CN113341765B - Coal-fired power plant CO with strictly controlled carbon emission 2 Flexible control method for trapping system - Google Patents
Coal-fired power plant CO with strictly controlled carbon emission 2 Flexible control method for trapping system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 27
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 20
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 20
- 239000003245 coal Substances 0.000 claims abstract description 11
- 238000000605 extraction Methods 0.000 claims abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 9
- 230000003321 amplification Effects 0.000 claims abstract description 6
- 238000003199 nucleic acid amplification method Methods 0.000 claims abstract description 6
- 230000000694 effects Effects 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000002485 combustion reaction Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000005096 rolling process Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 2
- 238000010248 power generation Methods 0.000 claims 1
- 230000008859 change Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 3
- 239000003546 flue gas Substances 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention is suitable for the technical field of automatic control of thermal engineering, and provides a coal-fired power plant CO with strictly controlled carbon emission 2 The flexible control method of the trapping system comprises the following steps: step S1: CO of large coal-fired power plant 2 Capturing a controlled variable and a control variable of the system; step S2: under the open loop condition, inputting M sequence signals to the coal feeding amount, the water feeding flow, the opening of a main steam valve, the lean solution flow and the steam extraction flow of a reboiler; step S3: obtaining CO of coal-fired power plant by using n4 sed algorithm in MATLAB identification tool box 2 Capturing a system state space model; by establishing a predictive controller based on an amplification state space model, CO of a coal-fired power plant can be effectively processed 2 The invention increases the soft constraint of output variable to the performance index, so that the trapping rate of the system is maintained at [ y ] min ，y max ]Between them, greatly reduce CO 2 The fluctuation of the trapping rate realizes the strict control of carbon emission.
Description
Technical Field
The invention belongs to the technical field of automatic control of thermal engineering, and particularly relates to a coal-fired power plant CO capable of strictly controlling carbon emission 2 The trapping system is flexible in regulation and control method.
Background
In order to realize the development targets of carbon peak and carbon neutralization in the early days, the CO of the coal-fired thermal power unit is needed 2 Maximum single emission source CO 2 Capturing and formulating strict CO 2 Emission standards. Post-combustion CO based on chemical solvent absorption 2 Trapping is the dominant technology in current pilot operations due to its technical maturity.
Meanwhile, new energy sources such as solar energy, wind energy and the like have the characteristics of discontinuity, fluctuation, uncertainty and the like, continuous and stable power supply is difficult to realize, and therefore, the coal-fired thermal power unit needs to have the capacity of deep peak regulation so as to maintain the supply and demand balance of a power grid, and when the coal-fired thermal power unit carries out deep peak regulation, the flue gas flow and the flue gas components of the unit also generate larger fluctuation with random group load, so that the coal-fired thermal power unit can carry out downstream CO 2 The stable operation of the trapping system has a certain influence and ensures that CO 2 The key index of the trapping rate generates larger fluctuation.
Disclosure of Invention
The invention provides a coal-fired power plant CO with strictly controlled carbon emission 2 Flexible regulation and control method of trapping system, aiming at solving the problem that CO is caused by deep peak regulation of coal-fired thermal power unit 2 The key index of the trapping rate is a problem of large fluctuation.
The invention is realized in such a way that the CO of the coal-fired power plant with the carbon emission strictly controlled 2 The flexible control method of the trapping system comprises the following steps:
step S1: CO of large coal-fired power plant 2 Capturing a controlled variable and a control variable of the system;
step S2: under the open loop condition, inputting M sequence signals to the coal feeding amount, the water feeding flow, the opening of a main steam valve, the lean solution flow and the steam extraction flow of a reboiler;
step S3: obtaining CO of coal-fired power plant by using n4 sed algorithm in MATLAB identification tool box 2 Capturing a system state space model;
step S4: adding an integral function, and considering the influence of undetectable disturbance and model mismatch on a state space model;
step S5: according to CO of coal-fired power plants 2 Capturing data input and output at the previous moment of the system and amplifying a state space equation, and estimating state quantity by using a Kalman filter;
step S6: estimating the CO of the future P time domain internal combustion coal power station according to the amplified state space model and the state estimation value 2 Capturing the output characteristics of the system;
step S7: setting relevant parameters of a controller;
step S8: setting a performance index of a predictive controller, and increasing output soft constraint on the trapping rate;
step S9: solving the performance index by adopting a quadratic programming quadprog solver, and calculating the optimal input quantity difference value in the future M moment;
step S10: calculating the optimal control quantity at the current moment;
step S11: outputting the optimal control quantity, and collecting CO of the coal-fired power plant 2 The output of the system is captured, after which steps S8 to S11 are repeatedly performed in each sampling period.
Preferably, in the step S1, the main steam pressure, the enthalpy value of the middle point, the generating capacity of the unit, the capturing rate and the reboiler temperature are selected to be the CO of the large-scale coal-fired power plant 2 And selecting controlled variables of the trapping system, namely, the coal feeding amount, the water feeding flow, the opening of a main steam valve, the lean solution flow and the steam extraction flow of a reboiler as corresponding controlled variables.
Preferably, the method takes the historical sampling value of the controlled variable as input, takes the historical sampling value of the controlled variable as output, and utilizes the n4 sed algorithm in the MATLAB tool box to obtain the CO of the coal-fired power station 2 And capturing a discrete state space model of the system and verifying by using model simulation data.
Preferably, according to large-scale coal-fired power plant CO 2 Capturing input data of the previous moment and output data of the previous moment of a system, and estimating the CO of the coal-fired power station at the current moment by using a Kalman filter 2 The state quantity of the system is captured.
Preferably, the rolling optimization method is utilized to carry out optimization solution on the future time domain controlled variable, and the trapping rate soft constraint is introduced into the performance index.
Preferably, the system further comprises a model predictive controller for calculating the optimal control quantity at the current moment meeting the constraint condition of the system by rolling and optimizing the performance index
Preferably, the method further comprises:
a first delay module for inputting CO of coal-fired power plant 2 Collecting a sampling value of the control variable of the whole system at the previous moment;
a second delay module for inputting CO of the coal-fired power plant 2 The sampled value of the controlled variable of the whole system at the previous moment is captured.
Preferably, also comprises
Kalman filter for estimating CO of coal-fired power plant at current moment according to input-output data of previous moment of system 2 Capturing state quantity of the system;
CO of large-scale coal-fired power plant 2 And the trapping system model outputs a controlled variable under the action of the optimal control variable at the current moment.
Compared with the prior art, the invention hasThe beneficial effects are that: the invention relates to a coal-fired power plant CO with strictly controlled carbon emission 2 Flexible control method of trapping system, through establishing predictive controller based on amplification state space model, CO of coal-fired power station can be effectively processed 2 The invention increases the soft constraint of output variable to the performance index, so that the trapping rate of the system is maintained at [ y ] min ，y max ]Between them, greatly reduce CO 2 The fluctuation of the trapping rate realizes the strict control of carbon emission.
Drawings
FIG. 1 is a schematic diagram of the method steps of the present invention;
FIG. 2 is a schematic diagram of the structure of the present invention;
FIG. 3 is a schematic diagram of a large coal-fired power plant CO according to the present invention 2 A schematic flow diagram of the trapping system;
FIG. 4 is a graph of the effect of the present invention on control of main vapor pressure as a given value changes;
FIG. 5 is a graph showing the control effect of the enthalpy value at the middle point when the given value is changed;
FIG. 6 is a graph showing the effect of controlling the generating capacity of the unit when a given value is changed;
FIG. 7 is a graph showing the effect of controlling the amount of coal fed when a given value is changed according to the present invention;
FIG. 8 is a graph showing the effect of controlling the water supply amount when a given value is changed according to the present invention;
FIG. 9 is a graph showing the effect of controlling the opening of the main steam valve when a given value is changed;
FIG. 10 is a graph showing the effect of the invention on the control of the trapping rate when a given value is changed;
FIG. 11 is a graph showing the reboiler temperature control effect upon a change in a setpoint in accordance with the present invention;
FIG. 12 is a graph showing the effect of lean flow control upon a change in a given value in accordance with the present invention;
FIG. 13 is a graph showing the effect of reboiler steam extraction flow control upon a change in a setpoint according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution: coal-fired power plant CO with strictly controlled carbon emission 2 The flexible control method of the trapping system comprises the following steps:
step S1: CO selected as large-scale coal-fired power plant 2 The controlled variable and the controlled variable of the trapping system are selected to be the CO of the large-scale coal-fired power plant, wherein the main steam pressure, the enthalpy value of the middle point, the generating capacity of the unit, the trapping rate and the reboiler temperature are selected 2 The controlled variable y (k) of the trapping system is selected to be the corresponding controlled variable u (k) of the coal feeding amount, the water feeding flow, the opening of the main steam valve, the lean liquid flow and the steam extraction flow of the reboiler;
step S2: under the open loop condition, M sequence signals are input to the coal feeding amount, the water feeding flow, the opening of a main steam valve, the lean solution flow and the reboiler steam extraction flow, and dynamic response data of the main steam pressure, the middle point enthalpy, the unit generating capacity, the capturing rate and the reboiler temperature are obtained. The dynamic response of parameters such as the generating capacity and the trapping rate of the unit needs to cover the main operation conditions;
step S3: obtaining CO of coal-fired power plant by using n4 sed algorithm in MATLAB identification tool box 2 Trapping system state space models, e.g. formula (1)
x(k+1)＝Ax(k)+Bu(k)
y(k)＝Cx(k)+Du(k) (1)
wherein ,
step S4: in order to realize the indifferent adjustment, an integral function is added to the formula (1), and the influence of undetectable disturbance and model mismatch on a state space model is considered, so that an amplification state space model shown in the formula (2) is constructed;
step S5: according to CO of coal-fired power plants 2 Capturing the input and output data of the system at the previous moment and the amplification state space equation in the formula (2), and estimating the state quantity x by using a Kalman filter e (k) As shown in formula (3)
x e (k+1|k)＝A e x e (k)+B e Δu(k)
P e (k+1|k)＝A e P e (k)A e T +Q n
L x ＝P e (k+1|k)C e T [C e P e (k+1|k)C e T +R n ] -1
x e (k+1)＝x e (k+1|k)+L x [y(k-1)-C e x e (k+1|k)]
P e (k+1)＝(I-L x C e )P e (k+1|k)
(3)
in the formula,xe (k+ 1|k) is a state estimate of time k versus time k+1; x is x e(k) and xe (k+1) is a state quantity at the time of k and at the time of k+1, respectively; p is p e (k) Estimating a covariance matrix for the posterior; q (Q) n and Rn Covariance matrices of state quantity and measured value noise respectively; l (L) x Is the optimal Kalman gain; y (k-1) is the measured value of the output variable at time k-1.
Step S6: based on the amplified state space model and the state estimation value x e (k) Estimating future P time domain internal combustion coal power station CO 2 The output characteristics of the trapping system are shown in the formula (4)
Y p ＝F x x e (k)+ΦΔU (4)
wherein ,
step S7: setting controller-related parameters including prediction time domain N p Control time domain M, output error weight matrix Q, control weight matrix R and state quantity covariance matrix Q n Covariance matrix R of measured value noise n And a relaxation variable matrix ρ. Sampling period T s Is generally selected in conformity with shannon's sampling theorem, and may be selected by using an empirical rule T 95 /T S Selected from =5 to 15, wherein T 95 An adjustment time of up to 95% for the transient; the predicted time domain P should contain the real dynamic part of the object as much as possible; m is generally 3 to 5; wherein T is s =30 seconds, p=20, m=5, q=diag ([ 90e1,0.78e1,8.0,8.5e6,6e 6)])，R＝diag([9900，200，8300，80，180])，Q n ＝eye(9)，R n ＝eye(9)，ρ＝diag([1，1，1，500，1])
Step S8: setting a performance index of a predictive controller, and increasing output soft constraint on the trapping rate, wherein the output soft constraint is as shown in a formula (5):
s.t.u min ≤u(k)≤u max ,Δu min ≤Δu(k)≤Δu max
y min -ε(k+i)≤y capture (k+i|k)≤y max +ε(k+i),1≤i≤N p
0≤ε(k+i)≤ε max (5)
wherein ε (k+i) is a relaxation variable; epsilon max Is the maximum value of the relaxation variable; Σ= [ ε (k+1) … ε (k+N) p )] T The method comprises the steps of carrying out a first treatment on the surface of the ρ is a corresponding weight matrix; y is min and ymax Respectively the trapping rate is N in future P Minimum and maximum values within a time of day; deltau min and Δumax Amplitude constraint of main input variables of the system; deltau min and Δumax Rate constraints for the system primary input variables;
wherein the constraint of the input and output variables is Deltau max ＝[5；15；0.03；20；10]T；Δu min ＝[-5；-15；-0.03；-20；-10]T；u min ＝[20；200；0.4；100；30]T；u max ＝[120；600；1；700；250]T is a T; minimum value y of trapping rate min =89% of the maximum value of the trapping rate y max =91%, maximum value epsilon of relaxation variable max ＝0.5％
Step S9: solving the performance index by adopting a quadratic programming quadprog solver, and calculating an optimal input quantity difference delta U in the future M moment;
step S10: calculating the optimal control quantity u (k) =u (k-1) +Δu (k) at the current moment; the method comprises the steps of carrying out a first treatment on the surface of the
Step S11: outputting the optimal control quantity, and collecting CO of the coal-fired power plant 2 The output of the system is captured, after which steps S8 to S11 are repeatedly performed in each sampling period.
In the present embodiment, as shown in fig. 1, the model predictive controller includes a model predictive controller, a first delay module, a second delay module, a kalman filter, and a large-scale coal-fired power plant CO 2 Capturing a system model; CO of large-scale coal-fired power plant 2 The input variable u (k) and the output variable y (k) of the trapping system respectively obtain delay variables u (k-1) and y (k-1) through a first delay unit and a second delay unit; delay variables u (k-1) and y (k-1) are used for obtaining estimated value x of state quantity at current moment through Kalman filter e (k) The method comprises the steps of carrying out a first treatment on the surface of the The inputs to the model predictive controller are respectively the set values y at the present moment r (k) Controlled variable y (k-1) at the previous time and state quantity estimated value x at the current time e (k) And calculating the optimal control variable u (k) at the current moment through the rolling optimization performance index.
In the present embodiment, as shown in fig. 3, CO is generated in a large-scale coal-fired power plant 2 The trapping system comprises: main units such as a boiler, a steam turbine, a generator, an absorption tower, a separation tower and the like; the main variables include: feeding ofCoal quantity, water supply flow, main steam valve opening, lean liquor flow, reboiler steam extraction flow, main steam pressure, intermediate point enthalpy, unit generating capacity, trapping rate and reboiler temperature.
In this embodiment, as shown in FIGS. 4-13, at an initial steady state condition u 1 ＝56.5333kg/s、u 2 ＝397.630kg/s、u 3 ＝86.31％、u 4 ＝497.680kg/s、u 5 ＝131.5056kg/s、y 1 ＝21.3693MPa、y 2 ＝2722.1325kJ/kg、y 3 ＝432.9270MW、y 4 ＝90％、y 5 Stable operation for 10 min at 392.2K, given values of 24.8430MPa, 2674.4886kJ/kg,552.790mw,90% and 392.2K, respectively, which after 100 min operation were 24.01MPa, 2702.4781kJ/kg,506.896mw,90% and 392.2K, respectively, the total simulation time of the system was 210 min.
In the present embodiment, as can be seen from the simulation results of FIGS. 4 to 13, the present invention provides a strict carbon emission coal-fired power plant CO 2 Flexible regulation and control method of trapping system can realize CO under the condition of wide-range change of working condition of thermal power generating unit 2 The strict regulation and control of the trapping rate can lead the trapping rate to quickly track a given value and greatly reduce the fluctuation amplitude (strictly maintained at [89%, 91%)]Between) to reduce the CO of the coal-fired power plant 2 Capturing CO of clean flue gas of system 2 Discharge amount. Meanwhile, the control method provided by the invention can also realize the rapid adjustment of other control variables such as the generating capacity of the unit, the pressure of main steam, the enthalpy value of the middle point, the temperature of the reboiler and the like, so that the control method is suitable for industrial application.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (7)
1. Coal-fired power plant CO with strictly controlled carbon emission 2 The flexible control method of the trapping system is characterized in that: the method comprises the following steps:
step S1: large coal-fired electricityStation CO 2 Capturing a controlled variable y (k) and a controlled variable u (k) of the system;
step S2: under the open loop condition, inputting M sequence signals to control variables, wherein the control variables comprise coal feeding amount, water feeding flow, main steam valve opening, lean solution flow and reboiler steam extraction flow, and collecting data of controlled variables, and the controlled variables comprise main steam pressure, middle point enthalpy value, unit power generation amount, trapping rate and reboiler temperature;
step S3: obtaining CO of coal-fired power plant by using n4 sed algorithm in MATLAB identification tool box 2 Trapping system state space models, e.g. formula (1)
x(k+1)＝Ax(k)+Bu(k)
y(k)＝Cx(k)+Du(k) (1)
wherein ,
step S4: the deadbeat adjustment is realized, the integral effect is added to the formula (1), and the influence of the undetectable disturbance and the model mismatch on the state space model is considered; constructing an amplification state space model shown in a formula (2);
step S5: according to CO of coal-fired power plants 2 Capturing the input and output data of the system at the previous moment and the amplification state space equation in the formula (2), and estimating the state quantity x by using a Kalman filter e (k) As shown in formula (3)
x e (k+1|k)＝A e x e (k)+B e Δu(k)
P e (k+1|k)＝A e P e (k)A e T +Q n
L x ＝P e (k+1|k)C e T [C e P e (k+1|k)C e T +R n ] -1
x e (k+1)＝x e (k+1|k)+L x [y(k-1)-C e x e (k+1|k)]
P e (k+1)＝(I-L x C e )P e (k+1|k) (3)
in the formula,xe (k+ 1|k) is a state estimate of time k versus time k+1; x is x e(k) and xe (k+1) is a state quantity at the time of k and at the time of k+1, respectively; p is p e (k) Estimating a covariance matrix for the posterior; q (Q) n and Rn Covariance matrices of state quantity and measured value noise respectively; l (L) x Is the optimal Kalman gain; y (k-1) is the measured value of the output variable at time k-1;
step S6: based on the amplified state space model and the state estimation value x e (k) Estimating future P time domain internal combustion coal power station CO 2 The output characteristic of the trapping system is as shown in formula (4);
Y p ＝F x x e (k)+ΦΔU (4)
wherein ,
step S7: setting relevant parameters of a model predictive controller;
step S8: setting a performance index of a predictive controller, and increasing output soft constraint on the trapping rate;
step S9: solving the performance index by adopting a quadratic programming quadprog solver, and calculating an optimal input quantity difference delta U in the future Nm moment;
step S10: calculating an optimal control variable u (k) =u (k-1) +Δu (k) at the current moment;
step S11: outputting the optimal control variable, and collecting CO of the coal-fired power plant 2 The output of the system is captured, after which steps S8 to S11 are repeatedly performed in each sampling period.
2. A coal-fired power plant CO with strictly controlled carbon emissions as claimed in claim 1 2 The flexible control method of the trapping system is characterized in that: in the step S1, the main steam pressure, the enthalpy value of the middle point, the generating capacity of the unit, the trapping rate and the reboiler temperature are selected to be the CO of the large-scale coal-fired power plant 2 And selecting controlled variables of the trapping system, namely, the coal feeding amount, the water feeding flow, the opening of a main steam valve, the lean solution flow and the steam extraction flow of a reboiler as corresponding controlled variables.
3. A coal-fired power plant CO with strictly controlled carbon emissions as claimed in claim 1 2 The flexible control method of the trapping system is characterized in that: taking a historical sampling value of a controlled variable as input, taking a historical sampling value of a controlled variable as output, and obtaining the CO of the coal-fired power plant by using an n4 sed algorithm in a MATLAB tool box 2 And capturing a discrete state space model of the system and verifying by using model simulation data.
4. A coal-fired power plant CO with strictly controlled carbon emissions as claimed in claim 1 2 The flexible control method of the trapping system is characterized in that: according to large-scale coal-fired power plant CO 2 Capturing input data of the previous moment and output data of the previous moment of a system, and estimating the CO of the coal-fired power station at the current moment by using a Kalman filter 2 The state quantity of the system is captured.
5. A coal-fired power plant CO with strictly controlled carbon emissions as claimed in claim 1 2 The flexible control method of the trapping system is characterized in that: and optimizing and solving the future time domain controlled variable by using a rolling optimization method, and introducing a trapping rate soft constraint into the performance index.
6. A coal-fired power plant CO with strictly controlled carbon emissions as claimed in claim 1 2 The flexible control method of the trapping system is characterized in that: further comprises:
a first delay module for inputting CO of coal-fired power plant 2 Collecting a sampling value of the control variable of the whole system at the previous moment;
a second delay module for inputting CO of the coal-fired power plant 2 The sampled value of the controlled variable of the whole system at the previous moment is captured.
7. A coal-fired power plant CO with strictly controlled carbon emissions as claimed in claim 1 2 The flexible control method of the trapping system is characterized in that: further comprises:
kalman filter for estimating CO of coal-fired power plant at current moment according to input-output data of previous moment of system 2 Capturing state quantity of the system;
CO of large-scale coal-fired power plant 2 And the trapping system model outputs a controlled variable under the action of the optimal control variable at the current moment.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06266408A (en) * | 1993-03-12 | 1994-09-22 | Hitachi Ltd | Adaptive control method for process and control system for process |
| CN107703745A (en) * | 2017-09-21 | 2018-02-16 | 东南大学 | MGT CCHP control systems based on economic forecasting control |
| CN110376886A (en) * | 2019-07-09 | 2019-10-25 | 东南大学 | A kind of Model Predictive Control Algorithm based on expansion state Kalman filter |
| CN110764419A (en) * | 2019-11-15 | 2020-02-07 | 江苏方天电力技术有限公司 | CO of large coal-fired power station2Capture global scheduling and predictive control system and method |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06266408A (en) * | 1993-03-12 | 1994-09-22 | Hitachi Ltd | Adaptive control method for process and control system for process |
| CN107703745A (en) * | 2017-09-21 | 2018-02-16 | 东南大学 | MGT CCHP control systems based on economic forecasting control |
| CN110376886A (en) * | 2019-07-09 | 2019-10-25 | 东南大学 | A kind of Model Predictive Control Algorithm based on expansion state Kalman filter |
| CN110764419A (en) * | 2019-11-15 | 2020-02-07 | 江苏方天电力技术有限公司 | CO of large coal-fired power station2Capture global scheduling and predictive control system and method |
Non-Patent Citations (1)
| Title |
|---|
| 主汽温多模型扰动抑制预测控制方法;赵慧荣等;《中国电机工程学报》;20141115;第34卷(第32期);第5763-5770页 * |
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