JPH02107707A - Method for predicting lowering and raising of furnace heat in blast furnace - Google Patents

Abstract

PURPOSE:To accurately predict raising and lowering of molten iron temp. in a furnace by measuring furnace temp. at each interval of the prescribed time with a thermometer arranged at furnace wall of the blast furnace and measuring solution loss of coke in the furnace. CONSTITUTION:The temps. in the furnace are measured at each interval of the prescribed time with plural inner wall thermometers arranged at vertical and outer circumferential directions of the furnace wall in the blast furnace, and in the case the raising or lowering value of this temps. difference exceeds the threshold value, the operation method is controlled by adjusting the charging quantity of raw material fuel from the furnace top. Further, the furnace top gas is analyzed at every prescribed times and the lowering of the furnace temp. caused by the solution loss of the coke reduced into CO with CO2 gas, is obtd. and in the case this moving average value is less than the threshold value, the lowering of the temp. in the blast furnace is predicted. By accurately predicting the furnace temp. based on lowering of the temp. caused by the above measured value of the furnace inner wall temp. and the solution loss of the coke, the furnace condition is stabilized and the temp. of the molten iron in the furnace is held to the prescribed value.

Description

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

(Prior Art) It has been known 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. 18, the relationship between the temperature at the side of the furnace bed and the temperature of the hot metal detected by the sensor (furnace sensor) 2 installed in the blast furnace 1 is subjected to linear regression as shown in FIG. 19. Based on this linear equation, the hot metal temperature Toio is calculated from the furnace belly layer side temperature.
It predicts.

On the other hand, conventionally, the amount of solution loss carbon (hereinafter referred to as "Tsuru Loss Cal"), which indicates the quality of the reduction state of the blast furnace, has been
Another prediction method is to predict the blast furnace thermal temperature based on the increase or decrease in the temperature. The increase in Turuloss Cff1 indicates that the so-called Turuloss reaction described below is promoted.

C+002-)2CO Since this reaction is an endothermic reaction, it can be predicted that the heat in the blast furnace will decrease.

Truloss Cff1 normally measures the proportions of CO, CO, N2, etc. in the furnace top gas, air blowing conditions, and raw material charging conditions at every analysis cycle (3 minutes/seconds) of gas chromatography that analyzes the composition of the furnace top gas. Conventionally, the decrease in furnace heat was controlled by the average value of the tsuru loss reaction every hour.

Figures 20 (a) and 20 (b) show d3, and the same figure (a) shows the changes over time of the crane loss Cff1 (j! 1) every 3 minutes and the average value (12) of the crane loss Cftt every hour, Figure (b) 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 (■). Figure 21 shows time 13 when the threshold ε2 was exceeded.
It shows the respective changes over time when the heating action A occurs. Similarly to FIG. 20, 11 in the figure indicates the instantaneous count every 3 minutes, and 12 indicates the hourly average loss Cff1. By comparing Fig. 21 and Fig. 21, it can be seen that due to the heating action, the drop in hot metal temperature as shown in ■ in Fig. 20 (b) can be avoided to some extent as shown in Fig. 21 J. .

(Problem to be Solved by the Invention) However, in the former method, it is necessary to insert the furnace probe 2 in order to measure the temperature near the inner wall inside the furnace, and therefore temperature measurement can only be performed at intermittent times. Naturally, there was a problem in that the accuracy of predicting the temperature of the hot metal deteriorated.

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. 19 has the problem that accurate prediction cannot necessarily be made.

In addition, in the latter method, when predicting the average value of the amount of vine loss C every hour, when there is a sudden increase in the amount of vine loss C, in the worst case, it is difficult to predict the furnace heat drop for about an hour. There was a problem that caused delays. For example, in the case of Fig. 21, the temperature of the hot metal is the control temperature T when heating action A is taken due to the prediction delay. 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 truss loss Cff1 measured at intervals of about 3 minutes,
As shown in 1, the individual variations are large and the noise component is large, so there is no sustainability of the data. Therefore, it is almost impossible to predict the decrease in furnace heat using the instantaneous value of the amount of truss loss C.

Further, as described above, since the trundle loss reaction is an endothermic reaction, the increase in the furnace heat of the blast furnace can be predicted to some extent from the decrease in the amount of trundle loss C. However, for the same reason as the furnace heat drop prediction, accurate prediction is difficult.

An object of the present invention is to provide a method for predicting a blast furnace heat decrease and a furnace heat increase, which solves the problems of the prior art described above and can accurately predict decreases and increases in hot metal temperature.

(Means for Solving the Problems) A blast furnace furnace heat drop prediction method according to the present invention includes installing an inner wall thermometer at a predetermined location of a blast furnace, and measuring the inner wall temperature difference at predetermined time intervals with the inner wall thermometer. a step of performing a first prediction that 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 step of performing a second prediction that gives an evaluation point for a predetermined period when the total value of the portions showing the value exceeds a threshold; and a moving average of the portions showing a positive value of the inner wall temperature difference at a certain time over a predetermined time width. A step of performing a third prediction that gives an evaluation point for a predetermined period when the sum of the values exceeds a threshold, and calculating the amount of solution loss carbon at each predetermined time interval, and the moving average value of the calculated values in a predetermined time width is A fourth prediction step in which an evaluation point is given when the dark value is exceeded, and the nitrogen amount in the furnace top gas component is determined at predetermined time intervals, and the moving average value of the determined value over a predetermined time width is determined as the threshold value. A fifth prediction step in which an evaluation point is given when the value is less than a threshold; 6
and a step of determining the hot metal temperature at predetermined time intervals and detecting the current furnace heat level based on the moving average value of the determined values over a predetermined time width. Is the prediction threshold of 3 the total value of each correspondence within a certain past period or the target of the total sum? 1!
The threshold values for the fourth to sixth predictions, which change from time to time based on the deviation, are the solution loss carbon amounts for each response within a certain period of time in the past.

It changes from moment to moment based on the average value of the nitrogen amount or the number of times of charging the raw material, and is based on the overall evaluation of the evaluation points of the first to sixth predictions and the detected furnace heat level,
The method further includes a step of predicting a blast furnace furnace heat drop.

In addition, the blast furnace furnace heat rise prediction method according to the present invention obtains the solution loss carbon layer at predetermined time intervals, and provides a first evaluation point when the moving average value of the obtained values at the predetermined intervals is less than a threshold value. The step of making a prediction and determining the amount of nitrogen in the furnace top gas component at predetermined time intervals;
A second prediction step in which an evaluation point is given when the moving average value of the obtained values in a predetermined time width exceeds a threshold value, and the number of times of charging the raw material from the top of the furnace is determined at each predetermined time interval. A third prediction step in which an evaluation point is given when the obtained value is lower than the dark value, and the molten metal temperature is obtained at predetermined time intervals, and based on the moving average value of the obtained values in the predetermined time width, the current condition is calculated. and a step of detecting a furnace heat level, wherein the threshold values of the first to third prediction means correspond to the corresponding solution loss carbon suspension within a certain period of time in the past.

The amount of nitrogen changes from moment to moment based on the average value of the number of times the raw material is charged, and is based on the comprehensive evaluation of the evaluation points of the first to third predictions and the detected furnace heat level,
The method further includes a step of predicting the blast furnace thermal field.

(Function) The blast furnace furnace heat drop prediction method according to the present invention predicts the furnace heat drop based on the comprehensive evaluation of the first to sixth predictions and the detected furnace heat level. The prediction can be changed depending on the difference.

Further, the blast furnace furnace thermal back prediction method according to the present invention includes the first to
Since the furnace heat rise prediction is performed based on the comprehensive evaluation of the third prediction and the detected furnace heat level, the prediction can be changed depending on the difference in the furnace heat level even if the comprehensive evaluation is the same.

(Example) Examples of the present invention will be described below, and the first to ninth prediction means described therein, the first to sixth predictions according to claim 1, and the first to sixth predictions according to claim 2 Table 1 shows the correspondence with the third prediction.

Table 1 The following factors are considered to be contributing factors to the decrease in blast furnace heat.

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, FQ　O+C→FC→−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 sharply. If this sudden temperature rise is detected, a decrease in furnace heat can be predicted.

B. Second Reason for Decrease in Furnace Heat Furthermore, the following factors can be considered as contributing factors to 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 relevant portion rapidly decreases. If this rapid temperature drop is detected, a decrease in furnace heat can be predicted.

Reasons for low furnace heat of C0.53Furthermore, 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 tuyeres to adjust the temperature of molten metal and the amount of hot metal is usually blown into the center of the furnace. However, for the same reason as A, B, a 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 to the peripheral part of the furnace, the temperature of the furnace wall gradually increases by -F. By detecting such a gradual temperature rise over a relatively long period of time, a decrease in furnace heat can be predicted.

D,]j~.--1: Figures 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, 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 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 the paragin 14 and the welding part 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. Efforts are being made 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 ""
・If the inner wall temperature before closing is Tj-1,i for one sampling time Δ at time j, then ■1. Inner wall temperature difference (difference value) between and Tj-1,i
1). This state is shown in FIG.

This difference value ΔTj, . Roughly, difference value 6
For negative j,i, V.

=0, and for other cases, multiply by the positive/negative parameter V, which indicates {circle around (2)}=1, and obtain a corrected difference value (positive difference value) CT· at time j.

CT−= =w−−v=−ΔT, −・−(2) J, l
l l J, first order,
Correction difference value CTj, for all FM sensors 3 and next (4)
According to the formula, if the field of this total difference value ST1 is larger than a predetermined threshold value ε1, it is assumed that there has been a rapid temperature rise, and an evaluation point of 1 is given for a predetermined period.

STI, ≧ε1...(4)
The above is the first prediction means based on reason A.

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

In equation (2), the positive and negative parameters V・ are the difference values 6 to 1
．． For those where is positive, v,=Q, that J,1
For values other than 1, set ■, = 1, then corrected difference value C
The absolute values of T, , , for all FM sensors 3 are summed, and this is calculated as Sr1. shall be.

Sr1. = 1cT,,l...(3) Then, according to equation (4), if the value of the sum of difference values ST2 based on equation (3) is greater than the predetermined threshold ε2. , it is assumed that there was a sudden temperature drop due to the descent of the raw ore, and an evaluation point of 1 is given for the predetermined period.

The third prediction means based on the reason of Sr1≧ε2 (4) σ- is as shown below.

As in the first prediction means, the positive/negative parameter Vi in equation (2) is J,1 v,=Q for negative difference values ΔT, and ■1- for other values. 1 and f. Also, at time j, before the sampling time (i.e., Δ
CTj, -.

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... (3) Then, according to the following equation (4), this moving average sum ST3,
If the value becomes 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) The first to third prediction means described above are each performed using the furnace wall temperature difference (difference value), so they can be accurately calculated regardless of the upper or lower absolute value of the furnace wall temperature. Predictions can be made. Moreover,
The FM sensor 3 can be placed to cover the entire circumference of the blast furnace due to its ease of installation and good temperature measurement response, and by being able to measure continuous inner wall temperature differences, more accurate predictions can be made. .

Moreover, - the above-described first to third prediction means can be realized by a computer. FIG. 6 is a flowchart 1 showing the flow of processing of the first prediction means. In the figure, in step S1, the furnace wall temperature Tj of each FM sensor 3 is
is measured at every sampling time Δ. Next, in step S2, the difference values of each FM sensor 3 are 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 S T1 is compared with a predetermined threshold value ε1, and if the equation (4) is satisfied, in step S5, the furnace temperature is reduced due to rapid flow around the furnace of the gas flow. It is assumed that a decrease in heat will occur, and a score of 1 is given for the 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, whereupon steps 81 to S4 are repeated to evaluate the decrease in furnace heat.

FIG. 7 is a flowchart showing the process flow of the second prediction means. In the figure, in step S11, the furnace wall temperature T of each FM sensor is measured at every sampling time. Next, in step S12, the difference value of each FM sensor 3 is calculated by t1 based on equation (1).

Then, in step S13, (2)', (3)'
A total absolute value 5T2J of negative difference values is determined based on the formula.

Furthermore, in step 814, the absolute value sum ST2. is compared with a predetermined threshold value ε2, and if the formula (4) is satisfied, it is assumed that a decrease in furnace heat will occur due to the increase in unloading speed in step 315, and the evaluation point is 1 for a predetermined period. give. On the other hand, (4)
If the formula is not satisfied, it is assumed that there is no abnormality and step S1
1 and repeat steps 311 to 814 to evaluate the decrease in furnace heat.

FIG. 8 is a flowchart showing the processing flow of the third f1 (111 means. In the same figure, step S21
The furnace wall temperature T, i of each FM sensor 3 is measured at every sampling time Δ. Next, in step S22, each F
The difference value of the M sensor 3 is multiplied by 4 based on equation (1).

Then, in step S23, the moving average sum 5T3j of the positive difference values in the time width nΔt is determined based on equation (21'', (3)').Furthermore, in step S24, the moving average sum 5T3j of the positive difference values is calculated. 3. is compared with a predetermined threshold value ε3, and if the equation (41) is satisfied, it is assumed that a decrease in furnace heat due to furnace body heat dissipation will occur in step S25, and an evaluation point of 1 is given for a predetermined period.

On the other hand, if the formula (41'' is not satisfied), it is assumed that there is no abnormality, and the process returns to step 821, whereupon steps 321 to S24 are repeated to evaluate the decrease in furnace heat.

[, 4th. Fifth prediction means Based on top gas component analysis by gas chromatography, ventilation conditions, raw material charging conditions, etc.
l-D) is calculated every sampling time t. Here, the truss C hanging at "time" is bound by X, and the time J
If the true loss Cff1 before the sampling time (that is, the Δtxk time song) is xj-, then the moving average xH of the predetermined time width nΔt at the current time j can be calculated as follows.

XH based on formula (5) is subtracted for each sampling time Δt, and XH is determined to be a predetermined threshold ε, (i=1~n)(εx1〈εx2...〈εx
An evaluation point i is given based on the maximum threshold value εxi when exceeding n)×1,
Evaluate.

X〉ε (i=1~n)...(6
)Hx+ 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 N2ff1J) has a strong negative correlation with truss loss Cff1. As a result, a decrease in blast furnace heat can be predicted.

As a result, the gas chronograph N2sui at the current time j is set as yj, and the gas chromatogram N,2ffi at the sampling time before time j (that is, ΔtXk hours before) is set as 'j.
j−, the predetermined time width nΔ at the current time j
The moving average VH of t can be calculated as y, = −1y j−++ (7°n+1k”0).

yH based on equation (7) is calculated for each sampling time Δ, and evaluation point 1 is given when VH falls below a predetermined threshold value ε5 according to equation (8) below to perform a furnace heat drop evaluation.

yH ε5 (8) The above is the fifth prediction means.

Furthermore, when calculating the moving average of the trail loss Cff1, which is the fourth prediction means, the instantaneous value of the trail loss C amount is shown in FIG.
As shown in a), the abnormal value E1. E2 may occur. Here, the truss loss Cff at time j
1 is xl, 1 sampling time Δ is the previous trail loss Cff
1 is X, then the absolute value ΔX of the difference value of the true loss Cff1 is ΔX, ""IX, -x, l ・19)
It becomes J J J-1. By comparing this ΔX with the threshold value ε2 as shown in the same figure (b), the abnormal value E1. Find E2 and draw the same figure (C
), a method of smoothing by replacing it with the immediately previous measured value can be considered. By applying this method, a more accurate moving average of the amount of truss loss C 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 831, εx1<εx2...εxn
The threshold values εx1 to ε are set with the magnitude of . Then, in step 832, the instantaneous value X· of the curve×n loss Ca1l is obtained at every sampling time Δ. Then, in step S33, the absolute value ΔXJ of the difference value of the amount of trailing loss C is calculated, and then in step 834, if the absolute value Δxj of the difference value is larger than the threshold value ε, in step S35, this instantaneous value X is abnormal. It is regarded as the value and replaced with the immediately previous measurement 1+fl x ·, and the process moves to step S36. On the other hand, if it is smaller than the threshold value ε7 in step S34, the process moves to step 836 without changing the instantaneous value XJ. Step 3
In step 36, the moving average XH of the time width nΔt is obtained, and in the next step 837, the evaluation point 1 is initially set to O.

Then, in step S38, a comparison is made between the true loss C moving average value XH and a threshold value εx1 (from i=o), and if XH≧εx1, the value of i is increased by 1 from O→1 in step S39, and step In step 840, it is determined that i=n, or in step 338, x
Step x(i+1) while increasing the value of threshold ε step by step until it is determined that
+1) Repeat steps S38 to S40 to calculate the evaluation score, and proceed to step 841. Also, if xH x εx1, steps 839 and 340 are never executed and the evaluation point i is O.
As a result, the process moves to step 341.

At step 841, steps 838 to 84
Output the evaluation point i obtained from 0.

As a matter of course, application of the abnormal value processing described above to a computer is based on the moving average value y of gas chromatography N2ff1.
The same can be achieved in the case of predicting the decrease in furnace heat due to H.

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, sixth and seventh prediction means Moving average xH of trail loss Cff1 based on formula (5) →
The non-bringing time is calculated every t, and according to the following formula (10),
) (εy1>εy2...>εy□). Evaluation points are given based on the evaluation criteria.

X 〈ε (j=1~m)
...(10) HVJ The above is the sixth prediction means. It can be said that the higher the "evaluation score" by this prediction means, the stronger the tendency for the blast furnace heat to rise.

In addition, the moving average VH of gas chromatography N2 based on equation (7)
is calculated for each sampling time Δ, and when yH exceeds a predetermined threshold value ε7, an evaluation point of 1 is given, and the furnace heat rise is evaluated using the following equation (11).

VH>ε7...(1
1) The above is the seventh prediction means.

The furnace heat drop prediction method using a moving average of the truss loss Cff1 including abnormal value correction described in the above can be realized using a computer. FIG. 11 is a flowchart showing the flow of the process.

In the same figure, first, in step 851, the threshold value εy1 is set to ε, 1>ε, 2...εym in n stages.
~ε is set. Then, in step S52, the instantaneous value X of the solenoid loss CF11 is obtained at each sampling time Δt. Then, in step S53, the Tsuru loss C
Find the absolute value ΔXJ of the difference value of ff1, then step 8
If the absolute value Δxj of the difference value is larger than the threshold value ε2 in step 54, in step 855 this instantaneous value
, is regarded as an abnormal value, and is replaced with the immediately preceding measurement taX, and the process moves to step S56. On the other hand, step S54
is smaller than the threshold ε, the process moves to step S56 without changing the instantaneous value XJ. Step S
In step S56, the moving average XH of the time width nΔt is obtained, and in the next step S57, the evaluation point is initialized to j&0.

Then, in step S58, a comparison is made between the most moving average value X of true loss C and a threshold value εy1 (from i=0), and if X ≦ε, 1, the value of j is increased by 1 from O→1 in step S59. , d3 in step S60 and i=m
In step S58, it is determined that ×14〉ε'
The value of @value ε is increased step by step until it is determined that /(j+1) t! While staying (j+1), steps 358 to 860 are repeated to obtain an evaluation point j, and the process moves to step 361. Also, if X s > ε, 1, then
Steps 359 and 360 are never executed and the evaluation point j
(YoO is selected and the process moves to step 361.

Finally, in step S61, steps 358 to S6
Output the evaluation score j obtained from 0.

As a matter of course, the application of the abnormal value processing described above to a computer is based on gas chromatography ('J2-hanging moving average value y
In the case of predicting the furnace heat rise due to H, it can be realized in the same way as J3.

Above) The 6th one that I missed. The seventh prediction means is the fourth prediction means. Like the fifth prediction means, since it is based on the moving average of the sampling time Δtffl, predictions can be obtained quickly and the accuracy can be said to be sufficiently reliable.

G, 8th. Ninth Prediction Means It is empirically known that the number of times raw materials are charged from the top of the furnace per predetermined time (hereinafter referred to as "charging pitch") affects the furnace heat. In other words, it is known that when the charging pitch increases, the furnace heat decreases, and when the charging pitch decreases, the furnace heat increases.

This is presumed to be mainly due to the following reasons.

■ When the charging pitch increases (decreases), the endothermic tsuru loss reaction is promoted (suppressed), and as a result, the furnace heat decreases (upward).

■ As the charging pitch goes up (down), the amount of tapped iron increases (or decreases), and the time from material charging to tapping becomes shorter (longer).

As a result, the time for the hot metal to exchange heat in the blast furnace decreases (increases), so the furnace heat decreases (increases).

Therefore, the charging pitch P I TCH is counted and the P I
TCH>88...If (12) is satisfied, an evaluation point of 1 is given and a furnace heat reduction evaluation is performed (eighth prediction means).

On the other hand, PITCH< ε9...(13)
If the above is satisfied, an evaluation point of 1 is given and a furnace heat rise evaluation is performed (ninth prediction means). Note that ε, ε9 (ε8>ε9) are charging pitches as threshold values.

FIG. 12 is a flowchart showing the processing flow of the eighth prediction means. In the same figure, in step S71, the charging pitch PITCH per unit time is calculated from the sampling data.
seek. Next, in step S72, this charging pitch PI
TCI-( is compared with a predetermined threshold value ε8,
If formula (12) is satisfied, an evaluation point of 1 is output in step 873. On the other hand, if the equation (12) is not satisfied, an evaluation point of 0 is output in step S72.

Note that the processing flow of the ninth prediction means is the same as the flowchart shown in FIG. 12, except that the comparison in step S72 is replaced by equation (12) with equation (13).

Ratio -1 Wataru Toyo11 Each threshold value ε9 of the first to ninth prediction methods described in p1 to Ω1
(ε, ε2.ε3.ε 1.ε,, ε.

I XI yJ 5, ε, and hereinafter collectively referred to as ``ε1''. ) is determined in advance so that the optimum furnace heat drop prediction rate or furnace heat Ueno prediction rate can be obtained. However, the initially optimal threshold value ε9 may become non-optimal due to changes in various conditions such as the production plan and raw material conditions during blast furnace operation. Therefore, it is necessary to change the threshold value ε'' to an optimal value during blast furnace operation, and learning of the threshold value ε8 is performed as shown below.

■ Threshold value learning for the first to third prediction means When measuring the furnace wall temperature of a blast furnace, the FM, which is a furnace wall thermometer,
As mentioned above, the temperature measurement point of the sensor 3 changes with the erosion of the furnace wall. Therefore, not only the value of the measured furnace wall temperature itself but also the peak value (maximum value) of the furnace wall temperature difference may vary depending on the erosion of the furnace wall. Since the first to third prediction means make predictions based on the furnace wall temperature difference, the prediction accuracy will also vary in such a case.

In order to solve this problem, the following processing is performed. First, go backwards in time from the current time t to the past,
Find the standard deviations σ to σ3 of each total ST1 to ST3 in this interval, and calculate J J
The threshold values ε 1 to ε 3 are determined from the standard deviations σ 1 to σ 3 using the following equations (14) to (16).

ε1=a1×σ1...(14)
ε2=a2xa2 ・(151ε3=
a3×σ3 ... (16) In addition, a
1 to a3 are coefficients.

FIG. 13A shows the time variation between the difference value of the furnace wall temperature and the threshold value ε" in the threshold value learning of the first to third prediction means.

The period t is a period in which the detection sensitivity of the FM sensor 3 is low due to falling walls, etc., and the period tb is a period in which the detection sensitivity of the FM sensor 3 is high. Therefore, fixing the threshold value ε1 to a certain field is often overlooked during the period ta;
In b, over-detection will occur. Considering this,
In the period t, the threshold ε" is set low, and in the period tb, the threshold ε" is set low.
It is desirable that the value be set high. With this learning method,
It can be seen that the threshold value ε9 can be set low in the period t 2 and the threshold value ε9 can be set too high in the period tb, and it is possible to detect a wall fall etc. with high accuracy.

■ Threshold learning of the 4th to 9th prediction means - Trunk loss Cff in sampling time of about a week
1. Gascro N21! and the average value of charging pitch change with changes in operating conditions during blast furnace operation. Therefore, following this, the threshold ε width ε,j.

ε5. It becomes necessary to change ε7 to ε9.

In order to solve this problem, the following processing is performed. Obtain the average values Sol, N2 and PITCH of the truss CT gas chromatography N2 and charging pitch that have gone up in the past for a predetermined period of time (a long period of about - weeks) from the current time t, and use these values to calculate the following (17) ~ By formula (22), the threshold εx
i, ε, j, ε5. ε7. Determine ε8°ε9.

εxi” 3 of+ΔS olU (i) (i=1
~n)...(17e, = S of - Δ5olD(
j) (j=1~m) ・18ε5−N2−ΔNut
...19ε1=N2+ΔND
...20ε8=PITCH+Δpu
...(21ε9=PITCH−ΔPD
...(22 Note that [Δ5OIU(i), Δ
5olD(j), ΔNU.

ΔND, ΔPU, and ΔPDI (>O) are preset values.

FIG. 138 is a flowchart showing the processing procedure of threshold value learning explained in ① and ②. In the same figure, first in step 391, the sum of positive difference values STj, the sum of absolute negative difference values 5T21 . Positive difference value moving average sum ST
3. Find standard width differences σ to σ3, respectively.

Then, using these standard deviations σ1 to σ3, threshold values ε1 to ε are determined from equations (14) to (16) in step 892.
Decide on 3.

Next, in step 893, the average values SOI, N2, and UPI rcI-1 of the turret loss C5, gas chromatography N2, and raw material charging pitch within a certain past period from the current time t are determined.
WfilL, in step S94, (17) to (22)
Thresholds εxi~ε, J, ε5. Determine ε7 to ε9. Thereafter, the process returns to step 891 and the threshold value is determined as needed by repeating the above-described process.

As explained in P■ and ■, by automatically determining the threshold value ε9 based on the change in each sampling data in the first to ninth prediction means at the appropriate time, the production plan and the Predictions can always be made with high accuracy regardless of changes in various conditions such as raw material conditions.

1, -jJ +zf p 工, L4. (By the first to fifth and eighth prediction means described in :a-, the evaluation score is calculated while changing the threshold value ε8 at any time as described in Hie.Then, the overall evaluation of this evaluation score, The final decrease in furnace heat is predicted from the furnace heat level, which will be described later.

Note that the term "furnace heat decrease" as used herein means that the moving average value of the hot metal temperature over a predetermined time period goes around the lower control limit temperature.

FIG. 14 is a flowchart showing the flow of the process, and the following description will be made with reference to the same figure.

In step 8101, 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, if the hold time (about 2 hours) is set at time t1, then from time t1 to time t
, +h, (that is, the data bold period) is the evaluation point f
〜f3 each changes from −× O → 1, then 1 → Q again
will not change. This data bold period is F
M sensor total value F (= f + f 2 + f
3) is set while initializing the fluctuation time tc when F=0→F>O, and from then on, the time defined by tc becomes the data hold period as described above.

Next, in step 5102, the F M sensor total value F
By determining whether or not is O, it is determined whether or not the data hold time h is set. If F=O''C-, there is no setting of the data hold time h, so the process goes to step 5105. On the other hand, if F≠0, the data hold time hr has already been set, so step 5
At 103, the variation time t. By comparing tC and hold time, it is checked whether the current time is in the data hold period or not. If tC≦h, it is in the data hold period, and the FM sensor total value F is initialized. Since it is not necessary, the process moves to step 5105. However, in step 5103 t. >hr, it is determined that the data hold period has ended, and in step 5104 the FM
Initialize the sensor total 1 shift F to II OII.

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. Because it has higher foresight than the fifth and eighth prediction means (predictions are made earlier), in order to comprehensively evaluate the prediction results for the same point in the future, the prediction results of the first to third prediction means are used. This is because it is necessary to hold it for a certain period of time.

Then, in step 8105, the evaluation point f5 by the fifth prediction means is calculated, and then in step 5106, the evaluation point f8 by the eighth prediction means is calculated.

Next, in step 8107, the intermediate evaluation point C1 is set to the next (2
3) Calculate from the formula.

C1=sqn(F+f +f) ・=(23
) However, the symbol "rson" indicates an operation to extract the positive or negative sign of the argument. Therefore, the FM sensor total value F
, if at least one of the evaluation points f, f8 is 1 or more, the intermediate evaluation point C1 is "1", and if the FM sensor total value F, evaluation points r, f8 are all rOJ, the intermediate evaluation point C1 is rOJ. becomes.

Then, in step 8108, the evaluation score i by the fourth prediction means is calculated. Note that n in equation (17) at this time is 3. Next, in step 5109, the overall evaluation C is determined according to the following equation (24).

C=CI+i...
(24) Then, in step 5110, the moving average Tpig of the hot metal temperature for a predetermined time width is calculated from the actual hot metal temperature measurement value "pio," and in step 5111, the moving average Tpig of the hot metal temperature is calculated based on the following: t1!Temperature T1.T2 (T1> 1)l
(IT2) and determine the furnace heat level TP as shown below.

If 'pio'1, TP=1 If T1≧'' pig≧T2, TP=2T If <T2, TP=3 1g Then, in step 5112, from the comprehensive evaluation C and the furnace heat level TP, a table is created. 2, perform the final furnace heat drop prediction. At this time, since the change in the intermediate evaluation C1 is O to 1 and the change in the evaluation point i is 0 to 3, the overall evaluation C1
is determined between O and 4.

Table 2 In Table 2, "-" indicates no alarm, rKJ indicates a mild alarm, and rJJ indicates a severe alarm.

Thereafter, the process returns to step 5101 and continues to predict the decrease in furnace heat.

FIG. 15 is a graph showing the effects of blast furnace heat drop prediction according to the embodiment of the present invention, and (a) shows the overall evaluation C1.
(b) shows the furnace heat temperature moving average "pio". Note that ■ is the control upper limit temperature, and "minax" is the pipe]! J! This is the upper limit temperature. Below is the furnace heat level T'P
Total without considering! Furnace heat drop prediction based only on ffi C (furnace heat drop prediction according to Table 2 with TP = 2 fixed, hereinafter referred to as "prediction A"), and based on the furnace heat level TP and comprehensive evaluation C. Furnace heat drop prediction (hereinafter referred to as “prediction B”)
”. ) will be compared with reference to FIG. Note that no heating action is performed here.

Time t (TP=1 since T > T1.)
a, pl(1), a mild alarm is output for prediction A, but no alarm is output for prediction B. From Fig. 15(b), the hot metal temperature moving average ``jio'' decreases after time ta, but
Eventually, the temperature will be within the control temperature (Tming〈T・＜−“1
llax). Therefore, since it can be determined that there was no need to perform the heating action at time t8 at 01g, the mild alarm based on prediction A becomes a false alarm.

Time t (TP=3 because T, <72) b
peg, no alarm is output for prediction A, but a mild alarm is output for prediction B. From Fig. 15(b), the hot metal temperature moving average "pio" decreases after time tb and falls slightly below the lower limit control temperature T1n.Therefore, it is necessary to perform a slight heating action at time tb. Therefore, the prediction B of outputting a mild alarm was correct, and the prediction A failed to output a minor alarm.

From the above, it can be seen that prediction B has better prediction accuracy than prediction.

In this way, based on the comprehensive evaluation C and the furnace heat level TP,
By changing the degree of alarm, the degree of heating action can be finely changed. 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 an unexpected rise in furnace heat due to excessive heating action.
Stable and economical blast furnace operation has become possible.

J, Blast Furnace Furnace Thermal Stress Prediction F, 6th section described in G. 7th. The ninth prediction means calculates the evaluation score while changing the threshold value ε1 at any time as described in the ratio. Then, the final furnace heat level is predicted based on the comprehensive evaluation of the evaluation points and the furnace heat level.

Here, the term "furnace heat rise" means that the moving average value of the melt'vc temperature over a predetermined period of 11 seconds exceeds the control upper limit temperature.

FIG. 16 is a flowchart showing the flow of the process, and the following description will be made with reference to the same figure.

First, in step 5121, the evaluation point f7 by the seventh prediction means is calculated, and then in step 5122, the evaluation point f9 by the ninth prediction means is set to 0.

Then, in step 5123, the intermediate evaluation score D1 is set to the next (
25) Calculate using formula.

D1=sgn (T7 to f9) −(25
) Thereafter, in step 5124, the evaluation score j by the sixth prediction means is calculated. In addition, (18) at this time
m in the formula is 3. Next, in step 8125, the overall evaluation is calculated according to the following equation (26).

D = D 1-1- j
...(26) Then, in step 8126, the hot metal temperature moving average Tpig for a predetermined time width is calculated from the hot metal temperature actual measurement value Tpig, and in step $127, this hot metal temperature moving average T 9 and the reference temperatures T, T (T> 1)l
(1121 T2), and as mentioned in T9, the furnace heat level TP
seek.

Then, in step 8128, a final furnace heat rise prediction is made according to Table 3 from the comprehensive evaluation and the furnace heat level TP. In addition, at this time, since the change in the intermediate evaluation D1 is O~1 and the change in the evaluation point j is O~3, the total total is 1l.
lliD is determined between 0 and 4.

Table 3 In Table 3, "-" indicates no alarm, rKJ indicates a mild alarm, and rJJG indicates a severe alarm.

Thereafter, the process returns to step 5121 and continues to predict the furnace heat rise.

FIG. 17 is a graph showing the effect of blast furnace heat rise prediction according to the embodiment of the present invention, in which (a) shows an overall evaluation of D;
(b) shows the furnace heat temperature moving average 'pig. In addition, 'manax' is the management upper limit temperature and 'manax' is the management lower limit temperature. Hereinafter, the furnace heat rise prediction (TP=
2, and the furnace heat height prediction according to Table 3, hereinafter referred to as "y; measurement C". ) and the furnace heat rise prediction (hereinafter referred to as "prediction D") based on the furnace heat level TP and comprehensive evaluation will be compared with reference to FIG. Note that no heat reduction action was taken here.

Time t (TP=3 from T ・ T2.)
In Cpl(1), a mild alarm is output for prediction C, but no alarm is output for prediction. From Fig. 17 (b), the moving average of hot metal temperature ", 1g is at time t. It rises afterward,
Ultimately, the temperature remains within the control temperature (T. Therefore, p
l(l ll1aX Since it can be determined that there was no need to take the heat-lowering action at time t, the mild alarm based on prediction C becomes a false alarm.

Time t (T, <TP=1 from T1.) d
peg, no alarm is output in prediction C, but a mild alarm is output in prediction B. From FIG. 17(b), the hot metal temperature moving average T rises to 1 g after time td, slightly exceeding the upper limit control temperature max. Therefore, since it is determined that it was necessary to take a mild heat-lowering action at time td, the prediction of the mild alarm output of the pre-ISO was correct, and the prediction C failed to output the mild alarm.

From the above, it can be seen that prediction accuracy is superior to prediction C.

In this way, based on the comprehensive evaluation and the furnace heat level TP,
By changing the level of the alarm, the level of heat reduction action can be changed in detail. By connecting the beams, it is now possible to select the necessary and sufficient heat-lowering action, which not only reliably prevents a rise in furnace heat, but also avoids unnecessary drops in furnace heat due to excessive heat-lowering action.
Stable and economical blast furnace operation has become possible.

In general, by using this furnace heat rise prediction method together with the furnace heat decrease prediction method described in 1D, more accurate blast furnace furnace heat prediction can be performed.

H1 Supplement: 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 be used as a substitute, although there are problems in terms of lifespan. It is. 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 were installed in 7 levels and in 4 directions, it goes without saying that they may be installed appropriately depending on the characteristics of the blast furnace.

In addition, the evaluation points by the fourth prediction means and the sixth prediction means were set to 0 to n and Om, respectively, because of Tsuruloss C.
This is because the prediction method using the moving average of f1I was given more importance than other prediction methods. Therefore, if there is no need to emphasize the prediction method using the moving average of the trail loss Cff1,
As with the evaluation scores by other prediction methods, the fourth. The sixth evaluation point may also be set to (0, 1). Also, 4th. If it is desired to place importance on a prediction means other than the sixth prediction means, the evaluation points of the prediction means to be emphasized may be set in multiple stages.

(Effects of the Invention) As explained above, according to the blast furnace furnace heat drop prediction method according to the present invention, the furnace heat drop is predicted based on the comprehensive evaluation of the first to sixth predictions and the detected furnace heat level. Therefore, it is possible to accurately predict the decrease in hot metal temperature.

On the other hand, according to the blast furnace furnace heat rise prediction method according to the present invention,
Since the furnace heat rise is predicted based on the comprehensive evaluation of the first to third predictions and the detected furnace heat level, it is possible to accurately predict the rise in hot metal temperature.

[Brief explanation of drawings]

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 sectional views, respectively. Conceptual diagram of FM sensor, installation explanatory diagram, Figure 4 is a graph showing the change in furnace wall temperature over time measured by FM sensor, Figure 5 is the furnace measured by FM sensor! ! ! A graph showing the change in temperature difference value over time, FIG. 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, and FIG. 8 is a flowchart showing the flow of processing of the second prediction means. Flowchart showing the flow of processing of the prediction means of No. 3, FIG. 9(a), (b), (c
) are graphs showing the instantaneous value of the trail loss Cff1 including abnormal values, the absolute value of the difference value of the trail loss C amount, and the instantaneous value of the trail loss C amount with abnormal values removed, respectively. ．． 6th. Figure 13A is a graph showing the advantages of threshold learning of the first to third prediction means, and Figure 138 is the flow of processing of the threshold learning method. FIG. 14 is a flowchart showing the process flow of a method for predicting a decrease in furnace heat according to an embodiment of the present invention, and FIG. 15 is a graph showing advantages of a method for predicting a decrease in furnace heat according to an embodiment of the present invention. 1st
FIG. 6 is a flowchart showing the process flow of a method for predicting a rise in furnace heat according to an embodiment of the present invention, and FIG. 17 is a graph showing advantages of a method for predicting a rise in furnace heat according to an embodiment of the present invention.
Fig. 18 is a side cross-sectional view showing the arrangement of the furnace belly sonde in the blast furnace in the prior art, Fig. 19 is a graph showing the correlation between the hot metal temperature and the temperature at the side of the furnace belly layer, and Fig. 20 is a graph of the
Figure 21 is a graph showing the time-based average value and the change in hot metal temperature over time. This is a graph shown in correspondence with .

Claims (2)

[Claims] (1) A step of installing an inner wall thermometer at a predetermined location of the blast furnace and measuring the inner wall temperature difference at predetermined time intervals with the inner wall thermometer, and a portion showing a positive value of the inner wall temperature difference at a certain time. The first point gives an evaluation point for a predetermined period when the total value exceeds a threshold.
a second step of 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 step of giving 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. Then, the amount of solution loss carbon is calculated at each predetermined time interval, and the moving average value of this calculated value over the predetermined time width is
A fourth prediction step that gives an evaluation point when the threshold value is exceeded; the amount of nitrogen in the furnace top gas component is determined at predetermined time intervals, and the moving average value of the determined values in the predetermined time width exceeds the threshold value. A fifth prediction step in which an evaluation point is given when the value falls below a threshold value, and a sixth step in which an evaluation point is given when the calculated value exceeds a threshold value, by calculating the number of times the raw material is charged from the top of the furnace at predetermined time intervals.
and a step of determining the hot metal temperature at predetermined time intervals and detecting the current furnace heat level based on the moving average value of the determined values over a predetermined time width. The threshold for the third prediction changes from time to time based on the total value or the standard deviation of the total for each correspondence within a certain period of time in the past, and the threshold for the fourth to sixth predictions The method changes from moment to moment based on the corresponding solution loss carbon amount, the nitrogen amount, or the average value of the number of times of charging the raw material, and the overall evaluation of the evaluation points of the first to sixth predictions and the detected furnace A blast furnace furnace heat drop prediction method further comprising the step of predicting a blast furnace furnace heat drop based on the heat level. (2) calculating the amount of solution loss carbon at each predetermined time interval, and performing a first prediction that gives an evaluation point when the moving average value of the calculated values at the predetermined interval is less than a threshold; A second step of calculating the amount of nitrogen in the furnace at predetermined time intervals and giving an evaluation point when the moving average of the determined values over a predetermined time width exceeds a threshold; A third prediction step is performed in which the number of times the hot metal is heated is calculated at each predetermined time interval, and an evaluation point is given when the calculated value is less than a threshold value. and detecting the current furnace heat level based on the moving average value in the time range, and the first to third prediction thresholds are the solution loss carbon amount for each response within a certain past period,
The amount of nitrogen changes from moment to moment based on the average value of the number of times the raw material is charged, and the blast furnace A blast furnace furnace heat rise prediction method further comprising a step of predicting a heat rise.