JPS63210211A - Prediction for lowering of furnace heat in blast furnace - Google Patents

Abstract

PURPOSE:To quickly and accurately execute the prediction, by measuring inner wall temp. in every time interval at the prescribed positions and executing generalized evaluation by plural means, giving evaluated point during the prescribed time, etc., when summary value of positive parts in the temp. differences exceeds the threshold value. CONSTITUTION:The temp. differences at the inner wall in a blast furnace are measured in every the prescribed time interval. One means or more among three means, such as the first means giving the evaluated points during the prescribed time when the summary value of the positive parts in the temp. differences exceeds the threshold value, the second means based on the negative parts as the same manner as the above and the third means giving the evaluated points during the prescribed time, when the sum total of running average values during the prescribed time in the above positive parts exceeds the threshold value, are provided. Further, the fourth means based on solution loss carbon quantity, and the fifth means based on nitrogen content in the top furnace gas, are provided. And, according to the generated evaluation by at least one means along the first-the third means and at least one means among the fourth and the fifth means, the lowering of the furnace heat is predicted.

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 humidity drop is extremely important in blast 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. 14, the relationship between the temperature in the local area of the blast furnace 1 detected by the sensor (belly sonde) 2 installed in the blast furnace 1 and the hot metal temperature is subjected to linear regression as shown in FIG. 15. Based on this linear equation, the hot metal temperature "pig" is calculated from the temperature at the local part of the furnace belly.
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 made intermittently, which naturally deteriorates the accuracy of predicting hot metal moisture. There was a problem with it being put away.

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 depletion shown in FIG. 15 has the problem that accurate prediction cannot necessarily be made.

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

C0CO2→2CO Since this reaction is an endothermic reaction, it can be predicted that the blast furnace heat will decrease.

The amount of turret loss C is calculated based on the ratio of Co, CO2, N2, etc. in the furnace top gas, the air blowing conditions, and the amount of raw material per feed per gas chromagraph analysis cycle (about 3 degrees), which normally analyzes the composition of the furnace top gas. Conventionally, the decrease in furnace heat was controlled by the average value of the amount of truss loss C for each hour.

In Fig. 16 (al, (b)), the figure (a) shows the crane loss every 3 minutes 0ffiil) and the crane loss Ct every hour.
The graph shows the change in the average value (12) over time, and FIG. 3B is a graph showing the change 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 (■). FIG. 17 shows the respective changes over time when the heating action A was initiated at time 13:00 when the threshold value ε2 was exceeded. As in Figure 16, I11 in the figure is an instantaneous value every 3 minutes, and 12 is 1.
It shows the time-averaged amount of vine loss C. Figure 16.

By comparing Fig. 17, it can be seen that due to heating action A, the temperature of hot metal decreases as shown in ■ in Fig. 16(b).
As shown in the figure, it can be seen that this can be avoided to some extent.

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 17,
Due to the prediction delay, heating action A is taken when the hot metal temperature is the control temperature T.

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 amount of truss loss C measured at intervals of about 3 degrees, individual variations as shown by A1 in Figs. 16 and 17. The data is not sustainable because the noise component is large. 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) The 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. It is to be.

(Means for Achieving the Object) In order to achieve the above object, the blast furnace furnace heat drop prediction method according to the present invention includes installing an inner wall temperature at a predetermined location of the blast furnace, and reducing the inner wall temperature. , a first prediction means that measures the inner wall temperature difference at each predetermined time interval and gives an evaluation point for a predetermined period when the total value of the portion indicating a positive value of the inner wall temperature difference at a certain time exceeds a threshold; a second prediction means for giving an evaluation point for a predetermined period when the total value of the portions indicating a negative value of the inner wall temperature difference at a time exceeds a threshold; and a portion indicating a positive value of the inner wall temperature difference at a certain time. and a third prediction means for giving an evaluation point for a predetermined period when the sum of the moving average values of the time width exceeds a threshold value, and the solution loss carbon suspension is calculated at each predetermined time interval. a fourth prediction means that gives an evaluation point when the moving average value of the determined values over a predetermined time width exceeds a threshold value; It further comprises at least one of the fifth prediction means for giving an evaluation point when the moving average value in the time width is less than the threshold value, and at least one of the first to third prediction means gives an evaluation point. Said No. 4. The blast furnace heat drop is predicted in accordance with the comprehensive evaluation of at least one evaluation point of the fifth prediction means.

(Example) A. First Reason for Reduction in Furnace Heat The following factors can be considered as causes for the reduction in furnace heat of the blast furnace.

High-temperature air (gas flow) blowing up from the blast furnace tuyeres to maintain the hot metal temperature and shape the 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　O+C−)Fe　10GO 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. Second Reason for Decrease in Furnace Heat The following may be considered as a cause of the decrease in furnace heat in the blast furnace.

If the unloading speed in the blast furnace increases for the same reason as Nyu,
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.

0.73 1 Round Bar/Region Furthermore, the following may be considered as contributing factors to the decrease in furnace heat of the blast furnace.

High-temperature air (gas flow) blown up from the blast furnace lining to adjust the temperature of the hot metal and the hot metal cloth is usually blown into the center of the furnace. However, for the same reason as in the N and LL cases, a portion of the gas flow may flow to the periphery of the furnace. If this condition continues for a long time, the heat dissipation of the gas flow from the furnace wall of the blast furnace will be greater than in 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 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 sensor disclosed in Japanese Utility Model Publication No. 59-16816 may be used, and FIG. 2 is a conceptual diagram showing the inner wall temperature gradient (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 that they are arranged at different lengthwise positions, and a sheath-type dummy rod 7 is connected to the tips of the temperature-sensing parts 6 to align the leading ends. In the sheath type dummy rod 7, two conductive wires 5 are insulated and buried in parallel, and the sheath type i1!
It has substantially the same thermal conductivity as the hot body 4. The FM sensor 3 is formed by keeping the sheath type temperature measuring element 4 in a non-thermal contact with each other using an insulating material 8 and storing it in a sheath tube 9.

FIG. 3 is an explanatory diagram of the film quality 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 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, 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. Improvements in performance, durability, and workability are being aimed at.

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 i-th FM sensor 3 at time j is Tj
, , and if one sampling time Δ at time j is the previous inner wall temperature T, then the inner wall temperature difference (difference value) between J-1,1 d... and T... is Δd J,
I J-i, 1 ... is J,1 ΔT,, -T,, -T, , ... (1)
J, l J, l J-1,1. This state is shown in FIG.

This difference value ΔTj, , is multiplied by the deviation W, taking into account the height of each FM sensor 3 in one circumferential direction, etc. Roughly speaking, the difference value Δ
For those where T j, , is negative, Vl-〇, for other cases, positive and negative parameters V that indicate ■, = 1
, is also multiplied, and the corrected difference at time j is 1 shift (positive difference 1 shift) 0 to 0. get.

J, ICT・・・V・・ΔT3. ...(2)J
, l l l J, first, the total sum of all the corrected difference values CT, , for J, 1 for the M sensor 3 is taken, and this is set as ST1.

ST1. - Σ CT, ... (3
) J4·IJ·I Then, according to the following equation (4), if the value of the total difference value ST1· becomes larger than the predetermined threshold value ε1, it is assumed that there has been a sudden temperature rise, and an evaluation point of 1 is given for the predetermined period.

ST1. ≧ε1 (4) The above is the first prediction means based on the reason of Nyu.

A second prediction means based on the reason for the ratio is as follows.

In equation (2), the positive and negative parameters Vi are the difference values ΔTj
, , is positive, v,=Q, and for other cases, it is set as ■・-1, and then the correction [ All FM sensor 3 Take the total sum for ST2. shall be.

ST2・-ΣIcT,・I...(3)J
2yo, J, 1 According to the following equation (4), if the value of the sum of the difference values ST2 based on equation (3) becomes larger than the predetermined threshold ε2, there will be a rapid temperature drop due to raw ore descent. , and will be given an evaluation point of 1 for a predetermined period of time.

'ST2. ≧ε2...(
4).

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

As in the first prediction means, the positive and negative parameters V in equation (2) are J, I for negative values of the difference value ΔK, and v1 for other values. =1.

Also, at time j, there is waste before the sampling time, that is, ΔtXk
Let the corrected difference value of CT j- be i, and the moving average of this corrected difference value with a predetermined time width nΔt at time j for all FM sensors 3, and according to the following equation (4), If the value of this moving average sum 5T3j becomes larger than the 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 first to third prediction means described above are each based on the furnace wall temperature difference (difference value), accurate predictions can be made regardless of whether the absolute value of the furnace wall temperature is above or below. It is possible to make predictions.Moreover, F
The M sensor 3 can be arranged to cover the entire circumference of the blast furnace due to its good workability and good temperature measurement 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 1 showing the process flow of the first prediction means. In the figure, in step s1, the furnace wall temperature Tj of each M sensor 3 is measured at every sampling time Δ. Then step s
In step 2, 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 d3, this positive difference value sum 5T1j is compared with a predetermined threshold value ε1, and formula (4) is satisfied by 2.
Then, in step s5, it is assumed that a decrease in furnace heat will occur due to rapid flow of gas flow to the periphery of the furnace, and an evaluation point of 1 is given for a predetermined period. On the other hand, if the formula (4) is not satisfied, it is assumed that there is no abnormality, and the process returns to step S1, and the furnace heat drop evaluation is performed by repeating steps $1 to $4.

FIG. 7 shows the flow of processing of the second prediction means 70-CH1
- It is. In the figure, in step S11, the furnace wall temperature Tj, , of each FM sensor is measured at every sampling time Δ. Next, in step S12, each FM sensor 3
Calculate the difference value based on equation (1).

Then, in step 813, (2)', (3)
The absolute value sum ST2 of the negative difference values based on the formula ' is calculated. Furthermore, in step 814, the absolute value sum 5T2j of this negative difference value is compared with a predetermined threshold value ε2, and if the equation (4) is satisfied, it is determined that the unloading speed has increased in step S15. It is assumed that a decrease in furnace heat 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 the process returns to step S11, and the following steps 811 to S1
Evaluate the furnace heat drop by repeating step 4.

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. is measured at sample J, every 1 ring time Δ. Next, in step S22, the difference value of each FM sensor 3 is calculated based on equation (1).

Then, in step 323, the moving average sum 5T3j of the positive difference values in the time width nΔt is determined based on equations (2) and (3). Furthermore, in step 3.24, this positive difference value moving average sum ST3 is compared with a predetermined threshold value ε3, and if the equation (4) is satisfied, in step 825, the furnace heat due to heat dissipation of the furnace body is It is assumed that a decrease will occur, and an evaluation point of 1 is given for a predetermined period of time.On the other hand, if the formula (4) is not satisfied, it is assumed that there is no abnormality and the process returns to step S21. Evaluate the decline.

E, 4th. The fifth prediction method is based on top gas component analysis, air blowing conditions, raw material charging conditions, etc.
g/1-1) ) is calculated for each sampling time Δt. Here, let the amount of pull loss C be X J at time j, and the sampling time before time j (that is, Δt
When the amount of loss C of Xk hours before) is set to Xj-, the moving average XH of the predetermined time width nΔt at the current time j can be calculated by S1 as follows.

xH based on equation (5) is calculated for each sampling time Δt, and using equation (6) below,
. ) × 1 is exceeded, the evaluation point i is given by the maximum threshold εxi,
Evaluate.

Hxi'=1 to 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 the amount of vine loss C.
Instead of an increase in ff1, a decrease in the blast furnace heat can be predicted by a decrease in the amount of gas chromatin N2.

As a result, if the gask opening N2 finger at the current time j is yj, and the gask opening Nff1 4 non-pull hours before time j (that is, Δtxk hours before) is yj-, then at the current time j The moving average VH of the predetermined time width nΔt can be calculated as follows.

yH based on equation (7) is calculated for each sampling time Δ, and VH is predetermined by equation (8) below to obtain a predetermined threshold value ε・(J'''he' m ) (εy i >
An evaluation point j is given based on the minimum threshold value ε,j when the value is less than εy 2 > ”・εylIl)J, and the furnace heat drop is evaluated.

yH〈εyj (to j・1・m)...
(8) The above is the fifth prediction means.

Furthermore, the fourth preliminary 4111 means, Tsururos Cff1
When calculating the moving average of , the instantaneous value of the amount of trailing loss C becomes an abnormal value E1. due to noise etc. as shown in FIG. 9(a).
E2 may occur. Here, if the crane loss Cff1 at time j is X, and the crane loss Cff1 before one sampling time Δ is -l...
・(9) J J J-1. By comparing this ΔX・ with the threshold value ε7 and J: in the same figure (b), abnormal values Fl and E2 are found, and the abnormal values Fl and E2 are found in the same figure (
As shown in C), a method of smoothing by replacing the measured value with the immediately previous measurement value can be considered. By applying this method, a more accurate moving average of the true loss Cff1 can be obtained, and as a result, prediction with considerably high accuracy is 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 S31, εx1<εx2...εxn
The threshold values εx1 to εxn are set according to the magnitude of . and,
In step S32, the instantaneous value X of the true loss CI is determined at every sampling time Δ. Then, in step S33, the absolute value ΔXJ of the difference value of the true loss Cff1 is determined,
Next, in step S34, if the absolute value ΔX of the difference value is larger than the threshold = 19 - value ε2, in step S35, this instantaneous value xj is regarded as an abnormal value, and the immediately previous measured value xj
-1, and the process moves to step S36. On the other hand, if it is smaller than the threshold value ε7 in step 834, the process moves to step S36 without changing the instantaneous value XJ. In step S36, the moving average X□ of the time width nΔt is determined, and in the next step 837, the evaluation point i is initialized to 0.

Therefore, in step 838, the moving average value X of the amount of trailing loss C is compared with the threshold value εx1 (from i-0), and if X ≧εx1, the value of i is increased by 1 such as 0 → 1 in step S39 , in step 840 it is determined that i=n, or in step 338 it is determined that X 〈ε
The evaluation point i is calculated by repeating steps 838 to S40 while increasing the value of the threshold εx(i+1) in stages until it is determined that Hx(i+1) is the threshold value εx(i+1), and the process proceeds to step S41. Further, if XH<εx1, steps S39 and 340 are not executed even once, and the evaluation point i is O.
As a result, the process moves to step 341.

Finally, in step 841, steps 838 to S4
Output the evaluation point i obtained from 0.

It should be noted that, as a matter of course, the above-described abnormal value processing can be similarly applied to a computer in the case of predicting a decrease in furnace heat using the moving average value yH of gas chromatography N2m.

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.

F. Comprehensive Prediction Means By using the evaluation scores of the first to fifth prediction means described in F., comprehensive prediction is performed as described below.

FIG. 11 is a flowchart showing the flow of the process, and the following description will be made with reference to the same figure.

In step 851, the evaluation points f of the first to third prediction means are
Find f. Once the evaluation points f to f3 change from 0 to 1, they hold that value during the data bold period, which will be described later. Therefore, the hold time question h r (2II'+
If the culm degree) is set at time t1, then time t ~
At time t1+1h (that is, during the data bold period), once each of the evaluation points f1 to f3 changes from O to 1, it does not change to 1-shi0 again. This data hold period is set at the same time as initializing the fluctuation time tC when the FM sensor υ total value F<-f1+f2+f3) changes from F=0→F>O, and from then on within the time specified by hr. As mentioned above, this is a data hold period.

Next, in step 852, the FM sensor total value F is O.
By determining whether or not the data hold time is
If F=O, there is no data hold time setting, so step S5
Move on to 5. On the other hand, if F≠O, since the data bold time t has already been set, the variable time t is set in step 853.

By comparing with the hold time hr, it is checked whether the current f is in the data hold period or not, and t ≦h
If so, it is during the data hold period r, so there is no need to initialize the FM sensor total value F, and the process moves to step S55. However, step 853
If t c > h r, it is determined that the data bold period has ended, and the FM sensor total value F is initialized to O'' in step S54.

In this way, the FM sensor total value F is determined while taking the data hold period into consideration. This means that the first to third prediction means are the fourth prediction means. Since it has higher foresight than the fifth prediction method (predictions can be obtained quickly), the prediction results of the first to third prediction methods must be held for a certain period of time in order to comprehensively evaluate the prediction results for the same point in the future. This is because it is necessary to keep it.

Then, in step 855, the evaluation score i by the fourth prediction means is calculated, and further in step 856,
An evaluation score j by the fifth prediction means is calculated. Next, in step S57, the overall evaluation C is determined according to the following formula.

C=wAF 10WB! +WCJ-(9
) Here, W, W, Wc are the first to third . 4th degreeB-23=weight for each of the fifth prediction means. This comprehensive evaluation C is examined in step S58, and if it is C-O, it is assumed that there is no tendency for the furnace heat to decrease at all, and the process returns to step 851, and the comprehensive evaluation C is determined again through steps 851 to 57. On the other hand, if C>0, in step S59, an alarm whose importance is changed according to the value of the comprehensive evaluation C is output.

Thereafter, the process returns to step 851 and continues to predict the decrease in furnace heat.

Figures 12 and 13 are graphs showing operation examples of comprehensive prediction based on comprehensive evaluation C, where (a) is the representative value of hot metal temperature, (b)
) is the sum of FM sensor positive difference values (first prediction means), (
C) is the sum of absolute values of negative difference values of the FM sensor (second prediction means), (d) is the moving average sum of positive difference values of the FM sensor (third prediction means), and (e) is the moving average value of gas chromatography N2fit. (Fourth prediction means), (f) shows the moving average value of Tsuru loss C (fifth prediction means), and (g) shows the change over time of the comprehensive evaluation C. The typical hot metal temperature value in (a) is the average value of the temperatures actually measured every 30 minutes during tapping into the hot metal pot (one tap for about 2 hours). The overall evaluation C is expressed as W A” in equation (9).
' W B-Wc-1, n= in equation (6) 1 (8)
m=3, and the range of comprehensive evaluation C is O~.

In Figure 12, the maximum value of the overall evaluation C is 9, indicating that it is necessary to output a serious alarm, and it is actually shown in Figure 12 (a).
Control temperature T as shown in . Typical hot metal temperatures have been measured that are considerably lower than . On the other hand, in Fig. 13, the maximum value of the comprehensive evaluation C is 3, and if this value is enough, a light alarm output is sufficient, and in fact the furnace heat decreases to the extent shown in Fig. 13 (a) ■. Only that has happened.

In this way, by changing the degree of alarm according to the value of the comprehensive evaluation C, it is possible to finely change the degree of heating action. As a result, it becomes possible to select the necessary and sufficient heating action, which not only reliably prevents a drop in furnace heat, but also prevents unnecessary rises in furnace heat due to excessive heating action, resulting in stable Moreover, economical blast furnace operation became possible.

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 may 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,
I installed 28 FM sensors + J3 in 7 levels and 4 directions, but
Of course, it is only necessary to install it appropriately depending on the characteristics of the blast furnace.

Furthermore, as mentioned in ε4, it is desirable for comprehensive prediction to use all of the first to fifth prediction means, but at least the first to third prediction means are used.
By using at least one of the prediction means, and at least one of the fourth and fifth prediction means, substantially the same effect as the example described in Hie can be expected. Further, the data hold period can also be set independently in the first to third prediction means, and the threshold value can be set in the fourth to third prediction means. Similar to the fifth prediction means, variations such as multiple steps are also possible.

(Effects of the Invention) As explained above, according to the present invention, a prediction can be obtained quickly, and since it is based on a comprehensive judgment of the first to fifth prediction means, a decrease in hot metal temperature can be predicted more accurately, and when necessary. You can take heat action according to your needs.

[Brief explanation of the drawing]

FIGS. 1(a) and (+)) are a side sectional view and a plan sectional view showing the arrangement of an FM sensor in the blast furnace wall, respectively, used in an embodiment of the present invention, and FIGS. 2 and 3 are [Conceptual diagram of M sensor, installation explanatory diagram, 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. , FIG. 6 is a flowchart showing the processing flow of the first prediction means, FIG. 7 is a flowchart showing the processing flow of the second prediction means, and FIG. 8 is a flowchart showing the processing flow of the third prediction means. The flowchart, Figures 9(a), (b), and (c) are the instantaneous values of the amount of trailing loss C including abnormal values, and the trailing loss C, respectively.
A graph showing the absolute value of the difference value of the mouth and the instantaneous value of the amount of true loss C after removing abnormal values, Fig. 10 is a flowchart showing the processing flow of the fourth prediction means, and Fig. 11 shows the processing of the comprehensive prediction means. Flowchart showing the flow, Figure 12, 1st
Figure 3 (a), (b), (cl, (d), (e)
, m, ((+) is the representative value of hot metal temperature, FM sensor positive difference value total, negative difference value absolute value total, positive difference value moving average total, moving average value of gas chromatin N2 amount, and tsuru loss C port. Fig. 14 is a side cross-sectional view showing the arrangement of the furnace belly sonde in the blast furnace in the conventional technology, and Fig. 15 is a graph showing the moving average value of and the change in the comprehensive evaluation C over time. Graph showing correlation, Figure 16 (a), (b)
17(a) and (b) are graphs showing the hourly average 1fj and hot metal humidity changes over time at Tsururosu C mouth, respectively.
2 is a graph showing the average value and the change in hot metal temperature over time.

Claims (1)

[Claims] (1) Install an inner wall thermometer at a predetermined location in the blast furnace, and measure the inner wall temperature difference at predetermined time intervals with the inner wall thermometer. A first system that gives evaluation points for a predetermined period when the total value exceeds a 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.
and a third prediction means that gives an evaluation point for a predetermined period when the sum of the moving average values for a predetermined time width of the portion showing a positive value of the inner wall temperature difference at a certain time exceeds a threshold value. a fourth prediction means which is provided with at least one and which calculates the amount of solution loss carbon at each predetermined time interval and gives an evaluation point when the moving average value of the calculated value over a predetermined time width exceeds a threshold; Further, at least one of a fifth prediction means for determining the amount of nitrogen in the component at each predetermined time interval and giving an evaluation point when the moving average value of the determined value over a predetermined time width is less than a threshold value; At least one evaluation point of the first to third prediction means and at least one of the fourth and fifth prediction means
A blast furnace heat drop prediction method that predicts a blast furnace heat drop based on a comprehensive evaluation of two evaluation points.