Method for indirectly measuring carbon content of molten steel based on big data analysis of converter flue gas

Abstract

The invention provides a method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis, and belongs to the technical field of ferrous metallurgy. Comprising the following steps: starting measurement to obtain initial molten iron information; converter flue gas components are obtained; according to the change of the flue gas components, performing flue gas component theoretical correction on the obtained flue gas components; performing smoke component experience correction on the theoretically corrected smoke components according to offline neural network learning; calculating the oxidation amount of each element after the experience correction of the flue gas components; calculating the carbon content in the molten pool; correcting the calculated carbon content in the molten pool according to experience; judging whether a smelting end point is reached; if so, outputting an end point carbon content result; and if not, returning. The converter molten steel end point carbon is indirectly measured through converter flue gas big data analysis, manual carbon measurement of the converter can be replaced, the labor intensity is reduced, the cost is reduced, the labor productivity is improved, and intelligent steelmaking is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of iron and steel metallurgy, and in particular to a method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis. Background Art

[0002] Converter steelmaking is a common steelmaking method. Oxygen top-blown converter is the main steelmaking method in the world today. The main purpose of converter smelting is to obtain molten steel with qualified composition and temperature. Due to the cumbersome production process of converter smelting and the complex physical and chemical reactions, it is difficult to effectively detect the composition and temperature in the converter production process. Although traditional manual sampling and testing can detect the composition and temperature of molten steel, it prolongs the smelting cycle, loses heat in the furnace and increases energy consumption, while increasing labor costs and material consumption.

[0003] The traditional method of determining the carbon content of molten steel can optimize the charge structure and predict the end carbon of molten steel according to the initial conditions and steel-making requirements, but lacks analysis of the smelting process, has low prediction accuracy, and cannot meet actual production needs. The end carbon prediction model based on the auxiliary gun uses the auxiliary gun to continuously sample at the end of converter smelting to predict the end carbon of molten steel. However, the consumption of the auxiliary gun probe is very large, which increases the cost of steelmaking.

[0004] Flue gas is an important byproduct in the converter smelting process, which can directly reflect the decarburization rate of the converter. According to the decarburization reaction rate, thermodynamic and kinetic analysis of the physical and chemical reactions in the converter can be performed to obtain the composition and temperature information in the furnace. The converter dynamic model based on flue gas analysis has the characteristics of no increase in smelting time / no additional consumption / real-time online prediction / high prediction accuracy.

[0005] The traditional method of analyzing the final carbon of the converter based on flue gas composition was initially to use the carbon content of molten iron, flue gas flow rate and carbon consumption indicated by the composition to calculate the final carbon of the converter molten steel based on the carbon balance. Foreign scholars Meyer and Uemura Takuro introduced an exponential decay model to predict the final carbon of the converter. Domestic scholars Liu Qing, Li Nan and others also proposed methods such as the converter flue gas analysis carbon determination model based on the mixing degree of the molten pool to determine the final carbon of the converter. These methods have problems such as the difficulty in accurately determining the initial carbon content of the molten pool and large fluctuations in the detection of flue gas components, which are not easy to implement in the actual operation of measuring the carbon content of molten steel.

[0006] References:

[0007] 1. Liu Kun, Steelmaking, 2009

[0008] 2. Liu Qing, Sun Jiankun, Li Nan, Cao Lingling, Lin Wenhui, Beijing University of Science and Technology

[0009] 3. Meyer and Uemura Takuro: Exponential decay model algorithm

[0010] 4. "Measurement Method of Converter End Carbon Content and Temperature Based on Least Squares Support Vector Machine with Feedback Model" Luo Liying Nanjing University of Science and Technology

[0011] 5. "Carbon Determination Model of Converter Flue Gas Analysis Based on Molten Pool Mixing Degree" by Li Nan; Lin Wenhui; Cao Lingling; Liu Qing; Sun Lefei; Liao Sangsang Summary of the invention

[0012] According to the technical problems mentioned in the above background technology, a method for indirectly measuring the carbon content of molten steel based on big data analysis of converter flue gas is provided.

[0013] The technical means adopted by the present invention are as follows:

[0014] A method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis comprises the following steps:

[0015] Step 1: Start measuring and obtain initial molten iron information;

[0016] Step 2: Obtain converter flue gas composition;

[0017] Step 3: According to the change of smoke components, the smoke components obtained in step 2 are corrected theoretically;

[0018] Step 4: Perform empirical correction on the smoke components after theoretical correction based on offline neural network learning;

[0019] Step 5: Calculate the oxidation amount of each element after empirical correction of flue gas composition;

[0020] Step 6: Calculate the carbon content in the molten pool;

[0021] Step 7: Correct the calculated carbon content in the molten pool based on experience;

[0022] Step 8: Determine whether the smelting end point has been reached; if it is the end point, output the end point carbon content result; if it is not the end point, return to execute step 2.

[0023] Furthermore, in step 2, due to the intake of air at the converter mouth and the secondary combustion in the flue, the flue gas composition detected by the gas analyzer is not equal to the initial flue gas composition of the initial molten pool reaction, so the flue gas composition needs to be corrected.

[0024] Furthermore, in step 2, the converter flue gas components are obtained by:

[0025] From the nitrogen balance in the flue gas, we can know that:

[0026] F n(i) +0.79·F空气(i) =F 烟气(i) N% (i) ;

[0027] Among them, F N(i) 、F 空气(i) 、F 烟气(i) 、N% (i) Respectively represent T i Nitrogen, furnace air, furnace flue gas, and nitrogen percentage content in flue gas at all times;

[0028] Get T i The air intake volume at the furnace mouth at this moment is:

[0029] F 空气(i) =(N% (i) ·F 烟气(i) -F N(i) ) / 0.79;

[0030] Assuming that the inhaled air is completely burned, the amount of oxygen in the secondary combustion is:

[0031] 0.21·F 空气(i) ;

[0032] The flue gas volume of CO2 after secondary combustion of flue gas is:

[0033] CO2% (i) ·F 烟气(i) -0.21·F 空气(i) ;

[0034] The amount of CO in flue gas is:

[0035] CO% (i) ·F 烟气(i) -2 0.21 F 空气(i)

[0036] Among them, CO2% (i) T i CO2 content in flue gas at the moment %; CO% (i) T i CO content in flue gas at the moment (%); due to the existence of secondary combustion:

[0037]

[0038] in, Indicates T i The CO2 flow rate generated by secondary combustion at the moment; a and c represent constants, a = 0.2, c = 0.75;

[0039] Therefore, the CO2 generated by the oxidation reaction between the oxygen supplied by the oxygen lance and the carbon element in the molten pool is:

[0040]

[0041] The generated CO is:

[0042]

[0043] Furthermore, the oxidation of each element in the molten pool is represented by the ratio of the distribution ratio using the Gibbs free energy:

[0044]

[0045] Among them, δ c , δ Si , δ Fe , δ Mn and δ p Respectively represent the distribution ratio of elements c, Si, Fe, Mn, and p; the G c , G Si , G Fe , G Mn and G p Represent the weight of elements C, Si, Fe, Mn, and P respectively.

[0046] Furthermore, in step 6, the carbon content of the molten pool is calculated based on the material balance in the converter:

[0047]

[0048] Among them, ω(C%) i Indicates T i Carbon content of molten steel at the moment (%); M represents the total mass of the molten pool, i.e. the sum of the mass of molten iron and scrap steel; Si i , Mn i , P i , C i Respectively represent T i The oxidation amount of each element at a given moment.

[0049] Furthermore, the influencing factors of the secondary combustion include: oxygen lance nozzle design, lance position, oxygen supply system, charging system and furnace shape.

[0050] Furthermore, in step 7, correction is made according to an empirical formula of the original carbon content of the molten iron and the smelting time; the empirical formula is:

[0051] F(C 初始 ,t 熔炼 );

[0052] Among them, C 初始 represents the initial carbon content, t 熔炼 Indicates the melting time.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] The present invention realizes the online measurement of the carbon content of the molten steel endpoint in the converter steelmaking production process, which is of great significance to the converter steelmaking production and quality control. Through the analysis of the converter flue gas big data, the converter steel endpoint carbon can be indirectly determined, which can replace the manual carbon measurement of the converter, reduce labor intensity, reduce costs, improve labor productivity, and promote intelligent steelmaking. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0057] Figure 2 This is a schematic diagram of the comparison between the simulated measurement value and the actual value based on the smoke composition of the present invention. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] like Figure 1 As shown, a method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis of the present invention comprises the following steps:

[0061] Step 1: Start measuring and obtain initial molten iron information;

[0062] Step 2: Obtain converter flue gas composition; In step 2, due to the intake of the converter mouth and the secondary combustion in the flue, the flue gas composition detected by the gas analyzer is not equal to the initial flue gas composition of the initial molten pool reaction, so the flue gas composition needs to be corrected. The influencing factors of the secondary combustion include: oxygen lance nozzle design, lance position, oxygen supply system, charging system and furnace shape.

[0063] In step 2, the converter flue gas components are obtained by:

[0064] From the nitrogen balance in the flue gas, we can know that:

[0065] F N(i) +0.79·F 空气(i) =F 烟气(i) N% (i) ;

[0066] Among them, F N(i) 、F 空气(i) 、F 烟气(i) 、N% (i) Respectively represent T i Nitrogen, furnace air, furnace flue gas, and nitrogen percentage content in flue gas at all times;

[0067] Get T i The air intake volume at the furnace mouth at this moment is:

[0068] F 空气(i) =(N% (i) ·F 烟气(i) -F N(i) ) / 0.79;

[0069] Assuming that the inhaled air is completely burned, the amount of oxygen in the secondary combustion is:

[0070] 0.21·F 空气(i) ;

[0071] The flue gas volume of CO2 after secondary combustion of flue gas is:

[0072] CO2% (i) ·F 烟气(i) -0.21·F 空气(i) ;

[0073] The amount of CO in flue gas is:

[0074] CO% (i) ·F 烟气(i) -2 0.21 F 空气(i)

[0075] Among them, CO2% (i)T i CO2 content in flue gas at the moment %; CO% (i) T i CO content in flue gas at the moment (%); due to the existence of secondary combustion:

[0076]

[0077] in, Indicates T i The CO2 flow rate generated by secondary combustion at the moment; a and c represent constants, a = 0.2, c = 0.75;

[0078] Therefore, the CO2 generated by the oxidation reaction between the oxygen supplied by the oxygen lance and the carbon element in the molten pool is:

[0079]

[0080] The generated CO is:

[0081]

[0082] Step 3: According to the change of smoke composition, the smoke composition obtained in step 2 is corrected theoretically, which is related to the specific production site);

[0083] Step 4: Performing empirical correction (linear correction) on the smoke components after theoretical correction based on offline neural network learning;

[0084] Step 5: Calculate the oxidation amount of each element after empirical correction of flue gas composition;

[0085] Step 6: Calculate the carbon content in the molten pool; the oxidation of each element in the molten pool is expressed by the ratio of the distribution ratio and the Gibbs free energy:

[0086]

[0087] Among them, δ c , δ Si , δ Fe , δ Mn and δ p Respectively represent the distribution ratio of elements c, Si, Fe, Mn, and p; the G c , G Si , G Fe , G Mn and G p Represent the weight of elements C, Si, Fe, Mn, and P respectively.

[0088] In step 6, the carbon content of the molten pool is calculated based on the material balance in the converter:

[0089]

[0090] Among them, ω(C%) i Indicates T i Carbon content of molten steel at the moment (%); M represents the total mass of the molten pool, i.e. the sum of the mass of molten iron and scrap steel; Si i , Mn i , P i , C i Respectively represent T i The oxidation amount of each element at a given moment.

[0091] Step 7: Correct the calculated carbon content in the molten pool based on experience (according to the empirical formula F(C 初始 ,t 熔炼 );

[0092] Step 8: Determine whether the smelting end point has been reached; if it is the end point, output the end point carbon content result; if it is not the end point, return to execute step 2.

[0093] The present invention collects the experimental converter steel grade (carbon content is 0.03%) flue gas composition data offline simulation measurement, and the comparison of the measurement results with the actual values ​​shows that the hit rate of the measurement results in the range of carbon content 0.025%-0.035% is 80%. Figure 2 shown.

[0094] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis, characterized in that: The following steps are involved: Step 1: Start measuring and obtain initial molten iron information; Step 2: Obtain converter flue gas composition; Step 3: According to the change of smoke components, the smoke components obtained in step 2 are corrected theoretically; Step 4: Perform empirical correction on the smoke components after theoretical correction based on offline neural network learning; Step 5: Calculate the oxidation amount of each element after empirical correction of flue gas composition; Step 6: Calculate the carbon content in the molten pool; Step 7: Correct the calculated carbon content in the molten pool based on experience; Step 8: Determine whether the smelting end point has been reached; if it is the end point, output the end point carbon content result; if it is not the end point, return to execute step 2.

2. The method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis according to claim 1 is characterized in that: In step 2, due to the intake of air at the converter mouth and the secondary combustion in the flue, the flue gas composition detected by the gas analyzer is not equal to the initial flue gas composition of the initial molten pool reaction, so the flue gas composition needs to be corrected.

3. The method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis according to claim 1, characterized in that: In step 2, the converter flue gas components are obtained by: From the nitrogen balance in the flue gas, we can know that: F N(i) +0.79·F 空气(i) =F 烟气(i) ·N% (i) ; Among them, F L(i) 、F 空气(i) 、F 烟气(i) 、N% (i) Respectively represent T i Nitrogen, furnace air, furnace flue gas, and nitrogen percentage content in flue gas at all times; Get T i The air intake volume at the furnace mouth at this moment is: F 空气(i) =(N% (i) ·F 烟气(i) -F N(i) ) / 0.79; Assuming that the inhaled air is completely burned, the amount of oxygen in the secondary combustion is: 0.21·F 空气(i) ； The flue gas volume of CO2 after secondary combustion of flue gas is: CO2% (i) ·F 烟气(i) -0.21·F 空气(i) ; The amount of CO in flue gas is: CO% (i) ·F 烟气(i) -2·0.21·F 空气(i) Among them, CO2% (i) T i CO2 content in flue gas at the moment %; CO% (i) T i CO content in flue gas at the moment (%); due to the existence of secondary combustion: in, Indicates T i The CO2 flow rate generated by secondary combustion at the moment; a and c represent constants, a = 0.2, c = 0.75; Therefore, the CO2 generated by the oxidation reaction between the oxygen supplied by the oxygen lance and the carbon element in the molten pool is: The generated CO is:

4. The method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis according to claim 1 is characterized in that: The oxidation of each element in the molten pool is expressed by the ratio of the distribution ratio and the Gibbs free energy: Among them, δ c , δ Si , δ Fe , δ Mn and δ p Respectively represent the distribution ratio of elements c, Si, Fe, Mn, and p; the G c , G Si , G Fe , G Mn and G p Represent the weight of elements C, Si, Fe, Mn, and P respectively.

5. The method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis according to claim 1, characterized in that: In step 6, the carbon content of the molten pool is calculated based on the material balance in the converter: Among them, ω(C%) i Indicates T i Carbon content of molten steel at the moment (%); M represents the total mass of the molten pool, i.e. the sum of the mass of molten iron and scrap steel; Si i , Mn i , P i , C i Respectively represent T i The oxidation amount of each element at a given moment.

6. The method for indirectly measuring the carbon content of molten steel based on converter flue gas big data analysis according to claim 2, characterized in that: The influencing factors of the secondary combustion include: oxygen lance nozzle design, lance position, oxygen supply system, charging system and furnace shape.

7. The method for indirectly measuring carbon content in molten steel based on converter flue gas big data analysis according to claim 1, characterized in that: In step 7, correction is performed according to the empirical formula of the original carbon content of the molten iron and the smelting time; the empirical formula is: F(C 初始 ,t 熔炼 ); Among them, C 初始 represents the initial carbon content, t 熔炼 Indicates the melting time.

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