Iron and steel industry load regulation and control method considering equipment inertia and dynamic data correction

Abstract

The invention provides an iron and steel industry load regulation and control method considering equipment inertia and dynamic data correction, and the method comprises the steps: building a load model of each production process based on the operation parameters of each production process in an iron and steel plant, and enabling the load model to reflect the material flow and energy flow in the production process; obtaining a power grid regulation and control demand, and adjusting the loads of the production processes in the iron and steel plant one by one based on the regulation and control demand according to the production sequence; when the load of the production process is adjusted, the reliable load adjustment capability of the production process is predicted based on the load model of the production process and the thermal inertia model, and the load adjustment amplitude of the production process and the production parameters of the subsequent production process are adjusted based on the prediction result, the thermal inertia model reflects the temperature change characteristic of the production equipment in the production process. The method can effectively reduce the influence of load regulation on the production of the steel plant.

Description

Technical Field

[0001] This invention relates to the field of industrial control technology, and in particular to a load control method for the steel industry that takes into account equipment inertia and dynamic data correction. Background Technology

[0002] As a typical example of a high-energy-consuming industry, the steel industry's load regulation is a crucial aspect of the power grid's demand-side response. Current technologies for load regulation in the steel industry often focus on a single energy source or a single production stage. For example, existing technologies typically collect electricity consumption data from blast furnaces or gas consumption from converters to establish simple load-output correlation models. Through grid-side dispatch instructions, steel companies reduce power consumption in processes such as rolling and sintering during peak electricity demand periods to achieve load regulation.

[0003] The existing load control methods in the steel industry rely on static data from steel plants (such as historical average energy consumption). They do not adequately consider the dynamic coupling relationship between material flow (such as the conversion of iron ore to molten iron to molten steel) and energy flow (such as the coordination of gas, steam, and electricity) in the "long process (sintering-blast furnace-converter-rolling)" or "short process (electric arc furnace-rolling)". The control strategies are often based on experience and judgment to reduce the load in a "one-size-fits-all" manner, which disrupts the continuity of production and has a significant impact on steel plant production. Summary of the Invention

[0004] This invention provides a load control method for the steel industry that takes into account equipment inertia and dynamic data correction, in order to solve the defect in the prior art that load control in steel plants affects production continuity, and to achieve the effect of reducing the impact of load control on production in steel plants.

[0005] This invention provides a load control method for the steel industry that takes into account equipment inertia and dynamic data correction, the method comprising: Based on the operating parameters of each production process in the steel plant, a load model is constructed for each production process, and the load model reflects the material flow and energy flow in the production process. Obtain the power grid control requirements, and adjust the load of each production process in the steel plant according to the production sequence and the control requirements; When adjusting the load of the production process, the load reliability adjustment capability of the production process is predicted based on the load model and thermal inertia model of the production process, wherein the thermal inertia model reflects the temperature change characteristics of the production equipment in the production process. Based on the prediction results, the load adjustment range of the production process and the production parameters of the subsequent production processes are adjusted.

[0006] According to the present invention, a load control method for the steel industry that takes into account equipment inertia and dynamic data correction is provided. The production process includes a sintering process. The energy flow model in the load model of the sintering process includes a balance relationship model between the actual combustion of fuel and blast furnace gas and the heat required for sintering, a heat balance model of the crusher, a power consumption model of the crusher, and a sintering energy balance model. The model for the balance between the actual combustion rate of fuel and blast furnace gas and the heat required for sintering is as follows: ; in, This refers to the amount of fuel input; The calorific value of the fuel; The combustion efficiency of fuel; This refers to the input amount of blast furnace gas; The calorific value of blast furnace gas. To improve the combustion efficiency of blast furnace gas, The heat required for sintering; The heat balance model for the pulverizer is as follows: ; in, For heat loss, , m is the mass of the output material. This refers to the specific heat capacity of the material. To reduce the temperature rise of the material after crushing, The overall heat transfer coefficient, The surface area of ​​the crusher. The surface temperature of the pulverizer. Ambient temperature; The energy consumption model for the crusher is as follows: ; Where t represents time. , For the power of the crusher, Indicates the difficulty of pulverizing the material. To handle the mass flow rate of materials, Initial particle size, The final particle size; The sintering energy balance model is as follows: ; in, It is the energy value of iron ore. It is the energy value of the cosolvent. The energy value of auxiliary materials, It is the energy value of the fuel. The energy value of sintered ore. It is about recovering energy. , , and The quantities of iron ore, flux, auxiliary materials, and fuel are respectively. According to the present invention, a load control method for the steel industry that takes into account equipment inertia and dynamic data correction is provided, wherein the production process includes blast furnace ironmaking process, converter steelmaking process and electric arc furnace steelmaking process; The energy flow model in the load model of the blast furnace ironmaking process includes the heat conduction model, heat convection model and radiation heat transfer model in the blast furnace. The heat conduction model is as follows: ; The heat convection model is as follows: The radiation heat transfer model is as follows: Where T represents temperature and t represents time. Represents the gradient. It is the thermal diffusivity. It is the input of heat. It is specific heat capacity. It's density. It is the convective heat transfer coefficient. It is the heat transferred through convective heat exchange between the blast furnace gas and the furnace wall. 2 represents the heat exchange area of ​​the blast furnace. It is the temperature of the gas. It's the air temperature. It is the radiant heat generated by the materials inside the blast furnace. It is the emissivity of radiation. It is the Stefan-Boltzmann constant. It is the ambient temperature; The energy flow model in the load model of the converter steelmaking process includes a heat balance equation and a temperature change iterative equation. The heat balance equation is: ; The iterative equation for temperature change is: ; in, It refers to heat; the superscripts Tot, HG, and HC represent total heat, heat generated, and heat consumed, respectively. and The masses of the metallic phase and the slag phase are respectively; and These are the specific heat capacities of the metallic phase and the slag phase, respectively. The energy flow model in the electric arc furnace steelmaking process includes a metal melting energy flow model; The energy flow model for metal melting is as follows: in, The melting power of solid metals. The heating power for solid metals. The heating power for liquid metal. The power of electric arc radiation. The radiant energy power of the oxygen fuel burner flame. For the heat power of solid waste, This represents the power used for heating that is transferred from the liquid phase to the solid phase via conduction. It is the convective power from the oxy-fuel burner. Coal is the energy generated from the combustion of coal. The power is transferred from the bath to the solid steel due to splashing water. This indicates the power of oxygen combustion in an electric arc furnace. This indicates the power of oxygen in supplying or participating in other physical processes. This represents the heat power carried away by the cooling liquid during the heat transfer process. This indicates the power loss.

[0007] According to the present invention, a load control method for the steel industry that takes into account equipment inertia and dynamic data correction is provided. The production process includes a continuous casting-rolling process. The energy flow model in the load model of the continuous casting-rolling process includes a heat energy consumption model of the reheat furnace, a temperature model of the reheat furnace, and a power consumption model of the rolling mill. The heat energy consumption model of the reheat furnace is as follows: ; For the heat energy consumption of the reheat furnace, For gas flow rate, Because of the low calorific value of the gas, The thermal efficiency of the heating furnace; The temperature model of the reheat furnace is: ; The temperature of the steel billet after heating. The initial temperature at which the steel billet enters the furnace. For the quality of steel billets, This refers to the specific heat capacity of steel. The power consumption model of the rolling mill is as follows: ; in, For the power consumption of the rolling mill, The force acting on the steel billet during the rolling process. For the rolling speed, For the efficiency of the rolling mill.

[0008] According to the present invention, a load control method for the steel industry that takes into account equipment inertia and dynamic data correction is provided. The production process further includes a self-generating process. The material flow model in the load model of the self-generating process includes a gas holder storage model. The energy flow model in the load model of the self-generating process includes an energy balance model of a gas boiler and a turbine, as well as an energy conversion model of a steam turbine generator. The gas holder storage model is as follows: ; Indicates the type of by-product gas; Indicates by-product gas The corresponding gas holder is The amount of stock at any given moment; , , They are respectively By-product gas The production volume, the amount of gas supplied to the self-owned power plant and the amount of gas supplied to the industrial production process; The energy balance model for the gas-fired boiler and turbine is as follows: ; This represents the calorific value of the fuel, where F is the summation index, indicating different types of fuel. This represents the amount of gas consumed by boiler j in a thermal power plant at time t. This represents the amount of steam at turbine inlet j at time t. This represents the water inlet flow rate at boiler inlet j at time t. Indicates boiler efficiency. This represents a correction factor for the turbine inlet steam flow rate. This indicates a correction factor for the boiler inlet water flow rate; The energy conversion model of the steam turbine generator is as follows: ; This represents the active power generated by turbine generator j at time t. This represents the amount of steam at turbine outlet j at time t. Indicates turbine efficiency. This represents the amount of steam extracted from the turbine at time t. This indicates the efficiency of the steam extraction process in a steam turbine. This represents a correction factor for the amount of steam supplied from the boiler to the turbine. This represents a correction factor for the amount of steam extracted from the turbine.

[0009] According to the present invention, a load control method for the steel industry that takes into account equipment inertia and dynamic data correction is provided, the method further includes: A multi-energy load dynamic state equation is constructed, and the production state of the steel plant is predicted based on the multi-energy load dynamic state equation to obtain the state prediction result. Based on the state prediction results, the preliminary predicted power of the steel plant is determined; The input data for historical periods is obtained and then input into a trained perturbation correction model to obtain the power perturbation result output by the perturbation correction model. The input data includes the production status, production control parameters, and external environmental information of the steel plant. Based on the power disturbance results and the preliminary predicted power, the target predicted power of the steel plant is determined.

[0010] The present invention also provides a load control device for the steel industry that takes into account equipment inertia and dynamic data correction, the device comprising: The model building module is used to construct load models for each production process in a steel plant based on the operating parameters of each production process. The load models reflect the material flow and energy flow in the production process. The load control module is used to acquire the power grid control demand and adjust the load of each production process in the steel plant according to the production sequence and the control demand. A thermal inertia embedding module is used to predict the reliable load adjustment capability of the production process based on the load model and thermal inertia model of the production process when adjusting the load of the production process. Based on the prediction results, the module adjusts the load adjustment range of the production process and the production parameters of the subsequent production process. The thermal inertia model reflects the temperature change characteristics of the production equipment in the production process.

[0011] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steel industry load control method taking into account device inertia and dynamic data correction as described above.

[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steel industry load control method taking into account equipment inertia and dynamic data correction as described above.

[0013] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steel industry load control method as described above, taking into account equipment inertia and dynamic data correction.

[0014] The steel industry load regulation method provided by this invention, which takes into account equipment inertia and dynamic data correction, obtains a load model for each production process by modeling the material flow and energy flow of each production process in a steel plant. When the power grid regulation demand is obtained, the method does not directly adjust the load of a certain production process based on the regulation demand. Instead, it adjusts the production process step by step. When adjusting each production process, the load reliability regulation capability of that production process is predicted based on the load model and thermal inertia model. Based on the prediction results, the production parameters and load adjustment range of subsequent production processes are adjusted. This allows subsequent production processes to adapt in advance to the changes in production parameters caused by the preceding production processes in response to the power grid regulation demand, avoiding the disruption of production continuity caused by a single process responding to the power grid regulation demand, and effectively reducing the impact of load regulation on steel plant production. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of the load control method for the steel industry that takes into account equipment inertia and dynamic data correction provided by the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the coupling relationship between various production processes in a steel plant within the steel industry load control method that takes into account equipment inertia and dynamic data correction, provided by this invention.

[0018] Figure 3 This is a schematic diagram of the load control device for the steel industry that takes into account equipment inertia and dynamic data correction provided by the present invention.

[0019] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The following is combined Figure 1-2This invention describes a load control method for the steel industry that takes into account equipment inertia and dynamic data correction. For example... Figure 1 As shown, the load control method for the steel industry that takes into account equipment inertia and dynamic data correction provided by the present invention includes the following steps: S110. Based on the operating parameters of each production process in the steel plant, construct a load model for each production process. The load model reflects the material flow and energy flow in the production process. S120. Obtain the power grid control requirements and adjust the load of each production process in the steel plant according to the production sequence and control requirements. S130. When adjusting the load of the production process, the load reliability adjustment capability of the production process is predicted based on the load model and thermal inertia model of the production process. The thermal inertia model reflects the temperature change characteristics of the generating equipment in the production process. S140. Adjust the load adjustment range of the production process and the production parameters of subsequent production processes based on the prediction results.

[0022] Load reliability regulation capability refers to the ability of electrical loads (in the context of a steel plant, this refers to the various equipment within the steel plant) in a power system to proactively and reliably adjust their power consumption and usage patterns based on the operating status of the power grid, electricity price signals, or dispatch instructions. The method provided by this invention models the material and energy flows of each production process in a steel plant, obtaining load models for each process. When the power grid regulation demand is received, instead of directly adjusting the load of a specific production process based on the regulation demand, the method adjusts the production processes step by step. During the adjustment of each production process, the load reliability regulation capability of that process is predicted based on the load model and thermal inertia model. Based on the prediction results, the production parameters and load adjustment range of subsequent production processes are adjusted. This allows subsequent production processes to adapt in advance to the changes in load reliability regulation capability caused by the preceding production processes responding to power grid regulation demands, avoiding disruption of production continuity caused by a single process responding to power grid regulation demands, and effectively reducing the impact of load regulation on steel plant production.

[0023] The inventors, through production process analysis of users in the steel industry, discovered that the steel industry's uniqueness lies in its core blast furnace / electric arc furnace, which connects multiple processes such as sintering, ironmaking, steelmaking, rolling, and self-owned power plants, relying on the synergistic characteristics of multiple energy sources including electricity, gas, and steam. This necessitates "continuous production across multiple processes and synergistic use of multiple energy sources." The core processes of the steel industry include pelletizing, coking, sintering, ironmaking (blast furnace), steelmaking (converter / electric arc furnace), self-owned power plants, and rolling, each relying on diverse energy sources. Production is divided into long and short processes: the long process includes sintering / pelletizing, blast furnace ironmaking, converter steelmaking, continuous casting, and rolling processes, using iron ore, flux, and coke as raw materials. Iron is obtained through sintering / pelletizing and blast furnace reduction, then oxygen is blown into the converter to produce steel, followed by continuous casting and rolling to form finished steel. The short process includes electric arc furnace, continuous casting, and rolling processes, using scrap steel as raw material. Steel is obtained through high-temperature melting in the electric arc furnace, followed by continuous casting and rolling to form finished steel.

[0024] Based on the interactive characteristics of production processes and energy, the energy-production system coupling characteristics of the steel industry load are characterized by a strong two-way coupling of material flow and energy flow. Specifically, this manifests as a high degree of coordination and dynamic feedback between material and energy flows in the production process. On the one hand, energy flow, as the fundamental support for the production process, directly determines the physicochemical reaction conditions and output efficiency of each production step (such as blast furnace, converter, electric arc furnace, etc.) through its stability and intensity of supply. On the other hand, the organization of production processes, production planning, and material flow strongly influence the energy system, affecting energy consumption intensity and temporal distribution. This results in the steel industry load generally exhibiting characteristics such as strong operational rigidity, large energy consumption fluctuation inertia, and limited adjustment flexibility. The core coupling variable between the production system and the energy system in the steel production process is the amount of coal gas and its calorific value. Furthermore, coal gas, electricity, and steam are three types of energy that permeate the entire steel production process, being consumed or generated at each production stage, thus establishing energy coupling relationships between each stage. Figure 2 As shown.

[0025] The method provided in this application constructs load models for each production process in a steel plant by collecting measured data of the operating parameters of each process. The operating parameters of the production processes used to construct the load models include, but are not limited to: (1) Power load data: including time-of-use power sampling values ​​of major electrical equipment (such as sintering machine, fan, rolling mill main motor, coal mill, etc.), with a time granularity better than 15 minutes.

[0026] (2) Steam and thermal energy data: covering steam flow rate, pressure and temperature, especially in the sintering process, hot blast stove and heating furnace system, which are key variables used to calculate thermal power.

[0027] (3) Gas data: The flow rate and calorific value parameters of blast furnace gas, converter gas and natural gas are key elements for thermal-electric coupling modeling.

[0028] (4) Production process parameters: such as sintering temperature, blast furnace tapping temperature, converter blowing time, rolling temperature curve, etc. These variables determine the trend of unit energy consumption.

[0029] (5) Production materials and operation data: including the material input and output of each stage of steel production, furnace schedule, key equipment start-up and shutdown records, etc.

[0030] Based on the characteristics of "continuous production of multiple processes and synergy of multiple energy sources" in steel production, the following section explains the multi-energy load model of material flow and energy flow involved in the main processes of steel production.

[0031] (1) Sintering process The production process of sintered ore mainly includes crushing and sintering. Input materials include ore powder, limestone, blast furnace gas, fuel, and water, while output materials include sintered ore. The energy flow models involved in the production process mainly include power consumption, electrical energy consumption, heat balance, energy balance, material temperature rise model, and heat loss model.

[0032] The total mass of the mixture after combining all input materials is:

[0033] In the formula, The input amount of mineral powder (unit: t). This represents the input amount of limestone (unit: t). This refers to the amount of water input.

[0034] The heat provided by both fuel and blast furnace gas is used for sintering. Assume the fuel combustion efficiency is... The combustion efficiency of blast furnace gas is The actual combustion rate of fuel and blast furnace gas is related to the heat required for sintering. The balance relationship can be expressed as:

[0035] In the formula, This refers to the amount of fuel input; The calorific value of the fuel; This refers to the input amount of blast furnace gas; This refers to the calorific value of blast furnace gas.

[0036] Assuming the mass loss rate during sintering is (Including mass losses such as moisture evaporation and gas escape), the final yield of sinter can be expressed as:

[0037] During the production process, the power consumption model of the crusher based on the Bond Work Index is as follows:

[0038] In the formula, For the power of the crusher, Indicates the difficulty of pulverizing the material. To handle the mass flow rate of materials, Initial particle size, This represents the final particle size.

[0039] Electricity consumption is expressed as:

[0040] In the formula, This refers to the electrical energy consumption of the crusher.

[0041] The heat balance equation is expressed as:

[0042] In the formula, This is due to heat loss.

[0043] The material heating model is as follows:

[0044] In the formula, This refers to the specific heat capacity of the material. This refers to the temperature rise of the material after crushing.

[0045] The heat loss model is as follows:

[0046] In the formula, The overall heat transfer coefficient, The surface area of ​​the crusher. The surface temperature of the pulverizer. The ambient temperature.

[0047] The sintering energy balance model is as follows:

[0048] In the formula, It is the energy value of iron ore. It is the energy value of the cosolvent. The energy value of auxiliary materials, It is the energy value of the fuel. The energy value of sintered ore. It is about recovering energy. , , and These are the masses of iron ore, flux, auxiliary materials, and fuel, respectively.

[0049] (2) Blast furnace ironmaking process The main input materials for the blast furnace process include ore pellets, sinter, coke, pulverized coal, and limestone, while the output materials are molten iron, slag, and blast furnace gas. The energy flow models involved in the production process mainly include heat conduction, heat convection, and radiation heat transfer process models.

[0050] Let the input of the pellet ore be... (Unit: t), the mass fraction of iron in the pellets is The input amount of iron in the pellet is:

[0051] The input amount of sintered ore is (Unit: t), the mass fraction of iron in the pellets is The input amount of iron in the pellet is:

[0052] The input amount of coke is (Unit: t), the fixed carbon content in coke is The ash content is ,and .

[0053] The input amount of pulverized coal is (Unit: t), the fixed carbon content in pulverized coal is The ash content is ,and .

[0054] The input amount of limestone is The magazine's quality score is .

[0055] Assume that the reduction rate of iron during the production of molten iron is... (That is, the proportion of iron element converted into molten iron). The production volume of molten iron can then be expressed as:

[0056] The ash from the combustion of coke and pulverized coal, as well as impurities from the decomposition of limestone, form waste residue. The yield of this waste residue can be expressed as:

[0057] The combustion of coke and pulverized coal produces carbon monoxide, forming blast furnace gas. The stoichiometric relationship between the combustion of fixed carbon in coke and the production of carbon monoxide is as follows: Let the ratio of coke and pulverized coal converted into carbon monoxide during combustion be... According to stoichiometry, the mass of blast furnace gas produced by the combustion of fixed carbon from coke and pulverized coal can be expressed as:

[0058] The heat conduction model in a blast furnace can be expressed as:

[0059] In the formula, T represents temperature and t represents time. Represents the gradient. It is the thermal diffusivity. It is the input of heat. It is specific heat capacity. It's density.

[0060] The heat convection model is expressed as:

[0061] In the formula, It is the convective heat transfer coefficient. It is the heat exchange area. It is the temperature of the gas. It's the air temperature. It is the heat transferred through convection between the blast furnace gas and the furnace wall.

[0062] The radiation heat transfer model is expressed as:

[0063] In the formula, It is the emissivity of radiation. It is the Stefan-Boltzmann constant. It is the ambient temperature. It is the radiant heat generated by the substances inside the blast furnace.

[0064] (3) Converter steelmaking process The input materials for the converter production process include scrap steel, molten iron, and limestone, while the output materials are molten steel, slag, and converter gas. The energy flow models involved in the production process mainly include heat balance and temperature change iterative models.

[0065] Let the input quantity of scrap steel be... (Unit: t), the mass fraction of iron in scrap steel is: The input amount of iron in the scrap steel is:

[0066] The input volume of molten iron is (Unit: t), the mass fraction of iron in molten iron is: The input amount of iron in the molten iron is:

[0067] Assume the iron recovery rate in the converter process is... The production volume of molten steel can then be expressed as:

[0068] During steelmaking, carbon oxidation produces converter gas, the main component of which is carbon monoxide. Assume the total carbon content of the scrap steel and molten iron is... And in the converter reaction there is If a certain proportion of carbon is oxidized to produce carbon monoxide, then the total amount of carbon is: The amount of converter gas generated is:

[0069] The heat balance equation in converter production can be expressed as:

[0070] In the formula, It represents heat, expressed in J; the superscripts Tot, HG, and HC represent total heat, heat generated, and heat consumed, respectively.

[0071] The iterative temperature change during the process can be represented as:

[0072] In the formula, and The masses (kg) of the metallic phase and the slag phase are respectively. and The specific heat capacities J of the metallic phase and the slag phase are respectively. ).

[0073] (4) Electric arc furnace steelmaking process The electric furnace production process is similar to that of a converter. Its input materials are mainly scrap iron, scrap steel, coke, and limestone. Coke is mainly used as a reducing agent. The output material is semi-finished steel (molten steel). The energy flow model involved in the production process is mainly the internal metal melting energy flow model.

[0074] Let the input amount of scrap iron be... (Unit: t), the iron content in scrap iron is (Mass fraction), scrap steel input quantity is (Unit: t), the iron content in scrap steel is (Quality score).

[0075] Assuming the total iron recovery rate is (Considering iron loss during the smelting process, such as oxidation loss), the iron content in the produced semi-finished steel is:

[0076] Assume that after smelting, the total mass of the molten steel is determined by iron and other elements (including alloying elements). For example, if only iron and other alloying elements are considered, the mass fraction of iron in the semi-finished steel is... The total mass of the semi-finished steel is:

[0077] The energy flow model for metal melting inside an electric arc furnace can be represented as: in, The melting power of solid metals. The heating power for solid metals. The heating power for liquid metal. The power of electric arc radiation. The radiant energy power of the oxygen fuel burner flame. For the heat power of solid waste, This represents the power used for heating that is transferred from the liquid phase to the solid phase via conduction. It is the convective power from the oxy-fuel burner. Coal is the energy generated from the combustion of coal. The power is transferred from the bath to the solid steel due to splashing water. This indicates the power of oxygen combustion in an electric arc furnace. This indicates the power of oxygen in supplying or participating in other physical processes. This represents the heat power carried away by the cooling liquid during the heat transfer process. This indicates the power loss.

[0078] (5) Continuous casting-rolling process The input materials in the continuous casting-rolling process mainly consist of by-product gases from previous production stages, as well as molten steel and water. The output materials are finished steel and scrap. The energy flow models involved in the production process mainly include models for heat energy consumption, temperature changes, and power consumption.

[0079] Let the injection volume of molten steel be... Molten steel mainly contains alloying elements such as carbon. Assuming that carbon and other alloying elements also undergo corresponding reactions during the refining process, the amount of finished steel produced after refining is:

[0080] In the formula, It is the initial mass fraction of the i-th element in the molten steel. It is the loss or change coefficient of the i-th element during the refining process.

[0081] The heat energy consumption of the reheat furnace in the continuous casting-rolling process is:

[0082] In the formula, The heat energy consumption of the reheat furnace (unit: kJ). Gas flow rate (unit: m³ / s). The lower calorific value of the gas (unit: kJ / m³). This refers to the thermal efficiency of the heating furnace.

[0083] The temperature of the reheat furnace can be expressed as:

[0084] In the formula, The temperature of the steel billet after heating. The initial temperature at which the steel billet enters the furnace. The mass of the steel billet (unit: kg). Specific heat capacity of steel (unit: kJ / (kg·°C)).

[0085] The power consumption of the rolling mill can be expressed as:

[0086] In the formula, Power consumption of the rolling mill (unit: kW) The force (in N) acting on the steel billet during the rolling process. The rolling speed (unit: m / s), For the efficiency of the rolling mill.

[0087] (6) Self-generating power process Steel plants often have their own power plants for self-generation. The main equipment in the process of these power plants includes gas-fired boilers, turbines, and steam turbine generators. Gas holders, as a crucial link between the steel production system and the power plant's energy supply system, play a role in balancing the output of by-product gas from the steel production system with the gas consumption of the power plant and other gas-using components.

[0088] The storage capacity of the gas holder can be expressed as:

[0089] In the formula, Indicates the type of by-product gas; Indicates by-product gas The corresponding gas holder is The amount of stock at any given moment; , , They are respectively By-product gas The amount of gas produced, the amount of gas supplied to the self-owned power plant, and the amount of gas supplied to industrial production processes.

[0090] The energy balance model for gas-fired boilers and turbines is as follows:

[0091] In the formula, This represents the calorific value of the fuel, where F is the summation index, indicating different types of fuel. This represents the amount of gas consumed by boiler j in a thermal power plant at time t. This represents the amount of steam at turbine inlet j at time t. This represents the water inlet flow rate at boiler inlet j at time t. Indicates boiler efficiency. This represents a correction factor for the turbine inlet steam flow rate. This represents the correction factor for the boiler inlet water flow rate.

[0092] The mechanical energy to electrical energy conversion model of a steam turbine generator is expressed as follows:

[0093] In the formula, This represents the active power generated by turbine generator j at time t. This represents the amount of steam at turbine outlet j at time t. Indicates turbine efficiency. This represents the amount of steam extracted from the turbine at time t. This indicates the efficiency of the steam extraction process in a steam turbine. This represents a correction factor for the amount of steam supplied from the boiler to the turbine. This represents a correction factor for the amount of steam extracted from the turbine.

[0094] The method provided in this application constructs a material-energy coupling model covering the entire steelmaking process by analyzing the production logic of both long and short processes in the steel industry. Furthermore, it is the first to incorporate the correlation chain of "coal gas recovery - self-owned power plant power generation - grid load substitution" into the model, overcoming the limitation of traditional models that cannot reflect the adjustable potential of the entire process, and achieving a systematic characterization of the steel load regulation potential.

[0095] As can be seen from the load model above, there is a process-related chain in steel plants: "gas recovery - power generation from self-owned power plants - grid-substituted load". This coupling mechanism can also be embedded into the prediction of load regulation capacity.

[0096] Due to the high thermal inertia of large-scale thermal equipment in the steel industry, forced regulation is prone to fluctuations. Considering the characteristics of continuous production and multi-energy synergy in steel plants, this application provides a method that breaks down grid load regulation demands into tiered tasks and designs a tiered regulation algorithm embedded in the model. Specifically, the "one-size-fits-all" forced regulation is broken down into multiple small steps. The dynamic impact of each adjustment on the furnace temperature field and other thermal balance parameters is simulated in real time. Using equipment thermal inertia and load models, and based on data such as blast flow and furnace wall temperature, the method predicts temperature changes at different times according to regulation demands. If there is a risk of temperature falling below the safety line, the subsequent regulation amplitude is automatically adjusted. Simultaneously, it links with subsequent processes such as converters and rolling mills, adapting to the production rhythm in advance. This avoids problems such as a surge in converter energy consumption and a shortened furnace lining life caused by a drop in molten iron temperature. This allows steel plants to ensure production continuity while responding to grid load regulation demands, reducing equipment maintenance costs, and achieving a win-win situation for both grid peak shaving and stable steel production.

[0097] Specifically, in the embedding of equipment thermal inertia, thermal inertia constraint equations are introduced. First, a temperature change model is established for equipment with significant thermal inertia, such as blast furnaces and heating furnaces, as follows:

[0098] Where t represents time and T represents temperature. Indicates equivalent heat capacity, For input heat flow, For system response time delay, For environmental heat dissipation, This represents the exothermic or endothermic power of the reaction. This equation macroscopically describes the dynamic process of heat load change, providing fundamental state variables for subsequent load prediction models.

[0099] When the equipment load changes in a short period of time, its actual output power It is not an instantaneous response, but can be approximated by a first-order hysteresis model as follows:

[0100] in, For process gain, For pure time delay, It is a time constant. The control input is used. This dynamic model characterizes the inertial response of the device power to the control signal, where s is a complex frequency domain variable in the Laplace transform.

[0101] In scenarios where equipment groups operate collaboratively, the gas storage capacity and pressure status of the gas holder also have a significant impact on the overall load regulation capability. The gas volume variation pattern can be described as follows:

[0102] Where t is time, The gas volume inside the cabinet. For output from various gas-generating equipment, and These are the gas consumption figures for the self-owned power plant and the process steps, respectively. This indicates additional losses.

[0103] Based on the aforementioned thermal inertia model, equipment power inertia response law equation, and gas holder gas volume change law equation, combined with the load model mentioned earlier, the changes in the equipment's load reliability regulation capability after load regulation can be calculated and predicted in advance. Based on the prediction results, the subsequent load regulation range of the equipment and the production parameters of the subsequent production processes of the equipment can be modified in advance to adapt to the changes, preventing load regulation from disrupting the continuity of production and reducing the impact of load regulation on steel plant production.

[0104] Furthermore, the method provided in this application also predicts the real-time power output of the steel plant. This allows the prediction results to be provided to the external power grid in advance as a reference for grid control policies, and also as a reference for load adjustment when receiving control requests from the external power grid. Specifically, this includes: A dynamic state equation for multi-energy load is constructed, and the production state of the steel plant is predicted based on the dynamic state equation for multi-energy load, thus obtaining the state prediction results. Based on the state prediction results, the preliminary predicted power of the steel plant is determined; The input data for historical periods is obtained and fed into the trained perturbation correction model to obtain the power perturbation result output by the perturbation correction model. The input data includes the production status of the steel plant, production control parameters, and external environmental information. Based on the power perturbation results and preliminary power forecasts, the target predicted power for the steel plant is determined.

[0105] By collecting data from industrial production sites, setting sampling frequencies and values, and calculating relevant initial variables and values, the foundation for establishing the "deduction" model is obtained. A multi-energy load model for steel production, built using actual data, is used to reconstruct the production state, while measured data is used to refine the deduction model. This forms a closed-loop verification system of "data-driven mechanism modeling, mechanism inversion of data value." The measured data includes the steel plant's daily hot rolling load production plan and equipment production power. Preparation time required for each production batch Production time The upper and lower limits of the quantity of slabs in each batch and The simulation data is generated by building a dynamic simulation platform to simulate load fluctuation trends under various potential conditions such as extreme weather, energy policy adjustments, and changes in user behavior. It also takes into account real-time time-of-use electricity prices and the base load excluding hot rolling production load. Specifically, in order to characterize the coupling relationship between equipment production and supply-demand cycle time when extrapolating power, a power constraint equation based on measured variables was established:

[0106] in, Indicates the first Average production power per batch This represents the equivalent energy consumption for the corresponding batch. The denominator reflects the combined impact of preparation and production time, and can dynamically characterize the inertial characteristics of equipment load utilization over time.

[0107] Meanwhile, to ensure consistency between batch capacity constraints and peak-shaving space calculations, this application introduces upper and lower limits on the number of slabs. These constraints limit the range of production rhythm changes, enabling the prediction model to assess the load adjustment space under different peak-shaving strategies while maintaining production line balance.

[0108] Based on the above measured parameters and inertial variables, the dynamic state equation for the multi-energy load is constructed as follows: Among them, state variables This includes equipment temperature, gas holder inventory, current batch power, and slab production, etc. For control input; and For the disturbance term and observation noise, g() represents the function processing. This indicates the production plan / operation status quantity. The equation represents the preliminary predicted power, and h() represents the function processing. Using measured data as constraints, this equation closely integrates the physical process with the production plan, reflecting the dynamic evolution of heat load over time and batches.

[0109] Based on the above dynamic state equations for multi-energy loads, the operating state of the steel plant at the next moment can be calculated. and based on The preliminary power forecast for the steel plant at the next moment is obtained.

[0110] In practical applications, the operation of steel plants is also affected by the external environment. The method provided in this application, after obtaining the initial predicted power, introduces a data-driven correction mechanism to form a combined prediction model of mechanism and data fusion, further improving the accuracy and generalization ability of the prediction. The specific correction model formula is as follows:

[0111] in, To predict power based on the mechanism calculated using the above state equations, For the perturbation correction function based on deep learning network, the input variables are... It includes historical status, control variables, and external environmental information. By integrating the authenticity of measured data with the broad coverage of extrapolated data, this combined model enables the prediction results to maintain physical consistency while possessing the ability to adaptively correct for complex operating environments.

[0112] Through the above process, rolling forecasts of the reliable regulation capabilities of steel industry users can be achieved with accuracy down to the minute. This lays the foundation for source-load coordination on the grid side, while striving to minimize disruption to industrial production and maximize economic benefits. It can further focus on diverse peak-shaving service demand scenarios, including but not limited to intraday short-term peak shaving, seasonal long-cycle peak shaving, and emergency backup peak shaving. The accurate forecasting system can determine the total amount of peak-shaving resources available, response speed, and duration under different time scales and load pressures, thus providing comprehensive and reliable technical support for developing scientific and efficient peak-shaving strategies.

[0113] The method provided in this application addresses the unique production logic of the steel industry, characterized by long-process, multi-stage, and highly coupled operations, self-circulating energy, and significant equipment inertia. It also addresses the key challenges of traditional control methods that fragment process coordination, neglect energy circulation, and struggle to adapt to equipment inertia. The method constructs a multi-energy load measurement-deduction model that integrates the coupling of the entire process, energy, materials, and environment, energy circulation adaptation, and equipment inertia coordination. This forms a steel-specific research framework that extends from in-depth analysis of the production process to precise load control strategies.

[0114] First, a comprehensive analysis of the long process of "sintering-blast furnace-converter-rolling steel-self-owned power plant" is conducted to reveal the deep coupling mechanism between the continuous material flow of molten iron and the energy flow of blast furnace gas power generation-steam / electricity coupling, and to accurately capture the chain effect of fluctuations in the preceding process on subsequent energy consumption and energy recovery. Secondly, taking into account the high proportion of energy recovery unique to the steel industry, a correlation chain of "coal gas recovery - self-owned power plant power generation - grid replacement load" is embedded to break through the limitation of traditional models that underestimate the adjustable load of energy cycle, realize the precise matching of surplus energy utilization with grid demand, and use measured data to correct the inference model to build a closed-loop verification system of "data-driven mechanism modeling and mechanism inversion data value". Finally, considering the lag characteristics of regulation in large thermal equipment, a stepped regulation algorithm is designed and embedded in the model to simulate the dynamic impact of load adjustment on the thermal balance of the equipment. This avoids forced regulation from disrupting production continuity, achieves coordinated load control throughout the entire process, reduces additional energy consumption for peak shaving, and improves the accuracy and stability of load regulation in the steel industry.

[0115] The following describes the load control device for the steel industry that takes into account equipment inertia and dynamic data correction provided by the present invention. The load control device for the steel industry that takes into account equipment inertia and dynamic data correction described below can be referred to in correspondence with the load control method for the steel industry that takes into account equipment inertia and dynamic data correction described above. Figure 3 As shown, the load control device for the steel industry that takes into account equipment inertia and dynamic data correction provided by the present invention includes the following modules: The model building module 310 is used to build load models for each production process based on the operating parameters of each production process in the steel plant. The load models reflect the material flow and energy flow in the production process. The load control module 320 is used to acquire the power grid control demand and adjust the load of each production process in the steel plant according to the production sequence and the control demand. The thermal inertia embedding module 330 is used to predict the reliable load adjustment capability of the production process based on the load model and thermal inertia model when adjusting the load of the production process. Based on the prediction results, the load adjustment range of the production process and the production parameters of the subsequent production process are adjusted. The thermal inertia model reflects the temperature change characteristics of the production equipment in the production process.

[0116] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a load control method for the steel industry that takes into account equipment inertia and dynamic data correction. This method includes: constructing load models for each production process based on the operating parameters of each production process in the steel plant; the load models reflecting the material and energy flows in the production processes; acquiring grid control requirements; adjusting the load of each production process in the steel plant according to the production sequence and based on the control requirements; predicting the reliable load regulation capability of the production process based on the load model and thermal inertia model; and adjusting the load adjustment range of the production process and the production parameters of subsequent production processes based on the prediction results. The thermal inertia model reflects the temperature change characteristics of the production equipment in the production process.

[0117] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as a software-powered unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the steel industry load regulation method provided by the above methods, which takes into account equipment inertia and dynamic data correction. The method includes: constructing a load model for each production process based on the operating parameters of each production process in the steel plant, wherein the load model reflects the material flow and energy flow in the production process; obtaining the power grid regulation demand, and adjusting the load of each production process in the steel plant one by one according to the production sequence and based on the regulation demand; when adjusting the load of the production process, predicting the reliable regulation capability of the load of the production process based on the load model and the thermal inertia model of the production process, and adjusting the load adjustment range of the production process and the production parameters of subsequent production processes based on the prediction results, wherein the thermal inertia model reflects the temperature change characteristics of the production equipment in the production process.

[0119] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a load regulation method for the steel industry that takes into account equipment inertia and dynamic data correction provided by the above methods. This method includes: constructing load models for each production process based on operating parameters of each production process in the steel plant, the load models reflecting the material and energy flows in the production processes; obtaining grid regulation requirements, and adjusting the loads of each production process in the steel plant sequentially based on the regulation requirements according to the production sequence; when adjusting the loads of the production processes, predicting the reliable load regulation capability of the production processes based on the load models and thermal inertia models of the production processes, and adjusting the load adjustment range of the production processes and the production parameters of subsequent production processes based on the prediction results, wherein the thermal inertia model reflects the temperature change characteristics of the production equipment in the production processes.

[0120] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A load control method for the steel industry that takes into account equipment inertia and dynamic data correction, characterized in that, The method includes: Based on the operating parameters of each production process in the steel plant, a load model is constructed for each production process, and the load model reflects the material flow and energy flow in the production process. Obtain the power grid control requirements, and adjust the load of each production process in the steel plant according to the production sequence and the control requirements; When adjusting the load of the production process, the load reliability adjustment capability of the production process is predicted based on the load model and thermal inertia model of the production process, wherein the thermal inertia model reflects the temperature change characteristics of the production equipment in the production process. Based on the prediction results, the load adjustment range of the production process and the production parameters of the subsequent production processes are adjusted.

2. The load control method for the steel industry considering equipment inertia and dynamic data correction according to claim 1, characterized in that, The production process includes a sintering process. The energy flow model in the load model of the sintering process includes a balance relationship model between the actual combustion of fuel and blast furnace gas and the heat required for sintering, a pulverizer heat balance model, a pulverizer power consumption model, and a sintering energy balance model. The model for the balance between the actual combustion rate of fuel and blast furnace gas and the heat required for sintering is as follows: ； in, This refers to the amount of fuel input; The calorific value of the fuel; The combustion efficiency of fuel; This refers to the input amount of blast furnace gas; The calorific value of blast furnace gas. To improve the combustion efficiency of blast furnace gas, The heat required for sintering; The heat balance model for the pulverizer is as follows: ； in, For heat loss, , m is the mass of the output material. This refers to the specific heat capacity of the material. To reduce the temperature rise of the material after crushing, The overall heat transfer coefficient, The surface area of ​​the crusher. The surface temperature of the pulverizer. Ambient temperature; The energy consumption model for the crusher is as follows: ; Where t represents time. , For the power of the crusher, Indicates the difficulty of pulverizing the material. To handle the mass flow rate of materials, Initial particle size, The final particle size; The sintering energy balance model is as follows: ； in, It is the energy value of iron ore. It is the energy value of the cosolvent. The energy value of auxiliary materials, It is the energy value of the fuel. The energy value of sintered ore. It is about recovering energy. , , and These are the masses of iron ore, flux, auxiliary materials, and fuel, respectively.

3. The load control method for the steel industry considering equipment inertia and dynamic data correction according to claim 1, characterized in that, The production process includes blast furnace ironmaking, converter steelmaking, and electric arc furnace steelmaking. The energy flow model in the load model of the blast furnace ironmaking process includes the heat conduction model, heat convection model and radiation heat transfer model in the blast furnace. The heat conduction model is as follows: ; The heat convection model is as follows: The radiation heat transfer model is as follows: Where T represents temperature and t represents time. Represents the gradient. It is the thermal diffusivity. It is the input of heat. It is specific heat capacity. It's density. It is the convective heat transfer coefficient. It is the heat transferred through convective heat exchange between the blast furnace gas and the furnace wall. 2 represents the heat exchange area of ​​the blast furnace. It is the temperature of the gas. It's the air temperature. It is the radiant heat generated by the materials inside the blast furnace. It is the emissivity of radiation. It is the Stefan-Boltzmann constant. It is the ambient temperature; The energy flow model in the load model of the converter steelmaking process includes a heat balance equation and a temperature change iterative equation. The heat balance equation is: ; The iterative equation for temperature change is: ; in, It refers to heat; the superscripts Tot, HG, and HC represent total heat, heat generated, and heat consumed, respectively. and The masses of the metallic phase and the slag phase are respectively; and These are the specific heat capacities of the metallic phase and the slag phase, respectively. The energy flow model in the electric arc furnace steelmaking process includes a metal melting energy flow model; The energy flow model for metal melting is as follows: in, The melting power of solid metals. The heating power for solid metals. The heating power for liquid metal. The power of electric arc radiation. The radiant energy power of the oxygen fuel burner flame. For the heat power of solid waste, This represents the power used for heating that is transferred from the liquid phase to the solid phase via conduction. It is the convective power from the oxy-fuel burner. Coal is the energy generated from the combustion of coal. The power is transferred from the bath to the solid steel due to splashing water. This indicates the power of oxygen combustion in an electric arc furnace. This indicates the power of oxygen in supplying or participating in other physical processes. This represents the heat power carried away by the cooling liquid during the heat transfer process. This indicates the power loss.

4. The load control method for the steel industry considering equipment inertia and dynamic data correction according to claim 1, characterized in that, The production process includes a continuous casting-rolling process, and the energy flow model in the load model of the continuous casting-rolling process includes a heat energy consumption model of the reheat furnace, a temperature model of the reheat furnace, and a power consumption model of the rolling mill. The heat energy consumption model of the reheat furnace is as follows: ; For the heat energy consumption of the reheat furnace, For gas flow rate, Because of the low calorific value of the gas, The thermal efficiency of the heating furnace; The temperature model of the reheat furnace is: ; The temperature of the steel billet after heating. The initial temperature at which the steel billet enters the furnace. For the quality of steel billets, This refers to the specific heat capacity of steel. The power consumption model of the rolling mill is as follows: ; in, For the power consumption of the rolling mill, The force acting on the steel billet during the rolling process. For the rolling speed, For the efficiency of the rolling mill.

5. The load control method for the steel industry considering equipment inertia and dynamic data correction according to claim 1, characterized in that, The production process also includes a self-generating power generation process. The material flow model in the load model of the self-generating power generation process includes a gas holder storage model, and the energy flow model in the load model of the self-generating power generation process includes an energy balance model of a gas boiler and a turbine, as well as an energy conversion model of a steam turbine generator. The gas holder storage model is as follows: ; Indicates the type of by-product gas; Indicates by-product gas The corresponding gas holder is The amount of stock at any given moment; , , They are respectively By-product gas The production volume, the amount of gas supplied to the self-owned power plant and the amount of gas supplied to the industrial production process; The energy balance model for the gas-fired boiler and turbine is as follows: ； This represents the calorific value of the fuel, where F is the summation index, indicating different types of fuel. This represents the amount of gas consumed by boiler j in a thermal power plant at time t. This represents the amount of steam at turbine inlet j at time t. This represents the water inlet flow rate at boiler inlet j at time t. Indicates boiler efficiency. This represents a correction factor for the turbine inlet steam flow rate. This indicates a correction factor for the boiler inlet water flow rate; The energy conversion model of the steam turbine generator is as follows: ； This represents the active power generated by turbine generator j at time t. This represents the amount of steam at turbine outlet j at time t. Indicates turbine efficiency. This represents the amount of steam extracted from the turbine at time t. This indicates the efficiency of the steam extraction process in a steam turbine. This represents a correction factor for the amount of steam supplied from the boiler to the turbine. This represents a correction factor for the amount of steam extracted from the turbine.

6. The load control method for the steel industry considering equipment inertia and dynamic data correction according to claim 1, characterized in that, The method further includes: A multi-energy load dynamic state equation is constructed, and the production state of the steel plant is predicted based on the multi-energy load dynamic state equation to obtain the state prediction result. Based on the state prediction results, the preliminary predicted power of the steel plant is determined; The input data for historical periods is obtained and then input into a trained perturbation correction model to obtain the power perturbation result output by the perturbation correction model. The input data includes the production status, production control parameters, and external environmental information of the steel plant. Based on the power disturbance results and the preliminary predicted power, the target predicted power of the steel plant is determined.

7. A load control device for the steel industry that takes into account equipment inertia and dynamic data correction, characterized in that, The device includes: The model building module is used to construct load models for each production process based on the operating parameters of the production processes in the steel plant. The load models reflect the material flow and energy flow in the production processes. The load control module is used to acquire the power grid control demand and adjust the load of each production process in the steel plant according to the production sequence and the control demand. A thermal inertia embedding module is used to predict the reliable load adjustment capability of the production process based on the load model and thermal inertia model of the production process when adjusting the load of the production process. Based on the prediction results, the module adjusts the load adjustment range of the production process and the production parameters of the subsequent production process. The thermal inertia model reflects the temperature change characteristics of the production equipment in the production process.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steel industry load control method as described in any one of claims 1 to 6, which takes into account equipment inertia and dynamic data correction.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steel industry load control method as described in any one of claims 1 to 6, which takes into account equipment inertia and dynamic data correction.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steel industry load control method as described in any one of claims 1 to 6, which takes into account equipment inertia and dynamic data correction.

Images