CN113584242A - Multi-blast-furnace automatic batching method, device and storage medium - Google Patents
Multi-blast-furnace automatic batching method, device and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 32
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 96
- 239000002994 raw material Substances 0.000 claims abstract description 67
- 229910052742 iron Inorganic materials 0.000 claims abstract description 48
- 239000008188 pellet Substances 0.000 claims abstract description 42
- 238000004364 calculation method Methods 0.000 claims abstract description 40
- 239000000571 coke Substances 0.000 claims abstract description 36
- 238000013178 mathematical model Methods 0.000 claims abstract description 34
- 239000011734 sodium Substances 0.000 claims abstract description 14
- 239000011701 zinc Substances 0.000 claims abstract description 14
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 claims abstract description 11
- 229910052725 zinc Inorganic materials 0.000 claims abstract description 11
- 229910052700 potassium Inorganic materials 0.000 claims abstract description 10
- 229910052708 sodium Inorganic materials 0.000 claims abstract description 10
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 claims abstract description 8
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims abstract description 8
- 239000011591 potassium Substances 0.000 claims abstract description 8
- 238000004590 computer program Methods 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 18
- 238000002156 mixing Methods 0.000 claims 1
- 239000002801 charged material Substances 0.000 description 5
- 239000004615 ingredient Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 230000002093 peripheral effect Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000003723 Smelting Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 239000003245 coal Substances 0.000 description 2
- 230000000704 physical effect Effects 0.000 description 2
- 239000000843 powder Substances 0.000 description 2
- 238000009628 steelmaking Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 229910000805 Pig iron Inorganic materials 0.000 description 1
- 229910052783 alkali metal Inorganic materials 0.000 description 1
- 150000001340 alkali metals Chemical class 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 229910052721 tungsten Inorganic materials 0.000 description 1
- 229910052720 vanadium Inorganic materials 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/008—Composition or distribution of the charge
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B2300/00—Process aspects
- C21B2300/04—Modeling of the process, e.g. for control purposes; CII
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- Organic Chemistry (AREA)
- Manufacture Of Iron (AREA)
Abstract
The application relates to a multi-blast-furnace automatic batching method, a multi-blast-furnace automatic batching device and a storage medium, wherein the multi-blast-furnace automatic batching method comprises the following steps: based on the determined blast furnace total ton iron cost calculation model and the calculation model of the preset element content, constructing a preset mathematical model by taking the minimum blast furnace total ton iron cost as a target function and the calculation model of the preset element content in each blast furnace and the total amount of the blast furnace raw materials as constraint conditions; solving based on the preset mathematical model to determine the charging amount of each charging raw material in each blast furnace; feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace; the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores. According to the scheme, the furnace entering raw materials are controlled on the premise that the furnace entering cost is the lowest, and the cost of the furnace entering raw materials is effectively controlled under the condition that the iron making requirement is met.
Description
Technical Field
The application relates to a multi-blast-furnace automatic batching method, a multi-blast-furnace automatic batching system and a storage medium, and belongs to the technical field of batching of blast-furnace charging raw materials in an iron-making process.
Background
In the process of blast furnace ironmaking, in order to improve the productivity and effectively ensure that the requirements of the steelmaking and ironmaking production processes on the quality of molten iron are met, the ingredients of the raw materials entering the blast furnace ironmaking are calculated, so that a blast furnace ironmaking ingredient calculation system needs to be designed scientifically.
In the prior art, the ingredient calculation process of blast furnace iron making is known before a blast furnace iron making ingredient calculation system is designed, and the ingredient calculation process of blast furnace iron making is actually based on the current smelting conditions and raw material conditions, and raw materials with different chemical compositions and physical properties are accurately combined according to certain quality requirements, so that the stability of the chemical compositions and the physical properties of iron-making products is guaranteed, qualified pig iron and proper slag compositions are obtained, and the consumption of required solvents and ores is accurately calculated. The quality and the yield of the smelting products are influenced by whether the batching scheme is reasonable or not and whether the batching calculation model is proper or not, so that the ore batching cost is directly influenced.
When a general blast furnace iron-making batching system calculates all furnace entering raw materials, because the blast furnace raw materials are various and have large quality fluctuation, the furnace entering amount of each blast furnace is generally considered to meet the requirements of iron making or steel making, and the cost of the raw materials is not considered too much, so that the cost of the furnace entering raw materials is relatively large.
In summary, the technical scheme that the cost of the raw materials entering the furnace can be controlled and the ironmaking requirement can be met is lacked in the prior technical scheme.
Disclosure of Invention
The application provides a technical problem of lacking a technical scheme which can control the cost of raw materials entering a furnace and simultaneously meet the requirement of iron making.
In order to solve the technical problem, the application provides the following technical scheme:
in a first aspect, according to an embodiment of the present application, there is provided a multi-blast furnace automatic batching method, including:
based on the determined blast furnace total ton iron cost calculation model and the calculation model of the preset element content, constructing a preset mathematical model by taking the minimum blast furnace total ton iron cost as a target function and the calculation model of the preset element content in each blast furnace and the total amount of the blast furnace raw materials as constraint conditions;
solving based on the preset mathematical model to determine the charging amount of each charging raw material in each blast furnace;
feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
In one embodiment, the method further comprises:
determining the blast furnace ton iron cost, and determining a blast furnace total ton iron cost calculation model and a calculation model of the content of preset elements in each blast furnace.
In one embodiment, the total ton iron cost of the blast furnace is modeled as:
wherein A is total ton iron cost of the blast furnace, PROiThe output of the i # blast furnace, A _ sinterjCost of j # sinter, GisinterjAmount of j # sinter supplied to i # blast furnace, GipelletKAmount of feed A _ pellet to the i # blast furnace for K # pelletskCost of k # pellets, GicokepAmount of supply of the coke ore K # to the blast furnace I # A _ makepMonovalent of K # coke, GilumpuAmount of i # blast furnace supplied to u # lump ore, A _ lumpuIs the unit price of u # lump ore, M is the amount of sintered ore, Q is the amount of pellets, W is the amount of coke ore, and V is the amount of lump ore.
In one embodiment, the total amount B of the blast furnace raw materials is:
in an embodiment, the model for obtaining the content of the preset element is:
wherein G isi(+ K) is the total content of K and Na elements in the i # blast furnace, Gi() The content of the zinc element in the i # blast furnace.
In a second aspect, an automatic batching control device for multiple blast furnaces according to an embodiment of the present application comprises:
the mathematical model building module is used for building a preset mathematical model based on the determined blast furnace total ton iron cost calculation model and the calculation model of the content of the preset elements, taking the lowest blast furnace total ton iron cost as a target function and taking the content of the preset elements in each blast furnace and the total amount of the raw materials entering the blast furnace as constraint conditions;
the mathematical model solving module is used for solving based on the mathematical model to determine the charging amount of each charging raw material in each blast furnace;
the feeding unit is used for feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
In one embodiment, the mathematical model building module is further configured to:
and determining a first mathematical model of the cost of the blast furnace ton iron and a second mathematical model of the content of the preset elements in each blast furnace.
In one embodiment, the first mathematical model is:
wherein A is total ton iron cost of the blast furnace, PROiThe output of the i # blast furnace, A _ sinterjCost of j # sinter, GisinterjAmount of j # sinter supplied to i # blast furnace, GipelletKAmount of feed A _ pellet to the i # blast furnace for K # pelletskCost of k # pellets, GicokepAmount of supply of the coke ore K # to the blast furnace I # A _ makepMonovalent of K # coke, GilumpuAmount of i # blast furnace supplied to u # lump ore, A _ lumpuIs the unit price of u # lump ore, M is the amount of sintered ore, Q is the amount of pellets, W is the amount of coke ore, and V is the amount of lump ore.
In a third aspect, according to an embodiment of the present application, there is provided a multi-blast furnace automatic batching device, the device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, the computer program being loaded and executed by the processor to implement the steps of the multi-blast furnace automatic batching method as in any one of the above.
In a fourth aspect, a computer-readable storage medium is provided according to an embodiment of the present application, which stores a computer program, and the computer program is used for implementing the steps of the multi-blast furnace automatic batching method according to any one of the above-mentioned items when being executed by a processor.
The beneficial effect of this application lies in:
according to the method provided by the embodiment of the application, the preset mathematical model is established based on the set objective function and the set constraint condition, the charging amount of each charging raw material in each blast furnace is determined based on the solving of the preset mathematical model, and then the charging raw materials are fed to the blast furnace according to the determined charging amount, so that the charging raw materials are controlled on the premise of lowest charging cost, and the cost of the charging raw materials is effectively controlled under the condition of meeting the iron-making requirement.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
Drawings
FIG. 1 is a schematic view of a feed system comprising a plurality of blast furnaces provided in one embodiment of the present application;
FIG. 2 is a flow chart of a method for automatic batching for multiple blast furnaces provided in an embodiment of the present application;
FIG. 3 is a flow chart of a method for automatic batching for multiple blast furnaces according to another embodiment of the present application;
FIG. 4 is a block diagram of an automatic batching device for multiple blast furnaces according to the present application;
FIG. 5 is a block diagram of a multi-blast furnace auto-batching provided in an embodiment of the present application.
Detailed Description
The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
Fig. 1 is a schematic diagram of a feeding system for multiple blast furnaces according to an embodiment of the present invention, for feeding the multiple blast furnaces in the system, the system including the multiple blast furnaces may include raw material ores for charging including # 1 sintered ore, # 2 sintered ore, # … …, M # sintered ore, # 1 pellet, # 2 pellet, … …, Q # pellet, # 1# coke ore, # 2 coke ore, … …, W # coke ore, # 1# lump ore, # 2 lump ore, … …, V # lump ore, and may be fed to a blast furnace system including # 1 blast furnace, # 2 blast furnace, … …, and N # blast furnace, wherein M, Q, W, V is an integer greater than or equal to 1, and the number of blast furnaces is greater than or equal to 1, and generally at least 2.
By adopting the method provided by the embodiment of the application, the amount of the raw material ores fed into each blast furnace can be calculated in a balanced manner, on one hand, the balance of the total consumption is met, the content of alkali metal in the blast furnace meets related requirements, and on the other hand, the lowest cost of the blast furnace ton iron is met, so that the production capacity is met, and meanwhile, the production cost is controlled.
Referring to fig. 2, an embodiment of the present application provides a multi-blast furnace automatic batching method, including:
s22, constructing a preset mathematical model by taking the lowest total ton iron cost of the blast furnace as a target function and the content calculation model of the preset elements in each blast furnace and the total amount of the raw materials entering the blast furnace as constraint conditions based on the determined total ton iron cost model of the blast furnace and the calculation model of the content of the preset elements;
step S24, solving based on the preset mathematical model, and determining the charging amount of each charging raw material in each blast furnace;
step S26, feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
In the embodiments of the present application, the predetermined elements mainly refer to elements harmful to the blast furnace, such as K element, Na element, Zn element, and the like, which are retained in the blast furnace, and the embodiments of the present application are described by taking K element, Na element, and Zn element as examples, and are not used to specifically limit the number of the predetermined elements and the specific elements.
In the embodiment of the present application, the lowest model of the total ton iron cost of the blast furnace is taken as an objective function, wherein the ton iron cost is the average cost of the charged raw materials and the ratio of the total cost of various charged raw materials to the total tonnage, and the constraint conditions are the content of the preset elements in the blast furnace and the total amount of the charged raw materials, that is, the present application aims to find the amount of each charged raw material which makes the cost of the charged raw materials lowest under the condition that the total charged raw materials are constant and the content of the preset elements in the blast furnace meets the reasonable requirement range.
According to the method provided by the embodiment of the application, the preset mathematical model is established based on the set objective function and the set constraint condition, the charging amount of each charging raw material in each blast furnace is determined based on the solving of the preset mathematical model, and then the charging raw materials are fed to the blast furnace according to the determined charging amount, so that the charging raw materials are controlled on the premise of lowest charging cost, and the cost of the charging raw materials is effectively controlled under the condition of meeting the iron-making requirement.
In the embodiment of the present application, referring to fig. 3, the method for feeding multiple blast furnace raw materials provided by the present application further comprises:
and step S21, determining a blast furnace ton iron cost calculation model and a calculation model of the preset element content in each blast furnace.
And determining a blast furnace ton iron cost calculation model and a calculation model of the content of the preset elements in each blast furnace.
In the embodiment of the application, firstly, a blast furnace ton iron cost calculation model and a calculation model of the preset element content are determined, then the determined blast furnace ton iron cost calculation model is taken as a target function, and the calculation model of the preset element content and the total amount of blast furnace charge are taken as constraint conditions to perform calculation, so that the optimal charge amount is determined.
In the embodiment of the present application, the cost calculation model for the blast furnace ton iron is as follows:
wherein A is total ton iron cost of the blast furnace, PROiThe output of the i # blast furnace, A _ sinterjCost of j # sinter, GisinterjAmount of j # sinter supplied to i # blast furnace, GipelletKAmount of feed A _ pellet to the i # blast furnace for K # pelletskCost of k # pellets, GicokepAmount of supply of the coke ore K # to the blast furnace I # A _ makepMonovalent of K # coke, GilumpuAmount of i # blast furnace supplied to u # lump ore, A _ lumpuIs the unit price of u # lump ore, M is the amount of sintered ore, Q is the amount of pellets, W is the amount of coke ore, and V is the amount of lump ore.
Further, the total amount B of the blast furnace raw materials is as follows:
in this embodiment of the present application, the model for obtaining the content of the predetermined element is:
wherein G isi(+ K) is the total content of K and Na elements in the i # blast furnace, Gi() The content of the zinc element in the i # blast furnace.
In the embodiment of the present application, in addition to the above-mentioned constraint conditions, the upper and lower limits of the charged material may be determined for each blast furnace according to actual conditions, and in the case where the upper and lower limits of the charged material are determined, the upper and lower limits of the charged material to be constrained are added to the constraint conditions, and the upper and lower limits of the charged material are more constrained when the charged amount of each charged material in each blast furnace is determined.
It is to be noted that, in the present application, the raw materials to be fed to each blast furnace are not limited to the above-mentioned ones, and when other raw materials to be fed are also included, the constraint of feeding each other is satisfied.
As follows, a specific example is illustrated:
in this specific example, 4 blast furnaces are taken as an example for explanation, and according to the method provided in the above example of the present application, the furnace charging raw materials in each blast furnace are respectively found in table 1:
TABLE 1 blast furnace feed materials data
It is noted that the feeding materials in this embodiment include # 1 coal powder and # 2 coal powder on the basis of sintered ore, pellet, lump ore and coke ore, and it is noted that when other feeding materials are included, a constraint model thereof may be added, and the application does not specifically limit the type and kind of the feeding materials.
Example 2
The embodiment of the present application further provides an automatic batching device for multiple blast furnaces, as shown in fig. 4, including:
the mathematical model construction module 41 is configured to construct a preset mathematical model based on the determined model of the total ton iron cost of the blast furnace and the calculation model of the content of the preset element, with the lowest total ton iron cost of the blast furnace as a target function, and with the content of the preset element in each blast furnace and the total amount of the raw materials charged into the blast furnace as constraint conditions;
the mathematical model solving module 42 is used for solving based on the mathematical model to determine the charging amount of each charging raw material in each blast furnace;
a feeding unit 43 for feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
And determining a blast furnace total ton iron cost calculation model and a calculation model of the content of the preset elements in each blast furnace.
In the embodiment of the application, the model for solving the total ton iron cost of the blast furnace is as follows:
wherein A is total ton iron cost of the blast furnace, PROiThe output of the i # blast furnace, A _ sinterjCost of j # sinter, GisinterjAmount of j # sinter supplied to i # blast furnace, GipelletKAmount of feed A _ pellet to the i # blast furnace for K # pelletskCost of k # pellets, GicokepAmount of supply of the coke ore K # to the blast furnace I # A _ makepIs the unit price of K # coke, GilumpuIs a u # blockAmount of ore supplied to i # blast furnace, A _ lumpuIs the unit price of u # lump ore, M is the amount of sintered ore, Q is the amount of pellets, W is the amount of coke ore, and V is the amount of lump ore.
Fig. 5 is a block diagram of an automatic batching device for multiple blast furnaces according to an embodiment of the present application, where the automatic batching device for multiple blast furnaces may be a desktop computer, a notebook computer, a palm computer, a cloud server, and other computing devices, and the device may include, but is not limited to, a processor and a memory. The multi-blast furnace automatic batching device of this embodiment at least comprises a processor and a memory, wherein the memory is stored with a computer program, the computer program is executable on the processor, and the processor executes the computer program to implement the steps of the multi-blast furnace automatic batching method embodiment, such as the steps of the multi-blast furnace automatic batching method shown in fig. 2. Or when the processor executes the computer program, the functions of the modules in the multi-blast furnace automatic batching device embodiment are realized.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the multi-blast furnace automatic batching device. For example, the computer program can be divided into a furnace entering standard content obtaining module, an enrichment amount calculating module and an adjusting module, and the specific functions of the modules are as follows:
the mathematical model building module is used for building a preset mathematical model based on the determined blast furnace total ton iron cost calculation model and the calculation model of the content of the preset elements, taking the lowest blast furnace total ton iron cost as a target function and taking the content of the preset elements in each blast furnace and the total amount of the raw materials entering the blast furnace as constraint conditions;
the mathematical model solving module is used for solving based on the mathematical model to determine the charging amount of each charging raw material in each blast furnace;
the feeding unit is used for feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
The processor may include one or more processing cores, such as: 4 core processors, 6 core processors, etc. The processor may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning. The processor is a control center of the multi-blast furnace automatic batching device and is connected with all parts of the whole multi-blast furnace automatic batching device by various interfaces and lines.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the multi-blast furnace automatic batching device by operating or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a memory device, or other volatile solid state storage device.
It is understood by those skilled in the art that the apparatus described in the present embodiment is only an example of a multi-blast furnace automatic batching apparatus, and does not constitute a limitation to the multi-blast furnace automatic batching apparatus, and in other embodiments, more or fewer components may be included, or some components may be combined, or different components may be included, for example, the calculating apparatus of the copper tube taper curve of the bloom crystallizer may further include an input/output device, a network access device, a bus, and the like. The processor, memory and peripheral interface may be connected by bus or signal lines. Each peripheral may be connected to the peripheral interface via a bus, signal line, or circuit board. Illustratively, peripheral devices include, but are not limited to: radio frequency circuit, touch display screen, audio circuit, power supply, etc.
Of course, the multi-blast furnace automatic batching device can also comprise fewer or more components, and the embodiment is not limited to the components.
Optionally, the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor is used for implementing the steps of the above-mentioned multi-blast furnace automatic batching method.
Optionally, the present application further provides a computer product comprising a computer readable storage medium having a program stored therein, the program being loaded and executed by a processor to implement the steps of the above-described multi-blast furnace automatic batching embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A multi-blast furnace automatic batching method is characterized by comprising the following steps:
based on the determined blast furnace total ton iron cost calculation model and the calculation model of the preset element content, constructing a preset mathematical model by taking the minimum blast furnace total ton iron cost as a target function and the calculation model of the preset element content in each blast furnace and the total amount of the blast furnace raw materials as constraint conditions;
solving based on the preset mathematical model to determine the charging amount of each charging raw material in each blast furnace;
feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
2. The method of claim 1, further comprising:
determining the blast furnace ton iron cost, and determining a blast furnace total ton iron cost calculation model and a calculation model of the content of preset elements in each blast furnace.
3. The method according to claim 1 or 2, wherein the blast furnace total ton iron cost is modeled as:
wherein A is total ton iron cost of the blast furnace, PROiThe output of the i # blast furnace, A _ sinterjCost of j # sinter, GisinterjAmount of j # sinter supplied to i # blast furnace, GipelletKAmount of feed A _ pellet to the i # blast furnace for K # pelletskCost of k # pellets, GicokepAmount of supply of the coke ore K # to the blast furnace I # A _ makepMonovalent of K # coke, GilumpuAmount of i # blast furnace supplied to u # lump ore, A _ lumpuIs the unit price of u # lump ore, M is the amount of sintered ore, Q is the amount of pellets, W is the amount of coke ore, and V is the amount of lump ore.
4. The method according to claim 3, wherein the total amount B of the blast furnace raw materials is as follows:
5. the method according to claim 1 or 2, wherein the model for obtaining the predetermined element content is:
wherein G isi(Na + K) is the total content of K element and Na element in the i # blast furnace, Gi(Zn) is the content of zinc element in the i # blast furnace.
6. The utility model provides a many blast furnaces automatic blending controlling means which characterized in that includes:
the mathematical model building module is used for building a preset mathematical model based on the determined blast furnace total ton iron cost calculation model and the calculation model of the content of the preset elements, taking the lowest blast furnace total ton iron cost as a target function and taking the content of the preset elements in each blast furnace and the total amount of the raw materials entering the blast furnace as constraint conditions;
the mathematical model solving module is used for solving based on the mathematical model to determine the charging amount of each charging raw material in each blast furnace;
the feeding unit is used for feeding each blast furnace according to the determined charging amount of each charging raw material in each blast furnace;
the preset elements are sodium elements, potassium elements and zinc elements, and the charging raw materials are at least one of sintered ores, pellets, coke ores and lump ores.
7. The apparatus of claim 6, wherein the mathematical model building module is further configured to:
determining the total ton iron cost calculation model of the blast furnace and the second mathematical model of the content of the preset elements in each blast furnace.
8. The apparatus of claim 6 or 7, wherein the blast furnace total ton iron cost is modeled as:
wherein A is total ton iron cost of the blast furnace, PROiThe output of the i # blast furnace, A _ sinterjCost of j # sinter, GisinterjAmount of j # sinter supplied to i # blast furnace, GipelletKAmount of feed A _ pellet to the i # blast furnace for K # pelletskCost of k # pellets, GicokepAmount of supply of the coke ore K # to the blast furnace I # A _ makepMonovalent of K # coke, GilumpuAmount of i # blast furnace supplied to u # lump ore, A _ lumpuIs the unit price of u # lump ore, M is the amount of sintered ore, Q is the amount of pellets, W is the amount of coke ore, and V is the amount of lump ore.
9. A multi-blast furnace automatic batching device, said device comprising a processor, a memory and a computer program stored in said memory and executable on said processor, characterized in that said computer program is loaded and executed by said processor to implement the steps of the multi-blast furnace automatic batching method according to any one of claims 1 to 5.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the multi-blast furnace auto-batching method according to anyone of the claims 1 to 5.
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