CN108021027B - Output power prediction system and method for supercritical circulating fluidized bed unit - Google Patents

Output power prediction system and method for supercritical circulating fluidized bed unit Download PDF

Info

Publication number
CN108021027B
CN108021027B CN201711167283.9A CN201711167283A CN108021027B CN 108021027 B CN108021027 B CN 108021027B CN 201711167283 A CN201711167283 A CN 201711167283A CN 108021027 B CN108021027 B CN 108021027B
Authority
CN
China
Prior art keywords
steam
unit
model
module
cfb
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201711167283.9A
Other languages
Chinese (zh)
Other versions
CN108021027A (en
Inventor
高明明
洪烽
刘吉臻
严国栋
陈�峰
伯运鹤
李玉红
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
North China Electric Power University
Original Assignee
North China Electric Power University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by North China Electric Power University filed Critical North China Electric Power University
Priority to CN201711167283.9A priority Critical patent/CN108021027B/en
Publication of CN108021027A publication Critical patent/CN108021027A/en
Application granted granted Critical
Publication of CN108021027B publication Critical patent/CN108021027B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The invention relates to the technical field of control of power station boiler units, and provides a system and a method for predicting output power of a supercritical circulating fluidized bed unit, wherein the system establishes a CFB boiler real-time combustion heat release model, a unit steam-water side working medium heat absorption model and a unit steam turbine work application model, and determines parameters in the models; identifying a functional relation among key parameters of the unit; and further establishing an output power prediction model of the circulating fluidized bed unit, and predicting the power of the dynamic unit according to input data and coal quality data of the unit in real time operation. The invention solves the technical problems that the control and regulation at two sides of the machine furnace are mutually influenced and the difference of the dynamic characteristics of the machine furnace is large due to the multivariable strong coupling characteristic of the supercritical CFB unit; a system control model is established, an input-output relation is determined, the output power is predicted on the basis of the known input quantity at the current moment and the input quantity at the future moment, and the change trend of the output quantity is predicted in advance; the method has high prediction precision and is suitable for popularization and application.

Description

Output power prediction system and method for supercritical circulating fluidized bed unit
Technical Field
The invention relates to the technical field of control of power station boiler units, in particular to a system and a method for predicting output power of a supercritical circulating fluidized bed unit.
Background
Under the promotion of energy conservation and emission reduction of energy enterprises in the global scope, China eliminates the backward capacity, and the pressure of comprehensive energy-saving planning and transformation of the coal-electric machine set is further increased. Compared with a pulverized coal furnace, the Circulating Fluidized Bed (CFB) boiler technology has a wider fuel adaptability range and lower air pollution control cost, and has been developed as one of the most successful clean coal combustion technologies for practical use. At present, most CFB units in China are subcritical parameters with rated surface pressure of steam at an outlet of a superheater being 14-22.2Mpa, and the CFB units do not have obvious superiority in the aspect of achieving lower power supply coal consumption. According to measurement and calculation, the power supply efficiency of a supercritical unit (the rated gauge pressure of steam at the outlet of a superheater is 22.2-31Mpa) is improved by 2.0% -2.5% compared with that of a subcritical parameter unit, and the power supply efficiency of an advanced supercritical unit reaches 45% -47%. The CFB combustion technology and the supercritical (supercritical) coal-fired power generation technology are mature, and the combination of the CFB combustion technology and the supercritical (supercritical) coal-fired power generation technology has the advantages of low cost pollution control and high power supply efficiency. The supercritical CFB technology in China is in a rapid development stage, the first 600MW supercritical CFB unit in the world runs well in a white horse power plant to play a demonstration purpose, and meanwhile, the supercritical unit with the same parameters is built in flat coal and Romania. The 350MW supercritical CFB unit forms a huge market, 14 units are put into operation, and 50 units are in order or installed.
With the continuous increase of large-capacity units and the increasing automation degree of power grid dispatching, the large-capacity units are required to operate in an Automatic Generation Control (AGC) mode, which puts new requirements on a rapid variable load system of a power plant unit. The performance of the operation control system is one of the main problems of the current industrial CFB unit. The multivariable strong coupling characteristics of the supercritical CFB unit are reflected in the mutual influence of control and regulation on two sides of the machine furnace, and the difference of the dynamic characteristics of the machine furnace is large. If a control model of the supercritical CFB unit coordination system can be established, the input-output relation is determined, the output power is predicted on the basis of the known input quantity at the current moment and the future moment, the change trend of the output quantity is predicted in advance, the design of unit control logic and algorithm is facilitated, and the operation control level of the unit is improved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a system and a method for predicting the output power of a supercritical circulating fluidized bed unit, which can predict the change trend of the output power of a supercritical CFB unit coordination control system in advance and improve the load control logic of the unit.
The invention discloses a supercritical circulating fluidized bed unit output power prediction system which comprises a supercritical CFB unit power prediction model module, a database module, a data selection and pretreatment module, a combustion heat calculation model module, a steam-water side model module, a steam turbine model module and a subfunction identification module;
the combustion heat calculation model module is used for establishing a CFB boiler real-time combustion heat release model and determining parameters in the model;
the steam-water side model module is used for establishing a steam-water side working medium heat absorption model of the CFB unit and determining steady-state parameters in the model;
the steam turbine model module is used for establishing a CFB unit steam turbine work-doing model and determining steady-state parameters in the model;
the subfunction identification module is used for identifying the functional relationship among the key parameters of the CFB unit;
the database module transmits real-time data, historical data and coal quality data of unit operation to the data selection and pretreatment module;
the data selection and preprocessing module is used for processing historical data and coal quality data and selecting training data for identifying model parameters and functional relations among the parameters; wherein the coal quality data comprises the volatile components, ash content and calorific value of the coal; the training data selection is that the input/output and intermediate state values of the model under the steady-state working condition of the unit operation need to be selected according to the requirements of model parameters and the training data corresponding to the static parameters need to be selected, and the input/output and intermediate state values of the model under the dynamic working condition of the unit need to be selected according to the training data corresponding to the dynamic parameters; the preprocessing link comprises selection and judgment of the working condition of the unit and elimination of singular values.
The supercritical CFB unit power prediction model module is used for establishing a supercritical CFB unit power prediction model and determining dynamic parameters in the model; and predicting the power of the dynamic unit according to the models established by the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the subfunction identification module, the determined corresponding parameters, the real-time operation input data of the unit and the coal quality data.
Further, the data selection and preprocessing module transmits historical data, real-time operation data and coal quality data of the unit to the supercritical CFB unit power prediction model module; and the data selection and preprocessing module transmits the historical data and the coal quality data of the unit to the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the subfunction identification module.
Further, the database module is connected with the data selection and preprocessing module, the data selection and preprocessing module is further connected with the supercritical CFB unit power prediction model module, the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the sub-function identification module, and the supercritical CFB unit power prediction model module is further connected with the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the sub-function identification module in a bidirectional connection mode.
The invention also provides a method for predicting the output power of the supercritical circulating fluidized bed unit, which comprises the following steps:
step one, establishing a CFB boiler real-time combustion heat release model, and determining parameters in the model;
step two, establishing a CFB unit steam-water side working medium heat absorption model, and determining steady-state parameters in the model;
establishing a CFB unit steam turbine work model, and determining steady-state parameters in the model;
identifying a functional relation among key parameters of the CFB unit;
step five, according to the models established in the step one to the step four and the determined corresponding parameters, utilizing historical data and coal quality data to identify dynamic parameters of the models and establishing a power prediction model of the supercritical CFB unit;
and step six, predicting the power of the dynamic unit according to input data and coal quality data of the unit running in real time.
Further, the first step specifically includes:
step 1.1, establishing a CFB boiler real-time combustion heat release model as follows:
Figure GDA0002756570900000031
in the formula, Qr(t) total heat released by combustion, MJ/s; b (t) is the instant carbon quantity which represents the unburned carbon residue in the furnace and has the unit of kg; a. theirAs the total air volume, in Nm3S; k is the total coefficient of the thermal model; mCIs the molar mass of carbon in kg/kmol; k is a radical ofcIs the burning rate constant of the carbon particles; dcIs the average diameter of the carbon particles in m; rhocIs the density of the carbon particles in kg/m3；RCThe overall combustion reaction rate of carbon, kg/s; h is the unit calorific value of the instant burning carbon, and the unit is MJ/kg; ko (Chinese character)2Is the total air quantity AirA correlation coefficient with oxygen concentration;
the amount of instantly-combusted carbon can be obtained from the formula (2)
Figure GDA0002756570900000032
In the formula WcThe coal feeding amount is kg/s; xcThe fuel quantity is the mass fraction of the base carbon,%; wPZThe slag discharge flow rate is kg/s; xc,pCarbon content of slag discharge is percent; wFLThe fly ash flow rate, kg/s; xc,fIs the carbon content of fly ash in percent. According to engineering experience, the carbon content of fly ash and the carbon content of discharged slag are ignored.
That is, the combustion speed Rc of the burning coal is proportional to the mass of the burning coal accumulated in the furnace and the total air volume.
Figure GDA0002756570900000033
And step 1.2, determining the total coefficient K of the thermal model by using the historical data and the coal quality data.
Further, the second step specifically comprises:
step 2.1, establishing a CFB unit steam-water side working medium heat absorption model:
Figure GDA0002756570900000034
Figure GDA0002756570900000035
where rhomAverage density of working medium in steam-water separator, kg/m3；hmThe average enthalpy value of the working medium in the steam-water separator is kJ/kg; p is a radical ofmSteam pressure at the outlet of the steam-water separator is Mpa; dfwThe water supply flow is kg/s; dsThe flow rate of steam at the outlet of the superheater is kg/s; h isfwThe enthalpy value of the feed water is kJ/kg; h issIs the enthalpy value of steam at the outlet of the superheater, kJ/kg; s1、s2Is a dynamic parameter; k is a radical of0The steam-water side heat absorption coefficient;
step 2.2, order
Figure GDA0002756570900000041
Establishing a CFB unit steam-water heating system model as shown in formulas (8) and (9):
Figure GDA0002756570900000042
Figure GDA0002756570900000043
in the formula hstThe steam ratio at the inlet of the steam turbine is also called main steam specific enthalpy, kJ/kg; dstThe main steam flow is kg/s; l, C1，C2，d1，d2For lumped model parameters in the heated section, l ═ hs/hm，C1＝b21-(b11/b12)×b22，C2＝b22-(b12/b11)×b21，d1＝b22/b12，d2＝b21/b11；
Step 2.3, determining steady state coefficients l and k of steam-water side working medium heat absorption model of CFB unit by utilizing historical data and coal quality data0：
For l, k0According to the formulas (8) and (9), the steady state condition can be obtained
Dfw0-Ds0＝0 (10)
Dfw0hfw0-Ds0hs0+k0Qr0＝0 (11)
The parameters with '0' subscript in the formula representing the steady state can obtain the formula of solving the static parameters:
Figure GDA0002756570900000044
Figure GDA0002756570900000045
further, the third step specifically comprises:
step 3.1, the dynamic process of the steam turbine is fast, and the dynamic characteristic is obtained by adopting a load shedding experimental method; in the control model, the governor stage pressure p1Output power N of the summing unitEApproximated by a first order inertial process.
Figure GDA0002756570900000046
In the formula NEThe unit power, MW; kT10-30 of dynamic time of the steam turbines；
Step 3.2, steam turbine valve regulation instruction gain k1The physical meaning of (1) is the pressure p before the machinestOpening u of steam turbine valveTThe unit load corresponding to the product of (a) represents the regulation stage pressure p1The proportional relationship between the variation of (A) and the variation of the unit load is
Figure GDA0002756570900000047
Further, step four, establishing a subfunction for the intermediate parameter which is difficult to obtain or measure in the model, specifically comprising:
step 4.1, steam-water separator outlet steam pressure pmThe pressure change in the water spraying temperature reduction is neglected, the pressure at the outlet of the superheater is equal to the main steam pressure, and the steam passing through the inlet of the steam turbine is the main steam pressure pstIs calculated to obtain
pm＝ps+△p＝pst+△p (16)
The steam endothermally expands in the superheater, causing an increase in the volume flow, which is the main cause of the superheater differential pressure, which can be denoted as pmEstablishing a subfunction of steam-water separator outlet steam pressure, main steam pressure and superheater differential pressure:
pm＝pst+g(pm) (17)；
step 4.2, the main steam flow entering the steam turbine is influenced by the opening of a valve of the steam turbine, the steam temperature and the pressure; in the operation process, the main steam flow is in direct proportion to the opening of a steam turbine valve; the steam temperature and the steam pressure jointly determine the steam density, and the steam density determines the amount of steam flowing through in unit time under the condition that the steam flow rate is not changed; during the operation of the boiler, although the load lifting amplitude of the unit is large, the temperature fluctuation of the main steam cannot exceed +/-10 ℃, and the main steam basically maintains stable. The temperature of the main steam is regarded as constant, including
Dst＝utf(pst) (18)；
4.3, the temperature fluctuation of the main steam does not exceed +/-10 ℃, and the main steam basically maintains stable; the main steam temperature is regarded as constant, the specific enthalpy h of the main steamstIs set to the main steam pressure pstFunction of (c):
hst＝h(pst) (19)；
and 4.4, identifying a subfunction (17) by using steady-state historical data of the superheater differential pressure and the main steam pressure under different loads of the unit, identifying a subfunction (18) by using steady-state historical data of the main steam flow, the main steam pressure, the main steam enthalpy value and the steam turbine valve opening degree under different loads of the unit, and identifying a subfunction (19) by using steady-state historical data of the main steam specific enthalpy and the main steam pressure under different loads of the unit.
Further, the fifth step specifically comprises:
step 5.1, determining a power prediction model of the supercritical CFB unit:
Figure GDA0002756570900000061
step 5.2, aiming at the model (20), taking the coal feeding, the total air volume, the water feeding flow and the steam turbine valve opening as input, calculating and outputting by the model, outputting the relative error sum of a calculated value and an actual value as an optimized objective function as a formula (21), and determining the dynamic parameter C by utilizing an optimization algorithm1，C2，d1，d2。
Figure GDA0002756570900000062
In the formula
Figure GDA0002756570900000063
Respectively representing the main steam pressure, the unit load, the mean value of the enthalpy value of the intermediate point and the deviation of the actual data output by the model.
Further, said C1In the range of 10000000-100000000 at an interval of 100; said C is2The range of (1) is 100000-2000000, and the interval is 10; d is1The range of (1) is 200-500, and the interval is 0.1; d is2The range of (1) is 5000-10000, and the interval is 0.1.
The invention has the beneficial effects that: the technical problems that control and regulation at two sides of the machine furnace are mutually influenced and the difference of the dynamic characteristics of the machine furnace is large due to the multivariable strong coupling characteristic of the supercritical CFB unit are solved; the control model of the supercritical CFB unit coordination system is established, the input-output relation is definite, the output power is predicted on the basis of knowing the input quantity at the current moment and the input quantity at the future moment, the change trend of the output quantity is predicted in advance, the design of unit control logic and algorithm is facilitated, and the operation control level of the unit is improved; the method has high prediction precision and is suitable for popularization and application.
Drawings
Fig. 1 is a schematic structural diagram of an output power prediction system of a supercritical circulating fluidized bed unit according to an embodiment of the present invention.
FIG. 2 is a graph showing the predicted effect of the output power of a 600MW supercritical circulating fluidized bed unit (sampling time is 120 seconds) applying the method of the invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that technical features or combinations of technical features described in the following embodiments should not be considered as being isolated, and they may be combined with each other to achieve better technical effects. In the drawings of the embodiments described below, the same reference numerals appearing in the respective drawings denote the same features or components, and may be applied to different embodiments.
The invention discloses a supercritical circulating fluidized bed unit output power prediction system which comprises a supercritical CFB unit power prediction model module, a database module, a data selection and pretreatment module, a combustion heat calculation model module, a steam-water side model module, a steam turbine model module and a subfunction identification module;
the combustion heat calculation model module is used for establishing a CFB boiler real-time combustion heat release model and determining parameters in the model;
the steam-water side model module is used for establishing a steam-water side working medium heat absorption model of the CFB unit and determining steady-state parameters in the model;
the steam turbine model module is used for establishing a CFB unit steam turbine work-doing model and determining steady-state parameters in the model;
the subfunction identification module is used for identifying the functional relationship among the key parameters of the CFB unit;
the database module transmits real-time data, historical data and coal quality data of unit operation to the data selection and pretreatment module;
the data selection and preprocessing module is used for processing historical data and coal quality data and selecting training data for identifying model parameters and functional relations among the parameters; wherein the coal quality data comprises the volatile components, ash content and calorific value of the coal; the training data selection is that the input/output and intermediate state values of the model under the steady-state working condition of the unit operation need to be selected according to the requirements of model parameters and the training data corresponding to the static parameters need to be selected, and the input/output and intermediate state values of the model under the dynamic working condition of the unit need to be selected according to the training data corresponding to the dynamic parameters; the preprocessing link comprises selection and judgment of the working condition of the unit and elimination of singular values.
The supercritical CFB unit power prediction model module is used for establishing a supercritical CFB unit power prediction model and determining dynamic parameters in the model; and predicting the power of the dynamic unit according to the models established by the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the subfunction identification module, the determined corresponding parameters, the real-time operation input data of the unit and the coal quality data.
The data selection and preprocessing module transmits unit historical data, real-time operation data and coal quality data to the supercritical CFB unit power prediction model module; and the data selection and preprocessing module transmits the historical data and the coal quality data of the unit to the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the subfunction identification module.
The database module is connected with the data selection and preprocessing module, the data selection and preprocessing module is further connected with the supercritical CFB unit power prediction model module, the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the sub-function identification module, and the supercritical CFB unit power prediction model module is further connected with the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the sub-function identification module in a bidirectional connection mode.
The embodiment of the invention provides a method for predicting the output power of a supercritical circulating fluidized bed unit, which comprises the following steps:
step 1.1) establishing a CFB boiler real-time combustion heat release model as follows:
Figure GDA0002756570900000081
in the formula, Qr(t) total heat released by combustion, MJ/s; b (t) is the instant carbon quantity which represents the unburned carbon residue in the furnace and has the unit of kg; a. theirAs the total air volume, in Nm3S; k is the total coefficient of the thermal model; mCIs the molar mass of carbon in kg/kmol; k is a radical ofcIs the burning rate constant of the carbon particles; dcIs the average diameter of the carbon particles in m; rhocIs the density of the carbon particles in kg/m3；RCThe overall combustion reaction rate of carbon, kg/s; h is the unit calorific value of the instant burning carbon, and the unit is MJ/kg; ko (Chinese character)2Is the total air quantity AirA correlation coefficient with oxygen concentration;
the instant carbon is obtained from the formula (2)
Figure GDA0002756570900000082
In the formula WcThe coal feeding amount is kg/s; xcThe fuel quantity is the mass fraction of the base carbon,%; wPZThe slag discharge flow rate is kg/s; xc,pCarbon content of slag discharge is percent; wFLThe fly ash flow rate, kg/s; xc,fIs the carbon content of fly ash in percent. According to engineering experience, the carbon content of fly ash and the carbon content of discharged slag are ignored.
That is, the combustion speed Rc of the burning coal is proportional to the mass of the burning coal accumulated in the furnace and the total air volume.
Figure GDA0002756570900000083
The La Nauze is integrated with the actual situation, the influence of the temperature on the combustion speed of the carbon particles is mainly considered, and the combustion rate constant k of the carbon particles in the circulating fluidized bed boiler is obtained according to practical summarycExpression (c):
kc＝0.513Texp(-9160/T) (4)
in the formula: t is the hearth bed temperature and the unit is K;
the carbon particle oxygen concentration can be approximately averaged in the control system and is determined by the total air volume PM (t) entering the furnace, and the expression is as follows:
Figure GDA0002756570900000084
in the formula: ko (Chinese character)2The value range of the correlation coefficient of the total air volume PM (t) and the oxygen concentration is 0.0040-0.0055, and is generally 0.0050; PM (t) is total air volume in Nm3/s；
Step 2.1) establishing a CFB unit steam-water side working medium heat absorption model:
Figure GDA0002756570900000085
Figure GDA0002756570900000086
where rhomAverage density of working medium in steam-water separator, kg/m3；hmThe average enthalpy value of the working medium in the steam-water separator is kJ/kg; p is a radical ofmAs soda waterSeparator outlet steam pressure, Mpa; dfwThe water supply flow is kg/s; dsThe flow rate of steam at the outlet of the superheater is kg/s; h isfwThe enthalpy value of the feed water is kJ/kg; h issIs the enthalpy value of steam at the outlet of the superheater, kJ/kg; s1、s2Is a dynamic parameter; k is a radical of0The steam-water side heat absorption coefficient.
Step 2.2) order
Figure GDA0002756570900000091
Establishing a CFB unit steam-water heating system lumped parameter model:
Figure GDA0002756570900000092
Figure GDA0002756570900000093
in the formula hstThe steam ratio at the inlet of the steam turbine is also called main steam specific enthalpy, kJ/kg; l, C1，C2，d1，d2For lumped model parameters in the heated section, l ═ hs/hm，C1＝b21-(b11/b12)×b22，C2＝b22-(b12/b11)×b21，d1＝b22/b12，d2＝b21/b11。
Step 2.3) determining steady state coefficients l and k of steam-water side working medium heat absorption model of CFB unit by using historical data and coal quality data0：
For l, k0According to the formulas (6) and (7), the steady state condition can be obtained
Dfw0-Ds0＝0 (10)
Dfw0hfw0-Ds0hs0+k0Qr0＝0 (11)
The parameters with '0' subscript in the formula representing the steady state can obtain the formula of solving the static parameters:
Figure GDA0002756570900000094
Figure GDA0002756570900000095
and 3.1) the dynamic process of the steam turbine is faster, and the dynamic characteristic can be obtained by adopting a load shedding experimental method. In the control model, the governor stage pressure p1Output power N of the summing unitECan be approximated by a first order inertial process.
Figure GDA0002756570900000096
In the formula NEThe unit power, MW; kTThe dynamic time of the steam turbine is 10-30 s.
Step 3.2) steam turbine valve regulation instruction gain k1The physical meaning of (1) is the pressure p before the machinestOpening u of steam turbine valveTThe unit load corresponding to the product of (a) represents the regulation stage pressure p1The proportional relationship between the variation of (A) and the variation of the unit load is
Figure GDA0002756570900000097
Step 4.1) steam-water separator outlet steam pressure pmThe pressure change in the water spraying temperature reduction is neglected, the pressure at the outlet of the superheater is equal to the main steam pressure, and the steam passing through the inlet of the steam turbine is the main steam pressure pstIs calculated to obtain
pm＝ps+△p＝pst+△p (16)
The steam endothermally expands in the superheater, causing an increase in the volume flow, which is the main cause of the superheater differential pressure, which can be denoted as pmEstablishing a subfunction of steam-water separator outlet steam pressure, main steam pressure and superheater differential pressure:
pm＝pst+g(pm) (17)
and 4.2) the main steam flow entering the steam turbine is influenced by a plurality of factors, wherein the factors mainly comprise the opening of a valve of the steam turbine, the steam temperature and the pressure. During operation, the main steam flow is considered to be in direct proportion to the opening of the steam turbine valve. The steam temperature and the steam pressure jointly determine the steam density, and under the condition that the steam flow rate is unchanged, the steam density determines the steam amount flowing through in unit time. During the operation of the boiler, although the load lifting amplitude of the unit is large, the temperature fluctuation of the main steam cannot exceed +/-10 ℃, and the main steam basically maintains stable. The temperature of the main steam is regarded as constant, including
Dst＝utf(pst) (18)
And 4.3) the temperature fluctuation of the main steam can not exceed +/-10 ℃, and the main steam is basically maintained stable. The main steam temperature is regarded as constant, the specific enthalpy h of the main steamstIs set to the main steam pressure pstFunction of (c):
hst＝h(pst) (19)
step 4.4) identifying a subfunction (17) by using steady-state historical data of superheater differential pressure and main steam pressure under different loads of the unit, identifying a subfunction (18) by using steady-state historical data of main steam flow, main steam pressure, main steam enthalpy value and steam turbine valve opening under different loads of the unit, and identifying a subfunction (19) by using steady-state historical data of main steam specific enthalpy and main steam pressure under different loads of the unit;
step 5.1) determining a power prediction model of the supercritical CFB unit:
Figure GDA0002756570900000101
step 5.2) aiming at the model (20), taking coal feeding, total air volume, water feeding flow and steam turbine valve opening as input and using the inputCalculating and outputting the model, taking the relative error sum of the output calculated value and the actual value as an optimized objective function as the formula (21), and determining the dynamic parameter C by using an optimization algorithm1，C2，d1，d2。
Figure GDA0002756570900000111
In the formula
Figure GDA0002756570900000112
Respectively representing the main steam pressure, the unit load, the mean value of the enthalpy value of the intermediate point and the deviation of the actual data output by the model.
Said C is1The range of (1) is 10000000-100000000, and the interval is 100; said C is2The range of (1) is 100000-2000000, and the interval is 10; d is1The range of (1) is 200-500, and the interval is 0.1; d is2The range of (1) is 5000-10000, and the interval is 0.1.
The present invention is further explained below with reference to specific examples.
Example 1
a. The 600MW supercritical CFB unit is used as a research object, and the power plant boiler is a supercritical once-through furnace, a single-hearth pant leg, a double-air-distribution-plate structural arrangement, a primary intermediate reheating, solid slag discharging and circulating fluidized bed combustion mode developed by eastern boiler factories. The boiler was designed with high ash, high sulfur, low calorific lean coal as shown in table 1.
TABLE 1 white horse 600MW supercritical circulating fluidized bed boiler design coal quality
Figure GDA0002756570900000113
The method of the invention can be used for obtaining the following nonlinear control model of the supercritical CFB boiler unit:
Figure GDA0002756570900000121
b. fuel side parameter and steam side static parameter identification
And (4) substituting the operation data according to the mechanism process from the coal feeding amount to the hearth heating amount in the step one to obtain K which is 0.00018.
When the unit is operating steadily, the parameters remain approximately steady. For l, k0，k1Four representative steady state values are selected from the historical data of the unit, each group corresponds to different operation conditions, the time length is 5 hours, the sampling period is 1min, and the average value of each statistical parameter is respectively obtained, as shown in table 2.
The average value l of the static parameters is 1.2743, k0＝1358.2，k1＝27.4。
TABLE 2 State parameter table for each steady state condition
Figure GDA0002756570900000122
c. Sub-function identification
And obtaining an expression of the function to be solved by adopting a regression analysis method. The method comprises the steps of collecting data of independent variables and dependent variables under different working conditions, selecting a group of proper regression analysis models, determining unknown parameters of the models by using a least square method, and finally carrying out credibility test on the models. Generally, an R square inspection criterion is adopted as a formula (40), the range of R is 0-1, the larger the R value is, the higher the reliability of the model is, and the lower the reliability is.
Figure GDA0002756570900000123
In the formula xiIs the actual data value; x'iIs a function value;
Figure GDA0002756570900000124
is the actual data mean.
And (4) counting the main steam flow, the main steam pressure, the main steam enthalpy and the steam turbine valve opening under different working conditions, and listing as follows.
TABLE 3 steam-water side parameter table under different working conditions
Figure GDA0002756570900000131
After analyzing the relationship between variables in table 3, the expression of f (-) is obtained by Matlab regression analysis function "nlnfit" as shown in formula (23).
Dst＝21.5μtpst (23)
The R square check value is 0.998, and the operation data of two sections of variable load working conditions of the unit are collected for further verifying the reliability of the function, wherein the first group lasts for 2 hours, and the second group lasts for 13 hours. The maximum relative error between the calculated value and the actual value of the first group of data is 2.86 percent, and the average relative error is 1.35 percent; the maximum relative error between the calculated and actual values for the second set of data was 2.95% and the average relative error was 0.84%. The fitting effect of the regression function is good, the error is small, and the correctness of the model mechanism analysis is proved.
For function h to be determinedst＝h(pst) From table 3, the actual data under different conditions can be obtained. Obtaining a calculation formula of the enthalpy value of the main steam through data regression analysis
hst＝-10.8165pst+3652 (24)
Similarly, the relationship between the superheater conduit differential pressure and the steam-water separator pressure is obtained according to the data in Table 4
Figure GDA0002756570900000132
TABLE 4 superheater differential pressure and steam-water separator pressure values
Figure GDA0002756570900000133
d. Dynamic identification of vapor and water side
The optimal dynamic parameters of the model are identified by using a genetic optimization algorithm, a certain 600MW supercritical CFB unit is selected for continuous 20-hour data, and the main steam pressure and the unit load pressure fluctuation are large and the duration is long in the operation process. According to the established supercritical CFB unit coordination control system model, coal feeding, total air volume, water feeding flow and steam turbine valve opening are used as input and are calculated and output by the model, and the relative error sum of a calculated value and an actual value is output to be used as an optimized objective function, namely
Figure GDA0002756570900000141
In the formula
Figure GDA0002756570900000142
Respectively representing the main steam pressure, the unit load, the mean value of the enthalpy value of the intermediate point and the deviation of the actual data output by the model.
Initial parameters are set as a population size of 50, an individual chromosome length of 40, a cross probability pc of 0.6, a mutation probability of 0.01 and a genetic generation number of 50. The collected data is used as the input of the model, and the dynamic parameter values of the model obtained by identification are as follows: c1＝11867000,C2＝907140,d1＝325.5,d2＝7292.3。
e. Model prediction and validation
In order to verify the model identification result established by the output power prediction method of the supercritical circulating fluidized bed unit and further verify the correctness of the model structure established by the method, dynamic operation data of a certain 600MW CFB unit for 20 continuous hours are additionally taken, model parameters adopt the result, the coal supply quantity, the total air quantity, the water supply enthalpy value and the opening degree of a steam turbine valve are taken as model inputs, and error statistics is carried out on the output power value and the actual value of the model.
To evaluate the accuracy of model predictions more specifically, root mean square errors rmse (root mean squared errors) are introduced to measure the deviation between observed and true values.
Figure GDA0002756570900000143
The actual curve and the response curve of the simulation model of the unit are shown in figure 2. The maximum relative error value of the model power output value and the actual value is 6.15%, the average relative error value is 1.31%, and the root mean square error is 7.23 MW. The overall error of model prediction is small, the trend in the dynamic process is accurate, and the data section is as long as 20 hours, so that the accuracy and the adaptability of the supercritical circulating fluidized bed unit output power prediction system and method can be embodied.
While several embodiments of the present invention have been presented herein, it will be appreciated by those skilled in the art that changes may be made to the embodiments herein without departing from the spirit of the invention. The above examples are merely illustrative and should not be taken as limiting the scope of the invention.

Claims (10)

1. A supercritical circulating fluidized bed unit output power prediction system is characterized by comprising a supercritical CFB unit power prediction model module, a database module, a data selection and pretreatment module, a combustion heat calculation model module, a steam-water side model module, a steam turbine model module and a subfunction identification module;
the combustion heat calculation model module is used for establishing a CFB boiler real-time combustion heat release model and determining parameters in the model; the CFB boiler real-time combustion heat release model is as follows:
Figure FDA0002756570890000011
in the formula, Qr(t) total heat released by combustion, MJ/s; b (t) is the instant carbon quantity which represents the unburned carbon residue in the furnace and has the unit of kg; a. theirAs the total air volume, in Nm3S; k is the total coefficient of the thermal model; mCIs the molar mass of carbon in kg/kmol; k is a radical ofcIs the burning rate constant of the carbon particles; dcIs the average diameter of the carbon particles in m; rhocIs the density of the carbon particles in kg/m3；RCThe overall combustion reaction rate of carbon, kg/s; h is the unit calorific value of the instant burning carbon, and the unit is MJ/kg; ko (Chinese character)2Is the total air quantity AirA correlation coefficient with oxygen concentration;
the steam-water side model module is used for establishing a steam-water side working medium heat absorption model of the CFB unit and determining parameters in the model; the CFB unit steam-water side working medium heat absorption model comprises the following steps:
Figure FDA0002756570890000012
Figure FDA0002756570890000013
where rhomAverage density of working medium in steam-water separator, kg/m3；hmThe average enthalpy value of the working medium in the steam-water separator is kJ/kg; p is a radical ofmSteam pressure at the outlet of the steam-water separator is Mpa; dfwThe water supply flow is kg/s; dsThe flow rate of steam at the outlet of the superheater is kg/s; h isfwThe enthalpy value of the feed water is kJ/kg; h issIs the enthalpy value of steam at the outlet of the superheater, kJ/kg; s1、s2Is a dynamic parameter; k is a radical of0The steam-water side heat absorption coefficient;
the steam turbine model module is used for establishing a CFB unit steam turbine work-doing model and determining steady-state parameters in the model; the dynamic process of the steam turbine is fast, and the dynamic characteristic is obtained by adopting a load shedding experimental method; in the control model, the governor stage pressure p1Output power N of the summing unitEThe first-order inertia process is used for approximation;
Figure FDA0002756570890000014
in the formula NEThe unit power, MW; kTThe dynamic time of the steam turbine is 10-30 s; p is a radical ofstIs the pressure before the machine uTOpening of the steam turbine valve, k1Gaining a steam turbine valve regulating instruction;
the subfunction identification module is used for identifying the functional relationship among the key parameters of the CFB unit; identifying subfunctions by using steady-state historical data of superheater differential pressure and main steam pressure under different loads of a unit, identifying subfunctions by using steady-state historical data of main steam flow, main steam pressure, main steam enthalpy value and steam turbine valve opening under different loads of the unit, and identifying subfunctions by using steady-state historical data of main steam specific enthalpy and main steam pressure under different loads of the unit; the database module transmits real-time data, historical data and coal quality data of unit operation to the data selection and pretreatment module; the coal quality data comprises the volatile components, ash content and heat value of the coal; the real-time data and the historical data of the unit operation comprise coal supply, total air volume, water supply flow and steam turbine valve opening;
the data selection and preprocessing module is used for processing historical data and coal quality data and selecting training data for identifying model parameters and the functional relation between the model parameters;
the supercritical CFB unit power prediction model module is used for establishing a supercritical CFB unit power prediction model and determining dynamic parameters in the model; predicting the power of the dynamic unit according to each model established by the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the subfunction identification module, the determined corresponding parameters, real-time data of unit operation and coal quality data;
the power prediction model of the supercritical CFB unit is as follows:
Figure FDA0002756570890000021
in the formula: h isstThe specific enthalpy of the main steam is kJ/kg; dstThe main steam flow is kg/s; l, C1，C2，d1，d2For lumped model parameters in the heated section, l ═ hs/hm，C1＝b21-(b11/b12)×b22，C2＝b22-(b12/b11)×b21，d1＝b22/b12，d2＝b21/b11。
2. The system for predicting the output power of the supercritical circulating fluidized bed unit according to claim 1, wherein the data selecting and preprocessing module transmits unit historical data, real-time operation data and coal quality data to the supercritical CFB unit power prediction model module; the data selection and preprocessing module respectively transmits the historical data and the coal quality data of the unit to the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the subfunction identification module; wherein the coal quality data comprises the volatile components, ash content and calorific value of the coal; the training data selection is that the input/output and intermediate state values of the model under the steady-state working condition of the unit operation need to be selected according to the requirements of model parameters and the training data corresponding to the static parameters need to be selected, and the input/output and intermediate state values of the model under the dynamic working condition of the unit need to be selected according to the training data corresponding to the dynamic parameters; the preprocessing link comprises selection and judgment of the working condition of the unit and elimination of singular values.
3. The system for predicting the output power of the supercritical circulating fluidized bed unit according to claim 1, wherein the database module is connected with the data selection and preprocessing module, the data selection and preprocessing module is further connected with the supercritical CFB unit power prediction model module, the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the sub-function identification module, and the supercritical CFB unit power prediction model module is further connected with the combustion heat calculation model module, the steam-water side model module, the steam turbine model module and the sub-function identification module in a bidirectional manner.
4. The output power prediction method of the supercritical circulating fluidized bed unit is characterized by comprising the following steps of:
step one, establishing a CFB boiler real-time combustion heat release model, and determining parameters in the model; the CFB boiler real-time combustion heat release model is as follows:
Figure FDA0002756570890000031
in the formula, Qr(t) total heat released by combustion, MJ/s; b (t) is the instant carbon quantity which represents the unburned carbon residue in the furnace and has the unit of kg; a. theirAs the total air volume, in Nm3S; k is the total coefficient of the thermal model; mCIs the molar mass of carbon in kg/kmol; k is a radical ofcIs the burning rate constant of the carbon particles; dcIs the average diameter of the carbon particles in m; rhocIs the density of the carbon particles in kg/m3；RCThe overall combustion reaction rate of carbon, kg/s; h is the unit calorific value of the instant burning carbon, and the unit is MJ/kg; ko (Chinese character)2Is the total air quantity AirA correlation coefficient with oxygen concentration;
step two, establishing a CFB unit steam-water side working medium heat absorption model, and determining steady-state parameters in the model; the CFB unit steam-water side working medium heat absorption model comprises the following steps:
Figure FDA0002756570890000032
Figure FDA0002756570890000033
where rhomAverage density of working medium in steam-water separator, kg/m3；hmThe average enthalpy value of the working medium in the steam-water separator is kJ/kg; p is a radical ofmSteam pressure at the outlet of the steam-water separator is Mpa; dfwThe water supply flow is kg/s; dsThe flow rate of steam at the outlet of the superheater is kg/s; h isfwThe enthalpy value of the feed water is kJ/kg; h issIs the enthalpy value of steam at the outlet of the superheater, kJ/kg; s1、s2Is a dynamic parameter; k is a radical of0The steam-water side heat absorption coefficient;
establishing a CFB unit steam turbine work model, and determining steady-state parameters in the model;
the dynamic process of the steam turbine is fast, and the dynamic characteristic is obtained by adopting a load shedding experimental method; in the control model, the governor stage pressure p1Output power N of the summing unitEThe first-order inertia process is used for approximation;
Figure FDA0002756570890000041
in the formula NEThe unit power, MW; kTThe dynamic time of the steam turbine is 10-30 s; p is a radical ofstIs the pressure before the machine uTOpening of the steam turbine valve, k1Gaining a steam turbine valve regulating instruction;
identifying a functional relation among key parameters of the CFB unit; identifying subfunctions by using steady-state historical data of superheater differential pressure and main steam pressure under different loads of a unit, identifying subfunctions by using steady-state historical data of main steam flow, main steam pressure, main steam enthalpy value and steam turbine valve opening under different loads of the unit, and identifying subfunctions by using steady-state historical data of main steam specific enthalpy and main steam pressure under different loads of the unit;
step five, according to the models established in the step one to the step four and the determined corresponding parameters, utilizing historical data and coal quality data to identify dynamic parameters of the models and establishing a power prediction model of the supercritical CFB unit;
the power prediction model of the supercritical CFB unit is as follows:
Qr＝KAirB
Figure FDA0002756570890000042
Figure FDA0002756570890000043
Figure FDA0002756570890000044
pm＝pst+g(pm)
Dst＝utf(pst)
hst＝h(pst)
in the formula: h isstThe specific enthalpy of the main steam is kJ/kg; dstThe main steam flow is kg/s; l, C1，C2，d1，d2For lumped model parameters in the heated section, l ═ hs/hm，C1＝b21-(b11/b12)×b22，C2＝b22-(b12/b11)×b21，d1＝b22/b12，d2＝b21/b11；
And step six, predicting the power of the dynamic unit according to the real-time operation data and the coal quality data of the unit.
5. The method for predicting the output power of the supercritical circulating fluidized bed unit according to claim 4, wherein the first step specifically comprises:
step 1.1, establishing a CFB boiler real-time combustion heat release model as follows:
Figure FDA0002756570890000051
in the formula, Qr(t) total heat released by combustion, MJ/s; b (t) is the instant carbon quantity which represents the unburned carbon residue in the furnace and has the unit of kg; a. theirAs the total air volume, in Nm3S; k is the total coefficient of the thermal model; mCIs the molar mass of carbon in kg/kmol; k is a radical ofcIs the burning rate constant of the carbon particles; dcIs the average diameter of the carbon particles in m; rhocIs the density of the carbon particles in kg/m3；RCThe overall combustion reaction rate of carbon, kg/s; h is the unit calorific value of the instant burning carbon, and the unit is MJ/kg; ko (Chinese character)2Is the total air quantity AirA correlation coefficient with oxygen concentration;
the instant carbon quantity B (t) can be obtained from the formula (2)
Figure FDA0002756570890000052
In the formula WcThe coal feeding amount is kg/s; xcThe fuel quantity is the mass fraction of the base carbon,%; wPZThe slag discharge flow rate is kg/s; xc,pCarbon content of slag discharge is percent; wFLThe fly ash flow rate, kg/s; xc,fCarbon content of fly ash,%; according to engineering experience, the carbon content of fly ash and the carbon content of discharged slag are ignored;
that is, the burning rate Rc of the burning coal is proportional to the mass of the burning coal accumulated in the furnace and the total air volume
Figure FDA0002756570890000053
And step 1.2, determining the total coefficient K of the thermal model by utilizing the historical operation data and the coal quality data.
6. The method for predicting the output power of the supercritical circulating fluidized bed unit according to claim 4, wherein the second step specifically comprises:
step 2.1, establishing a CFB unit steam-water side working medium heat absorption model:
Figure FDA0002756570890000054
Figure FDA0002756570890000055
where rhomAverage density of working medium in steam-water separator, kg/m3；hmThe average enthalpy value of the working medium in the steam-water separator is kJ/kg; p is a radical ofmSteam pressure at the outlet of the steam-water separator is Mpa; dfwThe water supply flow is kg/s; dsThe flow rate of steam at the outlet of the superheater is kg/s; h isfwThe enthalpy value of the feed water is kJ/kg; h issIs the enthalpy value of steam at the outlet of the superheater, kJ/kg; s1、s2Is a dynamic parameter; k is a radical of0The steam-water side heat absorption coefficient;
step 2.2, order
Figure FDA0002756570890000061
Establishing a CFB unit steam-water heating system model as shown in formulas (8) and (9):
Figure FDA0002756570890000062
Figure FDA0002756570890000063
step 2.3, determining steady state coefficients l and k of steam-water side working medium heat absorption model of CFB unit by utilizing historical operation data and coal quality data0：
For l, k0According to the formulas (8) and (9), the steady state condition can be obtained
Dfw0-Ds0＝0 (10)
Dfw0hfw0-Ds0hs0+k0Qr0＝0 (11)
The parameters with '0' subscript in the formula represent the parameters in the steady state, and the calculation formula of the steady state parameters can be obtained:
Figure FDA0002756570890000064
Figure FDA0002756570890000065
7. the method for predicting the output power of the supercritical circulating fluidized bed unit according to claim 4, wherein the third step specifically comprises:
step 3.1, the dynamic process of the steam turbine is fast, and the dynamic characteristic is obtained by adopting a load shedding experimental method; in the control model, the governor stage pressure p1Output power N of the summing unitEThe first-order inertia process is used for approximation;
Figure FDA0002756570890000066
in the formula NEThe unit power, MW; kTThe dynamic time of the steam turbine is 10-30 s;
step 3.2, steam turbine valve regulation instruction gain k1The physical meaning of (1) is the pressure p before the machinestOpening u of steam turbine valveTThe unit load corresponding to the product of (a) represents the regulation stage pressure p1The proportional relationship between the variation of (A) and the variation of the unit load is
Figure FDA0002756570890000067
8. The method for predicting the output power of the supercritical circulating fluidized bed unit according to claim 4, wherein the fourth step is performed on the intermediate parameter p which is difficult to obtain or measure in the modelmEstablishing a subfunction specifically comprises:
step 4.1, steam-water separator outlet steam pressure pmThe pressure change in the water spraying temperature reduction is neglected, the pressure at the outlet of the superheater is equal to the main steam pressure, and the steam passing through the inlet of the steam turbine is the main steam pressure pstIs calculated to obtain
pm＝ps+△p＝pst+△p (16)
The steam endothermally expands in the superheater, causing an increase in the volume flow, which is the main cause of the superheater differential pressure, which can be denoted as pmEstablishing a subfunction of steam-water separator outlet steam pressure, main steam pressure and superheater differential pressure:
pm＝pst+g(pm) (17)；
step 4.2, the main steam flow entering the steam turbine is influenced by the opening of a valve of the steam turbine, the steam temperature and the pressure; in the operation process, the main steam flow is in direct proportion to the opening of a steam turbine valve; the steam temperature and the steam pressure jointly determine the steam density, and the steam density determines the amount of steam flowing through in unit time under the condition that the steam flow rate is not changed; during the operation of the boiler, although the load lifting amplitude of the unit is large, the temperature fluctuation of the main steam cannot exceed +/-10 ℃, and the main steam basically maintains stable; the temperature of the main steam is regarded as constant, including
Dst＝utf(pst) (18)；
4.3, the temperature fluctuation of the main steam does not exceed +/-10 ℃, and the main steam basically maintains stable; the main steam temperature is regarded as constant, the specific enthalpy h of the main steamstIs set to the main steam pressure pstFunction of (c):
hst＝h(pst) (19)；
and 4.4, identifying a subfunction (17) by using steady-state historical data of the superheater differential pressure and the main steam pressure under different loads of the unit, identifying a subfunction (18) by using steady-state historical data of the main steam flow, the main steam pressure, the main steam enthalpy value and the steam turbine valve opening degree under different loads of the unit, and identifying a subfunction (19) by using steady-state historical data of the main steam specific enthalpy and the main steam pressure under different loads of the unit.
9. The method for predicting the output power of the supercritical circulating fluidized bed unit according to claim 4, wherein the step five specifically comprises:
step 5.1, determining a power prediction model of the supercritical CFB unit:
Figure FDA0002756570890000081
step 5.2, aiming at the model (20), taking the coal feeding, the total air volume, the water feeding flow and the steam turbine valve opening as input, calculating and outputting by the model, outputting the relative error sum of a calculated value and an actual value as an optimized objective function as a formula (21), and determining the dynamic parameter C by utilizing an optimization algorithm1，C2，d1，d2；
Figure FDA0002756570890000082
In the formula
Figure FDA0002756570890000083
Respectively representing the main steam pressure, the unit load, the mean value of the enthalpy value of the intermediate point and the deviation of the actual data output by the model.
10. The method for predicting the output power of the supercritical circulating fluidized bed unit according to claim 6 or 9, wherein C is1The range of (1) is 10000000-100000000, and the interval is 100; said C is2The range of (1) is 100000-2000000, and the interval is 10; d is1The range of (1) is 200-500, and the interval is 0.1; d is2The range of (1) is 5000-10000, and the interval is 0.1.
CN201711167283.9A 2017-11-21 2017-11-21 Output power prediction system and method for supercritical circulating fluidized bed unit Active CN108021027B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711167283.9A CN108021027B (en) 2017-11-21 2017-11-21 Output power prediction system and method for supercritical circulating fluidized bed unit

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711167283.9A CN108021027B (en) 2017-11-21 2017-11-21 Output power prediction system and method for supercritical circulating fluidized bed unit

Publications (2)

Publication Number Publication Date
CN108021027A CN108021027A (en) 2018-05-11
CN108021027B true CN108021027B (en) 2021-02-02

Family

ID=62080810

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711167283.9A Active CN108021027B (en) 2017-11-21 2017-11-21 Output power prediction system and method for supercritical circulating fluidized bed unit

Country Status (1)

Country Link
CN (1) CN108021027B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108919642B (en) * 2018-06-28 2021-03-26 东南大学 Optimal setting method for controller parameters of furnace-following machine coordination control system
CN111142381B (en) * 2019-12-17 2022-04-12 东南大学 Control-oriented NCB type steam turbine heating system composite dynamic modeling method
CN113378477B (en) * 2021-06-29 2023-05-26 西北师范大学 Deep learning method-based boiler superheater area high-low temperature prediction method

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103454918A (en) * 2013-07-31 2013-12-18 广东电网公司电力科学研究院 Decentralized nonlinear control method and system based on CFB nonlinear model
CN103742901A (en) * 2013-12-24 2014-04-23 广州市恒力安全检测技术有限公司 Method for determining consumption difference analysis optimized target value system of circulating fluidized bed unit
CN104864385A (en) * 2014-02-24 2015-08-26 北京国电智深控制技术有限公司 Method and device for calculating feed water flow instruction of supercritical unit
US9122260B2 (en) * 2008-03-03 2015-09-01 Alstom Technology Ltd Integrated controls design optimization
CN105276577A (en) * 2014-06-03 2016-01-27 国网山西省电力公司电力科学研究院 Large-scale circulating fluidized bed unit boiler main control pressure setting value optimization system
CN105864797A (en) * 2016-04-01 2016-08-17 浙江大学 Real-time prediction system and method for boiler entering heat value of circulating fluidized bed household garbage incineration boiler
CN106873370A (en) * 2017-03-07 2017-06-20 云南电网有限责任公司电力科学研究院 A kind of circulating fluidized bed unit cooperative control method
CN106897819A (en) * 2017-01-21 2017-06-27 华北电力大学 Coal-supplying amount rationality checking method and system during a kind of Properties of CFB varying duty
CN107270283A (en) * 2017-07-17 2017-10-20 安徽电气工程职业技术学院 A kind of Novel variable amount constrained forecast control method based on Properties of CFB

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9122260B2 (en) * 2008-03-03 2015-09-01 Alstom Technology Ltd Integrated controls design optimization
CN103454918A (en) * 2013-07-31 2013-12-18 广东电网公司电力科学研究院 Decentralized nonlinear control method and system based on CFB nonlinear model
CN103742901A (en) * 2013-12-24 2014-04-23 广州市恒力安全检测技术有限公司 Method for determining consumption difference analysis optimized target value system of circulating fluidized bed unit
CN104864385A (en) * 2014-02-24 2015-08-26 北京国电智深控制技术有限公司 Method and device for calculating feed water flow instruction of supercritical unit
CN105276577A (en) * 2014-06-03 2016-01-27 国网山西省电力公司电力科学研究院 Large-scale circulating fluidized bed unit boiler main control pressure setting value optimization system
CN105864797A (en) * 2016-04-01 2016-08-17 浙江大学 Real-time prediction system and method for boiler entering heat value of circulating fluidized bed household garbage incineration boiler
CN106897819A (en) * 2017-01-21 2017-06-27 华北电力大学 Coal-supplying amount rationality checking method and system during a kind of Properties of CFB varying duty
CN106873370A (en) * 2017-03-07 2017-06-20 云南电网有限责任公司电力科学研究院 A kind of circulating fluidized bed unit cooperative control method
CN107270283A (en) * 2017-07-17 2017-10-20 安徽电气工程职业技术学院 A kind of Novel variable amount constrained forecast control method based on Properties of CFB

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
基于GA-RBF网络广义预测控制在大型循环流化床机组中的应用;刘银波;《中国优秀硕士学位论文全文数据库》;20150415;C039-27 *
大型循环流化床锅炉燃烧状态监测研究;高明明;《中国博士学位论文全文数据库》;20150715;C039-7 *
循环流化床锅炉燃烧状态监测模型研究;孙亚洲;《中国优秀硕士学位论文全文数据库》;20170215;C039-122 *
超临界CFB锅炉主蒸汽压力控制系统研究;高明明等;《动力工程学报·第35卷第8期》;20150831;1-9 *

Also Published As

Publication number Publication date
CN108021027A (en) 2018-05-11

Similar Documents

Publication Publication Date Title
Park et al. Coupled fluid dynamics and whole plant simulation of coal combustion in a tangentially-fired boiler
CN108021027B (en) Output power prediction system and method for supercritical circulating fluidized bed unit
CN103759277B (en) Coal-fired power station boiler intelligent ash blowing closed loop control method, device and system
CN108087856B (en) A kind of supercritical circulating fluidized bed unit main steam pressure forecasting system and method
CN106768068A (en) Fired power generating unit fire coal calorific value and consumption of standard coal for power generation index on-line monitoring system and method
CN104571022B (en) Power consumption analysis model experimental systems and method based on coal consumption Yu controllable factor relationship
CN110263452B (en) Flue gas time distribution characteristic analysis method and system in flue and denitration system
Liu et al. An overview of modelling and simulation of thermal power plant
CN111931346A (en) Real-time calculation method and application of low calorific value of coal as fired in coal-fired boiler
Zhou et al. Multi‐objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms
CN111780127B (en) Garbage incinerator combustion management system
US7398652B1 (en) System for optimizing a combustion heating process
CN110298502B (en) Optimal oxygen quantity calculation method for boiler based on optimal energy efficiency
Wang et al. Mechanism modeling of optimal excess air coefficient for operating in coal fired boiler
Zhang et al. Dynamic model for subcritical circulating fluidized bed boiler-turbine units operated in a wide-load range
CN112664975B (en) Air volume control method suitable for pulverized coal fired boiler
CN112965386A (en) Coal water slurry gasification and coal-fired boiler ultra-low NOx emission integrated cooperative control method
CN106326562B (en) Energy storage quantification method for supercritical circulating fluidized bed boiler unit
CN115656461A (en) Coal electric unit real-time carbon emission monitoring method based on coal quality soft measurement
CN115145152A (en) Boiler combustion and denitration process collaborative optimization control method
Wang et al. Modeling for on-line monitoring of carbon burnout coefficient in boiler under partial load
Wang et al. Fault diagnosis analysis and health management of thermal performance of multi-source data fusion equipment based on fog computing model
Tang et al. Artificial neural networks model for predicting oxygen content in flue gas of power plant
CN113515042B (en) Multi-mode dry quenching Jiao Shaosun rate real-time computing system and computing method
Tang et al. Prediction Model of SO 2 Distribution in Boiler Based on Deep Neural Network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant