A method for predicting main steam pressure of subcritical circulating fluidized bed units under deep peak regulation

Abstract

The present invention provides a method for predicting the main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation. By constructing a CFB dynamic model of a subcritical circulating fluidized bed unit, on the basis of establishing a subcritical CFB boiler combustion, wall heat transfer, and steam-water working medium heat absorption model, a quantum genetic algorithm is used to optimize the model parameters, improve the model accuracy, and realize the prediction of the main steam pressure. The present invention is conducive to realizing the monitoring of the main steam pressure of the subcritical CFB unit under deep peak regulation, predicting the change trend of the main steam pressure under deep peak regulation in advance, improving the control performance of the unit, and ensuring the safe and stable operation of the unit.

Description

Technical Field

[0001] The invention belongs to the field of coordinated control of thermal power units and relates to a main steam pressure prediction method under deep peak regulation of a subcritical circulating fluidized bed unit. Background Art

[0002] The large-scale grid connection of renewable energy power generation can easily cause fluctuations in the load frequency of the power grid, seriously affecting the safe and stable operation of the power grid. In order to ensure the stability of the load frequency of the power grid and realize the consumption of renewable energy power generation, the circulating fluidized bed (CFB) generator set needs to perform deep peak-shaving operation, and the load adjustment range is changed from the original 50% to 100% rated load to 30% to 100% rated load. Most of the CFB units currently in operation are subcritical CFB units. When subcritical CFB units perform deep peak-shaving, the operating conditions often deviate from the design conditions, making the coordinated control of the CFB units more difficult, the control performance is reduced, and the operating parameters such as the main steam pressure and temperature of the unit fluctuate greatly, affecting the safe and economical operation of the unit. If the main steam pressure prediction under deep peak-shaving operation can be achieved, it will help the design of the unit control strategy and improve the unit's operation control level.

[0003] Subcritical CFB units adopt a unique fluidized combustion method. The combustion system has large inertia, strong coupling, and nonlinear characteristics, and the heat transfer process is complex, which makes it difficult to model and predict the main steam pressure. During deep peak-shaving operation, the operating characteristics of the unit under various load conditions vary greatly. Currently, there is little research on the main steam pressure modeling of subcritical CFB units under deep peak-shaving.

[0004] Purpose of the Invention

[0005] The purpose of the present invention is to solve the problem that the main steam pressure of the subcritical CFB unit under deep peak regulation in the prior art cannot be monitored and the control performance is poor, and to provide a main steam pressure prediction method for the subcritical circulating fluidized bed unit under deep peak regulation. On the basis of the subcritical CFB boiler combustion, wall heat transfer, and steam-water working medium heat absorption model, the main steam pressure prediction is realized, which is conducive to the monitoring of the main steam pressure under deep peak regulation of the subcritical CFB unit, and the change trend of the main steam pressure under deep peak regulation is predicted in advance, so as to improve the control performance of the unit. The present invention adopts a quantum genetic algorithm to optimize the model parameters, improve the model accuracy, and enhance the model's ability to predict the main steam pressure, which is conducive to the design of the control strategy of the deep peak regulation CFB unit and ensures the safe and stable operation of the unit. Summary of the invention

[0006] The present invention provides a method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation, comprising the following steps:

[0007] Step 1: construct a dynamic model of the power generation process of a subcritical CFB unit. According to the power generation process of the subcritical CFB unit, select coal quality, coal feed quantity instruction, primary air volume, secondary air volume, slag discharge volume, fly ash volume, feed water flow, main steam valve opening, and total desuperheating water flow as inputs of the constructed dynamic model of the power generation process;

[0008] Step 2: Establish combustion heat release, wall heat transfer, and steam-water working medium heat absorption models under deep peak regulation of the subcritical CFB unit, and determine the steady-state parameters in each established model based on historical data of steady-state conditions;

[0009] Step 3, fitting the functional relationship of key parameters using historical data of deep peak-shaving subcritical CFB units under various typical load conditions;

[0010] Step 4: Based on the dynamic model, model steady-state parameters, and functional relationships of key parameters established in steps 1 to 3, the dynamic historical data of variable load under deep peak regulation of CFB units are used in combination with coal quality parameters to identify the dynamic parameters in the model and establish a main steam pressure prediction model under deep peak regulation of subcritical CFB units. The main steam pressure under deep peak regulation is predicted based on the real-time operation data of subcritical CFB units and coal quality parameters.

[0011] Preferably, in step 1, the primary air volume and the secondary air volume adopt the volume flow rate under standard operating conditions; when the subcritical CFB unit is in deep peak regulation, the main steam temperature is controlled by 4 water spray desuperheaters, and the total flow rate of the cooling water is the sum of the flow rates of the 4 water spray desuperheaters.

[0012] Preferably, in step 2, when the deep peak-shaving subcritical CFB unit operates under ultra-low load conditions, when establishing the combustion heat release model, various heat losses are corrected according to the load conditions, i.e., the coal feed rate; the wall heat transfer model is established based on the dynamic bed temperature; the steady-state parameters in each model are taken as the average value under each typical load condition; and the historical data of the steady-state condition is taken from the distributed control system DCS of the subcritical CFB unit.

[0013] Further preferably, the process of establishing the combustion heat release model of the subcritical CFB unit under deep peak regulation includes: defining the combustion heat release of the subcritical CFB unit as the combustion heat release Q from the residual carbon in the furnace c Combustion heat release Q of volatile matter in coal v , that is, the total heat release Q of the subcritical CFB unit a It is expressed as shown in formula (1):

[0014] Q a =Q c +Q v (1)

[0015] Among them, the combustion of residual carbon in the furnace releases heat Q c With the burning rate R c , calorific value of residual carbon combustion H c Related, expressed as shown in formula (2):

[0016]

[0017] In formula (2), k 1 is the dimensionless coefficient; m B is the amount of residual carbon in the furnace, in kg; C o2 is the average oxygen concentration in the furnace of the CFB boiler, in mol / m 3 ;

[0018] The amount of residual carbon in the furnace m B It is expressed as shown in formula (3):

[0019]

[0020] In formula (3), X c is the fixed carbon content of coal on received basis, which is determined by the coal quality and expressed as % content; u B is the coal feeding instruction, unit is kg / s; t is the delay time, unit is s; X pz , X FL are the carbon contents of slag and fly ash, expressed as %; m pz 、m FL They are the slag discharge amount and fly ash amount, in kg / s;

[0021] The average oxygen concentration in the furnace C o2 It is expressed as shown in formula (4):

[0022]

[0023] In formula (4): V is the furnace volume, in m 3 ; A ir1 , A ir2 Primary air volume and secondary air volume respectively, in Nm 3 / s;k q is the molar conversion coefficient of the total air volume, in mol / m 3 ;k r is the molar conversion coefficient of oxygen content in the volatile matter, in mol / kg; v r is the volatile matter content of coal, which is determined by the coal quality and expressed as % content; k f is the proportionality coefficient.

[0024] Further preferably, the process of establishing the wall heat transfer model of the subcritical CFB unit under deep peak regulation is: the bed temperature Tb under deep peak regulation is expressed as shown in formula (5):

[0025]

[0026] In formula (5), c s m s is the heat storage coefficient of the furnace side, in kJ / ℃; Δq air is the change in the calorific value of air, expressed in kJ / m 3 ;h PZ is the enthalpy of the ash, in kJ / kg; Q 1 is the heat absorbed by the wall and the working fluid, in kJ / s;

[0027] The heat absorption of the wall and the working fluid is expressed as shown in formula (6):

[0028]

[0029] In formula (6), k 2 , n is the dimensionless model coefficient; D fw is the water flow rate, in kg / s; T st Main steam temperature, in °C.

[0030] Further preferably, the process of establishing the steam-water side working medium heat absorption model under deep peak regulation of the subcritical CFB unit is as follows: the main steam temperature can be expressed as a function of the main steam pressure and the coal feed rate, as shown in formula (7):

[0031] T st =f(p st ,u B ) (7),

[0032] In formula (7), p st is the main steam pressure, in MPa; f(·) is about p st 、u B Function of

[0033] The steam drum pressure p b It is expressed as shown in formula (8):

[0034]

[0035] In formula (8), C b is the total heat storage coefficient of soda and metal, in kJ / MPa; h fw is the feed water enthalpy value, in kJ / kg; D sw is the cooling water flow rate, in kg / s; D st Main steam flow rate, unit is kg / s; h st is the main steam enthalpy, in kJ / kg;

[0036] The feed water enthalpy is expressed as a function of the coal feed rate, as shown in formula (9):

[0037] h fw =g(u B )=au B 2 +bu B +c (9),

[0038] In formula (9), a, b, and c are function parameters, which are determined by function fitting;

[0039] The main steam enthalpy is expressed as shown in formula (10):

[0040] h st =h(p st ,u B ) (10),

[0041] In formula (10), h(·) is the st 、u B Function of

[0042] The main steam flow rate D st Expressed as the main steam pressure p st , Main steam valve opening u t Determine, as shown in formula (11):

[0043] D st =u t D(p st ) (11),

[0044] In formula (11), D(·) is the st Function of

[0045] The main steam pressure p of the subcritical CFB unit during deep peak regulation operation st It is expressed as shown in formula (12)-(13):

[0046] p st =p b -Δp (12),

[0047] Δp=p(r B ) (13),

[0048] In the formula, p b refers to the drum pressure, Δp represents the superheater pressure loss, and p(·) is about r B Function of

[0049] Further preferably, the steady-state parameters in each model determined by using the historical operating data of the steady-state condition in step 2 are expressed as shown in equations (14)-(16):

[0050]

[0051]

[0052]

[0053] The superscript “*” in the formula represents the parameters of each steady-state condition.

[0054] Preferably, in step 3, the least square method is used to fit the functional relationship of key parameters in combination with the operating data of the subcritical CFB unit under deep peak regulation. The functions to be fitted are Q 1 , f(·), g(·), h(·), D(·), p(·), and the criterion is to minimize the sum of squares of errors between the fitted value and the actual value of the function.

[0055] Preferably, in step 4, the low calorific value of coal is determined by coal quality parameters, and the dynamic parameters in each dynamic model are identified and optimized using a quantum genetic algorithm. The specific process is: setting the dynamic parameters c s m s , C b , the initial values ​​of t, coal quality, coal feed rate instruction, primary air volume, secondary air volume, slag discharge, fly ash, feed water flow, main steam valve opening, and total desuperheating water flow are used as the input of the dynamic model of the subcritical CFB unit power generation process to calculate the output of the dynamic model of the subcritical CFB unit power generation process. The sum of the absolute values ​​of the relative errors between the calculated output value and the actual operating parameter value is used as the objective function of identification. The dynamic parameters are iteratively updated and optimized using the quantum genetic algorithm. The objective function is expressed as shown in formula (17):

[0056]

[0057] In formula (17): Δp st,i , ΔT b,i They represent the deviations between the main steam pressure and bed temperature output values ​​of the dynamic model of the power generation process of the subcritical CFB unit at the i-th sampling point and the actual data;

[0058] Based on the real-time operation data and coal quality data of the subcritical CFB unit from the DCS system, the main steam pressure prediction of the subcritical CFB unit under deep peak regulation operation is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the main steam pressure prediction method under deep peak regulation of the subcritical circulating fluidized bed unit described in the present invention. DETAILED DESCRIPTION

[0060] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings. It should be noted that those skilled in the art should understand that the embodiments herein are only used to exemplarily describe the present invention and should not be regarded as limiting the scope of protection of the present invention. Any modification or equivalent substitution of the technical solution of the present invention that does not depart from the idea and purpose of the present invention shall fall within the scope of the present invention.

[0061] Figure 1 is a flowchart of the main steam pressure prediction method under deep peak regulation of the subcritical circulating fluidized bed unit of the present invention, such as Figure 1 As shown, the following steps are included:

[0062] Step 1: construct a dynamic model of the power generation process of a subcritical CFB unit. According to the power generation process of the subcritical CFB unit, select coal quality, coal feed quantity instruction, primary air volume, secondary air volume, slag discharge volume, fly ash volume, feed water flow, main steam valve opening, and total desuperheating water flow as inputs of the constructed dynamic model of the power generation process;

[0063] Step 2: Establish combustion heat release, wall heat transfer, and steam-water working medium heat absorption models under deep peak regulation of the subcritical CFB unit, and determine the steady-state parameters in each established model based on historical data of steady-state conditions;

[0064] Step 3, fitting the functional relationship of key parameters using historical data of deep peak-shaving subcritical CFB units under various typical load conditions;

[0065] Step 4: Based on the dynamic model, model steady-state parameters, and functional relationships of key parameters established in steps 1 to 3, the dynamic historical data of variable load under deep peak regulation of CFB units are used in combination with coal quality parameters to identify the dynamic parameters in the model and establish a main steam pressure prediction model under deep peak regulation of subcritical CFB units. The main steam pressure under deep peak regulation is predicted based on the real-time operation data of subcritical CFB units and coal quality parameters.

[0066] Specifically, in step 1, the primary air volume and the secondary air volume adopt the volume flow rate under standard operating conditions; when the subcritical CFB unit is in deep peak regulation, the main steam temperature is controlled by 4 water spray desuperheaters, and the total flow rate of the desuperheating water is the sum of the flow rates of the 4 water spray desuperheaters.

[0067] In step 2, when the deep peak-shaving subcritical CFB unit operates under ultra-low load conditions, when establishing the combustion heat release model, various heat losses are corrected according to the load conditions, that is, the coal feed rate; the wall heat transfer model is established based on the dynamic bed temperature; the steady-state parameters in each model are taken as the average value under each typical load condition; the historical data of the steady-state condition is taken from the distributed control system DCS of the subcritical CFB unit.

[0068] In step 3, the least square method is used to fit the functional relationship of the key parameters.

[0069] In step 4, the low calorific value of coal is determined by the coal quality parameters, and the dynamic parameters in each dynamic model are identified and optimized using a quantum genetic algorithm.

[0070] The invention is described in detail below through an embodiment.

[0071] Example

[0072] According to the steps included in the main steam pressure prediction method under deep peak regulation of the above subcritical circulating fluidized bed unit, the specific operations are as follows:

[0073] Selection of model inputs in step 1

[0074] For the subcritical CFB unit with deep peak regulation, the main steam pressure can characterize the balance between the boiler output energy and the energy required by the steam turbine. The boiler output energy is related to the combustion calorific value and various heat losses, and can be determined according to the coal quality, coal feed quantity instruction, primary air volume, secondary air volume, slag discharge volume, and fly ash volume. The steam-water working medium parameters of the CFB unit are related to the feed water flow, main steam valve opening, and total desuperheating water flow. Therefore, coal quality, coal feed quantity instruction, primary air volume, secondary air volume, slag discharge volume, fly ash volume, feed water flow, main steam valve opening, and total desuperheating water flow are selected as the input of the model.

[0075] Modeling of each dynamic model in step 2

[0076] Combustion heat release model for subcritical CFB units under deep peak regulation

[0077] The heat released by combustion of subcritical CFB units mainly comes from the heat released by combustion of residual carbon in the furnace Q c Combustion heat release Q of volatile matter in coal v Therefore, the total heat released by combustion is Q a It can be expressed as:

[0078] Q a =Q c +Q v (1)

[0079] The combustion of residual carbon in the furnace releases heat Q c With the burning rate R c , calorific value of residual carbon combustion H c Related:

[0080]

[0081] Where: k 1 is the dimensionless coefficient; m B is the amount of residual carbon in the furnace, kg; C o2is the average oxygen concentration in the furnace of the CFB boiler, mol / m 3 .

[0082] Residual carbon in the furnace m B It can be obtained by the following formula:

[0083]

[0084] Where: X c is the fixed carbon content of coal on received basis (determined by coal quality), %; u B is the coal feeding instruction, kg / s; t is the delay time, s; X pz , X FL are the carbon content of slag and fly ash, %; m pz , m FL They are slag discharge amount and fly ash amount, kg / s respectively.

[0085] Average oxygen concentration in the furnace C o2 It can be obtained by the following formula:

[0086]

[0087] Where: V is the furnace volume, m 3 ; A ir1 , A ir2 Primary air volume and secondary air volume, Nm 3 / s;k q is the molar conversion coefficient of the total air volume, mol / m 3 ;k r is the molar conversion coefficient of oxygen content in the volatile matter, mol / kg; v r is the received volatile matter fraction of coal (determined by coal quality), %; k f is the proportionality coefficient.

[0088] 2. Wall heat transfer model of subcritical CFB unit under deep peak regulation

[0089] The bed temperature of deep peak-shaving subcritical CFB units varies greatly, which has a great impact on the wall heat transfer coefficient and furnace side energy storage. b It can be obtained by the following formula:

[0090]

[0091] Where: c s m s is the heat storage coefficient of the furnace side, kJ / ℃; Δq air is the change in calorific value of air, kJ / m 3 ;h PZ is the enthalpy of ash, kJ / kg; Q 1 is the heat absorbed by the wall and the working fluid, kJ / s.

[0092] The heat absorption of the wall and the working fluid can be obtained by the following formula:

[0093]

[0094] Where: k 2 , n is the dimensionless model coefficient; D fw is the water flow rate, kg / s; T st is the main steam temperature, °C. 3. Heat absorption model of steam-water side working medium under deep peak regulation of subcritical CFB unit

[0095] When the subcritical CFB unit is in deep peak regulation operation, the main steam temperature can be expressed as a function of the main steam pressure and coal feed rate, that is,

[0096] T st =f(p st ,u B ) (7)

[0097] Where: p st is the main steam pressure, MPa; f(·) is about p st 、u B function.

[0098] According to the energy balance principle, the drum pressure p b It can be obtained by the following formula:

[0099]

[0100] Where: C b is the total heat storage coefficient of soda and metal, kJ / MPa; h fw is the feed water enthalpy, kJ / kg; D sw is the cooling water flow rate, kg / s; D st Main steam flow, kg / s; h st is the main steam enthalpy, kJ / kg.

[0101] By analyzing the deep peak load operation characteristics of CFB units, the feed water enthalpy can be expressed as a function of the coal feed rate, that is:

[0102] h fw =g(u B )=au B 2 +bu B +c (9)

[0103] Where: a, b, c are the parameters of the function, which can be determined by function fitting.

[0104] The main steam enthalpy can be obtained by the following formula:

[0105] hst =h(p st ,u B ) (10)

[0106] Where: h(·) is about p st 、u B function.

[0107] Main steam flow D st The main steam pressure p st , Main steam valve opening u t Sure:

[0108] D st =u t D(p st ) (11)

[0109] Where: D(·) is about p st function.

[0110] Considering the superheater pressure loss Δp, the main steam pressure p of the subcritical CFB unit during deep peak load operation is st It can be obtained by the following formula:

[0111] p st =p b -Δp (12)

[0112] Δp=p(r B ) (13)

[0113] Where: p(·) is the B function.

[0114] 4. Determination of model parameters

[0115] The steady-state parameters in the model can be determined using the historical operating data of the steady-state condition, namely

[0116]

[0117]

[0118]

[0119] The parameters with superscript “*” in the formula represent steady-state parameters.

[0120] Combined with the operation data of subcritical CFB units under deep peak regulation, the least square method is used to fit the functional relationship of key parameters. The functions to be fitted are Q 1 , f(·), g(·), h(·), D(·), p(·), and the criterion is to minimize the sum of squares of errors between the fitted value and the actual value of the function.

[0121] Set dynamic parameters c respectively s m s , C b , the initial value of t, coal quality, coal feed instruction, primary air volume, secondary air volume, slag discharge, fly ash, feed water flow, main steam valve opening, and total desuperheating water flow are used as the model input to calculate the model output. The sum of the absolute values ​​of the relative errors between the model output value and the actual operating parameter value is used as the objective function of identification. The dynamic parameters are iteratively updated and optimized using the quantum genetic algorithm. The objective function can be expressed as:

[0122]

[0123] Where: Δp st,i , ΔT b,i They respectively represent the deviation between the main steam pressure and bed temperature output values ​​of the model at the i-th sampling point and the actual data.

[0124] After the above model is established, the main steam pressure prediction of the unit under deep peak regulation operation can be realized based on the real-time operation data and coal quality data of the subcritical CFB unit from the DCS system.

[0125] In summary, the present invention has the following beneficial effects:

[0126] (1) A combustion heat release, wall heat transfer, and steam-water working medium heat absorption model suitable for deep peak-shaving subcritical CFB units was established, which is suitable for the prediction of main steam pressure under a wide range of load conditions and ultra-low load conditions.

[0127] (2) The least squares method is used to fit the functional relationship of the key parameters of the subcritical CFB unit under various load conditions. This functional relationship is applicable to various load conditions under deep peak regulation.

[0128] (3) The quantum genetic algorithm is used to optimize the dynamic parameters in the model with the minimum sum of squared errors as the objective function. This can improve the prediction accuracy of the main steam pressure, provide guidance for the operation of deep peak-shaving sub-CFB units, and help realize the automatic control of CFB units at full load.

Claims

1. A method for predicting main steam pressure under deep peak regulation of a subcritical circulating fluidized bed unit, characterized in that: The steps include: Step 1: construct a dynamic model of the power generation process of a subcritical CFB unit. According to the power generation process of the subcritical CFB unit, select coal quality, coal feed quantity instruction, primary air volume, secondary air volume, slag discharge volume, fly ash volume, feed water flow, main steam valve opening, and total desuperheating water flow as inputs of the constructed dynamic model of the power generation process; Step 2: Establish combustion heat release, wall heat transfer, and steam-water working medium heat absorption models under deep peak regulation of the subcritical CFB unit, and determine the steady-state parameters in each established model based on historical data of steady-state conditions; Step 3, fitting the functional relationship of key parameters using historical data of deep peak-shaving subcritical CFB units under various typical load conditions; In step 3, combined with the operating data of the subcritical CFB unit under deep peak regulation, the least square method is used to fit the functional relationship of the key parameters. The functions to be fitted are Q1, f(·), g(·), h(·), D(·), and p(·). The minimum sum of square errors between the fitting value and the actual value of the function is used as the criterion; wherein Q1 is the heat absorption of the wall and the working fluid, in kJ / s; f(·) is about p st 、u B is a function of; g(·) is a function of the amount of coal fed; h(·) is a function of p st 、u B function; D(·) is a function of p st function; p(·) is a function of r B Function of Step 4: Based on the dynamic model, model steady-state parameters, and functional relationships of key parameters established in steps 1 to 3, the dynamic historical data of variable load under deep peak regulation of the CFB unit are used in combination with coal quality parameters to identify the dynamic parameters in the model and establish a main steam pressure prediction model under deep peak regulation of the subcritical CFB unit; based on the real-time operation data of the subcritical CFB unit and coal quality parameters, the main steam pressure under deep peak regulation is predicted.

2. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 1, characterized in that: In step 1, the primary air volume and the secondary air volume adopt the volume flow rate under standard operating conditions; when the subcritical CFB unit is in deep peak regulation, the main steam temperature is controlled by 4 water spray desuperheaters, and the total flow rate of the desuperheating water is the sum of the flow rates of the 4 water spray desuperheaters.

3. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 1, characterized in that: In step 2, when the deep peak-shaving subcritical CFB unit operates under ultra-low load conditions, when establishing the combustion heat release model, various heat losses are corrected according to the load conditions, that is, the coal feed rate; the wall heat transfer model is established based on the dynamic bed temperature; the steady-state parameters in each model are taken as the average value under each typical load condition; the historical data of the steady-state condition is taken from the distributed control system DCS of the subcritical CFB unit.

4. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 3, characterized in that: The process of establishing the combustion heat release model of the subcritical CFB unit under deep peak regulation includes: defining the combustion heat release of the subcritical CFB unit as the combustion heat release Q from the residual carbon in the furnace c Combustion heat release Q of volatile matter in coal v , that is, the total heat release Q of the subcritical CFB unit a It is expressed as shown in formula (1): Q a =Q c +Q v (1), Among them, the combustion of residual carbon in the furnace releases heat Q c With the burning rate R c , calorific value of residual carbon combustion H c Related, expressed as shown in formula (2): In formula (2), k1 is a dimensionless coefficient; m B is the amount of residual carbon in the furnace, in kg; C o2 is the average oxygen concentration in the furnace of the CFB boiler, in mol / m 3 ; The amount of residual carbon in the furnace m B It is expressed as shown in formula (3): In formula (3), X c is the fixed carbon content of coal on a received basis, determined by the coal quality and expressed as % content; u B is the coal feeding quantity instruction, unit is kg / s; τ is the delay time, unit is s; X pz , X FL are the carbon contents of slag and fly ash, expressed as %; m pz 、m FL They are the slag discharge amount and fly ash amount, in kg / s; The average oxygen concentration in the furnace C o2 It is expressed as shown in formula (4): In formula (4): V is the furnace volume, in m 3 ; A ir1 , A ir2 Primary air volume and secondary air volume respectively, in Nm 3 / s;k q is the molar conversion coefficient of the total air volume, in mol / m 3 ;k r is the molar conversion coefficient of oxygen content in the volatile matter, in mol / kg; v r is the volatile matter content of coal, which is determined by the coal quality and expressed as % content; k f is the proportionality coefficient.

5. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 4, characterized in that: The process of establishing the wall heat transfer model of the subcritical CFB unit under deep peak regulation is as follows: the bed temperature Tb under deep peak regulation is expressed as shown in formula (5): In formula (5), c s m s is the heat storage coefficient of the furnace side, in kJ / ℃; Δq air is the change in the calorific value of air, expressed in kJ / m 3 ; h PZ is the enthalpy of the ash, in kJ / kg; Q1 is the heat absorbed by the wall and the working fluid, in kJ / s; The heat absorption of the wall and the working fluid is expressed as shown in formula (6): In formula (6), k2 and n are dimensionless model coefficients; D fw is the water flow rate, in kg / s; T st Main steam temperature, in °C.

6. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 5, characterized in that: The process of establishing the steam-water side working medium heat absorption model under deep peak regulation of subcritical CFB units is as follows: the main steam temperature can be expressed as a function of the main steam pressure and the coal feed rate, as shown in formula (7): T st =f(p st ,u B ) (7), In formula (7), p st is the main steam pressure, in MPa; f(·) is about p st 、u B Function of The drum pressure p b It is expressed as shown in formula (8): In formula (8), C b is the total heat storage coefficient of soda and metal, in kJ / MPa; h fw is the feed water enthalpy value, in kJ / kg; D sw is the cooling water flow rate, in kg / s; D st Main steam flow rate, unit is kg / s; h st is the main steam enthalpy, in kJ / kg; The feed water enthalpy is expressed as a function of the coal feed rate, as shown in formula (9): h fw =g(u B )=me B 2 +bu B +c (9), In formula (9), a, b, and c are function parameters, which are determined by function fitting; The main steam enthalpy is expressed as shown in formula (10): h st =h(p st ,u B ) (10), In formula (10), h(·) is the st 、u B Function of The main steam flow rate D st Expressed as the main steam pressure p st , Main steam valve opening u t Determine, as shown in formula (11): D st =u t D(p st ) (11), In formula (11), D(·) is the st Function of The main steam pressure p of the subcritical CFB unit during deep peak regulation operation st It is expressed as shown in formula (12)-(13): p st =p b -Δp (12), Δp=p(r B ) (13), In the formula, p b refers to the drum pressure, Δp represents the superheater pressure loss, and p(·) is about r B function.

7. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 6, characterized in that: The steady-state parameters in each model determined by using the historical operating data of the steady-state condition in step 2 are expressed as shown in equations (14)-(16): The superscript "*" in the formula represents the parameters of each steady-state condition.

8. The method for predicting main steam pressure of a subcritical circulating fluidized bed unit under deep peak regulation according to claim 7, characterized in that: In step 4, the low calorific value of coal is determined by the coal quality parameters, and the dynamic parameters in each dynamic model are identified and optimized using a quantum genetic algorithm. The specific process is: set the dynamic parameters c s m s , C b , the initial values ​​of τ, coal quality, coal feed rate instruction, primary air volume, secondary air volume, slag discharge, fly ash, feed water flow, main steam valve opening, and total desuperheating water flow are used as the input of the dynamic model of the subcritical CFB unit power generation process to calculate the output of the dynamic model of the subcritical CFB unit power generation process. The sum of the absolute values ​​of the relative errors between the calculated output value and the actual operating parameter value is used as the objective function of identification. The dynamic parameters are iteratively updated and optimized using the quantum genetic algorithm. The objective function is expressed as shown in formula (17): In formula (17): Δp st,i , ΔT b,i They represent the deviations between the main steam pressure and bed temperature output values ​​of the dynamic model of the power generation process of the subcritical CFB unit at the i-th sampling point and the actual data; Based on the real-time operation data and coal quality data of the subcritical CFB unit from the DCS system, the main steam pressure prediction of the subcritical CFB unit under deep peak regulation operation is realized.

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