CN107976976A - A kind of iron and steel enterprise's gas consumption equipment timing optimization method - Google Patents
A kind of iron and steel enterprise's gas consumption equipment timing optimization method Download PDFInfo
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Abstract
The invention discloses a kind of iron and steel enterprise's gas consumption equipment timing optimization method.On the basis of gas consumption characteristic model is established, establish gas consumption equipment timing optimization model, the model changes number at least for optimization aim so that power plant for self-supply's input thermal fluctuation is minimum with equipment running status, with power plant's coal gas heat, calorific value, flow etc. for constraints, mathematical problem is converted into 01 integer programming problems.The present invention solves mathematical problem model using multi-objective genetic algorithm, and the Choice of gene is designed, and " 0 ", the position of " 1 " in a matrix are arranged to the gene of algorithm, realize the simple and effective optimization of equipment sequential.
Description
Technical field
The invention belongs to iron and steel enterprise's energy source optimization scheduling field, a kind of more particularly to iron and steel enterprise's gas consumption equipment
Timing optimization method.
Background technology
Iron and steel enterprise's coal gas system is complicated, and equipment is numerous, is divided into Gas Production equipment, gas consumption equipment, coal gas
Buffer equipment.Production and consumption characteristics of each equipment to blast furnace gas, coke-stove gas and coal gas of converter are different, thus usually
The production consumption imbalance problem of coal gas system can occur, not only result in the waste of the energy, can also seriously affect iron and steel enterprise
Normal production.
Currently for the optimization problem of iron and steel enterprise's coal gas system, existing numerous studies simultaneously establish numerous Optimized Operations
Model, model be Optimal Operation Model (dynamic equilibrium of 5 minutes or so) in the short time mostly with to coal gas in the long period
Total amount Optimized model (static balancing of year, month, day), the former using adjust the amount of storage of gas chamber and power plant for self-supply and consumption as
Means carry out gas balance, and the latter is to adjust each equipment output as means progress gas balance, for set production in day border
Gas consumption equipment timing optimization under the conditions of duration (i.e. according to the production duration of each equipment in one day known to production requirement) is asked
Topic research is less, and there has been no research to use gas consumption equipment timing optimization method, is produced as iron and steel enterprise's coal gas system is adjusted
The means of balance optimizing are consumed, the present invention starts with from this angle, is that iron and steel enterprise's energy source optimization scheduling adjusts hand supplemented with a kind of
Section.
The thermal fluctuation problem of input power plant is ignored in conventional research mostly, and gas consumption equipment timing optimization is easily made
Frequently change into gas consumption operating status.In addition, many coal gas Optimal Operation Models have been directed to one-zero programming problem at present
(dominated variable value can only be 0 or 1 in model, and this kind of integer programming problem is known as one-zero programming problem), there is a small amount of research at present
Model comprising Zero-one integer programming problem is solved using genetic algorithm, but the gene set-up mode in its solution, make
It is more to obtain gene dimension, adds the solution difficulty of model.
The content of the invention
In order to solve the technical problem that above-mentioned background technology proposes, the present invention is intended to provide a kind of iron and steel enterprise's gas consumption
Equipment timing optimization method, can carry out timing optimization to iron and steel enterprise, maintaining coal gas production consumption balance, reducing input and provide electricity for oneself
Factory's thermal fluctuation, while reducing gas consumption equipment running status change number, reduces model solution difficulty.
In order to realize above-mentioned technical purpose, the technical scheme is that：
A kind of iron and steel enterprise's gas consumption equipment timing optimization method, comprises the following steps：
Step 1：By the device class of all consumption coal gas, it is divided into Gas Production equipment, gas consumption equipment and provides electricity for oneself
Factory, specific equipment is assigned in these classifications, establishes Gas Production model and gas consumption model respectively；
Step 2：Gas consumption equipment timing optimization model is established, which includes object function and constraints, described
Object function include power plant input thermal fluctuation minimize and gas consumption equipment running status change number minimize, it is described about
Beam condition includes power plant for self-supply's heat and constrains and input power plant's gas volume with calorific value on the occasion of constraint；
Step 3：Established using the non-linear Zero-one integer programming model based on II genetic algorithms of NSGA- come solution procedure 2
Timing optimization model.
Further, in step 1, the Gas Production equipment includes blast furnace, coke oven and converter, these three equipment are established
Following Gas Production model：
fpro_j(t)=fsum_j_t (1)
In above formula, fpro_j(t) represent j-th of Gas Production equipment when t is small in Gas Production amount, fsum_j_tRepresent
J-th of device history produces the average value of volume per hour.
Further, in step 1, the gas consumption equipment, which is further divided into, can not be interrupted production equipment and can be interrupted
Production equipment.
Further, the production equipment that can not be interrupted establishes following gas consumption device characteristics model：
(1) blast funnace hot blast stove, coke-oven plant, steel mill, ironmaking mouth and the gas consumption device characteristics model of dynamics factory：
In above formula, fpro_j(t) represent j-th of Gas Production equipment when t is small in Gas Production amount, fsum_j_tRepresent
J-th of device history produces the average value of volume per hour；
(2) the gas consumption device characteristics model of cold rolling kind equipment：
In above formula, fCon_ cold rollings(t) represent cold-rolling equipment when t is small in gas consumption volume；Cold rolling is represented to set
Standby history consumes the average value of volume per hour；α is coldRollRepresent the insulation consumption coefficient of cold-rolling equipment.
Further, the production equipment of being interrupted includes sintering plant, pelletizing plant, limekiln and hot rolling kind equipment, these
Equipment establishes following gas consumption device characteristics model：
In above formula, fcon_z(t) represent z-th of equipment when t is small in gas consumption volume,Represent history z-th
Equipment consumes the average value of volume, α per hourzRepresent the insulation consumption coefficient of z-th of equipment.
Further, in step 1, the power plant for self-supply establishes following gas consumption characteristic model：
(a) rich coal gas (meeting remaining coal gas after the needs of all gas consumption equipment) heat model：
In above formula, QtRepresent the heat of the mixed gas of t little Shi power plants for self-supply input, hbfg,hcog,hcfgRepresent respectively
Blast furnace, coke oven, the calorific value of converter, atjRepresent the coal gas volume that j-th of equipment is consumed when t is small, fsumcfg_tIt is small to represent t
When interior coal gas of converter production volume, fsumbfg_tRepresent t it is small when interior blast furnace gas production volume, fsumcog_tRepresent t it is small when it is interior
Coke-stove gas produces volume, hjRepresent the calorific value of j-th of equipment consumption coal gas；
(b) mixing substitutes (low heat value mixes calorific value of gas in replacement with high heating value gas) model：
fcog=(hcfg-hbfg)/(hcog-hbfg)*fcfg (6)
fbfg=(hcog-hcfg)/(hcog-hbfg)*fcfg (7)
In above formula, fcfgRepresent coal gas of converter volume, fcog、fbfgRepresent the coke-stove gas and blast furnace coal for replacing coal gas of converter
The volume of gas；
(c) surplus gas volume-based model：
In above formula, fcfg_before_tBefore substituting coal gas of converter for high coke oven gas, input power plant for self-supply coal gas of converter is calculated
Volume；fcfg_tRepresent t it is small when input power plant for self-supply coal gas of converter volume；PjcfgJ-th of gas consumption equipment is represented to disappear
Coal gas of converter proportion in the mixed gas of consumption；PjcogRepresent coke oven in the mixed gas of j-th of gas consumption equipment consumption
Coal gas proportion；PjbfgRepresent blast furnace gas proportion in the mixed gas of j-th of gas consumption equipment consumption；fcog_cfg
Required coke-stove gas volume when being substituted for coal gas of converter empty portions with high coke oven gas；fbfg_cfgFor coal gas of converter vacancy portion
Divide blast furnace gas volume required when being substituted with high coke oven gas；FSubstituteTo substitute coal gas of converter using above-mentioned mixing alternative model
When, the function between coal gas of converter volume and blast furnace, coke oven volume；fbfg_tRepresent t it is small when input power plant for self-supply blast furnace coal
Air volume；fcog_tRepresent t it is small when input power plant for self-supply coke-stove gas volume, N is gas consumption number of devices.
(d) surplus gas calorific value model：
In above formula, heattRepresent t it is small when surplus gas calorific value.
Further, in step 2, establish using the time as row, device numbering is the time series matrix Z of row, and equipment is just
Often operation is represented with " 1 ", and insulation of equipment state is represented with " 0 ", and optimization problem is equivalent to the value of each element in solution matrix Z,
And the value of each element is 0 or 1, sometime sequence matrix is as follows：
The gas consumption equipment timing optimization model of foundation is as follows：
Thermal fluctuation minimizes：
Gas consumption equipment running status change number and minimize：
Surplus gas is on the occasion of constraint：
Power plant for self-supply's heat is constrained with calorific value：
In above formula, average_Q represents the average value that power plant for self-supply inputs mixed gas heat per hour, zjiRepresent matrix
The i-th row jth column element in Z, heatminRepresent the calorific value lower limit of input power plant for self-supply mixed gas, heatmaxRepresent input certainly
The calorific value upper limit of standby power plant mixed gas, QminRepresent the lower limit of hour input power plant for self-supply heat, QmaxRepresent hour input certainly
The upper limit of standby power plant heat.
Further, the detailed process of step 3 is as follows：
(1) timing optimization model solution is carried out using genetic algorithm, and by " 0 " or " 1 " in time series
Input variable of the position as algorithm in matrix Z；
(2) constraint violation degree function is established：
Dis_f1t=(| heatt-heatmin|+|heatt-heatmax|-(heatmax-heatmin))2 (20)
Dis_f2t=(| Qt-Qmin|+|Qt-Qmax|-(Qmax-Qmin))2 (21)
In above formula, dis_f1t, dis_f2t represent the mixed gas calorific value and heat of i-th hour input power plant for self-supply respectively
Degree of restraint is violated, dis_f3t, dis_f4t represent high coke oven gas input power plant flow and violate degree of restraint, FViolateTo be total
Constraint violation degree function；
Constraint violation degree function weighs violation journey of the individual to constraints in gas consumption equipment timing optimization model
The size of degree, restriction range degree is bigger, and the individual is poorer, and all individuals can be arranged according to constraint violation degree in algorithm
Sequence, comes the individual nearer from feasible zone of foremost, is more probably feasible solution；
(3) according to the non-dominated ranking method of NSGA- II, each individual non-dominated ranking grade is obtained, grade is smaller,
Fewer better than the individual individual amount, the individual is more outstanding；The link of individual is being selected, using individual non-dominated ranking etc.
Level together decides on individual quality with two parameters of constraint violation degree rank.
The beneficial effect brought using above-mentioned technical proposal：
The present invention proposes a kind of coal gas system optimization method -- timing optimization different from the past, is iron and steel enterprise's coal gas
Optimized Operation supplements a kind of means of Optimized Operation.When solving-optimizing model, Mathematical Planning different from the past is asked
Solution mode, avoids a large amount of mathematical formulaes and calculating process, employs the solution scheme of genetic algorithm.Solving 0-1 paced beats
When the problem of drawing, it is input quantity not make each matrix element in time series matrix, but uses the position of " 0 " or " 1 "
For input quantity, the number of input quantity is reduced, reduces the solution difficulty of model.
Brief description of the drawings
Fig. 1 is the overall flow figure of the present invention；
Fig. 2 is certain iron and steel enterprise's by-product gas system diagram in embodiment；
Fig. 3 is thermal fluctuation minimum operating mode and actual comparison figure after embodiment timing optimization.
Embodiment
Below with reference to attached drawing, technical scheme is described in detail.
A kind of iron and steel enterprise's gas consumption equipment timing optimization method, including three parts, Part I are gas fittings
To classify and establish Gas Production, consumption characteristics model, Part II is the foundation of gas consumption equipment timing optimization model, the 3rd
Part is the solution of model, a kind of procedural block diagram such as Fig. 1 of iron and steel enterprise's timing optimization method based on genetic algorithm of the invention
It is shown.The object that model is directed to is the coal gas system of certain iron and steel enterprise, as shown in Fig. 2, the system includes Gas Production equipment,
Gas consumption equipment, coal gas buffering equipment, coal gas mixing station.
1. the embodiment of above-mentioned Part I：
Step 1：Gas fittings is classified：Gas fittings is divided into Gas Production equipment, gas consumption equipment, power plant for self-supply's (coal
Gas buffers equipment).
Gas Production equipment includes in the system：Blast furnace, converter, coke oven.Gas consumption equipment includes：Blast funnace hot blast stove,
Coke-oven plant, steel mill, smelts iron mouth, dynamics factory, cold-rolling mill, colored steel factory, 550 sintering (2 lines), 180 sintering (4 lines), pelletizing
Factory, limekiln, mechanism company, medium plate mill, Heavy Plate Plant, hot rolling mill, a milling train.Coal gas buffering equipment includes power plant for self-supply, height
Producer gas cabinet, converter gas cabinet, coke-oven coal gas holder.Coal gas mixing station has 1#~4# hybrid station.Each equipment has respective coal gas
Pipeline leads to three kinds of coal gas main pipelines, or leads to coal gas mixing station, and coal gas mixing station obtains coal gas from three kinds of coal gas main pipelines again.
It should be noted that it is not to connect per each equipment all while using the mixed gas of three kinds of coal gas in the pipeline of Figure of description 2
Connect in mode as can be seen that the coal gas constituent that each equipment uses is not quite similar.
In the production schedule of border, the production task (production duration) of each equipment can be obtained from day of iron and steel enterprise：Blast furnace,
When converter, coke oven, blast funnace hot blast stove, coke-oven plant, steel mill, ironmaking mouth, dynamics factory's operation 24 are small, cold-rolling mill, the operation of colored steel factory
20 it is small when, above equipment is because the characteristics of continuous production, is all not involved in timing optimization, therefore in Optimized model, as fixation
Value.550 sintering (2 lines) operation 32 it is small when, 180 sintering (4 lines) operation 40 it is small when, a milling train operation 20 it is small when, limekiln
Run 22 it is small when, pelletizing plant operation 20 it is small when, medium plate mill operation 20 it is small when, mechanism company operation 19 it is small when, Zhong Hou factories operation 20
Hour, when hot continuous rolling operation 21 is small, the industry characteristics of above equipment are divided into normal operation and keeping warm mode, due to its operating status
Between change, can be used as adjustment equipment participate in timing optimization.
It can be obtained by the historical data of certain iron and steel enterprise：
(1) Gas Production equipment：
Blast furnace gas aerogenesis flow：2673819m3/h
Coke-stove gas aerogenesis flow：214149.2m3/h
Coal gas of converter aerogenesis flow：163020.4m3/h
(2) gas consumption equipment：
1. timing optimization equipment is not may participate in：
Blast funnace hot blast stove gas consumption flow：1115739m3/ h (blast furnace gas)
Smelt iron mouth gas consumption flow：3880.417m3/ h (blast furnace gas)
Dynamics factory's gas consumption flow：83259.58m3/ h (blast furnace gas)
Steel mill gas consumption flow：46697.5m3/ h (blast furnace gas)
Coke-oven plant's gas consumption flow：547097.1m3/ h (blast furnace gas), 1610.417m3/ h (coke-stove gas)
Colored steel factory gas consumption flow：
Heating period gas consumption flow is：16407.3m3/ h, insulation consumption coefficient are 1/3 (coke-stove gas)
Cold-rolling mill gas consumption flow：
Heating period gas consumption flow is：76423.6m3/ h, (76.11% is blast furnace coal to insulation consumption coefficient for 1/3
Gas, 23.89% is coke-stove gas)
2. it may participate in timing optimization equipment：
One milling train gas consumption flow：99287m3/ h (blast furnace gas)
550 sintering single line gas consumption flows (2 lines)：31009m3/ h (coal gas of converter)
180 sintering single line gas consumption flows (4 lines)：14133m3/ h (blast furnace gas 58.39%, coke-stove gas 26.26%,
Coal gas of converter 15.36%)
Limekiln gas consumption flow：55400m3/ h (coal gas of converter)
Pelletizing plant's gas consumption flow：65200m3/ h (blast furnace gas 93.74%, coal gas of converter 6.26%)
Medium plate mill's gas consumption flow：81344m3/ h (blast furnace gas 58.36%, coke-stove gas 26.26%, coal gas of converter
15.36%)
Mechanism company gas consumption flow：3900m3/ h (blast furnace gas 64.52%, coke-stove gas 35.48%)
Heavy Plate Plant gas consumption flow：80000m3/ h (blast furnace gas 63.5%, coke-stove gas 25.57%, coal gas of converter
10.93%)
Hot continuous rolling gas consumption flow：302300m3/ h (blast furnace gas 76.11%, coke-stove gas 23.89%)
The insulation consumption coefficient of the kind equipment takes 0.2.
Step 2：Establish Gas Production device characteristics model
F in formulapro_bfg(t)、fpro_cog(t)、fpro_cfg(t) it is respectively blast furnace gas, coke-stove gas, coal gas of converter is in t
The aerogenesis volume of hour.
Step 3：Establish gas consumption device characteristics model
The consumed flow historical data of above-mentioned each equipment coal gas is brought into formula (2), (3), (4) respectively, can be obtained each
The specific consumption characteristics model of gas consumption equipment, such as the blast funnace hot blast stove that can not be interrupted in production equipment, colored steel factory, can between
(unit is all m for pelletizing plant in disconnected production equipment3)。
fCon_ blast funnace hot blast stoves(t)=1115739t ∈ normal operations (26)
F in formulaCon_ blast funnace hot blast stoves(t) it is the consumption volume of t interior blast funnace hot blast stoves when small, f in formulaCon_ colored steels factory(t) it is small for t
The consumption volume of Shi Nei colored steels factory, fCon_ pelletizing plants(t) it is the consumption volume of t little Shi Nei pelletizing plants.
Step 4：Establish the consumption characteristics model of power plant for self-supply
Step 4.1：Surplus gas heat model
Obtained by the historical data of the iron and steel enterprise, hbfg=3344kJ/m3, hcfg=6688kJ/m3, hcog=17347kJ/
m3.Colored steel factory calorific value is 17347kJ/m3, ironmaking mouth calorific value is 17347kJ/m3, coke-oven plant's calorific value is 3385.1kJ/m3, power
Factory's calorific value is 3344kJ/m3, a milling train calorific value is 3344kJ/m3, blast funnace hot blast stove calorific value is 3344kJ/m3, 550 sintering calorific values
For 6688kJ/m3, steel mill calorific value is 6688kJ/m3, limekiln calorific value is 6688kJ/m3, pelletizing plant's calorific value is 3553.4kJ/
m3, medium plate mill's calorific value is 7534.2kJ/m3, mechanism company calorific value is 8312.2kJ/m3, 180 sintering calorific values are 7534.2kJ/m3,
Heavy Plate Plant calorific value is 7290.3kJ/m3, hot continuous rolling calorific value is 6688.8kJ/m3, cold-rolling mill calorific value is 6688.8kJ/m3。
The consumption of calorie of all gas consumption equipment (including sintering line) is subtracted with the total amount of heat that coal gas is produced in certain hour,
The surplus gas heat of power plant for self-supply is as inputted, i.e., brings above-mentioned each equipment calorific value into formula (5) and establishes specific surplus gas
Heat model, as shown in formula (29)：
Step 4.2：Mix alternative model
The coke-stove gas of high heating value is mixed with the blast furnace gas of low heat value, instead of the coal gas of converter of middle calorific value, keeps mixing
Front and rear coal gas total flow is remained unchanged with coal gas total amount of heat.Two kinds of coal gas mixing are substituted always flows according to the front and rear coal gas of input mixing
Conservation and coal gas total amount of heat conservation are measured, as shown in formula (30)：
fbfg+fcog=fcfg
fbfg*hbfg+fcog*hcog=fcfg*hcfg
(30)
I.e.
Blast furnace gas and coke-stove gas ratio shared in mixed gas can be obtained：
Pcog=(hcfg-hbfg)/(hcog-hbfg)=(6688-3344)/(17347-3344)=23.88% (32)
Pbfg=(hcog-hcfg)/(hcog-hbfg)=(17347-6688)/(17347-3344)=76.12% (33)
Step 4.3：Surplus gas volume-based model
Within each small period, with high, burnt, coal gas of converter gas production, deduct what is consumed in each this hour of equipment
High Jiao turns gas consumption volume, then is met surplus gas volume (may be negative) after gas consumption device requirement.When turn
When producer gas is insufficient, is mixed and substituted using coal gas, mix the converter coal for substituting deficiency with coke-stove gas with blast furnace gas more than needed
Gas, last remaining all coal gas are passed through power plant for self-supply, i.e., such as formula (8)-(13).
Step 4.4：Surplus gas calorific value model
The heat of surplus gas is obtained by step 4.1, the volume of three kinds of surplus gas, surplus gas are obtained by step 4.3
Heat divided by three kinds of surplus gas cumulative volumes are the calorific value of surplus gas, such as formula (14).
2 establish timing optimization model
Timing optimization model includes object function and constraints, and the present invention inputs the ripple of power plant for self-supply's heat in order to reduce
It is dynamic, avoid equipment running status from frequently changing, the optimization aim of the model there are two：
Step 1：Establish optimization aim
First aim inputs thermal fluctuation to minimize power plant for self-supply, can by the surplus gas heat model of formula (29)
The heat for inputting power plant for self-supply per hour is known, using the concept attainment thermal fluctuation degree function of variance, such as formula (16), and because cold
Roll the specific periodicity of gas consumption of kind equipment, preceding 20 hours consume coal gas, it is rear 4 it is small when do not consume coal gas, therefore by formula (16)
Wave function adjusted, as shown in formula (34).
Average_Q in formula1The average value of heat, average_Q are inputted when small for preceding 202Heat is inputted when small for rear 4
Average value.
Second target changes number to minimize equipment running status, and element is included in formula (15) time series matrix
" 1 " and " 0 ", when in continuous time, the operating status of equipment is by " 0 " change " 1 ", or during " 1 " change " 0 ", that is, thinks the operation of equipment
State is changed, and can be subtracted each other in every a line of matrix with previous element and the latter element, all poor quadratic sums
The as change number of operating status, can obtain formula (17), and the equipment that may participate in timing optimization in the iron and steel enterprise has：550
Sinter (2 lines), 180 sintering (4 lines), a milling train, limekiln, pelletizing plant, medium plate mill, mechanism company, Heavy Plate Plant, heat is even
Roll, totally 13 equipment (line), i.e. N=15, then operating status changes number and minimizes as shown in formula (35).
Step 2：Determine constraints
Constraint one：According to the actual requirement of power plant for self-supply of iron and steel enterprise, the calorific value upper limit is 17347kJ/m3, calorific value lower limit is
3800kJ/m3, the heat upper limit is 2000GJ/h, and heat lower limit is 5000GJ/m3, input the mixed gas of power plant for self-supply, it is ensured that
For its calorific value no more than power plant for self-supply's claimed range, the heat inputted per hour can not exceed the acceptable heat model of power plant
Enclose, as shown in formula (36).
Constraint two：In the mathematical model of the present invention, coal gas is that first meet the needs of gas consumption equipment, is then inputted again
Power plant, if coal gas of converter deficiency, mixing substitute needed for consumption equipment.After mixing substitutes, it need to ensure to be input to power plant
In surplus gas, the volume of the blast furnace gas of low heat value and the coke-stove gas of high heating value is just, otherwise, can there are gas consumption to set
Standby coal gas demand can not be satisfied, and be constrained as shown in formula (37).
3. the solution of model
Step 1：Gene designs：The present invention is longer than insulation duration in order to reduce the gene dimension of individual, by run time
Equipment, the position of its soaking time section is represented with gene respectively, and the equipment of run time phrase insulation duration is distinguished with gene
Represent the position of its run time section.Such as：When normal operation 22 is small in limekiln one day, when insulation 2 is small.If using each
A hour respectively then needs 24 genes with a gene representation；If gene designing scheme using the present invention, two bases are only needed
Because come record the 2 of insulation it is small when when 24 is small in position, when 5 points to 6 points insulation once, 14 points to 15 points insulation once
When, 2 genes are respectively 6 and 15.
Step 2：Constrain processing scheme：Constraint violation degree function, including 4 parts are established, respectively expression (36) formula
(37) constraint formula is unsatisfactory for degree in, and function is bigger, it is more remote to represent that solution deviates feasible zone, such as formula (38), formula (39), formula
(40), shown in formula (41).By 4 partial summations, to violate constraint function, as shown in formula (42).
Dis_f1t=(| heatt-3800|+|heatt-17347|-(17347-3800))2 (38)
Dis_f2t=(| Qt-2*109|+|Qt-5*109|-(5*109-2*109))2 (39)
The present invention is solved using II multi-objective Genetics of NSGA-, and constraint processing method is：Constraint violation degree function value
Sequence and the integrated ordered definite individual quality of non-bad grade sequence.The present invention simultaneously designs individual gene, formula
(1) 13 rows 24 arrange in matrix, totally 312 elements, element value " 0 " or " 1 ".When determining one group of solution, i.e. 312 elements
When value all determines, then according to gas consumption model, it may be determined that all gas consumption equipment and the coal in power plant for self-supply's each hour
Gas consumption, then can be in the hope of interior surplus gas amount per hour, and finally obtains two target function values of the group solution, and total
Restriction range degree function value.
Because the Optimized model of the present invention is Bi-objective, therefore what is finally obtained is one group of Pareto Noninferior Solution Set, and solution is concentrated, fortune
Row state change number is from while dropping to 18 times 54 times, and power plant's thermal fluctuation desired value is from 8.8*1016Rise to 1,3*1018。
Such as the optimization solution that thermal fluctuation minimizes, carrying out practically operating mode are as follows：
550 one lines of sintering：6 points to 13 points, 17 points to 20 points, 21: 24 normal operations, other times insulation.
550 sintering two wires：0 point to 4 points, 8 points to 20 points, 21 points to 24 normal operations, other times insulation.
180 one lines of sintering：0 point to 7 points, 8 points to 9 points, 11 points to 12 points, 21 points to 24 normal operations, other times
Insulation.
180 sintering two wires：0 point to 4 points, 13 to 17 points, 21 points to 24 normal operations, other times insulation.180 sintering
Three lines：0 point to 1 point, 2 points to 3 points, 5 points to 7 points, 8 points to 9 points, 14 points to 16 points, 21 points to 24 normal operations, other when
Between keep the temperature.
180 four lines of sintering：0 point to 3 points, 4 points to 6 points, 12 points to 14 normal operations, other times insulation.One milling train：
0 point to 4 points, 7 points to 20 points, 21 points to 24 normal operations, other times insulation.
Limekiln：0 point to 2 points, 3 points to 20 points, 21 points to 24 normal operations, other times insulation.
Pelletizing plant：2 points to 8 points, 11 points to 20 points, 21 points to 24 normal operations, other times insulation.
Medium plate mill：4: 24 normal operations, other times insulation.
Mechanism company：0 point to 3 points, 5 points to 10 points, 13 points to 24 normal operations, other times insulation.Heavy Plate Plant：
0 point to 20 normal operation, other times insulation.
Hot continuous rolling：0 point to 21 normal operation, other times insulation.
Power plant's input heat of history actual operating data and the timing optimization operating mode is normalized respectively, is gone through
Power plant's input thermal fluctuation variance of history real data is 0.0083, power plant's input thermal fluctuation variance of the timing optimization operating mode
For 0.0002167, it is thermal fluctuation after timing optimization of the embodiment of the present invention to be reduced than actual fluctuation shown in 97.59%, Fig. 3
Minimize operating mode and actual comparison figure.It can be seen that the timing optimization method of the present invention can substantially reduce the heat ripple of power plant
It is dynamic, due to the selection of Bi-objective, two targets of number and power plant's thermal fluctuation can be changed to operating status at the same time and optimized.
Embodiment is merely illustrative of the invention's technical idea, it is impossible to protection scope of the present invention is limited with this, it is every according to
Technological thought proposed by the present invention, any change done on the basis of technical solution, each falls within the scope of the present invention.
Claims (8)
1. a kind of iron and steel enterprise's gas consumption equipment timing optimization method, it is characterised in that comprise the following steps：Step 1：By institute
There is the device class of consumption coal gas, be divided into Gas Production equipment, gas consumption equipment and power plant for self-supply, specific equipment is assigned to this
In a little classifications, Gas Production model and gas consumption model are established respectively；Step 2：Establish gas consumption equipment timing optimization mould
Type, the model include object function and constraints, and the object function includes power plant's input thermal fluctuation minimum and coal gas
Consume equipment running status and change number minimum, the constraints constrains and input including power plant for self-supply's heat electric with calorific value
Factory's gas volume is on the occasion of constraint；Step 3：Walked using based on the non-linear Zero-one integer programming model of II genetic algorithms of NSGA- to solve
The rapid 2 timing optimization models established.
2. iron and steel enterprise's gas consumption equipment timing optimization method according to claim 1, it is characterised in that in step 1,
The Gas Production equipment includes blast furnace, coke oven and converter, these three equipment establish following Gas Production model：
fpro_j(t)=fsum_j_t
In above formula, fpro_j(t) represent j-th of Gas Production equipment when t is small in Gas Production amount, fsum_j_tRepresent jth
A device history produces the average value of volume per hour.
3. iron and steel enterprise's gas consumption equipment timing optimization method according to claim 1, it is characterised in that in step 1,
The gas consumption equipment, which is further divided into, can not be interrupted production equipment and can be interrupted production equipment.
4. iron and steel enterprise's gas consumption equipment timing optimization method according to claim 3, it is characterised in that it is described can not between
Disconnected production equipment establishes following gas consumption device characteristics model：
(1) blast funnace hot blast stove, coke-oven plant, steel mill, ironmaking mouth and the gas consumption device characteristics model of dynamics factory：
T ∈ are normally produced
In above formula, fpro_j(t) represent j-th of Gas Production equipment when t is small in Gas Production amount, fsum_j_tRepresent jth
A device history produces the average value of volume per hour；
(2) the gas consumption device characteristics model of cold rolling kind equipment：
In above formula, fCon_ cold rollings(t) represent cold-rolling equipment when t is small in gas consumption volume；Represent cold-rolling equipment history
The average value of volume is consumed per hour；αCold rollingRepresent the insulation consumption coefficient of cold-rolling equipment.
5. iron and steel enterprise's gas consumption equipment timing optimization method according to claim 3, it is characterised in that described to be interrupted
Production equipment includes sintering plant, pelletizing plant, limekiln and hot rolling kind equipment, these equipment establish following gas consumption device characteristics
Model：
In above formula, fcon_z(t) represent z-th of equipment when t is small in gas consumption volume,Represent z-th of equipment of history
The average value of volume, α are consumed per hourzRepresent the insulation consumption coefficient of z-th of equipment.
6. iron and steel enterprise's gas consumption equipment timing optimization method according to claim 1, it is characterised in that in step 1,
The power plant for self-supply establishes following gas consumption characteristic model：
(a) rich coal gas heat model：
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In above formula, QtRepresent the heat of the mixed gas of t little Shi power plants for self-supply input, hbfg,hcog,hcfgRepresent respectively blast furnace,
Coke oven, the calorific value of converter, atjRepresent the coal gas volume that j-th of equipment is consumed when t is small, fsumcfg_tRepresent t it is small when interior turn
Producer gas produces volume, fsumbfg_tRepresent t it is small when interior blast furnace gas production volume, fsumcog_tRepresent t it is small when interior coke-oven coal
Gas produces volume, hjRepresent the calorific value of j-th of equipment consumption coal gas；
(b) alternative model is mixed：
fcog=(hcfg-hbfg)/(hcog-hbfg)*fcfg
fbfg=(hcog-hcfg)/(hcog-hbfg)*fcfg
In above formula, fcfgRepresent coal gas of converter volume, fcog、fbfgRepresent the coke-stove gas and blast furnace gas for replacing coal gas of converter
Volume；
(c) surplus gas volume-based model：
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In above formula, fcfg_before_tBefore substituting coal gas of converter for high coke oven gas, the body of input power plant for self-supply coal gas of converter is calculated
Product；fcfg_tRepresent t it is small when input power plant for self-supply coal gas of converter volume；PjcfgRepresent j-th of gas consumption equipment consumption
Coal gas of converter proportion in mixed gas；PjcogRepresent coke-stove gas in the mixed gas of j-th of gas consumption equipment consumption
Proportion；PjbfgRepresent blast furnace gas proportion in the mixed gas of j-th of gas consumption equipment consumption；fcog_cfgTo turn
Producer gas empty portions coke-stove gas volume required when being substituted with high coke oven gas；fbfg_cfgUsed for coal gas of converter empty portions
High coke oven gas blast furnace gas volume required when substituting；FSubstituteDuring to substitute coal gas of converter using above-mentioned mixing alternative model, turn
Function between producer gas volume and blast furnace, coke oven volume；fbfg_tRepresent t it is small when input power plant for self-supply blast furnace coal gas
Product；fcog_tRepresent t it is small when input power plant for self-supply coke-stove gas volume, N is gas consumption number of devices.
(d) surplus gas calorific value model：
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In above formula, heattRepresent t it is small when surplus gas calorific value.
7. iron and steel enterprise's gas consumption equipment timing optimization method according to claim 6, it is characterised in that in step 2,
Establish using the time as row, device numbering is the time series matrix Z of row, and equipment normal operation is represented with " 1 ", insulation of equipment state
Represented with " 0 ", optimization problem is equivalent to the value of each element in solution matrix Z, and the value of each element is 0 or 1, the coal gas of foundation
It is as follows to consume equipment timing optimization model：
Thermal fluctuation minimizes：
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Gas consumption equipment running status change number and minimize：
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Surplus gas is on the occasion of constraint：
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Power plant for self-supply's heat is constrained with calorific value：
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<mi>i</mi>
</msub>
<mo><</mo>
<mi>h</mi>
<mi>e</mi>
<mi>a</mi>
<msub>
<mi>t</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>Q</mi>
<mi>min</mi>
</msub>
<mo><</mo>
<mi>Q</mi>
<mi>i</mi>
<mo><</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
In above formula, average_Q represents the average value that power plant for self-supply inputs mixed gas heat per hour, zjiRepresent in matrix Z
The i-th row jth column element, heatminRepresent the calorific value lower limit of input power plant for self-supply mixed gas, heatmaxInput is represented to provide for oneself
The calorific value upper limit of power plant's mixed gas, QminRepresent the lower limit of hour input power plant for self-supply heat, QmaxHour input is represented to provide for oneself
The upper limit of power plant's heat.
8. state iron and steel enterprise's gas consumption equipment timing optimization method according to claim 7, it is characterised in that the specific mistake of step 3
Journey is as follows：
(1) timing optimization model solution is carried out using genetic algorithm, and the position of " 0 " or " 1 " in time series matrix Z is made
For the input variable of algorithm；
(2) constraint violation degree function is established：
Dis_f1t=(| heatt-heatmin|+|heatt-heatmax|-(heatmax-heatmin))2
Dis_f2t=(| Qt-Qmin|+|Qt-Qmax|-(Qmax-Qmin))2
<mrow>
<mi>d</mi>
<mi>i</mi>
<mi>s</mi>
<mo>_</mo>
<mi>f</mi>
<mn>3</mn>
<mi>t</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>b</mi>
<mi>f</mi>
<mi>g</mi>
<mo>_</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>f</mi>
<mn>2</mn>
</msup>
<mrow>
<mi>b</mi>
<mi>f</mi>
<mi>g</mi>
<mo>_</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>b</mi>
<mi>f</mi>
<mi>g</mi>
<mo>_</mo>
<mi>t</mi>
</mrow>
</msub>
<mo><</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<mi>d</mi>
<mi>i</mi>
<mi>s</mi>
<mo>_</mo>
<mi>f</mi>
<mn>4</mn>
<mi>t</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>g</mi>
<mo>_</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>f</mi>
<mn>2</mn>
</msup>
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>g</mi>
<mo>_</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>g</mi>
<mo>_</mo>
<mi>t</mi>
</mrow>
</msub>
<mo><</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
In above formula.Dis_f1t, dis_f2t represent i-th hour mixed gas calorific value for inputting power plant for self-supply respectively and heat is violated
Degree of restraint, dis_f3t, dis_f4t represent high coke oven gas input power plant flow and violate degree of restraint, FViolateFor total constraint
Violate degree function；
Constraint violation degree function weighs individual to the violation degree of constraints in gas consumption equipment timing optimization model
Size, restriction range degree is bigger, and the individual is poorer, and all individuals can be ranked up according to constraint violation degree in algorithm,
The individual nearer from feasible zone of foremost is come, is more probably feasible solution；
(3) according to the non-dominated ranking method of NSGA- II, each individual non-dominated ranking grade is obtained, grade is smaller, is better than
The individual individual amount is fewer, and the individual is more outstanding；Selection individual link, using individual non-dominated ranking grade with
Two parameters of constraint violation degree rank together decide on individual quality.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886531A (en) * | 2019-01-03 | 2019-06-14 | 新奥数能科技有限公司 | A kind of method, apparatus, readable medium and electronic equipment calculating energy efficiency of equipment |
| CN110910014A (en) * | 2019-11-20 | 2020-03-24 | 广州博依特智能信息科技有限公司 | Paper-making pulping scheduling method and device based on NSGA-II algorithm |
| CN112699613A (en) * | 2021-01-08 | 2021-04-23 | 中冶赛迪工程技术股份有限公司 | Multi-target integrated burdening optimization method, system, equipment and medium for iron making |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001199705A (en) * | 2000-01-12 | 2001-07-24 | Tokyo Gas Co Ltd | Apparatus for removing co in reformed gas by oxidation |
| CN101187967A (en) * | 2007-12-07 | 2008-05-28 | 冶金自动化研究设计院 | Gas dynamic simulation system for steel enterprise |
| CN103439926A (en) * | 2013-07-26 | 2013-12-11 | 同济大学 | Gas optimization scheduling device of iron and steel enterprise |
| CN104392334A (en) * | 2014-12-12 | 2015-03-04 | 冶金自动化研究设计院 | Joint optimized scheduling method for multiple types of generating sets of self-supply power plant of iron and steel enterprise |
| CN104881713A (en) * | 2015-05-22 | 2015-09-02 | 中冶南方工程技术有限公司 | Method for achieving decoupling of optimization algorithm and iron and steel enterprise energy integrated scheduling problem |
| CN104991531A (en) * | 2015-05-22 | 2015-10-21 | 中冶南方工程技术有限公司 | Method for determining optimized scheduling feasible solution of steel enterprise by-product gas system |
-
2017
- 2017-11-15 CN CN201711127829.8A patent/CN107976976B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001199705A (en) * | 2000-01-12 | 2001-07-24 | Tokyo Gas Co Ltd | Apparatus for removing co in reformed gas by oxidation |
| CN101187967A (en) * | 2007-12-07 | 2008-05-28 | 冶金自动化研究设计院 | Gas dynamic simulation system for steel enterprise |
| CN103439926A (en) * | 2013-07-26 | 2013-12-11 | 同济大学 | Gas optimization scheduling device of iron and steel enterprise |
| CN104392334A (en) * | 2014-12-12 | 2015-03-04 | 冶金自动化研究设计院 | Joint optimized scheduling method for multiple types of generating sets of self-supply power plant of iron and steel enterprise |
| CN104881713A (en) * | 2015-05-22 | 2015-09-02 | 中冶南方工程技术有限公司 | Method for achieving decoupling of optimization algorithm and iron and steel enterprise energy integrated scheduling problem |
| CN104991531A (en) * | 2015-05-22 | 2015-10-21 | 中冶南方工程技术有限公司 | Method for determining optimized scheduling feasible solution of steel enterprise by-product gas system |
Non-Patent Citations (1)
| Title |
|---|
| 施琦，等: "《钢铁企业副产煤气短周期优化调度模型》", 《钢铁》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886531A (en) * | 2019-01-03 | 2019-06-14 | 新奥数能科技有限公司 | A kind of method, apparatus, readable medium and electronic equipment calculating energy efficiency of equipment |
| CN109886531B (en) * | 2019-01-03 | 2021-03-23 | 新奥数能科技有限公司 | Method and device for calculating energy efficiency of equipment, readable medium and electronic equipment |
| CN110910014A (en) * | 2019-11-20 | 2020-03-24 | 广州博依特智能信息科技有限公司 | Paper-making pulping scheduling method and device based on NSGA-II algorithm |
| CN112699613A (en) * | 2021-01-08 | 2021-04-23 | 中冶赛迪工程技术股份有限公司 | Multi-target integrated burdening optimization method, system, equipment and medium for iron making |
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