Adjustable capacity calculation method based on long-process steel production process modeling

Abstract

The embodiment of the invention discloses an adjustable capacity calculation method based on long-process steel production process modeling, which comprises the following steps: analyzing a long-process steel production process, and determining the material flow, equipment start-stop and energy consumption relationship of each link; drawing a resource task network block diagram by using a resource task network method, and depicting a coupling relationship among the links; based on the resource task network method, an optimization scheduling model is established, and the maximum adjustable capacity is calculated; the method can analyze the whole long-process steel production process, fully considers the incidence relation between links based on a resource task network method, determines key elements such as material flow, equipment start and stop, energy consumption and the like, considers various requirements, establishes a long-process steel production process model, can determine the adjustable maximum capacity of steel enterprises in each time period, and improves the production efficiency. And theoretical support is provided for iron and steel enterprises to participate in power grid dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of steel production, and in particular to an adjustable capacity calculation method based on long-flow steel production process modeling. Background Art

[0002] Steel production is a complex, continuous industrial process involving multiple core production links. Currently, grid dispatching focuses on residential and commercial loads in load management, while the potential for industrial load regulation, particularly in the steel industry, has yet to be fully explored and utilized.

[0003] While some research has been conducted on steel production scheduling and load management, most existing solutions remain limited to optimizing a single process or a specific link, failing to fully consider the complex coupling relationships between these links. This limitation results in a lack of overall production coordination and flexibility within the continuous and complex steel production process, making it impossible to effectively and fully tap into the potential adjustability of the steel production process. Furthermore, existing research primarily focuses on modeling short-process steel mills, which consume a lot of electricity, and lacks analysis of long-process steel mills. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method for calculating adjustable capacity based on long-process steel production process modeling. By analyzing the entire long-process steel production process, an optimization scheduling model is established, and the maximum adjustable capacity within a preset time is calculated, providing theoretical support for steel enterprises to participate in power grid scheduling.

[0005] To achieve the above objectives, the present application provides, in a first aspect, a method for calculating adjustable capacity based on long-flow steel production process modeling, the method comprising:

[0006] Analyze the long-process steel production process to clarify the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link;

[0007] Use the resource-task network method to draw a resource-task network diagram to depict the coupling relationship between the various links;

[0008] Based on the resource-task network method, an optimization scheduling model is established to calculate the maximum adjustable capacity.

[0009] Optionally, the long-process steel production process includes pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting and steel rolling.

[0010] Optionally, the resource-task network diagram includes the abstraction of resources and tasks, wherein the resources include equipment, materials and energy, and the tasks include the pellet sintering, the blast furnace ironmaking, the converter steelmaking, the continuous casting and the steel rolling.

[0011] Optionally, the optimization scheduling model includes resource balance constraints, resource capacity limitations, waiting time constraints and production order requirements.

[0012] Optionally, the resource balancing constraint is expressed as:

[0013]

[0014] Among them, R t,n is the available quantity of resource n in time period t; μ t,n,k The amount of resource n generated or consumed by task k in time period t; x t,k is whether task k occurs in time period t; t,n is the quantity of externally provided / demanded resource n at the beginning of period t.

[0015] Optionally, the resource capacity limitation is expressed as:

[0016] LB n ≤R t,n ≤UB n ;

[0017] Among them, LB n and UB n are the storage lower and upper limits of resource n respectively.

[0018] Optionally, the waiting time constraint is expressed as:

[0019]

[0020] Among them, T n,waitMax The maximum waiting time allowed for furnace resource n.

[0021] Optionally, the production order requirement is expressed as:

[0022] R Te,steel -R 0,steel ≥LB steel,finall ;

[0023] Among them, R Te,steel and R 0,steel are the quantities of product steel at the end and beginning of the scheduling period respectively; LB steel,finall The daily output requirement for steel.

[0024] Optionally, the calculation target of the maximum adjustable capacity is to maximize the adjustable capacity of the steel enterprise within a preset time;

[0025] The objective function of the optimization scheduling model is expressed as:

[0026] maxF=abs(R t,e -P t,baseline );

[0027] Where F is the objective function, which represents the maximum adjustable capacity of the steel enterprise in time period t; R t,e P is the actual power consumption of the steel enterprise in the period t; t,baseline is the baseline load power of the steel enterprise in the period t.

[0028] A second aspect of the present application provides an adjustable capacity calculation device based on long-flow steel production process modeling, comprising:

[0029] The process analysis module is used to analyze the long steel production process and clarify the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link;

[0030] A block diagram analysis module is used to draw a resource task network block diagram using a resource task network method to depict the coupling relationship between the various links;

[0031] The model building and calculation module is used to build an optimization scheduling model and calculate the maximum adjustable capacity based on the resource task network method.

[0032] Optionally, the long-process steel production process includes pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting and steel rolling.

[0033] Optionally, the resource-task network diagram includes the abstraction of resources and tasks, wherein the resources include equipment, materials and energy, and the tasks include the pellet sintering, the blast furnace ironmaking, the converter steelmaking, the continuous casting and the steel rolling.

[0034] Optionally, the optimization scheduling model includes resource balance constraints, resource capacity limitations, waiting time constraints and production order requirements.

[0035] Optionally, the resource balancing constraint is expressed as:

[0036]

[0037] Among them, R t,n is the available quantity of resource n in time period t; μ t,n,k The amount of resource n generated or consumed by task k in time period t; x t,k is whether task k occurs in time period t; t,n is the quantity of externally provided / demanded resource n at the beginning of period t.

[0038] Optionally, the resource capacity limitation is expressed as:

[0039] LB n ≤R t,n ≤UB n ;

[0040] Among them, LB n and UB n are the storage lower and upper limits of resource n respectively.

[0041] Optionally, the waiting time constraint is expressed as:

[0042]

[0043] Among them, T n,waitMax The maximum waiting time allowed for furnace resource n.

[0044] Optionally, the production order requirement is expressed as:

[0045] R Te,steel -R 0,steel ≥LB steel,finall ;

[0046] Among them, R Te,steel and R 0,steel are the quantities of product steel at the end and beginning of the scheduling period respectively; LB steel,finall The daily output requirement for steel.

[0047] Optionally, the calculation target of the maximum adjustable capacity is to maximize the adjustable capacity of the steel enterprise within a preset time;

[0048] The objective function of the optimization scheduling model is expressed as:

[0049] maxF=abs(R t,e -P t,baseline );

[0050] Where F is the objective function, which represents the maximum adjustable capacity of the steel enterprise in time period t; R t,e P is the actual power consumption of the steel enterprise in the period t; t,baseline is the baseline load power of the steel enterprise in the period t.

[0051] A third aspect of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the first aspect and any possible implementation thereof.

[0052] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0053] The present application provides a method for calculating adjustable capacity based on modeling of a long-process steel production process. By analyzing the long-process steel production process, the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link is clarified; a resource task network block diagram is drawn using a resource task network method to characterize the coupling relationship between the links; based on the resource task network method, an optimization scheduling model is established to calculate the maximum adjustable capacity; by analyzing the entire long-process steel production process, based on the resource task network method, the correlation between the various links is fully considered, key factors such as material flow, equipment start-up and shutdown, energy consumption, etc. are clarified, and a full-process model of the long-process steel production is established taking into account various needs, so as to determine the maximum adjustable capacity of the steel enterprise in each time period, and provide theoretical support for the steel enterprise to participate in power grid scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] in:

[0056] Figure 1 A schematic flow chart of an adjustable capacity calculation method based on long-process steel production process modeling provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of a long steel production process provided in an embodiment of the present application;

[0058] Figure 3 A resource task network diagram for steel production provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of the structure of an adjustable capacity calculation device based on long-flow steel production process modeling provided in an embodiment of the present application;

[0060] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0062] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0063] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0064] This application mainly aims to provide a calculation method for the maximum adjustable capacity of steel enterprises, taking into account the coupling characteristics of the links in the steel production process, process technology constraints and production order requirements, with the goal of maximizing the adjustable capacity of the steel enterprise within a certain period of time, and establishing an optimization scheduling model that considers the entire steel production process. This method targets the long-process steel production process with blast furnace-converter as the core, analyzes the interactive relationship between material flow, equipment start-up and shutdown, energy consumption and production links, and uses the resource-task network method to abstract materials, equipment, energy, etc. as resources, and abstract production links as tasks. A resource-task network diagram is drawn to characterize the coupling relationship between each link, establish a long-process steel production process model, and calculate the maximum adjustable capacity of the steel enterprise within a preset time.

[0065] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0066] See also Figure 1 , is a flow chart of an adjustable capacity calculation method based on long-process steel production process modeling provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0067] 101. Analyze the long-process steel production process and clarify the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link.

[0068] The execution subject of the method in the embodiment of the present application can be an adjustable capacity calculation device based on long-process steel production process modeling. In practical applications, it can be implemented on an electronic device, and the above-mentioned electronic device can be a terminal device such as a computer.

[0069] As a typical high-energy-consuming industry, steel has high electricity consumption, is centralized and easy to control, and has great load adjustment potential. However, due to the strict production process restrictions in the production process, the specific production process must be clarified first when analyzing the load-adjustable capacity of steel.

[0070] In an optional embodiment, the long steel production process includes pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting and steel rolling.

[0071] Figure 2 A schematic diagram of a long-process steel production process provided in an embodiment of the present application.

[0072] For the traditional long process steel production process, its production process is as follows Figure 2 As shown, it mainly includes five core production links: pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting, and steel rolling. The details are as follows:

[0073] (1) Pellet sintering

[0074] Pellet sintering is the first step in the steel production process. Powdered iron ore is mixed with fuels such as pulverized coal and fed to a sintering machine. After sufficient heating, the ore particles bond together to form a sintered ore with a certain strength. This step utilizes equipment resources, sintering machines, and consumes material resources such as iron ore and energy resources such as electricity to produce sintered ore.

[0075] (2) Blast furnace ironmaking

[0076] Blast furnace ironmaking is a core process in steel production. Raw materials such as sintered ore, coke, and limestone are added to the blast furnace, where an intense high-temperature reduction reaction removes oxygen from the iron ore, resulting in molten iron. This process utilizes the blast furnace as a resource, consumes sintered ore as a resource, and consumes energy and electricity to produce molten iron.

[0077] (3) Converter steelmaking

[0078] Converter steelmaking involves refining molten iron from blast furnaces. Oxygen is added to the pig iron to oxidize it, removing impurities like carbon, sulfur, and phosphorus, ultimately producing qualified molten steel. This process utilizes converters, consuming both molten iron and electricity to produce molten steel.

[0079] (4) Continuous casting

[0080] The purpose of continuous casting is to cool molten steel produced in a converter through a mold into a solid billet. The molten steel flows from the converter into the tundish of the continuous casting machine, where it is poured into the crystallizer to cool and solidify, forming a billet. This process uses equipment resources, continuous casting machines, and consumes material resources, molten steel, and energy resources, electricity, to produce the billet.

[0081] (5) Steel rolling

[0082] Steel rolling involves preheating a cast billet and then rolling it through a series of rolling mills. The billet undergoes multiple stages of heating, compression, and deformation in the rolling mills, ultimately becoming steel products of varying specifications. This process utilizes equipment resources, such as rolling mills, and consumes material resources, such as the cast billet, as well as energy resources and electricity, to produce the material resource product, steel.

[0083] 102. Use the resource-task network method to draw a resource-task network diagram to depict the coupling relationship between the above links.

[0084] The resource task network (RTN) diagram mentioned in the embodiments of this application is composed of two basic elements: resources and tasks. Resources include various resources used in the production process, such as equipment, materials, and energy. Tasks refer to operations that need to be performed during the production process, such as processing, assembly, and transportation. Each task requires a certain amount of resources to complete. Tasks and resources are connected by connecting arcs to represent the interactive relationship between tasks and resources. This method can graphically display resource constraints, the execution order of tasks, and the temporal relationship between tasks.

[0085] In an optional embodiment, the resource-task network diagram includes the abstraction of resources and tasks, wherein the resources include equipment, materials and energy, and the tasks include the pellet sintering, the blast furnace ironmaking, the converter steelmaking, the continuous casting and the steel rolling.

[0086] Figure 3 A resource task network framework diagram for steel production provided in an embodiment of the present application.

[0087] Based on the analysis of the long steel production process in step 101 above, a resource task network diagram of the steel production process can be drawn as follows: Figure 3shown.

[0088] Figure 3 This diagram shows a resource-task network for steel production, encompassing the core components of the long steel production process and their interrelationships. Nodes of different colors and shapes are used to represent different types of resources and tasks, as well as their interactions.

[0089] Rectangular nodes: represent various tasks in the production process, such as pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting, and steel rolling.

[0090] Oval nodes: represent equipment resources used in the production process, such as sintering machines, blast furnaces, converters, continuous casting machines, and rolling mills.

[0091] The first type of circular node: represents energy resources, such as electricity.

[0092] The second type of circular nodes: represent material resources, such as iron ore, sintered ore, molten iron, molten steel, and cast embryos.

[0093] Arrows: Indicate the flow direction of resources and the dependencies between tasks.

[0094] Specifically, the sintering task consumes the raw material iron ore to generate the intermediate product sintered ore; the blast furnace ironmaking task uses the intermediate product sintered ore to generate the intermediate product molten iron through high-temperature reaction; the converter steelmaking task consumes the intermediate product molten iron, adds oxygen, and converts it into the intermediate product molten steel; the continuous casting task cools and solidifies the intermediate product molten steel to generate the intermediate product ingot; the steel rolling task heats the intermediate product ingot and performs multiple rolling to produce the final product: steel of different specifications.

[0095] 103. Based on the above resource task network method, an optimization scheduling model is established to calculate the maximum adjustable capacity.

[0096] Through the analysis of steps 101 and 102 above, a dynamic interactive relationship between resources and tasks can be established, which may specifically include the consumption, generation and conversion rules of material resources, equipment resources and power resources in core production links such as pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting and steel rolling.

[0097] Based on this, resource balance constraints can be established, using the input-output relationships between resources in each task to calculate material reserves, equipment idleness, and power consumption. Furthermore, by combining requirements such as storage device capacity, production order requirements, and molten iron and steel temperature, resource capacity constraints, waiting time constraints, and production order requirements can be further constructed. Finally, an optimization model for a long-process steel production process is constructed based on the resource-task network approach. The solution is based on maximizing the adjustable capacity (estimated electricity consumption minus historical electricity consumption), thereby determining the maximum adjustable capacity within the company's preset timeframe.

[0098] Assuming the total number of daily scheduling periods is T, the production process constraints can be given as:

[0099] 1) Resource balance constraints

[0100]

[0101] Where: R t,n is the available quantity of resource n in time period t; μ t,n,k The amount of resource n generated (+) or consumed (-) by task k in time period t; x t,k is whether task k occurs in time period t; t,n is the quantity of external supply (+) / demand (-) resource n at the beginning of period t.

[0102] 2) Resource capacity limitations

[0103] LB n ≤R t,n ≤UB n

[0104] Where: LB n and UB n are the storage lower and upper limits of resource n respectively.

[0105] 3) Waiting time constraint

[0106] To prevent molten iron and steel from staying outside the equipment for too long and causing quality damage, a waiting time constraint is set, as shown below:

[0107]

[0108] Where: T n,waitMax The maximum waiting time allowed for furnace resource n.

[0109] 4) Production order requirements

[0110] R Te,steel -R 0,steel ≥LB steel,finall

[0111] Where: RTe,steel and R 0,steel are the quantities of product steel at the end and beginning of the scheduling period respectively; LB steel,finall The daily output requirement for steel.

[0112] The objective function is determined as the maximum adjustable capacity of the steel enterprise in a certain period of time, which is expressed as follows:

[0113] maxF=abs(R t,e -P t,baseline )

[0114] Where: F is the objective function, that is, the maximum adjustable capacity of the steel enterprise in time period t; R t,e is the actual power consumption of the steel enterprise in period t; P t,baseline is the baseline load power of the steel enterprise in time period t, which can be given by the grid dispatching side and is a known quantity.

[0115] In the embodiment of the present application, through the modeling analysis of the constraints in the long-process steel production process, the modeling method clarifies the dynamic relationship between resources and tasks (such as consumption, generation or no direct connection). Since each production task is interconnected through the flow of material resources, the model can effectively describe the interaction and influence between tasks. By optimizing and solving this model, the maximum adjustable capacity can be calculated under the premise of ensuring the normal production process and production task requirements of the steel enterprise, thereby providing a theoretical basis and data support for steel enterprises to enter the power market and participate in grid interaction.

[0116] An existing optimization method for the steel industry to participate in electricity demand response (DDR) proposes a method for optimizing the scheduling of hot-rolling processes under DDR to achieve optimal steel plant economics. This method primarily involves collecting data on hot-rolling equipment, constructing a DDR model, and optimizing production scheduling. However, this method focuses solely on the scheduling of electrical equipment in the hot-rolling process. Although hot-rolling is a major electricity consumer, steel production is a continuous and interdependent process. Optimizing only the hot-rolling process without considering the coupling relationships with other processes (such as pelletizing, blast furnace ironmaking, converter steelmaking, and continuous casting) results in a localized optimization that fails to effectively coordinate production loads across different processes. This leads to unsynchronized load regulation in other processes, potentially causing production line bottlenecks, equipment overload, and resource waste. Furthermore, analyzing only a single process step in the production process fails to fully characterize the steel company's capacity for adjustment, potentially leading to inaccuracies in the calculation of adjustable capacity. Therefore, how to comprehensively model the long-term steel production process and fully tap the steel company's adjustable potential while fully considering process constraints, process coupling, and order demand remains a challenge that warrants further research.

[0117] The main purpose of this application is to model the entire long-process steel production process, taking into account the linkage relationship between each link, and maximizing the potential adjustable capacity of the overall production process. First, the entire process is divided into five core production links: pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting, and steel rolling. Using the resource-task network method, materials, equipment, energy, etc. are abstracted as resources, and production links are abstracted as tasks. The interaction between resources and tasks and the coupling relationship between each link are characterized, and constraints such as resource balance constraints, resource capacity limitations, waiting time constraints, and production order requirements are constructed to achieve modeling of the entire long-process steel production process. Based on this model, the maximum adjustable capacity of steel enterprises in each period is calculated.

[0118] Compared with the traditional analysis and modeling of a single process or local link of a steel enterprise, this invention analyzes the entire long-process steel production process in detail. Based on the resource-task network method, it fully considers the correlation between each link, clarifies key factors such as material flow, equipment start-up and shutdown, energy consumption, and considers process limitations, link coupling and production order requirements. It establishes a long-process steel production process model and determines the maximum adjustable capacity of the steel enterprise in each time period.

[0119] The method in the embodiment of the present application solves the problem of existing methods being limited to a single process by analyzing the entire long-process steel production process, covering multiple links from pellet sintering to steel rolling, and better clarifies the correlation between links; at the same time, it fully considers process limitations, link coupling and production order requirements, ensures the realization of product quality and production goals, avoids production losses or quality problems caused by ignoring these constraints, accurately calculates the maximum adjustable capacity of steel enterprises in each time period, and provides a theoretical basis and data support for steel enterprises to enter the power market and participate in grid interaction, which is conducive to guiding steel enterprises to actively participate in grid dispatching, helps to achieve peak shaving and valley filling, ensures the safe, stable and economical operation of the power system, and reduces the pressure of peak load on the grid.

[0120] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides an adjustable capacity calculation device based on long-process steel production process modeling.

[0121] Figure 4 This is a schematic diagram of the structure of an adjustable capacity calculation device based on long-process steel production process modeling provided in an embodiment of the present application. Figure 4 As shown, the adjustable capacity calculation device 400 based on the long-flow steel production process modeling includes:

[0122] Process analysis module 410 is used to analyze the long steel production process and clarify the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link;

[0123] The block diagram analysis module 420 is used to draw a resource task network block diagram using a resource task network method to depict the coupling relationship between the various links;

[0124] The model building and calculation module 430 is used to build an optimization scheduling model based on the resource task network method and calculate the maximum adjustable capacity.

[0125] Understandably, Figure 4 The relevant contents of each module in the above method embodiment have been described in detail, and the details can be referred to the contents of the method embodiment; Figure 4 The provided adjustable capacity calculation device 400 based on long-process steel production process modeling can perform the following operations: Figure 1 Any step in the illustrated embodiment, such as the block diagram analysis module 420, may generate Figure 3 The resource task network diagram shown is not described in detail here.

[0126] In one embodiment of the present application, an electronic device is also provided. Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502. The memory 502 stores a computer program. When the computer program is executed by the processor 501, the following operations are performed: Figure 1 The electronic device 500 may further include an input / output device, etc. In a specific embodiment, the electronic device may be a terminal device, etc.

[0127] In one embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor is caused to perform any step in the above method embodiment.

[0128] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0129] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for calculating adjustable capacity based on long-process steel production process modeling, characterized in that: The method comprises: Analyze the long-process steel production process to clarify the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link; Use the resource-task network method to draw a resource-task network diagram to depict the coupling relationship between the various links; Based on the resource-task network method, an optimization scheduling model is established to calculate the maximum adjustable capacity.

2. The adjustable capacity calculation method based on long-process steel production process modeling according to claim 1 is characterized in that: The long steel production process includes pellet sintering, blast furnace ironmaking, converter steelmaking, continuous casting and steel rolling.

3. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 2 is characterized in that: The resource-task network diagram includes the abstraction of resources and tasks, wherein the resources include equipment, materials and energy, and the tasks include the pellet sintering, the blast furnace ironmaking, the converter steelmaking, the continuous casting and the steel rolling.

4. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 3 is characterized in that: The optimization scheduling model includes resource balance constraints, resource capacity limitations, waiting time constraints and production order requirements.

5. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 4 is characterized in that: The resource balancing constraint is expressed as: Among them, R t,n is the available quantity of resource n in time period t; μ t,n,k The amount of resource n generated or consumed by task k in time period t; x t,k is whether task k occurs in time period t; t,n is the quantity of externally provided / demanded resource n at the beginning of period t.

6. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 5 is characterized in that: The resource capacity constraint is expressed as: LB n ≤R t,n ≤UB n ; Among them, LB n and UB n are the storage lower and upper limits of resource n respectively.

7. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 6 is characterized in that: The waiting time constraint is expressed as: Among them, T n,waitMax The maximum waiting time allowed for furnace resource n.

8. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 5 is characterized in that: The production order requirement is expressed as: in, and R 0,steel are the quantities of product steel at the end and beginning of the scheduling period respectively; LB steel,finall The daily output requirement for steel.

9. The adjustable capacity calculation method based on long-flow steel production process modeling according to claim 5 is characterized in that: The calculation target of the maximum adjustable capacity is to maximize the adjustable capacity of the steel enterprise within a preset time; The objective function of the optimization scheduling model is expressed as: maxF=abs(R t,e -P t,baseline ); Where F is the objective function, which represents the maximum adjustable capacity of the steel enterprise in time period t; R t,e P is the actual power consumption of the steel enterprise in the period t; t,baseline is the baseline load power of the steel enterprise in the period t.

10. An adjustable capacity calculation device based on long-process steel production process modeling, characterized in that: include: The process analysis module is used to analyze the long steel production process and clarify the relationship between material flow, equipment start-up and shutdown, and energy consumption in each link; A block diagram analysis module is used to draw a resource task network block diagram using a resource task network method to depict the coupling relationship between the various links; The model building and calculation module is used to build an optimization scheduling model and calculate the maximum adjustable capacity based on the resource task network method.

11. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 9.

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