CFB boiler SO2 emission optimization control method suitable for deep peak regulation

Abstract

The invention discloses a CFB boiler SO2 emission optimization control method suitable for deep peak regulation, and belongs to the technical field of thermal power generating unit green emission reduction, and the method comprises the following steps: S1, mechanism analysis; s2, constructing a partition SO2 emission model; s3, simulation verification and dynamic characteristic research; and S4, regulating and controlling the putting amount of the desulfurizing agent by adopting a stepped generalized predictive control method. According to the CFB boiler SO2 emission optimization control method adapting to deep peak regulation, the SO2 concentration prediction precision and the system adaptability under the variable load working condition are improved; the cooperative control capability of the sulfur-based pollutants under the complex boundary is enhanced, and SO2 emission control during deep peak regulation of the supercritical CFB boiler is optimized.

Description

Technical Field

[0001] This invention belongs to the technical field of green emission reduction in thermal power units, specifically relating to an optimized control method for SO2 emissions from CFB boilers adapted to deep peak shaving. Background Technology

[0002] Circulating fluidized bed (CFB) boilers, relying on their unique in-furnace desulfurization mechanism and multiphase flow characteristics, demonstrate significant advantages in the synergistic treatment of coal-fired pollutants. Their core technology lies in enhancing the reaction kinetics between limestone (CaCO3) and sulfur in fuel through gas-solid circulation, achieving an in-furnace desulfurization efficiency of no less than 95%, making them particularly suitable for the clean conversion of high-sulfur coal.

[0003] However, existing technologies have the following shortcomings: existing SO2 emission models mostly rely on the idealized calcium-to-sulfur ratio (Ca / S) assumption under steady-state conditions, which makes it difficult to reflect the dynamic desulfurization characteristics under actual operating conditions such as variable loads, and the analytical accuracy of the spatiotemporal distribution of homogeneous components in the furnace is insufficient; traditional PID control has significant limitations in dealing with the large lag and multivariate coupling problems of CFB boiler desulfurization systems, with slow response and difficulty in adapting to the time-varying characteristics of sulfur balance under wide load conditions; existing technologies are not accurate enough in analyzing the sulfur fluctuation characteristics under deep peak shaving conditions, and the optimization of desulfurizing agent dosing lacks specificity, resulting in insufficient stability of desulfurization efficiency and high desulfurizing agent consumption.

[0004] Therefore, a new method is urgently needed. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized control method for SO2 emissions from CFB boilers that is adapted to deep peak shaving. This method improves the accuracy of SO2 concentration prediction and system adaptability under variable load conditions; enhances the synergistic control capability of sulfur-based pollutants under complex boundary conditions; and optimizes SO2 emission control during deep peak shaving in supercritical CFB boilers.

[0006] To achieve the above objectives, this invention provides an optimized control method for SO2 emissions from a CFB boiler adapted to deep peak shaving, comprising the following steps:

[0007] S1. Mechanistic analysis of bed temperature, oxygen volume fraction, SO2 pollutant generation and removal mechanism in CFB boiler;

[0008] S2. Construct an SO2 emission model based on the mechanism analysis in S1, and output the parameters in the SO2 emission model;

[0009] S3. Based on the SO2 emission model in S2 and the parameters in the SO2 emission model, the variable load condition data is input into the SO2 emission model, and the SO2 concentration prediction results are output. At the same time, based on the SO2 emission model, the perturbation test is carried out on the manipulated variables, and the dynamic characteristic data and typical variable load condition data are output.

[0010] S4. Based on the SO2 emission model in S2, the SO2 concentration prediction results, dynamic characteristic data and typical variable load operating condition data output by S3, a step-type generalized predictive control method is adopted to regulate the amount of desulfurizing agent added.

[0011] Preferably, the mechanism analysis in S1 includes: the effect of oxygen volume fraction on combustion and pollutants, the importance of the reaction temperature window, the difference in redox atmosphere between the dense phase region and the dilute phase region, as well as the type of sulfur in the coal, the oxidation stage of sulfur during combustion, and the limestone calcination decomposition and desulfurization reaction process.

[0012] Preferably, the SO2 emission model constructed in S2 includes:

[0013] The SO2 generation rate equation is expressed as:

[0014]

[0015] in, This represents the self-generation rate of SO2, expressed in kg / s. The reaction rate for the oxidation of H2S to SO2 is expressed in kg / s. The reaction rate for the formation of SO2 from COS oxygen is expressed in kg / s. The reaction rate for the oxidation of sulfur in coke to SO2 is expressed in kg / s. This represents the molar mass of SO2, expressed in g / mol. This refers to the molar concentration of oxygen in the dense phase region, expressed in kmol / m³. 3 ; The reaction kinetic parameter constant for the oxidation of H2S to SO2 is given in m. 3 / (kmol·s); This refers to the molar concentration of H2S, in kmol / m³. 3 ; The reaction kinetic constant for the oxidation of COS to SO2 is given in m. 3 / (kmol·s); C COS COS molar concentration, in kmol / m 3 ; Dynamic parameters for SO2 production from sulfur oxidation in coke; w c_S R represents the sulfur mass fraction of coke. S,i The efficiency of sulfur precipitation from coal;

[0016] The sulfur mass fraction in coal depends on the fuel structure. A relationship coefficient is introduced to represent the relationship between the sulfur mass fraction in coke and the sulfur mass fraction in coal, expressed as:

[0017]

[0018] Where, μ S The coefficient representing the relationship between the carbon-sulfur content of the fuel and the base sulfur content of the fuel; w S The received basic sulfur mass fraction;

[0019] The equation for calculating the molar concentration of H2S is expressed as follows:

[0020]

[0021] w h_S This represents the mass fraction of sulfur in the volatile matter. The proportion of H2S in volatile sulfur; M S S is the molar mass of S, expressed in g / mol. The catalytic oxidation rate of H2S by CaO is expressed in kg / s.

[0022] The mass fraction of sulfur in volatile matter is expressed as:

[0023]

[0024] The reaction rate of CaO reacting with H2S to produce CaS is expressed as follows:

[0025]

[0026] in, The molar mass of H2S is expressed in g / mol. D0 is the catalytic oxidation coefficient of H2S by CaO; D0 is the effective diffusion coefficient, on the order of 102. -5 The unit is m 2 / s;d CaO ρ represents the particle size of CaO, in meters (m); CaO This is the density of CaO, in kg / m³. 3 R is the gas constant, with a value of 8.314 J / (mol·K); T yq It is the flue gas temperature, measured in Kelvin (K).

[0027] Preferably, the construction of the SO2 emission model in S2 also includes:

[0028] The equation for calculating the molar concentration of COS is expressed as follows:

[0029]

[0030] Where, η h_S(COS) The COS ratio in volatile sulfur;

[0031] The equation for calculating SO2 removal rate is expressed as follows:

[0032]

[0033] in, r is the SO2 removal rate in the furnace, expressed in kg / s. CaO ρ represents the CaO consumption rate, in kg / s; k1 is the correction factor for SO2 removal rate, a function of excess air coefficient and load conditions; m CaO The storage mass of active limestone is expressed in kg. The reaction rate constant of SO2;

[0034] The calculation method is as follows:

[0035]

[0036] Among them, S g The effective specific surface area of ​​limestone, in m². 2 / kg; ξ CaO The reactivity coefficient of limestone.

[0037] Preferably, the construction of the SO2 emission model in S2 also includes:

[0038] The mass balance equation for the production of CaO from limestone by calcination is expressed as:

[0039]

[0040] Among them, M CaO The value is the molar mass of CaO, expressed in g / mol. W is the molar mass of CaCO3, expressed in g / mol. CaO This represents the limestone feed rate, expressed in kg / s. This represents the mass fraction of CaCO3 in limestone. For CaCO3 calcination efficiency; r loss CaO loss rate;

[0041] The lumped parameter equation for the average SO2 concentration inside the furnace is expressed as:

[0042]

[0043] in, This represents the original SO2 emission concentration, in kmol / m³. 3 ;

[0044] The SO2 molar concentration in the dense phase region is expressed as:

[0045]

[0046] in, SO2 emission concentration in the dense phase region, in kmol / m³ 3 ;

[0047] The molar concentration of SO2 in the dilute phase region is expressed as:

[0048]

[0049] in, This refers to the SO2 emission concentration in the dilute phase region, expressed in kmol / m³. 3 ;

[0050] The formula for converting concentrations is expressed as follows:

[0051]

[0052] in, This indicates the converted on-site SO2 emission concentration, in kg / m³. 3 ; It is the volume fraction of oxygen in the air; This is the standard oxygen volume fraction for power plants, with a value of 6%.

[0053] Preferably, the disturbance test in S3 is specifically as follows:

[0054] Under 120MW low-load and steady-state load conditions, key operating variables affecting SO2 emissions were subjected to instantaneous changes of specific magnitudes. By observing the dynamic response of SO2 emission concentration, the correlation between sulfur migration pathways and removal efficiency was analyzed; this included four scenarios:

[0055] Scenario 1: The coal feed rate increases by 5% instantaneously;

[0056] Scenario 2: Primary airflow increases by 5%;

[0057] Situation 3: Secondary air volume increases by 5%;

[0058] Scenario 4: Limestone feed rate increased by 5%.

[0059] This invention also provides a CFB boiler SO2 emission optimization control system adapted to deep peak shaving, comprising:

[0060] The mechanism analysis module is used to perform mechanism analysis on the bed temperature, oxygen volume fraction, SO2 pollutant generation and removal mechanism in CFB boilers.

[0061] The partition model construction module is connected to the mechanism analysis module, constructs an SO2 emission model based on mechanism analysis, and outputs the parameters in the SO2 emission model.

[0062] The simulation verification and dynamic characteristics module is connected to the partition model construction module. Based on the SO2 emission model and the parameters in the SO2 emission model, it inputs the variable load condition data into the SO2 emission model and outputs the SO2 concentration prediction results. At the same time, based on the SO2 emission model, it performs disturbance tests on the manipulated variables and outputs dynamic characteristic data and typical variable load condition data.

[0063] The intelligent control strategy module, connected to the simulation verification and dynamic characteristics module, uses a step-wise generalized predictive control method to regulate the amount of desulfurizing agent based on the SO2 emission model, SO2 concentration prediction results, dynamic characteristic data, and typical variable load operating condition data.

[0064] Therefore, the present invention employs the above-mentioned optimized control method for SO2 emissions from CFB boilers adapted to deep peak shaving. Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0065] (1) The present invention constructs a dynamic coupling model for sulfur migration, which integrates multiple process mechanisms such as fuel sulfur release, limestone decomposition and gas-solid mass transfer. It overcomes the problem that traditional models are only applicable to steady-state conditions and cannot reflect the dynamic desulfurization characteristics under actual operating conditions such as variable load. It improves the accuracy of SO2 concentration prediction and system adaptability under variable load conditions.

[0066] (2) This invention proposes a step-wise generalized predictive control (GPC) method based on a controlled autoregressive integral moving average model, which integrates time-domain rolling optimization and parameter self-correction mechanism. This solves the problem that traditional PID control has response lag when dealing with large time delay and multivariable coupled systems, and it is difficult to achieve precise control of desulfurizer. This invention achieves precise control of desulfurizer and reduces desulfurizer consumption.

[0067] (3) This invention uses a regional SO2 generation-removal dynamic coupling equation to deeply analyze the synergistic effect mechanism of pollutant increase and decrease in the furnace, which solves the problem that the existing technology is not accurate enough in analyzing the sulfur fluctuation characteristics under deep peak shaving conditions and is not adaptable to deep peak shaving conditions. It accurately captures the sulfur fluctuation characteristics under variable load conditions and improves the synergistic control capability of sulfur-based pollutants under complex operating boundaries.

[0068] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0069] Figure 1 This is a variable load operating condition data diagram of an embodiment of the CFB boiler SO2 emission optimization control method adapted to deep peak shaving according to the present invention;

[0070] Figure 2This is a graph showing the SO2 emission prediction results and prediction error data under three variable load conditions in an embodiment of the SO2 emission optimization control method for CFB boilers adapted to deep peak shaving of the present invention. Figure 2 (a), (b), and (c) in the figure correspond to the predicted SO2 emissions under operating conditions 1, 2, and 3, respectively. Figure 2 In the figure, (d), (e), and (f) correspond to the error results under operating conditions 1, 2, and 3, respectively.

[0071] Figure 3 This is a step response curve of the dynamic model of SO2 emission concentration in the field, which is an embodiment of the SO2 emission optimization control method for CFB boilers adapted to deep peak shaving of the present invention. Figure 3 (a) in the figure is the step response curve after the coal feed rate increases by 5% instantaneously; Figure 3 (b) shows the step response curve after a 5% increase in airflow; Figure 3 (c) in the figure depicts the step response curve after a 5% increase in secondary air volume; Figure 3 (d) in the figure shows the step response curve when the limestone feed rate increases by 5%;

[0072] Figure 4 This is a data graph showing the optimization control effect of SO2 emissions from a CFB boiler according to an embodiment of the present invention, which is an optimized control method for SO2 emissions from a CFB boiler adapted to deep peak shaving. Figure 4 (a), (b), and (c) in the figure correspond to the data graphs of the optimized control effect of SO2 emissions from the CFB boiler under operating conditions 1, 2, and 3, respectively. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used in the present invention should have the ordinary meaning understood by those skilled in the art.

[0074] Example 1

[0075] The present invention provides an optimized control method for SO2 emissions from a CFB boiler adapted to deep peak shaving, comprising the following steps:

[0076] S1. Mechanistic analysis of bed temperature, oxygen volume fraction, SO2 pollutant formation and removal mechanism in CFB boiler. Mechanistic analysis includes the effect of oxygen volume fraction on combustion and pollutants, distinguishing the oxidation pathways of organic and inorganic sulfur in coal, the difference in redox atmosphere between dense and dilute phase regions, the type of sulfur in coal, the oxidation stage of sulfur during combustion, limestone calcination decomposition and desulfurization reaction process, etc.

[0077] In this step, sulfur compounds in coal are classified into two types: organic and inorganic. Inorganic sulfur is mostly pyrite, containing some sulfates and elemental sulfur; organic sulfur mainly exists in the form of thiophene and thiols. During coal combustion, sulfur oxidation occurs in two stages: first, organic sulfur is rapidly released as H2S and COS in the dense phase region along with volatiles, and is quickly oxidized to SO2; then, inorganic sulfur in the coke is oxidized, generating more SO2. Simultaneously, thermal power plants typically feed limestone and coal into the furnace together. The limestone is calcined and decomposed at high temperatures, releasing CO2 to generate CaO, which reacts with SO2 to form CaSO4, achieving desulfurization in the dilute phase region.

[0078] S2. Based on the mechanism analysis in S1, a mechanism modeling method was used to establish SO2 concentration models at the dense-dilute phase zone inside the furnace and at the inlet of the desulfurization tower. Through formula derivation, the relationship between the self-generation rate of SO2 and the reaction rates was clarified, as well as the calculation methods for key parameters such as the molar concentrations of H2S and COS, SO2 removal rate, and the mass balance of CaO generated from limestone calcination. Finally, the lumped parameter equations for the average SO2 concentration inside the furnace and the molar concentrations of SO2 in the dense and dilute phase zones were obtained, and the conversion method for the converted SO2 emission concentration was determined.

[0079] In this step, the self-generation rate of SO2 is jointly determined by the reaction rates of H2S oxidation, COS oxidation, and coke sulfur oxidation to SO2. The equation for the self-generation rate of SO2 is expressed as:

[0080]

[0081] in, This represents the self-generation rate of SO2, expressed in kg / s. The reaction rate for the oxidation of H2S to SO2 is expressed in kg / s. The reaction rate for the formation of SO2 from COS oxygen is expressed in kg / s. The reaction rate for the oxidation of sulfur in coke to SO2 is expressed in kg / s. This represents the molar mass of SO2, expressed in g / mol. This refers to the molar concentration of oxygen in the dense phase region, expressed in kmol / m³. 3 ; The reaction kinetic parameter constant for the oxidation of H2S to SO2 is given in m.3 / (kmol·s); This refers to the molar concentration of H2S, in kmol / m³. 3 ; The reaction kinetic constant for the oxidation of COS to SO2 is given in m. 3 / (kmol·s); C COS COS molar concentration, in kmol / m 3 ; Dynamic parameters for SO2 production from sulfur oxidation in coke; w c_S R represents the sulfur mass fraction of coke. S,i The efficiency of sulfur precipitation from coal;

[0082] The sulfur mass fraction in coal depends on the fuel structure. A relationship coefficient is introduced to represent the relationship between the sulfur mass fraction in coke and the sulfur mass fraction in coal, expressed as:

[0083]

[0084] Where, μ S The coefficient representing the relationship between the carbon-sulfur content of the fuel and the base sulfur content of the fuel; w S The received basic sulfur mass fraction;

[0085] After coal enters the furnace, it burns and releases volatiles. Considering that sulfur in the volatiles is released as H2S, catalytically oxidized by CaO, and discharged by the primary air, the molar concentration of H2S in the furnace is expressed as:

[0086]

[0087] Among them, w h_S This represents the mass fraction of sulfur in the volatile matter. The proportion of H2S in volatile sulfur; M S S is the molar mass of S, expressed in g / mol. The catalytic oxidation rate of H2S by CaO is expressed in kg / s.

[0088] The mass fraction of sulfur in volatile matter is expressed as:

[0089]

[0090] The reaction rate of CaO reacting with H2S to produce CaS is expressed as follows:

[0091]

[0092] in, The molar mass of H2S is expressed in g / mol. D0 is the catalytic oxidation coefficient of H2S by CaO; D0 is the effective diffusion coefficient, on the order of 102.-5 The unit is m 2 / s;d CaO ρ represents the particle size of CaO, in meters (m); CaO This is the density of CaO, in kg / m³. 3 R is the gas constant, with a value of 8.314 J / (mol·K); T yq It is the flue gas temperature, measured in Kelvin (K).

[0093] Considering the release of sulfur from volatiles in the form of COS, its oxidation, and its discharge by primary air, as well as the partial discharge of COS from the furnace by primary air, the molar concentration of COS in the furnace is expressed as:

[0094]

[0095] Where, η h_S(COS) The COS ratio in volatile sulfur;

[0096] For in-furnace desulfurization, a wet desulfurization method is used. The SO2 removal rate is expressed as:

[0097]

[0098] in, r is the SO2 removal rate in the furnace, expressed in kg / s. CaO ρ represents the CaO consumption rate, in kg / s; k1 is the correction factor for SO2 removal rate, a function of excess air coefficient and load conditions; m CaO The storage mass of active limestone is expressed in kg. The reaction rate constant of SO2;

[0099] The calculation method is as follows:

[0100]

[0101] Among them, S g The effective specific surface area of ​​limestone, in m². 2 / kg; ξ CaO The reactivity coefficient of limestone;

[0102] The mass balance of CaO produced by calcining limestone is expressed as follows:

[0103]

[0104] Among them, M CaO The value is the molar mass of CaO, expressed in g / mol. W is the molar mass of CaCO3, expressed in g / mol. CaO This represents the limestone feed rate, expressed in kg / s. This represents the mass fraction of CaCO3 in limestone. For CaCO3 calcination efficiency; r loss The CaO loss rate depends on the amount of active limestone reserves;

[0105] Combining the SO2 generation and removal rates, the lumped parameter equation for the average SO2 concentration in the furnace is obtained, expressed as:

[0106]

[0107] in, This represents the original SO2 emission concentration, in kmol / m³. 3 ;

[0108] The SO2 molar concentration in the dense phase region is expressed as:

[0109]

[0110] in, SO2 emission concentration in the dense phase region, in kmol / m³ 3 ;

[0111] The SO2 molar concentration in the dilute phase region is expressed as:

[0112]

[0113] in, This refers to the SO2 emission concentration in the dilute phase region, expressed in kmol / m³. 3 ;

[0114] The converted SO2 emission concentration at the measuring point is then converted to the on-site SO2 emission concentration, expressed as follows:

[0115]

[0116] in, This indicates the converted on-site SO2 emission concentration, in kg / m³. 3 ; It is the volume fraction of oxygen in the air; This is the standard oxygen volume fraction for power plants, with a value of 6%.

[0117] S3. A research framework for sulfur oxide formation characteristics was constructed based on a simulation platform. Dynamic simulation verification was carried out using historical operating data of a 350MW CFB boiler DCS under three-stage variable load conditions. The model fitting performance was evaluated using MAPE and MAE indices. Multidimensional perturbation tests were conducted on key operating variables such as coal feed rate, primary and secondary air volume, and limestone feed rate. The correlation between sulfur migration path and removal efficiency was analyzed by combining dynamic response characteristics, revealing the time-varying mechanism of bed temperature stratification and oxygen volume fraction distribution on sulfur balance.

[0118] like Figure 1 As shown, the verification was performed using three-stage historical operating data from a 350MW CFB boiler distributed control system (DCS). The specific operating load conditions were as follows:

[0119] Operating Condition 1: The load adjustment range is reduced from 270MW to 115MW, and the total load is reduced by 45%. The average load reduction rate between 7305s and 12810s is 1.7MW / min;

[0120] Operating Condition 2: The load regulation range increases from 140MW to 300MW, accounting for 46% of the total load. The average load growth rate between 16500s and 19320s is 1.2MW / min.

[0121] Operating Condition 3: The load adjustment range decreases from 270MW to 120MW, accounting for 43% of the total load. The average load growth rate between 11115s and 12930s is 2.8MW / min.

[0122] SO2 emission model prediction results under different load conditions are as follows Figure 2 As shown in (a), (b), and (c), the corresponding error results are as follows: Figure 2 As shown in (d), (e), and (f);

[0123] The error calculation results of the SO2 emission model under different load conditions, including mean absolute percentage error (MAPE), maximum absolute error (MAE), root mean square error (RMSE), and mean square error (MSE), are shown in Table 1:

[0124] Table 1. Error Results of SO2 Emission Model under Different Load Conditions

[0125]

[0126] like Figure 3As shown, the dynamic characteristics of the SO2 emission model were captured through perturbation testing. Under 120MW low-load and steady-state load conditions, instantaneous changes of specific amplitudes were applied to key operating variables affecting SO2 emissions. By observing the dynamic response of SO2 emission concentration, the correlation between sulfur migration pathways and removal efficiency was analyzed; this included four scenarios:

[0127] Scenario 1: The coal feed rate increases by 5% instantaneously;

[0128] Scenario 2: Primary airflow increases by 5%;

[0129] Situation 3: Secondary air volume increases by 5%;

[0130] Scenario 4: Limestone feed rate increased by 5%.

[0131] Figure 3 (a) in the figure shows the step response curve after a 5% instantaneous increase in coal feed rate. As the sulfur content in the coal increases, the combustion rate accelerates, SO2 generation increases, the molar ratio of Ca to S decreases, and the SO2 emission concentration increases.

[0132] Figure 3 Figure (b) shows the step response curve after a 5% increase in airflow. The addition of oxygen enhanced the CaO desulfurization reaction, diluted SO2, and reduced the in-situ SO2 emissions.

[0133] Figure 3 Figure (c) depicts the step response curve after a 5% increase in secondary air volume. Secondary air promotes CaO desulfurization, dilutes SO2, and oxidizes volatile sulfur. The curve initially decreases due to desulfurization and dilution, increases with oxidation, then decreases again as desulfurization becomes dominant, ultimately reducing in-situ SO2 emissions.

[0134] Figure 3 Figure (d) shows the step response curve when the limestone feed rate increases by 5%. The increase in feed rate improves the SO2 removal rate, reducing on-site SO2 emissions below the initial level. These analyses confirm that the dynamic model accurately reflects the dynamic characteristics of pollutant emissions;

[0135] S4. Based on the SO2 emission model in S2, the predicted SO2 concentration, dynamic characteristic data, and typical variable load operating condition data output from S3, a step-wise generalized predictive control method is used to regulate the desulfurizing agent dosage. The step-wise generalized predictive control method is an improvement on the traditional GPC algorithm, including replacing the recursive least squares method with the Adam optimizer and constructing a dynamic gain control matrix with an exponential decay factor. A full-process dynamic simulation is established based on the SO2 emission model to verify the regulation effect of this control strategy on SO2 emission concentration under different operating conditions. The results show that it can achieve rapid tracking of pollutant concentration and synergistic optimization of operating costs, reducing SO2 concentration overshoot and fluctuations.

[0136] In this step, such as Figure 4 As shown, to verify the effectiveness of stepped generalized predictive control (SGPC) in the synergistic treatment of sulfur-based pollutants, an SO2 emission model was constructed based on S2, and a dynamic simulation covering the entire process of in-furnace desulfurization was established. Typical long-cycle operating data sequences were selected, and core operating parameters such as coal feed rate and primary / secondary air volume ratio were used as input variables for the SGPC controller, while keeping the feedforward parameters constant during the rolling optimization phase. The setpoints for the original flue gas SO2 concentration were set at 1200, 1000, and 750 mg / m³ under three operating conditions. 3 As shown in Table 2:

[0137] Table 2 Comparison of SO2 Original Emission Control Performance

[0138]

[0139] Simulation results show that the constructed SGPC control architecture can achieve rapid tracking of pollutant concentration and coordinated optimization of operating costs under three operating conditions. After optimization control, the average value of the original SO2 emission concentration is lower than that before control, with smaller fluctuations and better regulation effect.

[0140] Therefore, the present invention adopts the above-mentioned optimized control method for SO2 emissions from CFB boilers adapted to deep peak shaving. This method improves the accuracy of SO2 concentration prediction and system adaptability under variable load conditions; enhances the synergistic control capability of sulfur-based pollutants under complex boundary conditions; and optimizes SO2 emission control during deep peak shaving of supercritical CFB boilers.

[0141] Finally, 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing SO2 emission control of a CFB boiler adapted to deep peak shaving, characterized in that, Includes the following steps: S1. Mechanistic analysis of bed temperature, oxygen volume fraction, SO2 pollutant generation and removal mechanism in CFB boiler; S2. Construct an SO2 emission model based on the mechanism analysis in S1, and output the parameters in the SO2 emission model; S3. Based on the SO2 emission model in S2 and the parameters in the SO2 emission model, the variable load condition data is input into the SO2 emission model, and the SO2 concentration prediction results are output. At the same time, based on the SO2 emission model, the perturbation test is carried out on the manipulated variables, and the dynamic characteristic data and typical variable load condition data are output. S4. Based on the SO2 emission model in S2, the SO2 concentration prediction results, dynamic characteristic data and typical variable load operating condition data output by S3, a step-type generalized predictive control method is adopted to regulate the amount of desulfurizing agent added.

2. The method for optimizing SO2 emissions from a CFB boiler adapted to deep peak shaving as described in claim 1, characterized in that, The mechanism analysis in S1 includes: the effect of oxygen volume fraction on combustion and pollutants, the importance of the reaction temperature window, the difference in redox atmosphere between the dense phase region and the dilute phase region, the type of sulfur in coal, the oxidation stage of sulfur during combustion, and the limestone calcination decomposition and desulfurization reaction process.

3. The method for optimizing SO2 emissions from a CFB boiler adapted to deep peak shaving as described in claim 1, characterized in that, The SO2 emission model constructed in S2 includes: The SO2 generation rate equation is expressed as: in, This represents the self-generation rate of SO2, expressed in kg / s. The reaction rate for the oxidation of H2S to SO2 is expressed in kg / s. The reaction rate for the formation of SO2 from COS oxygen is expressed in kg / s. The reaction rate for the oxidation of sulfur in coke to SO2 is expressed in kg / s. This represents the molar mass of SO2, expressed in g / mol. This refers to the molar concentration of oxygen in the dense phase region, expressed in kmol / m³. 3 ; The reaction kinetic parameter constant for the oxidation of H2S to SO2 is given in m. 3 / (kmol·s); This refers to the molar concentration of H2S, in kmol / m³. 3 ; The reaction kinetic constant for the oxidation of COS to SO2 is given in m. 3 / (kmol·s); C COS COS molar concentration, in kmol / m 3 ; Dynamic parameters for SO2 production from sulfur oxidation in coke; w c_S R represents the sulfur mass fraction of coke. S,i The efficiency of sulfur precipitation from coal; The sulfur mass fraction in coal depends on the fuel structure. A relationship coefficient is introduced to represent the relationship between the sulfur mass fraction in coke and the sulfur mass fraction in coal, expressed as: Where, μ S The coefficient representing the relationship between the carbon-sulfur content of the fuel and the base sulfur content of the fuel; w S The received basic sulfur mass fraction; The equation for calculating the molar concentration of H2S is expressed as follows: w h_S This represents the mass fraction of sulfur in the volatile matter. The proportion of H2S in volatile sulfur; M S S is the molar mass of S, expressed in g / mol. The catalytic oxidation rate of H2S by CaO is expressed in kg / s. The mass fraction of sulfur in volatile matter is expressed as: The reaction rate of CaO reacting with H2S to produce CaS is expressed as follows: in, The molar mass of H2S is expressed in g / mol. D0 is the catalytic oxidation coefficient of H2S by CaO; D0 is the effective diffusion coefficient, on the order of 102. -5 The unit is m 2 / s;d CaO ρ represents the particle size of CaO, in meters (m); CaO This is the density of CaO, in kg / m³. 3 R is the gas constant, with a value of 8.314 J / (mol·K); T yq It is the flue gas temperature, measured in Kelvin (K).

4. The method for optimizing SO2 emissions from a CFB boiler adapted to deep peak shaving as described in claim 3, characterized in that, The construction of SO2 emission models in S2 also includes: The equation for calculating the molar concentration of COS is expressed as follows: Where, η h_s(COS) The COS ratio in volatile sulfur; The equation for calculating SO2 removal rate is expressed as follows: in, r is the SO2 removal rate in the furnace, expressed in kg / s. CaO ρ represents the CaO consumption rate, in kg / s; k1 is the correction factor for SO2 removal rate, a function of excess air coefficient and load conditions; m CaO The storage mass of active limestone is expressed in kg. The reaction rate constant of SO2; The calculation method is as follows: Among them, S g The effective specific surface area of ​​limestone, in m². 2 / kg; ξ CaO The reactivity coefficient of limestone.

5. The method for optimizing SO2 emissions from a CFB boiler adapted to deep peak shaving as described in claim 4, characterized in that, The construction of SO2 emission models in S2 also includes: The mass balance equation for the production of CaO from limestone by calcination is expressed as: Among them, M CaO The value is the molar mass of CaO, expressed in g / mol. W is the molar mass of CaCO3, expressed in g / mol. CaO This represents the limestone feed rate, expressed in kg / s. This represents the mass fraction of CaCO3 in limestone. For CaCO3 calcination efficiency; r loss CaO loss rate; The lumped parameter equation for the average SO2 concentration inside the furnace is expressed as: in, This represents the original SO2 emission concentration, in kmol / m³. 3 ; The SO2 molar concentration in the dense phase region is expressed as: in, SO2 emission concentration in the dense phase region, in kmol / m³ 3 ; The molar concentration of SO2 in the dilute phase region is expressed as: in, This refers to the SO2 emission concentration in the dilute phase region, expressed in kmol / m³. 3 ; The formula for converting concentrations is expressed as follows: in, This indicates the converted on-site SO2 emission concentration, in kg / m³. 3 ; It is the volume fraction of oxygen in the air; This is the standard oxygen volume fraction for power plants, with a value of 6%.

6. The method for optimizing SO2 emissions from a CFB boiler adapted to deep peak shaving as described in claim 5, characterized in that, The specific perturbation test in S3 is as follows: Under 120MW low-load and steady-state load conditions, key operating variables affecting SO2 emissions were subjected to instantaneous changes of specific magnitudes. By observing the dynamic response of SO2 emission concentration, the correlation between sulfur migration pathways and removal efficiency was analyzed; this included four scenarios: Scenario 1: The coal feed rate increases by 5% instantaneously; Scenario 2: Primary airflow increases by 5%; Situation 3: Secondary air volume increases by 5%; Scenario 4: Limestone feed rate increased by 5%.

7. A CFB boiler SO2 emission optimization control system adapted to deep peak shaving, applied to the CFB boiler SO2 emission optimization control method adapted to deep peak shaving as described in any one of claims 1-6, characterized in that, include: The mechanism analysis module is used to perform mechanism analysis on the bed temperature, oxygen volume fraction, SO2 pollutant generation and removal mechanism in CFB boilers. The partition model construction module is connected to the mechanism analysis module, constructs an SO2 emission model based on mechanism analysis, and outputs the parameters in the SO2 emission model. The simulation verification and dynamic characteristics module is connected to the partition model construction module. Based on the SO2 emission model and the parameters in the SO2 emission model, it inputs the variable load condition data into the SO2 emission model and outputs the SO2 concentration prediction results. At the same time, based on the SO2 emission model, it performs disturbance tests on the manipulated variables and outputs dynamic characteristic data and typical variable load condition data. The intelligent control strategy module, connected to the simulation verification and dynamic characteristics module, uses a step-wise generalized predictive control method to regulate the amount of desulfurizing agent based on the SO2 emission model, SO2 concentration prediction results, dynamic characteristic data, and typical variable load operating condition data.

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