Big data-based online evaluation method for metallurgical effect of converter

Abstract

The invention belongs to the technical field of iron and steel metallurgy, and particularly relates to a converter metallurgy effect online evaluation method based on big data, after converter smelting is finished, corresponding data can be intelligently retrieved from a big data system such as iron and steel enterprise production and quality management; the method intelligently completes the evaluation of the smelting effects of carbon emission, metallurgical efficiency improvement, cost control, steel quality control and the like and the operation level of the converter smelting process of the current completion furnace, the evaluation process is intelligent and instant, relevant optimization can be carried out instantly, the defects that the smelting effect of the converter smelting production is not timely and incomplete in manual evaluation and the like in the past are overcome, and the production efficiency is improved. The method plays an important role in converter smelting low-carbon control, efficiency improvement and steel quality control improvement in China.

Description

Technical Field

[0001] The present invention relates to the technical field of iron and steel metallurgy, and particularly to an online evaluation method for converter metallurgical effect based on big data. Background Art

[0002] Converter steelmaking is an important process in China's iron and steel production. Its energy conservation, consumption reduction, efficiency improvement, and cost control are particularly important for improving China's modern iron and steel production level and realizing low-carbon iron and steel smelting. However, for a long time, due to the lack of a big data system for iron and steel production and the absence of carbon emission management, the evaluation of the carbon emission control level in converter smelting production has not been carried out. The carbon tax cost has not been considered in the converter production cost control. The evaluation of the converter smelting efficiency and operation level cannot be carried out online and immediately, and it cannot timely feedback and reflect the effects and levels of the current converter smelting process in many important aspects such as carbon emission control, efficiency, and cost control. It cannot be evaluated, analyzed, and optimized in time, which is not conducive to the optimization of the converter production 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 an online evaluation method for converter metallurgical effect based on big data.

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

[0005] An online evaluation method for converter metallurgical effect based on big data. After the converter smelting is completed, the metallurgical effect factors of this heat are determined online by using the steelmaking raw materials and process data of this heat, and the converter metallurgical effect is evaluated according to the metallurgical effect factors of the heat. In the present invention, the converter metallurgical effect not only includes smelting effects such as carbon emission, metallurgical efficiency improvement, cost control, and steel quality control, but also includes the operation level of the converter smelting process. The larger the metallurgical effect factor, the better the converter smelting effect and the higher the operation level.

[0006] As a preferred scheme of the online evaluation method for converter metallurgical effect based on big data according to the present invention, among them: when the metallurgical effect factor is in the top 10% within an evaluation period (such as within a week, within a month, within a quarter, or within half a year, etc.), it indicates good smelting effect and high operation level; when it is in the range of 10%-20%, it indicates relatively good smelting effect and relatively high operation level, and the relevant processes and operations need to be adjusted appropriately; when it is in the range of 20%-30%, it indicates general smelting effect and general operation level, and the relevant processes and operations need to be adjusted; when it is lower than 30%, it indicates poor smelting effect and poor operation level, and the relevant processes and operations need to be adjusted greatly.

[0007] As a preferred scheme of the online evaluation method for converter metallurgical effect based on big data according to the present invention, among them: the metallurgical effect factor S is:

[0008] S = a 1 × Ce + a 2 × t + a 3 × CO + a 4 × T + a 5 × C + a 6 × P + a 7 × Q slag + a 8 × Cost

[0009] Wherein, S is the metallurgical effect factor;

[0010] a 1 is the carbon emission factor; Ce is the carbon emission control level;

[0011] a 2 is the metallurgical efficiency factor; t is the metallurgical efficiency control level;

[0012] a 3 is the carbon-oxygen product control factor; CO is the carbon-oxygen product control level;

[0013] a 4 is the tapping temperature control factor for steelmaking, and T is the tapping temperature control level for steelmaking;

[0014] a 5 is the tapping carbon control factor for steelmaking, and C is the tapping carbon control level for steelmaking;

[0015] a 6 is the tapping phosphorus control factor for steelmaking, and P is the tapping phosphorus control level for steelmaking;

[0016] a 7 is the slagging control factor for steelmaking, Q slag is the slagging control level for steelmaking;

[0017] a 8 is the steelmaking cost control factor, and Cost is the steelmaking cost control level.

[0018] As a preferred solution of the online evaluation method for the converter metallurgical effect based on big data according to the present invention, wherein: it is determined whether it is necessary to adjust the converter smelting process according to the evaluation result; if adjustment is required, the adjustment direction can be determined according to the control levels of each evaluation control factor.

[0019] The beneficial effects of the present invention are as follows:

[0020] The present invention provides an online evaluation method for the metallurgical effect of a converter based on big data. After the converter smelting is completed, corresponding data can be intelligently retrieved from big data systems such as steel enterprise production and quality management. The intelligent evaluation of the smelting effects such as carbon emissions, improvement of metallurgical efficiency, cost control, and steel quality control, as well as the operation level of the converter smelting process for the current completed heat can be completed. The evaluation process is intelligent and immediate, and relevant optimizations can be carried out immediately, overcoming the disadvantages of untimely and incomplete manual evaluation of the smelting effects in previous converter smelting production, and playing an important role in the low-carbon control, efficiency improvement, and steel quality control improvement of converter smelting in China. Specific embodiments

[0021] The technical solutions in the embodiments will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] The present invention provides an online evaluation method for the metallurgical effect of a converter based on big data, that is, using the metallurgical big data systems being established by each steel enterprise to establish an evaluation method including converter smelting carbon dioxide emissions and carbon tax costs. This method can comprehensively evaluate the converter smelting effect and operation level through control factors, and can also separately evaluate the control levels of the converter in terms of carbon dioxide emissions, efficiency, end-point hit rate, cost, etc. After the converter smelting is completed, data is immediately retrieved from the production or quality management big data system for corresponding evaluation calculations, and comprehensive indexes in various aspects are quickly given to evaluate the smelting effect and operation level of this heat, and corresponding process technologies and operation levels are optimized, quickly improving the converter smelting process technology and operation level, and also improving the converter production management level.

[0023] According to one aspect of the present invention, the following technical solutions are provided:

[0024] An online evaluation method for the metallurgical effect of a converter based on big data. After the converter smelting is completed, the metallurgical effect factors of this heat are determined online using the steelmaking raw materials and process data of this heat, and the converter metallurgical effect is evaluated according to the metallurgical effect factors of the heat. In the present invention, the converter metallurgical effect not only includes smelting effects such as carbon emissions, improvement of metallurgical efficiency, cost control, and steel quality control, but also includes the operation level of the converter smelting process. The larger the metallurgical effect factor, the better the smelting effect and the higher the operation level.

[0025] Preferably, if the metallurgical effect factor is in the top 10% within an evaluation period (such as within a week, within a month, within a quarter, or within half a year, etc.), it indicates good smelting effect and high operation level; if it is in the range of 10%-20%, it indicates relatively good smelting effect and relatively high operation level, and the relevant processes and operations need to be adjusted appropriately; if it is in the range of 20%-30%, it indicates average smelting effect and average operation level, and the relevant processes and operations need to be adjusted; if it is below 30%, it indicates poor smelting effect and poor operation level, and the relevant processes and operations need to be adjusted significantly.

[0026] Preferably, the metallurgical effect factor S is:

[0027] S = a 1 ×Ce + a 2 ×t + a 3 ×CO + a 4 ×T + a 5 ×C + a 6 ×P + a 7 ×Q slag + a 8 ×Cost

[0028] Where:

[0029] S is the metallurgical effect factor;

[0030] a 1 is the carbon emission factor, and its value range is 0 - 0.8; Ce is the carbon emission control level;

[0031] a 2 is the metallurgical efficiency factor, and its value is 0 - 0.8; t is the metallurgical efficiency control level;

[0032] a 3 is the carbon-oxygen product control factor, and its value range is 0 - 0.5; CO is the carbon-oxygen product control level;

[0033] a 4 is the steelmaking end-point temperature control factor, and its value range is 0.2 - 0.8, T is the steelmaking end-point temperature control level;

[0034] a 5 is the steelmaking end-point carbon control factor, and its value range is 0.2 - 0.8, C is the steelmaking end-point carbon control level;

[0035] a 6 is the steelmaking end-point phosphorus control factor, and its value range is 0 - 0.8, P is the steelmaking end-point phosphorus control level;

[0036] a 7 is the steelmaking slagging control factor, and its value range is 0 - 0.6, Q slag is the steelmaking slagging control level;

[0037] a 8 is a steelmaking cost control factor, with a value range of 0 - 0.6, and Cost is the steelmaking cost control level.

[0038] Preferably, determine whether it is necessary to adjust the converter smelting process according to the evaluation results; if adjustment is needed, the adjustment direction can be determined according to the control levels of each evaluation control factor, and the adjustment intensity can be determined according to the ranking of the control levels of each evaluation control factor within the evaluation period.

[0039] Preferably, the carbon emission control level Ce is:

[0040]

[0041] Where: is the carbon dioxide emission, in tons; W steel is the amount of molten steel tapped, in tons; CeM is the maximum carbon emission within a recent period (week, month, quarter, year);

[0042] Carbon dioxide emission has the following expression:

[0043]

[0044] Where: M i is the amount of raw and auxiliary materials i consumed in converter smelting, including the amount of slag-making materials, consumed gas, etc., with unit i (tons or m 3 ); K i is the carbon emission factor corresponding to auxiliary material i, in tons of carbon dioxide per unit i; W HM is the amount of hot metal consumed, in tons; [%C] HM is the carbon percentage content of hot metal, wt%; W scrap is the amount of scrap consumed, in tons; [%C] scrap is the carbon percentage content of scrap, wt%; W steel is the amount of liquid steel, in tons; [%C] is the carbon percentage content of the liquid steel at the end of smelting, wt%; V 蒸汽 is the amount of recovered steam, t; K 蒸汽 is the carbon emission factor of steam, tCO 2 / t; V 煤气 is the amount of recovered gas, m 3 ; K 煤气 is the carbon emission factor of gas, tCO 2 / m 3 ; W slag is the amount of slag generated, in tons; K slag is the carbon emission factor of slag, tCO 2 / t.

[0045] When calculating the carbon dioxide emissions of a converter, the value ranges of the carbon dioxide emission factors of each substance in the above formula are calculated according to the values in Table 1.

[0046] Table 1 Carbon dioxide emission factors of each substance

[0047] Substance Carbon emission factor Substance Carbon emission factor Hot metal <![CDATA[1.855tCO 2 / t]]> Steam <![CDATA[0.195tCO 2 / t]]> Scrap steel <![CDATA[0tCO 2 / t]]> Argon <![CDATA[0.103 tCO 2 / 10 3 m 3 > Light-burned dolomite <![CDATA[1.1tCO 2 / t]]> Nitrogen <![CDATA[0.103 tCO 2 / 10 3 m 3 > Dolomite <![CDATA[0.471tCO 2 / t]]> Slag <![CDATA[0.3tCO 2 / t]]> Lime <![CDATA[0.950tCO 2 / t]]> Converter gas <![CDATA[0.47tCO 2 / 10 3 m 3 > Oxygen <![CDATA[0.355tCO 2 / 10 3 m 3 >

[0048] Preferably, the expressions of the steelmaking efficiency level t, the carbon-oxygen product control level CO, the tapping temperature control level T, the tapping carbon control level C, the tapping phosphorus control level P, and the slagging control level Q of the converter slag are as follows:

[0049] t = 2 - Δt / tM

[0050] Where: Δt is the smelting duration, in minutes; tM is the shortest converter smelting duration within a recent cycle (week, month, quarter, year), in minutes.

[0051] CO = 2 - [C%][O%] / [C%O%]M

[0052] Where: [%C] is the percentage content of carbon in the molten steel at the tapping end, wt%; [%O] is the percentage content of oxygen in the molten steel at the tapping end, wt%; [C%O%]M is the minimum carbon-oxygen product of the converter tapping molten steel within a recent cycle (week, month, quarter, year).

[0053] T = 1 - |T e -T g | / T g

[0054] Where: T e and T g are the tapping temperature and the target temperature of the converter, respectively, in K.

[0055] C = 1 - |[%C] - [%C] g | / [%C] g

[0056] Where: [%C] g is the target carbon content at the tapping end of the converter, wt%.

[0057] P = 2 - [%P] / [%P]M

[0058] Where: [%P] is the phosphorus content in the molten steel at the tapping end of the converter, wt%; [%P]M is the lowest phosphorus content in the molten steel at the tapping end of the converter within a recent cycle (week, month, quarter, year), wt%.

[0059] Q slag = 2 - Q / QM

[0060] Where: Q is the slag volume during tapping in converter steelmaking, in tons; QM is the minimum slag volume during tapping in converter steelmaking within the most recent cycle (week, month, quarter, year), in tons.

[0061] Preferably, the expression of the steelmaking cost control level Cost is as follows:

[0062] Cost = 2 - Co / Cog

[0063] Where: Co is the actual cost of converter steelmaking, in yuan; Cog is the lowest cost of converter steelmaking within the most recent cycle (week, month, quarter, year), in yuan; the actual cost of converter steelmaking is as follows:

[0064]

[0065] Where: Q i is the amount of the i-th raw and auxiliary material consumed in converter steelmaking, including the amount of hot metal, scrap steel, slag-making material, consumed gas, the number of oxygen and carbon determination probes, etc., with the unit i (tons, m 3 or pieces, etc.); Pr i is the unit price of the i-th raw and auxiliary material consumed in converter steelmaking, in yuan / unit i; W 蒸汽 is the amount of recovered steam, in t; Pr 蒸汽 is the unit price of steam, in yuan / t; V 煤气 is the amount of recovered gas, in m 3 ; Pr 煤气 is the unit price of gas, in yuan / m 3 ; is the carbon tax for carbon dioxide emissions, in yuan / tCO 2 .

[0066] Preferably, the data in the evaluation model are from the production and quality big data system of iron and steel enterprises. After the converter steelmaking is completed, the corresponding data can be retrieved from the big data system at any time for the metallurgical effect evaluation of this heat; each control factor a i in the formula can be adjusted accordingly according to the different emphases of the current evaluation direction.

[0067] Further preferably, Ce, t, CO, T, C, P, Q slag on the right side of formula (1) and Cost can also be evaluated separately to evaluate the smelting level and control effect of this heat in the corresponding aspects, and optimize the converter steelmaking process according to the corresponding data.

[0068] Further preferably, for the metallurgical effect evaluation of the first 30 heats of converter steelmaking using this method, since the values of CeM, tM, [C%O%]M, [%P]M, QM, and Cog are missing in the big data system, the corresponding initial values can be set first for evaluation according to the process and operation level of this converter steelmaking.

[0069] Further preferably, a corresponding program is compiled according to the calculation method of the metallurgical effect factor S, placed in the converter operating system, and data exchange is carried out with the corresponding production and quality management big data system of the steel enterprise. After the converter smelting 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.

[0070] Further preferably, according to the differences in the steel grades to be smelted and the converter smelting cycles, the converter smelting is subdivided into corresponding categories, such as the early stage, middle stage, late stage of the furnace campaign, low-carbon steel smelting, medium-carbon steel smelting, high-carbon steel smelting, etc. Intelligent classification evaluation is carried out on the CeM, tM, [C%O%]M, [%P]M, QM, Cog data of the corresponding categories.

[0071] The technical solution of the present invention will be further described below in conjunction with specific embodiments.

[0072] Example 1

[0073] A certain steel plant uses a 210-ton converter to produce Q345 steel. The process route of this steel grade is blast furnace-converter-CAS refining-LF refining-RH refining-continuous casting. The compositions of its hot metal, scrap and molten steel are as follows: hot metal (4.32% C, 0.32% Si, 0.12% Mn, 0.124% P, 0.027% S), scrap (0.15% C, 0.25% Si, 0.60% Mn, 0.02% P, 0.02% S), molten steel (0.09% C, 0.01% Si, 0.16% Mn, 0.038% P, 0.003% S). The consumption of raw and auxiliary materials for this heat are as follows: hot metal, 205.1t; scrap, 38.51t; lime, 4053kg; dolomite, 1282kg; oxygen, 9813m 3 ; argon, 220m 3 ; measuring probes, 2. The recovery amounts of steam, gas and slag recovered in steelmaking are as follows: slag, 17.21t; converter gas, 21090m 3 ; steam, 22.8t. The amount of molten steel produced is 224.12t.

[0074] The calculation method of the carbon emissions of this heat is as follows:

[0075] Carbon emission of hot metal + Carbon emission of scrap steel + Carbon emission of oxygen + Carbon emission of lime + Carbon emission of dolomite + Carbon emission of argon + Carbon emission of carbon monoxide - Carbon emission of steam - Carbon emission of slag - Carbon emission of converter gas = Hot metal consumption × Hot metal carbon emission factor + Scrap steel consumption × Scrap steel carbon emission factor + Oxygen consumption × Oxygen carbon emission factor + Lime consumption × Lime carbon emission factor + Dolomite consumption × Dolomite carbon emission factor + Argon consumption × Argon carbon emission factor + (Hot metal consumption × Hot metal carbon content + Scrap steel consumption × Scrap steel carbon content - Liquid steel output × Liquid steel carbon content) / 12 × 44 - Steam recovery × Steam carbon emission factor - Slag recovery × Slag carbon emission factor - Converter gas recovery × Converter gas carbon emission factor

[0076] = 205.1×1.855 + 38.51×0 + 9813 / 1000×0.355 + 4053 / 1000×0.95 + 1282 / 1000×1.1 + 220 / 1000×0.103 + (205.1×0.0432 + 38.51×0.0015 - 224.12×0.0009) / 12×44 - 22.8×0.195 - 17.21×0.3 - 21090 / 1000×0.47 = 401.67t。

[0077] The calculation method for the carbon emission control level Ce of this heat is as follows:

[0078]

[0079] The calculation method for the steelmaking efficiency level t of this heat is as follows:

[0080] t = 2 - Δt / tM = 2 - 35 / 29 = 0.793。

[0081] The calculation method for the carbon-oxygen product control level CO of this heat is as follows:

[0082] CO = 1 - [C%][O%] / [C%O%]M = 2 - 0.09×0.0345 / 0.0022 = 0.589。

[0083] The calculation method for the endpoint temperature control level T of this heat is as follows:

[0084] T = 1 - |T e - T g | / T g = 1 - |1916 - 1923| / 1923 = 0.997。

[0085] The calculation method for the endpoint target carbon control level C of this heat is as follows:

[0086] C = 1 - |[%C] - [%C] g | / [%C] g= 1 - |0.09 - 0.07| / 0.07 = 0.714。

[0087] The calculation method for the end-point target phosphorus control level P of this heat is:

[0088] P = 2 - [%P] / [%P]M = 1 - 0.038 / 0.022 = 0.273。

[0089] The slagging control level Q of the converter slag is calculated as follows:

[0090] Q slag = 2 - Q / QM = 2 - 1.5 / 1 = 0.5。

[0091] The calculation method for the production cost Co of this heat is:

[0092] Hot metal cost + scrap cost + lime cost + dolomite cost + oxygen cost + argon cost + probe cost - steam cost - gas cost + carbon tax cost = Hot metal consumption × Hot metal unit price + Scrap consumption × Scrap unit price + Lime consumption × Lime unit price + Dolomite consumption × Dolomite unit price + Oxygen consumption × Oxygen unit price + Argon consumption × Argon unit price + Probe consumption × Probe unit price - Steam recovery × Steam unit price - Converter gas recovery × Gas unit price + Carbon dioxide emissions × Carbon dioxide emission carbon tax

[0093] = 205.1×2457 + 38.51×2271 + 4053 / 1000×540 + 1282 / 1000×379 + 9813×0.6 + 220×2.5 + 2×200 - 22.8×100 - 21090×0.225 + 401.67×98 = 632837.23 yuan.

[0094] The calculation method for the production cost control level Cost of this heat is:

[0095] Cost = 2 - Co / Cog = 2 - 632837.23 / 600000 = 0.945。

[0096] Therefore, the calculation method for the metallurgical effect evaluation of this heat is:

[0097] S = a 1 × Ce + a 2 × t + a 3 × CO + a 4 × T + a 5 × C + a 6 × P + a 7 × Q slag + a 8×Cost = 0.7×0.88 + 0.6×0.793 + 0.4×0.589 + 0.6×0.997 + 0.6×0.714 + 0.6×0.273 + 0.3×0.25 + 0.3×0.945 = 2.8763。

[0098] The evaluation value of the metallurgical effect factor for this heat is 2.8763. This result is immediately displayed in the intelligent online system for converter metallurgical effect based on big data after the end of converter smelting. At the same time, the carbon emission control level Ce: 0.88; steelmaking efficiency level t: 0.793; carbon-oxygen product control level CO: 0.589; end-point temperature control level T: 0.997; end-point carbon control level C: 0.714; end-point phosphorus control level P: 0.273; slagging control level Q slag : 0.25; cost control level Cost: 0.945 are also displayed. The metallurgical effect factor for this heat is in the top 27% in the most recent month, indicating that the metallurgical effect of this heat is poor and there is a large room for improvement in terms of steelmaking efficiency, carbon-oxygen product, end-point carbon, end-point phosphorus, and tapping slagging control, and improvement is required.

[0099] Example 2

[0100] A certain steel plant uses a 210-ton converter to produce Q235; the process route for this steel grade is blast furnace-converter-CAS refining-continuous casting. The compositions of its hot metal, scrap, and molten steel are as follows: hot metal (4.5% C, 0.45% Si, 0.3% Mn, 0.13% P, 0.03% S), scrap (0.15% C, 0.1% Si, 0.3% Mn, 0.02% P, 0.02% S), molten steel (0.09% C, 0.01% Si, 0.13% Mn, 0.02% P, 0.03% S). The consumption amounts of raw and auxiliary materials for this heat are as follows: hot metal, 214 t; scrap, 34.48 t; lime, 5271 kg; dolomite, 1910 kg; oxygen, 11176 m 3 ; nitrogen, 3420 m 3 ; measuring probes, 2. The recovery amounts of steam, gas, and slag recovered during steelmaking are as follows: slag, 20.96 t; converter gas, 21523 m 3 ; steam, 23.4 t. The amount of molten steel produced is 228.6 t.

[0101] The calculation method for the carbon emissions of this heat is as follows:

[0102] Hot metal carbon emission + scrap carbon emission + oxygen carbon emission + lime carbon emission + dolomite carbon emission + argon carbon emission + carbon monoxide carbon emission - steam carbon emission - slag carbon emission - converter gas carbon emission = Hot metal consumption × Hot metal carbon emission factor + Scrap consumption × Scrap carbon emission factor + Oxygen consumption × Oxygen carbon emission factor + Lime consumption × Lime carbon emission factor + Dolomite consumption × Dolomite carbon emission factor + Nitrogen consumption × Nitrogen carbon emission factor + Hot metal consumption × Hot metal carbon content + Scrap consumption × Scrap carbon content - Liquid steel production × Liquid steel carbon content - Steam recovery × Steam carbon emission factor - Slag recovery × Slag carbon emission factor - Converter gas recovery × Converter gas carbon emission factor

[0103] = 214 × 1.855 + 34.48 × 0 + 11176 / 1000 × 0.355 + 5271 / 1000 × 0.95 + 1970 / 1000 × 1.1 + 3420 / 1000 × 0.103 + (205.1 × 0.045 + 34.48 × 0.0015 - 224.12 × 0.0009) / 12 × 44 - 23.4 × 0.195 - 20.96 × 0.3 - 21523 / 1000 × 0.47 = 420.77t。

[0104] The calculation method for the carbon emission control level Ce of this heat is as follows:

[0105]

[0106] The calculation method for the steelmaking efficiency level t of this heat is as follows:

[0107] t = 2 - Δt / tM = 2 - 35 / 32 = 0.906。

[0108] The calculation method for the carbon-oxygen product control level CO of this heat is as follows:

[0109] CO = 2 - [C%][O%] / [C%][O%]M = 2 - 0.09 × 0.0238 / 0.0019 = 0.873。

[0110] The calculation method for the end-point temperature control level T of this heat is as follows:

[0111] T = 1 - |T e -T g | / T g = 1 - |1915 - 1913| / 1913 = 0.999。

[0112] The calculation method for the end-point target carbon control level C of this heat is as follows:

[0113] C = 1 - |[%C] - [%C] g | / [%C] g = 1 - |0.09 - 0.1| / 0.1 = 0.9

[0114] The calculation method for the target phosphorus control level P at the end of this heat is as follows:

[0115] P = 2 - [%P] / [%P]M = 2 - 0.02 / 0.013 = 0.462.

[0116] The slagging control level Q of the converter slag is calculated as follows:

[0117] Q slag = 2 - Q / QM = 1 - 1.5 / 1 = 0.5.

[0118] The calculation method for the production cost Co of this heat is as follows:

[0119] Hot metal cost + scrap cost + lime cost + dolomite cost + oxygen cost + nitrogen cost + probe cost - steam cost - gas cost + carbon tax cost = Hot metal consumption × Hot metal unit price + Scrap consumption × Scrap unit price + Lime consumption × Lime unit price + Dolomite consumption × Dolomite unit price + Oxygen consumption × Oxygen unit price + Nitrogen consumption × Nitrogen unit price + Probe consumption × Probe unit price - Steam recovery × Steam unit price - Converter gas recovery × Gas unit price + Carbon dioxide emissions × Carbon dioxide emission carbon tax

[0120] = 214 × 2457 + 34.48 × 1065 + 5271 / 1000 × 540 + 1970 / 1000 × 379 + 11176 × 0.6 + 3420 × 0.21 + 2 × 200 - 23.4 × 100 - 21523 × 0.225 + 420.77 × 98 = 607589.13 yuan.

[0121] The calculation method for the production cost control level Cost of this heat is as follows:

[0122] Cost = 2 - Co / Cog = 2 - 605206.20 / 580000 = 0.952.

[0123] Therefore, the calculation method for the metallurgical effect evaluation of this heat is as follows:

[0124] S = a 1 × Ce + a 2 × t + a 3 × CO + a 4 × T + a 5 × C + a 6 × P + a 7 × Q slag + a 8×Cost = 0.8×0.85 + 0.6×0.906 + 0.4×0.873 + 0.6×0.999 + 0.6×0.9 + 0.6×0.462 + 0.3×0.5 + 0.5×0.952 = 3.6154。

[0125] The metallurgical effect evaluation value of this heat is 3.6154. This result is immediately displayed in the intelligent online system for converter metallurgical effect based on big data after the end of converter smelting. At the same time, the carbon emission control level Ce: 0.85; steelmaking efficiency level t: 0.906; carbon-oxygen product control level CO: 0.873; end-point temperature control level T: 0.999; end-point carbon control level C: 0.9; end-point phosphorus control level P: 0.462; slagging control level Q slag : 0.5; cost control level Cost: 0.952 are also displayed. The metallurgical effect factor value of this heat ranks in the top 12% in the evaluation in the recent month, indicating that the metallurgical effect of this heat is good, but there is still room for improvement in the end-point phosphorus and tapping slagging control, and improvement is needed.

[0126] 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 direct / indirect application in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A method for online evaluation of converter metallurgical effect based on big data, characterized in that: After the converter smelting is completed, the metallurgical effect factor of the furnace is determined online using the steelmaking raw materials and process data of the furnace. The converter smelting effect and operation level are evaluated based on the metallurgical effect factor of the furnace. The larger the metallurgical effect factor, the better the converter smelting effect and the higher the operation level.

2. The online evaluation method for converter metallurgical effect based on big data according to claim 1 is characterized in that: The metallurgical effect factor S is: S=a1×Ce+a2×t+a3×CO+a4×T+a5×C+a6×P+a7×Q slag +a8×Cost in: S is the metallurgical effect factor; a1 is the carbon emission factor; Ce is the carbon emission control level; a2 is the metallurgical efficiency factor; t is the metallurgical efficiency control level; a3 is the carbon oxygen accumulation control factor; CO is the carbon oxygen accumulation control level; a4 is the steelmaking end point temperature control factor, and T is the steelmaking end point temperature control level; a5 is the carbon control factor at the steelmaking end point, and C is the carbon control level at the steelmaking end point; a6 is the phosphorus control factor at the steelmaking end point, and P is the phosphorus control level at the steelmaking end point; a7 is the slag control factor for steelmaking, Q slag To control the slag level for steelmaking; a8 is the steelmaking cost control factor, and Cost is the steelmaking cost control level.

3. The online evaluation method for converter metallurgical effect based on big data according to claim 1 is characterized in that: Determine whether the converter smelting process and operation need 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 online evaluation method for converter metallurgical effect based on big data according to claim 1 is characterized in that: The carbon emission control level Ce is: Ce=2-{E CO2 / IN steel } / CeM in: is carbon dioxide emissions, tons; W steel is the steel output, tons; CeM is the maximum carbon emission in the recent cycle; CO2 emissions The expression is as follows: Where: M i The amount of raw and auxiliary materials i consumed in converter smelting, including slag-making materials and consumed gas, unit i; K i is the carbon emission factor corresponding to auxiliary material i, tCO2 / unit i; W HM is the amount of molten iron consumed, tons; [%C] HM is the carbon content of molten iron, wt%; W scrap is the amount of scrap steel consumed, tons; [%C] scrap is the carbon content of scrap steel, wt%; W steel is the amount of molten steel, tons; [%C] is the percentage of carbon in the molten steel at the end of smelting, wt%; V 蒸汽 is the amount of steam recovered, t; K 蒸汽 is the carbon emission factor of steam, tCO2 / t; V 煤气 is the amount of gas recovered, m 3 ; K 煤气 is the carbon emission factor of coal gas, tCO2 / m 3 ; W slag is the amount of slag generated, tons; K slag is the carbon emission factor of slag, tCO2 / t.

5. The online evaluation method for converter metallurgical effect based on big data according to claim 1 is characterized in that: Steelmaking efficiency level t, carbon oxygen content control level CO, steelmaking end temperature control level T, steelmaking end carbon control level C, steelmaking end phosphorus control level P, steelmaking slag control level Q slag The expressions are as follows: t=2-Δt / tM Where: Δt is the smelting time, minutes; tM is the shortest converter smelting time in the last cycle, minutes; CO=2-[C%][O%] / [C%O%]M Where: [%C] is the percentage of carbon in the molten steel at the end of smelting, wt%; [%O] is the percentage of oxygen in the molten steel at the end of smelting, wt%; [C%O%]M is the minimum carbon-oxygen product of the molten steel at the end of the converter in the last cycle; T=1-|T e -T g | / T g Where: T e and T g are the converter smelting end point temperature and target temperature, K respectively; C=1-|[%C]-[%C] g | / [%C] g Where: [%C] g is the target carbon content at the end point of converter smelting, wt%; P = 2 - [%P] / [%P] M Where: [%P] is the phosphorus content of molten steel at the end of converter smelting, wt%; [%P]M is the minimum phosphorus content of the steel liquid at the end of the converter in the last cycle, wt%; Q slag =2-Q / QM Where: Q is the slag amount for converter steelmaking, tons; QM is the minimum slag amount for converter steelmaking in the past cycle, tons.

6. The online evaluation method for converter metallurgical effect based on big data according to claim 1 is characterized in that: The expression of steelmaking cost control level Cost is as follows: Cost=2-Co / Cog Where: Co is the actual cost of converter smelting, RMB; Cog is the lowest cost of converter smelting in the recent period, RMB; The actual cost Co of converter smelting is expressed as follows: Where: Q i The amount of raw and auxiliary materials i consumed in converter smelting, including the amount of molten iron, scrap steel, slag material, consumed gas, and the number of oxygen and carbon measuring probes, unit i; Pr i is the unit price of the amount of raw and auxiliary materials i consumed in converter smelting, RMB / unit i; W 蒸汽 is the amount of steam recovered, t; Pr 蒸汽 is the unit price of steam, yuan / t; V 煤气 is the amount of gas recovered, m 3 ; Pr 煤气 is the unit price of gas, yuan / m 3 ; is the carbon tax on carbon dioxide emissions, yuan / tCO2.

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