JPS63210221A - Prediction of furnace heat in blast furnace - Google Patents

Abstract

PURPOSE:To accurately predict furnace heat, especially lowering of the furnace heat in a blast furnace, by adopting ARMA model introducing a variable to temp. difference at the inner wall in the blast furnace, average value of solution loss C quantity, etc., and a variable correcting the difference of iron tapping holes into a MA term. CONSTITUTION:At the time of predicting the variation with time of the furnace heat by the ARMA model using the operational result in the blast furnace, the undermentioned three kinds of variables are used in the MA term. That is, the furnace heat is predicted by adopting (A) the variable related to the temp. difference at the inner wall measured in every the prescribed time intervals by inner wall thermometer set at the prescribed positions in the blast furnace, (B) the average value of the above C quantity or N content In the top furnace gas component during prescribed time and (C) the variable correcting difference of the iron tapping holes, into the MA term. In this way, the variation of the molten iron temp. without any delay can be accurately predicted.

Description

DETAILED DESCRIPTION OF THE INVENTION (Industrial Application Field) The present invention relates to a blast furnace furnace heat prediction method for stable operation of a blast furnace.

(Prior art and its problems) It has been known for a long time that in order to maintain stable operation of a blast furnace, it is necessary to keep the temperature of hot metal constant. For this reason, blast furnace operators have always needed to predict changes in blast furnace heat.

Prediction of temperature drop is extremely important in blast furnace furnace thermal changes, especially since hot metal may solidify due to temperature drop and may no longer flow out of the blast furnace.

A method for predicting blast furnace heat is to consider the blast furnace as a black box and obtain a model through statistical analysis.
There is one based on an AR (Auto-Regressive) model.

Although the AR model described above has excellent characteristics as a model for predicting temporal fluctuations in the actual furnace heat level of a blast furnace, it has the following problems.

Fig. 16 is a diagram showing changes over time in the actual value and predicted value of hot metal temperature. In the figure, Ill is based on the actual value x 112 of the predicted value. With respect to changes in values, changes in predicted values tend to lag by approximately τ sampling time (i.e., where the sampling interval is Δt and τX△ is time), and the amplitude tends to be small.For this reason, it is difficult to fully perform the prediction function. Furthermore, there was a problem that the accuracy of the predicted value was not sufficiently accurate.

In order to solve this problem, ARM A (Auto-
Regressive Moving Average
) (autoregressive moving average) model is published in JP-A-60-248804゜ "Tetsu to Hagane J 1984, No. 1,
Although it is disclosed on page 54, etc., the current situation is that no definitive variable to be introduced into the MA term has been found yet.

(Objective of the Invention) The object of the present invention is to provide a blast furnace furnace heat prediction method that eliminates the problems of the above-mentioned prior art and can accurately predict hot metal temperature changes without delay in prediction. .

(Means for achieving the objective) In order to achieve the above objective, the blast furnace furnace heat prediction method according to the present invention uses the blast furnace operation results to predict temporal fluctuations in the blast furnace furnace heat level using the ARMA model. Variables related to the inner wall temperature difference measured at predetermined time intervals by inner wall thermometers installed at predetermined locations in the blast furnace, the average value of the amount of solution loss carbon or the amount of nitrogen in the furnace top gas component over a predetermined time width, and the output. A variable that compensates for the difference in the pigtail hole is introduced into the MA term.

(Example) 1. The first number introduced into the MA term The following can be considered as the first reason for the decrease in the furnace heat of the blast furnace.

High-temperature air (gas flow) blown up from the blast furnace lining to adjust the temperature and amount of hot metal is usually blown into the center of the furnace. However, due to reasons such as raw material charging conditions and charge distribution, a large amount of gas may suddenly flow to the periphery of the furnace.

As a result, Fe　+c−+　Fe　+C○ The endothermic reaction of is promoted and the furnace heat decreases.

By the way, when a large amount of gas flows around the furnace, Na
, when the deposits in the furnace such as Pb and the stagnation layer peel off and fall off the wall, the temperature of the furnace wall in that part increases rapidly. After that, the furnace heat will decrease sooner or later.

Moreover, the following can be considered as the second reason for the decrease in furnace heat of the blast furnace.

When the rate of unloading in the blast furnace increases due to raw material charging conditions, charge distribution, etc., the level of the cohesive zone in the blast furnace decreases due to so-called raw ore unloading, resulting in a decrease in furnace heat.

By the way, when the cohesive zone level decreases, the furnace wall temperature at the corresponding portion also decreases rapidly. After that, the furnace heat will decrease sooner or later. For the two reasons mentioned above,
It can be said that the difference in furnace wall temperature has a predictive value for the thermal change in the blast furnace. Therefore, an ARMA model can be considered in which a variable related to the furnace wall temperature difference is introduced into the MA term.

FIGS. 1(a) and 1(b) are a side sectional view and a plan sectional view, respectively, showing the arrangement of an inner wall thermometer used in an embodiment of the present invention. As shown in Figure (a), there are seven inner wall thermometers 3 in the height direction of the blast furnace 1 (three on the back).

(2 abdominal parts, 2 morning glory parts), and 4 pieces are installed in the circumferential direction of the blast furnace 1 as shown in FIG. In other words, a total of 28 inner wall thermometers 3 are installed in four directions and seven levels.

The inner wall thermometer is, for example, disclosed in Japanese Utility Model Application No. 57-81 by the present applicant.
531. The one disclosed in Japanese Utility Model Publication No. 59-16816 may be used, and FIG. 2 is a conceptual diagram showing the inner wall thermometer (hereinafter referred to as "rFM sensor") disclosed in the latter.

In the figure, reference numeral 4 denotes a sheath type thermometer having two conductive wires 5 buried in parallel insulatively and having a temperature sensing part 6 on the front end side. The temperature-sensing parts 6 are arranged in parallel so as to be placed at different parts in the length direction, and a roughly sheathed dummy rod 7 is connected to the tips of the temperature-sensing parts 6 to align the leading ends. The sheath type dummy rod 7 has two conductive wires 5 buried in parallel insulating manner, and has substantially the same thermal conductivity as the sheath type temperature measuring element 4. The FM sensor 3 is formed by keeping the sheath type temperature measuring element 4 in a non-contact manner with an insulating material 8 and housing it in a sheath tube 9.

FIG. 3 is an explanatory diagram of the installation of the FM sensor 3. In the figure, 10 to 13 are the furnace walls of the blast furnace, 1O is a brick, and 1
1 is Sudev, 12 is Stamp, and 13 is Ironhide. F
As shown in the figure, the M sensor 3 is installed inside the furnace wall by welding to a packing 14 and a welded portion 15. Note that 16 is a filler, and 17 is a milk injection port into which the filler 16 is poured.

Note that due to the installation and structure of the FM sensor 3 described here, the FM sensor 3 itself is eroded along with the erosion of the furnace wall, and the sheath type side heating body 4 may be exposed inside the furnace near the furnace wall. In addition to "furnace wall temperature", [furnace temperature near the furnace wall]
is being measured. Hereinafter, a concept including both will be described as "furnace wall temperature." As mentioned above, the FM sensor 3 has a large number of measurement points compared to conventional thermometers such as sheath thermometers, satisfies rapid temperature measurement response, and is capable of long-term continuous temperature measurement. Efforts are being made to improve reliability, durability, and workability.

Each FM sensor 3 measures the inner wall temperature of the blast furnace 1 at every predetermined sampling time Δt, as shown in FIG. Here, '1 degree of the first FM sensor 3 at time j is Tj
, , and if one sampling time △ at time j is the previous inner wall temperature T, then J-1, I T... and T... Which inner wall temperature difference (difference value) ΔTJ,
I J-1,1... is J,1 6-cho...-cho...-" j-1,i...(
1) J,l J,1. This state is shown in FIG.

This difference value Δ1. is multiplied by the weight W, taking into consideration the height J of each FM sensor 3, the circumferential direction, etc. Roughly speaking, if the difference value ΔTj, , is negative, vi−〇, otherwise, ■, = 1. Multiply by the positive/negative parameter V, and calculate the corrected difference at time j. Obtain the value (positive difference value) CT, .

J, ICT...=W... ・V... ・6 guns... (2)
J, I II I
J, 1st order, all FM sensors 3 with corrected difference value CT...
J, 1 and the sum of the difference values ST'l according to the following equation (4).

If the value of is larger than the predetermined threshold value ε1, it can be assumed that there has been a sudden rise in the hot water temperature on the furnace wall due to the falling of deposits inside the furnace, and by quantifying this phenomenon, MA
Can be introduced as a variable in a term.

ST1, ≧81 (4) Also, in equation (2), the positive and negative parameters Vi are converted to the difference value T1. For those where is positive, v,=O.

J,1 For other values, set v,=1, then apply J,1 to all FM sensors 3 of the absolute value of the corrected difference value CT, , and according to the following equation (4), (3) If the value of the total difference value 5T2j based on the formula becomes larger than the predetermined threshold ε2, it can be assumed that there has been a sudden temperature drop due to the raw ore descent, and by quantifying this event, it can be introduced as a variable in the MA term. can.

ST2. ≧82 ... (4) (4
) (formula (4)' is also acceptable) exists in the past by a time width NΔ from the current time t, then Fl (t
)=1, and if it does not exist, it is possible to introduce a variable F1 (t), which is F1 (t)−1, as an MA term.

In this case, the predicted hot metal temperature value 'S;' (t+
Δt2) can be estimated using the following equation.

V(t+△t2) =ΣaIHy(t+△t2) IniO+JF1(t)...
(5) The first term on the right side is the AR term, and the second term is the MA term.

Note that a and b are coefficients.

Figures 6 and 7 show examples of prediction using equation (5), where (a) is the hot metal temperature and (b) is the total positive difference value 5T1j
, (C) is a graph showing changes in Fl (t) over time.

Also, from the current time t, the time width NΔ is moved backwards into the past, (
4) Variable F2 (
It is also possible to introduce t) as an MA term as shown in the following equation (6).

V(t+△t2) =n a2. V(t-iΔt2)''-b2F2 (t) (6) Note that a and b are coefficients.

vi 2 Figures 8 and 9 show examples of prediction using equation (6), where (a) is the hot metal temperature and (b) is the sum of positive difference values STI・
, (C) is a J graph showing the change in F2 (t) over time.

From the second conventional method that leads to the terms Il and MA, it is known that the increase or decrease in the amount of solution loss carbon (hereinafter referred to as "Tru loss Cff1J"), which indicates the quality of the reduction state of the blast furnace, has a strong correlation with the temperature of the blast furnace heat. It is being In other words, an increase in the amount of trun loss C indicates that the so-called trun loss reaction shown below is promoted. This will result in a decline.

Therefore, it can be said that an increase or decrease in the amount of truss loss C can be predicted with respect to changes in blast furnace heat.

Therefore, AR that introduces the average value of the amount of truss loss C into the MA term
An MA model can be considered.

FIGS. 10(a) and 10(b) respectively show changes over time in the instantaneous value of the hot metal temperature and the average value of the truss loss C amount. In the same figure (a), Ll is an actual value, and F2 is a predicted value. The instantaneous value of truss loss C amount (Kg/1-p) is the C○ in the furnace top gas component.
, Go, N2, etc., ventilation conditions, raw material charging conditions, etc., it is calculated for each sampling time of gas chromatography, and with reference to FIG. t- (Δt1) (k=1 to N) is obtained by averaging the instantaneous values within a predetermined time width NΔt1 for each time width Δt1. On the other hand, the instantaneous value y(
t) is determined at every sampling time Δt2. Note that t is the current time.

Using such variables, using the ΔRMA model, the predicted value of the hot metal temperature '57 (t+Δt2) at one point in time can be estimated using the following equation.

V(t+Δt2) The first term on the right side is the AR term, and the second term is the MA term.

Note that a. and C. are coefficients.

J 1[[, two variables introduced into the MA term L 1. ! Although the variables mentioned in L can be expected to have a considerable effect even if they are introduced individually into the MA term, in order to further improve the prediction accuracy, it is necessary to introduce the two variables into the MA term at the same time.
It is possible to introduce it.

In this case, the predicted hot metal temperature value 'Sl' (t+Δt
2) can be estimated using the following equation.

V(t+Δt2) 1, X, a 1ty(t'Δt2)V(t+Δt
2) = 'Z a -y(t-tΔt2 >,,, 2+ b2F2 (t) Note that a, b, ca 1i 1 1j・21・b2・02j [is the coefficient.

The third change introduced in Sections IV and MA is that normally a blast furnace taps iron alternately using two tap holes, so for different tap holes A and B, the hot metal thirst per tap shown in Figure 11 is As shown in the change in the Maro average value over time, deviations often occur in the hot metal temperature, and in such cases, prediction accuracy is affected. Note that Δt3 is the sampling time for each tapping date.

Therefore, the pig iron tapping at the current time t and the predicted time (10Δ1.
) is not tapped from the same taphole, (8)
Predicted hot metal temperature value y(t+△j2
) is considered to be less accurate, so the following (10),
The prediction is made by correcting the difference in the tap hole according to equation (11).

10d z(t+△t2)...(10)
'Sl' (t+Δt2) 1d4z (t+Δt2) (11) Here, z(t) is a variable for correcting the difference in the tap hole, and its details will be described later. In addition, a , b , c3i
3 3jl 3. a4 and b4. C4j, d4 is a coefficient O
Figure 12 shows the hot metal temperature y(t) during intermittent pouring.
, is a graph showing the change over time of the taphole correction variable z(t>,
Ll is the hot metal temperature V(t), L3 is the taphole correction variable z(t
), time 1 . 1 is each A1 82 Tapping start time, tapping end time, time 1 at the taphole
．． 1 indicates the start of tapping at taphole B1 B1 8
2 time, which indicates the end time of tapping, and is predicted using equations (8) to 11) with n=2. Point P1 is the predicted value of time t2 at time t1, and both time t1 and predicted time +2 are the same tapping time tA1 to tA of taphole A.
Since it is within 2, there is no need to consider the difference in the tap hole, so the prediction is made using equation (8) or (9).

On the other hand, point P2 is the predicted value of time 1 (1) at time tA2 (+3), and time tA2 is the predicted value of B1
4. Time tB1 is the tapping time using taphole A, and since time tB1 is the tapping time using taphole B, they are not within the same tapping time. Therefore, prediction is performed using equation (10) or equation (11). Furthermore, at time tB1, the value of the taphole correction variable z (t) also changes in order to correct the difference in the tapholes.

The taphole correction variable z (t) is determined as follows. Focusing on the M hot metal temperatures that have increased backwards from the current time t in the past, find the average value Y, and calculate the average value Y of MA of the M hot metal temperatures related to taphole A and taphole B.
The average value YB of MB (MA+M=M) is calculated. Here, if the taphole at time 1o is A, z (t) = YA-Y - (12
) B, then Z (t) −Y8−Y ...(
13) to find the taphole correction variable z (tn).

Further, when tapping the same taphole 8 times (S≧2) consecutively, according to the formula (14) below, the formula (12) or (1
3) Change the taphole correction variable z (tn) obtained by the formula.

Here, k is l k l < 1 and if z (t,) > O, then k > 0 2 (1n) < 0, then k < Q. This is done in consideration of the heat retention effect of continuously tapping from the same tap hole. In other words, the continuous tapping warms the tap flute, so the actual hot metal temperature value rises higher than normal. Considering this temperature rise, the taphole correction variable z is set to a temperature rise corresponding to (Σ(lkl/1))4;l +2(to>1) than normal.
(tn). Furthermore, the control temperature, which is the standard for the hot metal temperature actual value, is changed in consideration of the temperature increase due to the continuous tapping described above.

In this way, by considering the difference in tapholes,
As shown in Figure 2, the predicted value V at time t81 (+4)
The value of (t, 1) is the point P when the tap is taphole A.
2', and in the case of taphole B, the point is P2.

Although we have explained the case of intermittent tapping in which there is a period of time between tapping, the overlapping part can also be used in the case of positive tapping in which tapping from tapholes A and B is partially overlapped. In this case, by focusing on one of the tapholes, the above (8)
) to (11) can be predicted. In this case, continuous tapping from the same taphole is impossible.

FIGS. 13(A) and 13(B) are flowcharts showing the processing procedure of the prediction method based on equations (8) and (10).

In the same figure, in step S1, the sum of positive difference values ST1 is calculated using equations (1) to (3), and in step S2, it is determined whether equation (4) is satisfied between the current time t and the past time width NΔt1. Determine. If formula (4) is satisfied even once,
In step $3, we set Fl (t) = 1, and once (
4) If the formula is not satisfied, in step S4
Let l (t)-1.

Then, in step S5, a period NΔt1 from the current time t
Going backwards in the past, calculate N average values u (Δt1 at t-) (k=1 to N) for each interval Δt of the true loss CI.

Note that steps S1 to S4 described above. The process of step S5 is performed every sampling time Δt2.

Then, in step S6, the current time t and the predicted time t
It is checked whether +Δt2 is within the same tapping time, and if it is within the same tapping time, in step S7, the hot metal temperature actual value y
(t) and the prediction result 'Sl' (t) (8)
The coefficients a, b, c of the equation are 1i 1
1J Decide. Next, in step S8, the predicted value V(t+Δt2) of the hot metal temperature one point ahead is calculated using the coefficient a11. calculated in step S7. b1. It is obtained from equation (8) using C1j, and the process moves to step 817.

On the other hand, if the current time t and the predicted time (with Δt2) are within the same tapping time in step S6, in step S9, going backwards from the current time t, focus on M hot metal temperatures, and The hot metal temperature average value Y, and M and M for the tap hole A and the tap hole B, respectively, among the M pieces.
(M,, +M8-M) hot metal temperature average value Y,
Calculate YB.

Eighth, in step S10, the taphole at the predicted time (t+△t2) is determined, and if it is the taphole A, in step 811, the predicted time (t+△t2) is determined by the formula (12;
) is determined, and if it is taphole B, the taphole compensation variable 7 (t+Δt2) is determined using equation (13) in step S12.

Furthermore, in step S13, it is checked whether the tap hole at the current time t and the predicted time (t+Δt2) are the same. Therefore, when tapping continuously from the same taphole, the taphole correction variable z(t
+Δt2). Next, in step 815,
The coefficients a, b, and cd of equation (10) are determined based on a comparison between the actual hot metal temperature value y(t) and the predicted result 'Sl'(t).

31 3 3j. 3 Furthermore, in step S16, the predicted value V(t+Δt2) of the hot metal temperature one point ahead is calculated using the coefficient a31. calculated in step 815. b3. Using c3j, d3 (10)
It is determined from the formula and the process moves to step 818.

Step S
The furnace heat is predicted by outputting in step 17, and thereafter the process returns to step s1 to continue the prediction.

FIGS. 14(A) and 14(B) are flowcharts showing the processing procedure of the prediction method based on equations (9) and (111).

Same 19 - In the figure, in step S21, the sum of positive difference values 5T1j is calculated using equations (1) to (3), and in step 322, it is determined whether equation (4) is satisfied while going backwards to the past time width NΔt1. . If formula (4) is satisfied, step S23
Let p be the number of times ST1, ≧81 occurs, then F2 (t)-p. On the other hand, if equation (4) is not satisfied even once, in step S24 F2 (
t) −〇. Then, in steps 825 to 837, steps 85 to S in the flowchart of FIG.
Processing similar to step 17 is performed.

In addition to the variable related to the furnace wall temperature difference (difference value) and the average value of truss loss C amount, which are highly predictive of changes in furnace heat, ARMA introduces into the MA term a variable that takes into account the difference in the tap hole.
The predicted values obtained by the model are quite accurate.

■, Supplementary note: In this example, we have described an example in which the event that satisfies equation (4) is digitized and introduced into the MA term, but the same effect can be obtained by digitizing the event that satisfies equation (4) and introducing it into the MA term. It has the effect of

In addition, in this example, an FM sensor was used as the inner wall thermometer, but ordinary temperature sensors (e.g., sheath heat, couple) can be used as substitutes, although there are problems with the lifespan. Even if a thermometer is used, the prediction accuracy will be slightly lower due to its reliability and low temperature response, but it can be used as a substitute.

Further, in this embodiment, 28 FM sensors 3 are installed in 7 levels and in 4 directions, but it goes without saying that they may be installed appropriately depending on the characteristics of the blast furnace.

Furthermore, when calculating the average of the amount of trailing loss C for each section Δt1, the instantaneous value of trailing loss Cfi may generate an abnormal value E1°F2 due to noise or the like as shown in FIG. 15(a). Here, if the amount of vine loss C at time j is X・, and the amount of vine loss C before one sampling time Δ is XJ-1, then the absolute value of the difference value of the amount of vine loss C △XJ is △ -X・1...
・(15) J J J-1. By comparing this ΔX· with the threshold value ε2 as shown in FIG. 2(b), the abnormal value E1. Find E2 and draw the same figure (C
), a method of smoothing by replacing it with the previous value can be considered. By applying this method, a more accurate average value of the truss loss C amount can be determined, and as a result, even more accurate prediction becomes possible.

In this embodiment, the average value for each section Δt1 is determined as the average value of the amount of trailing loss C within the predetermined period NΔt1, but a moving average value of a predetermined time width may be sampled at predetermined time intervals.

In addition, the amount of nitrogen (%) in the furnace top gas detected by gas chromatography (hereinafter referred to as gas chromatography N2 amount 1) has a strong negative correlation with the amount of tulle loss C;
A similar effect can be expected by using the minimum value of the two quantities in the MA term instead of the maximum value of the truss loss C quantity.

(Effects of the Invention) As explained above, according to the present invention, the variables related to the temperature difference in the inner wall of the blast furnace, the amount of truss loss C or the gas chromatography N2
By using the ARMA model using the average value of the amount and a variable that corrects the difference in the tap hole as the MA term, it is possible to accurately predict the blast furnace furnace temperature, especially the furnace heat decrease, without delay.

[Brief explanation of the drawing]

FIGS. 1(a) and (b) are a side sectional view and a plan sectional view showing the arrangement of an FM sensor used in an embodiment of the present invention, and FIGS. 2 and 3 are a conceptual diagram and a plan view of the FM sensor, respectively. The set sectional view, FIG. 4 is a graph showing the change in inner wall temperature over time, FIG. 5 is a graph showing the change in inner wall temperature difference over time, and FIGS. 6 to 9 each show a prediction example in an embodiment of the present invention. (a) is the hot metal temperature, (b) is the sum of positive difference values, (C
) is a graph showing changes over time in variables related to inner wall temperature difference,
Figures 10 (a) and (b) are graphs showing the changes over time in the instantaneous value of hot metal temperature and the average value of trough loss C, respectively. Graphs showing changes in hot metal temperature over time, Figure 12 is a graph showing changes in hot metal temperature and taphole correction variables over time, Figures 13 (8), (B) and Figures 14 (8), (8) are FIGS. 15(a), 15(b), and 15(c) are flowcharts showing the processing procedure of the prediction method in the embodiments of the present invention, respectively, and FIGS. 15(a), 15(b), and 15(c) are instantaneous values of the amount of vine loss C including abnormal values, and differential values of the amount of vine loss C, respectively. 16 is a graph showing the instantaneous value of the amount of turret loss C after removing the absolute value and the abnormal value, and FIG. 16 is a graph showing the prediction result based on the actual value of the hot metal temperature based on the conventional AR method.

Claims (1)

[Claims] (1) When using the blast furnace operation results to predict temporal fluctuations in the blast furnace heat level using the ARMA model, the inner wall temperature difference measured at predetermined time intervals by inner wall thermometers installed at predetermined locations in the blast furnace. The blast furnace furnace heat is characterized by introducing into the MA term a variable for correcting the amount of solution loss carbon or the amount of nitrogen in the top gas component over a predetermined time width, and a variable for correcting the difference in the tap hole. Prediction method.