Intelligent feeding method and feeding device for argon station of steel plant

Abstract

The invention discloses an intelligent feeding method and device for an argon station of a steel plant. The intelligent feeding method comprises the steps that parameters of molten steel temperature, carbon content and oxygen content are obtained, the optimal cold charge feeding amount is dynamically calculated through a cold charge feeding amount model, parameters of molten steel weight, alloy proportion and molten steel temperature are obtained, and the optimal argon flow and argon blowing duration are calculated through an argon flow model. According to the method, automatic and accurate control over the cold charge adding amount and argon gas regulation and control in the steelmaking process is achieved through a model algorithm, the steelmaking efficiency and the product quality are remarkably improved, and powerful support is provided for intelligent upgrading of the steel industry.

Description

Technical Field

[0001] The invention relates to a feeding method and a feeding device, in particular to an intelligent feeding method and a feeding device for an argon station in a steelmaking plant, belonging to the technical field of intelligent control of steelmaking. Background Art

[0002] In the complex process of steel smelting, especially in the LF refining stage, the treatment of molten steel is crucial to the quality of the final product. In this stage, argon blowing and stirring are required to accelerate gas absorption, promote impurity removal and achieve composition homogenization. However, the traditional manual feeding method is not only complicated and labor-intensive, but also requires operators to be exposed to a harsh environment of high temperature and radiation, posing a serious threat to health and affecting production efficiency. More importantly, it is difficult to achieve precise control of the amount of argon blowing manually, which is directly related to the quality stability of molten steel. Therefore, the development of a feeding device that can achieve automated and intelligent control is of great significance to improving smelting efficiency and product quality. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent charging method and a charging device for an argon station in a steelmaking plant, so as to realize automatic charging control of the argon station in the steelmaking plant.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] An intelligent charging method for an argon station in a steelmaking plant comprises the following steps:

[0006] S1. Obtain the parameters of molten steel temperature, carbon content and oxygen content, and dynamically calculate the optimal cold material feeding amount using the cold material feeding amount model;

[0007] S2. Obtain the molten steel weight, alloy ratio, and molten steel temperature parameters, and use the argon flow model to calculate the optimal argon flow rate and argon blowing time.

[0008] Furthermore, the cold material feeding amount model is specifically as follows:

[0009] Assume that the molten steel temperature is T(t), the target molten steel temperature is Ttarget, the current carbon content is C(t), the target carbon content is Ctarget, and the oxygen content is O(t);

[0010] The cold material feeding amount M(t) is solved by the following optimization problem:

[0011]

[0012] Among them, α is the weight coefficient of molten steel temperature, β is the weight coefficient of carbon content, and γ is the weight coefficient of oxygen content;

[0013] Indicates the cold material feeding rate;

[0014] The molten steel temperature T(t), the current carbon content C(t) and the oxygen content O(t) are collected in real time, the above optimization problem is solved by numerical method, and the cold material feed amount M(t) is updated in real time.

[0015] Furthermore, the constraint condition of the cold material feeding amount model is:

[0016] Molten steel temperature constraint: T min ≤T(t)≤T max , T min is the minimum temperature of the molten steel, T max is the maximum value of molten steel temperature;

[0017] Carbon content limit: C min ≤C(t)≤C max , C min is the minimum value of carbon content, C max is the maximum value of carbon content;

[0018] The amount of cold material fed is non-negative: M(t)≥0;

[0019] Cold material feeding rate limit: is the minimum value of the cold material feeding rate, It is the maximum value of the cold material feeding rate.

[0020] Furthermore, the argon gas flow model is specifically:

[0021] Assume that the weight of molten steel is W(t), the alloy ratio is R(t), the temperature of molten steel is T(t), the argon flow rate is Q(t), and the argon blowing time is t blow ;

[0022] The optimal control problem of argon gas flow can be expressed as:

[0023]

[0024] Among them, η is the weight coefficient of the ratio of argon flow rate to molten steel weight, ζ is the weight coefficient of the molten steel weight and the alloy ratio change rate;

[0025] Q target is the target total amount of argon;

[0026] is the rate of change of molten steel weight, is the rate of change of alloy proportion;

[0027] Obtain the molten steel weight W(t), alloy ratio R(t) and molten steel temperature T(t) in real time, solve the above optimization problem, and dynamically adjust the argon flow rate to Q(t) and the argon blowing time to t blow .

[0028] Furthermore, the constraint condition of the argon gas flow model is:

[0029] Argon flow constraint: Q min ≤Q(t)≤Q max , Q min is the minimum value of argon gas flow, Q max is the maximum value of the argon gas flow;

[0030] Argon blowing time constraint: t min ≤t blow ≤t amx , t min is the minimum argon blowing time, t amx is the maximum value of argon blowing time;

[0031] Non-negativity of molten steel weight and alloy ratio: W(t)≥0,R i (t)≥0.

[0032] An intelligent charging device for an argon station in a steel plant comprises a data processing module, a sensor module, an argon flow regulating module, a cold material adding module, a system alarm module and a mechanical cantilever. The sensor module collects steelmaking parameters and uploads them to the data processing module. The data processing module is provided with the running programs of step S1 and step S2 of the intelligent charging method for the argon station in the steel plant. The argon flow regulating module comprises a manual calibration submodule and an automatic processing submodule. The automatic processing submodule is connected to the data processing module and calls the running program of step S1. The cold material adding module calls the running program of step S2 and controls the mechanical cantilever to perform cold material adding. The system alarm module monitors the running status of each module and triggers an alarm when each module is abnormal.

[0033] Compared with the prior art, the present invention has the following advantages and effects: the present invention realizes the automatic and precise control of the amount of cold material added and the argon gas regulation in the steelmaking process through the model algorithm, which not only significantly improves the steelmaking efficiency and product quality, but also provides strong support for the intelligent upgrading of the steel industry. DETAILED DESCRIPTION

[0034] In order to elaborate on the technical scheme adopted by the present invention to achieve the predetermined technical purpose, the technical scheme in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all embodiments, and the technical means or technical features in the embodiments of the present invention can be replaced without paying creative labor. The present invention will be described in detail below in conjunction with the embodiments.

[0035] The present invention provides an intelligent charging method for an argon station in a steelmaking plant, comprising the following steps:

[0036] S1. Obtain the parameters of molten steel temperature, carbon content and oxygen content, and use the cold material feeding amount model to dynamically calculate the optimal cold material feeding amount.

[0037] The specific cold material feeding amount model is:

[0038] Assume that the temperature of the molten steel is T(t), the target temperature of the molten steel is Ttarget, the current carbon content is C(t), the target carbon content is Ctarget, and the oxygen content is O(t). The molten steel temperature T(t), the current carbon content C(t), and the oxygen content O(t) are all functions of time t;

[0039] The cold material feeding amount M(t) is solved by the following optimization problem:

[0040]

[0041] Among them, α is the weight coefficient of molten steel temperature, β is the weight coefficient of carbon content, and γ is the weight coefficient of oxygen content; the three weight coefficients are used to balance the importance of different parameters and the smoothness of the feeding rate.

[0042] Indicates the cold material feeding rate, which is used to control the stability of the feeding process and avoid sudden changes.

[0043] The constraints of the cold material feeding model are:

[0044] Molten steel temperature constraint: T min ≤T(t)≤T max , T min is the minimum temperature of the molten steel, T max is the maximum value of molten steel temperature;

[0045] Carbon content limit: C min ≤C(t)≤C max , C min is the minimum value of carbon content, C max is the maximum value of carbon content;

[0046] The amount of cold material fed is non-negative: M(t)≥0;

[0047] Cold material feeding rate limit: is the minimum value of the cold material feeding rate, It is the maximum value of the cold material feeding rate.

[0048] The molten steel temperature T(t), the current carbon content C(t) and the oxygen content O(t) are collected in real time, and the above optimization problem is solved by numerical methods (such as gradient descent method, Newton method, etc.) to obtain the optimal cold material feeding amount, and the cold material feeding amount M(t) is updated in real time to adjust the cold material feeding amount in real time.

[0049] S2. Obtain the molten steel weight, alloy ratio, and molten steel temperature parameters, and use the argon flow model to calculate the optimal argon flow rate and argon blowing time.

[0050] The specific argon flow model is:

[0051] Assume that the weight of molten steel is W(t), the alloy ratio is R(t), the temperature of molten steel is T(t), the argon flow rate is Q(t), and the argon blowing time is t blow Among them, the alloy ratio R(t) is expressed by the mass fraction of a certain key alloy element.

[0052] The optimal control problem of argon gas flow can be expressed as:

[0053]

[0054] Among them, η is the weight coefficient of the ratio of argon flow rate to molten steel weight, and ζ is the weight coefficient of the molten steel weight and the alloy ratio change rate.

[0055] Q target is the target total amount of argon, calculated based on the molten steel properties and alloy ratio.

[0056] is the rate of change of molten steel weight, It is the rate of change of alloy ratio, which is used to reflect the dynamic changes of molten steel composition and weight.

[0057] The constraints of the argon flow model are:

[0058] Argon flow constraint: Q min ≤Q(t)≤Q max , Q min is the minimum value of argon gas flow, Q max is the maximum value of the argon gas flow;

[0059] Argon blowing time constraint: t min ≤t blow ≤t amx , t min is the minimum argon blowing time, t amx is the maximum value of argon blowing time;

[0060] Non-negativity of molten steel weight and alloy ratio: W(t)≥0,R i (t)≥0.

[0061] Obtain the molten steel weight W(t), alloy ratio R(t) and molten steel temperature T(t) in real time, solve the above optimization problem, and dynamically adjust the argon flow rate to Q(t) and the argon blowing time to t blow , in order to achieve precise control of argon supply.

[0062] An intelligent charging device for an argon station in a steel plant comprises a data processing module, a sensor module, an argon flow regulating module, a cold material adding module, a system alarm module and a mechanical cantilever. The sensor module collects steelmaking parameters and uploads them to the data processing module. The data processing module is provided with the operation programs of step S1 and step S2 of the intelligent charging method for the argon station in the steel plant. The argon flow regulating module comprises a manual calibration submodule and an automatic processing submodule. The automatic processing submodule is connected to the data processing module and calls the operation program of step S1. According to the output result of the data processing module, the argon flow rate is finely adjusted to ensure the accuracy of the automatic control. The cold material adding module calls the operation program of step S2 and controls the mechanical cantilever to perform cold material adding. By continuously monitoring the temperature of the molten steel and according to the preset algorithm model, the cold material is automatically calculated and added to maintain the stability of the temperature of the molten steel. The system alarm module monitors the operation status of each module and triggers an alarm when each module is abnormal.

[0063] The present invention realizes the automated and precise control of cold material addition and argon gas regulation during the steelmaking process through a model algorithm, which not only significantly improves the steelmaking efficiency and product quality, but also provides strong support for the intelligent upgrading of the steel industry.

[0064] The present invention has a high degree of automation: it realizes comprehensive automated control of the charging and argon blowing processes, significantly reducing the labor intensity of operators. The present invention realizes precise control: through deep learning algorithms and high-precision sensor technology, it realizes precise control of the steelmaking process and ensures the stability of the molten steel quality. The present invention has high safety: the system is equipped with an automatic alarm function, which can detect and handle abnormal situations in time, ensuring the safe operation of the equipment. The present invention has strong adaptability: the present invention is suitable for ladles and refining furnaces in different states, and has strong adaptability and wide promotion and application value.

[0065] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent charging method for an argon station in a steelmaking plant, characterized in that The following steps are involved: S1. Obtain the parameters of molten steel temperature, carbon content and oxygen content, and dynamically calculate the optimal cold material feeding amount using the cold material feeding amount model; S2. Obtain the molten steel weight, alloy ratio, and molten steel temperature parameters, and use the argon flow model to calculate the optimal argon flow rate and argon blowing time.

2. The intelligent charging method for argon station in a steelmaking plant according to claim 1, characterized in that: The cold material feeding amount model is specifically as follows: Assume that the molten steel temperature is T(t), the target molten steel temperature is Ttarget, the current carbon content is C(t), the target carbon content is Ctarget, and the oxygen content is O(t); The cold material feeding amount M(t) is solved by the following optimization problem: Among them, α is the weight coefficient of molten steel temperature, β is the weight coefficient of carbon content, and γ is the weight coefficient of oxygen content; Indicates the cold material feeding rate; The molten steel temperature T(t), the current carbon content C(t) and the oxygen content O(t) are collected in real time, the above optimization problem is solved by numerical method, and the cold material feed amount M(t) is updated in real time.

3. The intelligent charging method for argon station in a steelmaking plant according to claim 2, characterized in that: The constraint condition of the cold material feeding amount model is: Molten steel temperature constraint: T min ≤T(t)≤T max , T min is the minimum temperature of the molten steel, T max is the maximum value of molten steel temperature; Carbon content limit: C min ≤C(t)≤C max , C min is the minimum value of carbon content, C max is the maximum value of carbon content; The amount of cold material fed is non-negative: M(t)≥0; Cold material feeding rate limit: is the minimum value of the cold material feeding rate, It is the maximum value of the cold material feeding rate.

4. The intelligent charging method for argon station in a steelmaking plant according to claim 1, characterized in that: The argon gas flow model is specifically: Assume that the weight of molten steel is W(t), the alloy ratio is R(t), the temperature of molten steel is T(t), the argon flow rate is Q(t), and the argon blowing time is t blow ; The optimal control problem of argon gas flow can be expressed as: Among them, η is the weight coefficient of the ratio of argon flow rate to molten steel weight, ζ is the weight coefficient of the molten steel weight and the alloy ratio change rate; Q target is the target total amount of argon; is the rate of change of molten steel weight, is the rate of change of alloy proportion; Obtain the molten steel weight W(t), alloy ratio R(t) and molten steel temperature T(t) in real time, solve the above optimization problem, and dynamically adjust the argon flow rate to Q(t) and the argon blowing time to t blow .

5. The intelligent charging method for argon station in a steelmaking plant according to claim 4, characterized in that: The constraints of the argon flow model are: Argon flow constraint: Q min ≤Q(t)≤Q max , Q min is the minimum value of argon gas flow, Q max is the maximum value of the argon gas flow; Argon blowing time constraint: t min ≤t blow ≤t amx , t min is the minimum argon blowing time, t amx is the maximum value of argon blowing time; Non-negativity of molten steel weight and alloy ratio: W(t)≥0,R i (t)≥0.

6. An intelligent charging device for an argon station in a steelmaking plant, characterized in that: It comprises a data processing module, a sensor module, an argon flow regulating module, a cold material adding module, a system alarm module and a mechanical cantilever. The sensor module collects steelmaking parameters and uploads them to the data processing module. The data processing module is provided with the operating programs of step S1 and step S2 of the intelligent charging method for the argon station of a steelmaking plant as claimed in claim 1. The argon flow regulating module comprises a manual calibration submodule and an automatic processing submodule. The automatic processing submodule is connected to the data processing module and calls the operating program of step S1. The cold material adding module calls the operating program of step S2 and controls the mechanical cantilever to perform cold material addition. The system alarm module monitors the operating status of each module and triggers an alarm when each module is abnormal.

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