CN106569517A - Coking waste-gas desulfurization process optimized control method - Google Patents
Coking waste-gas desulfurization process optimized control method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 174
- 230000008569 process Effects 0.000 title claims abstract description 101
- 238000006477 desulfuration reaction Methods 0.000 title claims abstract description 87
- 230000023556 desulfurization Effects 0.000 title claims abstract description 86
- 238000004939 coking Methods 0.000 title claims abstract description 21
- 239000002912 waste gas Substances 0.000 title abstract 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims abstract description 96
- 239000003546 flue gas Substances 0.000 claims abstract description 60
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 59
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims abstract description 58
- 229910021529 ammonia Inorganic materials 0.000 claims abstract description 48
- 239000007788 liquid Substances 0.000 claims abstract description 38
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 14
- 239000001301 oxygen Substances 0.000 claims abstract description 14
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 14
- 238000003062 neural network model Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims description 23
- 230000005540 biological transmission Effects 0.000 claims description 21
- 239000007789 gas Substances 0.000 claims description 21
- 238000013404 process transfer Methods 0.000 claims description 17
- 230000004044 response Effects 0.000 claims description 16
- 239000000779 smoke Substances 0.000 claims description 10
- 230000003111 delayed effect Effects 0.000 claims description 9
- 230000010355 oscillation Effects 0.000 claims description 9
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000003247 decreasing effect Effects 0.000 claims description 5
- 238000012546 transfer Methods 0.000 claims description 5
- 239000005864 Sulphur Substances 0.000 claims description 4
- GWEVSGVZZGPLCZ-UHFFFAOYSA-N Titan oxide Chemical compound O=[Ti]=O GWEVSGVZZGPLCZ-UHFFFAOYSA-N 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 230000003467 diminishing effect Effects 0.000 claims description 3
- 230000009977 dual effect Effects 0.000 claims description 3
- 230000037361 pathway Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 241000790917 Dioxys <bee> Species 0.000 claims description 2
- 230000007423 decrease Effects 0.000 claims description 2
- 239000004408 titanium dioxide Substances 0.000 claims description 2
- 238000012850 discrimination method Methods 0.000 claims 1
- FGIUAXJPYTZDNR-UHFFFAOYSA-N potassium nitrate Chemical compound [K+].[O-][N+]([O-])=O FGIUAXJPYTZDNR-UHFFFAOYSA-N 0.000 claims 1
- 238000010521 absorption reaction Methods 0.000 abstract description 5
- 239000012528 membrane Substances 0.000 abstract 1
- 230000007425 progressive decline Effects 0.000 abstract 1
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 description 8
- 229910052717 sulfur Inorganic materials 0.000 description 7
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 6
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 5
- 230000003009 desulfurizing effect Effects 0.000 description 5
- 238000005259 measurement Methods 0.000 description 5
- 239000011593 sulfur Substances 0.000 description 5
- 230000033228 biological regulation Effects 0.000 description 4
- 239000004202 carbamide Substances 0.000 description 4
- 235000013877 carbamide Nutrition 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 239000007921 spray Substances 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 3
- 230000032683 aging Effects 0.000 description 3
- TXKMVPPZCYKFAC-UHFFFAOYSA-N disulfur monoxide Inorganic materials O=S=S TXKMVPPZCYKFAC-UHFFFAOYSA-N 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000007935 neutral effect Effects 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- XTQHKBHJIVJGKJ-UHFFFAOYSA-N sulfur monoxide Chemical compound S=O XTQHKBHJIVJGKJ-UHFFFAOYSA-N 0.000 description 3
- 239000006096 absorbing agent Substances 0.000 description 2
- BFNBIHQBYMNNAN-UHFFFAOYSA-N ammonium sulfate Chemical compound N.N.OS(O)(=O)=O BFNBIHQBYMNNAN-UHFFFAOYSA-N 0.000 description 2
- 229910052921 ammonium sulfate Inorganic materials 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 235000019504 cigarettes Nutrition 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000029219 regulation of pH Effects 0.000 description 2
- AOSFMYBATFLTAQ-UHFFFAOYSA-N 1-amino-3-(benzimidazol-1-yl)propan-2-ol Chemical compound C1=CC=C2N(CC(O)CN)C=NC2=C1 AOSFMYBATFLTAQ-UHFFFAOYSA-N 0.000 description 1
- QGZKDVFQNNGYKY-UHFFFAOYSA-O Ammonium Chemical compound [NH4+] QGZKDVFQNNGYKY-UHFFFAOYSA-O 0.000 description 1
- 241001672694 Citrus reticulata Species 0.000 description 1
- PQUCIEFHOVEZAU-UHFFFAOYSA-N Diammonium sulfite Chemical compound [NH4+].[NH4+].[O-]S([O-])=O PQUCIEFHOVEZAU-UHFFFAOYSA-N 0.000 description 1
- 241000283074 Equus asinus Species 0.000 description 1
- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 description 1
- 239000002250 absorbent Substances 0.000 description 1
- 230000002745 absorbent Effects 0.000 description 1
- 235000011130 ammonium sulphate Nutrition 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 239000003517 fume Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000000746 purification Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000006722 reduction reaction Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 235000011149 sulphuric acid Nutrition 0.000 description 1
- 239000001117 sulphuric acid Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D21/00—Control of chemical or physico-chemical variables, e.g. pH value
- G05D21/02—Control of chemical or physico-chemical variables, e.g. pH value characterised by the use of electric means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- 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
- B01D53/46—Removing components of defined structure
- B01D53/48—Sulfur compounds
- B01D53/50—Sulfur oxides
- B01D53/501—Sulfur oxides by treating the gases with a solution or a suspension of an alkali or earth-alkali or ammonium compound
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D2251/00—Reactants
- B01D2251/20—Reductants
- B01D2251/206—Ammonium compounds
- B01D2251/2062—Ammonia
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D2258/00—Sources of waste gases
- B01D2258/02—Other waste gases
- B01D2258/0283—Flue gases
Abstract
The present invention relates to a coking waste-gas desulfurization process optimized control method applied to a desulfurization and denitrification integrated process device. The method includes the following steps that: a current working condition is determined through using a fuzzy identification algorithm according to flue gas temperature, flue gas flow rate, flue gas oxygen content and sulfur dioxide concentration at the entrance of the desulfurization and denitrification integrated process device; the optimal pH value of a desulfurization absorption liquid under the current working condition is determined by using a variable step size progressive decrease method; the amount of ammonia compensation under the current working condition is determined through using a neural network model; the amount of ammonia control under the current working condition is determined through using an inner membrane control method and according to the sulfur dioxide concentration at the entrance of the desulfurization and denitrification integrated process device; and the pH value of the desulfurization absorption liquid is controlled according to the amount of ammonia compensation and the amount of ammonia control and based on the optimal pH value of the desulfurization absorption liquid. With the coking waste-gas desulfurization process optimized control method provided by the embodiments of the invention adopted, the fastness and accuracy of control are improved, and the desulfurization and denitrification integrated process device can operate under a most economical and environmentally friendly state, and the operating cost of the desulfurization and denitrification integrated process device can be reduced.
Description
Technical field
The present invention relates to technical field of industrial control, more particularly to a kind of coking exhuast gas desulfurization procedure optimization control method.
Background technology
Sulfur dioxide and nitrogen oxides are main atmosphere pollutions, are the principal elements for affecting air quality.China is
Maximum coking manufacturing country in the world, due to due to history, for a long time, domestic coking industry coking flue gas is all without desulfurization
Denitration process and be directly discharged into air, become one of important source of atmospheric pollution.Formal enforcement from 1 day January in 2015《Refining
Coking pollutant emission standard》The discharge index of sulfur dioxide and nitrogen oxides to coking industry proposes strict and clear and definite
Quantization require.
Certain coke-oven plant takes the lead in using the desulfurization of wet-type ammonia forced turbulent in the country and forces oxidation carbamide denitrification integral work
Process and equipment (abbreviation desulfurization and denitrification integral process device) is used as a kind of important means for processing coking industry flue gas.The technique dress
Putting the equipment for including has air-introduced machine, heat recovery boiler, booster fan, desulfurizing tower, denitrating tower, ammonium sulfate circulating slot, solid-liquid point
From the relevant devices such as device, carbamide dissolving tank, pipeline and donkey pump, Detection of Process Parameters device, procedure parameter adjusting means, DCS
(Distributed Control System, dcs or Distributed Control System are a kind of computer controls to system
System) etc. (as shown in Figure 1).
The technical process of above-mentioned process unit is as follows：
Flue gas Jing air-introduced machines in process of coking send into heat recovery boiler, and flue-gas temperature is down to 160 DEG C of left sides by 300 DEG C
The right side, through booster fan, converged before into desulfurizing tower with ozone input channel, and the part NO in flue gas is quickly anti-with ozone
NO should be generated2.Flue gas enters desulfurizing tower enriching section, through spray, washing, is cooled to 60 DEG C or so, then Jing gas caps enter into it is de-
The absorber portion of sulfur tower, the desulfurization absorbing liquid counter current contacting with top spray；SO in flue gas2It is anti-with the ammonium sulfite in absorbent
Ammonium bisulfite, SO should be generated2It is able to removing purification.The liquid of absorber portion bottom is back to the reservoir of desulfurization tower bottom.For
Recover the absorbability of absorbing liquid, need to supplement ammonia.Desulfurizing tower top spray technique keeps the liquid level of reservoir in reasonable model
In enclosing.Liquid storage trench bottom blasts air, by the part (NH in reservoir4)2SO3It is oxidized to (NH4)2SO4, for sulphuric acid in enriching section
Spray-the evaporation-concentration of ammonium and subsequent treatment.
Flue after desulfurization is connected with ozone input channel, the ozone mixed in 60 DEG C or so of flue gas to after desulfurization,
Part NO in flue gas generates NO with ozone fast reaction2, denitrating tower bottom is subsequently entered, the carbamide with denitrating tower top spray
Solution counter current contacting, NO, NO2There is reduction reaction with the carbamide in solution and generate N2、CO2And H2O, completes denitration.Reach environmental protection
The flue gas of discharge standard enters air at the top of denitrating tower, so as to complete whole processing procedures of flue gas.
Said process can be divided into two subprocess, i.e. sweetening process and denitrification process.For sweetening process, to obtain
Higher desulfuration efficiency, the most important pH value for seeking to absorbing liquid in control tower, because it is to SO in flue gas in tower2Absorption
There is most direct impact.And at this stage there is following Railway Project in the control of its sweetening process：1st, controlled variable is directly net
SO in flue gas after change2Concentration, and (proportional integral differential control, P represents ratio, and I represents integration, D only with simple PID
Represent differential) control, overshoot is very big with delayed, and stability and the accuracy for controlling cannot be ensured completely；2nd, process of coking has
Sufficiently complex working condition, causes its multi-state characteristic, the index error such as the temperature of flue gas, flow, concentration under different operating modes
It is different very big；3rd, the requirement that pH value is only defined in technological requirement is 4.5-6.5, and scope is excessive and does not account for different flue gas conditions
Under difference, cause a large amount of wastes even the escaping of ammonia of raw material, be unfavorable for economy, the ring of desulfurization and denitrification integral process device
Protect operation.
In view of this, it is special to propose the present invention.
The content of the invention
In order to solve the problems referred to above of the prior art, it has been and has solved how to guarantee sweetening process in most economical environmental protection
Under the conditions of the technical problem that steadily carries out and a kind of coking exhuast gas desulfurization procedure optimization control method is provided.
To achieve these goals, there is provided technical scheme below：
A kind of coking exhuast gas desulfurization procedure optimization control method, the optimal control method is applied to desulfurization and denitrification integral
Process unit, the control method includes：
Flue-gas temperature, flue gas flow according to the desulfurization and denitrification integral process device portal, oxygen content of smoke gas and two
Sulfur oxide concentration, using fuzzy recognition algorithm, determines current working；
The method successively decreased using variable step, determines the optimum pH value of the desulfurization absorbing liquid under the current working；
The ammonia compensation dosage under the current working is determined using neural network model；
Based on the sulfur dioxide concentration that the desulfurization and denitrification integral process device is exported, determined using inner membrance control method
Ammonia controlled quentity controlled variable under the current working；
Based on the optimum pH value of the desulfurization absorbing liquid, control with reference to the ammonia compensation dosage and the ammonia controlled quentity controlled variable
Make the pH value of the desulfurization absorbing liquid.
Preferably, flue-gas temperature, flue gas flow, the flue gas according to the desulfurization and denitrification integral process device portal
Oxygen content and sulfur dioxide concentration, using fuzzy recognition algorithm, determine current working, specifically include：
The flue-gas temperature, the flue gas flow, the oxygen content of smoke gas and the sulfur dioxide concentration are set as into shape
State vector；
Clustered based on the state vector, determined sorting criterion, and state set is obtained by the sorting criterion；
By weighted fuzzy identification algorithm, the fuzzy membership relation square of the state vector and the state set is obtained
Battle array；
Based on the fuzzy membership relational matrix, the current working is determined by maximum membership grade principle.
Preferably, the method that the employing variable step successively decreases, determines the optimum of the desulfurization absorbing liquid under the current working
PH value, specifically includes：
The optimum pH value of the desulfurization absorbing liquid under the current working is determined according to following formula using variable step diminishing method：
In formula, the n represents iterationses, n>=2；The PHnRepresent pH value during nth iteration；The PHn-1Table
Show pH value during (n-1)th iteration；The WmaxWith the WminThe maximum and minimum value of step-length is represented respectively；The knRepresent and work as
Front iterationses；The kmaxRepresent maximum iteration time；T0 is represented after the desulfurization and denitrification integral process device stable operation
Initial time；The P (t0) represents the desulfurization and denitrification integral process device exiting flue gas sulfur dioxide concentration value；It is described
PLimitRepresent the sulfur dioxide concentration value for limiting；The T represents the sampling period；The ε represents termination threshold value.
Preferably, the neural network model is multiple radial basis function neural networks model in parallel.
Preferably, the sulfur dioxide concentration exported based on desulfurization and denitrification integral process device, using inner membrance control
Method determines the ammonia controlled quentity controlled variable under current working, specifically includes：
Desulphurization denitration process transfer function model is set up using closed loop two channel relay feedback frequency domain identification method；
Using the desulphurization denitration process transfer function model, exported based on the desulfurization and denitrification integral process device
Sulfur dioxide concentration, using internal model control method, determines ammonia controlled quentity controlled variable.
Preferably, the employing closed loop two channel relay feedback frequency domain identification method come set up desulphurization denitration process transmission
Function model, specifically includes：
According to the ammonia addition during the desulphurization denitration and the output of limit cycles oscillations, estimated using Fourier transform
Meter obtains the frequency response of the desulphurization denitration process and estimates；
Estimated according to the frequency response of the sweetening process, using identification algorithm and nonlinear optimization algorithm, estimated de-
Sulfur denitrification process transfer function model.
Preferably, it is described to be estimated according to the frequency response of the sweetening process, calculated using identification algorithm and nonlinear optimization
Method, estimates desulphurization denitration process transfer function model, specifically includes：
Estimated according to the frequency response of sweetening process, be defined below object function：
Wherein, the ciRepresent weights；The m represents selected phase place number；It is describedRepresent by the dual pathways after
Electrical characteristics test, in ωiUnder Multiple points frequency response estimate；G (the j ω0) represent frequency characteristic to be estimated；The J tables
Show the object function；
Based on object function, using identification algorithm, transmission function is obtained；
Using nonlinear optimization algorithm, the constant in the transmission function is solved, so that it is determined that the desulphurization denitration process
Transfer function model.
Preferably, the transmission function adds delayed transmission function for one order inertia；
The employing nonlinear optimization algorithm, solves the constant in the transmission function, so that it is determined that the desulphurization denitration
Process transfer function model, specifically includes：Using nonlinear optimization algorithm, solve the proportionality constant in the transmission function, be used to
Property constant and hysteresis constant, so that it is determined that the one order inertia of the desulphurization denitration process adds delayed transfer function model.
It is excellent that the embodiment of the present invention provides a kind of coking exhuast gas desulfurization process for being applied to desulfurization and denitrification integral process device
Change control method.The method includes：Flue-gas temperature, flue gas flow according to desulfurization and denitrification integral process device portal, flue gas
Oxygen content and sulfur dioxide concentration, using fuzzy recognition algorithm, determine current working；The method successively decreased using variable step, it is determined that
The optimum pH value of the desulfurization absorbing liquid under current working；The ammonia compensation dosage under current working is determined using neural network model；
Based on the sulfur dioxide concentration that desulfurization and denitrification integral process device is exported, determined under current working using inner membrance control method
Ammonia controlled quentity controlled variable；Based on the optimum pH value of desulfurization absorbing liquid, desulfurization absorption is controlled with reference to ammonia compensation dosage and ammonia controlled quentity controlled variable
The pH value of liquid.The embodiment of the present invention as controlled variable, ties pH value using the internal model control with feedforward neural network compensation
Structure, substantially increases the rapidity and accuracy of control, it is ensured that the SO2 contents control of flue gas is in the case where index is limited；Walked using becoming
The long strategy for successively decreasing change PH setting values is optimized to sweetening process, and SO2 concentration of flue gas can be made to reach under restriction index, also
Ammonia consumption can be minimized, it is to avoid the phenomenon of the escaping of ammonia occurs, and makes desulfurization and denitrification integral process plant running most
In the state of economic and environment-friendly, the control and optimization of sweetening process is realized, reduce operating cost.
Description of the drawings
Fig. 1 is the schematic diagram of desulfurization and denitrification integral process device；
Fig. 2 is the schematic flow sheet of the coking exhuast gas desulfurization procedure optimization control method according to the embodiment of the present invention；
Fig. 3 is the structural representation of the closed loop two channel relay feedback according to the embodiment of the present invention；
Fig. 4 is the structural representation of the two channel relay according to the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings describing the preferred embodiment of the present invention.It will be apparent to a skilled person that this
A little embodiments are used only for explaining the know-why of the present invention, it is not intended that limit the scope of the invention.
The basic thought of the embodiment of the present invention is：Using desulfurization absorbing liquid pH value as controlled variable, ammonia spirit adds and becomes a mandarin
Amount adopts the internal model control structure with feedforward compensation as control variable, meanwhile, by corresponding optimisation strategy, control is de-
The pH value of sulfur absorbing liquid, finds optimum pH value under current working, so as to ensure desulfurization and denitrification integral process device most economical
Steadily transport under the condition (sulfur dioxide concentration is in indication range and desulfurization absorbing liquid pH value is in neutral range for outlet) of environmental protection
OK.
The embodiment of the present invention proposes a kind of coking exhuast gas desulfurization procedure optimization control method.The method is applied to desulphurization denitration
Integral process device, as shown in Fig. 2 the method can include：
S100：Flue-gas temperature, flue gas flow, oxygen content of smoke gas and two according to desulfurization and denitrification integral process device portal
Sulfur oxide concentration, using fuzzy recognition algorithm, determines current working.
Sweetening process is divided into different operating modes, such as operating mode S1, operating mode S2, operating mode S3 by this step.Wherein, operating mode S1 can be with
It is dense including first entrance flue-gas temperature, first entrance flue gas flow, first entrance oxygen content of smoke gas and first entrance sulfur dioxide
Degree.Operating mode S2 can include that second entrance flue-gas temperature, second entrance flue gas flow, second entrance oxygen content of smoke gas and second enter
Mouth sulfur dioxide concentration.By that analogy, it may be determined that other operating modes.The embodiment of the present invention replaces operating mode with classification code name.
The flow velocity of flue gas, grey density characteristics are different under different operating modes, and specific category number is depending on practical situation.
Above-mentioned fuzzy recognition algorithm is preferably weighted fuzzy identification algorithm.
By taking weighted fuzzy identification algorithm as an example, can include the step of determine current working：
S101：By the flue-gas temperature of desulfurization and denitrification integral process device portal, flue gas flow, oxygen content of smoke gas and dioxy
Change sulphur concentration and be set as state vector.
S102：Cluster is carried out based on state vector and determines sorting criterion, state set is obtained by sorting criterion.
This step is clustered state vector as state sample collection.Clustering method can be calculated for K- averages etc.
Method.
Describe cluster process in detail by taking k- means clustering algorithms as an example below.
The individual objects of K (it takes positive integer) are first randomly selected as initial cluster centre.Then, calculate each object with it is each
The distance between individual seed cluster centre, distributes to each object apart from its nearest cluster centre.So constantly repeat, directly
To meeting end condition.
Using cluster result as sorting criterion, namely the foundation of setting operating mode species.For example, if current historical data
4 classes are divided into by clustering algorithm, then can determine that total operating mode classification is S1, S2, S3, S4Class.This step just operating mode can be divided into S1,
S2... class, obtain state set S, S={ S1, S2……Sn}.Divided by operating mode and recognition methodss, according to cigarette under different operating modes
The difference of gas characteristic " divides and rule control problem ".
S103：By weighted fuzzy identification algorithm, the fuzzy membership relational matrix of state vector and state set is obtained.
As an example, fuzzy membership relational matrix can be as follows：
Wherein, in formula, rij=λR{ei, sj, 0≤rij≤ 1 (i, j=1,2,3 ... n) represent the state quilt in state vector
It is chosen as SjThe probability of operating mode；λRRepresent the probability function of state vector and state set.Define ri=w1×r1j+w2×r2j+...+wn
×rnj, wherein w1, w2 ... wn represents weight.
S104：Based on fuzzy membership relational matrix, current working is determined by maximum membership grade principle.
Describe the process for determining operating mode in detail with a preferred embodiment below.
Setting state vector E={ e1, e2, e3, e4, in formula, e1To e4Entrance flue gas temperature, inlet flue gas stream are represented respectively
Speed, entrance SO2Concentration and inlet flue gas oxygen content.
Sorting criterion is determined based on the cluster of state sample collection, state set S is obtained by sorting criterion；Operating mode is divided into
S1, S2, S3, S4Class.S={ S1, S2, S3, S4}。
By weighted fuzzy identification, the fuzzy membership relational matrix R of state vector and state set is obtained, i.e.,：
In formula, rij=λR{ei, sj, 0≤rij≤ 1 (i, j=1,2,3,4) represents the e in state vectoriDimension index quilt
It is chosen as SjThe probability of operating mode；λRRepresent the probability function of state vector and state set.Define ri=w1×r1j+w2×r2j+w3×r13
+wn×rnj(wherein w represents weight, it is preferable that can respectively take 0.15,0.1,0.7,0.05) for the state belong to SjOperating mode it is general
Rate.
Based on state vector and the fuzzy membership relational matrix R of state set, can determine by maximum membership grade principle
Current working.
For example, compare current state and the different degrees of membership of operating mode 1,2,3,4,5 be respectively 0.2,0.3,0.4,
0.5th, 0.6, wherein maximum membership degree is 0.6, it is determined that current state corresponds to operating mode 5, and operating mode 5 is defined as into current working.
S110：The method successively decreased using variable step, determines the optimum pH value of the desulfurization absorbing liquid under current working.
Specifically, this step can include：Determine that desulfurization is inhaled under current working according to following formula using variable step diminishing method
Receive the optimum pH value of liquid：
In formula, n represents iterationses (n>=2)；PHnRepresent pH value during nth iteration；PHn-1Represent (n-1)th time repeatedly
For when pH value；WmaxAnd WminThe maximum and minimum value of step-length is represented respectively；knRepresent current iteration number of times；kmaxRepresent maximum
Iterationses, preferably take 5.
Due to requiring in technique, PH is typically chosen PH between 4.5 to 6.51=6；
Wherein,For stopping criterion for iteration.
In formula, t0 is represented after change setting value, the initial time after desulfurization and denitrification integral process device stable operation；t0
Depending on can be according to practical situation.Wherein, the condition of stable operation is：During desulphurization denitration, through extremely after regulated variable
It is few just to ensure within more than 2 hours the condition that device enters the even running phase.P (t0) represents that desulfurization and denitrification integral process device goes out
Mouth flue gas SO2Concentration value；PLimitRepresent the SO for limiting2Concentration value, it is therefore preferable to for 50 (mg/m3)；N=60/T+1, wherein T are to adopt
Sample cycle, i.e. n are that poor number of times is sought in sampling in one hour；ε represents termination threshold value, preferably takes 10%, i.e., when the next hour of stable state
Interior mean error is reached within the 10% of limiting concentration.
When stopping criterion for iteration is reached, it is believed that now pH value is optimum pH value.
After optimum pH value is obtained, disturbance factor can not be surveyed due to existing, for example：Ammonia spirit concentration, detection error with
And because desulfurization and denitrification integral process device is aging, Changes in weather etc. reason produce cannot measure uncontrollable disturbance
Deng the optimum pH value can occur minor variations.Thus, the embodiment of the present invention further takes indemnifying measure hereinafter described.
S120：The ammonia compensation dosage under current working is determined using neural network model.
Wherein, neural network model can adopt multiple neutral nets structure in parallel.Preferably, neutral net is for radially
Basis function neural network (RBFNN).By the way that using Parallel neural networks structure, the precision to complex working condition process can be improved,
The generalization ability of strength neural network (model).
By taking RBF neural as an example, in actual applications, three layers of RBF neural can be selected.Wherein it is possible to will be defeated
Enter layer to be configured to comprising desulfurization absorbing liquid circulating load, flue-gas temperature, inlet flue gas flow velocity, inlet flue gas sulfur dioxide concentration, de-
The data acquisition system of sulfur absorbing liquid pH value and inlet flue gas oxygen content and measurable disturbance；Can be using Gaussian function as intermediate layer；
Then weights are multiplied by, you can exported, in embodiments of the present invention, are output as ammonia compensation dosage, i.e. ammonia spirit addition.
The number of ammonia spirit addition directly affects pH value.
The embodiment of the present invention by obtaining ammonia compensation dosage, when have disturbance occur or operating mode be changed when, nerve net
Network calculates at once ammonia compensation dosage, rather than is calculated ammonia controlled quentity controlled variable again after control system feedback.So
Control can be prevented delayed, make ammonia addition play a role much sooner.
S130：It is true using inner membrance control method based on the sulfur dioxide concentration that desulfurization and denitrification integral process device is exported
Determine the ammonia controlled quentity controlled variable under current working.
Specifically, this step can include：
S131：Desulphurization denitration process transmission function mould is set up using closed loop two channel relay feedback frequency domain identification method
Type.
The regulation process of pH value is a typical non-linear process, such as, in the implementation process of the present invention, PH can be with
For 4.5-6.5, in the range of this, according to the characteristic of pH value, PH regulations process can be approximately a linear process.Example
Such as：If pH value is 7 or so, its regulation process has apparent non-linear, pH value from 7 more away from, the regulation process of pH value
It is non-linear weaker.In actual industrial process, PH typically each in 4-5 or so, pH value from 7 farther out, so, the regulation of pH value
Journey it is non-linear very weak.Therefore, the regulation process of pH value can be represented with transferring function by.
Again because coking flue gas desulfurization and denitrification is the process of the big inertia with strong disturbance and large time delay, Open-loop Identification
Process model inevitably will be affected by strong disturbance and the exceeded situation of fume emission occurs and occur.So, the present invention
Embodiment sets up desulphurization denitration process transfer function model using closed loop two channel relay feedback frequency domain identification method.
Fig. 3 schematically illustrates the structural representation of closed loop two channel relay feedback.Wherein, TR represents dual pathways relay
Characteristic；R represents desulfurization absorbing liquid target pH value；Ud represents ammonia controlled quentity controlled variable；D represents that measurable disturbance is (not such as：Ammonia spirit is dense
What the reasons such as degree, detection error, the aging, Changes in weather of desulfurization and denitrification integral process device were produced cannot measure uncontrollable
Disturbance etc.)；Yp represents actual measurement pH value.Wherein, surveying pH value can be from the pipeline for transporting absorbing liquid to tower top installed in desulfurizing tower bottom
In sensor measurement, and will be obtained in transmitting measured values to DCS system by the sensor.Two channel relay TR's
Structure is as shown in Figure 4.Wherein, the input of TR is e.Wherein, e=R-Yp, | e |≤ε.ε represents threshold value, such as it can be set as
0.1。
There is a stable operating point (namely steady operation point) in desulphurization denitration process, the operating point is current state,
A series of value of variables of desulphurization denitration process stabilization can be made.For example：In industrial processes, each variable or parameter are inscribed when a certain
Setting value, such as current time, it is 150 cubes of meter per seconds that ammonia addition is 50 cubes of meter per seconds, desulfurization absorbing liquid circulating load,
The operating point can be considered steady operation point.Setting steady operation point meaning be when working conditions change, can at once by each
State value required for specification of variables to operating mode after change, is conducive to the stability of desulfurization and denitrification integral process plant running.
Due to introducing nonlinear element in the structure of closed loop two channel relay feedback, so, TR structures can be near present operating point
There is limit cycles oscillations.Because the process of limit cycles oscillations is approximately linear process, so, it is possible to use the superposition of linear process
Principle, obtains the output of limit cycles oscillations, i.e.,Wherein,Represent the output of limit cycles oscillations；YpRepresent real
Survey pH value；R represents desulfurization absorbing liquid target pH value.Because limit cycles oscillations is a kind of controlled oscillation, so, frequency characteristic test
During occur not measurable disturbance can be controlled.
By the gain controllable pH value for adjusting TR, so discharge flue gas concentration is within limit value is required.The gain of TR is
For hp and hi in above-mentioned Fig. 4, gain difference, when being recognized, the maximum-minima of PH is just different.For example, Ke Yishe
It is all 2 to put hp and hi, then the excursion of PH is 2-9, and this scope is in actual applications unacceptable.If arranging hp
0.5 is with hi, then PH excursions are 4-5, this scope can be in actual applications to receive.By the gain for adjusting TR
To control pH value, so when transfer function model is recognized, pH value amplitude will not be excessive.
S1311：According to the ammonia addition during desulphurization denitration and the output of limit cycles oscillations, using Fourier transform
Estimation obtains the frequency response of desulphurization denitration process and estimates.
In order to overcome installed in denitration tower top, and the measurement positioned at the flue gas online chemical analysis instrument of smoke outlet is made an uproar
Sound, improves the precision estimated, the embodiment of the present invention adopts the frequency characteristic of the method calculating process of Fourier transform.Wherein, cigarette
Gas online chemical analysis instrument is used for measuring outlet flue gas concentration, i.e., for measurements of sulfur dioxide concentrations law.The online composition point of flue gas
The sulfur dioxide concentration of measurement is uploaded to DCS system by analyzer table.
As an example, the Fourier transform of input/output signal is determined according to following formula：
Wherein, u (t) represents input signal；Y (t) represents output signal；F represents Fourier transform；T0Represent time-domain signal
It is T in the cycle to be0Periodic signal；ω0Represent fundamental frequency, n round numbers.
Fundametal compoment, i.e. n=1, ω=ω can be taken0Situation, then the frequency response that can determine that sweetening process is estimated as：
G (j ω in above formula0) represent frequency characteristic to be estimated in transmission function.
For example, in actual applications, frequency characteristic estimation can be obtainedIts
In,Represent by two channel relay test, in ωiUnder frequency characteristic estimate (preferably Multiple points frequency response
Estimate).
S1312：Estimated according to the frequency response of sweetening process, using identification algorithm and nonlinear optimization algorithm, estimated
Desulphurization denitration process transfer function model.
It is preferably carried out in mode at some, this step specifically can include：
S13121：Estimated according to the frequency response of sweetening process, be defined below object function：
Wherein ciRepresent weights；M represents selected phase place number；That expression is tested by two channel relay,
In ωiUnder Multiple points frequency response estimate；G(jω0) represent frequency characteristic to be estimated；J represents object function.
By above-mentioned object function, estimation becomes a nonlinear optimal problem.
S13122：Based on object function, using identification algorithm, transmission function is obtained.
S13123：Using nonlinear optimization algorithm, the constant in above-mentioned transmission function is solved, so that it is determined that transmission function mould
Type.
Describe the process for determining transfer function model in detail with a preferred embodiment below.
Step A：Based on object function, using identification algorithm, following one order inertia plus delayed transmission function are obtained：
Wherein, GpS () represents the Laplace transform of output and the ratio of input；K represents proportionality constant；T represents that inertia is normal
Number；τ represents hysteresis constant.
Wherein, identification algorithm includes but is not limited to method of least square, particle swarm optimization algorithm.
Step B：Using nonlinear optimization algorithm, proportionality constant in above-mentioned transmission function, inertia constant and delayed are solved
Constant, so that it is determined that the one order inertia of desulphurization denitration process adds delayed transfer function model.
Above-mentioned nonlinear optimization algorithm includes but is not limited to particle cluster algorithm, genetic algorithm.
By using closed loop two channel relay feedback (being abbreviated as TRF) frequency domain identification method, the embodiment of the present invention can be with
The controlled mode of closed loop obtains the Multiple points frequency response of sweetening process according to specified phase place, and then estimates to obtain sweetening process
Transfer function model.
S132：Using desulphurization denitration process transfer function model, two exported based on desulfurization and denitrification integral process device
Sulfur oxide concentration, using internal model control method, determines ammonia controlled quentity controlled variable.
Wherein, inner membrance control method may refer to《Internal model control and its application》, Electronic Industry Press, here no longer goes to live in the household of one's in-laws on getting married
State.
This step is based on desulphurization denitration process transfer function model, using internal model control method, build feedback filter and
Internal mode controller.The titanium dioxide that desulfurization and denitrification integral process device can be exported using feedback filter and internal mode controller
Sulphur concentration is controlled in index.
Wherein, feedback filter adopts single order form：α represents constant.Feedback filtering
Device is used for feedback filtering in internal model control, can improve robustness.
S140：Based on the optimum pH value of desulfurization absorbing liquid, desulfurization suction is controlled with reference to ammonia compensation dosage and ammonia controlled quentity controlled variable
Receive the pH value of liquid.
Disturbance factor can not be surveyed due to existing, for example：Ammonia spirit concentration, detection error and due to desulphurization denitration one
Chemical industry process and equipment is aging, Changes in weather etc. reason is produced cannot measure uncontrollable disturbance etc., and this step is being taken off
On the basis of the optimum pH value of sulfur absorbing liquid, ammonia compensation dosage and ammonia controlled quentity controlled variable are added, so as to controlling desulfurization absorbing liquid
PH value, and then realize the optimal control to coking exhuast gas desulfurization process.
Although each step is described according to the mode of above-mentioned precedence in above-described embodiment, this area
Technical staff is appreciated that to realize the effect of the present embodiment, not necessarily in the execution of such order between different steps,
It (parallel) execution simultaneously or can be performed with the order for overturning, these simple changes all protection scope of the present invention it
It is interior.
So far, technical scheme is described already in connection with preferred implementation shown in the drawings, but, this area
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
On the premise of the principle of invention, those skilled in the art can make the change or replacement of equivalent to correlation technique feature, these
Technical scheme after changing or replacing it is fallen within protection scope of the present invention.
Claims (8)
1. a kind of coking exhuast gas desulfurization procedure optimization control method, the optimal control method is applied to desulfurization and denitrification integral work
Process and equipment, it is characterised in that the control method includes：
Flue-gas temperature, flue gas flow, oxygen content of smoke gas and titanium dioxide according to the desulfurization and denitrification integral process device portal
Sulphur concentration, using fuzzy recognition algorithm, determines current working；
The method successively decreased using variable step, determines the optimum pH value of the desulfurization absorbing liquid under the current working；
The ammonia compensation dosage under the current working is determined using neural network model；
Based on the sulfur dioxide concentration that the desulfurization and denitrification integral process device is exported, determined using inner membrance control method described
Ammonia controlled quentity controlled variable under current working；
Based on the optimum pH value of the desulfurization absorbing liquid, with reference to the ammonia compensation dosage and the ammonia controlled quentity controlled variable to control
State the pH value of desulfurization absorbing liquid.
2. optimal control method according to claim 1, it is characterised in that described according to the desulfurization and denitrification integral work
The flue-gas temperature of process and equipment entrance, flue gas flow, oxygen content of smoke gas and sulfur dioxide concentration, using fuzzy recognition algorithm, it is determined that
Current working, specifically includes：
By the flue-gas temperature, the flue gas flow, the oxygen content of smoke gas and the sulfur dioxide concentration be set as state to
Amount；
Clustered based on the state vector, determined sorting criterion, and state set is obtained by the sorting criterion；
By weighted fuzzy identification algorithm, the fuzzy membership relational matrix of the state vector and the state set is obtained；
Based on the fuzzy membership relational matrix, the current working is determined by maximum membership grade principle.
3. optimal control method according to claim 1, it is characterised in that the method that the employing variable step successively decreases, really
The optimum pH value of the desulfurization absorbing liquid under the fixed current working, specifically includes：
The optimum pH value of the desulfurization absorbing liquid under the current working is determined according to following formula using variable step diminishing method：
In formula, the n represents iterationses, n>=2；The PHnRepresent pH value during nth iteration；The PHn-1Expression n-th-
PH value during 1 iteration；The WmaxWith the WminThe maximum and minimum value of step-length is represented respectively；The knRepresent current iteration
Number of times；The kmaxRepresent maximum iteration time；T0 represents the starting after the desulfurization and denitrification integral process device stable operation
Time；The P (t0) represents the desulfurization and denitrification integral process device exiting flue gas sulfur dioxide concentration value；The PLimitRepresent
The sulfur dioxide concentration value of restriction；The T represents the sampling period；The ε represents termination threshold value.
4. optimal control method according to claim 1, it is characterised in that the neural network model is multiple radial direction bases
Function Neural Network model in parallel.
5. optimal control method according to claim 1, it is characterised in that described to be filled based on desulfurization and denitrification integral process
The sulfur dioxide concentration of outlet is put, the ammonia controlled quentity controlled variable under current working is determined using inner membrance control method, specifically included：
Desulphurization denitration process transfer function model is set up using closed loop two channel relay feedback frequency domain identification method；
Using the desulphurization denitration process transfer function model, based on the dioxy that the desulfurization and denitrification integral process device is exported
Change sulphur concentration, using internal model control method, determine ammonia controlled quentity controlled variable.
6. optimal control method according to claim 5, it is characterised in that the employing closed loop two channel relay feedback frequency
Domain discrimination method is specifically included setting up desulphurization denitration process transfer function model：
According to the ammonia addition during the desulphurization denitration and the output of limit cycles oscillations, estimated using Fourier transform
Frequency response to the desulphurization denitration process is estimated；
Estimated according to the frequency response of the sweetening process, using identification algorithm and nonlinear optimization algorithm, estimate desulfurization and take off
Nitre process transfer function model.
7. optimal control method according to claim 6, it is characterised in that described to be rung according to the frequency of the sweetening process
Should estimate, using identification algorithm and nonlinear optimization algorithm, estimate desulphurization denitration process transfer function model, specifically include：
Estimated according to the frequency response of sweetening process, be defined below object function：
Wherein, the ciRepresent weights；The m represents selected phase place number；It is describedRepresent special by dual pathways relay
Property test, in ωiUnder Multiple points frequency response estimate；G (the j ω0) represent frequency characteristic to be estimated；The J represents institute
State object function；
Based on object function, using identification algorithm, transmission function is obtained；
Using nonlinear optimization algorithm, the constant in the transmission function is solved, so that it is determined that desulphurization denitration process transmission
Function model.
8. optimal control method according to claim 7, it is characterised in that the transmission function adds delayed for one order inertia
Transmission function；
The employing nonlinear optimization algorithm, solves the constant in the transmission function, so that it is determined that the desulphurization denitration process
Transfer function model, specifically includes：Using nonlinear optimization algorithm, the proportionality constant, inertia in the solution transmission function is normal
Number and hysteresis constant, so that it is determined that the one order inertia of the desulphurization denitration process adds delayed transfer function model.
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