Dynamic adjustment method for optimal molten iron scrap ratio of converter

Abstract

The invention discloses a converter optimal molten iron scrap ratio dynamic adjustment method which is characterized by comprising the following steps: monitoring the temperature and chemical component data of molten iron entering a converter in real time; establishing a prediction model based on historical data and an intelligent algorithm, and predicting the molten iron temperature and chemical component change trend; calculating first influence data of the scrap steel adding amount on the molten iron temperature according to the physical thermal correction model, and calculating second influence data of element oxidation heat on the total heat according to the chemical thermal correction model; and the molten iron scrap ratio is dynamically adjusted according to the results of the first influence data and the second influence data, and the generated scrap ratio is transmitted to a converter control system in real time. According to the prediction model based on historical data and an intelligent algorithm, the temperature and component change trend of the molten iron is predicted in advance, so that the scrap ratio is adjusted in advance, the steelmaking efficiency is improved, the cost is reduced, and the environmental influence is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of converter smelting, in particular to a method for dynamically adjusting the optimal molten iron to scrap steel ratio of a converter. Background Art

[0002] The scrap ratio refers to the weight ratio of scrap steel to molten iron during the steelmaking process. The scrap ratio is a core economic and technical indicator in the converter production process. It has a direct impact on the temperature and composition control at the converter end point, and is therefore related to the optimization of multiple key economic and technical indicators. A reasonable ratio of molten iron to scrap steel is of great significance for reducing costs and improving production efficiency. However, in actual operation, due to the large fluctuations in the composition and temperature of molten iron, and the fact that scrap steel is usually pre-configured in the warehouse and used according to the established order of iron tapping, the adjustment of the scrap steel ratio faces a series of challenges:

[0003] 1) The adjustment of scrap ratio often cannot respond immediately to the temperature measurement and chemical composition changes before entering the furnace, resulting in the inability to quickly adapt to changes in molten iron quality.

[0004] 2) When different operators adjust the scrap steel ratio, due to differences in professional theoretical level and practical operation experience, their adjustment standards and methods are inconsistent.

[0005] 3) When the temperature and composition of the molten iron fluctuate greatly, the operator's response may not be timely enough and the adjustment accuracy may not be high, which will affect the effectiveness and efficiency of the converter smelting process. Summary of the Invention

[0006] In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method for dynamically adjusting the optimal molten iron-scrap ratio of the converter by establishing a prediction model based on historical data and intelligent algorithms to predict the temperature and composition change trends of molten iron in advance, thereby adjusting the scrap ratio in advance to improve steelmaking efficiency, reduce costs and reduce environmental impact.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for dynamically adjusting the optimal molten iron to scrap ratio of a converter, comprising the following steps:

[0008] Real-time monitoring of the temperature and chemical composition of molten iron entering the furnace;

[0009] Build a prediction model based on historical data and intelligent algorithms to predict the changing trends of molten iron temperature and chemical composition;

[0010] Calculating first impact data of scrap steel addition on molten iron temperature based on a physical heat correction model, and calculating second impact data of element oxidation heat on total heat based on a chemical heat correction model;

[0011] The molten iron scrap steel ratio is dynamically adjusted according to the results of the first influencing data and the second influencing data, and the generated scrap steel ratio is transmitted to the converter control system in real time.

[0012] As a further improvement of the present invention: the intelligent algorithm is used to screen the reference heats in the historical data that match the current molten iron tank parameters, and predict the change trend of the molten iron temperature and chemical composition of the current molten iron tank based on the molten iron temperature and chemical composition change data of the reference heats.

[0013] As a further improvement of the present invention: the historical data screening conditions include: based on the target molten iron tank iron tapping temperature ±5°C, the molten iron tank tare weight ±0.5 tons, the molten iron tank scrap steel addition amount ±0.5t, and the molten iron tank turnover time error range ±10min, the historical database is screened to determine the molten iron tank reference heat that meets the conditions.

[0014] As a further improvement of the present invention, the prediction of the current molten iron temperature and chemical composition change trend of the molten iron tank includes:

[0015] Predict the physical temperature data correction of the molten iron tank, including the correction of the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel mill and the correction of the effect of the iron tapping temperature of the molten iron tank on the inlet temperature of the steel mill.

[0016] As a further improvement of the present invention, the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant is corrected by using a physical thermal correction model to calculate the effect of each increase or decrease in room temperature scrap steel on the molten iron temperature. In the physical thermal correction model, the formula Q = m·c·ΔT is used, where m is the mass of the scrap steel entering the furnace, c is the specific heat capacity of steel 0.49k / kg·℃, and ΔT is the temperature difference.

[0017] As a further improvement of the present invention, the correction of the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant is based on the physical thermal correction model, which shows that every increase or decrease of 0.5 tons of room temperature scrap steel causes the temperature of 100 tons of molten iron to increase or decrease by 8.3°C.

[0018] As a further improvement of the present invention, the prediction of the current molten iron temperature and chemical composition change trend of the molten iron tank further includes:

[0019] Predict chemical composition corrections for hot metal ladle, including temperature and heat corrections based on chemical composition data deviations.

[0020] As a further improvement of the present invention: the temperature and heat correction based on the chemical composition data deviation includes: when the molten iron tank is full, obtaining the chemical composition data of the molten iron in the current molten iron tank, and selecting the converter number heat with the smallest composition error with the composition data of the molten iron in the current molten iron tank from the historical data as the reference heat.

[0021] As a further improvement of the present invention: the temperature and heat correction based on the chemical composition data deviation also includes: using a chemical heat correction model to correct the impact of changes in molten iron elements on the heat in the converter furnace, and calculating the scrap steel ratio by predicting the physical temperature and chemical heat entering and leaving the furnace. In the chemical heat correction model, the formula ΔT = ∑ (element mass change × unit oxidation heat) is used, and ΔT is the temperature difference.

[0022] As a further improvement of the present invention: the correction of the influence of the molten iron ladle tapping temperature on the steel plant furnace temperature includes subtracting the target molten iron ladle tapping temperature from the molten iron ladle tapping temperature that meets the historical database screening conditions.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention uses a prediction model based on historical data and intelligent algorithms to predict the temperature and composition change trends of molten iron in advance, thereby adjusting the scrap steel ratio in advance to improve steelmaking efficiency, reduce costs and minimize environmental impact.

[0025] 2. The present invention introduces an automated control system, which monitors the temperature and composition of molten iron in real time, dynamically adjusts the scrap steel ratio, and reduces the uncertainty of human intervention. Combining real-time monitoring data and intelligent algorithms can improve production efficiency, establish a prediction model based on historical data and intelligent algorithms, and predict the temperature and composition change trends of molten iron in advance to dynamically calculate and adjust the optimal ratio of molten iron to scrap steel, thereby improving the converter endpoint hit rate and reducing steel material consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0027] In order to enable a clear and complete understanding of the technical solution, the present invention is further described in conjunction with the embodiments and drawings. Obviously, the described embodiments are only some embodiments of the present invention, and all other embodiments obtained by technical personnel in the relevant field without making creative work are within the scope of protection of the present invention.

[0028] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0029] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] An embodiment of the present invention provides a method for dynamically adjusting the optimal molten iron to scrap ratio of a converter, comprising the following steps:

[0032] Real-time monitoring of the temperature and chemical composition of molten iron entering the furnace;

[0033] Build a prediction model based on historical data and intelligent algorithms to predict the changing trends of molten iron temperature and chemical composition;

[0034] Calculating first impact data of scrap steel addition on molten iron temperature based on a physical heat correction model, and calculating second impact data of element oxidation heat on total heat based on a chemical heat correction model;

[0035] The molten iron scrap steel ratio is dynamically adjusted according to the results of the first influencing data and the second influencing data, and the generated scrap steel ratio is transmitted to the converter control system in real time.

[0036] The present invention is based on a prediction model of historical data and intelligent algorithms, predicts the temperature and composition change trends of molten iron in advance, introduces an automated control system, monitors the temperature and composition of molten iron in real time, dynamically adjusts the scrap steel ratio, and reduces the uncertainty of human intervention. Combining real-time monitoring data and intelligent algorithms can improve production efficiency, establish a prediction model based on historical data and intelligent algorithms, predict the temperature and composition change trends of molten iron in advance, and dynamically calculate and adjust the optimal ratio of molten iron to scrap steel, thereby improving the converter endpoint hit rate and reducing steel material consumption.

[0037] In one embodiment of the present invention, the intelligent algorithm is used to screen the reference heats in the historical data that match the current molten iron tank parameters, and predict the change trend of the molten iron temperature and chemical composition of the current molten iron tank based on the molten iron temperature and chemical composition change data of the reference heats.

[0038] In one embodiment of the present invention, the historical data screening conditions include: based on the target molten iron tank tapping temperature ±5°C, the molten iron tank tare weight ±0.5 tons, the amount of scrap steel added to the molten iron tank ±0.5t, and the molten iron tank turnover time error range ±10min, the historical database is screened to determine the reference heat of the molten iron tank that meets the conditions.

[0039] Furthermore, the prediction of the current trend of changes in the molten iron temperature and chemical composition of the molten iron tank includes: predicting the correction of the physical temperature data of the molten iron tank, including the correction of the impact of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant and the correction of the impact of the molten iron tank tapping temperature on the inlet temperature of the steel plant. The correction of the impact of the molten iron tank tapping temperature on the inlet temperature of the steel plant includes the tapping temperature of the molten iron tank that meets the historical database screening conditions and minus the target molten iron tank tapping temperature.

[0040] Furthermore, the correction of the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant is carried out by using a physical thermal correction model to calculate the effect of each increase or decrease in room temperature scrap steel on the molten iron temperature. In the physical thermal correction model, the formula Q = m·c·ΔT is used, where m is the mass of the scrap steel entering the furnace, c is the specific heat capacity of steel (0.49k / kg·℃), and ΔT is the temperature difference.

[0041] Furthermore, the correction result of the physical thermal correction model for the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant is that every increase or decrease of 0.5 tons of room temperature scrap steel will cause the temperature of 100 tons of molten iron to increase or decrease by 8.3°C.

[0042] In this embodiment, since scrap steel is usually pre-configured in the warehouse, it is necessary to judge the temperature of the molten iron entering the furnace in advance to support the scrap steel ratio plan. The main influencing factors of the temperature of the molten iron entering the furnace include: the iron-making plant's iron-discharging temperature, the tare weight of the molten iron tank (the weight of the molten iron tank itself), the amount of scrap steel added to the molten iron tank, the empty molten iron tank turnover time (the time from the end of iron-adding at the steelmaking plant to the start of iron-adding at the ironmaking plant), and the heavy molten iron tank turnover time (the time from the end of iron-discharging at the ironmaking plant to the start of iron-adding at the steelmaking plant). The different physical heat of the molten iron arriving at the steelmaking plant is different.

[0043] This embodiment screens historical data based on the target molten iron tank tapping temperature ±5°C, molten iron tank tare weight ±0.5t, molten iron tank scrap addition amount ±0.5t, molten iron empty tank turnover time ±10min, molten iron heavy tank turnover time ±10min, and determines the molten iron tank times that meet specific conditions through screening through the historical database. The temperature of the molten iron tank times with specific conditions when entering the furnace is used as the predicted target molten iron physical temperature. There is a deviation between the molten iron tank times with specific conditions and the actual tank times entering the furnace data, so the deviation needs to be further corrected.

[0044] Correction of predicted hot metal ladle physical temperature data: Correction of the effect of scrap steel addition to the hot metal ladle on the steel mill inlet temperature: Calculate the effect of adding or removing 0.5 tons of room temperature scrap steel on the temperature of 100 tons of hot metal. The specific heat capacity of steel is generally around 0.49k / kg·C. The temperature drop can be calculated using the following formula:

[0045] Q=m·c·ΔT

[0046] Where m is the mass of scrap steel entering the furnace, c is the specific heat capacity of steel (0.49k / kg·℃), ΔT is the temperature difference,

[0047] In one embodiment, the mass of the scrap steel m = 0.5 tons = 500 kg, the temperature difference ΔT = 1270°C;

[0048] Assuming that the specific heat capacity of molten iron is the same as that of scrap steel, that is, c = 0.49 kJ / kg·C, first calculate the heat absorbed by the scrap steel at room temperature, and use Q = m·c·ΔT to calculate the heat of 0.5 tons of molten iron. It can be obtained that the heat capacity of molten iron Q = 406,371.5 kJ,

[0049] By calculating the effect of this heat on the temperature of 100 tons of molten iron, the mass of 100 tons of molten iron is:

[0050] m molten iron = 100 tons = 100,000 kg. Due to the law of conservation of heat, the heat absorbed by the scrap steel will be equal to the heat lost by the molten iron. Therefore, the temperature drop of the molten iron can be calculated by the following formula:

[0051] ΔT molten iron = Q / (m molten iron·C)

[0052] ΔT molten iron ≈ 8.3℃

[0053] It was found that every increase or decrease of 0.5 tons of room temperature scrap steel would cause the temperature of 100 tons of molten iron to increase or decrease by about 8.3℃.

[0054] The temperature drop of the molten iron tank during turnover is small due to the covered method and the turnover mode is fixed, so the temperature influence within ±10 minutes of the molten iron tank turnover time is not corrected.

[0055] Correction of the influence of molten iron ladle tapping temperature on steel mill inlet temperature: the tapping temperature of the molten iron ladle that meets the historical database screening conditions at the same time - the target tapping temperature of the molten iron ladle.

[0056] In one embodiment of the present invention, the predicting of the current trend of change of the molten iron temperature and chemical composition in the molten iron tank further includes:

[0057] Predict chemical composition corrections for hot metal ladle, including temperature and heat corrections based on chemical composition data deviations.

[0058] Furthermore, the temperature and heat correction according to the chemical composition data deviation includes: when the molten iron tank is full, obtaining the chemical composition data of the molten iron in the current molten iron tank, and selecting the converter heat with the smallest composition error with the composition data of the molten iron in the current molten iron tank from the historical data as the reference heat.

[0059] Furthermore, the temperature and heat correction based on the chemical composition data deviation also includes: using a chemical heat correction model to correct the impact of changes in molten iron elements on the heat in the converter furnace, and calculating the scrap steel ratio by predicting the physical temperature and chemical heat entering and leaving the furnace. In the chemical heat correction model, the formula ΔT = ∑ (element mass change × unit oxidation heat) is used, and ΔT is the temperature difference.

[0060] In this embodiment, at the ironworks, after the molten iron ladle is full, personnel collect samples from the molten iron. These samples are then sent to a laboratory for detailed analysis to determine the contents of elements such as carbon, silicon, manganese, phosphorus, sulfur, and titanium. Based on the analysis results, the converter heat with the smallest deviation from the target composition is selected as a reference heat. Due to the inherent variations in chemical elements, further correction for thermal effects is required. The planned scrap ratio for the desired heat is then calculated based on the first pour temperature and first pour composition of the reference heat, combined with the physical heat of the molten iron.

[0061] The effect of changes in molten iron elements on the heat inside the converter is corrected by using the following formula:

[0062] ΔT = ∑(percent change in element mass * unit heat of oxidation * total mass of molten iron) = ∑(change in element mass × unit heat of oxidation);

[0063] Take 100 tons of molten iron as an example:

[0064] Oxidation heat per unit mass of carbon: 32.7MJ / kg

[0065] Oxidation heat per unit mass of silicon: 14.5MJ / kg

[0066] Oxidation heat per unit mass of manganese: 10.5MJ / kg

[0067] Oxidation heat per unit mass of phosphorus: 6.5MJ / kg

[0068] Oxidation heat per unit mass of sulfur: 9.3MJ / kg

[0069] Oxidation heat per unit mass of titanium: 18.5MJ / kg

[0070] Calculate the scrap steel ratio by predicting the physical temperature and chemical heat of the furnace in and out in advance:

[0071] Temperature difference X: (predicted molten iron ladle entry temperature - reference heat molten iron ladle entry temperature) + {(molten iron silicon content - reference heat molten iron silicon content) * 3.2} + {(molten iron carbon content - reference heat molten iron carbon content) * 1.5} + {(molten iron titanium content - reference heat molten iron titanium content) * 3.1} + {(molten iron net weight - reference heat molten iron net weight) * 6.3} + {((predicted heat return ore addition amount + predicted heat limestone addition amount) - pre-set this furnace (return ore + limestone addition amount)) * 32} + (predicted heat first pouring temperature - pre-set this furnace first pouring temperature) + {(pre-set pig iron block addition amount - predicted heat pig iron block addition amount) * 6.5}

[0072] The scrap steel amount of this furnace is calculated as: X / 11.6 + predicted scrap steel amount of the furnace.

[0073] The scrap steel ratio refers to the weight ratio of scrap steel to molten iron during the steelmaking process. The scrap steel ratio is calculated based on the amount of scrap steel and molten iron in the furnace, and the generated scrap steel ratio is transmitted to the converter control system in real time.

[0074] The present invention uses a prediction model based on historical data and intelligent algorithms to predict the temperature and composition of molten iron entering the furnace in advance, thereby adjusting the scrap steel ratio in advance. It can quickly adapt to large changes in the physical and chemical heat of the molten iron, reduce the impact on converter smelting, improve the endpoint hit rate, and reduce steel material consumption.

[0075] The scrap steel ratio is calculated by combining the heat changes of chemical elements with the actual on-site smelting situation. It replaces manual experience with theoretical calculations and has the characteristics of high accuracy and stability.

[0076] In summary, after reading the present invention document, ordinary technicians in this field can make various other corresponding transformation schemes based on the technical solutions and technical concepts of the present invention without creative mental work, and all of them fall within the scope of protection of the present invention.

Claims

1. A method for dynamically adjusting the optimal ratio of molten iron to scrap steel in a converter, characterized in that: The following steps are involved: Real-time monitoring of the temperature and chemical composition of molten iron entering the furnace; Build a prediction model based on historical data and intelligent algorithms to predict the changing trends of molten iron temperature and chemical composition; Calculating first impact data of scrap steel addition on molten iron temperature based on a physical heat correction model, and calculating second impact data of element oxidation heat on total heat based on a chemical heat correction model; The molten iron scrap steel ratio is dynamically adjusted according to the results of the first influencing data and the second influencing data, and the generated scrap steel ratio is transmitted to the converter control system in real time.

2. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 1, characterized in that: The intelligent algorithm is used to screen the reference heats in the historical data that match the current molten iron tank parameters, and predict the change trend of the molten iron temperature and chemical composition of the current molten iron tank based on the molten iron temperature and chemical composition change data of the reference heats.

3. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 2, characterized in that: The historical data screening conditions include: based on the target molten iron tank tapping temperature ±5°C, the molten iron tank tare weight ±0.5 tons, the amount of scrap steel added to the molten iron tank ±0.5t, and the molten iron tank turnover time error range ±10min, the historical database is screened to determine the molten iron tank reference heat that meets the conditions.

4. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 3, characterized in that: The prediction of the current trend of the molten iron temperature and chemical composition in the molten iron tank includes: Predict the physical temperature data correction of the molten iron tank, including the correction of the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel mill and the correction of the effect of the iron tapping temperature of the molten iron tank on the inlet temperature of the steel mill.

5. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 4, characterized in that: The correction of the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant is performed by using a physical thermal correction model to calculate the effect of each increase or decrease in room temperature scrap steel on the molten iron temperature. In the physical thermal correction model, the formula Q = m·c·ΔT is used, where m is the mass of the scrap steel entering the furnace, c is the specific heat capacity of steel (0.49k / kg·℃), and ΔT is the temperature difference.

6. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 5, characterized in that: The correction of the effect of the amount of scrap steel added to the molten iron tank on the inlet temperature of the steel plant, the correction result of the physical thermal correction model is that every increase or decrease of 0.5 tons of room temperature scrap steel will cause the temperature of 100 tons of molten iron to increase or decrease by 8.3°C.

7. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 2, characterized in that: The prediction of the current trend of the temperature and chemical composition of the molten iron in the molten iron tank also includes: Predict chemical composition corrections for hot metal ladle, including temperature and heat corrections based on chemical composition data deviations.

8. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 7, characterized in that: The temperature and heat correction according to the chemical composition data deviation includes: when the molten iron tank is full, obtaining the chemical composition data of the molten iron in the current molten iron tank, and selecting the converter heat with the smallest composition error with the composition data of the molten iron in the current molten iron tank from the historical data as the reference heat.

9. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 8, characterized in that: The temperature and heat correction based on the chemical composition data deviation also includes: using a chemical heat correction model to correct the impact of changes in molten iron elements on the heat in the converter furnace, and calculating the scrap steel ratio by predicting the physical temperature and chemical heat entering and leaving the furnace. In the chemical heat correction model, the formula ΔT = ∑ (element mass change × unit oxidation heat) is used, where ΔT is the temperature difference.

10. The method for dynamically adjusting the optimal molten iron to scrap ratio of a converter according to claim 4, characterized in that: The correction of the influence of the molten iron ladle tapping temperature on the steel plant furnace temperature includes subtracting the target molten iron ladle tapping temperature from the molten iron ladle tapping temperature that meets the historical database screening conditions.

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