Optimization control method and system for aluminum slag smelting in blast furnace

Abstract

The invention provides an optimal control method and system for aluminum slag smelting in a blast furnace, and belongs to the technical field of blast furnace smelting. According to the method, the optimal magnesium-aluminum ratio RMg / Al is dynamically calculated through a piecewise function model, and a relational expression is established according to the Al2O3 content A of the iron ore entering the furnace; the magnesium adding amount is calculated by combining the current slag MgO content; and a viscosity prediction model is constructed, when eta obtained through calculation is smaller than or equal to 0.5 Pa.s, the current ore blending scheme is executed, otherwise, T, R2 or Mgadd is adjusted, and the slag viscosity is recalculated. Accurate regulation and control and viscosity prediction of the slag components are achieved, the blast furnace smelting efficiency and stability are improved, the steel yield is increased, and the application range is wide.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blast furnace smelting, in particular to an optimization control method and system for aluminum slag smelting in a blast furnace. BACKGROUND

[0002] In the field of steel metallurgy, especially in the process of blast furnace smelting, the treatment of aluminum slag has always been a key link. In the traditional blast furnace smelting process, when dealing with aluminum-containing iron ore, many challenges are faced. On the one hand, the formation of aluminum slag is closely related to the aluminum content in iron ore. When the aluminum content is too high, it will lead to complex slag composition, affecting the fluidity of the slag and the desulfurization effect. On the other hand, the existing technology lacks a precise quantitative model in controlling the composition of the slag, making it difficult to dynamically optimize key parameters such as the magnesium-aluminum ratio and binary basicity.

[0003] At present, although there is some research on the control of slag composition in the industry, most of them are still at the stage of qualitative analysis, lacking a systematic and operable control method. For example, in the control of the magnesium-aluminum ratio, most of them rely on empirical formulas and do not fully consider the real-time changes of the aluminum content in the iron ore entering the furnace, resulting in inaccurate magnesium addition, which not only wastes resources but also may affect the smooth operation of the blast furnace due to high slag viscosity.

[0004] In addition, for the prediction of slag viscosity, the existing models are often too complex to be quickly applied in actual production, or the precision is insufficient to effectively guide the adjustment of production parameters. In this case, blast furnace operators often have to resort to rough methods such as large-scale adjustment of temperature or basicity when facing abnormal slag viscosity, which not only increases energy consumption but also may cause damage to the blast furnace body.

[0005] At the same time, the types of iron ore used in blast furnace smelting are diverse, ranging from high-aluminum hematite to low-grade ore, with significant differences in aluminum content and chemical properties among different ores. The existing technology fails to develop differentiated ore matching strategies for these differences, resulting in difficulties in achieving ideal smelting results when dealing with certain specific ores.

[0006] In summary, the existing blast furnace smelting technology has many shortcomings in the treatment of aluminum slag, and it is urgent to develop an optimization control method that can accurately and dynamically control the composition of the slag, effectively predict and adjust the viscosity of the slag, and be suitable for various types of iron ore, in order to improve the efficiency and quality of blast furnace smelting, and reduce production costs and energy consumption. SUMMARY

[0007] In view of the above shortcomings of the prior art, the present application provides an optimization control method and system for aluminum slag smelting in a blast furnace, which realizes accurate regulation and control of slag composition and viscosity prediction, and improves the efficiency and stability of blast furnace smelting.

[0008] To achieve the above-mentioned purpose, the specific technical solutions of the present application are as follows:

[0009] In a first aspect, the present application provides an optimized control method for smelting aluminum slag in a blast furnace, comprising the following steps:

[0010] Dynamically calculating the optimal magnesium-aluminum ratio R by a piecewise function model Mg / Al , the formula is as follows:

[0011] ;

[0012] In the formula, R Mg / Al is the magnesium-aluminum ratio; A is the Al2O3 content of the iron ore entering the furnace;

[0013] Based on the optimal magnesium-aluminum ratio R Mg / Al calculated and the MgO content Mg current of the current slag, the amount of MgO needed to be supplemented Mg add is calculated, the formula is as follows:

[0014] ;

[0015] In the formula, Mg add is the amount of MgO needed to be supplemented; A is the Al2O3 content of the iron ore entering the furnace; R Mg / Al is the optimal magnesium-aluminum ratio; Mg current is the MgO content of the current slag;

[0016] Based on the furnace temperature T, the slag binary basicity R2 and the optimal magnesium-aluminum ratio R Mg / Al , the slag viscosity η is calculated by a viscosity prediction model, the formula is as follows:

[0017] ;

[0018] In the formula, η is the slag viscosity; A is the Al2O3 content of the iron ore entering the furnace; R Mg / Al is the magnesium-aluminum ratio; T is the furnace temperature; R2 is the slag binary basicity, which is the mass ratio of CaO and SiO2 in the slag;

[0019] When η ≤ 0.5 Pa·s, the current ore blending scheme is executed, otherwise T, R2 or Mg add is adjusted and recalculated.

[0020] Further, the control range of the furnace temperature T in the viscosity prediction model is 1500-1550℃.

[0021] Further, when η > 0.5 Pa·s, the furnace temperature T is preferentially increased to above 1520℃.

[0022] Further, the slag quality component control is: Al2O3 content 15%-16.5%, MgO content 8%-12.5%, magnesium aluminum ratio 0.25-0.8, binary basicity 1.1-1.16.

[0023] Further, when the Al2O3 content in the slag is 16.2%, the magnesium aluminum ratio is controlled to be 0.50±0.05, and the physical heat (i.e. the temperature of molten iron) is maintained >1500℃.

[0024] Further, the iron ore charged into the blast furnace includes but is not limited to at least one of high-aluminum hematite, aluminum limonite, complex intergrowth ore, and low-grade ore.

[0025] In the second aspect, the present application provides an optimized control system for smelting aluminum slag in a blast furnace, comprising the following modules:

[0026] The data acquisition module acquires the Al2O3 content of the iron ore charged into the blast furnace, the furnace temperature T, and the binary basicity R2 of the slag.

[0027] The data calculation module calculates the optimal magnesium aluminum ratio R Mg / Al , the amount of MgO needed to be supplemented Mg add , and the slag η.

[0028] The result judgment module executes the current ore blending scheme when η ≤ 0.5 Pa·s, and otherwise adjusts T or R2 and recalculates.

[0029] In the third aspect, the present application provides an electronic device, comprising a memory for storing computer program instructions, and a processor for executing the computer program instructions to complete the method.

[0030] In the fourth aspect, the present application provides a computer readable storage medium for storing computer readable computer program instructions, which are configured to execute the method when running.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] 1. The present application realizes dynamic adaptation of the magnesium aluminum ratio with the Al2O3 content through a piecewise function model, which improves the control precision by 30% compared with the traditional empirical formula, reduces the magnesium agent consumption by 18%-22%, and effectively solves the problem of slag component fluctuation caused by static parameters.

[0033] 2. The present application fuses the slag viscosity model with the multi-parameter coupling effect of furnace temperature, basicity and magnesium aluminum ratio, so that the viscosity prediction error is ≤ 0.05 Pa·s, which can better improve the steel production.

[0034] 3、The Al2O3 (15wt%-16.5wt%), MgO (8wt%-12.5 wt%) and R2 (1.1-1.16) wide range parameter design of the present application is suitable for high-aluminum hematite, aluminum limonite, composite intergrowth ore, low-grade ore and other ore varieties. DETAILED DESCRIPTION

[0035] The technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0036] In a first aspect, the present application provides an optimized control method for smelting of aluminum slag in a blast furnace, comprising the following steps:

[0037] The optimal magnesium-aluminum ratio R is dynamically calculated by a piecewise function model Mg / Al , and the formula is as follows:

[0038] ;

[0039] In the formula, R Mg / Al is the magnesium-aluminum ratio; A is the Al2O3 content of the iron ore entering the furnace;

[0040] Based on the calculated optimal magnesium-aluminum ratio R Mg / Al and the MgO content Mg current of the current slag, the amount of MgO to be supplemented Mg add is calculated, and the formula is as follows:

[0041] ;

[0042] In the formula, Mg add is the amount of MgO to be supplemented; A is the Al2O3 content of the iron ore entering the furnace; R Mg / Al is the optimal magnesium-aluminum ratio; Mg current is the MgO content of the current slag;

[0043] Based on the furnace temperature T, the slag binary basicity R2 and the optimal magnesium-aluminum ratio R Mg / Al , the slag viscosity η is calculated by a viscosity prediction model, and the formula is as follows:

[0044] ;

[0045] In the formula, η is the slag viscosity; A is the Al2O3 content of the iron ore entering the furnace; R Mg / Al is the magnesium-aluminum ratio; T is the furnace temperature; R2 is the slag binary basicity, and its value is the mass ratio of CaO and SiO2 in the slag;

[0046] The current ore blending scheme is executed when η ≤ 0.5 Pa·s; otherwise, T, R2, or Mg are adjusted. add And recalculate.

[0047] In some examples, the furnace temperature T in the viscosity prediction model is controlled within the range of 1500-1550℃.

[0048] In some examples, the slag quality composition is controlled as follows: Al2O3 content 15%-16.5%, MgO content 8%-12.5%, magnesium-aluminum ratio 0.25-0.8, and binary basicity 1.1-1.16.

[0049] In some examples, the iron ore fed into the furnace includes, but is not limited to, at least one of high-alumina hematite, aluminous limonite, complex symbiotic ore, and low-grade ore.

[0050] Example 1: Method for controlling slag with medium aluminum content

[0051] Iron ore samples were collected from the blast furnace feed, and the Al2O3 content (A) was determined to be 15.3% using X-ray fluorescence spectrometry. The optimal magnesium-aluminum ratio (R) was calculated using a piecewise function model. Mg / Al = 0.60 + 0.13 × (15.3 − 15) = 0.639;

[0052] Based on magnesium-aluminum ratio R Mg / Al = 0.639 and the current MgO content of the slag Mg current =8.6%, calculate the amount of MgO that needs to be added. add :

[0053] ;

[0054] With the furnace temperature set at T=1510℃ and the slag binary basicity R2=1.13, the viscosity prediction model was used to calculate:

[0055] ;

[0056] ;

[0057] If η = 0.38 Pa·s ≤ 0.5 Pa·s, execute the current ore blending scheme and add 1.30% lightly calcined magnesia powder into the furnace.

[0058] Example 2: Method for controlling high-alumina content slag

[0059] When the Al2O3 content A = 16.2% is detected in the iron ore sample fed into the blast furnace:

[0060] Calculate the magnesium-aluminum ratio: R Mg / Al= 0.72+0.18x(16.2-16)=0.756;

[0061] Current Mg current =9.8%:

[0062] ;

[0063] Furnace temperature T=1505℃, slag binary basicity R2=1.15:

[0064] ;

[0065] ;

[0066] Determine that η = 0.429 Pa·s ≤ 0.5 Pa·s, the current ore dressing scheme can be executed, and 2.78% of light burned magnesium powder is added to the furnace.

[0067] The inventors have found that when the slag viscosity η ≤ 0.5 Pa·s, the slag-iron separation efficiency can be improved by 20%-30%, the tapping time is shortened by 15%, and the output is directly increased; for each 0.1 Pa·s that η exceeds, the blast furnace needs to reduce the air by 3%-5%, resulting in a decrease in daily output of 2%-3%. At low viscosity (0.35-0.4 Pa·s), the hot metal reduction rate is increased by 1.8 times, the desulfurization efficiency (sulfur distribution ratio Ls reaches 35-40) is significantly optimized, and the hot metal qualification rate exceeds 99%, reducing the post-process processing time by 15 minutes / furnace. Maintaining η ≤ 0.5 Pa·s can reduce the coke ratio by 2-3 kg / t and increase the output by 0.5%-0.8%; but when relying on temperature rise to reduce viscosity, 3 kg / t of coal needs to be increased for each 10℃ increase, and long-term operation can easily result in an annual production capacity loss of 0.3%-0.5%. When η is controlled to 0.45±0.05 Pa·s, the blast furnace smooth running cycle is extended by 50% to 120-150 days, and the annual effective production time is increased by 7-10 days; when η < 0.5 Pa·s, the erosion of the furnace lining is accelerated, the major repair cycle is shortened, and the long-term production capacity loss is 8%-10%.

[0068] The above specific embodiments describe the implementation of the present application in detail, but the present application is not limited to the specific details in the above embodiments. Within the scope of the claims and technical concepts of the present application, the technical solutions of the present application can be modified and changed in many simple ways, and these simple modifications all belong to the protection scope of the present application.

Claims

1. An optimized control method for aluminum slag smelting in a blast furnace, characterized in that, Includes the following steps: The optimal magnesium-aluminum ratio is dynamically calculated using a piecewise function model, as shown in the following formula; ； In the formula, R Mg / Al The optimal magnesium-aluminum ratio is given; A represents the Al2O3 content of the iron ore fed into the furnace. Based on the optimal magnesium-aluminum ratio and the current MgO content in the slag, the amount of MgO that needs to be added is calculated using the following formula: ； In the formula, Mg add A represents the amount of MgO that needs to be added; A represents the Al2O3 content of the iron ore fed into the furnace; R represents the amount of MgO that needs to be added. Mg / Al The optimal magnesium-aluminum ratio; Mg current This represents the current MgO content of the slag. Based on furnace temperature, slag binary basicity, and optimal magnesium-aluminum ratio, slag viscosity is calculated using a viscosity prediction model, as shown in the following formula: ； In the formula, η is the slag viscosity; A is the Al2O3 content of the iron ore fed into the furnace; R Mg / Al The optimal magnesium-aluminum ratio is given; T is the furnace temperature; R2 is the binary basicity of the slag. The current ore blending scheme is executed when η ≤ 0.5 Pa·s; otherwise, T, R2, or Mg are adjusted. add And recalculate.

2. The optimized control method for aluminum slag smelting in a blast furnace according to claim 1, characterized in that, The furnace temperature T is adjustable within a range of 1500-1550℃.

3. The optimized control method for aluminum slag smelting in a blast furnace according to claim 2, characterized in that, When η > 0.5 Pa·s, the furnace temperature T should be increased to above 1520℃.

4. The optimized control method for aluminum slag smelting in a blast furnace according to claim 1, characterized in that, The quality composition of the slag is controlled as follows: Al2O3 content 15%-16.5%, MgO content 8%-12.5%, magnesium-aluminum ratio 0.25-0.8, and binary basicity 1.1-1.

16.

5. The optimized control method for aluminum slag smelting in a blast furnace according to claim 4, characterized in that, When the Al2O3 content in the slag is 16.2%, the magnesium-aluminum ratio is controlled at 0.50±0.05, and the physical heat is maintained at >1500℃.

6. The optimized control method for aluminum slag smelting in a blast furnace according to claim 1, characterized in that, The iron ore fed into the furnace includes, but is not limited to, at least one of high-alumina hematite, aluminous limonite, complex symbiotic ore, and low-grade ore.

7. An optimized control system for aluminum slag smelting in a blast furnace, used to implement the method described in any one of claims 1-6, characterized in that, Includes the following modules: Data acquisition module: Collects Al2O3 content, furnace temperature T, and slag binary basicity R2 of the iron ore fed into the furnace; Data Calculation Module: Calculates the magnesium-aluminum ratio R Mg / Al The amount of MgO that needs to be supplemented (Mg) add Slag viscosity η; Result judgment module: When η ≤ 0.5 Pa·s, execute the current ore blending scheme; otherwise, adjust T or R2 and recalculate.

8. An electronic device, characterized in that, include: A memory for storing computer program instructions; and a processor for executing the computer program instructions to perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used for storing computer-readable computer program instructions, which are configured to execute the method of any one of claims 1-6 at runtime.

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