KR101185279B1 - Method for predicting of drum index of cokes - Google Patents
Method for predicting of drum index of cokes Download PDFInfo
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- KR101185279B1 KR101185279B1 KR20100028492A KR20100028492A KR101185279B1 KR 101185279 B1 KR101185279 B1 KR 101185279B1 KR 20100028492 A KR20100028492 A KR 20100028492A KR 20100028492 A KR20100028492 A KR 20100028492A KR 101185279 B1 KR101185279 B1 KR 101185279B1
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Abstract
The method for predicting cold strength of coke according to the present invention collects indicator data on quality conditions of raw coal under predetermined test conditions, and makes independent variables of coke based on indicator data on quality conditions of raw coal as independent variables. Obtaining a basic prediction model by obtaining a regression coefficient on cold strength; And collecting indicator data on operating conditions under predetermined raw coal conditions, and calculating the regression coefficients of the independent variables on the cold strength of coke based on the basic predictive model. Obtaining a model; And predicting the cold strength of the coke by the final prediction model.
Description
The present invention relates to a method for predicting cold strength of coke, and more particularly, to a method for predicting cold strength of coke capable of predicting cold strength (DI) of coke reflecting coal quality and charging conditions.
In general, the use of coke is necessary in the blast furnace steelmaking method, and various mixing indicators are utilized in the mixing design process of raw coal in order to stably supply high strength coke.
In large blast furnaces operated at high pulverized coal injection ratios and low coke rates, the cold index ("DI"), which represents the mechanical strength of the coke, is a very important indicator.
An object of the present invention is to provide a method for predicting cold strength of coke, which can accurately predict cold strength (DI) of coke reflecting coal quality and charging conditions.
Cold strength prediction method of coke according to the present invention for achieving the above object, collects the indicator data on the quality conditions of the raw coal in a predetermined test conditions, and the indicator data on the quality conditions of the raw coal as an independent variable Obtaining a basic predictive model by calculating a regression coefficient of the independent variables on the cold strength of coke; And collecting indicator data on operating conditions under predetermined raw coal conditions, and calculating the regression coefficients of the independent variables on the cold strength of coke based on the basic predictive model. Obtaining a model; And predicting the cold strength of the coke by the final prediction model.
In addition, it is preferable that the index regarding the quality condition includes the flow rate (LMF), volatile matter (VM), total expansion (TD), and total inert component (TI) of the raw coal.
In addition, it is preferable that a prediction model having an index on quality conditions as an independent variable satisfies Equation 1 below.
&Quot; (1) "
DI = 104.8-1.17 × (VM) -0.0335 × TI + 12.35 × (LMF)-0.12 × (TD)
(Correlation coefficient R 2 is 0.821 or more)
[Wherein DI is the cold strength of the manufactured coke (%), VM is the volatile content (%), LMF is the fluidity (log ddpm), TD is the total expansion (%), TI is the total inert component (%).]
Moreover, it is preferable that the index | index regarding the operating conditions of raw material coal is charge density (BD).
In addition, it is preferable that the predictive model having an index on the quality condition as an independent variable satisfies Equation 2 below.
&Quot; (2) "
DI = 104.8-1.17 × (VM) -0.0335 × TI + 12.35 × (LMF)-0.12 × (TD) + 12BD
(Correlation coefficient R 2 is 0.821 or more)
[Wherein DI is the cold strength (%) of the manufactured coke, VM is the volatile content (%), LMF is the flow rate (log ddpm), TD is the total expansion (%), TI is the total inert component (%), BD ( bulk density is the loading density (t-coal / m 3 ).]
Moreover, it is preferable that predetermined | prescribed raw coal conditions are volatile matters: 22 to 27%, and LMF: 2.0 to 3.2.
Moreover, it is preferable that predetermined test furnace conditions are charging density: 750 kg / m <3> , the moisture in a test furnace: 8.0-0.3%, and the temperature of a test furnace: 1100 degreeC.
According to the present invention, it is possible to easily predict the cold strength of the coke, and to accurately evaluate the coke quality impact through the prediction equation using the loading condition as a variable along with the quality conditions such as volatile matter (VM), flowability (LMF) and the like. Therefore, there is an advantage that the operation utilization of the coke quality prediction equation is increased.
In addition, because the cold strength of the coke can be predicted, the characteristics of the process are well reflected, the prediction accuracy is very high, and the predictive model can be easily supplemented according to the change in operating conditions. There is an advantage that can reduce the coke manufacturing cost.
That is, the present invention improves the degree of coke strength prediction equation by adding a loading density factor representing charging conditions to existing coal quality-related influence factors.
1 is a chart showing the results of the single-type quality analysis of the raw coal and carbonization test results of the blended coal species,
2 is a view of measuring cold strength (DI) for each quality-related index according to the present invention,
3 is a graph illustrating a relationship between DI predicted values and measured values using a basic predictive model according to the present invention;
4 is a view showing the measurement of the cold strength (DI) for the operating conditions related index according to the present invention.
Hereinafter, exemplary embodiments of the present invention will be described with reference to the accompanying drawings.
Coke's cold strength (DI) prediction method according to the present invention is configured to reflect the indicators related to coal quality and operating conditions of the various indicators together. That is, in the present invention, a coke quality prediction equation is derived, and an optimal influence factor is selected and quantified in order to improve the accuracy of the coke quality prediction equation.
First, in order to obtain high-strength coke in the manufacture of coke to which the present invention is applied, various compounding indicators are utilized in the process of designing the raw coal. Such blending indicators include the carbonization of coal (average reflectance of vitrinite, volatile matter (VM), etc.), cohesiveness (logarithm maximum fluidity (LMF), total dilatation (TD), ash content). Use impact factors related to coal quality, such as (ASH).
Next, when manufacturing the coke to which the present invention is applied, the bulk density of the coal in the carbonization chamber of the coke oven varies depending on the charging conditions of the coal. As a result, a quality deviation of the manufactured coke occurs.
Therefore, in the present invention, when the coke strength prediction equation is deduced in consideration of the coal quality only, the prediction accuracy is lowered, and thus the quality influence according to the charging density is also quantified and reflected in the coke quality prediction equation.
The cold strength (DI) for each quality-related index according to the present invention is utilized. This quality-related index is used as various compounding indexes in the process of designing raw coal to stably obtain high strength coke.
Such compounding indexes include carbon volatility (VM, Volatile Matter), strength index (SI), Mean Reflectance of Vitrinite of Coal Texture (RM), Indicators of cohesiveness include Log Maximum Fluidity (LMF), Composition Balance Index (CBI), Total Dilatation (TD), and Total Inert (TI). Ash (ASH) and the like.
The blending indicators are blended so as to fall within a predetermined blending range, and the blending design of the raw coal is performed in consideration of the quality condition of the coke, for example, the cold strength (DI) of the manufactured coke, within the target range.
Therefore, in the present invention, in order to secure a high-quality coke in a stable manner, actual data on coal carbonization, coking property, and ash are collected from the operation data, and the regression coefficient on the cold strength of the coke is obtained from these data. Basic prediction model of coke cold strength was obtained.
First, according to the method for predicting the cold strength of coke according to an embodiment of the present invention, various data on the mixing index of raw coal under predetermined test conditions, for example, the carbonization of coal, the index indicating the cohesiveness and ash Collect actual data for
Specifically, the data on the blending indicators include volatilized Matter (VM) as an indicator of the carbonization of coal, and Log Maximum Fluidity (LMF), Total Dilatation (TD), Total Inert (TI)
After collecting various data on the mixing index of raw coal, the cold coke (DI) of manufactured coke is used as a dependent variable, and the statistical significance level among the collected coke is analyzed to analyze the cold coke (DI). Choose correlated factors as independent variables.
1 is a chart showing the results of a single type quality analysis of raw coal and the carbonization test results of blended coal types.
Fig. 1 shows the results of a single type quality analysis of raw coal and the carbonization test result of blended coal species. Specifically, in the coke test furnace in which 40 kg of raw coal is charged, 12 single coal species and 9 mixed coals are tested. Drying under conditions, the quality of the coal and the cold strength (DI; 150/15,%) of the coke produced were measured. Here, the predetermined test furnace conditions were the charging density: 750 kg / m 3 , the moisture in the test furnace: 8.003%, and the temperature of the test furnace (drying temperature): 1100 ° C. Here, one single coal and one mixed coal are exemplarily illustrated.
This is to prepare the coke in a test furnace (test oven) by mixing the raw coal in various mixing methods, and to obtain the cold strength (DI) of the coke in order to evaluate the quality of the manufactured coke, the cold strength (DI) of the coke If you use multiple overlapping indices (eg, LMF, TD, TI) to predict, the prediction equation becomes complicated.
Therefore, the flow rate (LMF), volatile matter (VM), total dilatation (TD) and total inert component (TI) of raw coal which can be easily measured and correlated with the cold strength (DI) of coke are Select as an independent variable.
2 is a view showing the measurement of the cold strength (DI) for each quality-related index according to the present invention.
2, in the present invention, a basic prediction model was obtained by obtaining a regression coefficient of the independent variable of raw coal on the cold strength of coke.
Specifically, step-by-step regression analysis of the relationship between the independent variables of raw coal (LMF), volatile matter (VM) and total expansion (TD, Total Dilatation), total inert (TI) and cold strength of coke ( Stepwise regression analysis). This manner will be easily understood by those skilled in the art, and thus detailed descriptions are omitted.
In addition, the regression coefficient of VM, the regression coefficient of LMF, the regression coefficient of total expansion (TD), and the regression coefficient of total inert component (TI) can be obtained from the regression analysis. A predetermined prediction model between the dependent variables is obtained, and the prediction model satisfies Equation 1 below.
[Equation 1]
DI = 104.8-1.17 × (VM) -0.0335 × TI + 12.35 × (LMF)-0.12 × (TD)
(Correlation coefficient R 2 is 0.821 or more)
Here, DI is the cold strength (%) of the coke prepared, VM is the volatile content (%), LMF is the flow rate (log ddpm), TD is the total expansion (%), TI means the total inert component (%).
According to the basic prediction model calculated as described above, there is an advantage that can predict the cold strength (%) of the coke using quality-related indicators.
3 is a graph illustrating a relationship between DI predicted values and actual measured values using a basic predictive model.
3 is a graph showing the relationship between the DI predicted value and the measured value using the basic predictive model, and the straight line L is a straight line showing the case where the DI predicted value and the measured value are the same. This shows that most of the predictions of cold strength according to quality-related indices coincide.
In FIG. 3, reference numeral A and reference numeral B denote portions in which the DI predicted value and the measured value are partially inconsistent. The discrepancy between the DI predicted value and the measured value varies in the bulk density of the coal in the carbonization chamber of the coke oven according to the charging condition of the coal. As a result, a quality deviation of the manufactured coke occurs.
4 is a view showing the measurement of the cold strength (DI) for the operating conditions related index according to the present invention.
Therefore, in the present invention, when deriving the coke strength prediction equation considering only coal quality, the prediction accuracy is lowered, so that the final prediction equation is derived by quantifying the quality impact according to the loading density and reflecting it in the coke quality prediction equation. Referring to Figure 4, it can be seen that the cold strength DI is affected by the loading density (B) which is an indicator of the operating conditions. Here, it can be seen that reference numeral M of FIG. 4 denotes a linearity of the charging density B and the cold strength DI.
First, in order to evaluate the effect of the loading density of coal on the coke strength, the dry density test was carried out while changing the loading density to 0.7 ~ 0.8 t-coal / m 3 . This was carried out under the same distillation test conditions. The quality of the charged coal used was 22 to 27% for volatile matter and LMF for 2.0 to 3.2.
As such, the final predictive model in the present invention uses the basic predictive model in the present invention, and uses the charging density related to the charging condition as an independent variable. Here, the final predictive model is obtained by calculating the regression coefficient of the loading density on the cold strength of the coke. The cold strength of the coke was predicted by the final predictive model obtained as described above. Since the description is the same as the above-described basic prediction model, a detailed description thereof will be omitted.
The regression coefficient of BD can be obtained through the regression analysis according to the result shown in FIG. 4, and a predetermined prediction model between the independent variable and the dependent variable is obtained from the BD, and the prediction model satisfies Equation 2 below. .
&Quot; (2) "
DI = 104.8-1.17 × (VM) -0.0335 × TI + 12.35 × (LMF)-0.12 × (TD) + 12BD
(Correlation coefficient R 2 is 0.821 or more)
Here, DI means the cold strength (%) of the coke manufactured, VM is the volatile content (%), LMF is the flow rate (log ddpm), TD is the total expansion (%), TI is the total inert component (%), BD (bulk density) is a charge density (t-coal / m 3 ).
According to the final prediction model calculated as described above, there is an advantage that the cold strength (%) of the coke can be predicted by using the quality-related index and the operating condition index.
The final predictive model derived in this manner can correct a discrepancy in the relationship between the DI predicted value and the measured value using the basic predictive model illustrated in FIG. 3. That is, there is almost no difference between the predicted value and the measured value using the final predictive model satisfying Equation (2).
Therefore, if the cold strength (DI) value of the coke predicted using the predictive model satisfies the desired coke quality condition, it is regarded as the blending and charging condition of the raw material. If the predicted value is out of the desired cold strength, the wrong blending and charging Judging by the condition, the independent variable can be adjusted to satisfy the desired coke quality condition.
Using the predictive model, the cold strength of the coke can be easily estimated, and the coke quality influence can be accurately evaluated through the prediction formula using the loading condition as a variable along with the quality conditions such as volatile matter (VM) and fluidity (LMF). It is possible to increase the operational utilization of the coke quality prediction equation.
In addition, because the cold strength of the coke can be predicted, the characteristics of the process are well reflected, the prediction accuracy is very high, and the prediction model can be easily supplemented according to the change of operating conditions.The prediction model can be applied to a computer to automate the compounding process. There is an advantage that can reduce the coke manufacturing cost.
While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.
Claims (7)
Collecting indicator data on operating conditions under predetermined raw coal conditions, and based on the basic predictive model, the regression coefficient of the independent variable on the cold strength of the coke is determined by using the indicator data on the operating conditions as independent variables. Obtaining a final predictive model; And
And predicting the cold strength of the coke by the final predictive model.
Indicators relating to the above quality conditions,
A method of predicting cold strength of coke comprising the flow rate (LMF), volatile matter (VM), total dilation (TD), and total inert component (TI) of the raw coal.
Prediction model having the index on the quality condition as an independent variable satisfies the cold strength of the coke characterized in that the following equation (1).
&Quot; (1) "
DI = 104.8-1.17 × (VM) -0.0335 × TI + 12.35 × (LMF)-0.12 × (TD)
(Correlation coefficient R 2 is 0.821 or more)
[Wherein DI is the cold strength of the manufactured coke (%), VM is the volatile content (%), LMF is the fluidity (log ddpm), TD is the total expansion (%), TI is the total inert component (%).]
The index concerning the operating conditions of the raw coal is a charging density (BD), the cold strength prediction method of coke.
Prediction model having the index on the quality condition as an independent variable satisfies the cold strength of the coke characterized in that the following equation (2).
&Quot; (2) "
DI = 104.8-1.17 × (VM) -0.0335 × TI + 12.35 × (LMF)-0.12 × (TD) + 12BD
(Correlation coefficient R 2 is 0.821 or more)
[Wherein DI is the cold strength (%) of the manufactured coke, VM is the volatile content (%), LMF is the flow rate (log ddpm), TD is the total expansion (%), TI is the total inert component (%), BD ( bulk density is the loading density (t-coal / m 3 ).]
The predetermined raw coal condition is a volatile matter: 22 ~ 27%, LMF: 2.0 ~ 3.2 cold strength prediction method of the coke, characterized in that.
The predetermined test furnace conditions are loading density: 750kg / m 3 , moisture in the test furnace: 8.003%, the temperature of the test furnace: 1100 ℃, characterized in that the cold strength prediction method of the coke.
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| CN104089845A (en) * | 2014-07-16 | 2014-10-08 | 拜城县众泰煤焦化有限公司 | Caking index tester and application method thereof |
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| CN104164246B (en) * | 2014-08-22 | 2016-02-03 | 江苏沙钢集团有限公司 | The Coal Blending Expert System of a kind of applicable top dress coke oven |
| CN106033483A (en) * | 2015-03-19 | 2016-10-19 | 中国矿业大学 | Coal quality express-analysis based on support vector machine |
| KR102299553B1 (en) * | 2019-12-20 | 2021-09-07 | 현대제철 주식회사 | Prediction method for cold strength of coke |
| KR102299551B1 (en) * | 2019-12-20 | 2021-09-07 | 현대제철 주식회사 | Evaluation method for reflectance distribution of cold strength index of coal blend |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000063846A (en) | 1998-08-19 | 2000-02-29 | Nippon Steel Corp | Estimation of coke's strength |
| JP2002180065A (en) | 2000-10-03 | 2002-06-26 | Kawasaki Steel Corp | Method for estimating coke strength for metallurgy |
| JP2004026902A (en) | 2002-06-21 | 2004-01-29 | Nippon Steel Corp | Coke strength estimation method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000063846A (en) | 1998-08-19 | 2000-02-29 | Nippon Steel Corp | Estimation of coke's strength |
| JP2002180065A (en) | 2000-10-03 | 2002-06-26 | Kawasaki Steel Corp | Method for estimating coke strength for metallurgy |
| JP2004026902A (en) | 2002-06-21 | 2004-01-29 | Nippon Steel Corp | Coke strength estimation method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104089845A (en) * | 2014-07-16 | 2014-10-08 | 拜城县众泰煤焦化有限公司 | Caking index tester and application method thereof |
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