LF refining metallurgy effect big data intelligent online evaluation method

Abstract

The invention belongs to the technical field of iron and steel metallurgy, and particularly relates to an LF refining metallurgy effect big data intelligent online evaluation method, which is characterized in that an LF refining carbon dioxide emission intensity, LF refining effect and operation level evaluation method is established by utilizing a metallurgy big data system which is currently established by each iron and steel enterprise; the control level of LF refining in the aspects of carbon dioxide emission, efficiency, end point hit rate, cost and the like can be evaluated, and the method can also comprehensively evaluate the effect and operation level of LF refining through weighting coefficients. After LF refining is finished, data can be called from a production or quality management big data system in real time, corresponding evaluation calculation is carried out, indexes in all aspects and comprehensive indexes are rapidly given, the carbon dioxide emission intensity, the refining effect and the operation level are evaluated, and corresponding process technology and operation level optimization is carried out. And the LF refining technology and operation level are rapidly improved, and the LF refining production management level is also improved.

Description

Technical Field

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

[0002] Energy conservation, consumption reduction, efficiency improvement and cost control in LF refining are particularly important for improving the level of modern iron and steel production and realizing low-carbon and efficient iron and steel smelting. However, for a long time, due to the lack of a big data system for iron and steel production, the evaluation of the carbon emission intensity level in LF refining production has not been carried out, and the evaluation of LF refining production cost control, refining efficiency and operation level cannot be carried out online and immediately. It cannot timely feedback and reflect the effects and levels of many important aspects such as online emission control, efficiency, and cost control in the current LF refining process, and cannot be evaluated, analyzed and optimized in time, which is not conducive to the optimization of the LF refining process and operation level, nor to the improvement of production management. Summary of the Invention

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

[0004] According to one aspect of the present invention, the following technical solution is provided:

[0005] A big data intelligent online evaluation method for the metallurgical effect of LF refining. After the LF refining is completed, the refining effect factor of this heat is determined online by using the process data of this heat, and the metallurgical effect of LF refining is evaluated according to the level of the refining effect factor of this heat and its high and low ranking in the recent period. In the present invention, the metallurgical effect of LF refining includes the carbon dioxide emission intensity of LF refining, the refining effect and the operation level.

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

[0007] R = a 1 ×Slag + a 2 ×t + a 3 ×E + a 4 ×T + a 5 ×S + a 6 ×P + a 7 ×N + a 8 ×Cost - a 9 ×X + a 10 ×Ce

[0008] Wherein, R is the refining effect factor;

[0009] a 1is the weighting factor for the refining slag condition, with a value range of 0 - 0.9; Slag is the control level of the refining slag condition;

[0010] a 2 is the weighting factor for the refining efficiency, with a value range of 0 - 0.9; t is the control level of the refining efficiency;

[0011] a 3 is the weighting factor for the energy consumption, with a value range of 0.1 - 0.5; E is the control level of the energy consumption;

[0012] a 4 is the weighting factor for the control of the refining end - point temperature, with a value range of 0.1 - 0.8; T is the control level of the refining end - point temperature;

[0013] a 5 is the weighting factor for the control of S at the refining end - point, with a value range of 0 - 0.9; S is the control level of sulfur at the refining end - point;

[0014] a 6 is the weighting factor for the control of P at the refining end - point, with a value range of 0.1 - 0.9; P is the control level of phosphorus at the refining end - point;

[0015] a 7 is the weighting factor for the control of N at the refining end - point, with a value range of 0 - 0.5; N is the control level of nitrogen at the refining end - point;

[0016] a 8 is the weighting factor for the control of the refining cost, with a value range of 0 - 0.5; M is the control level of the refining cost;

[0017] a 9 is the weighting factor for the hit of the refining end - point composition, with a value range of 0 - 0.9; X is the hit level of the refining end - point composition;

[0018] a 10 is the weighting factor for the refining carbon emission intensity, with a value range of 0 - 0.9; Ce is the control level of the refining carbon emission intensity.

[0019] As a preferred embodiment of the big data intelligent online evaluation method for LF refining metallurgical effect described in the present invention, specifically: the LF refining effect and operation level are evaluated according to the refining effect factors of each heat, and the ranking within the corresponding evaluation period (weekly, monthly, quarterly) is given. If the ranking of the refining effect factors of this heat is in the top 10%, it can be considered that its refining effect is good, the operation level is high, and less optimization and adjustment of the process operation are required; if the ranking of the refining effect factors of this heat is in the range of 10%-20% of the top, it can be considered that its refining effect is relatively good, the operation level is relatively high, and the relevant processes and operations need to be adjusted appropriately; if it is in the range of 20%-30% of the top, it indicates that the refining effect is average, the operation level is average, and the relevant processes and operations need to be adjusted; if it is lower than 30%, it indicates that the refining effect is poor, the operation level is poor, and the relevant processes and operations need to be adjusted significantly.

[0020] If adjustment is needed, the adjustment direction can be determined according to the control levels of each evaluation control factor. If the hitting level X of the final refining composition is 0, it indicates that the control of the final composition is good, otherwise both the process and operation of the final refining composition control need to be improved. If the control level of other evaluation control factors is higher than 0.9, it indicates that the control level of this control factor is high and no adjustment or optimization of the relevant process operation is required; if it is lower than 0.9, it indicates that the control level of this control factor is insufficient and the relevant process operation needs to be adjusted and optimized.

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

[0022] The present invention provides a big data intelligent online evaluation method for LF refining metallurgical effect. By using the metallurgical big data systems being established by various steel enterprises, an evaluation method for LF refining carbon dioxide emission intensity, LF refining effect and operation level is established, which can evaluate the control levels of LF refining in aspects such as carbon dioxide emission, efficiency, final hitting rate, cost, etc. This method can also comprehensively evaluate the effect and operation level of LF refining through a weighting coefficient. After LF refining is completed, data can be retrieved from the production or quality management big data system immediately for corresponding evaluation calculations, quickly giving various indicators and comprehensive indicators, evaluating the carbon dioxide emission intensity, refining effect and operation level, and optimizing the corresponding process technologies and operation levels, rapidly improving the process technology and operation level of LF refining, and also improving the production management level of LF refining. Detailed implementation manners

[0023] The technical solutions in the embodiments will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the scope of protection of the present invention.

[0024] The present invention provides a big data intelligent online evaluation method for the metallurgical effect of LF refining, that is, by using the metallurgical big data systems being established by various steel enterprises, an online weighted intelligent evaluation model is established. The model comprehensively and intelligently considers the refining carbon emission intensity, heating efficiency, refining efficiency, nitrogen increase control effect, refining time consumption, component and temperature hit rate, etc. of this heat. Through the weighted coefficient, the comprehensive level and metallurgical effect of the refining of this heat can be intelligently evaluated, and the focus direction of the intelligent optimization of the refining effect evaluation can be adjusted through the weighted coefficient. The present invention can, on the basis of the existing refining metallurgical big data platform, immediately evaluate the carbon dioxide emission, refining effect and operation level, and immediately feedback the advantages, deficiencies and improvement space of this heat of refining process in terms of carbon emission, refining cost, efficiency and steel quality control, which is conducive to strengthening the refining production management and can help improve the operation level of operators.

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

[0026] A big data intelligent online evaluation method for the metallurgical effect of LF refining. After the LF refining is completed, the process data of this heat is used to online determine the refining effect factors of this heat, and the metallurgical effect of LF refining is evaluated according to the refining effect factors of the heat, and the ranking situation within the corresponding evaluation period (week, month, quarter) is given.

[0027] Preferably, if the ranking of the refining effect factor is in the top 10%, it can be considered that its refining effect is good, the operation level is high, and the process operation requires less optimization and adjustment; if the ranking of the refining effect factor is in the range of 10%-20%, it can be considered that its refining effect is relatively good, the operation level is relatively high, and the relevant processes and operations need to be appropriately adjusted; if it is in the range of 20%-30%, it indicates that the refining effect is average, the operation level is average, and the relevant processes and operations need to be adjusted; if it is lower than 30%, it indicates that the refining effect is poor, the operation level is poor, and the relevant processes and operations need to be greatly adjusted.

[0028] In the present invention, the metallurgical effect of LF refining 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 = a 1 ×Slag + a 2 ×t + a 3 ×E + a 4 ×T + a 5 ×S + a 6 ×P + a 7 ×N + a 8 ×Cost - a 9 ×X + a 10 ×Ce

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

[0032] a 1 is the weighted factor of the refining slag condition, with a value range of 0 - 0.9; Slag is the control level of the refining slag condition;

[0033] a 2 is the weighted factor of the refining efficiency, with a value range of 0 - 0.9; t is the control level of the refining efficiency;

[0034] a 3 is the weighted factor of the energy consumption, with a value range of 0.1 - 0.5; E is the control level of the energy consumption;

[0035] a 4 is the weighted factor of the control of the final refining temperature, with a value range of 0.1 - 0.8; T is the control level of the final refining temperature;

[0036] a 5 is the weighted factor of the control of S at the end of refining, with a value range of 0 - 0.9; S is the control level of sulfur at the end of refining;

[0037] a 6 is the weighted factor of the control of P at the end of refining, with a value range of 0.1 - 0.9; P is the control level of phosphorus at the end of refining;

[0038] a 7 is the weighted factor of the control of N at the end of refining, with a value range of 0 - 0.5; N is the control level of nitrogen at the end of refining;

[0039] a 8 is the weighted factor of the control of the refining cost, with a value range of 0 - 0.5; M is the control level of the refining cost;

[0040] a 9 is the weighted factor of the hit of the final refining composition, with a value range of 0 - 0.9; X is the hit level of the final refining composition;

[0041] a 10 is the weighted factor of the refining carbon emission intensity, with a value range of 0 - 0.9; Ce is the control level of the refining carbon emission intensity.

[0042] Preferably, it is determined whether it is necessary to adjust the LF refining process according to the evaluation results; if adjustment is required, the adjustment direction can be determined according to the control levels of each evaluation control factor.

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

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

[0045] Where: (%FeO) is the iron oxide content in the slag, wt%; (%FeO) Min is the minimum value of (%FeO) in the refining slag at the end of LF refining for this steel grade or similar steel grades within a recent reference evaluation period.

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

[0047] t = 2 - Δt / Δt Min

[0048] Where: Δt is the smelting duration, in minutes; Δt Min is the shortest refining duration within a recent period (week, month, quarter, year), in minutes.

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

[0050] E = F / F Max

[0051] Where: F is the heating-up efficiency, K / (kW·h / t); F Max is the maximum refining heating-up efficiency within a recent period (week, month, quarter, year); the expression for the heating-up 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 molten steel, t; ΔT is the amount of temperature rise, K. Preferably, the expression for the refining end-point temperature control level T is:

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

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

[0056] Preferably, the expression for the refining end-point sulfur control level S is:

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

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

[0059] When refining non-sulfur-controlled steel by LF, [%S] ≤ [%S] gWhen, 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 for the phosphorus control level P at the end of refining is:

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

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

[0064] When refining non-phosphorus-controlled steel by LF, [%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 for the nitrogen control level N at the end of refining is:

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

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

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

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

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

[0072] Cost = 2 - Co / Co Min

[0073] Where: Co is the LF refining cost, including the costs of slag formers, alloys, electricity, electrodes, and gases consumed during the refining process, in yuan; Co Min is the minimum value of the LF refining cost for this steel grade or a similar steel grade within a recent reference evaluation period, in yuan;

[0074] Preferably, the expression for the LF refining cost 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 former i, in tons; Pr SMi is the unit price of the slag former, in yuan / ton;

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

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

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

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

[0081] Preferably, the expression for the hitting level of the refining end-point composition is:

[0082] When the content [%i] of component i (excluding N, P, and S) in the molten steel at the LF refining end-point ≤ [%i] llimt then

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

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

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

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

[0087] X i =0

[0088] X = ∑X i

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

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

[0091] X i is the refining end - point composition hit level of element i.

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

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

[0094] In the formula: E CO2 is the carbon dioxide emission, ton; W steel is the tapping weight, ton; CeM is the minimum carbon emission within a recent cycle (week, month, quarter, year), that is, the minimum value, ton carbon dioxide emission / ton steel; the expression of the carbon dioxide emission E CO2 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 emission factor of carbon dioxide for the power consumption of LF refining in this enterprise, in tons of carbon dioxide / kW·h, and the value is taken according to the carbon dioxide emission factor of the enterprise's electricity consumption verified by the national authoritative department;

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

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

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

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

[0101] Preferably, each 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 for metallurgical effect, operation level and carbon dioxide emission evaluation; each weighting factor in the formula can be adjusted accordingly according to different emphases of the current evaluation.

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

[0103] Furthermore, for the evaluation of the metallurgical effect of the first 10 furnaces of LF refining of a certain steel grade adopted by this method, since the values of (%FeO) Min , Δt Min , F Max , Co Min , CeM are missing in the big data system, the corresponding initial values can be set first according to the process and operation level of the LF refining of this steel grade for evaluation.

[0104] Furthermore, corresponding programs are compiled according to the evaluation model, placed in the LF refining operation system, and data exchange is carried out with the corresponding production and quality management big data system of the steel enterprise. After the LF refining is completed, various data are intelligently retrieved by the system for automatic evaluation, and the corresponding evaluation data and indicators are stored in the system; technicians, operators and managers can retrieve the corresponding data indicators for evaluation and optimize the process and operation.

[0105] Furthermore, when evaluating according to the evaluation model, the LF refining needs to be subdivided into corresponding categories according to the differences in the refined steel grades and the thermal states of the ladles before LF refining, and the (%FeO) Min , Δt Min , F Max , Co Min , CeM data intelligent classification evaluation is adopted.

[0106] Furthermore, when calculating the carbon emission intensity of LF refining, the value ranges of the carbon dioxide emission factors of various substances are calculated according to the values in Table 1.

[0107] Table 1 Value ranges of carbon dioxide emission factors of various substances in LF refining

[0108] Substance Carbon dioxide emission factor Substance Carbon dioxide emission factor Calcium carbide <![CDATA[5.067tCO 2 / t]]> Copper <![CDATA[4.23tCO 2 / t]]> SiC <![CDATA[15.9tCO 2 / t]]> Electrolytic manganese <![CDATA[0.94tCO 2 / t]]> Lime <![CDATA[0.950tCO 2 / t]]> Metallic manganese <![CDATA[1.5tCO 2 / t]]> Fluorite <![CDATA[3.614tCO 2 / t]]> Medium-carbon ferromanganese <![CDATA[0.043 tCO 2 / t]]> Carbon powder <![CDATA[3.67tCO 2 / t]]> Medium-carbon ferrochrome <![CDATA[0.061tCO 2 / t]]> Nitrogen <![CDATA[0.103 tCO 2 / 10 3 m 3 > Electrode <![CDATA[3.663tCO 2 / t <!-- 5 -->]]> Argon <![CDATA[0.103 tCO 2 / 10 3 m 3 > Ferromolybdenum <![CDATA[0.018tCO 2 / t]]> Aluminum <![CDATA[14.4tCO 2 / t]]> Sponge titanium <![CDATA[2.5tCO 2 / t]]> Ferrosilicon <![CDATA[5.05tCO 2 / t]]> Ferrotitanium <![CDATA[1.74tCO 2 / t]]> Silicomanganese <![CDATA[0.055tCO 2 / t]]> Electrode <![CDATA[3.663tCO 2 / t]]>

[0109] The technical solution of the present invention will be further described below in conjunction with specific embodiments.

[0110] Example 1

[0111] A steel plant uses 210-ton LF refining to produce wheel steel. The process route for this steel grade is blast furnace - converter - CAS refining - LF refining - continuous casting. The in-station composition and end composition of the molten steel are as follows: In-station 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), end 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 consumption of raw and auxiliary materials for this heat are as follows: refining slag, 399 kg; lime, 1302 kg; fluorite, 160 kg; silicomanganese, 702 kg; aluminum ingot, 249 kg; ferrotitanium, 222 kg; electrode, 656 kg; power consumption, 6331 kW·h; argon, 57 m 3 The refining duration of this heat is about 61 min, the molten steel weight is 217.4 t, and the tapping temperature is 1846.15 K. The required tapping temperature is between 1838.15 K and 1848.15 K. Therefore, the target refining temperature is taken as the middle value of the tapping temperature range, which is 1843.15 K.

[0112] The calculation method of the refining slag condition factor Slag for this heat is as follows:

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

[0114] The calculation method of the metallurgical efficiency level t for this heat is as follows:

[0115] t = 2 - Δt / ΔtMin = 2 - 61 / 35 = 0.26

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

[0117] F = ΔT / (ΔW / W steel ) = 49 / (6331 / 217.4) = 1.68 K / (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 for this heat is as follows:

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

[0121] This heat is a sulfur-controlled steel, and the calculation method for its final 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 a non-phosphorus-controlled steel, and the calculation method for its final phosphorus control level P is:

[0124] [%P] ≤ [%P] g , so P = 1

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

[0126] [%N] ≤ [%N] g , so N = 1

[0127] The calculation method for 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 for the refining cost control level Cost of this heat is as follows:

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

[0133] The calculation method for the refining end - point composition hitting level X of this heat is: All component compositions at the LF refining end - point of this heat meet the requirements of the steel grade, X = 0

[0134] The carbon emission intensity E CO2 of this heat

[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 the evaluation of the refining metallurgical effect of this heat is:

[0141] R = a 1 × Slag + a 2 × t + a 3 × E + a4 ×T + a 5 ×S + a 6 ×P + a 7 ×N + a 8 ×Cost - a 9 ×X + a 10 ×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 evaluation value of the refining metallurgical effect of this heat is 3.016. This result is immediately displayed in the online real-time big data evaluation system for refining effect and operation level after the LF furnace refining is completed; and it shows that its ranking is in the top 35% among the evaluation values of the LF refining metallurgical effect of gear steel in the recent month; also shown are the control levels of the slag condition of this heat of refining, Slag: 0.73; refining efficiency level t: 0.26; energy efficiency control level E: 0.54; end-point temperature control level T: 0.998; end-point sulfur control level S: 0.67; end-point phosphorus control level P: 1; end-point nitrogen control level N: 1; cost control level Cost: 0.82; hit rate of the end-point molten steel composition X: 0; carbon emission control level Ce during the refining process: 0.678. It indicates that the metallurgical effect and operation level of this heat are poor, and there is a large room for improvement in the control of refining slag condition, refining efficiency, energy efficiency control, end-point sulfur and carbon emission during the refining process, and improvement is needed.

[0144] Example 2

[0145] A steel plant uses 210-ton LF refining to produce carbon steel JB65Mn. The process route for this steel grade is blast furnace - converter - CAS refining - LF refining - continuous casting. The inlet and end-point compositions of the molten steel are as follows: Inlet composition (0.2639% C, 0.166% Si, 0.8004% Mn, 0.0152% P, 0.0168% S, 0.0206% Als, 0.0034% N), end-point composition (0.64% C, 0.23% Si, 0.99% Mn, 0.016% P, 0.001% S, 0.018% Als, 0.0032% N); the target compositions are: 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 consumptions of raw and auxiliary materials for this heat are as follows: refining slag, 345 kg; lime, 883 kg; fluorite, 115 kg; ferrosilicon, 80 kg; aluminum ingot, 85 kg; silicomanganese, 319 kg; carburizer, 270 kg; electrode, 537 kg; power consumption, 5194 kW·h; argon, 43 m 3 The refining duration of this heat is about 45 min, the molten steel weight is 220.2 t, and the heating rate is 4.5 °C / min; the tapping temperature is 1796.15 K, and the required tapping temperature is between 1793.15 K and 1803.15 K. Therefore, the target refining temperature is taken as the middle value of the tapping temperature range, which is 1798.15 K.

[0146] The calculation method for the refining slag condition factor Slag of this heat is as follows:

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

[0148] The calculation method for the metallurgical efficiency level t of this heat is as follows:

[0149] t = 2 - Δt / ΔtMin = 2 - 45 / 30 = 0.5

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

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

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

[0153] The calculation method for the end-point temperature control level T of this heat is as follows:

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

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

[0156] [%S] ≤ [%S] g , so S = 1

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

[0158] [%P] ≤ [%P] g , so P = 1

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

[0160] [%N] ≤ [%N] g , so N = 1

[0161] The calculation method for 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 for the hitting level X of the refining end-point composition of this heat is as follows: When the contents of components in the molten steel at the LF refining end-point all meet the requirements, X = 0;

[0167] The carbon emission intensity E of this heat CO2 is calculated as follows:

[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 as follows:

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

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

[0174] R = a 1 × Slag + a 2 × t + a 3 × E + a4 ×T + a 5 ×S + a 6 ×P + a 7 ×N + a 8 ×Cost - a 9 ×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 evaluation value of the refining metallurgical effect of this heat is 3.472. This result is immediately displayed in the online big data real-time evaluation system for refining effect and operation level after the LF furnace refining is completed, and it shows that it ranks in the top 22% among the evaluation values of the LF refining metallurgical effects of similar steel grades of carbon steel JB65Mn in the recent month; also displayed at the same time are the control levels of the slag condition of this heat of refining, Slag: 0.79; refining efficiency level t: 0.5; energy efficiency control level E: 0.83; end-point temperature control level T: 0.999; end-point sulfur control level S: 1; end-point phosphorus control level P: 1; end-point nitrogen control level N: 1; cost control level Cost: 0.88; hit rate of the end-point molten steel composition X: 0; carbon emission control level Ce during the refining process: 0.699. It shows that the metallurgical effect and operation level of this heat are average, and there is a large room for improvement in the control of refining slag condition, refining efficiency, energy efficiency control, and carbon emission during the refining process, and improvement is needed.

[0177] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural transformation made using the content of the specification of the present invention under the inventive concept of the present invention, or any direct / indirect application in other related technical fields is 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 LF refining is completed, the furnace process data is used to determine the furnace refining effect factor online. According to the furnace refining effect factor and its ranking in the recent evaluation cycle, the LF refining metallurgical effect is evaluated.

2. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is characterized in that: 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 the refined slag condition, ranging from 0 to 0.9; Slag is the control level of the 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, ranging from 0.1 to 0.5; E is the energy consumption control level; a4 is the refining endpoint temperature control weighting factor, ranging from 0.1 to 0.8; T is the refining endpoint temperature control level; 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 refining endpoint P control weighting factor, ranging from 0.1 to 0.9; P is the refining endpoint phosphorus control level; 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 hit weighting factor of the refined endpoint component, ranging from 0 to 0.9; X is the hit level of the refined endpoint component; 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.

3. 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.

4. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is 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 refining slag (%FeO) of this steel grade in the recent reference evaluation cycle.

5. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is 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, minutes.

6. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is characterized in that: 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 It is the maximum refining heating efficiency in the last cycle; the expression of heating efficiency F is 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; The expression of refining end point 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.

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

8. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1 is characterized in that: The expression of the refining cost control level Cost is: Cost=2-What / What 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 is the lowest LF refining cost of this 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, 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 .

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

10. The LF refining metallurgical effect big data intelligent online evaluation method according to claim 1, characterized in that: The calculation formula for 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 last cycle, i.e. the minimum value, ton of carbon dioxide emissions / 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 carbon dioxide emission factor of the enterprise's LF refining electricity consumption, tons of carbon dioxide / kW·h, which is determined by the enterprise's electricity carbon dioxide emission factor 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 in the 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 in LF refining process, tons; K El is the carbon emission factor of the electrode, tons of carbon dioxide / ton.