CN113160899A - NSGA-II algorithm-based sintering material multi-objective optimization method - Google Patents
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Abstract
The invention relates to an iron-making sintering burdening multi-objective optimization method based on NSGA-II algorithm, belonging to the technical field of computer application, comprising the following steps: defining the proportioning result of the iron-containing powder, the fuel and the flux as a decision variable and determining the constraint condition of the decision variable; constructing a multi-objective optimization model according to 6 different indexes of the sinter, such as pig iron cost, sintering cost, blast furnace coke ratio and the like; and solving the optimization model by adopting a multi-objective evolutionary algorithm NSGA-II, and performing multiple calculations to enable the solution to approach the Pareto optimal front edge more widely and uniformly, and finally outputting an optimal solution set of the sintering ingredient proportion. The algorithm is mature and stable, and shows strong optimizing capability for both theoretical test functions and actual production problems.
Description
Technical Field
The invention belongs to the field of computer application blending sintering burdening, and relates to a sintering burdening multi-objective optimization method based on an NSGA-II algorithm.
Background
Sintering is one of key processes of concentrate in blast furnace ironmaking production, and the main function of sintering is to enable iron concentrate and powder ore in ironmaking raw materials to become large sintered ore with certain strength after sintering so as to meet the requirement of a blast furnace on the granularity of furnace burden; the sintering burdening aims at accurately and reasonably collocating different varieties of iron-containing powder, sintering fuel and sintering flux according to sintering requirements, so that the chemical components of the uniformly mixed ore meet the requirements of sintering production, the requirements of a blast furnace on furnace burden components are met, the sintering performance and the metallurgical performance of the uniformly mixed ore are improved, and the high quality, high yield and low consumption of iron-making production are facilitated; the cost of iron per ton, the sintering cost and the coke ratio of the blast furnace are reduced, so that the cost is saved; the improvement of the metallurgical value, the iron-containing grade and the blast furnace coefficient of the furnace charge is beneficial to blast furnace smelting and the improvement of the quality of pig iron. Therefore, the optimized batching of the sinter is an extremely important task.
Disclosure of Invention
Technical problem to be solved
Aiming at the existing technical problems, the invention provides a sintering batching multi-target optimization method based on an NSGA-II algorithm, the algorithm has strong optimization capability, the complexity is reduced by fast non-dominated sorting, the diversity of the population is uniformly maintained by congestion calculation and relatively, the best individual is reserved by operation strategy selection, the level of the population is improved, and the solution is more widely and uniformly close to the optimal front edge of Pareto.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a sintering material multi-objective optimization method based on an NSGA-II algorithm comprises the following steps:
step 101: defining the proportion result of the iron-containing powder, the sintering fuel and the sintering flux in the sintering mixture as a decision variable and determining the constraint condition of the decision variable, wherein the proportion of the iron-containing powder is xiAnd the proportion of sintering fuel is as follows: y isjAnd the proportion of sintering flux: z is a radical ofk. The constraints are as follows:
0≤xi≤1,i∈(1,2,…,l)
0≤yj≤1,j∈(1,2,…,m)
0≤zk≤1,k∈(1,2,…,n)
in the formula: l is the number of kinds of iron-containing powder; m is the number of types of sintering fuel; n is the number of sintering flux species.
Step 102: 6 different objective functions according to the pig iron cost, the sintering cost, the blast furnace coke ratio, the sintered ore metallurgical value, the blast furnace coefficient and the sintered ore total iron of the sintered ore are as follows:
the cost of pig iron is as follows: f (1) ═ cal _ ironCostPerTon (X, Y, Z)
Sintering cost: f (2) ═ cal _ sinterCost (X, Y, Z)
Blast furnace coke ratio: f (3) ═ cal _ forecast cokeratio (X, Y, Z)
Sintered ore metallurgical value: f (4) ═ cal _ sinterMetlluValue (X, Y, Z)
Blast furnace coefficient: f (5) ═ cal _ forecast coeffient (X, Y, Z)
Sintering ore total iron: f (6) ═ cal _ ratio TFe (X, Y, Z)
The established multi-objective optimization model is as follows:
s.t.
xi∈X,i∈(1,2,…,l)
yj∈Y,j∈(1,2,…,m)
zk∈Z,k∈(1,2,…,n)
0≤xi≤1,i∈(1,2,…,l)
0≤yj≤1,j∈(1,2,…,m)
0≤zk≤1,k∈(1,2,…,n)
step 103: simulating a biological evolution process, carrying out chromosome coding, population initialization, selection, crossing, mutation, rapid non-dominated sorting, congestion degree calculation and comparison, and elite strategy selection. The method comprises the following specific steps: firstly, randomly generating an initial population with the size of N, and obtaining a first generation progeny population through three basic operations of selection, crossing and variation of a genetic algorithm after non-dominated sorting; secondly, from the second generation, merging the parent population and the offspring population, performing rapid non-dominant sorting, simultaneously performing crowding degree calculation on the individuals in each non-dominant layer, and selecting proper individuals according to the non-dominant relationship and the crowding degree of the individuals to form a new parent population; and finally, generating a new offspring population through the basic operation of the genetic algorithm, and so on until the condition of program end is met.
(III) advantageous effects
The invention has the beneficial effects that: the algorithm is mature and stable, and shows strong optimizing capability for both theoretical test functions and actual production problems. The method is applied to a sintering batching multi-objective optimization model, and the optimal proportioning parameters are obtained through optimization by utilizing the established objective function, so that the sintering cost is saved, and the quality of the sintered ore is improved
Drawings
FIG. 1 is a schematic diagram of a sintering batch multi-objective optimization method based on NSGA-II algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process for implementing NSGA-II in a sintering batch multi-objective optimization method based on NSGA-II algorithm according to an embodiment of the present invention;
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
In the method of the embodiment, the software environment is Windows system, JVM and Matlab.
As shown in fig. 1: the embodiment discloses a sintering material multi-objective optimization method based on NSGA-II algorithm, which comprises the following steps:
step 101: the method comprises the steps of giving 10 iron-containing powder materials (i.e. comprehensive concentrate, Shenma iron concentrate, ferric oxide powder, four-burning high-return, Jinxinxin iron concentrate, black hawk mountain concentrate, old burning high-return, imported hajing, fish red iron concentrate, blast furnace fly ash, wherein l is 10), 2 sintering fuels (old burning coke powder, blue carbon, m is 2) and 2 sintering fluxes (limestone powder, modified lime, and n is 2) in sintering ingredients, wherein the total amount of given ore is 1000kg, and the proportion of solid fuel is 65 kg/ton of ore.
Step 102: designing a fitness model, and constructing a multi-objective optimization model according to 6 different indexes of pig iron cost, sintering cost, blast furnace coke ratio, sintered ore metallurgical value, blast furnace coefficient and sintered ore total iron of a sintered ore;
step 103: solving the multi-objective optimization model by adopting a multi-objective evolutionary algorithm NSGA-II, simulating a biological evolution process, chromosome coding, population initialization, selection, crossing, mutation, rapid non-dominated sorting, congestion degree calculation and comparison, and elite strategy selection, wherein the specific process is shown in FIG. 2. And carrying out simulation experiments to obtain a final result.
In summary, the NSGA-II algorithm-based sintering material multi-objective optimization method provided by the embodiment of the invention optimizes the proportion of the sintering material by adopting the NSGA-II algorithm, and not only improves the metallurgical performance but also saves the cost when the method is put into practical production. The sintering material multi-objective optimization method based on the NSGA-II algorithm has great advantages and important practical application values in the aspects of economy, quality and accuracy.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and are not limited thereto; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications and substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. A sintering material multi-objective optimization method based on an NSGA-II algorithm is characterized by comprising the following steps:
step 101: defining the proportioning result of the iron-containing powder, the sintering fuel and the sintering flux in the sintering ingredients as a decision variable, and determining the constraint condition;
step 102: constructing a multi-target optimization model according to 6 different indexes of pig iron cost, sintering cost, blast furnace coke ratio, sintered ore metallurgical value, blast furnace coefficient and sintered ore total iron of the sintered ore;
step 103: solving the multi-target optimization model by adopting a multi-target evolution algorithm NSGA-II, simulating a biological evolution process, carrying out chromosome coding, carrying out population initialization, selecting, crossing, carrying out mutation, carrying out rapid non-dominated sorting, carrying out crowdedness calculation and comparison, and carrying out elite strategy selection, so that the diversity of the solution is ensured, the solution more widely and uniformly approaches to the Pareto optimal front, and finally, an optimal solution set of the sintering ingredient proportion is output.
2. The method of claim 1, wherein in step 101, the ratio of the iron-containing powder, the sintering fuel and the sintering flux in the sintering batch is defined as a decision variable, and the constraint condition is determined, wherein the ratio of the iron-containing powder is xiAnd the proportion of sintering fuel is as follows: y isjAnd the proportion of sintering flux: z is a radical ofkThe constraint conditions are as follows:
0≤xi≤1,i∈(1,2,…,l)
0≤yj≤1,j∈(1,2,…,m)
0≤zk≤1,k∈(1,2,…,n)
in the formula: l is the number of kinds of iron-containing powder; m is the number of types of sintering fuel; n is the number of sintering flux species.
3. The method as claimed in claim 1, wherein in step 102, 6 different objective functions according to the pig iron cost, sintering cost, blast furnace coke ratio, sintered ore metallurgical value, blast furnace coefficient and sintered ore total iron of the sintered ore are as follows:
the cost of pig iron is as follows: f (1) ═ cal _ ironCostPerTon (X, Y, Z)
Sintering cost: f (2) ═ cal _ sinterCost (X, Y, Z)
Blast furnace coke ratio: f (3) ═ cal _ forecast cokeratio (X, Y, Z)
Sintered ore metallurgical value: f (4) ═ cal _ sinterMetlluValue (X, Y, Z)
Blast furnace coefficient: f (5) ═ cal _ forecast coeffient (X, Y, Z)
Sintering ore total iron: f (6) ═ cal _ ratio TFe (X, Y, Z)
The established multi-objective optimization model is as follows:
s.t.
xi∈X,i∈(1,2,…,l)
yj∈Y,j∈(1,2,…,m)
zk∈Z,k∈(1,2,…,n)
0≤xi≤1,i∈(1,2,…,l)
0≤yj≤1,j∈(1,2,…,m)
0≤zk≤1,k∈(1,2,…,n)
4. the method according to claim 1, wherein in step 103, the method further comprises the sub-steps of: chromosome coding, population initialization, selection, crossing, mutation, rapid non-dominated sorting, congestion degree calculation and comparison, and elite strategy selection.
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| CN114021913A (en) * | 2021-10-22 | 2022-02-08 | 中冶南方工程技术有限公司 | Blast furnace burden optimization method based on differential evolution algorithm, electronic equipment and storage medium |
| CN114707368A (en) * | 2022-06-07 | 2022-07-05 | 南京玻璃纤维研究设计院有限公司 | Method and system for accurately designing components of ore fibers |
| CN117151428A (en) * | 2023-10-27 | 2023-12-01 | 泉州装备制造研究所 | NSGA-II-based warp knitting machine stock planning method |
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Cited By (5)
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| CN117151428A (en) * | 2023-10-27 | 2023-12-01 | 泉州装备制造研究所 | NSGA-II-based warp knitting machine stock planning method |
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