Ammonia-coal ratio dynamic regulation and control method and system

Abstract

The invention relates to the technical field of mixed combustion power generation, in particular to an ammonia-coal ratio dynamic regulation and control method and system.The method comprises the steps that working condition signals including the boiler load, the hearth temperature, the primary air volume, the NOx tail gas concentration, the ammonia feeding amount and the coal feeding amount are collected; establishing a discrete time state space model based on the working condition signal, and vectorizing model parameters; carrying out online identification on vectorized model parameters by adopting a hybrid algorithm based on a particle swarm and recursive least squares; according to the identified current model parameters, a preset prediction step length and a control step length, a model prediction control objective function is constructed, and an optimal future control increment sequence is obtained through online solving of a particle swarm algorithm; and dynamically regulating and controlling the ammonia-coal ratio by using the optimal control increment sequence. According to the method, the model can automatically track working condition changes, and the adaptability is high; on the premise of meeting emission indexes, the ammonia feeding amount is minimized, ammonia water is saved, and the cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hybrid combustion power generation, and particularly relates to an ammonia-coal ratio dynamic regulation method and system. BACKGROUND

[0002] Although the installed capacity of renewable energy has exceeded that of coal power, due to the fact that the available hours of solar energy and wind energy are much lower than that of coal power, there is still a certain gap between the power generation of renewable energy and the power generation of coal power, and in the short term, energy security still needs to be guaranteed by coal power. In the long term, coal power is transforming from a main power supply to a basic and system regulating power supply that provides reliable capacity, peak regulation, and frequency modulation.

[0003] Modern coal-fired power plants often use ammonia injection denitration technology to reduce NO x x emissions, and the denitration efficiency is highly dependent on the ratio of ammonia injection to coal quantity (i.e., ammonia-coal ratio). However, the traditional ammonia-coal ratio is generally set as a fixed value or adjusted based on experience, which is difficult to cope with dynamic disturbances such as load fluctuations and fuel heat value changes, and is prone to cause a decrease in denitration efficiency or ammonia escape.

[0004] The information disclosed in this BACKGROUND section is only intended to enhance the understanding of the general background of the application, and should not be considered as recognition or suggestion that this information forms the prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application provides an ammonia-coal ratio dynamic regulation method and system, thereby effectively solving the problems in the background art.

[0006] In order to achieve the above purpose, the technical solution adopted by the present application is as follows: an ammonia-coal ratio dynamic regulation method, comprising the following steps:

[0007] Collecting working condition signals including boiler load, furnace temperature, primary air quantity, NO x x concentration, injected ammonia quantity, and coal quantity;

[0008] Establishing a discrete-time state space model based on the working condition signals, and vectorizing the model parameters;

[0009] Using a hybrid algorithm based on particle swarm and recursive least squares to perform online identification on the vectorized model parameters;

[0010] According to the identified current model parameters, a preset prediction step, and a control step, constructing a model predictive control objective function, and obtaining an optimal future control increment sequence by online solving through a particle swarm algorithm;

[0011] Dynamically regulating the ammonia-coal ratio by using the optimal control increment sequence.

[0012] Further, the discrete-time state space model is:

[0013]

[0014] wherein x is a state vector x=[T f , C NOx ] T , T f is a furnace temperature, C NOx is a NO x tail gas concentration, u is a control variable, i.e., an ammonia / coal ratio u=a / m, a is an ammonia injection amount, and m is a coal injection amount; d is a disturbance variable, d=[Q, F air ] T , Q is a boiler load, and F air is a primary air flow, A, B, C, and E are model parameter matrices of state variables, control variables, output variables, and disturbance variables, respectively, y is a predicted NO x tail gas concentration, k is a kth sampling time, and a superscript T represents a transpose of a matrix or a vector, which converts a column vector into a row vector.

[0015] Further, the vectorization of the model parameters includes:

[0016] The matrices A, B, C, and E are respectively expanded into one-dimensional vectors to obtain a vectorized matrix θ:

[0017] θ=[vec(A) T , vec(B) T , vec(C) T , vec(E) T ] T ∈ R d ;

[0018] wherein d=n 2 +nm+pn+nq, n is a dimension of a state variable, m is a dimension of a control variable, p is a dimension of an output variable, and q is a dimension of a disturbance variable.

[0019] Further, the hybrid algorithm based on a particle swarm and a recursive least square is used to perform online identification of the vectorized model parameters, and includes the following steps:

[0020] First, a particle swarm algorithm is used to search on historical data to obtain initial estimated values of the model parameters and initial values of a covariance matrix of a recursive least square algorithm;

[0021] After a new set of working condition data is collected, the model parameters and the covariance matrix are updated according to the recursive least square algorithm;

[0022] Every set of a set of sampling periods, with the latest set of sampling periods of working condition data as input, with the current model parameter as the center, running a short-term particle swarm algorithm in a set range to modify the model parameter, and resetting the covariance matrix.

[0023] Further, it also includes:

[0024] When the working condition mutation or the model prediction error exceeds the threshold value is detected, a short-term particle swarm algorithm is triggered to modify the model parameter.

[0025] Further, the target function is:

[0026]

[0027] In the formula, Np is the prediction step length, Nc is the control step length, and λ is the control amount change penalty factor, Target NO x Tail gas concentration limit

[0028] The target function also includes the following constraint conditions:

[0029]

[0030] In the formula, Δu(k)=u(k)-u(k-1).

[0031] The application also includes an ammonia-coal ratio dynamic regulation system using the method described above, and the system includes:

[0032] The acquisition unit is used to acquire working condition signals including boiler load, furnace temperature, primary air volume, NO x Tail gas concentration, ammonia injection amount, and coal supply amount;

[0033] The modeling unit is used to establish a discrete-time state-space model based on the working condition signals, and to vectorize the model parameters;

[0034] The parameter identification unit is used to perform online identification on the vectorized model parameters by using a hybrid algorithm based on particle swarm and recursive least squares;

[0035] The solving unit is used to construct a model predictive control target function according to the identified current model parameters, a preset prediction step length, and a control step length, and to obtain an optimal future control increment sequence by online solving through a particle swarm algorithm;

[0036] The regulation unit is used to dynamically regulate the ammonia-coal ratio by using the optimal control increment sequence.

[0037] The application also comprises a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method as described above when executing the computer program.

[0038] The application also comprises a storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the method as described above.

[0039] The application has the advantages that: the model can automatically track the working condition changes and has strong adaptability by using the hybrid algorithm based on particle swarm and recursive least square to perform online identification on the vectorized model parameters; the particle swarm algorithm has global search capability in nonlinear and multi-peak optimization, and can find the near-optimal ammonia-coal ratio under variable working conditions, so that the ammonia injection amount is minimized, ammonia water is saved, and the cost is reduced under the premise of meeting the emission indicators. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0041] Figure 1 The flow chart of the method of the present application;

[0042] Figure 2 The structural schematic diagram of the system of the present application;

[0043] Figure 3 The structural schematic diagram of the computer device of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments.

[0045] Embodiment 1:

[0046] As shown in the figure: an ammonia-coal ratio dynamic regulation method, comprising the following steps: Figure 1

[0047] Collecting working condition signals including boiler load, furnace temperature, primary air volume, NOx concentration, tail gas concentration, ammonia injection amount and coal supply amount; x Establishing a discrete-time state space model based on the working condition signals, and vectorizing the model parameters;

[0048] Establishing a discrete-time state space model based on the working condition signals, and vectorizing the model parameters; ​

[0049] The hybrid algorithm based on particle swarm and recursive least square is used to identify the vectorized model parameters on line;

[0050] According to the identified current model parameters, the preset prediction step and the control step, a model predictive control target function is constructed, and an optimal future control increment sequence is obtained by solving the function on line through the particle swarm algorithm;

[0051] The optimal control increment sequence is used to dynamically control the ammonia-coal ratio.

[0052] By using the hybrid algorithm based on particle swarm and recursive least square to identify the vectorized model parameters on line, the model can automatically track the working condition change and has strong adaptability; the particle swarm algorithm has global search ability in nonlinear and multi-peak optimization, and can find a near-optimal ammonia-coal ratio under variable working conditions, so that the ammonia injection amount is minimized, the ammonia water is saved, and the cost is reduced under the premise of meeting the emission index.

[0053] In the embodiment, the discrete-time state space model is:

[0054]

[0055] In the formula, x is a state vector x=[T f , C NOx ] T , T f is the furnace temperature, C NOx is the NO x tail gas concentration, u is the control amount, i.e. the ammonia-coal ratio u=a / m, a is the ammonia injection amount, and m is the coal injection amount; d is the disturbance variable, d=[Q, F air ] T , Q is the boiler load, F air is the primary air volume, A, B, C and E are model parameter matrices of state variables, control variables, output variables and disturbance variables respectively, y is the predicted NO x tail gas concentration, k is the kth sampling time, and the superscript T represents the transpose of the matrix or vector, which changes the column vector into a row vector.

[0056] The model parameters are vectorized, including:

[0057] The matrices A, B, C and E are respectively expanded into one-dimensional vectors to obtain a vectorized matrix θ:

[0058] θ=[vec(A) T , vec(B) T , vec(C) T , vec(E) T ] T ∈R d ;

[0059] Where d = n 2 +nm+pn+nq, where n is the dimension of the state variables, m is the dimension of the control variables, p is the dimension of the output variables, and q is the dimension of the perturbation variables.

[0060] The algorithm employs a hybrid approach based on particle swarm optimization and recursive least squares to identify the vectorized model parameters online, including the following steps:

[0061] First, try using the particle swarm optimization algorithm to search on historical data to obtain initial estimates of model parameters and initial values ​​of the covariance matrix of the recursive least squares algorithm.

[0062] After collecting a new set of operating condition data, the model parameters and covariance matrix are updated according to the recursive least squares algorithm;

[0063] Every set number of sampling periods, the working condition data from the most recent set number of sampling periods is used as input. Centered on the current model parameters, a short-time particle swarm optimization algorithm is run within a set range to correct the model parameters and reset the covariance matrix.

[0064] As a preferred embodiment of the above, it further includes:

[0065] When a sudden change in operating conditions is detected or the model prediction error exceeds the threshold, a short-term particle swarm algorithm is immediately triggered to correct the model parameters.

[0066] In this embodiment, the objective function is:

[0067]

[0068] In the formula, Np is the prediction step size, Nc is the control step size, and λ is the penalty factor for changes in the control quantity. For target NO x Exhaust gas concentration limits

[0069] The objective function also includes the following constraints:

[0070]

[0071] In the formula, Δu(k)=u(k)-u(k-1).

[0072] like Figure 2 As shown, this embodiment also includes a dynamic control system for the ammonia-to-coal ratio, using the method described above. The system includes:

[0073] The data acquisition unit is used to collect data including boiler load, furnace temperature, primary air volume, and NO. x Operating signals for exhaust gas concentration, ammonia dosage, and coal feed rate;

[0074] The modeling unit is used to establish a discrete-time state-space model based on operating condition signals and to vectorize the model parameters.

[0075] The parameter identification unit is used to identify vectorized model parameters online using a hybrid algorithm based on particle swarm optimization and recursive least squares.

[0076] The solving unit is used to construct the model prediction control objective function based on the identified current model parameters, preset prediction step size and control step size, and solve it online using the particle swarm algorithm to obtain the optimal future control increment sequence;

[0077] The control unit is used to dynamically regulate the ammonia-coal ratio using the optimal control increment sequence.

[0078] Example 2:

[0079] This embodiment includes a method for dynamic control of the ammonia-to-coal ratio, comprising the following steps:

[0080] S1: Real-time data collection of boiler load, furnace temperature, primary air volume, and NO. x Operating condition signals such as exhaust gas concentration, ammonia dosage, and coal feed rate;

[0081] S2: Establish a discrete-time state-space model based on the acquired signals, and vectorize the model parameters as θ;

[0082] S3: The model parameter θ is identified online using a PSO-RLS hybrid algorithm, where:

[0083] S31: In the offline stage, the initial parameter estimate θ(0) and the initial value P(0) of the covariance matrix of the recursive least squares algorithm RLS are obtained by searching on historical data using the particle swarm optimization (PSO) algorithm.

[0084] S32: During the online phase, for each new set of sampled data received, θ(k) and P(k) are updated according to the recursive least squares (RLS) algorithm;

[0085] S33: Every preset sampling period Tpso, using the most recent Nr samples as input, and based on the current θ(k) as the center, run a short-term PSO within the range [θ(k)±Δmax] to correct θ(k) and reset P(k);

[0086] S4: Based on the current model θ(k), the preset prediction step size Np and control step size Nc, construct the model predictive control (MPC) objective function, and use the PSO algorithm to solve it online to obtain the optimal future control increment sequence;

[0087] S5: Update the current ammonia-to-coal ratio u(k) = u(k-1) + Δu* with the optimal control increment, and apply it to the denitrification system;

[0088] S6: Repeat steps S1 to S5 to form a closed-loop dynamic control.

[0089] The specific steps are as follows:

[0090] Acquisition signals: Boiler load Q, furnace temperature T f Primary air volume F air NO x Exhaust gas concentration C NOx The amount of ammonia added (a) and the amount of coal fed (m) are shown.

[0091] Establish a discrete-time state-space model:

[0092]

[0093] In the formula, x is the state vector x = [T f C NOx ] T T f C represents the furnace temperature. NOx NO x The exhaust gas concentration, u is the controlled variable, i.e., the ammonia-to-coal ratio u = a / m, where a is the ammonia input and m is the coal feed rate; d is the disturbance variable, d = [Q, F] air ] T Q is the boiler load, F air Let y represent the primary air volume, A, B, C, and E be the model parameter matrices for the state variable, control variable, output variable, and disturbance variable, respectively, and y be the predicted NO. x Exhaust gas concentration, k is the kth sampling time, and the superscript T indicates the transpose of a matrix or vector, which transforms a column vector into a row vector.

[0094] Vectorizing the model parameters includes:

[0095] Expand matrices A, B, C, and E into one-dimensional vectors to obtain the vectorized matrix θ:

[0096] θ=[vec(A) T ,vec(B) T ,vec(C) T ,vec(E) T ] T ∈R d ;

[0097] Where d = n 2 +nm+pn+nq, where n is the dimension of the state variables, m is the dimension of the control variables, p is the dimension of the output variables, and q is the dimension of the perturbation variables.

[0098] First, try using the particle swarm optimization algorithm to search on historical data to obtain initial estimates of model parameters and initial values ​​of the covariance matrix of the recursive least squares algorithm.

[0099] After collecting a new set of operating condition data, the model parameters and covariance matrix are updated according to the recursive least squares algorithm;

[0100] Every set number of sampling periods, the working condition data from the most recent set number of sampling periods is used as input. Centered on the current model parameters, a short-time particle swarm optimization algorithm is run within a set range to correct the model parameters and reset the covariance matrix.

[0101] I. Initial offline PSO identification:

[0102] 1. Particle Swarm Optimization:

[0103] Group size: P;

[0104] Maximum number of iterations:

[0105] Inertia weight: ω∈[0.9,0.4] (linearly decreasing);

[0106] Learning factors: c1, c2;

[0107] 2. Fitness function:

[0108] Using offline historical data {u(k),d(k),y(k)}, after reconstructing the state-space model for a given θ, calculate the sum of squared prediction errors:

[0109]

[0110] 3. Iterative Process:

[0111] For each particle j = 1, ..., P, initialize its position, velocity, and individual optimality;

[0112] For t = 0, ..., T max pso -1:

[0113] Calculate the fitness of each particle Update pbest and the globally optimal gbest; update velocity and position according to the following formula:

[0114]

[0115] Constraint: If If the value exceeds the preset boundary, it will be truncated.

[0116] 4. Output: After the iteration, take the θ(0) corresponding to gbest and reshape it to A(0), B(0), C(0), E(0); let the initial covariance matrix of RLS be P0 = αI d (α is a relatively large constant).

[0117] II. Online RLS Update:

[0118] When a new observation (u(k),d(k),y(k)) arrives at time k:

[0119] 1. Construct the regression vector:

[0120] Based on the "expansion form" of the discrete state-space model, for example:

[0121] y(k)=Cx(k)=C(Ax(k-1)+Bu(k-1)+Ed(k-1));

[0122] This can be written in linear regression form: y(k)=φ(k) T θ(k-1)+ε(k);

[0123] Where φ(k) contains components of x(k-1), u(k-1), and d(k-1).

[0124] 2. Gain Calculation:

[0125]

[0126] Where λ∈(0,1] is the forgetting factor.

[0127] 3. Parameter update:

[0128] θ(k)=θ(k-1)+K(k)[y(k)-φ(k) T θ(k-1)];

[0129] 4. Covariance Update:

[0130]

[0131] 5. Restore the model matrix:

[0132] θ(k) is split and reshaped back into A(k), B(k), C(k), and E(k) for further prediction and control.

[0133] 3. Regular PSO correction:

[0134] To prevent RLS from getting trapped in local optima under nonlinear or abrupt conditions, PSO is run again every Tpso sampling periods (e.g., 300s) based on the most recent Nr samples {φ(i), y(i)}:

[0135] 1. Construct the search range centered on the current θ(k):

[0136] Θ min =θ(k)-△ max Θ max =θ(k)+△ max

[0137] 2. Short-term PSO optimization:

[0138] Iteration only Step, update gbest;

[0139] 3. Reset RLS:

[0140] Let θ(k) ← new gbest, P(k) ← αId.

[0141] The relevant parameters of the model are shown in Table 1:

[0142] Table 1 Key Parameter Settings

[0143] Parameter Value Explanation PSO population size P 20–50 Small size is OK, 20 can be selected when real-time requirement is high PSO maximum iteration Tmax 50–100 100 can be taken in offline stage, 30 in online correction RLS forgetting factor λ 0.98–0.995 Tends to 1, higher weight on historical data Initial covariance coefficient α 10 6 -10 8 ]] Guarantees fast convergence in initial stage Correction period Tpso 200-500 samples Adjust according to system response characteristics Correction data window length Nr 100-300 samples Match with correction period

[0144] For a 600MW coal-fired power unit, the specific control steps are as follows:

[0145] Data preprocessing: Sample every 5 seconds, denoise and then normalize;

[0146] Model identification: The PSO-RLS hybrid algorithm was used to obtain A, B, C, and E in the initial 50 iterations;

[0147] MPC parameter selection: prediction step size Np = 10, control step size Nc = 3, penalty factor λ = 0.5;

[0148] PSO settings: Population size P = 30, maximum number of iterations The inertia weight ω decreases linearly from 0.9 to 0.4;

[0149] Real-time operation: After the system is running stably, NO x Emissions maintained at <100 mg / Nm 3 Ammonia slip rate <2%.

[0150] Please see Figure 3 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0151] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0152] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0153] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0154] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0155] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0156] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0157] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0158] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0159] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0160] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for dynamic control of the ammonia-to-coal ratio, characterized in that, Includes the following steps: Data collected includes boiler load, furnace temperature, primary air volume, and NO. x Operating signals for exhaust gas concentration, ammonia dosage, and coal feed rate; A discrete-time state-space model is established based on the aforementioned operating condition signal, and the model parameters are vectorized. A hybrid algorithm based on particle swarm optimization and recursive least squares is used to identify the vectorized model parameters online. Based on the identified current model parameters, preset prediction step size and control step size, a model prediction control objective function is constructed, and the optimal future control increment sequence is obtained by solving the particle swarm algorithm online. The optimal control increment sequence is used to dynamically adjust the ammonia-coal ratio.

2. The method for dynamic control of the ammonia-to-coal ratio according to claim 1, characterized in that, The discrete-time state-space model is as follows: In the formula, x is the state vector x = [T f C NOx ] T T f C represents the furnace temperature. NOx NO x The exhaust gas concentration, u is the controlled variable, i.e., the ammonia-to-coal ratio u = a / m, where a is the ammonia input and m is the coal feed rate; d is the disturbance variable, d = [Q, F] air ] T Q is the boiler load, F air Let y represent the primary air volume, A, B, C, and E be the model parameter matrices for the state variable, control variable, output variable, and disturbance variable, respectively, and y be the predicted NO. x Exhaust gas concentration, k is the kth sampling time, and the superscript T indicates the transpose of a matrix or vector, which transforms a column vector into a row vector.

3. The method for dynamic control of the ammonia-to-coal ratio according to claim 2, characterized in that, The process of vectorizing model parameters includes: Expand matrices A, B, C, and E into one-dimensional vectors to obtain the vectorized matrix θ: θ=[thing(A) T ,thing(B) T ,thing(C) T ,thing(E) T ] T ∈R d ; Where d = n 2 +nm+pn+nq, where n is the dimension of the state variables, m is the dimension of the control variables, p is the dimension of the output variables, and q is the dimension of the perturbation variables.

4. The method for dynamic control of the ammonia-to-coal ratio according to claim 1, characterized in that, The online identification of the vectorized model parameters using a hybrid algorithm based on particle swarm optimization and recursive least squares includes the following steps: First, use the particle swarm optimization algorithm to search on historical data to obtain the initial estimated values ​​of the model parameters and the initial values ​​of the covariance matrix of the recursive least squares algorithm. After collecting a new set of operating condition data, the model parameters and covariance matrix are updated according to the recursive least squares algorithm; Every set number of sampling periods, using the working condition data from the most recent set number of sampling periods as input, and centering on the current model parameters, a short-time particle swarm optimization algorithm is run within a set range to correct the model parameters and reset the covariance matrix.

5. The method for dynamic control of the ammonia-to-coal ratio according to claim 4, characterized in that, Also includes: When a sudden change in operating conditions is detected or the model prediction error exceeds the threshold, a short-time particle swarm algorithm is immediately triggered to correct the model parameters.

6. The method for dynamic control of the ammonia-to-coal ratio according to claim 1, characterized in that, The objective function is: In the formula, Np is the prediction step size, Nc is the control step size, and λ is the penalty factor for changes in the control quantity. For target NO x Exhaust gas concentration limits The objective function also includes the following constraints: In the formula, Δu(k)=u(k)-u(k-1).

7. A dynamic control system for the ammonia-to-coal ratio, characterized in that, Using the method of any one of claims 1 to 6, the system comprises: The data acquisition unit is used to collect data including boiler load, furnace temperature, primary air volume, and NO. x Operating signals for exhaust gas concentration, ammonia dosage, and coal feed rate; The modeling unit is used to establish a discrete-time state-space model based on the operating condition signal and to vectorize the model parameters. The parameter identification unit is used to identify the vectorized model parameters online using a hybrid algorithm based on particle swarm optimization and recursive least squares. The solving unit is used to construct the model prediction control objective function based on the identified current model parameters, preset prediction step size and control step size, and solve it online using the particle swarm algorithm to obtain the optimal future control increment sequence; The control unit is used to dynamically control the ammonia-coal ratio using the optimal control increment sequence.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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