CN110618706B - Multistage intelligent denitration on-line optimization control system based on data driving - Google Patents
Multistage intelligent denitration on-line optimization control system based on data driving Download PDFInfo
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- 238000005457 optimization Methods 0.000 title abstract description 27
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 abstract description 222
- 229910021529 ammonia Inorganic materials 0.000 abstract description 111
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 30
- 239000003546 flue gas Substances 0.000 description 30
- 238000002347 injection Methods 0.000 description 14
- 239000007924 injection Substances 0.000 description 14
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 12
- 238000000034 method Methods 0.000 description 11
- 230000001105 regulatory effect Effects 0.000 description 11
- 239000002245 particle Substances 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 9
- 239000003245 coal Substances 0.000 description 9
- 239000003054 catalyst Substances 0.000 description 5
- 241000267854 Brustiarius nox Species 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000005507 spraying Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003009 desulfurizing effect Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
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- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
- B01D53/34—Chemical or biological purification of waste gases
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- B01D53/8625—Nitrogen oxides
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- B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
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- B01D53/74—General processes for purification of waste gases; Apparatus or devices specially adapted therefor
- B01D53/86—Catalytic processes
- B01D53/90—Injecting reactants
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- G05D7/06—Control of flow characterised by the use of electric means
- G05D7/0617—Control of flow characterised by the use of electric means specially adapted for fluid materials
- G05D7/0629—Control of flow characterised by the use of electric means specially adapted for fluid materials characterised by the type of regulator means
- G05D7/0635—Control of flow characterised by the use of electric means specially adapted for fluid materials characterised by the type of regulator means by action on throttling means
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention relates to a multistage intelligent denitration on-line optimization control system based on data driving, which is used for controlling ammonia escape to be the lowest value on the premise of ensuring that NOx emission does not exceed a standard.
Description
Technical Field
The invention relates to the technical field of automatic control of thermal power plants, in particular to a multistage intelligent denitration on-line optimization control system based on data driving.
Background
The vast majority of power plants currently complete the ultra-low emission reconstruction work, and the NOx emission concentration must be controlled at 50mg/Nm according to relevant environmental regulations 3 Under the range, the NOx emission index is controlled to be 35mg/Nm according to the development trend 3 Within the range.
Due to various factors such as large delay inertia characteristic of the denitration process object, poor representativeness of measuring points in the operation process, small adjustment margin and the like, the denitration automatic adjustment quality of the coal-fired power plant is poor.
In denitration control, insufficient ammonia injection amount can cause NOx exceeding, and the environment-friendly assessment value is not qualified; if ammonia is sprayed excessively, ammonia can escape greatly, NH 3 The catalyst is adhered to the surface of the catalyst to block the pore canal of the catalyst, so that the catalyst is deactivated and the air preheater is blocked, and the catalyst is dispersed in the flue gas to corrode pipelines and downstream equipment, thereby endangering the operation safety of a unit.
At present, after the denitration ammonia injection of a plurality of units is automatically put into operation, the fluctuation of NOx is large, the oscillation is not easy to stabilize, and especially when the load is changed, the coal mill is started and stopped and the coal is blended for combustion, the dynamic deviation of NOx is large, and the development requirements of the intellectualization, informatization and digitization of a power plant can not be met.
Disclosure of Invention
Aiming at the situation, the invention aims to overcome the defects of the prior art and provide a multistage intelligent denitration on-line optimization control system based on data driving so as to control ammonia escape to be at a minimum value on the premise that NOx emission does not exceed the standard.
The technical scheme of the invention is as follows:
the multistage intelligent denitration on-line optimization control system based on data driving comprises five control stages, namely a rapid protection stage, an intelligent feedforward stage, an accurate control stage, an ammonia escape control stage and an on-line optimization stage, wherein an ammonia flow deviation value of total ammonia demand and actual ammonia flow is obtained through the five control stages, and the opening of an ammonia flow regulating valve is correspondingly regulated according to the ammonia flow deviation value, so that ammonia escape is controlled to be at a minimum value on the premise that NOx emission is not out of standard;
wherein:
fast protection stage: the ammonia demand is calculated through the NOx concentration value and the change rate of the clean flue gas to obtain the primary ammonia demand, so that the ammonia can be rapidly increased, the phenomenon of exceeding standard caused by sudden rise of the NOx concentration is prevented, and the NOx emission is controlled to be not exceeding standard;
intelligent feed-forward stage: the method comprises the steps that through taking the concentration of NOx at an SCR inlet on the A/B side of a boiler, the load of a unit, the coal quantity, the air quantity, the measured value of the concentration of the SCR outlet on the A/B side of the boiler and the set guide value of the concentration of NOx at the SCR outlet on the A/B side of the boiler as input variables, a feedforward control module formed by a linear variable parameter model structure outputs the secondary demand of ammonia, and when any variable changes, the flow of the ammonia is directly changed, so that the rapidity of NOx emission control is realized;
accurate control level: a set value prediction control loop and a cascade negative feedback prediction control loop;
the set value prediction control loop comprises a NOx concentration calculation module formed by a linear variable parameter model structure, a net flue gas NOx concentration set value is used as an input variable, the net flue gas NOx concentration set value is output as a boiler A/B side SCR outlet NOx concentration set guide value, the boiler A/B side SCR outlet NOx concentration set guide value is used as one variable of a feedforward control module in the intelligent feedforward stage, meanwhile, the concentration set guide value and a boiler A/B side SCR outlet NOx concentration correction value output by the ammonia slip control stage are summed to obtain a boiler A/B side SCR outlet NOx concentration set value, the concentration set value is compared with a boiler A/B side SCR outlet NOx concentration measured value to obtain a deviation value of the boiler A/B side SCR outlet NOx concentration set value and the measured value, and the deviation value is used as an input quantity of the cascade negative feedback prediction control loop;
the cascade negative feedback prediction control loop comprises a prediction controller based on a compact generalized prediction model, wherein the input of the prediction controller is the deviation value between a set value of the concentration of NOx at an SCR outlet at the A/B side of a boiler and a measured value, the output is the three-level demand of ammonia, the nitrogen oxide value at the outlet of a denitration reactor under the current ammonia injection amount can be obtained by calculation in advance through the prediction model, whether the ammonia injection amount is proper or not is pre-judged in advance, and then an adjustment recommended value of the ammonia injection amount is given;
summing the primary ammonia demand, the secondary ammonia demand and the tertiary ammonia demand to obtain total ammonia demand;
transmitting the deviation value of the total ammonia demand and the actual ammonia flow to a PID controller of a power plant denitration system to perform opening adjustment control of an ammonia flow adjusting valve;
ammonia slip control stage: correcting the NOx concentration setting guide value of the SCR outlet on the side A/B of the boiler according to the ammonia slip measured value to obtain a corrected value of the NOx concentration of the SCR outlet on the side A/B of the boiler, and improving the NOx emission concentration value of the SCR outlet to realize the purpose of reducing ammonia slip;
on-line optimization stage: and according to the historical operation data, the parameters of the other four control stages are updated on line through a particle swarm optimization algorithm.
Preferably, the starting conditions of the fast protection stage are:
a. NOx concentration value of clean flue gas>47mg/Nm 3 The method comprises the steps of carrying out a first treatment on the surface of the b. Rate of change of NOx concentration value of clean flue gas>5mg/Nm 3 /min；
When the conditions a and b are satisfied simultaneously, outputting the first-level ammonia demand, wherein the output value is as follows:
ammonia current flow value x (clean flue gas NOx current concentration value-47) x 5% xprotection action amplitude coefficient
The phenomenon of exceeding standard caused by sudden rise of the concentration of NOx can be prevented, and the emission of NOx is controlled to be not exceeding standard.
Preferably, the fast protection level has the highest priority.
Preferably, the specific structure of the feedforward control module of the intelligent feedforward stage is as follows:
assuming that the input of the system at the time t is u (t), the output is y (t), I and J are orders of the model autoregressive part and the moving average part, respectively, and ζ (t) is white noise, the linear parameter variation model of the system can be expressed as:
wherein p, q=1, 2, …, n; i=1, 2, … I 1 ；r＝1,2,…,m；j＝1,2,…,J r The method comprises the steps of carrying out a first treatment on the surface of the s=1, 2, …, S; m is the number of input variables, n is the number of output variables, and S is the number of selected reference working conditions;
and->
For inputting weight coefficient, ++>
Is an exponential factor polynomial, ++>
And->
For weight bias, ++>
Is a function->
A center point of the selected reference working condition point; delta is a scaling factor, delta > 0./>
In the control system, the number m of input variables is 6, the input variables are respectively the concentration u (1) of the NOx at the inlet of the SCR at the side A/B of the boiler, the unit load u (2), the coal quantity u (3), the air quantity u (4), the measured value u (5) of the concentration of the SCR at the outlet of the SCR at the side A/B of the boiler and the set guide value u (6) of the concentration of the NOx at the outlet of the SCR at the side A/B, the number n of output variables is 1, the number y (1) of output variables is the secondary demand of ammonia, the number S of reference working conditions is 7, and x is a time-varying parameter of the system, and the unit load is selected as the time-varying parameter;
preferably, in the cascade negative feedback prediction control loop, the structure of the prediction controller based on the compact generalized prediction model is as follows:
y(k+j)＝L j (q -1 )Δu(k-1)+y 0 (k+j)
wherein: j=1, Λ, P; p is the predicted step number; p is the predicted step number; k is the current moment; l is the number of control domain items; u is the system input; y0 is the contribution of past inputs and outputs to future time output
The nitrogen oxide value at the outlet of the denitration reactor under the current ammonia injection amount can be calculated in advance through a prediction model;
preferably, the starting conditions of the ammonia slip control stage are:
a. NOx concentration of clean flue gas<40mg/Nm 3 ；
b. The setting value of the ammonia spraying regulating valve is not changed within 10 min;
c. ammonia slip averages greater than 10ppm;
when the conditions a, B and c are met simultaneously, overlapping the correction value of the NOx concentration of the SCR outlet on the side A/B of the boiler with the set guide value of the NOx concentration of the SCR outlet on the side A/B of the boiler every 15min so as to achieve the purpose of improving the NOx concentration value and reduce ammonia slip;
after the ammonia escape control level intervenes the automatic control, if the average value of the NOx concentration value of the clean flue gas for 10min exceeds 45mg/Nm 3 The correction values are not superimposed.
In the five control stages, the rapid protection stage calculates the ammonia demand through the NOx concentration value and the change rate of the clean flue gas to obtain the ammonia primary demand, and can rapidly increase ammonia and prevent the exceeding of standard caused by sudden rise of the NOx concentration, thereby controlling the NOx emission not to exceed standard; the intelligent feedforward stage takes the concentration of NOx at an inlet of the SCR at the A/B side of the boiler, the load of a unit, the coal quantity, the air quantity, the measured value of the concentration of the outlet of the SCR at the A/B side of the boiler and the set guide value of the concentration of the NOx at the outlet of the SCR at the A/B side of the boiler as input variables to output the secondary demand quantity of ammonia, and when any variable changes, the flow rate of the ammonia is directly changed to realize the rapidity of the emission control of the NOx; the accurate control level obtains three-level ammonia demand, and finally obtains total ammonia demand, and the ammonia flow deviation value of the total ammonia demand and the actual ammonia flow is transmitted to a PID controller of a power plant denitration system to carry out opening adjustment control of an ammonia flow regulating valve; and on the premise that the NOx emission is not out of standard, controlling ammonia slip to be at a minimum.
Drawings
Fig. 1 is a schematic diagram of the multistage intelligent denitration on-line optimization control system of the invention, and the marked meanings in the diagram are shown in the following table:
FIG. 2 is an algorithm flow chart of particle swarm optimization algorithm (PSO) in the online optimization stage of the present invention.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to examples.
The invention comprises five control stages, namely a rapid protection stage, an intelligent feedforward stage, a precise control stage, an ammonia escape control stage and an online optimization stage, wherein the five control stages are used for obtaining an ammonia flow deviation value of total ammonia demand and actual ammonia flow, and correspondingly adjusting the opening of an ammonia flow regulating valve according to the ammonia flow deviation value so as to control ammonia escape to be at a minimum value on the premise that NOx emission is not out of standard;
wherein:
fast protection stage: the ammonia demand is calculated through the NOx concentration value and the change rate of the clean flue gas to obtain the primary ammonia demand, so that the ammonia can be rapidly increased, the phenomenon of exceeding standard caused by sudden rise of the NOx concentration is prevented, and the NOx emission is controlled to be not exceeding standard;
intelligent feed-forward stage: the method comprises the steps that through taking the concentration of NOx at an SCR inlet on the A/B side of a boiler, the load of a unit, the coal quantity, the air quantity, the measured value of the concentration of the SCR outlet on the A/B side of the boiler and the set guide value of the concentration of the NOx at the SCR outlet on the A/B side of the boiler as input variables, the ammonia secondary demand is output through a feedforward control module formed by a linear variable parameter model structure (linear parameter varying, LPV, namely a network model 1 in a schematic diagram), and when any variable changes, the ammonia flow is directly changed, so that the rapidity of NOx emission control is realized;
accurate control level: a set value prediction control loop and a cascade negative feedback prediction control loop;
the set value prediction control loop comprises a linear variable parameter model structure (linear parameter varying, LPV, namely a network model 2 in a schematic diagram) to form a NOx concentration calculation module, a net flue gas NOx concentration set value is taken as an input variable, a set guide value of the NOx concentration of an SCR outlet on the side A/B of a boiler is output, the set guide value of the NOx concentration of the SCR outlet on the side A/B of the boiler is taken as one variable of a feedforward control module in an intelligent feedforward stage, meanwhile, the set guide value of the NOx concentration and a correction value of the NOx concentration of the SCR outlet on the side A/B of the boiler output by an ammonia slip control stage are summed to obtain a set value of the NOx concentration of the SCR outlet on the side A/B of the boiler, the set value of the NOx concentration is compared with a measured value of the NOx concentration of the SCR outlet on the side A/B of the boiler to obtain a deviation value of the set value of the NOx concentration of the SCR outlet on the side A/B of the boiler, and the deviation value is taken as an input quantity of the cascade negative feedback prediction control loop;
the cascade negative feedback prediction control loop comprises a prediction controller based on a compact generalized prediction model, wherein the input of the prediction controller is the deviation value between a set value of the concentration of NOx at an SCR outlet at the A/B side of a boiler and a measured value, the output is the three-level demand of ammonia, the nitrogen oxide value at the outlet of a denitration reactor under the current ammonia injection amount can be obtained by calculation in advance through the prediction model, whether the ammonia injection amount is proper or not is pre-judged in advance, and then an adjustment recommended value of the ammonia injection amount is given;
summing the primary ammonia demand, the secondary ammonia demand and the tertiary ammonia demand to obtain total ammonia demand;
transmitting the deviation value of the total ammonia demand and the actual ammonia flow to a PID controller (an inner loop controller) of a power plant denitration system to carry out opening adjustment control of an ammonia flow regulating valve;
ammonia slip control stage: correcting the NOx concentration setting guide value of the SCR outlet on the side A/B of the boiler according to the ammonia slip measured value to obtain a corrected value of the NOx concentration of the SCR outlet on the side A/B of the boiler, and improving the NOx emission concentration value of the SCR outlet to realize the purpose of reducing ammonia slip;
on-line optimization stage: and according to the historical operation data, the parameters of the other four control stages are updated on line through a particle swarm optimization algorithm.
The starting conditions of the rapid protection stage are as follows:
a. NOx concentration value of clean flue gas>47mg/Nm 3 The method comprises the steps of carrying out a first treatment on the surface of the b. Rate of change of NOx concentration value of clean flue gas>5mg/Nm 3 /min；
When the conditions a and b are satisfied simultaneously, outputting the first-level ammonia demand, wherein the output value is as follows:
ammonia current flow value x (clean flue gas NOx current concentration value-47) x 5% xprotection action amplitude coefficient
The phenomenon of exceeding standard caused by sudden rise of the concentration of NOx can be prevented, and the emission of NOx is controlled to be not exceeding standard.
The rapid protection stage comprises a clean flue gas NOx concentration protection control module C (NOx) and is used for collecting a clean flue gas NOx concentration value and a clean flue gas NOx concentration value change rate, judging starting conditions of the rapid protection stage and sending output ammonia primary demand to an addition block ADD to be summed with ammonia secondary demand.
The NOx concentration of the clean flue gas is the NOx concentration after the desulfurizing tower and the NOx concentration at the inlet of the chimney, and the concentration value and the change rate of the concentration value can be directly collected through the original denitration system of the power plant.
The protection action amplitude coefficient is obtained through on-line optimization stage calculation, and the general value range is 0.6-1.5.
The fast protection level has the highest priority.
The specific structure of the feedforward control module of the intelligent feedforward stage is as follows:
assuming that the input of the system at the time t is u (t), the output is y (t), I and J are orders of the model autoregressive part and the moving average part, respectively, and ζ (t) is white noise, the linear parameter variation model of the system can be expressed as:
wherein p, q=1, 2, …, n; i=1, 2, … I 1 ；r＝1,2,…,m；j＝1,2,…,J r The method comprises the steps of carrying out a first treatment on the surface of the s=1, 2, …, S; m is the number of input variables, n is the number of output variables, and S is the number of selected reference working conditions;
and->
For inputting weight coefficient, ++>
Is an exponential factor polynomial, ++>
And->
For weight bias, ++>
Is a function->
A center point of the selected reference working condition point; delta is a scaling factor, delta > 0.
In the control system, the number m of input variables is 6, the input variables are respectively the concentration u (1) of the NOx at the inlet of the SCR at the side A/B of the boiler, the unit load u (2), the coal quantity u (3), the air quantity u (4), the measured value u (5) of the concentration of the SCR at the outlet of the SCR at the side A/B of the boiler and the set guide value u (6) of the concentration of the NOx at the outlet of the SCR at the side A/B, the number n of output variables is 1, the number y (1) of output variables is the secondary demand of ammonia, the number S of reference working conditions is 7, and x is a time-varying parameter of the system, and the unit load is selected as the time-varying parameter;
the time-varying parameters in the linear variable parameter model directly determine the time-varying characteristics of parameters in a system equation, a system is selected to be measurable in the mechanism analysis process of a modeling object, time variables closely related to the model parameter change are particularly important, whether the time-varying parameter x is selected appropriately relates to the rationality of the time-varying parameter model of the whole system, and for the denitration ammonia injection control system, the system variables are easily measured because the unit load corresponds to the fuel quantity and the air quantity, so that the time-varying parameter is set as the unit load (current load) in the feedforward control model.
The NOx concentration of the SCR inlet on the side A/B of the boiler, the load of a unit, the coal quantity, the air quantity and the concentration measured value of the SCR outlet on the side A/B of the boiler are directly collected through an original denitration system of a power plant, and the set instruction value of the NOx concentration of the SCR outlet on the side A/B is output by a prediction control loop of a set value of the NOx of clean flue gas in the accurate control stage.
Weight coefficient
And->
Weight bias +.>
And->
The scaling factor delta is calculated by an on-line optimization stage, delta typically being around 0.75.
And after the feedforward control module outputs the ammonia secondary demand, the ammonia secondary demand is sent to the adding block ADD and summed with the ammonia primary demand.
The NOx concentration calculation module in the set value prediction control loop and the feedforward control module of the intelligent feedforward stage both adopt linear variable parameter model structures, so the model structures are the same, and the difference is that the number m of input variables of the NOx concentration calculation module is 1, the input variables are the NOx concentration set value of the clean flue gas, the number n of output variables is 1, the output is the SCR outlet NOx concentration set guide value of the side A/B of the boiler, the number S of reference working conditions is 7, and the NOx concentration set value of the clean flue gas is selected as a time-varying parameter;
the set value of the NOx concentration of the purified flue gas is generally 25-45, and the national environmental protection regulation is 50 out of standard.
Weight coefficient
And->
Weight bias +.>
And->
The scaling factor delta is obtained through on-line optimization stage calculation;
in the cascade negative feedback prediction control loop, the structure of a prediction controller based on a compact generalized prediction model is as follows:
y(k+j)＝L j (q -1 )Δu(k-1)+y 0 (k+j)
wherein: j=1, Λ, P; p is the predicted step number; k is the current moment; l is the number of control domain items; u is the system input; y is 0 Contributions of past inputs and outputs to future time outputs; the nitrogen oxide value at the outlet of the denitration reactor under the current ammonia injection amount can be calculated in advance through a prediction model.
The set system k is input as a deviation value u (t) between a set value of the concentration of NOx at an SCR outlet of the boiler A/B side and a measured value, and output as three-level ammonia demand y (t), and the set system k comprises:
A(q -1 )y(k)＝B(q -1 )u(k-1) (10)
the following formula (10) is deformed:
factors can be factorized using long division
To decompose, if j items before decomposition are found
Wherein:
is a quotient polynomial->
J=1, Λp, which is the remainder polynomial.
Equation (12) is in fact the diphentine equation.
Substituting formula (12) into formula (11), and using formula (10) to obtain:
y(k)＝E j (q -1 )B(q -1 )Δu(k-1)+q -j F j y(k) (13)
the predicted output at the future j time instant can be expressed as:
y(k+j)＝q j E j (q -1 )B(q -1 )Δu(k-1)+F j y(k) (14)
e using "1" as the dividing polynomial j (q -1 )B(q -1 ) The long division decomposition can be performed:
wherein:
corresponding to present and future control input parts;
Then there are:
y(k+j)＝q j G j (q -1 )Δu(k-1)+H j (q -1 )Δu(k-1)+F j y(k) (16)
wherein: j=1, Λ, P; p is the number of predicted steps.
As can be seen from formula (16): predicting output of a future time systemThe key is to determine the operator polynomial G j (q -1 )、H j (q -1 ) And F j (q -1 ) Is a calculation method of (a).
To determine the coefficient G in the predictive relation (16) j (q -1 )、H j (q -1 ) And F j (q -1 ) The following method can be adopted:
obtained according to formula (12):
formula (17) may be changed to:
F j (q -1 )＝q j [E j+1 (q -1 )-E j (q -1 )]A(q -1 )Δ+q -1 F j+1 (q -1 ) (18)
because E is j (q -1 )、E j+1 (q -1 ) The j-th and (j+1) -th order quotient polynomials, respectively, so:
e j+1,j ＝q j [E j+1 (q -1 )-E j (q -1 )] (19)
and because of a 0 =1, q on both sides of comparative formula (18) 0 The secondary term coefficient is obtained:
e j+1,j ＝f j0 (20)
formula (20) is substituted into formula (19):
f j0 ＝q j [E j+1 (q -1 )-E j (q -1 )] (21)
the formula (21) is obtained by substituting the formula (18):
F j+1 (q -1 )＝q[F j (q -1 )-A(q -1 )Δf j0 ]
or F j+1 (q -1 )＝q[F j (q -1 )-f j0 ]-f j0 {q[A(q -1 )-1]-A(q -1 )}
F is obtained by replacing subscript (j+1) in the above formula with subscript j j (q -1 ) Is of the recurrence of (1)：
F j (q -1 )＝q[F j-1 (q -1 )-f j-1,0 ]-f j-1,0 {q[A(q -1 )-1]-A(q -1 )} (22)
Multiplying B (q) on both sides of formula (21) -1 ) And is obtained by using the formula (15):
f j0 B(q -1 )+H j (q -1 )＝q j [G j+1 (q -1 )-G j (q -1 )]+q -1 H j+1 (q -1 ) (23)
due to G j (q -1 )、G j+1 (q -1 ) Respectively E j (q -1 )、B(q -1 ) Regarding the j-th and (j+1) -th order quotient polynomials of the "1" polynomial, the first term to the right of the equation ((23) equation) is constant, that is:
g j+1,j ＝q j [G j+1 (q -1 )-G j (q -1 )] (24)
let (23) be equal to the left
I.e.
W j (q -1 )＝f j0 B(q -1 )+H j (q -1 ) (25)
Formula (24) and formula (25) are substituted into formula (23):
W j (q -1 )＝g j+1,j +q -1 H j+1 (q -1 ) (26)
comparison of (26) two sides q 0 The coefficients are obtained:
g j+1,j ＝w j0 (27)
formula (27) is substituted into formula (26) to obtain:
H j+1 (q -1 )＝q[W j (q -1 )-w j0 ] (28)
the subscript (j-1) is substituted for the subscript j in formula (25) to obtain W j-1 (q -1 ) Is as follows:
W j-1 (q -1 )＝f j-1,0 B(q -1 )+H j-1 (q -1 ) (29)
replacing subscript (j+1) of formula (28) with subscript j, H can be obtained j (q -1 ) Is as follows:
H j (q -1 )＝q[W j-1 (q -1 )-w j-1,0 ] (30)
formula (24) may be written as:
G j+1 (q -1 )＝G j (q -1 )+q -j w j,0 (31)
the subscript j is used to replace the subscript (j+1) in the above formula to obtain G j (q -1 ) Is as follows:
G j (q -1 )＝G j-1 (q -1 )+q -(j-1) w j-1,0 (32)
or (b)
Comparison type (33)
It can be seen that:
g ji ＝w i-1,0 (34)
formula (16) can be rewritten as:
y(k+j)＝[q j G j (q -1 )]Δu(k-1)+y 0 (k+j) (35)
wherein: y is 0 (k+j)＝H j (q -1 )Δu(k-1)+F j (q -1 ) y (k) is the contribution of past inputs and outputs to the future (k+j) instant output, j=1, Λp.
The starting conditions of the recurrence formula are: g 0 (q -1 )＝0，H 0 (q -1 )＝0，F 0 (q -1 )＝1。
The first term coefficient polynomial to the right of the equal sign of equation (35) can be determined as follows:
if the number of selected control field entries is M, equation (36) should be rewritten as:
thus, formula (35) is rewritable as:
y(k+j)＝L j (q -1 )Δu(k-1)+y 0 (k+j) (38)
wherein: j=1, Λp.
Equation (38) is a relation for predicting the system output at a future time point used by the closed-loop prediction module.
The starting conditions of the ammonia slip control stage are:
a. NOx concentration of clean flue gas<40mg/Nm 3 ；
b. The setting value of the ammonia spraying regulating valve is not changed within 10 min; the ammonia injection regulating valve setting value is a value (required and expected) given by a running operator;
c. ammonia slip averages greater than 10ppm;
when the conditions a, B and c are met simultaneously, overlapping the correction value of the NOx concentration of the SCR outlet on the side A/B of the boiler with the set guide value of the NOx concentration of the SCR outlet on the side A/B of the boiler every 15min so as to achieve the purpose of improving the NOx concentration value and reduce ammonia slip;
after the ammonia escape control level intervenes the automatic control, if the average value of the NOx concentration value of the clean flue gas for 10min exceeds 45mg/Nm 3 The correction value is not overlapped any more;
if the following occurs:
1) The operator can also cut off the automatic control manually;
2) The operator modifies the ammonia injection regulating valve setting value;
the boiler a/B side SCR outlet NOx concentration correction value is reset to zero.
The ammonia slip control stage comprises an ammonia slip controller C (NH 3) which is used for collecting a net flue gas NOx concentration value, an ammonia injection regulating valve set value and an ammonia slip average value, judging starting conditions of the ammonia slip control stage, and sending an output correction value to an addition block ADD to be summed with a boiler A/B side SCR outlet NOx concentration set guiding value.
The online optimization stage operates data according to the following histories: the method comprises the following steps of (1) carrying out online updating of relevant parameters of the other four control stages through a particle swarm optimization algorithm (Particle Swarm Optimization, PSO), wherein the relevant parameters comprise the concentration of NOx at an SCR inlet of a boiler A/B side, the concentration of NOx at an SCR outlet of the boiler A/B side, the concentration of net flue gas NOx, the load of a unit, the coal quantity, the air quantity, the ammonia flow and the ammonia slip quantity, and the PSO algorithm is described as follows:
assuming a population size of N, the coordinate position of particle i (i=1, 2, l, N) in d-dimensional space can be expressed as x i ＝(x i1 ,x i2 ,L,x id ) The velocity of movement of the particles is defined as the distance the particles move during each iteration, v i ＝(v i1 ,v i2 ,L,v id ) And (3) representing. At the kth iteration, the flight velocity v of particle i in the d-th dimension subspace id The adjustment update is performed according to the following formula:
the particles adjust their position by:
wherein the variable x represents the relevant on-line optimization parameters of the four control stages respectively;
for the quick protection level, the online optimization parameter x is a protection action amplitude coefficient;
for the intelligent feedforward stage, on-line optimization parameters x are respectively input weight coefficients for corresponding linear variable parameter models
And->
Weight bias +.>
And->
Scaling factor delta;
for a precise control stage, on-line optimization parameters x are respectively input weight coefficients for corresponding linear variable parameter models
And->
Weight bias +.>
And->
Scaling factor delta, and proportional and integral factors of the PID controller;
for the ammonia slip control stage, the online optimization parameter x is a correction value of the NOx concentration of the SCR outlet on the side A/B of the boiler, and the initial value is +3.
PSO algorithm flow chart, as shown in FIG. 2.
The hardware part of the online optimization control system is connected with a computer of a power plant denitration system by adopting a PLC controller, and the system has good technical effects after actual operation, and the 660MW unit implementation effects are as follows:
1) Short term precision control
The load of the unit is 470MW to 516MW, the set value of the concentration of NOx at the SCR outlet on the A/B side of the boiler is changed to 41-35-39-43, and the maximum dynamic deviation is achieved<5mg/Nm 3 Steady state deviation<2mg/Nm 3 Adjusting the time to 5-8min.
2) Ammonia slip control
And the ammonia slip control level automatic intervention regulating system automatically regulates the correction value according to the ammonia slip mean value, and reduces the ammonia slip by 55.6% in a range with smaller influence of NOx at the SCR outlet of the side A/B of the boiler.
3) Mid-term stability control
The 8h running condition is that the average value of the NOx concentration of the clean flue gas is 40mg/Nm 3 (37-46), boiler A/B side SCR inlet NOx (292-470) mg/Nm 3 Maximum rate of change 20mg/Nm 3 And/min, the load change is 330-484 MW.
4) Medium and long term stability control
The running condition of 7 days is that the average value of the NOx concentration of the clean flue gas is 41mg/Nm 3 (35-47), boiler A/B side SCR inlet NOx (290-610) mg/Nm 3 The load of the unit changes 330-484 MW, and the instantaneous and average values exceed the standard 0 times.
5) Long term stability control
The running condition of 14 days is that the average value of the NOx concentration of the clean flue gas is 41mg/Nm 3 Boiler A/B side SCR inlet NOx (270-650) mg/Nm 3 The load of the unit changes by 280-620 MW, and the instantaneous and average values exceed the standard 0 times.
Claims (4)
1. The multistage intelligent denitration on-line optimization control system based on data driving is characterized by comprising five control stages, namely a rapid protection stage, an intelligent feedforward stage, an accurate control stage, an ammonia escape control stage and an on-line optimization stage, wherein the five control stages are used for obtaining an ammonia flow deviation value of total ammonia demand and actual ammonia flow, and correspondingly adjusting the opening of an ammonia flow regulating valve according to the ammonia flow deviation value so as to control ammonia escape to be at a minimum value on the premise that NOx emission is not out of standard;
wherein:
fast protection stage: the ammonia demand is calculated through the NOx concentration value and the change rate of the clean flue gas to obtain the primary ammonia demand, so that the ammonia can be rapidly increased, the phenomenon of exceeding standard caused by sudden rise of the NOx concentration is prevented, and the NOx emission is controlled to be not exceeding standard;
intelligent feed-forward stage: the method comprises the steps that through taking the concentration of NOx at an SCR inlet on the A/B side of a boiler, the load of a unit, the coal quantity, the air quantity, the measured value of the concentration of the SCR outlet on the A/B side of the boiler and the set guide value of the concentration of NOx at the SCR outlet on the A/B side of the boiler as input variables, a feedforward control module formed by a linear variable parameter model structure outputs the secondary demand of ammonia, and when any variable changes, the flow of the ammonia is directly changed, so that the rapidity of NOx emission control is realized;
accurate control level: a set value prediction control loop and a cascade negative feedback prediction control loop;
the set value prediction control loop comprises a NOx concentration calculation module formed by a linear variable parameter model structure, a net flue gas NOx concentration set value is used as an input variable, the net flue gas NOx concentration set value is output as a boiler A/B side SCR outlet NOx concentration set guide value, the boiler A/B side SCR outlet NOx concentration set guide value is used as one variable of a feedforward control module in the intelligent feedforward stage, meanwhile, the concentration set guide value and a boiler A/B side SCR outlet NOx concentration correction value output by the ammonia slip control stage are summed to obtain a boiler A/B side SCR outlet NOx concentration set value, the concentration set value is compared with a boiler A/B side SCR outlet NOx concentration measured value to obtain a deviation value of the boiler A/B side SCR outlet NOx concentration set value and the measured value, and the deviation value is used as an input quantity of the cascade negative feedback prediction control loop;
the cascade negative feedback prediction control loop comprises a prediction controller based on a compact generalized prediction model, wherein the input of the prediction controller is the deviation value between a set value of the concentration of NOx at an SCR outlet at the A/B side of a boiler and a measured value, the output is the three-level demand of ammonia, the nitrogen oxide value at the outlet of a denitration reactor under the current ammonia injection amount can be obtained by calculation in advance through the prediction model, whether the ammonia injection amount is proper or not is pre-judged in advance, and then an adjustment recommended value of the ammonia injection amount is given;
summing the primary ammonia demand, the secondary ammonia demand and the tertiary ammonia demand to obtain total ammonia demand;
transmitting the deviation value of the total ammonia demand and the actual ammonia flow to a PID controller of a power plant denitration system to perform opening adjustment control of an ammonia flow adjusting valve;
ammonia slip control stage: correcting the NOx concentration setting guide value of the SCR outlet on the side A/B of the boiler according to the ammonia slip measured value to obtain a corrected value of the NOx concentration of the SCR outlet on the side A/B of the boiler, and improving the NOx emission concentration value of the SCR outlet to realize the purpose of reducing ammonia slip;
on-line optimization stage: according to the historical operation data, the parameters of the other four control stages are updated on line through a particle swarm optimization algorithm;
the specific structure of the feedforward control module of the intelligent feedforward stage is as follows:
assuming that the input of the system at the time t is u (t), the output is y (t), I and J are orders of the model autoregressive part and the moving average part, respectively, and ζ (t) is white noise, the linear parameter variation model of the system can be expressed as:
wherein p, q=1, 2, …, n; i=1, 2, … I 1 ；r＝1,2,…,m；j＝1,2,…,J r The method comprises the steps of carrying out a first treatment on the surface of the s=1, 2, …, S; m is the number of input variables, n is the number of output variables, and S is the number of selected reference working conditions;
and->
For inputting weight coefficient, ++>
Is an exponential factor polynomial, ++>
And->
For weight bias, ++>
Is a function->
A center point of the selected reference working condition point; delta is a scaling factor, delta > 0;
in the control system, the number m of input variables is 6, the input variables are respectively the concentration u (1) of the NOx at the inlet of the SCR at the side A/B of the boiler, the unit load u (2), the coal quantity u (3), the air quantity u (4), the measured value u (5) of the concentration of the SCR at the outlet of the SCR at the side A/B of the boiler and the set guide value u (6) of the concentration of the NOx at the outlet of the SCR at the side A/B, the number n of output variables is 1, the y (1) of output is the secondary demand of ammonia, the number S of reference working conditions is 7, and x is a time-varying parameter of the system, and the unit load is selected as the time-varying parameter;
in the cascade negative feedback prediction control loop, the structure of a prediction controller based on a compact generalized prediction model is as follows:
y(k+j)＝L j (q -1 )Δu(k-1)+y 0 (k+j)
wherein: j=1, Λ, P; p is the predicted step number; p is the predicted step number; k is the current moment; l is the number of control domain items; u is the system input; y0 is the contribution of past inputs and outputs to future time output
The nitrogen oxide value at the outlet of the denitration reactor under the current ammonia injection amount can be obtained in advance through calculation by a prediction model.
2. The data-driven multistage intelligent denitration on-line optimization control system according to claim 1, wherein the starting conditions of the rapid protection stage are as follows:
a. NOx concentration value of clean flue gas>47mg/Nm 3 The method comprises the steps of carrying out a first treatment on the surface of the b. Rate of change of NOx concentration value of clean flue gas>5mg/Nm 3 /min；
When the conditions a and b are satisfied simultaneously, outputting the first-level ammonia demand, wherein the output value is as follows:
ammonia current flow value x (clean flue gas NOx current concentration value-47) x 5% xprotection action amplitude coefficient
The phenomenon of exceeding standard caused by sudden rise of the concentration of NOx can be prevented, and the emission of NOx is controlled to be not exceeding standard.
3. The data-driven multistage intelligent denitration on-line optimization control system according to claim 1 or 2, wherein the priority of the rapid protection stage is highest.
4. The data-driven multistage intelligent denitration on-line optimization control system according to claim 1, wherein the starting conditions of the ammonia slip control stage are as follows:
a. NOx concentration of clean flue gas<40mg/Nm 3 ；
b. The setting value of the ammonia spraying regulating valve is not changed within 10 min;
c. ammonia slip averages greater than 10ppm;
when the conditions a, B and c are met simultaneously, overlapping the correction value of the NOx concentration of the SCR outlet on the side A/B of the boiler with the set guide value of the NOx concentration of the SCR outlet on the side A/B of the boiler every 15min so as to achieve the purpose of improving the NOx concentration value and reduce ammonia slip;
after the ammonia escape control level intervenes the automatic control, if the average value of the NOx concentration value of the clean flue gas for 10min exceeds 45mg/Nm 3 The correction values are not superimposed.
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| CN119668089B (en) * | 2025-02-19 | 2025-06-13 | 江西江投能源技术研究有限公司 | Cascade control method of chemical ammonia addition based on improved particle swarm optimization feedforward model |
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