CN105116855A - Optimal control method for flue gas circulating fluidized bed desulphurization - Google Patents
Optimal control method for flue gas circulating fluidized bed desulphurization Download PDFInfo
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- CN105116855A CN105116855A CN201510435036.7A CN201510435036A CN105116855A CN 105116855 A CN105116855 A CN 105116855A CN 201510435036 A CN201510435036 A CN 201510435036A CN 105116855 A CN105116855 A CN 105116855A
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- desulfurizer
- flue gas
- lime hydrate
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 67
- 239000003546 flue gas Substances 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 30
- AXCZMVOFGPJBDE-UHFFFAOYSA-L calcium dihydroxide Chemical compound [OH-].[OH-].[Ca+2] AXCZMVOFGPJBDE-UHFFFAOYSA-L 0.000 claims abstract description 108
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims abstract description 58
- 238000004891 communication Methods 0.000 claims abstract description 41
- 238000006477 desulfuration reaction Methods 0.000 claims abstract description 39
- 230000023556 desulfurization Effects 0.000 claims abstract description 32
- 229910002092 carbon dioxide Inorganic materials 0.000 claims abstract description 29
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims abstract description 28
- 239000001569 carbon dioxide Substances 0.000 claims abstract description 25
- 239000000920 calcium hydroxide Substances 0.000 claims abstract description 20
- 229910001861 calcium hydroxide Inorganic materials 0.000 claims abstract description 20
- 235000011116 calcium hydroxide Nutrition 0.000 claims abstract description 20
- 239000007789 gas Substances 0.000 claims description 36
- 239000003245 coal Substances 0.000 claims description 31
- 238000013459 approach Methods 0.000 claims description 29
- 238000006243 chemical reaction Methods 0.000 claims description 21
- 230000003009 desulfurizing effect Effects 0.000 claims description 17
- 239000000446 fuel Substances 0.000 claims description 15
- 239000003795 chemical substances by application Substances 0.000 claims description 12
- 239000002245 particle Substances 0.000 claims description 11
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 8
- 230000007423 decrease Effects 0.000 claims description 7
- 238000013461 design Methods 0.000 claims description 7
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- 238000010521 absorption reaction Methods 0.000 claims description 6
- 230000009897 systematic effect Effects 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 230000033228 biological regulation Effects 0.000 claims description 5
- 230000001276 controlling effect Effects 0.000 claims description 5
- 230000007246 mechanism Effects 0.000 claims description 5
- 239000000779 smoke Substances 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 3
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 3
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000001133 acceleration Effects 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 229910052799 carbon Inorganic materials 0.000 claims description 3
- 238000002485 combustion reaction Methods 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 239000010881 fly ash Substances 0.000 claims description 3
- 239000008187 granular material Substances 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 239000005431 greenhouse gas Substances 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 239000001257 hydrogen Substances 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- HNBFUFIYQWYCDM-UHFFFAOYSA-N oxygen(2-) sulfane titanium(4+) Chemical compound [O--].[O--].S.[Ti+4] HNBFUFIYQWYCDM-UHFFFAOYSA-N 0.000 claims description 3
- 229910052698 phosphorus Inorganic materials 0.000 claims description 3
- 229910052717 sulfur Inorganic materials 0.000 claims description 3
- 239000011593 sulfur Substances 0.000 claims description 3
- 239000002699 waste material Substances 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 description 10
- 238000004378 air conditioning Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 241001269238 Data Species 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 3
- 235000011941 Tilia x europaea Nutrition 0.000 description 3
- 239000004571 lime Substances 0.000 description 3
- 239000006096 absorbing agent Substances 0.000 description 2
- 239000011575 calcium Substances 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 238000004868 gas analysis Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003134 recirculating effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000002745 absorbent Effects 0.000 description 1
- 239000002250 absorbent Substances 0.000 description 1
- 239000002956 ash Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- JGIATAMCQXIDNZ-UHFFFAOYSA-N calcium sulfide Chemical compound [Ca]=S JGIATAMCQXIDNZ-UHFFFAOYSA-N 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000003517 fume Substances 0.000 description 1
- 150000004677 hydrates Chemical class 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
- VWDWKYIASSYTQR-UHFFFAOYSA-N sodium nitrate Chemical compound [Na+].[O-][N+]([O-])=O VWDWKYIASSYTQR-UHFFFAOYSA-N 0.000 description 1
- 238000004073 vulcanization 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Treating Waste Gases (AREA)
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
Abstract
The invention discloses an optimal control method for flue gas circulating fluidized bed desulphurization in the technical field of thermal power plant desulphurization. A DCS system is connected with a flue gas circulating fluidized bed and a PLC-based sulfur dioxide optimal control system. The sulfur dioxide optimal control system is formed as follows: a sulfur dioxide prediction module and a carbon dioxide calculation module are respectively connected with a data communication module and a hydrated lime control module, and the hydrated lime control module is connected with the data communication module. The data communication module and the DCS system exchange data. According to the invention, the content of CO2 in the flue gas at the inlet of a desulfurization tower is calculated through an SO2 prediction model. The feeding amount of hydrated lime can be adjusted timely, dynamically and accurately. Excessive emission of SO2 and the waste of hydrated lime and resources are avoided fundamentally, and CO2 emission is reduced to a certain extent. The method is of high reference value in engineering.
Description
Technical field
The invention belongs to fired power generating unit technical field of desulfurization, be specifically related to a kind of optimal control method of flue gas circulating fluidized bed desulfurization.
Background technology
Circulating Fluidized Bed Flue Gas Desulfurization Technology is a kind of Novel desulphurization technique of the exploitation eighties in 20th century, and it originates from cement and metallurgical roasting technique, is progressively applied to power station desulfurization and flue gas of refuse burning process field afterwards.It is based on recirculating fluidized bed, by the repeatedly circulation of absorbing agent, the time of extension of absorbent and smoke contacts, greatly improves the utilization factor of absorbing agent, can under comparatively low calcium-sulphur ratio, close to or reach the desulfuration efficiency of wet processing.Circulating fluidized bed dry desulfur technology, system is simple, speculates relatively low, causes the attention of more and more national gradually, and this technology is in the application of current business, a kind of flue gas desulfurization technique that single tower processing power is maximum, desulfurization comprehensive benefit is superior.
At present, although circulating fluidized bed dry desulfur technology widely applies power plant, and installed capacity constantly increases, and this technology also exists some problems in actual motion and operating process.Recirculating fluidized bed control system is a multivariate, multitask, and has the Complex Nonlinear System of time variation, coupling and randomness, under noise, load disturbance and some other changes in environmental conditions, controls difficulty larger.Wherein, the outstanding problem main manifestations in desulfurization is as follows: first, and the current concrete grammar that regulates of sulfur dioxide concentration to discharge is: adopt flue gas analysis instrument to measure the SO of desulfurizer entrance and exit
2concentration, with outlet and entrance SO
2concentration, respectively as main regulation amount and auxiliary adjustment amount, if exceed emission concentration standard, then regulates the feeding coal of lime hydrate, by that analogy, then carries out measuring, regulates, measures, until the concentration of emission of sulphuric dioxide meets emission standard.Obviously, there is larger delay in gas analysis instrument output signal, poor as directly used the vibration that may cause control loop for real-time control loop, can not as direct regulating parameter; Secondly, what boiler load fluctuate the exhaust gas volumn brought significantly changes also to measure and adjustment causes larger difficulty; Again, because the requirement of country to fume emission index is more and more stricter, if reach higher desulfuration efficiency, just need to add a large amount of desulfurizing agents, a large amount of lime hydrates can rest in desulfurizer, affects the desulfurizing agent circulation in desulfurizer, easily there is the problem of couch bed, and lime hydrate consumption is large, and Ca/S mol ratio can be made to increase, operating cost increases.In addition, excessive desulfurizing agent can bring again the very large additional grey quantity of slag and physical thermal loss, increases desulfurizer hearth abrasion simultaneously, affects Desulfurization, and then increase cost, affect the economy of desulfurization.
The present invention proposes a kind of optimal control method of flue gas circulating fluidized bed desulfurization, establish SO
2the hard measurement forecast model of concentration of emission, overcomes the hysteresis quality of lime hydrate control system, because lime hydrate control system postpones the SO that brings when solving lifting load
2discharge the problem exceeded standard; Meanwhile, the present invention utilizes desulfurizer entrance CO
2amount assist and control the feeding coal of lime hydrate, lime hydrate is controlled more accurate, decreases the waste of lime hydrate resource, certain degree decreases CO
2discharge capacity; The hard measurement forecast model that the present invention proposes has higher accuracy, can realize controlling more accurately to lime hydrate quantity delivered, engineering has higher reference.
Summary of the invention
The object of the invention is to the optimal control method proposing a kind of flue gas circulating fluidized bed desulfurization, DCS system connects flue gas circulating fluidized bed and based on the sulphuric dioxide Optimal Control System of PLC respectively, and described optimization system comprises: data communication module, sulphuric dioxide prediction module, carbon dioxide calculate module and lime hydrate control module.Wherein, data communication module and DCS system exchange data mutually, and sulphuric dioxide prediction module and carbon dioxide calculate module connecting communication module and lime hydrate control module respectively, lime hydrate control module connection data communication module.It is characterized in that, described optimal control method comprises as lower part:
1) data communication module exchanges data by ModBus communications protocol and DCS system;
2) sulphuric dioxide prediction module reads real-time running data by data communication module from DCS controller: desulfurizer gas approach sulfur dioxide concentration, desulfurizer gas approach flue gas volume, calcium hydroxide particle footpath, desulfurizer exiting flue gas flow velocity and lime hydrate feeding coal, uses the concentration of slaked lime desulfurizing mechanism model prediction desulfurizer outlet sulphuric dioxide;
3) carbon dioxide calculates module and reads real-time running data by data communication module from DCS controller: the volume of desulfurizer gas approach flue gas, sulphuric dioxide and carbon monoxide, enter boiler total blast volume and coal-supplying amount, calculates the volume of desulfurizer gas approach carbon dioxide.
4) lime hydrate control module is in conjunction with sulphuric dioxide predicted value, carbon dioxide calculating value, desulfurizer fire box temperature, desulfurizer furnace pressure and the real-time running data of lime hydrate feeding coal that read by data communication module, through fuzzy PID regulation, the instruction of lime hydrate feeding coal is sent to DCS controller by data communication module, thus instruction is issued field apparatus by DCS system.Wherein, sulphuric dioxide predicted value, carbon dioxide calculating value and desulfurizer entrance titanium dioxide sulfur number are as given instruction, and desulfurizer fire box temperature and desulfurizer furnace pressure are as feedforward instruction.
Described data communication module reads real time data and comprises: desulfurizer gas approach flue gas volume V
y, m
3/ s; Desulfurizer gas approach sulphuric dioxide volume
m
3/ s; Desulfurizer gas approach carbon monoxide volume V
cO, m
3/ s; Enter the total blast volume V of boiler
f, m
3/ s; Enter the coal-supplying amount B of boiler
v, kg/s; Desulfurizer gas approach sulfur dioxide concentration
mg/m
3; Calcium hydroxide particle footpath r
s, mm; Desulfurizer exiting flue gas flow velocity u
f, m/s; Lime hydrate feeding coal, kg/s.The real time data that communication module reads adopts Vladimir Romanovskiy criterion to reject abnormal data, and concrete steps are as follows:
Step 2.1: determine suspicious data X successively
j, j ∈ [1, n], n are gathered data amount check;
Step 2.2: then calculate the ordered series of numbers mean value deleted after suspicious numerical value
and standard deviation
wherein X
ifor normal data;
Step 2.3: the residual error calculating suspicious data:
Step 2.4: if | ε
j| >K σ, then suspicious data is rejected, if | ε
j| <K σ, then can continue to use suspicious data, wherein K is test coefficient;
The optimal control method of described flue gas circulating fluidized bed desulfurization, is characterized in that, sulphuric dioxide prediction module uses the concentration of slaked lime desulfurizing mechanism model prediction desulfurizer outlet sulphuric dioxide, comprises the following steps:
Step 3.1: according to conversion ratio and the relation of time
calculate the conversion ratio of desulfurizing agent lime hydrate, wherein: X
bfor the conversion ratio of lime hydrate, %; The reaction of desulfurizing agent in the absorption tower complete time is
for hydrated lime particle density, kg/m
3;
for lime hydrate molal weight, kg/mol; r
sfor hydrated lime particle radius, mm;
for SO in desulfurizer
2partial pressure, Pa; T is temperature in desulfurizer, K; D
efffor SO
2effective diffusion cofficient in product layer, gets 4 × 10
?10cm
2/ s; R is universal gas constant, gets 8.314J/ (molK); The residence time of desulfurizing agent in absorption tower is
ρ
yfor smoke density, kg/m
3; u
ffor desulfurizer exiting flue gas flow velocity, m/s; d
cfor desulfurizer outlet fly ash granule diameter, mm; H is desulfurizer height, m; μ is gas motion viscosity, gets 45.6 × 10
?6pa.s; G is acceleration of gravity, gets 9.8N/kg.
Step 3.2: according to lime hydrate reaction model in desulfurizer
obtain the reaction rate of lime hydrate.Wherein,
for the reaction rate of lime hydrate, kg/s;
for lime hydrate feeding coal, kg/s.
Step 3.3: according to desulfurizer outlet SO
2the forecast model of concentration of emission
calculate SO
2prediction concentrations, wherein,
for SO
2prediction concentrations, mg/m
3;
for desulfurizer entrance SO
2generating rate, mg/s; V
yfor desulfurizer inlet flue gas volume, m
3/ s;
for SO
2molal weight, g/mol; V is the volume of desulfurizer, m
3; α is desulfurizer air leakage coefficient, %.
The optimal control method of described flue gas circulating fluidized bed desulfurization, is characterized in that, carbon dioxide calculates module and uses
calculate the carbon dioxide volume in desulfurizer inlet flue gas, wherein, V
yfor gas approach flue gas volume, m
3/ s;
for gas approach sulphuric dioxide volume, m
3/ s; V
cOfor gas approach carbon monoxide volume, m
3/ s; V
ffor entering the total blast volume of boiler, m
3/ s; B
vfor entering the coal-supplying amount of boiler, kg/s, greenhouse gas is
theoretical air requirement required for every kilogram of coal combustion is V
t=0.089 (C
ar+ 0.375S
ar)+0.265H
ar-0.033O
ar, %; C
arfor fuel as received basis carbon content, %; H
arfor fuel as received basis hydrogen richness, %; O
arfor fuel as received basis oxygen content, %; N
arfor fuel as received basis nitrogen content, %; S
arfor fuel as received basis sulfur content, %.
The optimal control method of described flue gas circulating fluidized bed desulfurization, is characterized in that, in lime hydrate control module, fuzzy controller arranges and comprises the following steps:
Step 5.1: fuzzy controller is that the expression formula of error e and error rate ec is as follows using error e and error rate ec as input:
ec(k)＝e(k)-e(k-1)(2)
Wherein,
for lime hydrate flow instruction;
for actual lime hydrate flow value; E (k) is the deviation between command value and actual value; Ec (k) is the deviation ratio between command value and actual value; E (k-1) is the deviation between a upper moment command value and actual value.
Step 5.2: according to systematic error e and error rate ec variation range, use suitable fuzzy reasoning table, obtain k
p, k
i, k
dthe fuzzy control table that three parameters are adjusted respectively.
Step 5.3: systematic error e and error rate ec variation range are defined as the domain in fuzzy set, e, ec={ ?6 ， ?5 ， ?4 ?3 ， ?2 ， ?1,0,1,2,3,4,5,6}, its fuzzy subset is e, ec={NB, NM, NS, O, PS, PM, PB}, subset elements represents negative large respectively, in negative, negative little, zero, just little, center, honest.If e, ec and k
p, k
i, k
dnormal Distribution, therefore can obtain each fuzzy subset's degree of membership.
Step 5.4: according to each fuzzy subset's degree of membership assignment table and each parameter fuzzy Controlling model, the fuzzy matrix of application fuzzy synthetic reason design pid parameter, find corrected parameter and bring following formula calculating into:
k
p＝k'
p+Δk
p(3)
k
i＝k
i'+Δk
i(4)
k
d＝k'
d+Δk
d(5)
Wherein, k'
p, k
i', k'
dfor the initial value of P, I and D parameter; Δ k
p, Δ k
i, Δ k
dbe respectively the self-adjusting amount of the pid parameter drawn by fuzzy reasoning according to error e and error rate ec.
Step 5.5: by the current k obtained
p, k
i, k
d, give topworks through PID controller by instruction.
In order to the correctness of model is inherently described, the present invention at home certain power plant has carried out experimental study.Under stationary conditions, got one group of floor data at interval of 20 seconds, collect 2100 groups of floor datas altogether and test, experimental result shows: sulphuric dioxide forecast model has higher precision of prediction, and its average relative error is 2.28%; Concentration of emission mean value in optimization system before sulphuric dioxide optimization is 217.57mg/m
3, the concentration of emission mean value after optimization is 172.92mg/m
3, meet emission standard; Lime hydrate feeding coal reduces 6.13%; CO2 emissions reduce 5.21% relatively; In desulfurizer, desulfuration efficiency is up to 93%.Wherein, main control parameters all keeps in allowed limits, and main control parameters is as follows: k
p=0.40; k
i=1.0; k
d=0.01; Desulfurizer outlet medial temperature is 73 ~ 75 degrees Celsius; Control atmospheric pressure is greater than 0.65MPa; Desulfurizer pressure reduction regulating loop setting range is 1000 ~ 1200Pa; Bag-type dust pressure reduction is 1300 ~ 1600Pa.
Further, in order to the practicality of model is described, the present invention at home certain power plant has carried out the vehicle air-conditioning research of flue gas cycle fluid-bed sweetening.Time under stationary conditions with lifting load, the modules being placed in PLC is by the DCS real-time exchange data of data communication module and power plant, shown by a large amount of experimental datas: the optimal control method of this flue gas circulating fluidized bed desulfurization under various operating mode and coal varitation time, all comparatively stably can control the discharge capacity of sulphuric dioxide, meet emission standard, and the lime hydrate semi-invariant in desulfurizer reduces, and has saved the energy and capital to a large extent.This optimal control method, when implementing, can not affect the normal operation of power plant, reads the real time data of power plant, good optimal control result is used for the actual production of power plant, can realizes engineer applied comparatively neatly.
The invention has the beneficial effects as follows the hysteresis quality overcoming lime hydrate control system, because lime hydrate control system postpones the SO that brings when solving lifting load
2discharge the problem exceeded standard, it also avoid the problem such as thermal loss increase, cost increase that lime hydrate input amount is too much caused simultaneously; The present invention also utilizes smoke inlet CO
2amount assist and control the feeding coal of lime hydrate, certain degree decreases CO
2discharge capacity.Forecast model of the present invention has higher accuracy, can realize controlling more accurately to lime hydrate quantity delivered, engineering has higher reference.
Accompanying drawing explanation
Fig. 1 is a kind of optimal control method schematic diagram of flue gas circulating fluidized bed desulfurization;
Fig. 2 is the optimization system schematic diagram that sulphuric dioxide prediction controls with lime hydrate;
Fig. 3 is the predicted value of sulfur dioxide emissioning concentration forecast model and the comparison diagram of actual value;
Fig. 4 is the optimal control design sketch of flue gas circulating fluidized bed desulfurization;
Fig. 5 is the vehicle air-conditioning design sketch of flue gas circulating fluidized bed desulfurization:
Embodiment
The present invention proposes a kind of optimal control method of flue gas circulating fluidized bed desulfurization, below in conjunction with accompanying drawing and instantiation, the present invention is described in detail.
Fig. 1 is a kind of optimal control method schematic diagram of flue gas circulating fluidized bed desulfurization, DCS system connects flue gas cycle fluidized bed and the sulphuric dioxide Optimal Control System based on PLC respectively, and optimization system comprises: data communication module, sulphuric dioxide prediction module, carbon dioxide calculate module and lime hydrate control module.Wherein, data communication module and DCS system exchange data mutually, and sulphuric dioxide prediction module and carbon dioxide calculate module connection data communication module and lime hydrate control module respectively, lime hydrate control module connecting communication module.
Described data communication module exchanges data by ModBus communications protocol and DCS system, reads real time data and comprises: desulfurizer gas approach flue gas volume V
y, m
3/ s; Desulfurizer gas approach sulphuric dioxide volume
m
3/ s; Desulfurizer gas approach carbon monoxide volume V
cO, m
3/ s; Enter the total blast volume V of boiler
f, m
3/ s; Enter the coal-supplying amount B of boiler
v, kg/s; Desulfurizer gas approach sulfur dioxide concentration
mg/m
3; Calcium hydroxide particle footpath r
s, mm; Desulfurizer exiting flue gas flow velocity u
f, m/s; Lime hydrate feeding coal, kg/s.The real time data that communication module reads adopts Vladimir Romanovskiy criterion to reject abnormal data, and concrete steps are as follows:
Step 2.1: determine suspicious data X successively
j, j ∈ [1, n], n are gathered data amount check;
Step 2.2: then calculate the ordered series of numbers mean value deleted after suspicious numerical value
and standard deviation
wherein X
ifor normal data;
Step 2.3: the residual error calculating suspicious data:
Step 2.4: if | ε
j| >K σ, then suspicious data is rejected, if | ε
j| <K σ, then can continue to use suspicious data, wherein K is test coefficient;
Fig. 2 is the optimization system schematic diagram that sulphuric dioxide prediction controls with lime hydrate, lime hydrate control module is in conjunction with sulphuric dioxide predicted value, carbon dioxide calculating value, desulfurizer fire box temperature, desulfurizer furnace pressure and the real-time running data of lime hydrate feeding coal that read by data communication module, through fuzzy PID regulation, the instruction of lime hydrate feeding coal is sent to DCS controller by data communication module, thus instruction is issued field apparatus by DCS system.Wherein, sulphuric dioxide predicted value, carbon dioxide calculating value and desulfurizer entrance titanium dioxide sulfur number are as given instruction, and desulfurizer fire box temperature and desulfurizer furnace pressure are as feedforward instruction.Wherein, f (x
1) be desulfurizer entrance CO
2volume is converted into the polygronal function of lime hydrate quality; F (x
2) be desulfurizer entrance SO
2concentration is converted into the polygronal function of lime hydrate quality; F (x
3) for desulfurizer temperature inversion be the polygronal function of lime hydrate quality; F (x
4) be converted into the polygronal function of lime hydrate quality for desulfurizer furnace pressure.
Described sulphuric dioxide prediction module reads the real time data of data communication module, uses slaked lime desulfurizing mechanism model prediction desulfurizer outlet sulfur dioxide concentration, comprises the following steps:
Step 3.1: according to conversion ratio and the relation of time
calculate the conversion ratio of desulfurizing agent lime hydrate, wherein: X
bfor the conversion ratio of lime hydrate, %; The reaction of desulfurizing agent in the absorption tower complete time is
for hydrated lime particle density, kg/m
3;
for lime hydrate molal weight, kg/mol; r
sfor hydrated lime particle radius, mm;
for SO in desulfurizer
2partial pressure, Pa; T is temperature in desulfurizer, K; D
efffor SO
2effective diffusion cofficient in product layer, gets 4 × 10
?10cm
2/ s; R is universal gas constant, gets 8.314J/ (molK); The residence time of desulfurizing agent in absorption tower is
ρ
yfor smoke density, kg/m
3; u
ffor desulfurizer exiting flue gas flow velocity, m/s; d
cfor desulfurizer outlet fly ash granule diameter, mm; H is desulfurizer height, m; μ is gas motion viscosity, gets 45.6 × 10
?6pa.s; G is acceleration of gravity, gets 9.8N/kg.
Step 3.2: according to lime hydrate reaction model in desulfurizer
obtain the reaction rate of lime hydrate.Wherein,
for the reaction rate of lime hydrate, kg/s;
for lime hydrate feeding coal, kg/s.
Step 3.3: according to desulfurizer outlet SO
2the forecast model of concentration of emission
calculate SO
2prediction concentrations, wherein,
for SO
2prediction concentrations, mg/m
3;
for desulfurizer entrance SO
2generating rate, mg/s; V
yfor desulfurizer inlet flue gas volume, m
3/ s;
for SO
2molal weight, g/mol; V is the volume of desulfurizer, m
3; α is desulfurizer air leakage coefficient, %.
Described carbon dioxide calculates module and reads real time data from data communication module, uses
calculate the carbon dioxide volume in desulfurizer inlet flue gas, wherein, V
yfor gas approach flue gas volume, m
3/ s;
for gas approach sulphuric dioxide volume, m
3/ s; V
cOfor gas approach carbon monoxide volume, m
3/ s; V
ffor entering the total blast volume of boiler, m
3/ s; B
vfor entering the coal-supplying amount of boiler, kg/s, greenhouse gas is
theoretical air requirement required for every kilogram of coal combustion is V
t=0.089 (C
ar+ 0.375S
ar)+0.265H
ar-0.033O
ar, %; C
arfor fuel as received basis carbon content, %; H
arfor fuel as received basis hydrogen richness, %; O
arfor fuel as received basis oxygen content, %; N
arfor fuel as received basis nitrogen content, %; S
arfor fuel as received basis sulfur content, %.
Described lime hydrate control module is in conjunction with sulfur dioxide concentration predicted data, carbon dioxide volume computing data and the real-time feeding coal data of lime hydrate that read by communication module, through fuzzy PID regulation, the instruction of lime hydrate feeding coal is sent to DCS controller by data communication module, thus instruction is issued field apparatus by DCS system.Wherein, PID adjuster with fuzzy control takes corresponding Fuzzy PID to control the output of lime hydrate flow according to the control signal of input, and fuzzy-adaptation PID control implement body arranges and comprises the following steps:
Step 5.1: fuzzy controller is that the expression formula of error e and error rate ec is as follows using error e and error rate ec as input:
ec(k)＝e(k)-e(k-1)(2)
Wherein,
for lime hydrate flow instruction;
for actual lime hydrate flow value; E (k) is the deviation between command value and actual value; Ec (k) is the deviation ratio between command value and actual value; E (k-1) is the deviation between a upper moment command value and actual value.
Step 5.2: according to systematic error e and error rate ec variation range, use suitable fuzzy reasoning table, obtain k
p, k
i, k
dthe fuzzy control table that three parameters are adjusted respectively is as follows:
Table 1k
pfuzzy reasoning table
Table 2k
ifuzzy reasoning table
Table 3k
dfuzzy reasoning table
Step 5.3: systematic error e and error rate ec variation range are defined as the domain in fuzzy set, e, ec={ ?6 ， ?5 ， ?4 ?3 ， ?2 ， ?1,0,1,2,3,4,5,6}, its fuzzy subset is e, ec={NB, NM, NS, O, PS, PM, PB}, subset elements represents negative large respectively, in negative, negative little, zero, just little, center, honest.If e, ec and k
p, k
i, k
dnormal Distribution, therefore can obtain each fuzzy subset's degree of membership.
Step 5.4: according to each fuzzy subset's degree of membership assignment table and each parameter fuzzy Controlling model, the fuzzy matrix of application fuzzy synthetic reason design pid parameter, find corrected parameter and bring following formula calculating into:
k
p＝k'
p+Δk
p(3)
k
i＝k
i'+Δk
i(4)
k
d＝k'
d+Δk
d(5)
Wherein, k'
p, k
i', k'
dfor the initial value of P, I and D parameter; Δ k
p, Δ k
i, Δ k
dbe respectively the self-adjusting amount of the pid parameter drawn by fuzzy reasoning according to error e and error rate ec.
Step 5.5: by the current k obtained
p, k
i, k
d, give topworks through PID controller by instruction.
In order to the correctness of model is inherently described, the present invention at home certain power plant has carried out experimental study.Under stationary conditions, got one group of floor data at interval of 20 seconds, collect 2100 groups of floor datas altogether and test, experimental result as shown in Figure 3, SO in figure
2the model predication value of concentration of emission is consistent with actual measured value trend, and both average relative errors are 2.28%, in allowed limits, also proves the SO set up simultaneously
2concentration of emission forecast model has higher precision of prediction.On the basis that sulphuric dioxide forecast model is correct, and then carried out the optimal control of sulphuric dioxide, as shown in Figure 4, the concentration of emission mean value in optimization system before sulphuric dioxide optimization is 217.57mg/m to its result
3, the concentration of emission mean value after optimization is 172.92mg/m
3, meet emission standard, indicate the validity of flue gas circulating fluidized bed desulfurization optimal control method.In addition, experiment medium-slaking lime feeding coal reduces 6.13% relatively, and CO2 emissions reduce 5.21% relatively, and in desulfurizer, desulfuration efficiency is up to 93%.Wherein, main control parameters all keeps in allowed limits, and main control parameters is as follows: k
p=0.40; k
i=1.0; k
d=0.01; Desulfurizer outlet medial temperature is 73 ~ 75 DEG C; Control atmospheric pressure is greater than 0.65MPa; Desulfurizer pressure reduction is 1000 ~ 1200Pa; Bag-type dust pressure reduction is 1300 ~ 1600Pa.
Further, in order to the practicality of model is described, the present invention at home certain power plant has carried out the vehicle air-conditioning research of flue gas cycle vulcanization bed desulfurization.Time under stationary conditions with lifting load, the modules being placed in PLC is by the DCS real-time exchange data of data communication module and power plant, shown by a large amount of experimental datas: the optimal control method of this flue gas circulating fluidized bed desulfurization under various operating mode and coal varitation time, control SO that all can be comparatively stable
2discharge capacity, meet emission standard, and lime hydrate semi-invariant in desulfurizer reduces, and has saved the energy and capital to a large extent.Fig. 5 is the vehicle air-conditioning design sketch of flue gas circulating fluidized bed desulfurization, and it is the result of a real-time online optimal control of the present invention, and result shows: under different load conditions, by regulating niter ash quantity all can control SO preferably
2discharge capacity, in 10 hours period of vehicle air-conditioning, SO
2discharge capacity average emission concentration is 159.53mg/m
3, meet emission standard.In addition, vehicle air-conditioning medium-slaking lime feeding coal decreases 6.36% than the lime hydrate feeding coal estimated, in desulfurizer, desulfuration efficiency is up to 93.89%, improves 1.57%, Ca/S=1.1 ~ 1.3 than the desulfuration efficiency estimated.Wherein: main control parameters and main adjustment parameter all keep in allowed limits, and design parameter value is as table 4:
Table 4
The present invention, on the basis taking into full account lime hydrate store status and memory space in desulfurizer, establishes SO
2the forecast model of concentration of emission, this model can Real-Time Monitoring desulfuration in furnace state, in advance by SO
2concentration of emission feeds back to lime hydrate feed control system, fundamentally overcomes the hysteresis quality of lime hydrate control system, because lime hydrate control system postpones the SO that brings when solving lifting load
2discharge the problem exceeded standard; The present invention simultaneously utilizes desulfurizer entrance CO
2amount assist and control the feeding coal of lime hydrate, lime hydrate feed is controlled more accurate, decreases the waste of lime hydrate resource, certain degree decreases CO
2discharge capacity; According to test repeatedly and a large amount of historical data analysis, the SO in the present invention
2forecast model has higher precision, can more adequately control lime hydrate quantity delivered, engineering has higher practical value.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (6)
1. the optimal control method of a flue gas circulating fluidized bed desulfurization, wherein, DCS system connects flue gas circulating fluidized bed and based on the sulphuric dioxide Optimal Control System of PLC respectively, described sulphuric dioxide Optimal Control System calculates module connection data communication module and lime hydrate control module respectively by sulphuric dioxide prediction module and carbon dioxide, and lime hydrate control module connection data communication module forms; Data communication module and DCS system exchange data mutually; It is characterized in that, described optimal control method comprises as lower part:
1) data communication module exchanges data by ModBus communications protocol and DCS system;
2) sulphuric dioxide prediction module reads real-time running data by data communication module from DCS system: desulfurizer gas approach sulfur dioxide concentration, desulfurizer gas approach flue gas volume, calcium hydroxide particle footpath, desulfurizer exiting flue gas flow velocity and lime hydrate feeding coal, uses the concentration of slaked lime desulfurizing mechanism model prediction desulfurizer outlet sulphuric dioxide;
3) carbon dioxide calculates module and reads real-time running data by data communication module from DCS system: the volume of desulfurizer gas approach flue gas, sulphuric dioxide and carbon monoxide, enter boiler total blast volume and coal-supplying amount, calculates the volume of desulfurizer gas approach carbon dioxide;
4) lime hydrate control module is in conjunction with sulphuric dioxide predicted value, carbon dioxide calculating value, desulfurizer fire box temperature, desulfurizer furnace pressure and the real-time running data of lime hydrate feeding coal that read by data communication module, through fuzzy PID regulation, the instruction of lime hydrate feeding coal is sent to DCS controller by data communication module, thus instruction is issued field apparatus by DCS system; Wherein, sulphuric dioxide predicted value, carbon dioxide calculating value and desulfurizer entrance titanium dioxide sulfur number are as given instruction, and desulfurizer fire box temperature and desulfurizer furnace pressure are as feedforward instruction.
2. the optimal control method of flue gas circulating fluidized bed desulfurization according to claim 1, is characterized in that, described data communication module reads real time data and comprises: desulfurizer gas approach flue gas volume V
y, m
3/ s; Desulfurizer gas approach sulphuric dioxide volume
m
3/ s; Desulfurizer gas approach carbon monoxide volume V
cO, m
3/ s; Enter the total blast volume V of boiler
f, m
3/ s; Enter the coal-supplying amount B of boiler
v, kg/s; Desulfurizer gas approach sulfur dioxide concentration
mg/m
3; Calcium hydroxide particle footpath r
s, mm; Desulfurizer exiting flue gas flow velocity u
f, m/s; Lime hydrate feeding coal, kg/s; The real time data that communication module reads adopts Vladimir Romanovskiy criterion to reject abnormal data, and concrete steps are as follows:
Step 2.1: determine suspicious data X successively
j, j ∈ [1, n], n are gathered data amount check;
Step 2.2: then calculate the ordered series of numbers mean value deleted after suspicious numerical value
x
iand standard deviation
Wherein X
ifor normal data;
Step 2.3: the residual error calculating suspicious data:
Step 2.4: if | ε
j| >K σ, then suspicious data is rejected, if | ε
j| <K σ, then can continue to use suspicious data, wherein K is test coefficient.
3. the optimal control method of flue gas circulating fluidized bed desulfurization according to claim 1, is characterized in that, described sulphuric dioxide prediction module uses the concentration of slaked lime desulfurizing mechanism model prediction desulfurizer outlet sulphuric dioxide, comprises the following steps:
Step 3.1: according to conversion ratio and the relation of time
calculate the conversion ratio of desulfurizing agent lime hydrate, wherein: X
bfor the conversion ratio of lime hydrate, %; The reaction of desulfurizing agent in the absorption tower complete time is
Min;
for hydrated lime particle density, kg/m
3;
for lime hydrate molal weight, kg/mol; r
sfor hydrated lime particle radius, mm;
for SO in desulfurizer
2partial pressure, Pa; T is temperature in desulfurizer, K; D
efffor SO
2effective diffusion cofficient in product layer, gets 4 × 10
-10cm
2/ s; R is universal gas constant, gets 8.314J/ (molK); The residence time of desulfurizing agent in absorption tower is
min; ρ
yfor smoke density, kg/m
3; u
ffor desulfurizer exiting flue gas flow velocity, m/s; d
cfor desulfurizer outlet fly ash granule diameter, mm; H is desulfurizer height, m; μ is gas motion viscosity, gets 45.6 × 10
-6pa.s; G is acceleration of gravity, gets 9.8N/kg;
Step 3.2: according to lime hydrate reaction model in desulfurizer
obtain the reaction rate of lime hydrate; Wherein,
for the reaction rate of lime hydrate, kg/s;
for lime hydrate feeding coal, kg/s;
Step 3.3: according to desulfurizer outlet SO
2the forecast model of concentration of emission
Calculate SO
2prediction concentrations, wherein,
for SO
2prediction concentrations, mg/m
3;
for desulfurizer entrance SO
2generating rate, mg/s; V
yfor desulfurizer inlet flue gas volume, m
3/ s;
for SO
2molal weight, g/mol; V is the volume of desulfurizer, m
3; α is desulfurizer air leakage coefficient, %.
4. the optimal control method of flue gas circulating fluidized bed desulfurization according to claim 1, is characterized in that, described carbon dioxide calculates module and uses
Calculate the carbon dioxide volume in desulfurizer inlet flue gas, wherein, V
yfor gas approach flue gas volume, m
3/ s;
for gas approach sulphuric dioxide volume, m
3/ s; V
cOfor gas approach carbon monoxide volume, m
3/ s; V
ffor entering the total blast volume of boiler, m
3/ s; B
vfor entering the coal-supplying amount of boiler, kg/s, greenhouse gas is
%; Theoretical air requirement required for every kilogram of coal combustion is V
t=0.089 (C
ar+ 0.375S
ar)+0.265H
ar-0.033O
ar, %; C
arfor fuel as received basis carbon content, %; H
arfor fuel as received basis hydrogen richness, %; O
arfor fuel as received basis oxygen content, %; N
arfor fuel as received basis nitrogen content, %; S
arfor fuel as received basis sulfur content, %.
5. the optimal control method of flue gas circulating fluidized bed desulfurization according to claim 1, is characterized in that, in described lime hydrate control module, fuzzy controller arranges and comprises the following steps:
Step 5.1: fuzzy controller is that the expression formula of error e and error rate ec is as follows using error e and error rate ec as input:
ec(k)＝e(k)-e(k-1)(2)
Wherein,
for lime hydrate flow instruction;
for actual lime hydrate flow value; E (k) is the deviation between command value and actual value; Ec (k) is the deviation ratio between command value and actual value; E (k-1) is the deviation between a upper moment command value and actual value;
Step 5.2: according to systematic error e and error rate ec variation range, use suitable fuzzy reasoning table, obtain k
p, k
i, k
dthe fuzzy control table that three parameters are adjusted respectively;
Step 5.3: systematic error e and error rate ec variation range are defined as the domain in fuzzy set, e, ec={-6 ,-5 ,-4,-3 ,-2 ,-1,0,1,2,3,4,5,6}, its fuzzy subset is e, ec={NB, NM, NS, O, PS, PM, PB}, subset elements represents negative large respectively, in negative, negative little, zero, just little, center, honest; If e, ec and k
p, k
i, k
dnormal Distribution, therefore can obtain each fuzzy subset's degree of membership;
Step 5.4: according to each fuzzy subset's degree of membership assignment table and each parameter fuzzy Controlling model, the fuzzy matrix of application fuzzy synthetic reason design pid parameter, find corrected parameter and bring following formula calculating into:
k
p＝k'
p+Δk
p(3)
k
i＝k′
i+Δk
i(4)
k
d＝k'
d+Δk
d(5)
Wherein, k'
p, k '
i, k'
dfor the initial value of P, I and D parameter; Δ k
p, Δ k
i, Δ k
dbe respectively the self-adjusting amount of the pid parameter drawn by fuzzy reasoning according to error e and error rate ec;
Step 5.5: by the current k obtained
p, k
i, k
d, give topworks through PID controller by instruction.
6. the optimal control method of flue gas circulating fluidized bed desulfurization according to claim 1, it is characterized in that, stable with under the operating mode of lifting load, according to sulphuric dioxide predicted value and carbon dioxide calculating value, concentration of emission lime hydrate feeding coal being regulated to control to sulphuric dioxide meets emission standard, decreases the discharge capacity of lime hydrate feeding coal and carbon dioxide simultaneously.
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