CN107976976B - Time sequence optimization method for gas consumption equipment of iron and steel enterprise - Google Patents
Time sequence optimization method for gas consumption equipment of iron and steel enterprise Download PDFInfo
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Abstract
The invention discloses a time sequence optimization method for coal gas consumption equipment of a steel enterprise. On the basis of establishing a coal gas consumption characteristic model, a coal gas consumption equipment time sequence optimization model is established, the model takes the minimum input heat fluctuation of a self-contained power plant and the minimum change times of the equipment operation state as optimization targets, and takes the heat quantity, the heat value, the flow quantity and the like of the coal gas of the power plant as constraint conditions, so that a mathematical problem is converted into a 0-1 integer programming problem. The invention adopts a multi-target genetic algorithm to solve a mathematical problem model, designs a gene selection scheme, and sets the positions of '0' and '1' in a matrix as the genes of the algorithm, thereby realizing simple and effective optimization of equipment time sequence.
Description
Technical Field
The invention belongs to the field of energy optimization scheduling of iron and steel enterprises, and particularly relates to a time sequence optimization method for gas consumption equipment of an iron and steel enterprise.
Background
The gas system of the iron and steel enterprise has complex structure and numerous devices, and is divided into gas production equipment, gas consumption equipment and gas buffering equipment. The production and consumption characteristics of each device for blast furnace gas, coke oven gas and converter gas are different, so that the problem of unbalanced production and consumption of a gas system often occurs, energy waste is caused, and the normal production of iron and steel enterprises is seriously influenced.
At present, a great deal of optimized scheduling models are researched and established aiming at the optimization problem of the gas system of the iron and steel enterprise, the models are mostly optimized scheduling models in a short time (dynamic balance of about 5 minutes) and optimized models for the total amount of gas in a longer time (static balance of year, month and day), the former carries out gas balance by taking the adjustment of the storage amount and the consumption amount of a gas cabinet and a self-contained power plant as means, the latter carries out gas balance by taking the adjustment of the production amount of each device as means, the method has less research on the time sequence optimization problem of the gas consumption equipment under the condition of set production duration within a day (namely the production duration of each equipment within a day is known according to production requirements), and no research adopts a gas consumption equipment time sequence optimization method as a means for adjusting the production and consumption balance optimization of a gas system of an iron and steel enterprise.
In the past, the problem of heat fluctuation input into a power plant is mostly ignored in the research, and the time sequence optimization of the gas consumption equipment is easy to cause frequent change of the gas consumption running state. In addition, at present, many gas optimization scheduling models relate to a 0-1 planning problem (the value of a limiting variable in the model is only 0 or 1, and the integer planning problem is called as a 0-1 planning problem), a small amount of research is carried out at present to solve the model comprising the 0-1 integer planning problem by adopting a genetic algorithm, but the gene setting mode in the solution scheme of the problem increases the gene dimension and increases the solving difficulty of the model.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention aims to provide a time sequence optimization method for gas consumption equipment of a steel enterprise, which can optimize the time sequence of the gas consumption equipment of the steel enterprise, maintain the balance of gas production and consumption, reduce the fluctuation of heat input into a self-contained power plant, reduce the change times of the running state of the gas consumption equipment and simultaneously reduce the difficulty in model solution.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a time sequence optimization method for gas consumption equipment of a steel enterprise comprises the following steps:
step 1: classifying all gas-consuming equipment into gas production equipment, gas-consuming equipment and self-contained power plants, classifying specific equipment into the categories, and respectively establishing a gas production model and a gas consumption model;
step 2: establishing a gas consumption equipment time sequence optimization model, wherein the model comprises an objective function and constraint conditions, the objective function comprises minimization of power plant input heat fluctuation and minimization of the change times of the running state of the gas consumption equipment, and the constraint conditions comprise self-contained power plant heat and heat value constraint and input power plant gas amount positive value constraint;
and step 3: and (3) solving the time sequence optimization model established in the step (2) by adopting a non-linear 0-1 integer programming model based on the NSGA-II genetic algorithm.
Further, in step 1, the gas production equipment comprises a blast furnace, a coke oven and a converter, and the following gas production models are established by the three equipment:
fpro_j(t)＝fsum_j_t(1)
in the above formula, fpro_j(t) represents the gas production of the jth gas production facility in the tth hour, fsum_j_tRepresents the average of the historical hourly production volumes for the jth plant.
Further, in step 1, the gas consuming apparatus is further divided into an uninterruptible production apparatus and a discontinuous production apparatus.
Further, the uninterruptible production equipment establishes the following characteristic model of the gas consumption equipment:
the characteristic models of coal gas consumption equipment of a blast furnace hot blast stove, a coking plant, a steel plant, an iron smelting port and a power plant are as follows:
in the above formula, fpro_j(t) represents the gas production of the jth gas production facility in the tth hour, fsum_j_tAverage value representing the historical hourly production volume of the jth plant;
(II) a characteristic model of gas consumption equipment of cold rolling equipment:
in the above formula, fcon _ Cold Rolling(t) represents the gas consumption volume of the cold rolling equipment in the t hour;
representsAverage value of historical hourly consumption volume of cold rolling equipment α coldRolling millRepresenting the heat preservation consumption coefficient of the cold rolling equipment.
Further, the discontinuously producible apparatuses include sintering plants, pelletizing plants, lime kilns and hot rolling type apparatuses, which establish characteristic models of gas consumption apparatuses as follows:
in the above formula, fcon_z(t) represents the gas consumption volume of the z-th plant during the t-hour,
average value representing the historical z-th device hourly consumption volume, αzRepresenting the insulation consumption coefficient of the z-th equipment.
Further, in step 1, the self-contained power plant establishes the following gas consumption characteristic model:
(a) rich gas (gas left after meeting the requirements of all gas consuming equipment) thermal model:
in the above formula, QtRepresenting the heat of the mixed gas input from the self-contained power plant at the t hour, hbfg,hcog,hcfgRespectively represent the heat values of a blast furnace, a coke furnace and a convertertjRepresents the volume of gas consumed by the jth plant at the t hour, fsumcfg_tRepresenting the volume of converter gas production in the t hour, fsumbfg_tRepresents the blast furnace gas production volume in the t hour, fsumcog_tRepresents the coke oven gas production volume in the t hour, hjRepresents the calorific value of the consumed gas of the jth equipment;
(b) mixed substitution (mixed substitution of low calorific value gas and high calorific value gas for medium calorific value gas) model:
fcog＝(hcfg-hbfg)/(hcog-hbfg)*fcfg(6)
fbfg＝(hcog-hcfg)/(hcog-hbfg)*fcfg(7)
in the above formula, fcfgRepresenting the volume of the converter gas, fcog、fbfgRepresents the volume of coke oven gas and blast furnace gas that replaces converter gas;
(c) volume model of surplus gas:
in the above formula, fcfg_before_tCalculating the volume of converter gas input into a self-contained power plant before replacing converter gas with blast furnace coke oven gas; f. ofcfg_tRepresenting the volume of converter gas input into the self-contained power plant at the t hour; pjcfgRepresenting the proportion of the mixed gas transfer furnace gas consumed by the jth gas consumption equipment; pjcogRepresenting the proportion of the coke oven gas in the mixed gas consumed by the jth gas consumption equipment; pjbfgRepresenting the proportion of the coal gas in the mixed gas consumed by the jth coal gas consumption equipment; f. ofcog_cfgCoke oven gas body needed when the converter gas vacancy part is replaced by high coke oven gasAccumulating; f. ofbfg_cfgThe volume of blast furnace gas required when the converter gas vacancy part is replaced by the blast furnace gas; fSubstitutionThe function between the volume of the converter gas and the volume of the blast furnace and the coke oven when the converter gas is replaced by the mixed substitution model; f. ofbfg_tRepresents the volume of blast furnace gas input from the backup power plant at the t hour; f. ofcog_tRepresenting the volume of coke oven gas input into the self-contained power plant at the t hour, and N is the number of gas consumption equipment.
(d) The surplus gas heat value model:
in the above formula, heattRepresenting the calorific value of the surplus gas at the t hour.
Further, in step 2, a time series matrix Z with time as a row and equipment number as a column is established, the normal operation of the equipment is represented by "1", the equipment heat preservation state is represented by "0", the optimization problem is equivalent to solving the value of each element in the matrix Z, the value of each element is 0 or 1, and a certain time series matrix is as follows:
the established gas consumption equipment time sequence optimization model is as follows:
minimization of thermal fluctuations:
the number of changes of the operating state of the gas consumption equipment is minimized:
positive value constraint of surplus coal gas:
self-contained power plant heat and heat value constraint:
in the above formula, average _ Q represents the average value of heat input into the mixed gas per hour from the power plant, zjiRepresents the ith row and jth column element, heat, in matrix ZminRepresenting the lower limit of the calorific value of the mixed gas input into the self-contained power plant, heatmaxRepresenting the upper limit of the calorific value of the mixed gas fed to the self-contained power plant, QminRepresents the lower limit of hourly heat input from the power plant, QmaxRepresenting the upper limit of heat input to the self-contained power plant in hours.
Further, the specific process of step 3 is as follows:
(1) adopting a genetic algorithm to carry out time sequence optimization model solution, and carrying out time sequence on ' 0 ' or ' 1
The position in the matrix Z is used as an input variable of the algorithm;
(2) establishing a constraint violation degree function:
dis_f1t＝(|heatt-heatmin|+|heatt-heatmax|-(heatmax-heatmin))2(20)
dis_f2t＝(|Qt-Qmin|+|Qt-Qmax|-(Qmax-Qmin))2(21)
in the above formula, dis _ f1t and dis _ f2t represent the mixture input from the backup power plant at the ith hourThe combined gas heat value and the heat quantity violate the constraint degree, dis _ F3t and dis _ F4t represent the violation constraint degree of the flow of the coke oven gas input into the power plant, FViolation ofAs a function of the total constraint violation;
the constraint violation degree function measures the violation degree of the individual on the constraint conditions in the time sequence optimization model of the gas consumption equipment, the larger the constraint range degree is, the worse the individual is, all the individuals can be sorted according to the constraint violation degree in the algorithm, and the closer the individual arranged at the front is to the feasible domain, the more possible the individual is to be a feasible solution;
(3) obtaining the non-dominant ranking grade of each individual according to the non-dominant ranking method of NSGA-II, wherein the smaller the ranking is, the smaller the number of individuals superior to the individual is, the more excellent the individual is; in the link of selecting individuals, the individual quality is determined by adopting two parameters of an individual non-dominated sorting level and a constraint violation degree sorting level.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the invention provides a time sequence optimization method which is different from the prior gas system optimization method, and supplements an optimized scheduling means for the optimized scheduling of the gas of the iron and steel enterprises. When the optimization model is solved, the method is different from the traditional mathematical programming solving mode, a large number of mathematical formulas and calculation processes are avoided, and a solving scheme of a genetic algorithm is adopted. When the 0-1 integer programming problem is solved, each matrix element in the time sequence matrix is not used as an input quantity, and the position of 0 or 1 is used as the input quantity, so that the number of the input quantities is reduced, and the solving difficulty of the model is reduced.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a diagram of a by-product gas system of a certain iron and steel enterprise in the example;
FIG. 3 is a graph comparing the heat fluctuation minimizing operation condition with the actual value after the time sequence optimization of the embodiment.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
A time sequence optimization method for gas consumption equipment of a steel enterprise comprises three parts, wherein the first part is classification of the gas equipment and establishment of a gas production and consumption characteristic model, the second part is establishment of a time sequence optimization model of the gas consumption equipment, and the third part is solution of the model. The model is aimed at a gas system of a certain steel enterprise, and the system comprises gas production equipment, gas consumption equipment, gas buffer equipment and a gas mixing station as shown in figure 2.
1. Embodiments of the first section above:
step 1: classification of gas equipment: the gas equipment is divided into gas production equipment, gas consumption equipment and self-contained power plants (gas buffer equipment).
The gas production equipment in the system comprises: blast furnace, converter, coke oven. The gas consumption equipment comprises: blast furnace hot blast stove, coke-oven plant, steel plant, ironmaking mouth, power plant, cold rolling mill, color plate factory, 550 sintering (2 lines), 180 sintering (4 lines), pelletizing plant, lime kiln, machine company, medium plate factory, hot rolling factory, one rolling mill. The gas buffering equipment comprises a self-contained power plant, a blast furnace gas chamber, a converter gas chamber and a coke oven gas chamber. The gas mixing station has a 1# to 4# mixing station. Each device has its own gas pipeline leading to three main gas pipelines or to a gas mixing station which obtains gas from the three main gas pipelines. It should be noted that not every equipment uses the mixed gas of three kinds of gas at the same time, and it can be seen from the pipeline connection mode of figure 2 in the specification that the components of the gas used by each equipment are different.
From the day-to-day production plan of the iron and steel enterprise, the production tasks (production time length) of each equipment can be obtained: blast furnace, converter, coke oven, blast furnace hot blast stove, coke oven plant, steel plant, ironmaking mouth, power plant operate 24 hours, cold rolling mill, color sheet factory operate 20 hours, above-mentioned equipment do not participate in the time sequence optimization because of the characteristic of production continuity, so in optimizing the model, regard it as the fixed value. The production characteristics of the equipment are divided into normal operation and heat preservation state, and the equipment can be used as adjusting equipment to participate in time sequence optimization due to the change of the operation state of the equipment.
Historical data from a certain iron and steel enterprise can be obtained:
the coal gas production equipment comprises:
gas production flow of blast furnace gas: 2673819m3/h
Gas production flow of coke oven gas: 214149.2m3/h
Gas production flow of converter gas: 163020.4m3/h
(II) gas consumption equipment:
① not participating in timing optimization devices:
blast furnace hot blast stove gas consumption flow: 1115739m3H (blast furnace gas)
The gas consumption flow of an ironmaking opening is as follows: 3880.417m3H (blast furnace gas)
The gas consumption flow of the power plant: 83259.58m3H (blast furnace gas)
The gas consumption flow of the steel plant: 46697.5m3H (blast furnace gas)
The gas consumption flow of the coking plant is as follows: 547097.1m3H (blast furnace gas), 1610.417m3H (Coke oven gas)
Air consumption flow of color plate factory:
the gas consumption flow during the temperature rise period is as follows: 16407.3m3H, thermal insulation coefficient of consumption 1/3 (Coke oven gas)
Air consumption flow of the cold rolling mill:
the gas consumption flow during the temperature rise period is as follows: 76423.6m3H, thermal insulation coefficient of consumption 1/3 (76.11% blast furnace gas, 23.89% coke oven gas)
② may participate in the timing optimization device:
the air consumption flow of a rolling mill is as follows: 99287m3H (blast furnace gas)
550 sintering single line gas consumption flow (2 lines): 31009m3H (converter gas)
180 single line gas consumption (4 lines) for sintering: 14133m3H (blast furnace gas 58.39%, coke oven gas 26.26%, converter gas 15.36%)
Lime kiln gas consumption flow: 55400m3H (converter gas)
The gas consumption flow of the pelletizing plant is as follows: 65200m3H (blast furnace gas 93.74%, converter gas 6.26%)
The gas consumption flow of the middle plate factory is as follows: 81344m3H (blast furnace gas 58.36%, coke oven gas 26.26%, converter gas 15.36%)
Mechanism company gas consumption flow: 3900m3H (blast furnace gas 64.52%, coke oven gas 35.48%)
The gas consumption flow of the medium plate factory is as follows: 80000m3H (blast furnace gas 63.5%, coke oven gas 25.57%, converter gas 10.93%)
The gas consumption flow of hot continuous rolling is as follows: 302300m3H (blast furnace gas 76.11%, coke oven gas 23.89%)
The heat preservation consumption coefficient of the equipment is 0.2.
Step 2: establishing a characteristic model of gas production equipment
In the formula fpro_bfg(t)、fpro_cog(t)、fpro_cfg(t) the gas production volumes of blast furnace gas, coke oven gas and converter gas in the t hour respectively.
And step 3: establishing a characteristic model of gas consumption equipment
The historical data of the gas consumption flow of each device are respectively brought into the formulas (2), (3) and (4), so that specific consumption characteristic models of each gas consumption device can be obtained, such as a blast furnace hot blast stove in an uninterruptible production device, a color plate factory, a pellet factory in an uninterruptible production device (the unit is m all)3)。
fcon _ blast furnace hot blast stove(t) 1115739 t e normal operation (26)
In the formula fcon _ blast furnace hot blast stove(t) is the consumption volume of the blast furnace hot blast stove in the t hour, wherein fcon _ color plate works(t) is the consumption volume of the color plate mill in the t hour, fcon _ pellet plant(t) is the consumption volume of the pellet mill in the t hour.
And 4, step 4: establishing a consumption characteristic model of a self-contained power plant
Step 4.1: surplus gas heat model
Obtained from historical data of the iron and steel enterprise, hbfg＝3344kJ/m3，hcfg＝6688kJ/m3，hcog＝17347kJ/m3. The heat value of the color plate factory is 17347kJ/m3The heat value of an iron making mouth is 17347kJ/m3The heat value of the coke-oven plant is 3385.1kJ/m3The heat value of the power plant is 3344kJ/m3The heat value of the one-rolling mill is 3344kJ/m3The heat value of the blast furnace hot blast stove is 3344kJ/m3550 sintering heat value of 6688kJ/m3The heat value of the steel plant is 6688kJ/m3The heat value of the lime kiln is 6688kJ/m3The calorific value of the pellet mill is 3553.4kJ/m3The heat value of the medium plate factory is 7534.2kJ/m3The calorific value of the mechanical company is 8312.2kJ/m3180-degree sintering heat value of 7534.2kJ/m3The heat value of the medium plate factory is 7290.3kJ/m3The heat value of the hot continuous rolling is 6688.8kJ/m3The heat value of the cold rolling mill is 6688.8kJ/m3。
Subtracting the consumed heat of all gas consuming equipment (including a sintering line) from the total heat of the gas generated in a certain hour to obtain the surplus gas heat input into the self-contained power plant, namely, bringing the heat values of the equipment into formula (5) to establish a specific surplus gas heat model, wherein the formula (29) is as follows:
step 4.2: hybrid surrogate model
The high-calorific-value coke oven gas and the low-calorific-value blast furnace gas are mixed to replace the converter gas with the medium calorific value, and the total flow and the total heat of the gas before and after mixing are kept unchanged. The two kinds of coal gas are mixed and replaced according to the total flow conservation and the total heat conservation of the coal gas before and after the input mixing, and the formula (30) shows that:
fbfg+fcog＝fcfg
fbfg*hbfg+fcog*hcog＝fcfg*hcfg
(30)
namely, it is
The proportion of the blast furnace gas and the coke oven gas in the mixed gas is obtained as follows:
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: volume model of surplus gas
In each hour period, the gas production rate of the high coke, coke and converter gas is used, and the volume consumption of the high coke-to-gas consumed in each equipment in the hour is deducted, so that the volume (possibly negative) of the surplus gas meeting the requirements of the gas consumption equipment is obtained. When the converter gas is insufficient, the converter gas is replaced by mixing the gas, the insufficient converter gas is replaced by mixing the surplus blast furnace gas and the coke oven gas, and finally all the surplus gas is introduced into a self-contained power plant, namely the formulas (8) to (13).
Step 4.4: surplus gas heat value model
The heat of the surplus coal gas is obtained in the step 4.1, the volumes of the three surplus coal gases are obtained in the step 4.3, and the heat of the surplus coal gas is divided by the total volume of the three surplus coal gases to obtain the heat value of the surplus coal gas as shown in the formula (14).
2 establishing a time sequence optimization model
The time sequence optimization model comprises an objective function and constraint conditions, and in order to reduce the fluctuation of heat input into the self-contained power plant and avoid frequent change of the running state of equipment, the optimization objectives of the model are two:
step 1: establishing an optimization goal
The first objective is to minimize the fluctuation of the input heat of the self-contained power plant, the heat input into the self-contained power plant per hour can be known by the surplus gas heat model of formula (29), the heat fluctuation degree function is obtained by using the concept of variance, as shown in formula (16), and the fluctuation function of formula (16) is adjusted as shown in formula (34) because the gas consumption of the cold-rolling equipment is specific and periodic, the gas is consumed in the first 20 hours and the gas is not consumed in the last 4 hours.
Wherein average _ Q1Average _ Q, the average of the heat input over the first 20 hours2The average of the heat input after 4 hours.
The second objective is to minimize the number of times of change of the operation state of the equipment, the time sequence matrix of equation (15) contains elements "1" and "0", when the operation state of the equipment changes from "0" to "1" or "1" to "0" in continuous time, that is, the operation state of the equipment is considered to have changed, in each row of the matrix, the former element is subtracted from the latter element, and the sum of squares of all differences is the number of times of change of the operation state, equation (17) can be obtained, and the equipment in the steel enterprise, which can participate in the time sequence optimization, has: 550 sintering (2 lines), 180 sintering (4 lines), one rolling mill, lime kiln, pellet mill, medium plate mill, machining company, medium plate mill, hot continuous rolling, 13 devices (lines) in total, that is, N is 15, the number of times of operating state change is minimized as shown in formula (35).
Step 2: determining constraints
Restraining one: according to the actual requirement of the self-contained power plant of the iron and steel enterprise, the upper limit of the calorific value is 17347kJ/m3The lower limit of the calorific value is 3800kJ/m3The upper limit of the heat quantity is 2000GJ/h, and the lower limit of the heat quantity is 5000GJ/m3The heat value of the mixed gas input into the self-contained power plant is ensured not to exceed the range required by the self-contained power plant, and the heat input per hour also cannot exceed the range of the heat acceptable by the power plant, as shown in a formula (36).
And (2) constraining: in the mathematical model of the invention, the coal gas meets the requirements of coal gas consumption equipment firstly, then the coal gas is input into a power plant, and if the converter coal gas required by the consumption equipment is insufficient, the coal gas is mixed and substituted. After mixing and replacing, the volume of blast furnace gas with low calorific value and coke oven gas with high calorific value in the surplus gas input into the power plant needs to be ensured to be positive, otherwise, the gas requirement of gas consumption equipment can not be met, and the constraint is shown as a formula (37).
3. Solving of models
Step 1: gene design: in order to reduce the gene dimension of an individual, the invention uses genes to respectively represent the positions of the heat preservation time periods of equipment with the running time longer than the heat preservation time period, and uses genes to respectively represent the positions of the heat preservation time periods of equipment with the running time shorter than the heat preservation time period. For example: the lime kiln normally runs for 22 hours in one day, and the temperature is kept for 2 hours. If one gene is used for each hour, 24 genes are needed; if the gene design scheme of the invention is adopted, only two genes are needed to record the positions of 2 hours of heat preservation within 24 hours, and when the heat preservation is carried out once from 5 to 6 points and the heat preservation is carried out once from 14 to 15 points, the 2 genes are respectively 6 and 15.
Step 2: and (3) constraint processing scheme: and establishing a constraint violation degree function which comprises 4 parts and respectively represents the unsatisfied degree of the constraint expression in the expression (36) and the expression (37), wherein the larger the function is, the farther the solution deviates from the feasible region is, and the functions are shown as the expression (38), the expression (39), the expression (40) and the expression (41). The 4 parts are summed to violate the constraint function, as shown in equation (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 invention adopts NSGA-II multi-target inheritance to solve, and the constraint processing method comprises the following steps: and determining the quality of the individual by the comprehensive ordering of the constraint violation degree function value ordering and the non-quality level ordering. The invention designs individual genes, the matrix of the formula (1) has 13 rows and 24 columns, and 312 elements in total, and the elements take the value of '0' or '1'. When a group of solutions is determined, namely the values of 312 elements are determined, the gas consumption of all gas consumption equipment and the gas consumption of the self-contained power plant in each hour can be determined according to the gas consumption model, then the surplus gas in each hour can be obtained, and finally two objective function values of the group of solutions and the total constraint range degree function value are obtained.
Because the optimization model of the invention is a dual target, a group of pareto non-inferior solution sets are obtained finally, the solution sets are obtained, the number of the operation state changes is reduced from 54 times to 18 times, and simultaneously, the target value of the heat fluctuation of the power plant is reduced from 8.8 x 1016Up to 1,3 x 1018. For example, an optimal solution for minimizing heat fluctuation, the specific operating conditions are as follows:
550 sintering one line: and (4) normal operation is carried out from point 6 to point 13, from point 17 to point 20 and from point 21 to point 24, and heat preservation is carried out at other times.
550 two lines of sintering: and (4) from 0 point to 4 point, 8 point to 20 point, 21 point to 24 point and keeping the temperature at other times.
180, sintering for one line: and (3) normally operating from 0 point to 7 points, from 8 points to 9 points, from 11 points to 12 points and from 21 points to 24 points, and keeping the temperature at other times.
180, sintering the second wire: and (4) from 0 point to 4 points, 13 to 17 points, and 21 to 24 points, and keeping the temperature at other times. 180, sintering three wires: and (3) from 0 point to 1 point, from 2 point to 3 point, from 5 point to 7 point, from 8 point to 9 point, from 14 point to 16 point, from 21 point to 24 point, and keeping the temperature at other times.
180 sintering four lines: and (4) keeping the temperature for other times, wherein the temperature is from 0 point to 3 points, from 4 points to 6 points, from 12 points to 14 points. A rolling mill: and (4) from 0 point to 4 point, from 7 point to 20 point, from 21 point to 24 point and keeping the temperature at other times.
Lime kiln: and (4) normally operating from 0 point to 2 points, from 3 points to 20 points and from 21 points to 24 points, and keeping the temperature at other times.
Pelletizing plant: normal operation is carried out from point 2 to point 8, from point 11 to point 20, from point 21 to point 24, and the temperature is kept at other times.
Middle plate factory: and 4, normal operation is carried out at 24 points, and heat preservation is carried out at other times.
Mechanism company: and (4) normally operating from 0 point to 3 points, from 5 points to 10 points, from 13 points to 24 points, and keeping the temperature at other times. Medium plate factory: and (5) normally operating from 0 to 20, and keeping the temperature at other times.
Hot continuous rolling: and (5) normally operating from 0 to 21, and keeping the temperature at other times.
The historical actual operation data and the power plant input heat under the time sequence optimization working condition are respectively subjected to normalization processing, the power plant input heat fluctuation variance of the historical actual data is 0.0083, the power plant input heat fluctuation variance of the time sequence optimization working condition is 0.0002167, the fluctuation is reduced by 97.59% compared with the actual fluctuation, and a comparison graph of the heat fluctuation minimization working condition and the actual value after the time sequence optimization of the embodiment of the invention is shown in fig. 3. The time sequence optimization method can greatly reduce the heat fluctuation of the power plant, and can simultaneously optimize two targets of the change times of the running state and the heat fluctuation of the power plant due to the selection of the double targets.
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.
Claims (7)
1. A time sequence optimization method for gas consumption equipment of a steel enterprise is characterized by comprising the following steps:
step 1: classifying all gas-consuming equipment into gas production equipment, gas-consuming equipment and self-contained power plants, classifying specific equipment into the categories, and respectively establishing a gas production model and a gas consumption model;
the self-contained power plant establishes the following gas consumption characteristic model:
(a) the heat model of rich gas:
in the above formula, QtRepresenting the heat of the mixed gas input from the self-contained power plant at the t hour, hbfg,hcog,hcfgRespectively represent the heat values of a blast furnace, a coke furnace and a convertertjRepresents the volume of gas consumed by the jth plant at the t hour, fsumcfg_tRepresenting the volume of converter gas production in the t hour, fsumbfg_tRepresents the blast furnace gas production volume in the t hour, fsumcog_tRepresents the coke oven gas production volume in the t hour, hjRepresents the calorific value of the consumed gas of the jth equipment;
(b) mixing substitution models:
fcog＝(hcfg-hbfg)/(hcog-hbfg)*fcfg
fbfg＝(hcog-hcfg)/(hcog-hbfg)*fcfg
in the above formula, fcfgRepresenting the volume of the converter gas, fcog、fbfgRepresents replacementThe volume of the coke oven gas and the blast furnace gas of the converter gas;
(c) volume model of surplus gas:
in the above formula, fcfg_before_tCalculating the volume of converter gas input into a self-contained power plant before replacing converter gas with blast furnace coke oven gas; f. ofcfg_tRepresenting the volume of converter gas input into the self-contained power plant at the t hour; pjcfgRepresenting the proportion of the mixed gas transfer furnace gas consumed by the jth gas consumption equipment; pjcogRepresenting the proportion of the coke oven gas in the mixed gas consumed by the jth gas consumption equipment; pjbfgRepresenting the proportion of the coal gas in the mixed gas consumed by the jth coal gas consumption equipment; f. ofcog_cfgThe coke oven gas volume required when the converter gas vacancy part is replaced by the high coke oven gas; f. ofbfg_cfgThe volume of blast furnace gas required when the converter gas vacancy part is replaced by the blast furnace gas; fSubstitutionWhen the mixed substitution model is used for replacing converter gas, the volume of the converter gas is between that of the blast furnace and that of the coke ovenA function of (a); f. ofbfg_tRepresents the volume of blast furnace gas input from the backup power plant at the t hour; f. ofcog_tRepresenting the volume of coke oven gas input into the self-contained power plant in the t hour, wherein N is the number of gas consumption equipment;
(d) the surplus gas heat value model:
in the above formula, heattRepresenting the calorific value of the surplus gas in the t hour;
step 2: establishing a gas consumption equipment time sequence optimization model, wherein the model comprises an objective function and constraint conditions, the objective function comprises minimization of power plant input heat fluctuation and minimization of the change times of the running state of the gas consumption equipment, and the constraint conditions comprise self-contained power plant heat and heat value constraint and input power plant gas amount positive value constraint;
and step 3: and (3) solving the time sequence optimization model established in the step (2) by adopting a non-linear 0-1 integer programming model based on the NSGA-II genetic algorithm.
2. The time sequence optimization method for the gas consumption equipment of the steel enterprise according to claim 1, wherein in the step 1, the gas production equipment comprises a blast furnace, a coke oven and a converter, and the following gas production models are established for the three types of equipment:
fpro_j(t)＝fsum_j_t
in the above formula, fpro_j(t) represents the gas production of the jth gas production facility in the tth hour, fsum_j_tRepresents the average of the historical hourly production volumes for the jth plant.
3. The time-series optimization method for gas consumption equipment of steel enterprises according to claim 1, wherein in step 1, the gas consumption equipment is further divided into uninterruptible production equipment and interruptible production equipment.
4. The time-series optimization method for gas consumption equipment of steel enterprises according to claim 3, wherein the uninterruptible production equipment establishes the following characteristic model of the gas consumption equipment:
characteristic models of coal gas consumption equipment of blast furnace hot blast furnaces, coking plants, steel plants, iron smelting ports and power plants;
(II) a characteristic model of gas consumption equipment of cold rolling equipment:
in the above formula, fcon _ Cold Rolling(t) represents the gas consumption volume of the cold rolling equipment in the t hour;
average value representing the historical hourly consumption volume of a cold rolling plant αCold rollingRepresenting the heat retention consumption coefficient, t, of the cold rolling plant0At the beginning of the warming period, t1At the end of the warming period and at the beginning of the transition period, t2The end point of the transition period and the start point of the incubation period, t3At the end of the incubation period and at the beginning of the cooling period, t4The end of the cooling period.
5. The time-series optimization method for gas consumption equipment of steel enterprises according to claim 3, wherein the interruptible production equipment comprises sintering plants, pelletizing plants, lime kilns and hot rolling type equipment, and the following characteristic models of the gas consumption equipment are established for the equipment:
in the above formula, fcon_z(t) represents the gas consumption volume of the z-th plant during the t-hour,
average value representing the historical z-th device hourly consumption volume, αzSecurity on behalf of z-th deviceTemperature coefficient of consumption.
6. The time sequence optimization method for gas consumption equipment of the steel enterprises as claimed in claim 1, wherein in the step 2, a time sequence matrix Z with time as row and equipment number as column is established, the normal operation of the equipment is represented by '1', the heat preservation state of the equipment is represented by '0', the optimization problem is equivalent to solving the value of each element in the matrix Z, the value of each element is 0 or 1, and the established time sequence optimization model for the gas consumption equipment is as follows:
minimization of thermal fluctuations:
the number of changes of the operating state of the gas consumption equipment is minimized:
positive value constraint of surplus coal gas:
self-contained power plant heat and heat value constraint:
in the above formula, average _ Q represents the average value of heat input into the mixed gas per hour from the power plant, zjiRepresents the ith row and jth column element, heat, in matrix ZminRepresenting the lower limit of the calorific value of the mixed gas input into the self-contained power plant, heatmaxRepresenting the upper limit of the calorific value of the mixed gas fed to the self-contained power plant, QminRepresents the lower limit of hourly heat input from the power plant, QmaxRepresenting the upper limit of heat input to the self-contained power plant in hours.
7. The time-series optimization method for gas consumption equipment of steel enterprises according to claim 6, wherein the specific process of the step 3 is as follows:
(1) adopting a genetic algorithm to solve a time sequence optimization model, and taking the position of '0' or '1' in a time sequence matrix Z as an input variable of the algorithm;
(2) establishing a constraint violation degree function:
dis_f1t＝(|heatt-heatmin|+|heatt-heatmax|-(heatmax-heatmin))2
dis_f2t＝(|Qt-Qmin|+|Qt-Qmax|-(Qmax-Qmin))2
in the above formula, dis _ F1t and dis _ F2t represent violation constraint degrees of the heat value and heat quantity of the mixed gas input from the backup power plant at the ith hour, dis _ F3t and dis _ F4t represent violation constraint degrees of the flow quantity of the coke oven gas input into the power plant, and FViolation ofAs a function of the total constraint violation;
the constraint violation degree function measures the violation degree of the individual on the constraint conditions in the time sequence optimization model of the gas consumption equipment, the larger the constraint range degree is, the worse the individual is, all the individuals can be sorted according to the constraint violation degree in the algorithm, and the closer the individual arranged at the front is to the feasible domain, the more possible the individual is to be a feasible solution;
(3) obtaining the non-dominant ranking grade of each individual according to the non-dominant ranking method of NSGA-II, wherein the smaller the ranking is, the smaller the number of individuals superior to the individual is, the more excellent the individual is; in the link of selecting individuals, the individual quality is determined by adopting two parameters of an individual non-dominated sorting level and a constraint violation degree sorting level.
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