CN114819409A - Intelligent optimization method and system for sintering ore blending - Google Patents
Intelligent optimization method and system for sintering ore blending Download PDFInfo
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Abstract
The invention provides an intelligent optimization method and system for sintering ore blending; relates to the technical field of big data processing. The intelligent optimization method for sintering ore blending comprises the following steps: acquiring material characteristics of each to-be-selected raw fuel, and determining cost performance indexes of the to-be-selected raw fuel based on the material characteristics; screening out sintering raw materials from the raw fuel to be selected based on the cost performance index; determining an objective function for calculating a cost of the sintering raw material required per unit weight of the sintered ore; and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient. The method can reduce the cost of the sinter.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to an intelligent optimization method and system for sintering ore blending.
Background
Sintering is a process of uniformly mixing mineral powder raw materials, auxiliary materials, solvents and other materials according to a predefined mineral material proportion and firing to produce sintered ore. In the sinter ore blending process, the blending is usually performed based on manual experience. However, with the increasing demand of steel, the quantity of high-quality ore powder is less and less, and the raw material of the ore powder is changed frequently. The mode of batching depending on manual experience cannot adapt to the changing ore powder raw materials, and the production efficiency is difficult to improve.
Disclosure of Invention
The invention provides an intelligent optimization method and system for sintering ore blending, which are used for solving the defect that the prior art cannot be updated in time depending on manual ore blending experience and improving the production efficiency.
The invention provides an intelligent optimization method for sintering ore blending, which comprises the following steps:
acquiring material characteristics of each to-be-selected raw fuel, and determining cost performance indexes of the to-be-selected raw fuel based on the material characteristics;
screening out sintering raw materials from the raw fuel to be selected based on the cost performance index;
determining an objective function for calculating a cost of the sintering raw material required per unit weight of the sintered ore;
and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient.
According to an embodiment of the present invention, the determining the cost performance index of the candidate raw fuel based on the material characteristics includes:
inputting the material characteristics of the candidate raw fuel into a preset cost calculation model to obtain the estimated cost of the candidate raw fuel, wherein the cost calculation model is constructed through a deep neural network;
determining the actual cost of the candidate raw fuel;
and determining the cost performance index of the candidate raw fuel based on the estimated cost and the actual cost of the candidate raw fuel.
According to an embodiment of the present invention, the determining the objective function includes:
determining the required sintering weight of the sintering ore;
obtaining the objective function based on the firing weight and the cost of each sintering material required for the firing weight;
the cost of each sintering material is the product of the actual cost of the sintering material and the candidate coefficient.
According to an embodiment of the present invention, the determining the proportioning coefficient of each sintering raw material based on the objective function includes:
determining constraint information of the sinter;
and determining the proportioning coefficient of the sintering raw material by combining the constraint information and the objective function.
According to an embodiment of the present invention, the determining a proportioning coefficient of each sintering raw material based on the objective function includes:
solving the objective function through an intelligent optimization algorithm, and enabling the value of the objective function to be minimum under the condition that the constraint information is met, wherein the intelligent optimization algorithm comprises one or more of a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm;
and when the value of the objective function is minimum, taking the candidate coefficient as the proportioning coefficient of the sintering raw materials, wherein the sum of the proportioning coefficients of all the sintering raw materials is 1.
According to an embodiment of the present invention, the determining constraint information of the sintering ore includes:
and determining the chemical composition constraint, the raw material inventory constraint and the harmful element constraint of the sinter to obtain the constraint information of the sinter.
The invention also provides an intelligent optimization system for sintering ore blending, which comprises:
the data acquisition module is used for acquiring the material characteristics of each to-be-selected raw fuel and determining the cost performance index of the to-be-selected raw fuel based on the material characteristics;
the cost performance calculation module is used for screening sintering raw materials from the to-be-selected raw fuel based on the cost performance indexes;
a model building module for determining an objective function for calculating the cost of the sintering raw material required for a unit weight of sinter;
and the proportioning determination module is used for determining the proportioning coefficient of each sintering raw material based on the objective function and sintering ore blending by adopting the proportioning coefficient.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the intelligent optimization method for the sintering ore blending.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for intelligent optimization of sinter blending as described in any of the above.
The invention also provides a computer program product comprising a computer program, wherein the computer program is used for realizing the intelligent optimization method for the sintering ore blending when being executed by a processor.
According to the intelligent optimization method for ore blending sintering, the ore blending sintering device and the electronic equipment, on one hand, sintered materials are screened according to the cost performance index of the raw fuel to be selected, so that the ore blending cost performance can be improved, and the mineral resource can be saved. On the other hand, the proportioning coefficient of the sintering raw materials is determined based on the cost of the sintering raw materials required for the unit weight of the sintered ore, and the ore proportioning coefficient of the minimum cost can be used, thereby reducing the cost of the sintered ore.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of an intelligent optimization method for ore blending sintering provided by the invention;
FIG. 2 is a schematic diagram of a system architecture of an intelligent optimization system for sinter ore blending provided by the invention;
FIG. 3 is one of the schematic structural diagrams of the intelligent optimization system for sinter ore blending provided by the invention;
FIG. 4 is one of the schematic display interfaces of the intelligent optimization system for sinter ore blending provided by the invention;
FIG. 5 is a second schematic view of a display interface of the intelligent optimization system for sinter ore blending according to the present invention;
FIG. 6 is a second schematic structural diagram of an intelligent optimization system for sinter ore blending according to the present invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The intelligent optimization method for ore blending sintering, the ore blending sintering device and the electronic equipment in the embodiment of the invention are described below with reference to the accompanying drawings.
The embodiment firstly provides an intelligent optimization method for sinter blending, and fig. 1 shows a flow chart of the intelligent optimization method for sinter blending. As shown in fig. 1, the intelligent optimization method for sinter ore blending may include the following steps:
step 10: and acquiring the material characteristics of each to-be-selected raw fuel, and determining the cost performance index of the to-be-selected raw fuel based on the material characteristics.
The raw fuel to be selected can comprise various materials such as iron ore powder, a solvent, carbon powder and the like required by sinter production. The same type of material may also be of multiple types, with different manufacturers offering different materials with different chemical compositions and prices. The material properties of the candidate raw fuel may include the type of candidate raw fuel, such as limonite, hematite, etc.; the name of the raw fuel to be selected, such as brazilian dust and the like; the price of the raw fuel to be selected, namely the unit price; chemical components of the raw fuel to be selected and the like; or the properties of the raw fuel to be selected, such as mineral powder, concentrate powder and the like; the particle size of the raw fuel to be selected, various information such as the assimilation temperature, and the like are not particularly limited in this embodiment.
And determining the cost performance index of each raw fuel to be selected according to the material characteristics of the raw fuel to be selected. For example, the cost/performance ratio index of the candidate raw fuel may be a difference between an estimated cost and an actual cost of the candidate raw fuel, and may be expressed as:
wherein the content of the first and second substances,
representing the cost performance index of the ith raw fuel to be selected;
the estimated cost of the ith candidate raw fuel;
is the actual cost of the ith candidate raw fuel. The actual cost refers to the price of the candidate raw fuel on the market, and if the difference between the estimated cost and the actual cost is larger, the higher the cost performance of the candidate raw fuel is.
The current price of the candidate raw fuel, namely the estimated cost, can be predicted by utilizing the past price of the candidate raw fuel. For example, if the price of material a in last 4 months of the year is 100 dollars per ton, then the estimated cost of material a in the last 4 months of the day may be determined to be 100 dollars per ton. Illustratively, the estimated cost of each material may be predicted more accurately by building a model. Specifically, a cost calculation model is constructed in advance, and the material characteristics of the candidate raw fuel are input into the preset cost calculation model to obtain the estimated cost of the candidate raw fuel. And then determining the actual cost of each type of the raw fuel to be selected, and determining the cost performance index of the raw fuel to be selected based on the estimated cost and the actual cost of the raw fuel to be selected.
The price of the material can be estimated from multiple dimensions through the cost measurement model, and the accuracy of cost estimation is improved.
The cost calculation model may be a Deep Neural Networks (DNN) model. Previously collecting historical data, wherein the historical data comprises a plurality of material characteristics, and each material characteristic can comprise the moisture (H) of the material 2 O), burnout, total iron (TFe) content, ferrous iron (FeO) content, silicon dioxide (SiO) 2 ) Content, aluminum oxide (Al) 2 O 3 ) Content, calcium oxide (CaO) content, magnesium oxide (MgO) content, manganese oxide (MnO) content, sulfur (S) content, phosphorus (P) content, nickel (Ni) content, and the like. Each item is also included in the history dataThe strip material characteristics correspond to the price of the material history. And converting each material characteristic into an input vector to form a training data set of the cost calculation model. And (4) taking the price of each material characteristic as a corresponding label to train the cost measurement model. The trained cost estimation model can be arranged on a corresponding system or platform to estimate the future price of the material.
For example, the raw materials for sintering are less in the types of solvents and carbon powders, and the price is generally stable, while the variety of mineral powders is large, and the price fluctuation is large. The material characteristics of each mineral powder in the candidate raw fuel can be converted into vectors, and the vectors are input into a trained cost measurement model to obtain the current price of each mineral powder, namely the estimated cost. The actual price of the ore dust, i.e. the actual cost, is then determined by the supplier of the primary fuel to be selected. And calculating a value obtained by subtracting the actual cost from the estimated cost, namely a cost performance index. When the estimated price of the raw material for sintering such as mineral powder is higher than the actual price, the current price of the raw material is lower than the conventional price, namely, the raw material has higher cost performance, and the raw material can be used for sintering production, so that the production cost can be reduced.
Step 20: and screening out sintering raw materials from the raw fuel to be selected based on the cost performance index.
For example, according to the cost performance index, the raw fuels to be selected can be ranked from large to small, and then the materials with higher cost performance ranked in the top are selected as sintering raw materials. For example, the mineral powder ranked at the top n positions according to the index of performance/cost ratio of the screening may be used as the sintering material, and n may be various values such as 3, 4, 5, and the like, which is not limited in this embodiment. And respectively screening the materials with higher neutral value of the type to form sintering raw materials according to the type required by actual production. The sintering raw material is a material to be mixed together in the sintering production.
Step 30: an objective function for calculating the cost of sintering material required per unit weight of sintered ore is determined.
The cost of the sintering raw material required per unit weight of the sintered ore is the total cost of the material required divided by the fired weight of the sintered ore. It is therefore necessary to determine the required fired weight of the sintered ore first. The fired weight may be the weight of the sintered ore obtained after the primary sintering production is performed. The fired weight may be set by a person, for example, 500 or 1000 tons, and the present embodiment is not particularly limited thereto.
After determining the firing weight, an objective function is constructed based on the firing weight and the cost required for each sintering material. The cost required for each sintering material is the product of the actual cost of the sintering material and the candidate coefficient. The candidate coefficients may include a plurality of sets of proportioning coefficients of the sintering raw materials, or may include a range of proportioning coefficients. Illustratively, the objective function may be expressed as:
wherein i is the serial number of the sintering raw materials, the sintering raw materials are n in total, and n is a positive integer; the n sintering raw materials comprise iron ore concentrate, bentonite, dedusting ash and other auxiliary materials;
the actual cost of the ith sintering raw material;
the candidate coefficient of the ith sintering raw material; z is the value of the objective function. M is the required sintering weight of the sintered ore. The fired weight can be calculated by the following formula, as shown in formula (3):
wherein the content of the first and second substances,
the value (%) of the burn-out of the i-th sintering material;
the removal rate of sulfur (S) may be 0.95, 0.9, etc.;
is the S content of the ith sintering raw material;
the FeO content of the i-th sintering raw material.
And (3) solving the formula (2) and the formula (3) according to a plurality of candidate coefficients of each sintering raw material, and calculating the cost required by the objective function under the conditions of different mixture ratios.
Step 40: and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient.
Wherein, the proportioning coefficient refers to the proportion of each sintering raw material required by each ton of sintering ore. Based on the cost under different proportioning conditions calculated by the objective function, the proportioning coefficient of the sintering raw material can be determined from the candidate coefficients. Illustratively, when the value of the objective function meets a preset condition, the candidate coefficient of the sintering raw material in the condition is taken as the proportioning coefficient. For example, when the objective function is minimized, the candidate coefficient at that time may be used as the final proportioning coefficient of the sintering material. That is, the objective function is solved, and the minimum value of the objective function is determined
. The sum of the proportioning coefficients of all the sintering raw materials is 1, namely,
。
the cost corresponding to the proportioning coefficient when the objective function obtains the minimum value is the lowest, ore proportioning is carried out according to the proportioning coefficient, the cost of sintering production can be saved, meanwhile, the proportioning of each sintering raw material can be accurately determined, mineral resources can be saved, and the waste of resources is avoided.
The preset condition of the objective function may be determined in advance, for example, when the value of the objective function is smaller than a preset value, the candidate coefficient is used as the matching coefficient, for example, when the value of the objective function is larger than a specific value, the candidate coefficient is used as the matching coefficient, and the like.
In actual production, not only the cost needs to be controlled, but also the inherent characteristics of iron ore resources, such as chemical components and the like, the sintering performance of the mixed material, the production requirement of the blast furnace, the control of harmful elements entering the blast furnace and the like need to be considered. When the proportioning coefficient of the sintering raw material is determined based on the objective function, the constraint information of the sintering ore can be determined firstly; and combining the constraint information and the objective function, and determining the proportioning coefficient of the sintering raw materials.
For example, the constraint information of the sintered ore blending may include a chemical composition constraint, a harmful element constraint, and the like, and may further include other constraints, such as a raw material inventory constraint, and the like, which is not limited in this embodiment.
Wherein, the chemical composition constraint means that all sintering raw materials include chemical compositions in the range of sintering finished ore. Specifically, it can be expressed as:
wherein the content of the first and second substances,
TFe content contained in each ton of the ith sintering raw material;
SiO as the i-th sintering material 2 Content (c);
is contained in each ton of the i-th sintering raw material
Content (c);
is contained in each ton of the i-th sintering raw material
Content (c);
the basicity of the ith sintering raw material per ton
；
Is contained in each ton of the i-th sintering raw material
Content (c);
is contained in each ton of the i-th sintering raw material
Content (c);
is the S content contained in each ton of the ith sintering material.
For example, the constraint conditions of the sintered ore blending may further include a raw material inventory constraint, which may be expressed as:
wherein the content of the first and second substances,
and
the maximum value and the minimum value of the raw material ratio of the ith sintering raw material are respectively expressed. Determining, based on the amount of resources stored, if sufficient resources are stored,
and
may be set to 0% and 100%, respectively.
Illustratively, the harmful element constraint refers to a constraint of harmful elements charged into the blast furnace. The detrimental element constraint may be expressed as:
wherein the content of the first and second substances,
、
、
、
、
、
respectively represent harmful elements of copper (Cu) and potassium oxide (K) in the i-th sintering raw material 2 O), sodium oxide (Na) 2 O), alkali metal, zinc (Zn), lead (Pb);
、
、
、
、
、
indicates Cu and K contained in all the sintering raw materials 2 O、Na 2 The maximum values of O, alkali metal, Zn and Pb are generally 100、3、3、3、0.25、100。
Illustratively, the sinter mix may also include other constraints, such as a ratio coefficient sum of all sinter raw materials of 1. Namely:
solving the formulas (2) to (6) through an intelligent optimization algorithm to obtain the proportioning coefficient which simultaneously satisfies the formulas (2) to (6) and the value of the formula (2) under the condition of the proportioning coefficient, namely the cost of the sintering raw material. The intelligent optimization algorithm may include other group intelligent optimization algorithms such as a particle swarm optimization algorithm, an ant colony optimization algorithm, a genetic algorithm, and the like, which is not limited in this embodiment. By solving the above equations (2) to (6), the optimal proportioning coefficient of each sintering material at the lowest cost can be obtained. And then carrying out ore blending, uniform mixing and sintering based on the determined proportioning coefficient to obtain the finished sintered ore. In the embodiment, the conditions of the blast furnace entering, the cost of ore blending raw materials and the requirements of components in the sintered ore are fully considered during ore blending, the sintering production process can be accurately controlled, the sintered ore meeting the requirements can be produced with the minimum cost as far as possible, the production cost is reduced, and the production quality is ensured.
Further, the embodiment also provides an intelligent optimization system for sintering ore blending. The intelligent optimization method for the sintering ore blending can be applied to the intelligent optimization system for the sintering ore blending. Fig. 2 shows a system framework diagram of the intelligent optimization system for sinter ore blending provided by the embodiment. As shown in fig. 2, the sinter ore blending intelligent optimization system 200 may include:
the material characteristics of each candidate raw fuel are converted into vectors, and the vectors are input into the cost calculation model 201, so that the estimated cost of the candidate raw fuel is obtained. The cost performance index of each candidate raw fuel is calculated by the cost performance calculation module 202. The cost performance calculation module 202 may calculate a difference between the estimated cost and the actual cost of the candidate raw fuel as a cost performance index of the candidate raw fuel. The intelligent sintering ore blending optimization system 200 can screen the raw fuel to be selected with higher cost performance as the sintering raw material according to the cost performance index. The selected sintering raw materials are input into the ore blending optimization model 203, and the proportioning coefficient of each sintering raw material is determined. The ore blending optimization model 203 is a model obtained by modeling according to the above equations (2) to (6). The objective function of the model may be equation (2). The optimal proportioning coefficient meeting the conditions can be calculated through intelligent optimization algorithms such as a particle swarm optimization algorithm, an ant colony optimization algorithm and the like.
Fig. 3 shows a schematic structural diagram of the intelligent sinter ore blending optimization system 200. As shown in fig. 3, the intelligent sinter blending optimization system 200 may include three modules, which are: material source data 301; ore blending calculations 302 and ingredient plan retrospective 303. The material source data 301 module may store material characteristics of various materials, among other things. And provides an interface for a user to inquire and modify the material characteristics. The ore blending calculation 302 module can be used for executing the steps 10 to 40 to obtain an ore blending scheme with the lowest cost per ton of iron. Namely, the cost performance index of each material is calculated, the materials for ore blending are screened out based on the cost performance index, and the proportioning coefficient of the ore blending materials required by each ton of sintered ore is determined. The blending scheme tracing 303 module can be used for storing the blending scheme and providing an inquiry interface for a user to inquire the blending scheme.
For example, the sinter blending intelligence optimization system 200 may provide a user page for a user to view or modify material properties. As shown in fig. 4, the sinter intelligent optimization mining system 200 may provide a user page 400. A list of material properties for all materials is displayed in the control 401 in the user page 400. The control 402 is used for selecting materials, and a user can select the materials corresponding to the control 402 by clicking the control 402 to serve as the raw fuel to be selected. Or all selections of material may be implemented via control 403. Also included in user page 400 is a control 404, where control 404 is used to initiate cost calculations. When the sintering and ore-blending intelligent optimization system 200 receives an operation of clicking the control 402 by a user, determining the raw fuel to be selected by the user according to the operation. When the operation of clicking the control 403 is received, all materials are determined to be the raw fuel to be selected. When the operation of clicking the control 404 is received, the material characteristics of the selected to-be-selected raw fuel are input into the cost measurement model, and a combination of materials with higher cost performance is determined from the to-be-selected raw fuel according to the obtained cost performance index and is used as a sintering raw material.
The determined sintering material may be displayed in the control 405. For example, the candidate raw fuels with higher cost performance are: australian FMG super powder, BHP Yandi powder and Kimbaba powder, the control 405 may display "Australian FMG super powder + BHP Yandi powder + Kimbaba powder", and the combination of these three materials is used as sintering material. In addition, the user page 400 further includes a control 406 for saving the screened material combination scheme. When the intelligent sintering and ore-blending optimization system 200 receives the operation of clicking the control 406, the currently determined material combination, such as the material combination displayed in the control 405, is saved as a combination scheme of sintering raw materials.
For example, the sinter ore blending intelligent optimization system 200 may determine a plurality of combined recipes for sintering raw materials, each of which may be identified by a recipe number. In addition, the intelligent optimization system 200 for sinter ore blending can also provide another user page to display different schemes, so that the user can compare different schemes conveniently. Fig. 5 is a graph showing the effect of the ratio coefficient of the sintering material. As shown in FIG. 5, the sinter blending intelligent optimization system 200 may provide a user page 500 to display the results of the determination of the proportioning coefficients for each solution. The proportioning coefficients of the sintering materials in each combination scheme can be displayed in the user page 500. For example, there are currently three material combination schemes, scheme 1, scheme 2 and scheme 3, respectively. The proportions of each type of material in the three scenarios, such as concentrate fraction, limonite fraction, fines fraction, etc., may be displayed in controls 501 in the user page 500. In addition, other types of proportions of the components, such as the total iron content, the ferrous iron content, and the like, may also be displayed in the control 501, and the embodiment is not limited thereto.
In addition, the sintering ore blending intelligent optimization system 200 may also provide a page to display information such as the cost required for each recipe, the cost required for each sintering raw material, and the like, and the embodiment is not limited thereto.
In an exemplary embodiment, fig. 6 shows another block diagram of the sinter ore blending intelligent optimization system. As shown in fig. 6, the intelligent sintering and ore-blending optimization system 60 may include a data obtaining module 61, configured to obtain a material characteristic of each candidate raw fuel, and determine a cost performance indicator of the candidate raw fuel based on the material characteristic; a cost performance calculation module 62, configured to screen out a sintering raw material from the to-be-selected raw fuel based on the cost performance index; a model building module 63 for determining an objective function for calculating the cost of the sintering material required per unit weight of sinter; and a proportioning determination module 64, configured to determine a proportioning coefficient of each sintering raw material based on the objective function, and perform sintering ore blending by using the proportioning coefficient.
In an embodiment of the present invention, the cost performance calculating module 62 specifically includes: the cost prediction module is used for inputting the material characteristics of the candidate raw fuel into a preset cost calculation model to obtain the estimated cost of the candidate raw fuel, wherein the cost calculation model is constructed through a deep neural network; the cost acquisition module is used for determining the actual cost of the raw fuel to be selected; and the index determining module is used for determining the cost performance index of the candidate raw fuel based on the estimated cost and the actual cost of the candidate raw fuel.
In an embodiment of the present invention, the model building module 63 specifically includes: the weight determination module is used for determining the sintering weight of the needed sintering ore; a function determination module for deriving the objective function based on the firing weight and a cost of each sintering material required for the firing weight; wherein the cost of each sintering material is the product of the actual cost of the sintering material and the candidate coefficient.
In an embodiment of the present invention, the ratio determining module 64 is specifically configured to: solving the objective function through an intelligent optimization algorithm, and enabling the value of the objective function to be minimum under the condition that the constraint information is met, wherein the intelligent optimization algorithm comprises one or more of a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm; and when the value of the objective function is minimum, taking the candidate coefficient as the proportioning coefficient of the sintering raw materials, wherein the sum of the proportioning coefficients of all the sintering raw materials is 1.
In an embodiment of the present invention, the ratio determining module 64 specifically includes: the constraint determining module is used for determining constraint information of the sintering ore; and the function solving module is used for determining the proportioning coefficient of the sintering raw materials by combining the constraint information and the objective function.
In one embodiment of the invention, the constraint determination module may be configured to: and determining the chemical component constraint and the harmful element constraint of the sintered ore to obtain the constraint information of the sintered ore.
It should be noted that the intelligent optimization system for sintering ore blending provided in the embodiment of the present application can implement all the method steps implemented in the embodiment of the intelligent optimization method for sintering ore blending, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in this embodiment are omitted here.
Fig. 7 illustrates a physical structure diagram of an electronic device, and as shown in fig. 7, the electronic device may include: a processor (processor)710, a communication Interface (Communications Interface)720, a memory (memory)730, and a communication bus 740, wherein the processor 710, the communication Interface 720, and the memory 730 communicate with each other via the communication bus 740. Processor 710 may invoke logic instructions in memory 730 to perform a sinter ore blending intelligence optimization method comprising: acquiring material characteristics of each to-be-selected raw fuel, and determining cost performance indexes of the to-be-selected raw fuel based on the material characteristics; screening out sintering raw materials from the to-be-selected raw fuel based on the cost performance index; determining an objective function for calculating a cost of the sintering raw material required per unit weight of the sintered ore; and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient.
In addition, the logic instructions in the memory 730 can be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, when the computer program is executed by a processor, a computer can execute the method for intelligent optimization of sinter blending provided by the above methods, and the method includes: acquiring material characteristics of each to-be-selected raw fuel, and determining cost performance indexes of the to-be-selected raw fuel based on the material characteristics; screening out sintering raw materials from the raw fuel to be selected based on the cost performance index; determining an objective function for calculating a cost of the sintering raw material required per unit weight of the sintered ore; and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the intelligent sinter ore blending optimization method provided by the above methods, the method including: acquiring material characteristics of each to-be-selected raw fuel, and determining cost performance indexes of the to-be-selected raw fuel based on the material characteristics; screening out sintering raw materials from the raw fuel to be selected based on the cost performance index; determining an objective function for calculating a cost of the sintering raw material required per unit weight of the sintered ore; and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; 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 technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent optimization method for sintering ore blending is characterized by comprising the following steps:
acquiring material characteristics of each to-be-selected raw fuel, and determining cost performance indexes of the to-be-selected raw fuel based on the material characteristics;
screening out sintering raw materials from the raw fuel to be selected based on the cost performance index;
determining an objective function for calculating a cost of the sintering raw material required per unit weight of the sintered ore;
and determining the proportioning coefficient of each sintering raw material based on the objective function, and sintering and ore blending by adopting the proportioning coefficient.
2. The intelligent sinter blending optimization method according to claim 1, wherein the determining cost performance indicators of the candidate raw fuels based on the material characteristics includes:
inputting the material characteristics of the candidate raw fuel into a preset cost calculation model to obtain the estimated cost of the candidate raw fuel, wherein the cost calculation model is constructed through a deep neural network;
determining the actual cost of the candidate raw fuel;
and determining the cost performance index of the candidate raw fuel based on the estimated cost and the actual cost of the candidate raw fuel.
3. The intelligent sinter ore blending optimization method according to claim 1, wherein the determining an objective function comprises:
determining the required sintering weight of the sintering ore;
obtaining the objective function based on the firing weight and the cost of each sintering material required for the firing weight;
the cost of each sintering material is the product of the actual cost of the sintering material and the candidate coefficient.
4. The intelligent sinter blending optimization method according to claim 3, wherein the determining the proportioning coefficient of each sintering raw material based on the objective function comprises:
determining constraint information of the sinter;
and determining the proportioning coefficient of the sintering raw material by combining the constraint information and the objective function.
5. The intelligent sinter blending optimization method according to claim 4, wherein the determining proportioning coefficients of each sintering raw material based on the objective function comprises:
solving the objective function through an intelligent optimization algorithm, and enabling the value of the objective function to be minimum under the condition that the constraint information is met, wherein the intelligent optimization algorithm comprises one or more of a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm;
and when the value of the objective function is minimum, taking the candidate coefficient as the proportioning coefficient of the sintering raw materials, wherein the sum of the proportioning coefficients of all the sintering raw materials is 1.
6. The intelligent optimization method for ore blending for sintering according to claim 4, wherein the determining constraint information of the sinter includes:
and determining the chemical composition constraint, the raw material inventory constraint and the harmful element constraint of the sinter to obtain the constraint information of the sinter.
7. An intelligent optimization system for sinter ore blending, comprising:
the data acquisition module is used for acquiring the material characteristics of each to-be-selected raw fuel and determining the cost performance index of the to-be-selected raw fuel based on the material characteristics;
the cost performance calculation module is used for screening sintering raw materials from the to-be-selected raw fuel based on the cost performance indexes;
a model building module for determining an objective function for calculating the cost of the sintering raw material required for a unit weight of sinter;
and the proportioning determination module is used for determining the proportioning coefficient of each sintering raw material based on the objective function and sintering ore blending by adopting the proportioning coefficient.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent sinter blending optimization method according to any one of claims 1 to 6 when executing the program.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent sinter blending optimization method as claimed in any one of claims 1 to 6.
10. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the intelligent sinter blending optimization method as claimed in any one of claims 1 to 6.
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