A big data intelligent online evaluation method for LF refining metallurgical effect

Abstract

The present invention belongs to the field of iron and steel metallurgy technology, specifically a LF refining metallurgical effect big data intelligent online evaluation method, which utilizes the metallurgical big data system being established by various steel enterprises to establish a LF refining carbon dioxide emission intensity, LF refining effect and operation level evaluation method, which can evaluate the control level of LF refining in carbon dioxide emissions, efficiency, end point hit rate, cost, etc. The method can also comprehensively evaluate the effect and operation level of LF refining through weighted coefficients. After LF refining is completed, data can be immediately retrieved from the production or quality management big data system, and corresponding evaluation calculations can be performed to quickly provide various indicators and comprehensive indicators, evaluate carbon dioxide emission intensity, refining effect and operation level, and optimize the corresponding process technology and operation level, quickly improve the LF refining process technology and operation level, and also improve the LF refining production management level.

Description

Technical Field

[0001] The present invention relates to the technical field of iron and steel metallurgy, and in particular to a big data intelligent online evaluation method for LF refining metallurgical effects. Background Art

[0002] Energy conservation, consumption reduction, efficiency improvement, and cost control in LF refining are crucial for improving modern steel production and achieving low-carbon, high-efficiency steelmaking. However, due to the lack of a big data system for steel production, carbon emission intensity assessments of LF refining have long been unavailable. Furthermore, online and real-time assessments of LF refining cost control, refining efficiency, and operational performance cannot be conducted. This lack of timely feedback on the effectiveness and performance of many key aspects of the LF refining process, such as online emission control, efficiency, and cost control, hinders timely evaluation, analysis, and optimization, hindering the optimization of LF refining processes and operational levels, as well as the improvement of production management. Summary of the Invention

[0003] In order to solve the problems existing in the prior art, the main purpose of the present invention is to propose a big data intelligent online evaluation method for LF refining metallurgical effects.

[0004] According to one aspect of the present invention, the present invention provides the following technical solutions:

[0005] A method for intelligent online evaluation of LF refining metallurgical effects based on big data is described. After LF refining is completed, the refining effect factor of the heat is determined online using the process data from the heat. The LF refining metallurgical effect is evaluated based on the refining effect factor and its ranking within a recent cycle. In the present invention, the LF refining metallurgical effect includes LF refining carbon dioxide emission intensity, LF refining effect, and operating level.

[0006] As a preferred solution of the LF refining metallurgical effect big data intelligent online evaluation method described in the present invention, the refining effect factor R is:

[0007] R=a1×Slag+a2×t+a3×E+a4×T+a5×S+a6×P+a7×N+a8×Cost-a9×X+a 10 ×Ce

[0008] Among them, R is the refining effect factor;

[0009] a1 is the weighting factor of refined slag condition, ranging from 0 to 0.9; Slag is the control level of refined slag condition;

[0010] a2 is the refining efficiency weighting factor, ranging from 0 to 0.9; t is the refining efficiency control level;

[0011] a3 is the energy consumption weighting factor, ranging from 0.1 to 0.5; E is the energy consumption control level;

[0012] a4 is the refining endpoint temperature control weighting factor, ranging from 0.1 to 0.8; T is the refining endpoint temperature control level;

[0013] a5 is the refining endpoint S control weighting factor, ranging from 0 to 0.9; S is the refining endpoint sulfur control level;

[0014] a6 is the P control weighting factor at the refining endpoint, ranging from 0.1 to 0.9; P is the phosphorus control level at the refining endpoint;

[0015] a7 is the refining endpoint N control weighting factor, ranging from 0 to 0.5; N is the refining endpoint nitrogen control level;

[0016] a8 is the refining cost control weighting factor, ranging from 0 to 0.5; M is the refining cost control level;

[0017] a9 is the refinement endpoint component hit weighting factor, ranging from 0 to 0.9; X is the refinement endpoint component hit level;

[0018] a 10 is the refining carbon emission intensity weighting factor, ranging from 0 to 0.9; Ce is the refining carbon emission intensity control level.

[0019] As a preferred embodiment of the LF refining metallurgical effect big data intelligent online evaluation method described in the present invention, the LF refining effect and operation level are evaluated according to the furnace refining effect factor, and the ranking within the corresponding evaluation period (week, month, quarter) is given. If the furnace refining effect factor ranks in the top 10%, it can be considered that the refining effect is good, the operation level is high, and the process operation requires less optimization and adjustment; if the furnace refining effect factor ranks in the top 10-20%, it can be considered that the refining effect is good, the operation level is high, and the relevant process and operation need to be appropriately adjusted; if it ranks in the top 20-30%, it indicates that the refining effect is average and the operation level is average, and the relevant process and operation need to be adjusted; if it is below 30%, it indicates that the refining effect is poor and the operation level is poor, and the relevant process and operation need to be significantly adjusted.

[0020] If adjustments are necessary, the direction of adjustment can be determined based on the control level of each evaluated control factor. If the refining endpoint component hit level X = 0, it indicates that the endpoint component is well controlled. Otherwise, the refining endpoint component control process and operation require improvement. If the control level of other evaluated control factors is above 0.9, it indicates that the control level of the control factor is high and the related process operation does not require adjustment or optimization. If it is below 0.9, it indicates that the control level of the control factor is insufficient and the related process operation needs to be adjusted and optimized.

[0021] The beneficial effects of the present invention are as follows:

[0022] The present invention proposes a method for intelligent online evaluation of LF refining metallurgical effect big data. Utilizing the metallurgical big data system being established by various steel companies, a method for evaluating LF refining carbon dioxide emission intensity, LF refining effect, and operating level is established. This method can evaluate the control level of LF refining in terms of carbon dioxide emissions, efficiency, endpoint hit rate, cost, etc. The method can also comprehensively evaluate the effect and operating level of LF refining through weighted coefficients. After LF refining is completed, data can be immediately retrieved from the production or quality management big data system for corresponding evaluation calculations, and various indicators and comprehensive indicators can be quickly given to evaluate carbon dioxide emission intensity, refining effect, and operating level. The corresponding process technology and operating level are optimized, rapidly improving the LF refining process technology and operating level, and also improving the LF refining production management level. DETAILED DESCRIPTION

[0023] The following will be a clear and complete description of the technical solutions in the embodiments. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0024] The present invention proposes a method for intelligent online evaluation of LF refining metallurgical effects based on big data. This method utilizes the metallurgical big data systems currently being established by various steel companies to establish an online weighted intelligent evaluation model. The model comprehensively and intelligently considers factors such as carbon emission intensity, heating efficiency, refining efficiency, nitrogen addition control effect, refining time, composition, and temperature hit rate during the refining cycle. Weighted coefficients are used to intelligently evaluate the overall level and metallurgical effect of the refining cycle, and the refining effect evaluation focus can be intelligently adjusted using the weighted coefficients. Based on the existing refining metallurgical big data platform, the present invention can instantly evaluate carbon dioxide emissions, refining effects, and operational levels, providing instant feedback on the strengths, weaknesses, and room for improvement of the refining process in terms of carbon emissions, refining costs, efficiency, and steel quality control. This model is conducive to strengthening refining production management and improving the operational level of operators.

[0025] According to one aspect of the present invention, the present invention provides the following technical solutions:

[0026] A big data intelligent online evaluation method for LF refining metallurgical effect is proposed. After LF refining is completed, the process data of the furnace is used to determine the refining effect factor of the furnace online. The LF refining metallurgical effect is evaluated based on the refining effect factor, and the ranking within the corresponding evaluation period (week, month, quarter) is given.

[0027] Preferably, if the refining effect factor ranks in the top 10%, it can be considered that the refining effect is good, the operation level is high, and the process operation requires less optimization and adjustment; if the refining effect factor ranks in the top 10-20%, it can be considered that the refining effect is good, the operation level is high, and the relevant processes and operations need to be appropriately adjusted; if it ranks in the top 20-30%, it indicates that the refining effect and the operation level are average, and the relevant processes and operations need to be adjusted; if it is below 30%, it indicates that the refining effect and the operation level are poor, and the relevant processes and operations need to be significantly adjusted.

[0028] In the present invention, the LF refining metallurgical effect includes the LF refining carbon dioxide emission intensity, the LF refining effect and the operation level.

[0029] Preferably, the refining effect factor R is:

[0030] R=a1×Slag+a2×t+a3×E+a4×T+a5×S+a6×P+a7×N+a8×Cost-a9×X+a 10 ×Ce

[0031] Among them, R is the refining effect factor;

[0032] a1 is the weighting factor of refined slag condition, ranging from 0 to 0.9; Slag is the control level of refined slag condition;

[0033] a2 is the refining efficiency weighting factor, ranging from 0 to 0.9; t is the refining efficiency control level;

[0034] a3 is the energy consumption weighting factor, ranging from 0.1 to 0.5; E is the energy consumption control level;

[0035] a4 is the refining endpoint temperature control weighting factor, ranging from 0.1 to 0.8; T is the refining endpoint temperature control level;

[0036] a5 is the refining endpoint S control weighting factor, ranging from 0 to 0.9; S is the refining endpoint sulfur control level;

[0037] a6 is the P control weighting factor at the refining endpoint, ranging from 0.1 to 0.9; P is the phosphorus control level at the refining endpoint;

[0038] a7 is the refining endpoint N control weighting factor, ranging from 0 to 0.5; N is the refining endpoint nitrogen control level;

[0039] a8 is the refining cost control weighting factor, ranging from 0 to 0.5; M is the refining cost control level;

[0040] a9 is the refinement endpoint component hit weighting factor, ranging from 0 to 0.9; X is the refinement endpoint component hit level;

[0041] a 10 is the refining carbon emission intensity weighting factor, ranging from 0 to 0.9; Ce is the refining carbon emission intensity control level.

[0042] Preferably, whether the LF refining process needs to be adjusted is determined based on the evaluation results; if adjustment is required, the adjustment direction can be determined based on the control level of each evaluation control factor.

[0043] Preferably, the expression of the refined slag condition control level Slag is:

[0044] Slag = 2-(%FeO) / (%FeO) Min

[0045] Where: (%FeO) is the iron oxide content of the slag, wt%; (%FeO) Min It is the lowest value of the refined slag (%FeO) at the LF refining endpoint of this steel grade or similar steel grades in the past reference evaluation period.

[0046] Preferably, the expression of the refining efficiency control level t is:

[0047] t=2-Δt / Δt Min

[0048] Where: Δt is the smelting time, minutes; Δt Min The shortest refining time in the last cycle (week, month, quarter, year), in minutes.

[0049] Preferably, the expression of energy consumption control level E is:

[0050] E=F / F Max

[0051] Where: F is the heating efficiency, K / (kW·h / t); F Max The maximum refining heating efficiency in the past cycle (week, month, quarter, year); the expression of heating efficiency F is as follows:

[0052] F=ΔT / (ΔW / W steel )

[0053] Where: ΔW is the refining power consumption, kW·h; W steel is the amount of refined steel liquid, t; ΔT is the temperature rise, K. Preferably, the expression of the refining end temperature control level T is:

[0054] T=1-|T e -T g | / T g

[0055] Where: T e is the refining end temperature, K; Tg is the refining target temperature, K.

[0056] Preferably, the expression of the refining endpoint sulfur control level S is:

[0057] When LF refines sulfur-controlled steel, S = 1-|[%S]-[%S] M | / [%S] M

[0058] Where: [%S] is the sulfur content at the end of refining, wt%; [%S] M is the target median value of sulfur content control, wt%;

[0059] When LF refines non-controlled sulfur steel, [%S]≤[%S] g When S=1; [%S]>[%S] g When S=0;

[0060] Where: [%S] is the sulfur content of the molten steel at the end of refining, wt%; [%S] g is the target sulfur content at the end of refining, wt%.

[0061] Preferably, the expression of the phosphorus control level P at the refining endpoint is:

[0062] When LF refines phosphorus-controlled steel, P = 1-|[%P]-[%P] M | / [%P] M

[0063] Where: [%P] is the phosphorus content at the end of refining, wt%; [%P] M is the target median value of phosphorus content control, wt%;

[0064] When LF refines non-phosphorus controlled steel, [%P]≤[%P] g When P=1; [%P]>[%P] g When P=0;

[0065] Where: [%P] is the phosphorus content of the molten steel at the end of refining, wt%; [%P] g is the target phosphorus content at the end of refining, wt%.

[0066] Preferably, the expression of the refining endpoint nitrogen control level N is:

[0067] When LF refines nitrogen-controlled steel, N=1-|[%N]-[%N] M | / [%N] M

[0068] Where: [%N] is the nitrogen content at the end of refining, wt%; [%N] M is the target median value of nitrogen content control, wt%;

[0069] When LF refines non-nitrogen controlled steel, [%N]≤[%N] g When N=1, [%N]>[%N] g When N=0;

[0070] Where: [%N] is the liquid nitrogen content of the refined steel at the end point, wt%; [%N] g is the target nitrogen content at the end of refining, wt%.

[0071] Preferably, the expression of the refining cost control level Cost is:

[0072] Cost=2-Co / Co Min

[0073] Where: Co is the LF refining cost, including the cost of slag-making agent, alloy, electricity, electrode and gas consumed in the refining process, RMB; Co Min The lowest LF refining cost of this or similar steel grades in the last reference evaluation period, RMB;

[0074] Preferably, the LF refining cost expression is as follows:

[0075] Co=∑SM i ×Pr SMi +∑M i ×Pr Mi +El×Pr El +ΔW×Pr e +∑G i ×Pr Gi

[0076] Where: SM i is the consumption of slag material i, tons; Pr SMi is the unit price of slag material, RMB / ton;

[0077] M i is the consumption of alloy i, including aluminum blocks, ferrotitanium, high carbon ferrochrome, medium carbon ferrochrome, silicon manganese, ferrosilicon, medium carbon ferromanganese, copper, electrolytic manganese, metallic manganese, sponge titanium, ferromolybdenum, etc., tons; Pr Mi is the unit price of alloy i, yuan / ton;

[0078] El is the consumption of graphite electrode during heating, tons; Pr El is the unit price of electrode, yuan / ton;

[0079] ΔW is the power consumption of the refining process, kW·h, Pr e is the unit price of industrial electricity, RMB / kW·h;

[0080] G i is the gas consumption in the refining process, m 3 ;Pr Giis the unit price of gas i, yuan / m 3 .

[0081] Preferably, the expression for the refinement endpoint component hit level is:

[0082] When the content of component i (except N, P, S) in molten steel at the end of LF refining is [%i]≤[%i] llimt hour,

[0083] X i =x i (|[%i]-[%i] llimt |) / [%i] llimt

[0084] When the content of component i (except N, P, S) in molten steel at the end of LF refining is [%i]≥[%i] ulimt hour,

[0085] X i =x i (|[%i]-[%i] ulimt |) / [%i] ulimt

[0086] At the end point of LF refining, the content of component i (except N, P, S) in molten steel [%i] llimt <[%i]<[%i] ulimt hour,

[0087] X i =0

[0088] X=∑X i

[0089] Where: [%i] llimt and [%i] ulimt are the upper or lower limit of the control target of component i, wt%;

[0090] x i is the severity factor of the component i exceeding the standard, 0-100;

[0091] X i is the refined endpoint component hit level of element i.

[0092] Preferably, the calculation formula for the smelting carbon emission intensity control level is:

[0093] Ce=2-(E CO2 / W steel ) / CeM

[0094] Where: E CO2 is carbon dioxide emissions, tons; W steelSteel output, tons; CeM is the minimum carbon emission in the past cycle (week, month, quarter, year), that is, the minimum value, tons of carbon dioxide emissions / ton of steel; carbon dioxide emissions E CO2 The expression is as follows:

[0095] E CO2 =ΔW×Ke+∑SM i ×K Si +∑M i ×K Mi +∑G i ×K Gi +El×K El

[0096] Where: Ke is the CO2 emission factor of the company's LF refining electricity consumption, tons of CO2 / kW·h, and is determined based on the company's electricity CO2 emission factor approved by the national authority;

[0097] SM i is the mass of slag-forming agent i added in LF refining process, tons; K Si is the carbon emission factor of slagging agent i, tons of CO2 / ton;

[0098] M i K is the mass of alloy i added during LF refining process, tons; Mi is the carbon emission factor of alloy i, tons of CO2 / ton;

[0099] G i is the volume of gas i consumed in the LF refining process, m 3 ;K Gi is the carbon emission factor of gas i, tons of carbon dioxide / m 3 ;

[0100] El is the loss of electrode in LF refining process, tons; K El is the carbon emission factor of the electrode, tons of carbon dioxide / ton.

[0101] Preferably, the data comes from the production and quality big data system of the steel enterprise. After the LF smelting is completed, the corresponding data can be retrieved from the big data system to evaluate the metallurgical effect, operation level and carbon dioxide emissions; the weighting factors in the formula can be adjusted accordingly according to the different focuses of the current evaluation.

[0102] Furthermore, Slag, t, E, T, S, P, N, Cost, X, and Ce can also be evaluated separately to evaluate the smelting level and control effect in the corresponding aspects, and optimize the converter smelting process based on the corresponding data.

[0103] Furthermore, for the evaluation of the metallurgical effect of the first 10 LF refining heats of a certain steel grade adopted by this method, due to the (%FeO) in the big data systemMin , Δt Min 、F Max 、Co Min If the CeM value is missing, the corresponding initial value can be set for evaluation based on the LF refining process and operation level of the steel grade.

[0104] Furthermore, a corresponding program is compiled based on the evaluation model, placed in the LF refining operating system, and data is exchanged with the corresponding production and quality management big data system of the steel enterprise. After the LF refining is completed, the system intelligently retrieves various data, performs automatic evaluation, and stores the corresponding evaluation data and indicators in the system; technicians, operators and managers can retrieve corresponding data indicators for evaluation and optimize processes and operations.

[0105] Furthermore, when evaluating according to the evaluation model, it is necessary to subdivide LF refining into corresponding categories according to the differences in refined steel grades and the differences in the thermal state of the ladle before LF refining, and use the corresponding category (%FeO) Min , Δt Min 、F Max 、Co Min , CeM data intelligent classification and evaluation.

[0106] Furthermore, when calculating the carbon emission intensity of LF refining, the range of carbon dioxide emission factors of each substance is calculated according to the values ​​in Table 1.

[0107] Table 1 Carbon dioxide emission factor values ​​for various substances in LF refining

[0108] substance CO2 emission factor substance CO2 emission factor calcium carbide <![CDATA[5.067tCO2 / t]]> copper <![CDATA[4.23tCO2 / t]]> SiC <![CDATA[15.9tCO2 / t]]> Electrolytic manganese <![CDATA[0.94tCO2 / t]]> lime <![CDATA[0.950tCO2 / t]]> Manganese metal <![CDATA[1.5tCO2 / t]]> fluorite <![CDATA[3.614tCO2 / t]]> Medium carbon ferromanganese <![CDATA[0.043tCO2 / t]]> Toner <![CDATA[3.67tCO2 / t]]> Medium carbon ferrochrome <![CDATA[0.061tCO2 / t]]> Nitrogen <![CDATA[0.103tCO2 / 10 3 m 3 ]]> electrode <![CDATA[3.663tCO2 / t <!-- 5 -->]]> Argon <![CDATA[0.103tCO2 / 10 3 m 3 ]]> Ferromolybdenum <![CDATA[0.018tCO2 / t]]> aluminum <![CDATA[14.4tCO2 / t]]> Titanium sponge <![CDATA[2.5tCO2 / t]]> Ferrosilicon <![CDATA[5.05tCO2 / t]]> Ferrotitanium <![CDATA[1.74tCO2 / t]]> Silicon manganese <![CDATA[0.055tCO2 / t]]> electrode <![CDATA[3.663tCO2 / t]]>

[0109] The technical solution of the present invention is further described below with reference to specific embodiments.

[0110] Example 1

[0111] A steel plant uses 210 tons of LF refining to produce wheel steel. The process route of this steel is blast furnace-converter-CAS refining-LF refining-continuous casting. The incoming composition and final composition of the molten steel are as follows: incoming composition (0.0578% C, 0.0744% Si, 0.4011% Mn, 0.0089% P, 0.0194% S, 0.0494% Als, 0.0036% N, 0.008% Ti), final composition (0.08% C, 0.22% Si, 1.12% Mn, 0.013% P, 0.037% S, 0.033 % Als, 0.0041% N, 0.022% Ti); the target composition is: C (0.065%-0.09%), Si (0.1%-0.3%), Mn (1.1%-1.25%), P (≤0.015%), S (0.050%-0.060%), Als (0.02%-0.045%), N (≤0.008%), Ti (0.02%-0.03%). The raw and auxiliary material consumption for this batch is as follows: refined slag, 399kg; lime, 1302kg; fluorite, 160kg; silicomanganese, 702kg; aluminum block, 249kg; ferrotitanium, 222kg; electrode, 656kg; power consumption, 6331kW·h; argon, 57m 3 The refining time of this furnace is about 61 minutes, the amount of molten steel is 217.4 tons, and the outlet temperature is 1846.15K. The outlet temperature is required to be between 1838.15K and 1848.15K, so the refining target temperature is the middle value of the outlet temperature range of 1843.15K.

[0112] The calculation method of the refined slag condition factor Slag of this heat is:

[0113] Slag = 2-(%FeO) / (%FeO) Min =2-0.75 / 0.59=0.73

[0114] The calculation method of the metallurgical efficiency level t of the heat is:

[0115] t=2-Δt / ΔtMin=2-61 / 35=0.26

[0116] The calculation method of the energy consumption control level E of this heat is:

[0117] F=ΔT / (ΔW / W steel )=49 / (6331 / 217.4)=1.68K / (kW·h·t)

[0118] E=F / F Max =1.68 / 3.11=0.54

[0119] The calculation method of the end temperature control level T of this heat is:

[0120] T=1-|T e -T g | / T g =1-|1846.15-1843.15| / 1843.15=0.9984

[0121] This heat is sulfur-controlled steel, and the calculation method for its endpoint sulfur control level S is:

[0122] S=1-|[%S]-[%S] M | / [%S] M =1-|0.037-0.055| / 0.055=0.67

[0123] This heat is non-phosphorus controlled steel, and the calculation method for its endpoint phosphorus control level P is:

[0124] [%P]≤[%P] g , so P = 1

[0125] This heat is non-nitrogen controlled steel, and the calculation method for its endpoint nitrogen control level N is:

[0126] [%N]≤[%N] g , so N = 1

[0127] The calculation method of the refining cost Co of this heat is:

[0128] Co=M 精炼渣 ×Pr 精炼渣 +M 石灰 ×Pr 石灰 +M 萤石 ×Pr 萤石 +M 硅锰 ×Pr 硅锰 +M 铝块 ×Pr 铝块 +M 钛铁 ×Pr 钛铁

[0129] +M 电极 ×Pr 电极 +Q 电 ×Pr 电 +V 氩气 ×Pr 氩气

[0130] =0.399×701.77+1.302×479+0.16×1903+0.702×5560+0.249×18773+0.222×12104+0.656×12673+6331×0.7+57×0.21=25230 yuan

[0131] The calculation method of the refining cost control level Cost of this heat is:

[0132] Cost=2-Co / Cost=2-25230 / 21318=0.82

[0133] The calculation method of the hit level X of the refining end point of this heat is: the components of each component at the end point of the LF refining of this heat meet the steel grade composition requirements, X=0

[0134] The carbon emission intensity E of this heat CO2 The calculation method is:

[0135] E CO2 =ΔW×Ke+∑SM i ×K Si +∑M i ×K Mi +∑G i ×K Gi +El×K El =ΔW×Ke+K 萤石 ×SM 萤石

[0136] +K 石灰 ×SM 石灰 +M 铝 ×K 铝 +M 硅锰 ×K 硅锰 +M 钛铁 ×K 钛铁 +G 氩气 ×K 氩气 +El×K El

[0137] =0.504×6.331+3.614×0.16+0.95×1.302+14.4×0.249+0.055×0.702+1.74×0.222+0.103×0.102+3.663×0.656=11.5

[0138] The calculation method for the carbon emission intensity control level Ce of this heat is:

[0139] Ce=2-(E CO2 / W steel ) / CeM=2-(11.5 / 217.4) / 0.04=0.678

[0140] Therefore, the calculation method for evaluating the refining metallurgical effect of this heat is:

[0141] R=a1×Slag+a2×t+a3×E+a4×T+a5×S+a6×P+a7×N+a8×Cost-a9×X+a10 ×Ce

[0142] =0.5×0.73+0.4×0.26+0.3×0.54+0.5×0.998+0.6×0.67+0.3×1+0.3×1+0.5×0.82+0.7×0.678=3.016

[0143] The refining metallurgical performance evaluation value for this heat was 3.016. This result was immediately displayed in the real-time big data online evaluation system for refining performance and operating levels after the LF furnace refining. It also ranked in the top 35% of gear steel LF refining metallurgical performance evaluation values ​​for the past month. Also displayed were the slag control level (Slag) of 0.73, refining efficiency level (t) of 0.26, energy efficiency level (E) of 0.54, endpoint temperature control level (T) of 0.998, endpoint sulfur control level (S) of 0.67, endpoint phosphorus control level (P) of 1, endpoint nitrogen control level (N) of 1, cost control level (Cost) of 0.82, endpoint molten steel composition hit rate (X) of 0, and refining process carbon emission control level (Ce) of 0.678. This indicates that the metallurgical performance and operating level of this heat are poor, and significant room for improvement is needed in controlling slag condition, refining efficiency, energy efficiency, endpoint sulfur, and refining process carbon emissions.

[0144] Example 2

[0145] A steel mill uses 210 tons of LF refining to produce carbon steel JB65Mn. The process for this steel grade is blast furnace-converter-CAS refining-LF refining-continuous casting. The incoming composition and final composition of the molten steel are: incoming composition (0.2639% C, 0.166% Si, 0.8004% Mn, 0.0152% P, 0.0168% S, 0.0206% Als, 0.0034% N), final composition (0.64% C, 0.23% Si, 0.99% Mn, 0.016% P, 0.001% S, 0.018% Als, 0.0032% N); the target composition is: C (0.64%-0.68%), Si (0.2%-0.37%), Mn (0.95%-1.1%), P (≤0.02%), S (≤0.005%), Als (0.006%-0.03%), N (≤0.0055%). The raw and auxiliary material consumption for this batch is as follows: refined slag, 345kg; lime, 883kg; fluorite, 115kg; ferrosilicon, 80kg; aluminum block, 85kg; silicon manganese, 319kg; carburizer, 270kg; electrode, 537kg; power consumption, 5194kW·h; argon, 43m 3The refining time of this furnace is about 45 minutes, the molten steel volume is 220.2 tons, the heating rate is 4.5℃ / min, the outlet temperature is 1796.15K, and the outlet temperature is required to be between 1793.15K and 1803.15K. Therefore, the refining target temperature is the middle value of the outlet temperature range of 1798.15K.

[0146] The calculation method of the refined slag condition factor Slag of this heat is:

[0147] Slag = 2-(%FeO) / (%FeO) Min =2-0.74 / 0.61=0.79

[0148] The calculation method of the metallurgical efficiency level t of the heat is:

[0149] t=2-Δt / ΔtMin=2-45 / 30=0.5

[0150] The calculation method of the energy consumption control level E of this heat is:

[0151] F=ΔT / (ΔW / W steel )=53 / (5194 / 220.2)=2.21K / (kW·h / t)

[0152] E=F / F Max =2.12 / 2.56=0.83

[0153] The calculation method of the end temperature control level T of this heat is:

[0154] T=1-|T e -T g | / T g =1-|1796.15-1798.15| / 1798.15=0.9989

[0155] This heat is non-sulfur controlled steel, and the calculation method for its endpoint sulfur control level S is:

[0156] [%S]≤[%S] g , so S=1

[0157] This heat is non-phosphorus controlled steel, and the calculation method for its endpoint phosphorus control level P is:

[0158] [%P]≤[%P] g , so P = 1

[0159] This heat is non-nitrogen controlled steel, and the calculation method for its endpoint nitrogen control level N is:

[0160] [%N]≤[%N] g , so N = 1

[0161] The calculation method of the refining cost Co of this heat is:

[0162] Co=M 精炼渣 ×Pr 精炼渣 +M 石灰 ×Pr 石灰 +M 萤石 ×Pr 萤石 +M 硅铁 ×Pr 硅铁 +M 硅锰 ×Pr 硅锰 +M 铝块 ×Pr 铝

[0163] 块 +M 增碳剂 ×Pr 增碳剂 +M 电极 ×Pr 电极 +Q 电 ×Pr 电 +V 氩气 ×Pr 氩气

[0164] =0.345×701.77+0.883×479+0.115×1903+0.08×5789+0.319×5560+0.085×18773+0.27×2399+0.537×12673+5194×0.7+43×0.21=15814 yuan

[0165] Cost=2-Co / Co Min =2-15814 / 14134=0.88

[0166] The calculation method of the refining end point composition hit level X of this heat is: if the component contents in the molten steel at the end point of LF refining all meet the requirements, X=0;

[0167] The carbon emission intensity E of this heat CO2 The calculation method is:

[0168] E CO2 =ΔW×Ke+∑SM i ×K Si +∑M i ×K Mi +∑G i ×K Gi +El×K El =ΔW×Ke+K 萤石 ×SM 萤

[0169] 石 +K 石灰 ×SM 石灰 +M铝 ×K 铝 +M 硅锰 ×K 硅锰 +M 硅铁 ×K 硅铁 +M 碳粉 ×K 碳粉 +G 氩气 ×K 氩气

[0170] =0.504×5.194+3.614×0.115+0.95×0.883+14.4×0.085+0.055×0.319+5.05×0.08+3.67×0.27+0.103×0.077+3.663×0.537=8.589

[0171] The calculation method for the carbon emission intensity control level Ce of this heat is:

[0172] Ce=2-(E CO2 / W steel ) / CeM=2-(8.589 / 220.2) / 0.03=0.699

[0173] Therefore, the calculation method for evaluating the refining metallurgical effect of this heat is:

[0174] R=a1×Slag+a2×t+a3×E+a4×T+a5×S+a6×P+a7×N+a8×Cost-a9×X+a 10 ×Ce

[0175] =0.5×0.79+0.4×0.5+0.3×0.83+0.5×0.9989+0.6×1+0.3×1+0.3×1+0.5×0.88+0.7×0.699=3.472

[0176] The refining metallurgical performance evaluation value for this heat was 3.472. This result was immediately displayed in the online real-time big data evaluation system for refining performance and operating level after the LF furnace refining completed. It also ranked in the top 22% of LF refining metallurgical performance evaluation values ​​for similar carbon steel grades, including JB65Mn, in the past month. Also displayed were the slag control level (Slag) of 0.79, refining efficiency level (t) of 0.5, energy efficiency level (E) of 0.83, endpoint temperature control level (T) of 0.999, endpoint sulfur control level (S) of 1, endpoint phosphorus control level (P) of 1, endpoint nitrogen control level (N) of 1, cost control level (Cost) of 0.88, endpoint molten steel composition hit rate (X) of 0, and refining process carbon emission control level (Ce) of 0.699. This indicates that the metallurgical performance and operating level of this heat are average, with significant room for improvement in slag control, refining efficiency, energy efficiency, and refining process carbon emissions.

[0177] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention specification under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A big data intelligent online evaluation method for LF refining metallurgical effect, characterized in that: After a certain LF refining heat is completed, the refining effect factor of the heat is determined online using the process data of the heat. Based on the level of the refining effect factor and its ranking in the recent evaluation cycle, the LF refining metallurgical effect is evaluated. The LF refining metallurgical effect includes the LF refining carbon dioxide emission intensity, LF refining effect and operation level; The refining effect factor R is: R=a1×Slag+a2×t+a3×E+a4×T+a5×S+a6×P+a7×N+a8×Cost-a9×X+a 10 ×Ce Among them, R is the refining effect factor; a1 is the weighting factor of refined slag condition, ranging from 0 to 0.9; Slag is the control level of refined slag condition; a2 is the refining efficiency weighting factor, ranging from 0 to 0.9; t is the refining efficiency control level; a3 is the energy consumption weighting factor, with a value range of 0.1-0.5; E is the energy consumption control level; the expression of energy consumption control level E is: E=F / F Max , where: F is the heating efficiency, K / (kW·h / t); F Max The maximum refining heating efficiency in the past cycle; the heating efficiency F is expressed as follows: F = ΔT / (ΔW / W steel ), where: ΔW is the refining power consumption, kW·h; W steel is the amount of refined steel liquid, t; ΔT is the temperature rise, K; a4 is the refining endpoint temperature control weighting factor, which ranges from 0.1 to 0.8; T is the refining endpoint temperature control level; the expression of the refining endpoint temperature control level T is: T=1-|T e -T g | / T g , where: T e is the refining end temperature, K; T g is the refining target temperature, K; a5 is the refining endpoint S control weighting factor, ranging from 0 to 0.9; S is the refining endpoint sulfur control level; a6 is the P control weighting factor at the refining endpoint, ranging from 0.1 to 0.9; P is the phosphorus control level at the refining endpoint; a7 is the refining endpoint N control weighting factor, ranging from 0 to 0.5; N is the refining endpoint nitrogen control level; a8 is the refining cost control weighting factor, ranging from 0 to 0.5; M is the refining cost control level; a9 is the refinement endpoint component hit weighting factor, ranging from 0 to 0.9; X is the refinement endpoint component hit level; a 10 is the refining carbon emission intensity weighting factor, ranging from 0 to 0.9; Ce is the refining carbon emission intensity control level.

2. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is characterized in that: Determine whether the LF refining process needs to be adjusted based on the evaluation results; if adjustment is necessary, determine the adjustment direction based on the control level of each evaluation control factor.

3. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1, characterized in that: The expression of the refined slag condition control level Slag is: Slag=2-(%FeO) / (%FeO) Min Where: (%FeO) is the iron oxide content of the slag, wt%; (%FeO) Min It is the lowest value of the LF refining endpoint slag (%FeO) of the steel grade in the recent reference evaluation period.

4. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1, characterized in that: The expression of refining efficiency control level t is: t=2-Δt / Δt Min Where: Δt is the smelting time, minutes; Δt Min The shortest refining time in the last cycle, in minutes.

5. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1, characterized in that: The expression of sulfur control level S at the end of refining is: When LF refines sulfur-controlled steel, S=1-|[%S]-[%S] M | / [%S] M Where: [%S] is the sulfur content of the molten steel at the end of refining, wt%; [%S] M is the median value of sulfur content control, wt%; When LF refines non-sulfur controlled steel, [%S]≤[%S] g When S=1; [%S]>[%S] g When S=0; Where: [%S] is the sulfur content of the molten steel at the end of refining, wt%; [%S] g is the target sulfur content at the end of refining, wt%; The expression of phosphorus control level P at the refining endpoint is: When LF refines phosphorus-controlled steel, P=1-|[%P]- [%P] M | / [%P] M Where: [%P] is the phosphorus content of the molten steel at the end of refining, wt%; [%P] M is the median value of phosphorus content control, wt%; When LF refines non-phosphorus controlled steel, [%P]≤[%P] g When P=1; [%P]>[%P] g When P=0; Where: [%P] is the phosphorus content of the molten steel at the end of refining, wt%; [%P] g is the target phosphorus content at the end point of refining, wt%; The expression of nitrogen control level N at the refining endpoint is: When LF refines nitrogen-controlled steel, N=1-|[%N]-[%N] M | / [%N] M Where: [%N] is the nitrogen content of liquid steel at the end of refining, wt%; [%N] M is the median value of nitrogen content control, wt%; When LF refines non-nitrogen controlled steel, [%N]≤[%N] g When N=1, [%N]>[%N] g When N=0; Where: [%N] is the nitrogen content of liquid steel at the end of refining, wt%; [%N] g is the target nitrogen content at the end of refining, wt%.

6. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1, characterized in that: The expression of the refining cost control level Cost is: Cost=2-Co / Co Min Where: Co is the LF refining cost, including the cost of slag-making agent, alloy, electricity, electrode and gas consumed in the refining process, RMB; Co Min The lowest value of LF refining cost of steel grade in the recent reference evaluation period, RMB; the expression of LF refining cost Co is as follows: Co=∑SM i ×Pr SMi +∑M i ×Pr Mi +El×Pr El +ΔW×Pr e +∑G i ×Pr Gi Where: SM i is the consumption of slag material i, tons; Pr SMi is the unit price of slag material i, yuan / ton; M i is the consumption of alloy i, tons; Pr Mi is the unit price of alloy i, yuan / ton; El is the consumption of graphite electrode during heating, tons; Pr El is the unit price of electrode, yuan / ton; ΔW is the power consumption of the refining process, kW·h, Pr e is the unit price of industrial electricity, RMB / kW·h; G i is the gas consumption in the refining process, m 3 ; Pr Gi is the unit price of gas i, yuan / m 3 .

7. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is characterized in that: The expression for the hit level of the refined endpoint component is: When the content of component i in the molten steel at the end of LF refining is [%i]≤[%i] llimt hour, X i =x i (|[%i]-[%i] llimt |) / [%i] llimt When the content of component i in the molten steel at the end of LF refining is [%i]≥[%i] ulimt hour, X i =x i (|[%i]-[%i] ulimt |) / [%i] ulimt At the end of LF refining, the content of component i in the liquid steel [%i] llimt <[%i]<[%i] ulimt hour, X i =0 X=∑X i Where: [%i] ulimt is the upper limit of component i, wt%; [%i] llimt is the lower limit of component i, wt%; x i is the severity factor of the component i exceeding the standard, 0-100; X i is the refined endpoint component hit level of element i; The steel component i is the component other than N, P and S.

8. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1, characterized in that: The calculation formula for the smelting carbon emission intensity control level is: Ce=2-(E CO2 / IN steel ) / CeM Where: E CO2 is carbon dioxide emissions, tons; W steel Steel output, tons; CeM is the minimum carbon emission in the past cycle, that is, the minimum value, tons of carbon dioxide emissions per ton of steel; carbon dioxide emissions E CO2 The expression is as follows: AND CO2 =ΔW×Ke+∑SM i ×K Si +∑M i ×K Mi +∑G i ×K Gi +El×K El Where: Ke is the CO2 emission factor of LF refining electricity consumption, tons of CO2 / kW·h, which is determined by the CO2 emission factor of electricity consumption approved by the national authority; SM i K is the mass of slag-forming agent i added in LF refining process, tons; Si is the carbon emission factor of slagging agent i, tons of CO2 / ton; M i K is the mass of alloy i added during LF refining process, tons; Mi is the carbon emission factor of alloy i, tons of CO2 / ton; G i is the volume of gas i consumed in the LF refining process, m 3 ;K Gi is the carbon emission factor of gas i, tons of carbon dioxide / m 3 ; El is the loss of electrode during LF refining process, tons; K El is the carbon emission factor of the electrode, tons of carbon dioxide / ton.