KR101245307B1 - Forcasting methods for carbon composition of pig iron - Google Patents

Abstract

The present invention is a method for predicting the content ratio of carbon in the molten iron, using the raw data known to the molten iron temperature and the content of carbon in the molten iron at that time to obtain the fuzzy function defined by F1, F2, F3, A plant fuzzy model defined as Equation 1 and Equation 2 below using manganese, sulfur, silicon, silicon, and titanium as variables, predicts the carbon content in the molten iron.

[Equation 1]

If the molten iron temperature is F1, Y ₁ = a ₁ + a ₁₁ x ₁₁ + a ₁₂ x ₁₂ + ........ + x _1n

If the molten iron temperature is F2, Y ₂ = a ₂ + a ₂₁ x ₂₁ + a ₂₂ x ₂₂ + ........ + x _2n

If the molten iron temperature is F3, Y3 = a ₃ + a ₃₁ x ₃₁ + a ₃₂ x ₃₂ + ........ + x _3n

&Quot; (2) "

Y = Y ₁ in the range of [melting temperature ≤ 1305 (° C)].

Y = {Y ₁ × (1345-x) / 40} + {Y ₂ × (x-1305) / 40} in the range of [1305 (° C) <melting temperature ≤ 1345 (° C)]

Y = {Y ₂ × (1385-x) / 40} + {Y ₃ × (x-1345) / 40} in the range of [1345 (° C.) <Melting point temperature ≦ 1385 (° C.)]

Y = Y ₃ in the range of [1385 (° C) <molten iron temperature].

Molten iron, carbon, plant fuzzy model

Description

Method of predicting carbon content ratio in molten iron {FORCASTING METHODS FOR CARBON COMPOSITION OF PIG IRON}

1 is a conceptual diagram illustrating a prediction method according to the present invention.

2 is a graph illustrating a molten iron temperature as a fuzzy function.

Figure 3 is a graph showing the results predicted by the plant fuzzy model of the present invention.

The present invention relates to a method for easily predicting the carbon content of molten iron from the plant purge model using the molten iron temperature and the component ratios of phosphorus, manganese, sulfur, silicon, and titanium contained in the molten iron.

In general, the content components and temperature control of the product are made to meet the components of the product required by the customer in the steelmaking process. Above all, accurate prediction of carbon among the components of the molten iron is necessary to efficiently carry out the drilling work to lower the carbon ratio of the molten iron to the level required by the customer.

Inaccurate prediction of carbon content in molten iron leads to inaccurate control of temperature and composition of the molten iron during blow control, and this inadequate control of temperature and composition of molten metal leads to final product quality and additional work, resulting in lower productivity, quality It spreads to phenomena such as poor and unbalanced process flow.

Therefore, although it is necessary to accurately predict the proportion of carbon in the molten iron in order to minimize the decrease in productivity due to poor control at the work site of the molten metal, phosphorus, manganese, silicon, sulfur, etc. are easily As can be expected, the proportion of carbon is not.

The present invention was devised to solve the above problems, and it is easy to predict carbon content of molten iron from the plant fuzzy model using the ratio and temperature of phosphorus, manganese, sulfur, silicon, and titanium, which are easily known among molten iron components. To provide an invention.

In order to achieve the above object, the method of predicting the carbon component in the molten iron provided by the present invention,

The molten iron temperature and the content of carbon in the molten iron are then fuzzy-functioned using known raw data, and phosphorus, manganese, sulfur, silicon, silicon, and titanium in molten iron are defined as Equations 1 and 2 below. The estimated plant purge model is used to predict the proportion of carbon in the molten iron.

[Equation 1]

If molten iron temperature is F1, then Y ₁ = a ₁ + a ₁₁ x ₁₁ + a ₁₂ x ₁₂ + ........ + x _1n

If molten iron is F2, then Y ₂ = a ₂ + a ₂₁ x ₂₁ + a ₂₂ x ₂₂ + ........ + x _2n

If molten iron is F3, then Y3 = a ₃ + a ₃₁ x ₃₁ + a ₃₂ x ₃₂ + ........ + x _3n

&Quot; (2) &quot;

If molten iron temperature ≤ 1305 (℃), Y = Y ₁

If 1305 (℃) <molten iron temperature ≤ 1345 (℃), Y = {Y ₁ × (1345-x) / 40} + {Y ₂ × (x-1305) / 40}

If 1345 (℃) <molten iron temperature ≤1385 (℃), Y = {Y ₂ × (1385-x) / 40} + {Y ₃ × (x-1345) / 40}

If 1385 (℃) <Melting Temperature, Y = Y ₃

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.

1 is a conceptual diagram illustrating a prediction method of the present invention.

Referring to this, the prediction method of the present invention proceeds to step S10 of fuzzy functioning the molten iron temperature.

The molten iron temperature used here is what was collected by carrying out the past blow job at an industrial site, and the molten iron temperature and the ratio of carbon content in molten iron at that time are related. The more the molten iron temperature used in this purge-functionalizing step (S10), the more accurate the molten iron temperature and the carbon content ratio in the molten iron can be obtained as shown in FIG. 2 is a graph illustrating a molten iron temperature as a fuzzy function. In the embodiment of the present invention has three functions for each section according to the molten iron temperature.

The molten iron temperature up to 1345 (° C) is defined by the F1 function, which produces a transition point at 1305 (° C), before which it has a constant value but decreases linearly with respect to the conversion point.

The molten iron temperature of 1305 (℃) to 1385 (℃) is defined as the F2 function, and at 1345 (℃), the conversion point is increased linearly and decreases linearly based on the conversion point.

After the molten iron temperature 1345 (° C) is defined as an F3 function, 1385 (° C) rises linearly before the conversion point and has a constant value after the conversion point.

Next, in the plant fuzzy model defined by Equations 1 and 2 below, the ratio of the phosphorus, manganese, sulfur, silicon, and titanium and the molten iron temperature of the molten iron at the time point to be known are obtained. It may be (S20, S30). 3 is a graph showing the results predicted by the plant fuzzy model below.

If molten iron temperature is F1, then Y ₁ = a ₁ + a ₁₁ x ₁₁ + a ₁₂ x ₁₂ + ........ + x _1n

If molten iron is F2, then Y ₂ = a ₂ + a ₂₁ x ₂₁ + a ₂₂ x ₂₂ + ........ + x _2n

If molten iron is F3, then Y3 = a ₃ + a ₃₁ x ₃₁ + a ₃₂ x ₃₂ + ........ + x _3n

If molten iron temperature ≤ 1305 (℃), Y = Y ₁

If 1305 (℃) <molten iron temperature ≤ 1345 (℃), Y = {Y ₁ × (1345-x) / 40} + {Y ₂ × (x-1305) / 40}

If 1345 (℃) <molten iron temperature ≤1385 (℃), Y = {Y ₂ × (1385-x) / 40} + {Y ₃ × (x-1345) / 40}

If 1385 (℃) <Melting Temperature, Y = Y ₃
Here, Y ₁ represents a regression equation for predicting the ratio of carbon in the molten iron when the molten iron temperature is F1, and x ₁₁ to x _1n represent phosphorus (P), manganese (Mn), and sulfur (S) in molten iron. , The ratio of silicon (Si) and titanium (Ti) and the molten iron temperature, a ₁ , a ₁₁ , a ₁₂ .... means the coefficient of the regression equation predicting the carbon (C) ratio in the molten iron.
Y ₂ is a regression equation for predicting the ratio of carbon in molten iron when the molten iron temperature is F2, and x ₂₁ to x _2n are phosphorus (P), manganese (Mn), sulfur (S), and silicon in molten iron. (Si), the ratio of titanium (Ti) and the molten iron temperature, and a ₂ , a ₂₁ , a ₂₂ .... is the coefficient of the prediction of the regression equation of the carbon (C) ratio in the molten iron.
Y ₃ is a regression equation for predicting the carbon ratio in molten iron when the molten iron temperature is F3, and x ₃₁ to x _3n are phosphorus (P), manganese (Mn), sulfur (S), and silicon in molten iron. (Si) and the ratio of titanium (Ti) and molten iron temperature, and a ₃ , a ₃₁ , a ₃₂ .... are the coefficients of the predicted regression equation for the ratio of carbon in the molten iron.

As described above, since the plant purge model represented by Equations 1 and 2 is formulated into a multiple regression model, it can be derived as a regression equation. In this case, the constants of Equations 1 and 2 are obtained through a least square method that minimizes the square of the error. Here, the error is the difference between the molten iron temperature obtained through the drilling operation in the past industrial field and the actual value and the predicted value of carbon, phosphorus, manganese, silicon, phosphorus, sulfur and titanium associated with this temperature.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. It will be understood that various modifications and changes may be made without departing from the scope of the appended claims.

According to the present invention, it is possible to easily obtain the content ratio of carbon using phosphorus, manganese, sulfur, silicon, and titanium, which can solve the above-mentioned problems and easily obtain the content ratio of molten iron.

Claims (2)

The fuzzy function defined as F1, F2, F3 is obtained by using the raw data of the molten iron temperature and the ratio of carbon content in the molten iron at that time. The plant fuzzy model defined as 1 and Equation 2 is solved to predict the proportion of carbon in the molten iron, The molten iron temperature up to 1345 ℃ is defined by the F1 function, the molten iron temperature from 1305 ℃ to 1385 ℃ is defined by the F2 function, and the molten iron temperature after 1345 ℃ is defined by the F3 function. The F1 function makes a conversion point at 1305 ° C, before that it has a constant value but decreases linearly with respect to the conversion point, The F2 function makes a transition point at 1345 ° C and increases linearly before then decreases linearly with respect to the conversion point, The F3 function rises linearly at 1385 ° C before the transition point. After the conversion point, a method for predicting the proportion of carbon in the molten iron having a constant value. [Equation 1] If the molten iron temperature is F1, Y ₁ = a ₁ + a ₁₁ x ₁₁ + a ₁₂ x ₁₂ + ........ + x _1n If the molten iron temperature is F2, Y ₂ = a ₂ + a ₂₁ x ₂₁ + a ₂₂ x ₂₂ + ........ + x _2n If the molten iron temperature is F3, Y3 = a ₃ + a ₃₁ x ₃₁ + a ₃₂ x ₃₂ + ........ + x _3n &Quot; (2) &quot; Y = Y ₁ in the range of [melting temperature ≤ 1305 (° C)]. Y = {Y ₁ × (1345-x) / 40} + {Y ₂ × (x-1305) / 40} in the range of [1305 (° C) <melting temperature ≤ 1345 (° C)] Y = {Y ₂ × (1385-x) / 40} + {Y ₃ × (x-1345) / 40} in the range of [1345 (° C.) <Melting point temperature ≦ 1385 (° C.)] Y = Y ₃ in the range of [1385 (° C) <molten iron temperature]. Here, Y ₁ represents a regression equation for predicting the ratio of carbon in the molten iron when the molten iron temperature is F1, and x ₁₁ to x _1n represent phosphorus (P), manganese (Mn), and sulfur (S) in molten iron. , The ratio of silicon (Si) and titanium (Ti) and the molten iron temperature, a ₁ , a ₁₁ , a ₁₂ .... is the coefficient of the regression equation predicting the carbon (C) ratio in the molten iron, Y ₂ is a regression equation for predicting the ratio of carbon in molten iron when the molten iron temperature is F2, and x ₂₁ to x _2n are phosphorus (P), manganese (Mn), sulfur (S), and silicon in molten iron. (Si), the ratio of titanium (Ti) and the molten iron temperature, a ₂ , a ₂₁ , a ₂₂ .... is the coefficient of the predictive regression equation of carbon (C) ratio in the molten iron, Y ₃ is a regression equation for predicting the carbon ratio in molten iron when the molten iron temperature is F3, and x ₃₁ to x _3n are phosphorus (P), manganese (Mn), sulfur (S), and silicon in molten iron. (Si) and the ratio of titanium (Ti) and molten iron temperature, and a ₃ , a ₃₁ , a ₃₂ .... are the coefficients of the predicted regression equation for the ratio of carbon in the molten iron. delete

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