CN110026068A - A kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system and feed forward control method - Google Patents
A kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system and feed forward control method Download PDFInfo
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- B01D53/34—Chemical or biological purification of waste gases
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- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
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- B01D53/34—Chemical or biological purification of waste gases
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Abstract
The invention discloses a kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system and feed forward control method, by coal fired power plant CO2Trapping system sees the multi-variable system of five inputs-five output as, chooses main steam pressure, steam-water separator exports enthalpy, unit generation amount, CO2Capture rate and reboiler temperature are main controlled variable, and choosing unit coal-supplying amount, confluent, main steam valve, lean solution flow and reboiler steam flow is corresponding control variable.The present invention uses BP neural network technology, establishes large-scale coal fired power plant CO2The inversion model of trapping system, so as to calculate required control variable according to given value, realization controls in advance, the big lag characteristic of total system can be effectively treated, improve the dynamic regulation quality of outlet side；In addition, by increasing amendment of the PID control compensator realization to neural network contrary modeling, to enhance its disturbance rejection and uncertain ability, so that control system adapts to industry spot and needs.
Description
Technical field
The present invention relates to thermal technics field, especially a kind of large-scale coal fired power plant based on Neural network inverse control
CO2Trapping system and feed forward control method.
Background technique
Fired power generating unit is current CO2Greenhouse effects are caused very big influence by the most important emission source of gas.Based on change
CO after the burning of absorption2Trapping technique is to realize CO2Trapping, the important measures for reducing greenhouse gas emission.It is absorption with MEA
CO after the burning of solvent2Trapping technique with its high efficiency, high economy, technology maturation and is convenient for the advantages that adjusting, becomes current
Business CO in the world2The mainstream of trapping technique；Meanwhile CO after burning2Trapping technique haves no need to change existing thermal power unit operation knot
Structure can effectively run plus catching apparatus after back-end ductwork, reduce cost of investment.
CO after fired power generating unit and burning2Trapping system has close coupling characteristic.It is instructed according to network load, fired power generating unit needs
Load peak regulation is participated in, tail flue gas therefore meeting random groups load generate fluctuation, and flue gas fluctuates meeting influence downstream CO therewith2Trapping
System produces bigger effect the key variables such as capture rate, reboiler temperature；On the other hand, CO after burning2In trapping system again
Boiling device steam is provided by bleeder steam, and the pumping of this stock can reduce unit generation amount, influences peak load regulation.In view of fired power generating unit
Coupled characteristic between trapping system, it is therefore desirable to comprehensively consider the two, system optimizes control as a whole.Together
When, studies have shown that large-scale coal fired power plant trapping system, there are biggish inertia and delay, disturbance measures noise, is probabilistic
In the presence of can also have controller certain interference effect, it is difficult to obtain good Control platform.At present for large-scale coal fired power plant CO2
Trapping system generallys use regulatory PID control scheme, it is difficult to successfully manage big delay, the close coupling characteristic of controlled device.
Summary of the invention
Technical problem to be solved by the present invention lies in provide a kind of large-scale coal fired power plant based on Neural network inverse control
CO2Trapping system and feed forward control method can reduce dynamic deviation caused by big inertia, be controlled in advance, improve control
Quality.
In order to solve the above technical problems, the present invention provides a kind of large-scale coal fired power plant CO based on Neural network inverse control2
Trapping system, comprising: target value setting unit 1, nerve network reverse controller 2, PID control compensator 3, large-scale coal fired power plant
CO2Trap total system model 4, the first delay cell 5 and the second delay cell 6；Target value setup unit 1 has two-way output,
It is connected respectively with nerve network reverse controller 2 and PID compensating controller 3；Target value setup unit 1 exports r (k+1) and large-scale combustion
Coal power station CO2It traps total system model 4 and exports input of the deviation e (k) of y (k+1) as PID control compensator 3, solve
Compensate input variable uPID(k)；Large-scale coal fired power plant CO2The input quantity u (k) of total system model 4 is trapped as PID control compensation
Device 3 exports uPID(k) u is exported with nerve network reverse controller 2NNThe sum of (k)；Large-scale coal fired power plant CO2Trap total system model
4 input variable u (k) and output variable y (k+1) by the first delay cell 5 and the second delay cell 6, is postponed respectively
Variable u (k-1) and y (k)；First delay cell 5 and 6 output variable u (k-1) of the second delay cell and y (k) and target value are set
Unit 1 exports r (k+1) and inputs as nerve network reverse controller 2, calculates output uNN(k)。
Correspondingly, a kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system feed forward control method, packet
Include following steps:
(1) main steam pressure is chosen, steam-water separator exports enthalpy, unit generation amount, CO2Capture rate and reboiler temperature
For large-scale coal fired power plant CO2The controlled variable of trapping system model 4 chooses unit coal-supplying amount, confluent, main steam valve, poor
Flow quantity and reboiler steam flow are corresponding control variable；
(2) under closed-loop case, change the controlled variables given values such as flue gas, capture rate, carry out closed loop response test；Setting
Sampling period T obtains large-scale coal fired power plant CO under different flue gases, capture rate load2The control amount of trapping system model 4 and controlled
Stable state, the dynamic parameter of amount；
(3) by large-scale coal fired power plant CO2The control measure of trapping system model 4 is according to as output, by large-scale coal fired power plant
CO2The controlled volume data of trapping system model 4 carry out off-line training as input, using BP neural network, determine large-scale coal-fired
Power station CO2The inverse system model of trapping system model 4, such as formula (1):
uNN(k)=f (y (k+1), y (k) ..., y (k-n1),u(k-1),…,u(k-n2)) (1)
(4) control loop is set, controls steam-water separator using unit Limestone control main steam pressure, using confluent
Outlet enthalpy controls unit generation amount using main steam valve door, utilizes lean solution flow control CO2Capture rate is steamed using reboiler
Vapour flow control reboiler temperature；
(5) relevant parameter of PID control compensator 3, including proportional gain k are setP, integration time constant Ti, the differential gain
kd, derivative time constant Td；
(6) target value setup unit 1 is exported to the output u (k- of r (k+1) and the first delay cell 5, the second delay cell 6
1) the output u of k moment nerve network reverse controller 2 is calculated using formula (1) respectively as input variable with y (k)NN(k)；
(7) target value setup unit 1 is exported into r (k+1) and large-scale coal fired power plant CO2Trapping system model 4 exports y (k+
1) it is compared, calculates output error e (k)；Output error is used as the input of PID control compensator 3, calculates compensation input
Measure uPID(k)；Using formula (2):
(8) k moment large size coal fired power plant CO is calculated24 reality output of trapping system model；Using formula (3):
U (k)=uNN(k)+uPID(k) (3)
(9) step (6) is executed repeatedly in the period later to step (8), is obtained corresponding control amount, is realized indifference control
System.
Preferably, in step (2), the selection rule of sampling time T is T95/ T=5~15, wherein T95For the list of object
Position step response process rises to 95% regulating time.
Preferably, in step (5), proportional gain kP, integration time constant Ti, differential gain kd, derivative time constant Td's
Selection rule is Ziegler-Nichols practical tuning method.
The invention has the benefit that the present invention is by using the feed forward control method based on Neural network inverse control, energy
Enough improve dynamic regulation quality；Simultaneously by introducing PID compensating controller, prediction model mismatch, disturbance etc. can be effectively treated
Caused by influence, to guarantee the Control platform of co-feeding system.
Detailed description of the invention
Fig. 1 is control system architecture schematic diagram of the invention.
Fig. 2 is large-scale coal fired power plant CO of the invention2Trapping system flow diagram.
Fig. 3 (a) is the present invention and conventional PID controllers coal unit outlet side main steam pressure in given value Spline smoothing
The contrast schematic diagram of power control effect.
Fig. 3 (b) is the present invention and conventional PID controllers coal unit outlet side steam-water separation in given value Spline smoothing
The contrast schematic diagram of device outlet enthalpy control effect.
Fig. 3 (c) is the present invention and conventional PID controllers coal unit outlet side unit generation in given value Spline smoothing
Measure the contrast schematic diagram of control effect.
Fig. 4 (a) is the present invention and conventional PID controllers coal unit input side coal-supplying amount control in given value Spline smoothing
The contrast schematic diagram of effect processed.
Fig. 4 (b) is the present invention and conventional PID controllers coal unit input side confluent control in given value Spline smoothing
The contrast schematic diagram of effect processed.
Fig. 4 (c) is the present invention and conventional PID controllers coal unit input side main steam valve in given value Spline smoothing
The contrast schematic diagram of door aperture control effect.
Fig. 5 (a) is the present invention and conventional PID controllers CO in given value Spline smoothing2Trapping system outlet side CO2It catches
The contrast schematic diagram of collection rate control effect.
Fig. 5 (b) is the present invention and conventional PID controllers CO in given value Spline smoothing2Trapping system outlet side boils again
The contrast schematic diagram of device temperature control effect.
Fig. 6 (a) is the present invention and conventional PID controllers CO in given value Spline smoothing2Trapping system input side lean solution
The contrast schematic diagram of flow control effect.
Fig. 6 (b) is the present invention and conventional PID controllers CO in given value Spline smoothing2Trapping system input side boils again
The contrast schematic diagram of device steam flow control effect.
Specific embodiment
As shown in Figure 1, a kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system, comprising: target value
Setting unit 1, nerve network reverse controller 2, PID control compensator 3, large-scale coal fired power plant CO2Trapping total system model 4,
First delay cell 5 and the second delay cell 6；Target value setup unit 1 has two-way output, respectively with nerve network reverse controller
2 are connected with PID compensating controller 3；Target value setup unit 1 exports r (k+1) and large-scale coal fired power plant CO2Trap total system
Model 4 exports input of the deviation e (k) of y (k+1) as PID control compensator 3, solves compensation input variable uPID(k)；Greatly
Type coal fired power plant CO2The input quantity u (k) for trapping total system model 4 is that PID control compensator 3 exports uPID(k) and nerve net
Network inverse controller 2 exports uNNThe sum of (k)；Large-scale coal fired power plant CO2Trap input variable u (k) and the output of total system model 4
Variable y (k+1) by the first delay cell 5 and the second delay cell 6, obtains lagged variable u (k-1) and y (k) respectively；First
Delay cell 5 and 6 output variable u (k-1) of the second delay cell and y (k) and target value setup unit 1 export r (k+1) as mind
It is inputted through network inverse controller 2, calculates output uNN(k)。
As shown in Fig. 2, large-scale coal fired power plant CO2Trapping system includes: main steam pressure, steam-water separator outlet enthalpy,
Unit generation amount, CO2It capture rate, reboiler temperature and unit coal-supplying amount, confluent, main steam valve, lean solution flow and boils again
The primary variables such as device steam flow.A kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system feedforward control
Method includes the following steps:
(1) main steam pressure is chosen, steam-water separator exports enthalpy, unit generation amount, CO2Capture rate and reboiler temperature
For large-scale coal fired power plant CO2The controlled variable of trapping system model 4 chooses unit coal-supplying amount, confluent, main steam valve, poor
Flow quantity and reboiler steam flow are corresponding control variable；
(2) under closed-loop case, change the controlled variables given values such as flue gas, capture rate, carry out closed loop response test；Setting
Sampling period T obtains large-scale coal fired power plant CO under different flue gases, capture rate load2The control amount of trapping system model 4 and controlled
Stable state, the dynamic parameter of amount；
(3) by large-scale coal fired power plant CO2The control measure of trapping system model 4 is according to as output, by large-scale coal fired power plant
CO2The controlled volume data of trapping system model 4 carry out off-line training as input, using BP neural network, determine large-scale coal-fired
Power station CO2The inverse system model of trapping system model 4, such as formula (1):
uNN(k)=f (y (k+1), y (k) ..., y (k-n1),u(k-1),…,u(k-n2)) (1)
(4) control loop is set, controls steam-water separator using unit Limestone control main steam pressure, using confluent
Outlet enthalpy controls unit generation amount using main steam valve door, utilizes lean solution flow control CO2Capture rate is steamed using reboiler
Vapour flow control reboiler temperature；
(5) relevant parameter of PID control compensator 3, including proportional gain k are setP, integration time constant Ti, the differential gain
kd, derivative time constant Td；
(6) target value setup unit 1 is exported to the output u (k- of r (k+1) and the first delay cell 5, the second delay cell 6
1) the output u of k moment nerve network reverse controller 2 is calculated using formula (1) respectively as input variable with y (k)NN(k)；
(7) target value setup unit 1 is exported into r (k+1) and large-scale coal fired power plant CO2Trapping system model 4 exports y (k+
1) it is compared, calculates output error e (k)；Output error is used as the input of PID control compensator 3, calculates compensation input
Measure uPID(k)；Using formula (2):
(8) k moment large size coal fired power plant CO is calculated24 reality output of trapping system model；Using formula (3):
U (k)=uNN(k)+uPID(k) (3)
(9) step (6) is executed repeatedly in the period later to step (8), is obtained corresponding control amount, is realized indifference control
System.
Embodiment:
(1) large-scale coal fired power plant CO is determined2Trapping system control loop and corresponding control amount and controlled volume, as shown in table 1:
Table 1
(2) sampling time T=30s is set, is neural network input using controlled volume data, controller data is nerve net
Network data establish coal fired power plant CO using BP neural network tool box2Trapping system inversion model.The neural network contain two layers it is hidden
Layer is hidden, neuron number is respectively 20 and 5, and training function is traingdm；
(3) according to given value r (k+1) and past input data u (k-1) and output data y (k), nerve network reverse is calculated
Controller exports uNN(k)；
(4) PID control compensator relevant parameter is set, as shown in formula (4):
(5) deviation is calculated.E (k)=r (k+1)-y (k+1)；
(6) PID control compensation output is calculated according to deviation e (k) and formula (5):
(7) subsequent time unit coal-supplying amount, confluent, main steam valve, lean solution flow and reboiler steam flow u are calculated
(k)=uNN(k)+uPID(k)；
(8) Optimal Control amount u (k) is exported, the nerve network reverse input of subsequent time is calculated and updated according to measuring signal
uNN(k).Thereafter in each sampling period, (3) step is repeated to (8) step.
The present invention is based on pairs of the control effect of the feed forward control method of Neural network inverse control and traditional PID control effect
Such as shown in attached drawing 3 (a)-attached drawing 6 (b).It is u in initial steady state operating condition1=60.4620kg/s, u2=425.2630kg/s, u3
=92.31%, u4=513.4947kg/s, u5=135.874kg/s, y1=21.3693MPa, y2=2722.1325kJ/kg, y3
=432.9270MWe, y4=90%, y5When=392.2k, at 600 seconds, output target value change respectively be 24.8430MPa,
2674.4886kJ/kg, 540MWe, 90%, 392.2k change again in 10500 seconds output target values after running a period of time and are
24.01MPa, 2702.4781kJ/kg, 506.896MWe, 90%, 392.2k.System is run 20400 seconds in total, for convenience of observing
Compare, carried out taking point for the sampling period with 30 seconds, draw.By being based on Neural network inverse control shown in attached drawing 3 (a)-attached drawing 6 (b)
Feedforward controller control effect it is more preferable, fluctuate small, fast response time；Simultaneously because the effect of PID compensating controller, practical defeated
Out and given value does not have deviation.
The present invention is large-scale coal fired power plant CO2Multivariable of the trapping system as one five five output of input, uses
Feed forward control techniques based on Neural network inverse control, choose unit coal-supplying amount, confluent, main steam valve, lean solution flow and
Reboiler steam flow is control variable, controls main steam pressure respectively, steam-water separator exports enthalpy, unit generation amount, CO2
Capture rate and reboiler temperature.On the one hand can Prediction System input quantity, control in advance, the big of total system can be successfully managed
Delay characteristic；In addition, shadow caused by prediction model adaptation, disturbance etc. can be effectively treated by introducing PID compensating controller
It rings, to guarantee the Control platform of total system.
By the present invention in that total system can be estimated in advance with the feed forward control method based on nerve network reverse controller
The control variable needed can preferably realize the coordinated control of total system, improve the dynamic regulation quality of system；Meanwhile
By increasing PID compensating controller, error correction is carried out to neural network analog model, to realize indifference control, and increases and is
The ability for anti-external disturbance and the uncertain disturbances of uniting enables control system preferably to adapt to industry spot, improves control
Quality.
Claims (4)
1. a kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system characterized by comprising target value is set
Set unit (1), nerve network reverse controller (2), PID control compensator (3), large-scale coal fired power plant CO2Trap total system mould
Type (4), the first delay cell (5) and the second delay cell (6)；Target value setup unit (1) has two-way output, respectively with nerve
Network inverse controller (2) is connected with PID compensating controller (3)；Target value setup unit (1) exports r (k+1) and large-scale coal-fired electricity
Stand CO2Input of the deviation e (k) of total system model (4) output y (k+1) as PID control compensator (3) is trapped, is solved
Compensate input variable uPID(k)；Large-scale coal fired power plant CO2The input quantity u (k) of total system model (4) is trapped as PID control benefit
Repay device (3) output uPID(k) u is exported with nerve network reverse controller (2)NNThe sum of (k)；Large-scale coal fired power plant CO2The whole system of trapping
The input variable u (k) and output variable y (k+1) of system model (4) pass through the first delay cell (5) and the second delay cell respectively
(6), lagged variable u (k-1) and y (k) are obtained；First delay cell (5) and the second delay cell (6) output variable u (k-1) with
Y (k) and target value setup unit (1) output r (k+1) are inputted as nerve network reverse controller (2), calculate output uNN(k)。
2. a kind of large-scale coal fired power plant CO based on Neural network inverse control2Trapping system feed forward control method, which is characterized in that
Include the following steps:
(1) main steam pressure is chosen, steam-water separator exports enthalpy, unit generation amount, CO2Capture rate and reboiler temperature are big
Type coal fired power plant CO2The controlled variable of trapping system model (4) chooses unit coal-supplying amount, confluent, main steam valve, lean solution
Flow and reboiler steam flow are corresponding control variable；
(2) under closed-loop case, change flue gas, capture rate controlled variable given value, carry out closed loop response test；Setting sampling week
Phase T obtains large-scale coal fired power plant CO under different flue gases, capture rate load2The control amount of trapping system model (4) and controlled volume
Stable state, dynamic parameter；
(3) by large-scale coal fired power plant CO2The control measure of trapping system model (4) is according to as output, by large-scale coal fired power plant CO2
The controlled volume data of trapping system model (4) carry out off-line training as input, using BP neural network, determine large-scale coal-fired electricity
Stand CO2The inverse system model of trapping system model (4), such as formula (1):
uNN(k)=f (y (k+1), y (k) ..., y (k-n1),u(k-1),…,u(k-n2)) (1)
(4) control loop is set, is exported using unit Limestone control main steam pressure, using confluent control steam-water separator
Enthalpy controls unit generation amount using main steam valve door, utilizes lean solution flow control CO2Capture rate utilizes reboiler steam stream
Amount control reboiler temperature；
(5) relevant parameter of PID control compensator (3), including proportional gain k are setP, integration time constant Ti, the differential gain
kd, derivative time constant Td；
(6) by target value setup unit (1) output r (k+1) and the first delay cell (5), the output u of the second delay cell (6)
(k-1) the output u of k moment nerve network reverse controller (2) is calculated using formula (1) respectively as input variable with y (k)NN
(k)；
(7) by target value setup unit (1) output r (k+1) and large-scale coal fired power plant CO2Trapping system model (4) exports y (k+1)
It is compared, calculates output error e (k)；Output error is used as the input of PID control compensator (3), calculates compensation input
Measure uPID(k)；Using formula (2):
(8) k moment large size coal fired power plant CO is calculated2Trapping system model (4) reality output；Using formula (3):
U (k)=uNN(k)+uPID(k) (3)
(9) step (6) is executed repeatedly in the period later to step (8), is obtained corresponding control amount, is realized indifference control.
3. the large-scale coal fired power plant CO based on Neural network inverse control as claimed in claim 22Trapping system feedforward control side
Method, which is characterized in that in step (2), the selection rule of sampling time T is T95/ T=5~15, wherein T95For the unit of object
Step response process rises to 95% regulating time.
4. the large-scale coal fired power plant CO based on Neural network inverse control as claimed in claim 22Trapping system feedforward control side
Method, which is characterized in that in step (5), proportional gain kP, integration time constant Ti, differential gain kd, derivative time constant Td's
Selection rule is Ziegler-Nichols practical tuning method.
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| CN110687792A (en) * | 2019-11-04 | 2020-01-14 | 东南大学 | CO after chemical adsorption combustion2Anti-smoke disturbance fuzzy control method of trapping system |
| CN110737198A (en) * | 2019-10-09 | 2020-01-31 | 东南大学 | Large-scale coal-fired power plant CO based on BP neural network2Capture system prediction control method |
| 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 |
| CN111871164A (en) * | 2020-06-15 | 2020-11-03 | 浙江工业大学 | Intelligent regulation and control method for low-temperature plasma catalytic degradation of CVOCs |
| CN113393905A (en) * | 2021-06-03 | 2021-09-14 | 东南大学 | Chemical absorption of CO2Dynamic robust soft measurement system and method of trapping system |
| CN113467237A (en) * | 2021-06-22 | 2021-10-01 | 东南大学 | Dynamic modeling method for main steam temperature based on deep learning |
| CN114397922A (en) * | 2021-09-29 | 2022-04-26 | 北京百利时能源技术股份有限公司 | Temperature control system of carbon dioxide capture reboiler of coal-fired power plant |
| CN116679572A (en) * | 2023-08-03 | 2023-09-01 | 北京绿能碳宝科技发展有限公司 | Carbon dioxide trapping self-learning method based on deep Q learning network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0710901B1 (en) * | 1994-11-01 | 2000-09-27 | The Foxboro Company | Multivariable nonlinear process controller |
| CN1828656A (en) * | 2006-04-13 | 2006-09-06 | 东南大学 | Electricity-generating terminal contest price decision information obtaining and processing method based on Markov chain |
| CN101856590A (en) * | 2010-06-11 | 2010-10-13 | 清华大学 | Carbon capturing system and method for controlling electric carbon coordination in carbon capturing power plant after combustion |
| CN201711089U (en) * | 2010-07-08 | 2011-01-19 | 陕西正大环保科技有限公司 | pH regulating control system of wet process desulfurization equipment of small and middle sized boilers |
| US20120143382A1 (en) * | 2010-12-07 | 2012-06-07 | Alstom Technology Ltd. | Optimized integrated controls for oxy-fuel combustion power plant |
| CN105498497A (en) * | 2016-01-05 | 2016-04-20 | 中国科学院自动化研究所 | Flue gas desulfurization and denitration integrated equipment controlled through multiple variables and control method thereof |
| CN107694337A (en) * | 2017-11-03 | 2018-02-16 | 吉林省电力科学研究院有限公司 | Coal unit SCR denitrating flue gas control methods based on network response surface |
-
2019
- 2019-04-08 CN CN201910276056.2A patent/CN110026068B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0710901B1 (en) * | 1994-11-01 | 2000-09-27 | The Foxboro Company | Multivariable nonlinear process controller |
| CN1828656A (en) * | 2006-04-13 | 2006-09-06 | 东南大学 | Electricity-generating terminal contest price decision information obtaining and processing method based on Markov chain |
| CN101856590A (en) * | 2010-06-11 | 2010-10-13 | 清华大学 | Carbon capturing system and method for controlling electric carbon coordination in carbon capturing power plant after combustion |
| CN201711089U (en) * | 2010-07-08 | 2011-01-19 | 陕西正大环保科技有限公司 | pH regulating control system of wet process desulfurization equipment of small and middle sized boilers |
| US20120143382A1 (en) * | 2010-12-07 | 2012-06-07 | Alstom Technology Ltd. | Optimized integrated controls for oxy-fuel combustion power plant |
| CN103339441A (en) * | 2010-12-07 | 2013-10-02 | 阿尔斯通技术有限公司 | Optimized integrated controls for oxy-fuel combustion power plant |
| CN105498497A (en) * | 2016-01-05 | 2016-04-20 | 中国科学院自动化研究所 | Flue gas desulfurization and denitration integrated equipment controlled through multiple variables and control method thereof |
| CN107694337A (en) * | 2017-11-03 | 2018-02-16 | 吉林省电力科学研究院有限公司 | Coal unit SCR denitrating flue gas control methods based on network response surface |
Non-Patent Citations (2)
| Title |
|---|
| SHAHROKHI, M: "Modeling, simulation and control of a methanol synthesis fixed-bed reactor", 《CHEMICAL ENGINEERING SCIENCE》 * |
| 揭超: "基于神经网络的电厂烟气二氧化碳捕集过程建模", 《计算机与应用化学》 * |
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| CN114397922A (en) * | 2021-09-29 | 2022-04-26 | 北京百利时能源技术股份有限公司 | Temperature control system of carbon dioxide capture reboiler of coal-fired power plant |
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