CN107450325B - CO after a kind of burning2The Multi model Predictive Controllers of trapping system - Google Patents
CO after a kind of burning2The Multi model Predictive Controllers of trapping system Download PDFInfo
- Publication number
- CN107450325B CN107450325B CN201710795146.3A CN201710795146A CN107450325B CN 107450325 B CN107450325 B CN 107450325B CN 201710795146 A CN201710795146 A CN 201710795146A CN 107450325 B CN107450325 B CN 107450325B
- Authority
- CN
- China
- Prior art keywords
- trapping
- matrix
- time
- future
- pressure cylinder
- 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
Links
- 238000000034 method Methods 0.000 claims abstract description 33
- 238000000605 extraction Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims description 43
- 238000002485 combustion reaction Methods 0.000 claims description 22
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 9
- 239000007788 liquid Substances 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 238000000691 measurement method Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 2
- 230000000630 rising effect Effects 0.000 claims description 2
- 230000007704 transition Effects 0.000 claims description 2
- 238000011835 investigation Methods 0.000 abstract description 5
- 230000000694 effects Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 2
- 239000003546 flue gas Substances 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 108010076504 Protein Sorting Signals Proteins 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000008929 regeneration Effects 0.000 description 1
- 238000011069 regeneration method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Medicines Containing Antibodies Or Antigens For Use As Internal Diagnostic Agents (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses CO after a kind of burning2The Multi model Predictive Controllers of trapping system, the forecast Control Algorithm is with CO after the burning based on chemisorption2Trapping system is controlled device, and lean solution valve opening and turbine low pressure cylinder steam extraction valve opening are that system controls input quantity, CO2Capture rate and reboiler temperature are system output quantity；It is primarily based on subspace state space system identification, the data generated using system operation establish the local state spatial model of system at different operating points；Then using the nonlinear Distribution of the method investigation controlled device of gap metric；And then predictive controller is established at suitable local operating point, and design subordinating degree function for its weighted array, establish CO after burning2Trapping system multiple model predictive control system.Method of the invention has good global nonlinear Control ability, can effectively adapt to the demand of a wide range of variable working condition of system, fast track CO2Capture rate setting value improves CO2The level of trapping system depth fast and flexible operation.
Description
Technical Field
The invention relates to the technical field of predictive control methods, in particular to post-combustion CO2A multi-model predictive control method for an entrapment system.
Background
With the increasing severity of greenhouse effect and related climate ecological problems, CO emission reduction2Has become a key measure for the international society to cope with climate change. The thermal power generating unit is CO as main equipment for power supply2The most stable and concentrated emission source, 30-40% of the world and 40-50% of CO in China2The discharge comes from the thermal power generating unit. While the new energy technology is actively developed and the power generation efficiency of the thermal power generating unit is improved, the CO of the thermal power generating unit2Capture is recognized by numerous authorities as the realization of large-scale CO within the next 30 years2The most direct and effective technical means for emission reduction.
In the existing thermal power generating unit CO2Post-combustion CO capture technology based on chemical absorption2Capture technology for directly separating CO from flue gas generated after combustion in power plant2Has excellent inheritance and better technical applicability to the existing units, and is the current CO2The mainstream technology adopted by the power station is captured. Due to CO2The trapping needs to extract a large amount of steam from a low-pressure cylinder in the thermal power generating unit for lean liquid regeneration, and the implementation of the steam has great influence on the generating efficiency of the thermal power generating unit. To this end, CO2The trapping system must be practicalExtensive flexible operation, e.g. CO reduction during times of urgent power demand or high electricity prices2The collection rate is improved in the period of higher environmental protection pressure or higher carbon value. However, with CO2The trapping device operates in a large range under variable working conditions, and the system of the trapping device presents stronger nonlinear characteristics, so that the control performance of the traditional prediction controller designed based on a linear model is reduced, and the stability is reduced. Thus a post combustion CO2It is necessary to incorporate the development of predictive control algorithms for flue gas signal utilization in the capture system.
Disclosure of Invention
The invention aims to solve the technical problem of providing CO after combustion2Multi-model predictive control method for a capture system capable of improving CO2The quality of the large-range variable working condition of the trapping system is adjusted, and the capability of rapid, deep and flexible operation of the trapping system is improved.
To solve the above technical problems, the present invention provides a post-combustion CO2The multi-model predictive control method of the trapping system comprises the following steps:
(1) CO after combustion2The trapping system is switched to a manual state, and the opening u of the lean flow valve is used near working points with different trapping ratesaAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbinebFor input, to CO2The capture system is excited to obtain CO2Trapping rate yaAnd reboiler temperature ybOpen loop response data of (a);
(2) the sampling period Ts is selected so as toIn order to be an input, the user can select,for output, a subspace identification method is utilized to construct CO at working condition points with different capture rates2Capturing a system local state space model;
(3) analyzing the difference between adjacent local models by using a gap measurement method, and investigating CO2A non-linear profile of the trapping system;
(4) establishing a model predictive controller at a proper local working point; at each sampling moment, CO of the system is estimated in a certain time in the future by utilizing each sub-model respectively2Trapping rateAnd reboiler temperatureObtaining the local optimal lean solution flow valve opening u through optimization calculationi a-opAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbinei b-op；
(5) Designing a final appropriate membership function, and carrying out weighted combination on the outputs of all the controllers to obtain the final opening u of the lean flow valvea-opAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbineb-opAnd act on CO2A capture system; whereinωiIs the membership function corresponding to the ith controller, which is a function of the scheduling variable, the trapping rate CR, ui a-opAnd ui b-opCalculating an optimal control signal for the ith controller;
(6) fixing the prediction matrix psi output from each local controllerx、ψu、ψyAnd (5) repeating the steps (3) to (4) to realize continuous control.
Preferably, in step (2), T95/Ts5-15, wherein T95 is the adjustment time of the transition process rising to 95%.
Preferably, in the step (2), CO at different trapping rate working points is constructed2The method comprises the following steps of collecting a system local state space model:
(21) output data y and input data u from 0 th time to 2N + j-2 th time continuously obtained near a given working condition point are respectively arranged in a Hankel matrix form:
wherein N is the number of rows in the matrix, and N is greater than CO2Capturing the system order, j is the number of matrix columns, Y and U respectively represent Hankel matrix formed by output data and input data, and YfAnd YpFuture data and past data, U, representing output data, respectivelyfAnd UpFuture data and past data, y, representing input data, respectivelyjDenotes the jth output data, ujRepresents the jth input data;
(22) let Wp＝[(Yp)T (Up)T]TThe following matrix is subjected to QR decomposition:
obtaining a matrix L:
(23) thereby obtaining a matrix Lw＝L(:,1:N(m+l))，LuL (: N (m + L) +1: end), m being the input variable dimension, L being the output variable dimension, L (: 1: N (m + L)) representing the first N (m + L) columns of the matrix L, L (: N (m + L) +1: end) representing all columns of the matrix L following the N (m + L) +1 columns;
(24) to LwPerforming singular value decomposition on the matrix:
obtaining a matrix gammaN＝U1(S1)1/2And further obtaining: model parametersWhereinShowing the deletion of the preceding l rowsN，Γ NRepresenting Γ for the removed l rowsN，Represents Moore-Penrose pseudo-inverse; model parameter C ═ ΓN(1: l:) may be selected from gammaNDirectly obtaining in the first line l;
(25) solving a system of linear equations:
model parameters B and D are obtained.
Preferably, in the step (3), the gap metric between adjacent local models is calculated, and the specific steps are as follows:
(31) for two adjacent local models P1、P2Performing orthogonal right co-prime decomposition to obtain:
(32) calculation model P1、P2Distance between:
wherein H∞A special Hardy canonical space:σmax(G (j ω)) represents the maximum singular value of G (j ω); q is H∞An arbitrary function in space;
(33) measurement of the gap between two systems
Preferably, in step (4), the formula (1) is used to estimate the future periodCO of inter-system2Capture rate and reboiler temperature
Wherein the prediction matrix psix、ψu、ψyRespectively as follows:
respectively at time k CO2The estimated state, input and output of the trapping system, F is the observer gain, ufFor the input data at Nu time instants in the future, is the future NyThe estimated output of the time of day system,
the performance indicator function J is calculated using the following formula:
wherein Q isfAnd RfIs a weight matrix for adjusting the quality of input/output control, rfis the future N1Time system CO2A sequence of capture rates and reboiler temperature setpoints,
respectively representing the time k +1 to k + N1Time system CO2Trapping rate raAnd reboiler temperature rbThe setting value is set, is the future NyTime system CO2A capture rate and reboiler temperature predictive value sequence,
respectively representing the time k +1 to k + NyTime system CO2Trapping rate yaAnd reboiler temperature ybThe value to be estimated is estimated in advance,
Δufis the future NuOpening signal u of time lean flow valveaAnd the opening signal u of the steam extraction valve of the low-pressure cylinderbSequence ofAn increment of (d);
wherein
CO2Amplitude constraints (u) of lean liquid flow valve and low-pressure cylinder steam extraction valve opening signals u of capture systemmin,umax) And an incremental constraint (Δ u)min,Δumax) Comprises the following steps:
wherein u ismin,umaxRespectively representing the minimum value and the maximum value, delta u, of the opening signals u of the lean liquid flow valve and the low-pressure cylinder steam extraction valvemin,ΔumaxRespectively representing the minimum increment and the maximum increment of the lean solution flow valve and the low-pressure cylinder steam extraction valve opening signals u;
substituting the formula (1) into the formula (2) at each sampling moment, and minimizing the performance index function J under the condition of satisfying the formulas (3) and (4) to obtain an optimal control increment sequence uf：
Extracting optimal control increment sequence ufThe first block u ink+1As optimal lean liquid flow valve and low-pressure cylinder extraction valve opening degree signals
The invention has the beneficial effects that: the multi-model predictive control method has good global nonlinear control capability and is applied to CO after combustion in a thermal power station2The capture system can effectively meet the requirement of large-range variable working conditions of the system and quickly track CO2Capture rate set value, increase of CO2The depth of the trapping system is rapid and flexible.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention.
FIG. 2 is a diagram illustrating the results of the non-linear investigation according to the present invention.
FIG. 3 is a schematic diagram of a membership function designed according to the present invention.
FIG. 4 shows the CO ratio of the multi-model predictive control (solid line) and the conventional PID control (dashed line) of the present invention2The control effect under the small range change of the set value of the trapping rate is compared with a schematic diagram (the dotted line is the set value).
FIG. 5 shows the CO of the multi-model predictive control (solid line) and the conventional linear predictive control (dotted line) of the present invention2The control effect under the large-range change of the set value of the trapping rate is compared with a schematic diagram (the dotted line is the set value).
Detailed Description
The multi-model predictive control method is applied to CO after combustion of certain 1MW thermal power generating unit2In the simulation model of the trapping system, the control aims to make CO meet the input constraint condition2The capture rate and the reboiler temperature tracking set value are adopted to realize CO2The large-range variable working condition operation of the trapping system.
Post combustion CO of the invention2Multi-model predictive control method for capture system with post-combustion CO based on chemisorption2The collecting system is controlled object, the opening of lean solution valve and the opening of steam extraction valve of low-pressure cylinder of steam turbine are system control input quantity, CO2Firstly, establishing a local state space model of the system at different working condition points by using data generated by system operation based on a subspace identification method; then, the nonlinear distribution of the controlled object is investigated by using a clearance measurement method, a predictive controller is established at a proper local working condition point, membership function is designed and combined in a weighting way, and CO after combustion is established2The trapping system multi-model predictive control system. Compared with the traditional predictive control, the method improves CO2The quality of the trapping system is controlled in large-range variable working condition operation, and the flexible operation capability of the trapping system is enhanced.
As shown in FIG. 1, the post-combustion CO of the present invention2Trapping system multi-model predictionThe measurement control method specifically comprises the following steps:
step 1, designing a lean flow valve opening signal u which changes once in 30 seconds and lasts 30000 seconds near a certain given trapping rate working pointaAnd the steam extraction valve opening signal u of the low-pressure cylinder of the steam turbinebExciting the system to obtain a series of CO2Trapping rate yaAnd reboiler temperature ybOpen loop response data of (a);
step 2, selecting a sampling period Ts30s, toIn order to be an input, the user can select,for output, a subspace identification method is utilized to construct CO at working condition points with different capture rates2The method comprises the following steps of collecting a system local state space model:
a: the 1000 sets of output data Y and amplified input data U obtained consecutively were arranged in a Hankel matrix form (2N + j-2 ═ 1000), respectively:
wherein, N is the number of matrix lines, and N is 10; n is greater than CO2Capturing the system order, wherein j is the number of matrix columns, the larger the matrix column number is, the better the matrix column number is, Y and U respectively represent Hankel matrix formed by output and input data, and Y isfAnd YpFuture data and past data, U, representing output data, respectivelyfAnd UpFuture data and past data, y, representing input data, respectivelyjDenotes the jth output data, ujRepresents the jth input data;
b: let Wp＝[(Yp)T (Up)T]TThe following matrix is subjected to QR decomposition:
obtaining a matrix L:
c: obtain the matrix Lw＝L(:,1:N(m+l))，LuL (: N (m + L) +1: end), m ═ 2, m is the input variable dimension, L ═ 2, L is the input output variable dimension, L (: 1: N (m + L)) means the first N (m + L) column of L, L (: N (m + L) +1: end) means all columns of L following the N (m + L) +1 column;
d: to LwPerforming singular value decomposition on the matrix:
obtaining a matrix gammaN＝U1(S1)1/2And further obtaining: model parametersWhereinShowing the deletion of the preceding l rowsN，Γ NRepresenting Γ for the removed l rowsN，Represents Moore-Penrose pseudo-inverse; model parameter C ═ ΓN(1: l:) may be selected from gammaNDirectly in the first l line of (1).
Subspace matrix lw＝Lw(1:l,:),lu＝Lu(1:l,1:m)；
E: solving a system of linear equations:
model parameters B and D are obtained.
Step 3, using the method of gap measurement, analyzing each adjacentDifferences between local models, investigation of CO2The non-linear distribution of the trapping system comprises the following specific steps:
a: for two adjacent local models P1、P2Performing orthogonal right co-prime decomposition to obtain:
b: calculation model P1、P2Distance between:
wherein H∞A special Hardy canonical space:σmax(G (j ω)) represents the maximum singular value of G (j ω); q is H∞And taking Q as 1 for any function in the space.
C: measurement of the gap between two systems
D: and selecting local working condition points according to the result of the gap measurement and designing a membership function. In this example, the results of the gap degree investigation are shown in fig. 2, and it can be seen that the system nonlinearity is strong in the low trapping rate and high trapping rate regions, and thus the membership degree function is designed as shown in fig. 3.
And 4, step 4: and establishing a model predictive controller at a proper local working point. At each sampling moment, the CO of the system in a future period is estimated by adopting a formula (1)2Capture rate and reboiler temperature
Wherein the prediction matrix psix、ψu、ψyRespectively as follows:
respectively at time k CO2The estimated state, inputs and outputs of the system are captured, F is the observer gain. u. offFor the future NuThe input data of each moment in time, is the future NyThe estimated output of the time of day system,in this example, take Nu＝2，Ny＝100。
Taking the formula (2) as a function of the performance index:
wherein,is a weight matrix for adjusting the quality of input/output control,rfis the future NyTime system CO2A sequence of capture rates and reboiler temperature setpoints, respectively representing the time k +1 to k + NyTime system CO2Trapping rate raAnd reboiler temperature rbThe setting value is set,
is a future Ny time system CO2A capture rate and reboiler temperature predictive value sequence,
respectively representing the time k +1 to k + NyTime system CO2Trapping rate yaAnd reboiler temperature ybThe value to be estimated is estimated in advance,can be described by formula (1), taking Ny＝10；ΔufIs the future NuOpening signal sequence of lean solution flow valve and low-pressure cylinder steam extraction valveAn increment of (2), whereinNu＝2。
Considering CO2Amplitude constraint (u) of capture system valve opening signalmin＝[0.4 0.02]T,umax＝[1 0.075]T) And an incremental constraint (Δ u)min＝[-0.007 -0.001]T,Δumax＝[0.007 0.001]T)：
Substituting (1) into the performance index formula (2) at each sampling moment, and minimizing (2) under the condition of satisfying constraints (3) and (4) to obtain a local control increment sequence uf：Extracting a sequence of local control increments ufThe first block u ink+1As local lean liquid flow valve and low-pressure cylinder extraction valve opening signals
Step 5, utilizing the membership function to carry out weighted combination on the outputs of all the controllers to obtain the final opening u of the barren solution flow valvea-opAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbineb-opAnd act on CO2A capture system. WhereinωiIs the membership function corresponding to the ith controller, which is a function of the scheduling variable, the trapping rate CR, ui a-opAnd ui b-opThe calculated optimal control signal for the ith controller.
Step 6, fixing the prediction matrix psi output from each local controllerx、ψu、ψyAnd repeating the steps 3-4 to realize continuous control.
This example is for comparison of post-combustion CO in the present invention2The control effects of a multi-model predictive control method, a conventional proportional-integral-derivative control method and a general predictive control method of the trapping system are subjected to two groups of simulation tests: simulation experiment 1, CO2The initial capture rate of the capture system is stabilized at 80 percent, and CO is generated when t is 15min and 115min2TrappingThe rate set point was slowly changed from 80% to 70% and 75%, respectively, and the reboiler temperature set point was held constant at 383K; simulation experiment 2, CO2The initial capture rate of the capture system is stabilized at 80 percent, t is 15min and 115min, and CO is obtained2The capture rate setpoint was slowly changed from 80% to 90% and 55%, respectively, and the reboiler temperature setpoint was held constant at 383K.
As shown in FIG. 4, in CO2The invention is directed to post-combustion CO when the capture rate set point is increased or decreased2The optimal control effect curve of the trapping system is obviously superior to that of a conventional proportional-integral controller, and the trapping system has satisfactory set value tracking and adjusting capacity. As shown in FIG. 5, in CO2Under the condition that the set value of the capturing rate is changed in a large range, the optimization control method can better coordinate the steam extraction of the reboiler and the flow of the barren solution, realize the rapid tracking control of the capturing rate, effectively avoid the controller oscillation caused by the mismatch of the linear model in the large-range variable working condition operation, have a more stable control effect, and improve the CO2The operational quality of the capture system.
Post combustion CO of the invention2Multi-model predictive control method for trapping system, CO establishment using subspace identification method2Models of the trapping system at different working condition points are selected, a prediction controller is established, a membership function is designed on the basis of nonlinear investigation of the trapping system, and the large-range variable working condition operation level of the system is greatly improved on the premise of keeping all the advantages of conventional linear prediction control, so that the CO is further improved2The capability of rapid deep flexible operation of the trapping system.
While the invention has been shown and described with respect to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the scope of the invention as defined in the following claims.
Claims (4)
1. Post combustion CO2A multi-model predictive control method for an entrapment system, comprising the steps of:
(1) CO after combustion2The trap system switches toManually operating at the operating points with different capture rates and with the opening u of the lean flow valveaAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbinebFor input, to CO2The capture system is excited to obtain CO2Trapping rate yaAnd reboiler temperature ybOpen loop response data of (a);
(2) the sampling period Ts is selected so as toIn order to be an input, the user can select,for output, a subspace identification method is utilized to construct CO at working condition points with different capture rates2Capturing a system local state space model;
(3) analyzing the difference between adjacent local models by using a gap measurement method, and investigating CO2A non-linear profile of the trapping system;
(4) establishing a model predictive controller at a proper local working point; at each sampling moment, CO of the system is estimated in a certain time in the future by utilizing each sub-model respectively2Trapping rateAnd reboiler temperatureObtaining the local optimal lean solution flow valve opening u through optimization calculationi a-opAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbinei b-op(ii) a Predicting CO of a system in a future period of time by using formula (1)2Capture rate and reboiler temperature
Wherein the prediction matrix psix、ψu、ψyRespectively as follows:
uk,ykrespectively at time k CO2The estimated state, input and output of the trapping system, F is the observer gain, ufFor the input data at Nu time instants in the future, is the future NyThe estimated output of the time of day system,
the performance indicator function J is calculated using the following formula:
wherein Q isfAnd RfIs a weight matrix for adjusting the quality of input/output control,rfis the future NyTime system CO2A sequence of capture rates and reboiler temperature setpoints,
respectively representing the time k +1 to k + NyTime system CO2Trapping rate raAnd reboiler temperature rbThe setting value is set, is the future NyTime system CO2A capture rate and reboiler temperature predictive value sequence,
respectively representing the time k +1 to k + NyTime system CO2Trapping rate yaAnd reboiler temperature ybThe value to be estimated is estimated in advance,
Δufis the future NuOpening signal u of time lean flow valveaAnd the opening signal u of the steam extraction valve of the low-pressure cylinderbSequence ofAn increment of (d);
wherein
CO2Amplitude constraints (u) of lean liquid flow valve and low-pressure cylinder steam extraction valve opening signals u of capture systemmin,umax) And an incremental constraint (Δ u)min,Δumax) Comprises the following steps:
wherein u ismin,umaxRespectively representing the minimum value and the maximum value, delta u, of the opening signals u of the lean liquid flow valve and the low-pressure cylinder steam extraction valvemin,ΔumaxRespectively representing the minimum increment and the maximum increment of the lean solution flow valve and the low-pressure cylinder steam extraction valve opening signals u;
substituting the formula (1) into the formula (2) at each sampling moment, and minimizing the performance index function J under the condition of satisfying the formulas (3) and (4) to obtain an optimal control increment sequence uf：
Extracting optimal control increment sequence ufThe first block u ink+1As optimal lean liquid flow valve and low-pressure cylinder extraction valve opening degree signals
(5) Designing a final appropriate membership function, and carrying out weighted combination on the outputs of all the controllers to obtain the final opening u of the lean flow valvea-opAnd the opening signal u of the steam extraction valve of the low-pressure cylinder of the steam turbineb-opAnd act on CO2A capture system; whereinωiIs the membership function corresponding to the ith controller, which is a function of the scheduling variable, the trapping rate CR, ui a-opAnd ui b-opCalculating an optimal control signal for the ith controller;
(6) fixing the prediction matrix psi output from each local controllerx、ψu、ψyAnd (5) repeating the steps (3) to (4) to realize continuous control.
2. Post combustion CO according to claim 12The multi-model predictive control method for a trapping system, characterized in that, in the step (2), T95/Ts5-15, wherein T95 is the adjustment time of the transition process rising to 95%.
3. Post combustion CO according to claim 12The multi-model predictive control method of the trapping system is characterized in that in the step (2), CO at working condition points with different trapping rates are constructed2The method comprises the following steps of collecting a system local state space model:
(21) output data y and input data u from 0 th time to 2N + j-2 th time continuously obtained near a given working condition point are respectively arranged in a Hankel matrix form:
wherein N is the number of rows in the matrix, and N is greater than CO2Capturing the system order, j is the number of matrix columns, Y and U respectively represent Hankel matrix formed by output data and input data, and YfAnd YpFuture data and past data, U, representing output data, respectivelyfAnd UpFuture data and past data, y, representing input data, respectivelyjDenotes the jth output data, ujRepresents the jth input data;
(22) let Wp＝[(Yp)T (Up)T]TThe following matrix is subjected to QR decomposition:
obtaining a matrix L:
(23) thereby obtaining a matrix Lw＝L(:,1:N(m+l))，LuL (: N (m + L) +1: end), m being the input variable dimension, L being the output variable dimension, L (: 1: N (m + L)) representing the first N (m + L) columns of the matrix L, L (: N (m + L) +1: end) representing all columns of the matrix L following the N (m + L) +1 columns;
(24) to LwPerforming singular value decomposition on the matrix:
obtaining a matrix gammaN＝U1(S1)1/2And further obtaining: model parametersWhereinShowing the deletion of the preceding l rowsN，Γ NRepresenting Γ for the removed l rowsN，Represents Moore-Penrose pseudo-inverse; model parameter C ═ ΓN(1: l:) may be selected from gammaNDirectly obtaining in the first line l;
(25) solving a system of linear equations:
model parameters B and D are obtained.
4. Post combustion CO according to claim 12The multi-model predictive control method of the trapping system is characterized in that in the step (3), a gap metric value between adjacent local models is calculated, and the specific steps are as follows:
(31) for two adjacent local models P1、P2Performing orthogonal right co-prime decomposition to obtain:
(32) calculation model P1、P2Distance between:
wherein H∞A special Hardy canonical space:σmax(G (j ω)) represents the maximum singular value of G (j ω); q is H∞An arbitrary function in space;
(33) measurement of the gap between two systems
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710795146.3A CN107450325B (en) | 2017-09-06 | 2017-09-06 | CO after a kind of burning2The Multi model Predictive Controllers of trapping system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710795146.3A CN107450325B (en) | 2017-09-06 | 2017-09-06 | CO after a kind of burning2The Multi model Predictive Controllers of trapping system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107450325A CN107450325A (en) | 2017-12-08 |
| CN107450325B true CN107450325B (en) | 2019-11-12 |
Family
ID=60494970
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710795146.3A Active CN107450325B (en) | 2017-09-06 | 2017-09-06 | CO after a kind of burning2The Multi model Predictive Controllers of trapping system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107450325B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108508748B (en) * | 2018-03-28 | 2020-11-03 | 东南大学 | Effective operation control method for photo-thermal auxiliary combustion post-CO 2 capture system |
| CN108710356B (en) * | 2018-06-19 | 2021-01-26 | 东南大学 | Effective post-combustion CO2Operation control method for trapping coal-fired power generation system |
| CN108958031B (en) * | 2018-07-19 | 2021-02-09 | 东南大学 | Post combustion CO2Coordinated prediction control method for trapping coal-fired power generation system |
| CN109100940A (en) * | 2018-09-28 | 2018-12-28 | 河海大学常州校区 | A kind of Multi model Predictive Controllers based on gap metric weighting function |
| CN109188911B (en) * | 2018-09-28 | 2019-07-23 | 东南大学 | One kind is for CO after burning2The improvement INA feed forward control method of trapping system |
| CN110286593B (en) * | 2019-07-02 | 2022-04-26 | 东南大学 | Multi-target prediction control-based control method for carbon dioxide capture system after chemisorption combustion |
| CN110442027A (en) * | 2019-08-16 | 2019-11-12 | 河海大学常州校区 | A kind of gap multi-model weighting function methods of self-tuning |
| CN110737198B (en) * | 2019-10-09 | 2022-11-18 | 东南大学 | Large-scale coal-fired power plant CO based on BP neural network 2 Capture system prediction control method |
| CN110658722B (en) * | 2019-10-18 | 2022-04-26 | 河海大学常州校区 | Self-equalization multi-model decomposition method and system based on gap |
| CN110687792B (en) * | 2019-11-04 | 2022-04-26 | 东南大学 | Anti-smoke disturbance fuzzy control method for carbon dioxide capture system after chemical adsorption combustion |
| CN110764419B (en) * | 2019-11-15 | 2022-06-10 | 江苏方天电力技术有限公司 | CO of large coal-fired power plant2Capture global scheduling and predictive control system and method |
| CN111045327B (en) * | 2019-11-28 | 2021-10-01 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Automatic manual switching method based on generalized predictive control |
| CN111694279B (en) * | 2020-06-30 | 2022-09-23 | 河海大学常州校区 | Multivariable nonlinear system self-adaptive equalization multi-model decomposition control method |
| CN113110030B (en) * | 2021-04-20 | 2022-06-28 | 兰州理工大学 | CO (carbon monoxide)2Trapped DMC-PID cascading system and control method thereof |
| CN114397922B (en) * | 2021-09-29 | 2023-03-14 | 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 |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102841539B (en) * | 2012-09-10 | 2016-05-11 | 广东电网公司电力科学研究院 | Based on the subcritical control method for coordinating of multi-model PREDICTIVE CONTROL |
| CN104216287A (en) * | 2014-08-20 | 2014-12-17 | 上海交通大学 | Multi-model adaptive control method and system of non-minimum phase nonlinear system |
| CN105955021A (en) * | 2016-05-11 | 2016-09-21 | 杭州电子科技大学 | Multi-level and multi-model weighted predictive functional control method for electric heating furnace |
| CN106610587A (en) * | 2016-12-28 | 2017-05-03 | 中国电力科学研究院 | Temperature multi-model prediction function control method and device |
| CN106842955B (en) * | 2017-03-15 | 2019-08-20 | 东南大学 | CO after burning with exhaust gas volumn Disturbance Rejection2Trapping system forecast Control Algorithm |
| CN106842962A (en) * | 2017-04-13 | 2017-06-13 | 东南大学 | Based on the SCR denitration control method for becoming constraint multiple model predictive control |
-
2017
- 2017-09-06 CN CN201710795146.3A patent/CN107450325B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN107450325A (en) | 2017-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107450325B (en) | CO after a kind of burning2The Multi model Predictive Controllers of trapping system | |
| Wu et al. | Flexible operation of post-combustion solvent-based carbon capture for coal-fired power plants using multi-model predictive control: A simulation study | |
| CN106842955B (en) | CO after burning with exhaust gas volumn Disturbance Rejection2Trapping system forecast Control Algorithm | |
| CN110764419B (en) | CO of large coal-fired power plant2Capture global scheduling and predictive control system and method | |
| CN105426956A (en) | Ultra-short-period photovoltaic prediction method | |
| Wu et al. | Nonlinear dynamic analysis and control design of a solvent-based post-combustion CO2 capture process | |
| CN111552175B (en) | Overall optimization scheduling and rapid variable load control method for supercritical coal-fired power plant-carbon capture system after chemical adsorption combustion | |
| CN105260800A (en) | Photovoltaic module temperature prediction method and device | |
| CN110543988A (en) | Photovoltaic short-term output prediction system and method based on XGboost algorithm | |
| CN104050517A (en) | Photovoltaic power generation forecasting method based on GRNN | |
| Zhang et al. | The application of slide technology in PV maximum power point tracking system | |
| CN110737198B (en) | Large-scale coal-fired power plant CO based on BP neural network 2 Capture system prediction control method | |
| CN113094860B (en) | Industrial control network flow modeling method based on attention mechanism | |
| CN108229721A (en) | The Forecasting Methodology of pyrolysis of coal product based on Speed Controlling Based on Improving BP Neural Network | |
| CN104865827B (en) | Oil pumping unit oil extraction optimization method based on multi-working-condition model | |
| CN103336867A (en) | Proton exchange membrane fuel cell model optimizing process method | |
| CN105281372A (en) | Multi-target multi-main-body distributed game optimization method for distributed energy sources | |
| CN109032117B (en) | ARMA model-based single-loop control system performance evaluation method | |
| CN108958031B (en) | Post combustion CO2Coordinated prediction control method for trapping coal-fired power generation system | |
| Sulaiman et al. | An artificial immune-based hybrid multi-layer feedforward neural network for predicting grid-connected photovoltaic system output | |
| Alqahtani et al. | Photovoltaic power forecasting model based on nonlinear system identification | |
| Vadivel et al. | Stabilization of photovoltaic systems with fuzzy event-triggered communication | |
| Tai et al. | Power prediction of photovoltaic power generation based on LSTM model with additive Attention mechanism | |
| Ding et al. | Forecast of pv power generation based on residual correction of markov chain | |
| CN105514975A (en) | Energy efficiency prediction method of photovoltaic power generation system |
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 |