulverized coal in the combustion chamber.

[0012] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

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

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

[0015] Temperature sensors and flame detection sensors are deployed at multiple locations in the combustion chamber to obtain the temperature field distribution and combustion status.

[0016] Wear monitoring sensors are installed on key heated surfaces or easily worn parts to obtain real-time data on changes in equipment wear rate;

[0017] A combustion efficiency detection unit is installed on the exhaust side or at another suitable location to evaluate the combustion efficiency index in real time.

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

[0019] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

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

[0021] Based on the acquired real-time data, mathematical characterization functions for equipment wear rate, combustion efficiency, and coal powder distribution uniformity are defined.

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

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

[0024] Where α, β, and γ are weighting coefficients, determined according to production needs 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 uniformity function.

[0025] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

[0026] The specific constraints mentioned include:

[0027] Coal powder conveying flow rate threshold, safe range of combustion temperature, airflow uniformity index, equipment stress limit, and operation execution delay.

[0028] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

[0029] The dynamic iteration algorithm includes,

[0030] Based on the aforementioned multi-objective optimization model, the parameters collected in real time are read, an initial solution is set, and the number of iterations is specified;

[0031] In each iteration, a multi-objective optimization algorithm is used to perform crossover, mutation, and fitness evaluation on candidate solutions; the fitness function is given by a combination of equipment wear rate, combustion efficiency, and allocation balance.

[0032] Candidate solutions that do not meet the constraints (such as pulverized coal flow rate, temperature limit, airflow uniformity, etc.) are modified or discarded, and the fitness is re-evaluated after modification.

[0033] When the preset number of iterations is reached, the set of candidate allocation schemes obtained in the current iteration is output.

[0034] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

[0035] The candidate allocation scheme includes,

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

[0037] Set up an airflow distribution scheme for the duct, including the supply air volume and the ratio of secondary air to primary air;

[0038] For different areas within the combustion chamber, corresponding pulverized coal and air supply distribution are provided;

[0039] For areas with high wear rates, optimize the air supply angle at those locations.

[0040] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

[0041] The optimal allocation strategy includes,

[0042] For the set of candidate allocation schemes output by the dynamic iterative algorithm, the equipment wear rate, combustion efficiency, allocation balance and operability are scored by multiple factors according to the fuzzy logic evaluation rules.

[0043] Based on pre-set weights, the candidate solution with the highest fuzzy evaluation score is selected as the optimal allocation strategy;

[0044] After executing the optimal allocation strategy, the system's performance is monitored in real time, and the monitoring data is input into the fuzzy logic evaluation module. If the evaluation result deviates from the expected target, a new round of iterative optimization or fine-tuning of the strategy is triggered.

[0045] When the optimal allocation strategy needs to balance low wear rate and high combustion efficiency, conflicting objectives are coordinated through hierarchical prioritization, and the final output is a comprehensive optimal solution that takes into account both equipment life and combustion performance.

[0046] As a preferred technical solution for an adjustable and optimized pulverized coal distribution method under deep peak-shaving conditions in coal-fired power units,

[0047] The specific details of dynamically adjusting the valve opening, distributor angle, and airflow guiding device of the pulverized coal conveying pipeline include:

[0048] Based on the pulverized coal flow ratio determined by the optimal allocation strategy, commands are issued to the valve actuator to increase or decrease the opening degree. 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 pulverized coal distributor can be adjusted to change the flow direction and distribution path of the pulverized coal. When a high wear rate is detected at a certain point, the angle of the distributor can be adjusted appropriately to divert the pulverized coal to a channel with less wear.

[0050] The beneficial effects of this invention are as follows: Step S1, by real-time acquisition of pulverized coal conveying parameters, combustion state parameters, and equipment wear parameters of the combustion system, including pulverized coal particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, airflow velocity, equipment wear monitoring data, and combustion efficiency indicators, can comprehensively and accurately grasp the current operating status of the combustion system, forming a complete data closed-loop feedback system. Through real-time monitoring and recording of the above data, the necessary data information foundation for precise optimization control is provided, ensuring the reliability and accuracy of subsequent optimization decisions, ultimately achieving the beneficial effect of rapid response to changes in actual operating conditions and avoiding misadjustment or loss of control caused by data lag.

[0051] Step S2 involves constructing a multi-objective optimization model based on the parameters collected in step S1. This model aims to minimize equipment wear rate, maximize combustion efficiency, and achieve balanced coal powder distribution. It sets multi-dimensional constraints, including a coal powder delivery flow rate threshold, a safe combustion temperature range, and airflow uniformity indicators, thus achieving a quantitative description and effective trade-off of system operation objectives. By clearly defining the target relationship and boundary constraints between equipment wear and combustion efficiency, extreme operating conditions are prevented during optimization. This avoids the situation where combustion efficiency is pursued at the expense of equipment safety, ultimately achieving a beneficial balance between equipment protection, improved combustion efficiency, and balanced coal powder distribution.

[0052] Step S3 involves solving the multi-objective optimization model constructed in step S2 using a dynamic iterative algorithm to generate several candidate allocation schemes. The optimal allocation strategy is then selected using a fuzzy logic evaluation method. This effectively overcomes the shortcomings of traditional static optimization strategies, which are often singular and rigid, ensuring the dynamic adaptability of the selected allocation scheme to complex actual combustion conditions. Specifically, through continuous optimization using the dynamic iterative algorithm and the multi-dimensional evaluation process using fuzzy logic, the final selected scheme achieves a comprehensive balance between equipment wear rate, pulverized coal distribution uniformity, and combustion efficiency. This enables the optimized scheme to adaptively adjust to real-time changing conditions, effectively improving the control accuracy and robustness of the combustion system against complex load changes.

[0053] Step S4: Based on the optimal distribution strategy obtained in step S3, the valve opening, distributor angle, and airflow guiding device of the pulverized coal conveying pipeline are dynamically adjusted to achieve balanced distribution of pulverized coal in multiple areas of the combustion chamber. This effectively solves the problems of localized high temperatures and severe equipment wear caused by uneven distribution of pulverized coal in the combustion chamber. By implementing precise dynamic control of pulverized coal flow rate and airflow path, not only is refined regulation of the combustion process achieved, reducing wear on key parts of the combustion equipment and extending the service life of the equipment, but also a significant improvement in combustion efficiency is achieved. This results in the beneficial effects of reducing equipment maintenance costs and improving the economic efficiency and safety of equipment operation. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

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

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0059] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0060] Example 1

[0061] Reference Figure 1 This embodiment provides a method for adjustable and optimized allocation of pulverized coal under deep peak-shaving conditions in coal-fired power units, specifically including the following steps:

[0062] S1. Real-time acquisition of pulverized coal conveying parameters, combustion state parameters, and equipment wear parameters of the combustion system. These parameters include at least pulverized coal particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, airflow velocity, equipment wear monitoring data, and combustion efficiency indicators. It should be noted that in this step:

[0063] S1.1 acquires the following real-time data through a multi-sensor network deployed in pulverized coal conveying pipelines, combustion chambers, and key heating surfaces:

[0064] (1) Coal powder particle distribution: It is detected by a particle concentration sensor installed in the coal powder conveying pipeline and is denoted as C(t). It is used to characterize the coal powder particle concentration and distribution at the cross section through which the pipeline flows per unit time.

[0065] (2) Pressure of conveying pipeline: It is measured by a pressure sensor installed on the outer wall or interface of the pulverized coal conveying pipeline and is recorded as P(t). It is used to monitor the dynamic changes of pulverized coal conveying pressure in the combustion system.

[0066] (3) Temperature field distribution in the combustion chamber: Temperature is obtained by deploying temperature sensors at multiple key locations in the combustion chamber and recorded as T(t,x,y,z). Local temperature values ​​can be recorded based on the three-dimensional coordinates (x,y,z) of the combustion chamber.

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

[0068] (5) Equipment wear monitoring data: obtained by wear sensors installed on key heated surfaces or easily worn parts, denoted as W(t), which reflects the change in equipment wear rate over time;

[0069] (6) Combustion efficiency index: The combustion efficiency is measured by the combustion efficiency detection unit on the exhaust side or other suitable locations and is denoted as E(t). It is used to evaluate the real-time efficiency level of the combustion process under different loads and operating conditions.

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

[0071] S1.3 constructs the above parameters into a multidimensional data vector X(t), where

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

[0073] Where C(t) represents the distribution of pulverized coal particles; P(t) represents the pressure of the conveying pipeline; T(t,x,y,z) represents the temperature field distribution in the combustion chamber; V(t) represents the airflow 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) are the internal coordinates of the combustion chamber.

[0074] S1.4 The collected multidimensional data vector X(t) is sent to the data processing and monitoring platform, and the real-time data is initially screened and cleaned according to the pre-set threshold, anomaly judgment rules and data integrity verification process to remove obviously erroneous or failed sensor data.

[0075] S1.5 inputs the filtered and cleaned multidimensional data into the modeling and optimization unit on which subsequent steps depend, so as to lay the data foundation for multi-objective analysis and real-time optimization of pulverized coal distribution and anti-wear control.

[0076] S2. Construct a multi-objective optimization model based on the parameters. The model aims to minimize equipment wear rate, maximize combustion efficiency, and achieve uniform coal powder distribution. Constraints include a coal powder conveying flow rate threshold, a safe combustion temperature range, and airflow uniformity indicators. It should be noted that in this step:

[0077] Based on the acquired real-time data, mathematical characterization functions for equipment wear rate, combustion efficiency, and coal powder distribution uniformity are defined.

[0078] Establish the following multi-objective optimization model:

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

[0080] Where α, β, and γ are weighting coefficients, determined according to production needs 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 uniformity function.

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

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

[0083] The equipment wear rate function W(X) is used to quantify the wear condition of key heated surfaces or easily worn parts;

[0084] The combustion efficiency function E(X) is used to characterize the degree of complete combustion of pulverized coal per unit time. It can usually be calculated in combination with thermal efficiency, flue gas composition or flame detection indicators.

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

[0086] S2.2 Combining the three objective functions mentioned above, weighting coefficients α, β, and γ (all non-negative real numbers, and α+β+γ=1 or can be scaled proportionally according to technical requirements under a certain operating condition) are set to reflect the priority of different optimization objectives in the overall strategy. Based on the multi-objective optimization concept, the comprehensive problem of "minimizing equipment wear rate W(X)", "maximizing combustion efficiency E(X)", and "coal powder distribution balance 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] Where W(X) is the equipment wear rate function, it should be as small as possible;

[0089] E(X) is the combustion efficiency function, which should be as large as possible. Therefore, it appears in the form of "-β·E(X)" in the overall objective function.

[0090] U(X) is the coal powder distribution uniformity function. The larger the value, the more uniform the distribution. It appears in the objective function in the form of "-γ·U(X)" to maximize the distribution uniformity.

[0091] X represents the decision variables to be optimized (including valve opening, pulverized coal flow distribution, air volume and velocity adjustment, etc.), which comprehensively reflect the various adjustable parameters of the combustion system control.

[0092] S2.3 When constructing the objective function, the following constraints need to be defined (to meet the requirements of combustion safety and actual operating conditions):

[0093] Coal powder conveying flow rate threshold constraint:

[0094] Q min ≤Q pipe，i (t)≤Q max ,

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

[0096] 2) Combustion temperature safety range constraints:

[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 These are the upper and lower limits of the safe operating range of the combustion chamber;

[0099] 3) Constraints on airflow uniformity:

[0100] ΔV(t)≤δ

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

[0102] 4) Equipment stress limitations:

[0103] |F mechanical，j (t)|≤F limit

[0104] Among them, F mechanical，j (t) represents the actual load or stress value borne by a key heated surface or mechanical component j at time t; F limit Designed for safety;

[0105] 5) Operation execution delay:

[0106] Δt exec ≤τ allow

[0107] Where, Δt exec This indicates the time difference between issuing a regulation command and executing it on the valve opening, distributor angle, and airflow guiding device; τ allow This is a limit on the allowable operational delay.

[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] Wherein, Ω represents the set of all feasible decision spaces, including the range of values ​​for adjustable parameters such as valve opening, pulverized coal flow ratio, air supply volume, and distributor angle.

[0118] S2.5 uses the above-mentioned multi-objective optimization model as the core mathematical framework for combustion control and equipment wear prevention, providing objective functions and constraints for the dynamic iterative algorithm or other optimization strategies (such as evolutionary algorithms, fuzzy logic decision-making, etc.) adopted in step S3, so as to achieve real-time and comprehensive optimal control of the combustion system.

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

[0120] S3.1 Initialization and Data Acquisition

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

[0122] (2) Obtain the parameters collected in real time in step S1 (such as coal particle distribution, temperature field distribution, equipment wear rate, combustion efficiency index, etc.) 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 N of the number of iterations. max Population size (or number of candidate solutions) P, crossover rate, mutation rate, etc., the specific values ​​can be set according to production needs or experience.

[0124] S3.2 Iterative generation of candidate solutions

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

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

[0127] Mutation operations are performed on some of the descendant solutions to change adjustable parameters such as valve opening, pulverized coal flow distribution, or airflow distribution in the duct, in order to enhance the diversity of the search.

[0128] (2) Fitness assessment:

[0129] Based on the equipment wear rate function W(X), combustion efficiency function E(X), and pulverized coal distribution uniformity function U(X), a comprehensive evaluation is performed 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 weighting 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 candidate solution is in terms of wear rate, combustion efficiency and distribution balance.

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

[0134] If a candidate solution violates the constraints set in step S2 (such as pulverized coal flow threshold, temperature range, airflow uniformity index, equipment stress limit, etc.), the relevant candidate solution will be corrected.

[0135] After correction, reassess its fitness; if it cannot be corrected or still seriously violates the constraints after correction, 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" cycle until the preset number of iterations N is reached. max Or it satisfies the convergence criterion;

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

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

[0140] Coal powder flow rate or valve opening parameters corresponding to multiple coal powder conveying pipelines;

[0141] Airflow distribution scheme for ducts (including supply air volume and the ratio of secondary air to primary air);

[0142] The distribution of pulverized coal and air in different areas of the combustion chamber is differentiated for areas with localized high temperatures or uneven distribution.

[0143] For areas where high wear rates are detected, corresponding measures to optimize the air supply angle or pulverized coal flow direction are provided to reduce wear at those locations.

[0144] S3.4 Fuzzy Logic Evaluation and Optimal Allocation Strategy Selection

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

[0146] Set up fuzzy membership functions or fuzzy rule bases, and score them comprehensively based on dimensions such as "low wear rate", "high efficiency", "high distribution uniformity" and "ease of operation".

[0147] The scheme with the highest fuzzy evaluation score is designated as the optimal allocation strategy X. * ;

[0148] The optimal allocation strategy can be expressed as:

[0149]

[0150] in, This represents the optimal pulverized coal flow rate (or valve opening) for the m-th pipeline. This indicates the airflow distribution parameters in the duct. This indicates adjustable parameters such as the distributor angle.

[0151] (3) Strategy adjustment and feedback:

[0152] If the real-time monitoring data deviates or fails to meet the target after the optimal allocation strategy is executed, a new round of iterative optimization or fine-tuning of the strategy will be triggered.

[0153] When a balance needs to be struck between low wear rate and high combustion efficiency, conflicting objectives are coordinated through a priority-based hierarchical approach, ultimately outputting a balanced solution with optimal overall performance.

[0154] S4. Based on the optimal allocation strategy, dynamically adjust the valve opening, distributor angle, and airflow guiding device of the pulverized coal conveying pipeline to achieve balanced multi-zone distribution of pulverized coal within the combustion chamber. This step specifically includes the following:

[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 executed instruction. valve The response limit θ of the implementing agency needs to be considered. max and minimum step size θ min ;

[0158] (3) Issue adjustment commands to increase or decrease the opening degree, and monitor whether the real-time response matches the expected value; if a deviation occurs, perform a second fine adjustment;

[0159] (4) 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.

[0160] S4.2 Distributor Angle Adjustment:

[0161] (1) Based on the set angle φ of the pulverized coal distributor in the optimal allocation strategy * Adjust the internal guide vanes or rotatable distribution components of the distributor accordingly.

[0162] (2) When a high wear rate is detected at a certain distribution channel or a certain heated surface, the distributor angle φ should be adjusted first. adjust To change the flow direction and distribution path of pulverized coal;

[0163] (3) By appropriately diverting the flow, the pulverized coal is "guided" to channels with relatively less wear or lower temperature, so as to ensure that the load in the local high wear area is reduced;

[0164] (4) If the angle adjustment causes significant fluctuations in the local temperature distribution, further optimization and fine-tuning will be carried out by combining the real-time feedback data of S1 and S2.

[0165] S4.3 Airflow guiding device control:

[0166] (1) Based on the actual working conditions of the combustion chamber, the ratio of primary air to secondary air, air volume and flow rate are controlled in a coordinated manner.

[0167] (2) Change the adjustment position δ of the airflow guiding device (such as damper, guide vane, etc.) air This allows it to work in coordination 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 uniformity of coal powder and air mixing and avoid local overheating or local oxygen enrichment.

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

[0170] S4.4 Monitoring and Dynamic Feedback:

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

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

[0173] (3) When the wear rate of a specific area decreases and the combustion efficiency is not significantly reduced, the current adjustment scheme can be maintained; if a decrease in combustion efficiency is observed, the distributor or airflow distribution parameters need to be adjusted appropriately to balance efficiency and wear prevention.

[0174] The dynamic adjustment rule for valve opening is that the control cycle of the actuator can be set according to actual production needs, such as updating the command every 5 or 10 seconds; if Δθ valve Exceeding the hardware limit θ max Then it is restricted to θ max And prompt the operator or iterate and optimize again; if Δθ valve <θ min If the current adjustment amount is too small, it will have a limited impact on the flow rate and can be temporarily ignored or accumulated to be executed together in the next cycle.

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

[0176] Multiple distributors within the combustion chamber can be controlled in separate zones, and the angle φ of each distributor can be set individually, for example:

[0177] φ = [φ1, φ2, ..., φ n ]

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

[0179] When a certain part W is detected high When the wear rate is high, prioritize fine-tuning the φ corresponding to that area. i This directs the pulverized coal or airflow away from the high-wear zone;

[0180] A better allocation path can be sought by iteratively searching the value of φ (similar to the micro-loop of the S3 algorithm).

[0181] The coordinated control method of airflow guiding devices lies in:

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

[0183] If the adjustment of the valve or distributor causes excessive changes in the combustion chamber temperature field, adjust the air supply by increasing or decreasing the air volume or changing the guide device δ. air To compensate for the location;

[0184] Maintain a moderate air volume adjustment range, such as controlling the ratio of primary air to secondary air within the range of [1:1.2, 1:2.0] (the specific ratio depends on the boiler design), to avoid large fluctuations that could lead to unstable combustion.

[0185] This invention saves the collected data into data records and uses these data records for closed-loop control, specifically in the following ways:

[0186] All executed commands (valve opening θ) valve Distributor angle φ, airflow guide δ air The final status of the data must be transmitted back to the central control system in real time.

[0187] The data on coal powder particle concentration, temperature field, wear rate, and combustion efficiency collected in step S1 are compared to verify the actual implementation effect.

[0188] If high wear or a significant decrease in combustion efficiency is detected in certain parts, new iterative optimizations or manual interventions can be initiated while meeting safety constraints, until the goal of balancing wear prevention and efficiency is achieved.

[0189] During the adjustment process, it is necessary to ensure the safety of the equipment (wear prevention) while also taking into account combustion efficiency and environmental protection requirements (such as NOx emissions). Therefore, it is necessary to gradually verify and iterate in actual engineering.

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

[0191] It should be understood that numerous specific implementation decisions can be made during the development of any practical implementation, such as in any engineering or design project. Such development efforts may be complex and time-consuming, but for those skilled in the art who benefit from this disclosure, the development effort will be a routine work of design, manufacturing, and production without requiring much experimentation.

[0192] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for adjustable and optimized allocation of pulverized coal under deep peak-shaving conditions in coal-fired power units, characterized in that: This includes real-time acquisition of coal powder conveying parameters, combustion status parameters, and equipment wear parameters of the combustion system. The parameters include at least coal powder particle distribution, conveying pipeline pressure, combustion chamber temperature field distribution, airflow velocity, equipment wear monitoring data, and combustion efficiency indicators. A multi-objective optimization model is constructed based on the parameters. The model aims to minimize equipment wear rate, maximize combustion efficiency and coal powder distribution uniformity. The constraints include coal powder conveying flow rate threshold, safe range of combustion temperature and airflow uniformity index. The multi-objective optimization model is solved by a dynamic iterative algorithm to generate candidate allocation schemes, and the optimal allocation strategy is selected based on fuzzy logic evaluation. According to the optimal distribution strategy, the valve opening, distributor angle and airflow guiding device of the pulverized coal conveying pipeline are dynamically adjusted to achieve balanced distribution of pulverized coal in multiple areas of the combustion chamber. Constructing a multi-objective optimization model based on the parameters includes defining mathematical representation functions for equipment wear rate, combustion efficiency, and coal powder distribution uniformity based on the acquired real-time data. Establish the following multi-objective optimization model: ； Where α, β, and γ are weighting coefficients, determined based on production needs and operating conditions. Let the wear rate be a function of the equipment. Let be the combustion efficiency function. This is a function for the uniformity of pulverized coal distribution. The candidate allocation scheme includes: For multiple pulverized coal conveying pipelines, provide the corresponding pulverized coal flow rate or valve opening parameters for each pipeline; Set up an airflow distribution scheme for the duct, including the supply air volume and the ratio of secondary air to primary air; For different areas within the combustion chamber, corresponding pulverized coal and air supply distribution are provided; For areas with high wear rates, optimize the airflow angle at those locations; The specific details of dynamically adjusting the valve opening, distributor angle, and airflow guiding device of the pulverized coal conveying pipeline include: Based on the pulverized coal flow ratio determined by the optimal allocation strategy, commands are issued to the valve actuator to increase or decrease the opening degree. The adjustment range needs to be combined with the upper limit of the actuator's response and the minimum step size. Adjust the angle of the guide vanes or rotatable distribution components inside the pulverized coal distributor to change the flow direction and distribution path of the pulverized coal; when a high wear rate is detected at a certain point, adjust the angle of the distributor to divert the pulverized coal to a channel with less wear.

2. The adjustable and optimized coal powder allocation method based on deep peak-shaving state of coal-fired power units according to claim 1, characterized in that, The various parameters acquired in real time are obtained by a multi-sensor network installed in the pulverized coal conveying pipeline, combustion chamber, and key heating surfaces. Specifically, this includes: deploying pressure sensors, flow sensors, and pulverized coal particle concentration sensors on the pulverized coal conveying pipeline to acquire pipeline pressure, pulverized coal conveying velocity, and real-time particle distribution information; deploying temperature sensors and flame detection sensors at multiple locations in the combustion chamber to acquire temperature field distribution and combustion status; deploying wear monitoring sensors on key heating surfaces or easily worn parts to acquire real-time change data of equipment wear rate; and deploying a combustion efficiency detection unit on the exhaust side to evaluate combustion efficiency indicators in real time.

3. The adjustable and optimized coal powder allocation method based on deep peak shaving of coal-fired units according to claim 2, characterized in that, The specific constraints include: Coal powder conveying flow rate threshold, safe range of combustion temperature, airflow uniformity index, equipment stress limit, and operation execution delay.

4. The adjustable and optimized coal powder allocation method based on deep peak shaving state of coal-fired power units according to claim 3, characterized in that: The dynamic iterative algorithm includes reading parameters collected in real time, setting an initial solution, and specifying the number of iterations based on the aforementioned multi-objective optimization model; In each iteration, a multi-objective optimization algorithm is used to perform crossover, mutation, and fitness evaluation on candidate solutions; the fitness function is given by a combination of equipment wear rate, combustion efficiency, and allocation balance. Candidate solutions that do not meet the constraints are modified or discarded, and the fitness is re-evaluated after modification. When the preset number of iterations is reached, the set of candidate allocation schemes obtained in the current iteration is output.

5. The adjustable and optimized coal powder allocation method based on deep peak shaving of coal-fired units according to claim 4, characterized in that: The optimal allocation strategy includes a set of candidate allocation schemes output by the dynamic iterative algorithm, and multi-factor scoring of equipment wear rate, combustion efficiency, allocation balance and operability based on fuzzy logic evaluation rules. Based on pre-set weights, the candidate solution with the highest fuzzy evaluation score is selected as the optimal allocation strategy; After executing the optimal allocation strategy, the system's performance is monitored in real time, and the monitoring data is input into the fuzzy logic evaluation module. If the evaluation result deviates from the expected target, a new round of iterative optimization or fine-tuning of the strategy is triggered. When the optimal allocation strategy needs to balance low wear rate and high combustion efficiency, conflicting objectives are coordinated through hierarchical prioritization, and the final output is a comprehensive optimal solution that takes into account both equipment life and combustion performance.

6. The adjustable and optimized coal powder distribution method based on deep peak shaving state of coal-fired power units according to claim 5, characterized in that: The solution with the highest fuzzy evaluation score is designated as the optimal allocation strategy. ; The optimal allocation strategy can be expressed as: ； in, This represents the optimal pulverized coal flow rate for the m-th pipeline. This indicates the airflow distribution parameters in the duct. Indicates the angle of the distributor.

7. The adjustable and optimized coal powder distribution method based on deep peak shaving state of coal-fired power units according to claim 6, characterized in that: The valve opening adjustment includes reading the valve opening parameters of each pipeline in the optimal allocation strategy; Calculate the range of the executed instructions The response capacity of the implementing agency needs to be considered. and minimum step size ; Issue adjustment commands to increase or decrease the opening degree, and monitor whether the real-time response matches the expected value; If a deviation occurs, a second fine-tuning will be performed. Record the final valve opening. This information is then incorporated into subsequent monitoring and feedback loops to dynamically update the pulverized coal flow rate within the pipeline.

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