JPS63210222A - Prediction of lowering of furnace heat in blast furnace - Google Patents

Abstract

PURPOSE:To quickly and accurately execute the prediction, by changing so as to improve the accuracy of a generalized threshold value for five predicting means giving evaluated point for the prescribed time, etc., when summary value of positive part in temp. difference at an inner wall in every the prescribed time intervals exceeds the threshold value. CONSTITUTION:The temp. difference at the inner wall in a blast furnace is measured in every the prescribed time intervals. One means or more among five means, such as the first means giving the evaluated point for the prescribed time when the summary value of the positive parts in the temp. positive parts in the temp. difference exceeds the threshold value, the second means based on the negative parts as the same manner as the above, the third means giving the evaluated point during the prescribed time when sum total of running average values of the above positive parts during the prescribed time exceeds the threshold and the fourth means and the fifth means based on respectively solution loss carbon quantity and nitrogen content in the top furnace gas, are provided. And, by changing in every moment is generalized threshold value, the prediction accuracy of lowering degree of the furnace heat is improved.

Description

DETAILED DESCRIPTION OF THE INVENTION (Industrial Field of Application) The present invention relates to a method for predicting heat drop in a blast furnace 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.

As a method for predicting blast furnace furnace heat, Japanese Patent Application Laid-Open No. 60-39107
There are some that have been disclosed. This method is based on the viewpoint that the charge temperature around the furnace belly has a strong correlation with the hot metal temperature.
As shown in FIG. 13, the relationship between the temperature in the local area of the blast furnace 1 detected by the sensor (belly probe) 2 and the hot metal temperature is subjected to linear regression as shown in FIG. 14. Based on this linear equation, the hot metal temperature 'pig' is determined from the temperature in the local area of the furnace abdomen.
It predicts.

However, with this method, it is necessary to insert the furnace probe 2 in order to measure the temperature near the inner wall of the furnace, which means that temperature measurements can only be carried out intermittently, which naturally deteriorates the accuracy of hot metal temperature prediction. There was a problem.

Further, even if the hot metal temperature is the same, the furnace temperature may change due to changes in the production plan, raw material charging conditions, etc. Therefore, the linear equation based on the absolute value of the furnace wall temperature shown in FIG. 14 has the problem that accurate prediction cannot necessarily be made.

On the other hand, conventionally, solution loss carbon amount (hereinafter referred to as "Tsuru loss C amount"), which indicates the quality of the reduction state of the blast furnace, has been used.
Another prediction method is to predict the blast furnace heat based on the increase or decrease of . An increase in the amount of Turuloss C indicates that the so-called Turuloss reaction described below is promoted.

c+co2->2CO: Since this reaction is an endothermic reaction, it can be predicted that the blast furnace heat will decrease.

Tsururos CM analyzes the proportions of GO, GO, N2, etc. in the top gas, air blowing conditions, and raw material charging conditions every analysis period (about 3 minutes) of gas chromatography, which normally analyzes the top gas composition. Conventionally, the decrease in furnace heat was controlled by the average value of the amount of truss loss C per hour.

Figure 15 (a) and (b) show the time-dependent changes in the amount of vine loss C (j21 >) every 3 minutes and the average value of the amount of vine loss C (12) every hour, Same figure A
- (b) is a graph showing changes in hot metal temperature over time.

In the figure, although the threshold value ε1 is exceeded at time 17, no action is taken to raise the temperature, and thereafter the hot metal temperature drops significantly as shown in (■). Figure 16 shows time 1 when the threshold ε2 was exceeded.
It shows each change over time when the heating action A was started at 3 o'clock. As in Figure 15, 11 in the figure is the instantaneous value every 3 minutes, j! 2 indicates the hourly average amount of Tsuru loss C. By comparing FIG. 15 and FIG. 16, it can be seen that the temperature increase of the hot metal as shown in ■ in FIG. 15(b) can be avoided to some extent by heating action A, as shown in FIG. 16.

However, the problem with predicting the average value of the amount of vine loss C every hour is that when there is a sudden increase in the amount of vine loss C, in the worst case, there is a delay in predicting the decrease in furnace heat by about an hour. There was a point. For example, in the case of Figure 16,
The temperature of the hot metal is the control temperature T when heating action A is taken due to a delay in prediction. It has become a problem since it has fallen below a certain level. Therefore, in order to avoid this problem, when predicting the decrease in furnace heat using the instantaneous value of the trunnion loss Cm measured at intervals of about 3 - 9 times, individual variations as shown in 12 in Figs. 15 and 16. is large and the noise component is large, so the data is not sustainable. Therefore, it is almost impossible to predict the decrease in furnace heat using the instantaneous value of the amount of truss loss C.

(Object of the Invention) An object of the present invention is to provide a blast furnace furnace heat drop prediction method that can solve the problems of the prior art described above, obtain predictions as quickly as possible, and accurately predict the drop in hot metal temperature. That's true.

(Means for Achieving the Object) In order to achieve the above object, the blast furnace heat drop prediction method according to the present invention includes installing an inner wall thermometer at a predetermined location of the blast furnace,
A first method that measures the inner wall temperature difference at predetermined time intervals using the inner wall thermometer, and gives an evaluation point for a predetermined period when the total value of the portion showing a positive value of the inner wall temperature difference at a certain time exceeds a threshold value. a second prediction means for giving an evaluation point for a predetermined period when the total value of the portion indicating a negative value of the inner wall temperature difference at a certain time exceeds a threshold; a third prediction means that gives an evaluation point for a predetermined period when the sum of the moving average values of a predetermined time width of the part showing the value exceeds a threshold; a fourth prediction means for giving an evaluation point when the moving average value over a predetermined time width of the values exceeds a threshold value; and a fifth prediction means that gives an evaluation point when the moving average value of When predicting the degree of decrease in furnace heat by comparison, the comprehensive threshold value is changed from time to time during blast furnace operation so as to increase the accuracy of predicting the degree of decrease in furnace heat.

(Example) is possible.

High-temperature air (gas flow) blown up from the blast furnace tuyeres 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　+CO 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. If this rapid temperature rise is detected, a decrease in furnace heat can be predicted.

B, IWλ亘亘P 柕IL王IRU Additionally, the following factors are considered to be contributing factors to the decrease in furnace heat of the blast furnace.

When the unloading speed in the blast furnace increases for the same reason as N-,
The level of the cohesive zone in the blast furnace decreases due to so-called raw ore descent, causing 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. If this rapid temperature drop is detected, a decrease in furnace heat can be predicted.

C0 Third Furnace Heat Reduction In addition, the following factors are considered to be contributing factors to the decrease in the furnace heat of the blast furnace.

High-temperature air (gas flow) blown up from the blast furnace tuyeres to adjust the degree of IA and the amount of hot metal is usually blown into the center of the furnace. However, for the same reason as the specific gravity, part of the gas flow may flow to the periphery of the furnace.

If this state continues for a long time, heat dissipation from the gas flow from the blast furnace wall becomes greater than during normal operation, resulting in a decrease in furnace heat.

By the way, when a part of the gas flow steadily flows into the periphery of the furnace, the temperature of the furnace wall gradually increases. If such a gradual temperature rise over a relatively long period of time is detected, a decrease in furnace heat can be predicted.

D. First to Third Predicting Means 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 the figure (a), the inner wall thermometer 3
7 pieces in the height direction of blast furnace 1 (3 pieces 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, 211 inner wall thermometers 3 are installed at 7 levels in 4 directions.

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 shows an inner wall thermometer (J
y, this is called the rFM sensor. ) is a conceptual diagram showing.

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 walls of the blast furnace, 10 is a brick, and 1
1 is a stave, 12 is a stamp, and 13 is an iron skin. 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 temperature sensing element 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, compared to conventional thermometers such as sheathed thermocouples, the FM sensor 3 has a large number of measurement points, satisfies rapid temperature measurement response, is capable of long-term continuous temperature measurement, and is highly reliable. The aim is to improve performance, 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, the inner wall temperature of the first FM sensor 3 at time j is Tj
, , and if one sampling time Δ at time j is the previous inner wall temperature, , then J−1,I T・, and T, , which inner wall temperature difference (difference value) ΔTJ,
(J-1,1... is J,1 ΔTj, 1=Tj, i'j-1.i...(
1). This state is shown in FIG.

This difference value ΔK1. is multiplied by the weight W, taking into consideration the height J of each FM sensor 3, the circumferential direction, etc. Roughly speaking, for negative difference values ΔT・, viJ,1 10, for other values, multiply by the positive/negative parameter V, which indicates v,=1, and calculate the corrected difference at time j. Obtain the value (positive difference value) CTj, .

CT,, -W, -V, -ΔT,, ・(
2) J, l l l
J, first-order, the sum of the corrected difference values CT, , for all FM sensors 3, J,1, is taken and this is set as 5T1j.

Then, according to the following equation (4), if the value of the total difference value 5T1j becomes larger than the predetermined threshold value ε1, it is assumed that there has been a rapid temperature rise, and an evaluation point of 1 is given for the predetermined period.

8dō1·≧81 (4) The above is the first prediction means based on the reason of Nyu.

The second prediction means based on the reason B1 is as shown below.

In equation (2), the positive and negative parameters Vi are 6 differential values,
For those where ・is positive, v,=Q, that J,l
For anything other than 1, v,
=l, and then the sum of the absolute values of the corrected difference values CTj and i for all FM sensors 3 is taken, and this is set as 5T2j.

ST2. −ΣlcT,,l...(3)
.

According to the following (4)', if the value of the total difference value ST2 based on the equation (3) ° becomes larger than the predetermined threshold ε2, it is determined that there has been a rapid temperature drop due to the raw ore descent. Regarded as such, an evaluation point of 1 will be given for a predetermined period of time.

ST2. ≧82 (4).

The third prediction means based on the reason for Ω4 is as shown below.

As with the first prediction means, the positive and negative parameters ■· in equation (2) are as follows: VH=Q, for negative difference values ΔTj, .
For other cases, it is set to vl-1. Also time j
The corrected difference value before the sampling time (i.e., ΔtxklIIa) is CTj-b,.

Then, for all FM sensors 3 of the value at time j of the moving average of this corrected difference value with a predetermined time width nΔt, and then (4
) According to the formula, if the value of this moving average sum ST3 is larger than a predetermined threshold value ε3, it is assumed that there has been a gradual temperature rise for a long period of time, and an evaluation point of 1 is given for the predetermined period.

ST3・≧ε3...(4)"Since the above-mentioned first to third prediction means are each performed based on the furnace wall temperature difference (difference value), Accurate predictions can be made.Moreover,
The FM sensor 3 can be placed so as to cover the entire circumference of the blast furnace due to its good workability and good side gutter response, and by being able to grasp continuous inner wall temperature differences, more accurate predictions can be made.

Further, the first to third prediction means described above can be realized by a computer. FIG. 6 is a flowchart showing the processing flow of the first prediction means. In the same figure,
In step S1, the furnace wall temperature Tj, , of each FM sensor 3 is measured at every sampling time Δ. Next, step S2
In this step, the difference value of each FM sensor 3 is calculated based on equation (1).

Then, in step S3, a total positive difference value 5T1j is determined based on equations (2) and (3). Furthermore, in step S4, this positive difference value sum 5T1j is compared with a predetermined threshold value ε1, and if equation (4) is satisfied, in step S5, the furnace heat due to the sudden flow of the gas flow around the furnace is reduced. It is assumed that a decrease will occur and a score of 1 is given for a predetermined period. On the other hand, if the equation (4) is not satisfied, it is assumed that there is no abnormality and the process returns to step S1.
~ Evaluate the furnace heat drop by repeating step S4.

FIG. 7 is a flowchart showing the flow of processing by the second prediction means. In the figure, in step S11, the furnace wall temperature T of each FM sensor is measured every 1 time period Δ. Next, in step S12, the difference value of each M sensor 3 is calculated based on equation (1).

Then, in step 813, the sum of negative difference values ST2. based on equation (3) is calculated. seek. Furthermore, step 81
In step 4, this negative difference value sum ST2 is compared with a predetermined threshold value ε2, and if the formula (4) is satisfied, a decrease in furnace heat occurs due to the increase in unloading speed in step 815. 1 evaluation point for the specified period.
give. On the other hand, if the (4-inch formula) is not satisfied, it is assumed that there is no abnormality and the process returns to step S11.
Furnace heat reduction evaluation is performed by repeating steps 1 to S14.

FIG. 8 is a flowchart showing the processing flow of the third prediction means. In the figure, in step S21, the furnace wall temperature T1 of each FM sensor 3. Measure the Δ between the rings FR and 1 at each sample J. Next, step S22
In this step, the difference value of each FM sensor 3 is calculated based on equation (1).

Then, in step S23, the moving average sum ST3 of the positive difference value in the time width nΔt based on the formula (3) is calculated.
, find. Furthermore, in step 824, this positive difference value moving average sum 5T3j and a predetermined threshold value ε
3, and if formula (4) is satisfied, step S
In step 25, it is assumed that a decrease in furnace heat due to heat dissipation from the furnace body will occur, and an evaluation point of 1 is given for the predetermined period. On the other hand, (4)
”If the formula is not satisfied, it is assumed that there is no abnormality and step S
21, and the furnace heat reduction evaluation is performed by repeating steps 821 to 824.

E, 4th. Fifth prediction means Based on top gas component analysis by gas chromatography, ventilation conditions, raw material charging conditions, etc., the trunnion loss Cff1 (
Kg/1-11) is calculated for each sampling time Δt. Here, let the amount of trail loss C at time j be
Assuming that the amount of loss C of k hours ago) is X J -k, the moving average XH of the predetermined time width nΔt at the current time j can be calculated as follows.

XH based on equation (5) is calculated for each sampling time Δt, and XH is determined by equation (6) below to a predetermined threshold ε ・(i −= 1~rl)(εx1<εx2 ・・
・An evaluation point i is given based on the maximum threshold value ε when <εxn)×1 is exceeded, and evaluation is performed.

x H>εxi (i=1~n)
-(6) The above is the fourth prediction means.

In addition, the amount of nitrogen (%) in the furnace top gas detected by gas chromatography (hereinafter referred to as "gas chromatography N2 amount") has a strong negative correlation with vine loss l, and instead of increasing the amount of vine loss C, A decrease in blast furnace heat can be predicted by a decrease in the amount of gas chromatin N2.

As a result, the amount of gas chromatography N2 at the current time j is set as Vj, and the amount of gas chromatography Nfjt at a sampling time before time j (that is, △tXk hours before) is set as Vj4.
Then, the moving average VH of the predetermined time width nΔt at the current time j can be calculated as follows: y −Σ yj−′ n+1 1” (7).

Calculate y)I based on equation (7) for each sampling time Δ, and use equation (8) below to calculate the threshold value ε, (j...1~n)(εy1>εy2...・〉ε
yo) An evaluation point j is given based on the minimum threshold value ε,j when the value falls below J, and the furnace heat decrease is evaluated.

yH<εyj(j-1~n)...(8
) The above is the fifth prediction means.

Furthermore, when calculating the moving average of the amount of vine loss C, which is the fourth prediction means, the instantaneous value of the amount of vine loss C is shown in Fig. 9(a).
As shown in , the abnormal value E1. due to noise etc. E2 may occur. Here, the amount of truss loss C at time j is expressed as
, 1 sampling time △ is the previous tsuru loss C amount
-1, the absolute value ΔX of the difference value of the amount of truss loss C,
J is ΔX・=lx・−X・1
...(9) 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)
One possible method is to smooth the data by replacing it with the previous measured value, as shown in the figure below.

By applying this method, more accurate truss C
A moving average of fi is determined, and as a result, highly accurate prediction becomes possible.

A method for predicting a decrease in furnace heat by using a moving average of the amount of truss loss C, which includes such abnormal value correction, can be realized using a computer. FIG. 10 is a flowchart showing the flow of the process. In the figure, first, in step 831, εx1<εx2...εxn
Threshold values εx1 to εxn are set with the magnitude of . and,
In step S32, the instantaneous value Xj of the amount of trailing loss C is obtained at every sampling time Δ. In step S33, the absolute value ΔX of the difference value of the amount of trailing loss C is calculated, and then in step S34, if the absolute value ΔXj of the difference value is larger than the threshold ε7, in step S35, this instantaneous value Regarded as an abnormal value, it is replaced with the immediately previous measured value X, and the process moves to step 836.

On the other hand, if it is smaller than the threshold value ε7 in step 833, the process moves to step S36 without changing the instantaneous value XJ. In step S36, the moving average XH of the time width nΔt is calculated, and in the next step S37, the evaluation point 1 is changed to 0.
Initialize to .

Then, in step S38, the moving average value of the trail loss C amount is compared with the threshold value εx1 (from i=0), and if XH≧εx1, the value of i is increased by 1 from O→1 in step S39, and step In step S40, it is determined that i=n, or in step 838, XH<εx(i+
Steps S38 to S840 are repeated while increasing the threshold value εx(i+1) in stages until it is determined that the evaluation point i is determined as 1), and the process proceeds to step S41. Also X
If H<εx1, steps 339 and 840 are never executed, the evaluation point i is set to 0, and the process moves to step S41.

Finally, in step 841, steps 838-84
Output the evaluation point i obtained from 0.

Note that, of course, the above-mentioned abnormal value processing.

Application to a computer can be similarly realized in the case of predicting a decrease in furnace heat using the moving average value y8 of the amount of gas chromatin N2.

4. mentioned above. Since the fifth prediction means is based on a moving average for each sampling time Δ, prediction can be obtained quickly and the accuracy can be said to be sufficiently reliable.

Comprehensive prediction is performed as described below by using the evaluation score of at least one of the first to fifth prediction means described in dan-''L negative jJq and E. This comprehensive prediction method is the first
~ Let the evaluation points of the fifth prediction means be f ~ f 1 and the weights ω ~ ω5, respectively (prediction means that are not used have a weight of 0)
) C=ω f +ω f +ω3f3 +ω4 f4+ω5 f5...(1
0), and the value of this comprehensive evaluation C is set as a comprehensive threshold value ε that is set in advance in multiple stages. 1
~ε. . By comparing this, predictions are made according to the furnace heat reduction level 0 to n. In this way, the overall rating is C
By changing the degree of alarm according to the value of , the heating action can be changed in detail. As a result, it becomes possible to select the necessary and sufficient heating action, and of course it is possible to reliably prevent a decrease in furnace heat.
Stable and economical blast furnace operation is possible without causing an unnecessary rise in furnace heat due to excessive heating action.

However, there is a possibility that the prediction of the furnace heat reduction level based on the comprehensive evaluation C, which was initially optimal, is no longer optimal due to changes in various conditions such as the production plan and raw material conditions during blast furnace operation. Therefore, the overall threshold value ε determines the furnace heat reduction level based on the value of the overall evaluation C. 1~ε. . must be changed appropriately during blast furnace operation, and the overall threshold ε.

1~ε. , 23 - Proceed as shown below.

FIG. 11 is a flowchart showing the process flow of the comprehensive prediction means based on the evaluation points of all the first to fifth prediction means.

This will be explained below with reference to the same figure.

First, in step S51, a comprehensive evaluation C is calculated using equation (1o). Next, in step 852, threshold hot metal temperatures T H (i-1 to n) that determine the furnace heat reduction level are set in descending order (see FIG. 12). Then, in step S53, the values of a large number of comprehensive evaluations C over a certain period of time that have been reversed in the past,
The relationship between the lower actual temperature of hot metal after a predetermined period predicted by each comprehensive evaluation C is investigated, and an approximate straight line of CT is derived by the least squares method as shown below.

FIG. 12 is a graph showing the correlation between the lower actual temperature of hot metal and the value of comprehensive evaluation C, where x indicates measured data. L is a CT approximation straight line determined by the least squares method from the measurement data, and the linear equation can be determined as shown in (11) below.

C=aT×b River (11) Obtained in step S53 (,T approximate straight line L (17)-24
Based on the set, in step 854, each comprehensive threshold value ε,i corresponding to each threshold value ε,i is determined as shown by the broken line in the section of FIG. Then, in step S55, the comprehensive evaluation C calculated in step S51 and each comprehensive threshold value ε determined in step 854. By comparing with i, the furnace heat reduction level 1 (ε ≦C
1)) Determine. Finally, an alarm corresponding to the furnace heat decrease level l is output to warn of a decrease in furnace heat, and thereafter the process returns to step 851 and steps 851 to S56 are repeated to predict a decrease in furnace heat.

In this way, by constantly and automatically updating the comprehensive threshold value εci based on the correlation between the value of the comprehensive evaluation C and the actual hot metal temperature value T, even if the production plan or raw material conditions change during blast furnace operation, , accurate predictions can be made according to the level of furnace heat drop.

G. Supplementary note: In the first to third prediction means in this example, an FM sensor was used as the inner wall thermometer, but a normal temperature sensor (for example, a sheathed thermocouple) can also be used as a substitute, although it has problems with its lifespan. It is possible. Furthermore, a stave thermometer or a brick-embedded thermometer can be used as a substitute, although the prediction accuracy will be slightly lower due to their reliability and low temperature response.

Furthermore, in the first to third prediction means in this example,
Although 28 FM sensors 3 are arranged in seven levels and in four directions, it goes without saying that they may be installed appropriately depending on the characteristics of the blast furnace.

Furthermore, as mentioned in L-, it is desirable to use all of the first to fifth prediction means for the comprehensive prediction, but at least the first to fifth prediction means are used.
By using at least one of the prediction means described above, it is possible to expect almost the same effect as the example described in L-engineering. In addition, instead of updating the overall threshold value, a modification example such as changing at least one threshold value of each prediction means can be considered, and the first to third prediction means also have multiple prediction means similar to the fourth and fifth prediction means. It is also conceivable to provide a threshold value.

(Effects of the Invention) As explained above, according to the present invention, predictions can be obtained quickly, and the comprehensive prediction automatically improves the accuracy of predicting the degree of decrease in furnace heat by changing the comprehensive threshold value, etc. It is possible to more accurately predict the drop in hot metal temperature and take action to raise the temperature as necessary.

[Brief explanation of the drawing]

FIGS. 1(a) and 3(b) are a side sectional view and a plan sectional view, respectively, showing the arrangement of an FM sensor used in an embodiment of the present invention in the blast furnace wall, and FIGS. 2 and 3 are respectively A conceptual diagram and an installation explanatory diagram of the FM sensor. Figure 4 is a graph showing the change over time in the furnace wall temperature measured by the FM sensor. Figure 5 is a graph showing the change over time in the difference value of the furnace wall temperature measured by the FM sensor. 6 is a flowchart showing the flow of processing of the first prediction means, FIG. 7 is a flowchart showing the flow of processing of the second prediction means, FIG. 8 is a flowchart showing the flow of processing of the third prediction means, Figures 9 (a), (b), and (c) are instantaneous values of the amount of trail loss C including abnormal values, respectively.
Absolute value of the difference value of quantity, Tsuru loss Cf with abnormal values removed
A graph showing the instantaneous value of f1, Fig. 10 is a flowchart showing the processing flow of the fourth prediction means, Fig. 11 is a flowchart showing the processing flow of the comprehensive prediction means, and Fig. 12 shows the comprehensive evaluation and hot metal temperature performance. Graph showing the relationship with the value, 1st
Figure 3 is a side cross-sectional view showing the arrangement of a furnace belly sonde in a blast furnace according to the prior art, Figure 14 is a graph showing the correlation between hot metal temperature and furnace belly layer side temperature, Figure 15 (a), (b) is a graph showing the hourly average value of the amount of vine loss C and the change in hot metal temperature over time. Figures 16 (a) and (b) are the 1-hour average values of the amount of vine loss C when arm heat action occurs. and is a graph showing changes in hot metal temperature over time.

Claims (1)

[Claims] (1) An inner wall thermometer is installed at a predetermined location in the blast furnace, and the inner wall temperature difference is measured at a predetermined time interval with the inner wall thermometer, and the sum of the portions that show a positive value of the inner wall temperature difference at a certain time. The first one gives an evaluation point for a predetermined period when the value exceeds the threshold.
and a second means for giving an evaluation point for a predetermined period when the total value of the portion indicating a negative value of the inner wall temperature difference at a certain time exceeds a threshold value.
a third prediction means for giving an evaluation point for a predetermined period when the sum of moving average values of a portion showing a positive value of the inner wall temperature difference at a certain time over a predetermined time width exceeds a threshold; A fourth prediction means that calculates the amount of carbon at predetermined time intervals and gives an evaluation point when the moving average value of the calculated values over a predetermined time width exceeds a threshold; evaluation of the at least one prediction means, comprising at least one of a fifth prediction means that calculates for each interval and gives an evaluation point when the moving average value of the calculated value in a predetermined time width is less than a threshold value; When predicting the degree of furnace heat drop by comparing the comprehensive evaluation value based on points with a predetermined comprehensive threshold value, the comprehensive threshold value is changed from time to time during blast furnace operation so as to increase the accuracy of predicting the degree of furnace heat drop. A blast furnace furnace heat drop prediction method characterized by changing.