JPH0730371B2 - Blast furnace furnace heat drop prediction method - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a blast furnace heat drop prediction method for performing stable operation of a blast furnace.

(Prior Art) It is conventionally known that it is necessary to keep the hot metal temperature constant in order to maintain stable operation of a blast furnace. Therefore, the blast furnace operator always had to predict the thermal change in the blast furnace.

In the heat change of the blast furnace, it is very important to predict the temperature decrease because the hot metal may solidify due to the temperature decrease and may not flow out of the blast furnace.

As a method for predicting the heat of the blast furnace, there is a method disclosed in JP-A-60-39107. This method is based on the fact that the temperature of the charge around the furnace belly has a strong correlation with the hot metal temperature.
Sensor (furnace bellows) 2 installed in blast furnace 1 as shown in the figure
The relationship between the temperature around the furnace abdomen and the hot metal temperature detected by the method is linearly regressed as shown in FIG. Based on this linear equation, the hot metal temperature T _pig is predicted from the temperature around the furnace abdomen.

(Problems to be Solved by the Invention) However, it is necessary to insert the furnace belly sonde 2 in order to measure the temperature in the vicinity of the inner wall of the furnace. Therefore, the temperature measurement can be performed only at the intermittent time point, and the hot metal temperature prediction accuracy is high. However, there was a problem that it deteriorated.

Further, even if the hot metal temperature is the same value, the temperature in the furnace may change due to changes in the production plan and raw material charging conditions. Therefore, the linear equation based on the absolute value of the furnace wall temperature shown in FIG.

An object of the present invention is to solve the above-mentioned problems of the prior art and to provide a blast furnace heat drop prediction method capable of accurately predicting a drop in hot metal temperature.

(Means for Solving the Problem) The method for predicting heat loss in a blast furnace according to the present invention is to install an inner wall thermometer at a predetermined location of the blast furnace, and measure the inner wall temperature difference at predetermined time intervals with the inner wall thermometer. , A first predicting means for predicting a furnace heat drop when a total value of portions showing a positive value of the inner wall temperature difference at a certain time exceeds a threshold value, and a negative value of the inner wall temperature difference at a certain time Second predicting means for predicting furnace heat drop when the total value of the parts exceeds a threshold value;
At least one predicting means of the third predicting means for predicting the furnace heat drop when the sum of the moving average values of the portion showing the positive value of the inner wall temperature difference at a certain time over the predetermined time width exceeds the threshold value. When using the blast furnace heat drop prediction by using the threshold value of the first to third predicting means, it is changed momentarily based on the total value or the standard deviation of the total corresponding to each corresponding within the past certain period .

(Operation) In the present invention, the threshold values of the first to third predicting means are
In order to change every moment based on the standard value of the total value or the total sum of each correspondence within the past certain period,
The decrease in prediction accuracy due to the change in the sensitivity of the inner wall thermometer can be compensated by the change in the threshold value.

(Examples) A. First reason for decrease in furnace heat The following are possible causes of decrease in furnace heat in the blast furnace.

Hot air temperature (gas flow) for adjusting the hot metal temperature and hot metal amount blown from the tuyere of the blast furnace is usually blown into the central part of the furnace. However, a large amount of gas flow may suddenly flow around the inside of the furnace due to the conditions of raw material charging, distribution of charged materials, and the like.
As a result, the endothermic reaction of FeO + C → Fe + CO is promoted and the furnace heat is reduced.

By the way, when a large amount of gas flow flows into the periphery of the furnace, Na,
The in-furnace deposits such as K and Pb and the stagnant layer separate and fall off the wall, causing the temperature of the furnace wall to rise rapidly. If this sudden temperature rise is detected, the furnace heat drop can be predicted.

B. Second Reason for Lowering Furnace Heat In addition, the following factors can be considered as one of the causes of lowering the furnace heat of the blast furnace.

If the unloading speed in the blast furnace increases for the same reason as in A. , the cohesive zone level in the blast furnace decreases due to so-called raw mine descent,
Furnace heat drop occurs.

By the way, when the cohesive zone level is lowered, the temperature of the furnace wall in the relevant portion is also rapidly lowered. If this rapid temperature drop is detected, the furnace heat drop can be predicted.

C. Third Reason for Reducing Furnace Heat In addition, the following factors can be considered as a cause of lowering furnace heat.

Hot air temperature (gas flow) for adjusting the hot metal temperature and hot metal amount blown from the tuyere of the blast furnace is usually blown into the central part of the furnace. However, due to the same reason as A. and B. , a part of the gas flow may flow to the periphery of the furnace. If this state continues for a long time, the heat dissipation of the gas flow from the furnace wall of the blast furnace becomes larger than that during normal operation, and as a result, the furnace heat decreases.

By the way, when a part of the gas flow steadily flows to the inner peripheral portion of the furnace, the temperature of the furnace wall gradually rises. By detecting such a gradual temperature rise for a relatively long time, a decrease in furnace heat can be predicted.

D. First to Third Prediction Means FIGS. 1 (a) and 1 (b) are a side sectional view and a plan sectional view showing the arrangement of an inner wall thermometer used in an embodiment of the present invention. As shown in FIG. 2A, the inner wall thermometer 3 has seven pieces in the height direction of the blast furnace 1 (three pieces on the back, two pieces on the abdomen, and the bosh section 2).
4) in the circumferential direction of the blast furnace 1 as shown in FIG. That is, a total of 28 inner wall thermometers 3 are installed in 7 levels in 4 directions.

The inner wall thermometer is, for example, the actual applicant's 57-81531,
You may use what was disclosed in Japanese Utility Model Publication No. 59-16816.
The figure is a conceptual diagram showing the inner wall thermometer disclosed in the latter (hereinafter referred to as "FM sensor").

In the figure, reference numeral 4 denotes a sheath-type temperature measuring element having two conducting wires 5 embedded in parallel insulatively and having a temperature-sensing portion 6 on the front end side. The temperature-sensitive parts 6 are arranged in parallel so as to be arranged at different parts in the length direction, and the sheath type dummy rod 7 is connected to the tip of the temperature-sensitive part 6 to align the leading edge. The sheath type dummy rod 7 has two conductive wires 5 embedded in parallel in an insulating manner, and has substantially the same thermal conductivity as the sheath type temperature measuring element 4. The FM sensor 3 keeps the sheath type temperature measuring element 4 out of contact with the insulating material 8,
It is formed by being housed in the sheath tube 9.

FIG. 3 is an explanatory diagram of installation of the FM sensor 3. In the figure, 10 to 13 are furnace walls of the blast furnace, 10 is a brick, 11 is a stave, 12 is a stamp, and 13 is an iron skin. As shown in the figure, the FM sensor 3 is installed inside the furnace wall by welding to the packing 14 and the welded portion 15. In addition, 16 is a filler, 17 is a milk injection port, and the part where the filler 16 is poured.

Because of the installation and structure of the FM sensor 3 described here, the FM sensor 3 itself may be eroded as well as the erosion of the furnace wall, and the sheath-type temperature measuring element 4 may be exposed in the furnace near the furnace wall.
Actually, it means that the "temperature inside the furnace near the furnace wall" is measured together with the "temperature inside the furnace wall". Hereinafter, the concept including both is described as “furnace wall temperature”. As described above, the FM sensor 3 has a large number of temperature measuring points P as compared with the thermometer such as the conventional sheath thermocouple.
It has a temperature sensing part 6 in FIG. 2 and P _{1 to} P ₅ in FIG. 3, and it satisfies the quick temperature measurement response and is capable of continuous temperature measurement for a long time. Improvement of durability, improvement of durability,
Workability has been improved.

Further, the FM sensor 3 normally uses the temperature measuring point P ₁ closest to the inner wall, and when the temperature measuring point P ₁ becomes unusable due to disconnection due to erosion of the furnace wall, the temperature measuring point P ₁ closest to the inner wall next. _{Use 2} .
Hereinafter, the temperature measuring points P ₃ , P ₄ , and P _{5 will be} sequentially used, and one of the usable temperature measuring points that is closer to the inner wall 1 will be used. Therefore, the distance (hereinafter, referred to as “measurement distance”) 1 from the inner wall to the nearest and usable temperature measuring point (P ₁ ) immediately after the FM sensor 3 is installed is short as shown in FIG.

As shown in FIG. 4, each FM sensor 3 measures the inner wall temperature of the blast furnace 1 at every predetermined sampling time Δt. Here, the inner wall temperature of the i-th FM sensor 3 at time j is T _{j, i,} and the inner wall temperature one sampling time Δt before the time j is T _{j.
j-1, i} , the inner wall temperature difference (difference value) ΔT _{j, i} between T _{j, i} and T _{j-1 , i} is ΔT _{j, i} = T _{j, i} −T _{j-1, i} … (1) This state is shown in FIG.

This difference value ΔT _{j, i} is multiplied by the weight w _{i in} consideration of the height, circumferential direction, etc. of each FM sensor 3. Further, if the difference value ΔT _{j, i} is negative, it is also multiplied by a positive / negative parameter v _i indicating v _i = 0 and v _i = 1 otherwise, and the corrected difference value at time j is obtained. (Positive difference value) CT _{j, i} is obtained.

CT _{j, i} = w _i · v _i · ΔT _{j, i} (2) Next, the sum of the correction difference values CT _{j, i} for all the FM sensors 3 is taken, and this is designated as ST1 _j .

Then, according to the following equation (4), if the value of the sum ST1 _j of the difference values becomes larger than a predetermined threshold value ε ₁ , it is considered that there is a rapid temperature rise, and the decrease in furnace heat is predicted.

ST1 _j ≧ ε ₁ (4) The above is the first prediction means based on the reason of A. The second predictive measure based on the reason of B. is as follows.

(2) In the equation, for the positive and negative parameter v _i is the difference value [Delta] T _{j, i} is a positive thing, v _i = 0, for the other ones, and v _i = 1, then the correction All F of absolute values of difference value CT _{j, i}
The sum for the M sensor 3 is taken and designated as ST2 _j .

Then, according to the following formula (4) ′, if the value of the difference value sum ST2 _j based on the formula (3) ′ becomes larger than a predetermined threshold value ε _2, it is considered that there was a rapid temperature drop due to the downfall of the mine,
Predict furnace heat drop.

The third prediction means based on the reason of ST2 _j ≧ ε ₂ (4) ′ C. is as follows.

The positive / negative parameter v _i in the equation (2) is the same as in the first prediction means.
If the difference value ΔT _{j, i} is negative, v _i = 0, and otherwise v _i = 1. Further, a correction difference value before k sampling time (that is, Δt × k time before) at time j is CT _{jk, i,} and a predetermined time width nΔt of this correction difference value is set.
The sum of the values of the moving average at time j for all FM sensors 3 is taken, and this is designated as ST3 _j .

Then, according to the following equation (4) ″, when the value of the moving average sum ST3 _j becomes larger than a predetermined threshold value ε _3, it is considered that the temperature rise is gentle for a long period of time, and the furnace heat drop is predicted.

ST3 _j ≧ ε ₃ (4) ″ Since the first to third predicting means described above are each performed by the difference in the furnace wall temperature (difference value), they are accurate regardless of the absolute value of the furnace wall temperature. Moreover, due to its good workability and good temperature measurement response, the FM sensor 3 can be placed so as to cover the entire circumference of the blast furnace, and the continuous inner wall temperature difference can be grasped. You can make accurate predictions.

The above-mentioned first to third predicting means can be realized by a computer. FIG. 6 is a flowchart showing the flow of processing of the first predicting means. In the figure,
In step S1, the furnace wall temperature T _{j, i} of each FM sensor 3 is measured every sampling time Δt. Next, in step S2
The difference value of the FM sensor 3 is calculated based on the equation (1).

Then, in step S3, the positive difference value sum ST1 _j based on the expressions (2) and (3) is obtained. Further, in step S4, this positive difference value sum ST1 _j is compared with a predetermined threshold value ε _1, and if the formula (4) is satisfied, step S5
At, it is assumed that the furnace heat drop due to the rapid gas flow around the furnace will occur, and an alarm is output. on the other hand,
When the equation (4) is not satisfied, it is considered that there is no abnormality, the process returns to step S1, and the steps S1 to S4 are repeated to predict the furnace heat drop.

FIG. 7 is a flowchart showing the processing flow of the second predicting means. In the figure, in step S11, the furnace wall temperature T _{j, i} of each FM sensor is measured every sampling time Δt.
Next, in step S12, the difference value of each FM sensor 3 is calculated based on the equation (1).

Then, in step S13, the absolute value sum ST2 _j of the negative difference values based on the expressions (2) ′ and (3) ′ is obtained. Further, in step S14, the absolute value sum ST2 _j of the negative difference values is compared with a predetermined threshold value ε ₂ ,
If the equation (4) 'is satisfied, it is considered that the furnace heat drop due to the increase in the unloading speed will occur in step S15, and an alarm is output. On the other hand, when the equation (4) 'is not satisfied, it is considered that there is no abnormality, the process returns to step S11, and the steps S11 to S14 are repeated to predict the furnace heat drop.

FIG. 8 is a flow chart showing the flow of processing of the third predicting means. In the figure, in step S21, the furnace wall temperature T _{j, i} of each FM sensor 3 is measured every sampling time Δt. Next, in step S22, the difference value of each FM sensor 3 is calculated based on the equation (1).

Then, in step S23, the moving average sum ST3 _j in the time width nΔt of the positive difference values based on the expressions (2) ″ and (3) ″ is obtained. Further, in step S24, this positive difference value moving average sum ST3 _j and a predetermined threshold value ε ₃
If the expression (4) ″ is satisfied, step S25
It is assumed that there will be a decrease in furnace heat due to heat dissipation from the furnace body, and an alarm is output. On the other hand, if the expression (4) ″ is not satisfied, it is considered that there is no abnormality and the process returns to step S21
The furnace heat drop prediction is performed by repeating steps S21 to S24 below.

E. Learning of Thresholds Each threshold ε ^* (ε ₁ , ε ₁ , of the _first to third prediction means described in D.
Either ε ₂ or ε ₃ , and hereinafter “ε ^* ” is used generically. ) Is obtained in advance so as to obtain the optimum predicted rate of decrease in furnace heat. However, the initially optimal threshold value ε ^* may not be optimal during blast furnace operation due to changes in various conditions such as the production plan and raw material conditions. Therefore, it is necessary to change the threshold value ε ^* to an optimum value during the operation of the blast furnace, and the learning of the threshold value ε ^* is performed as follows.

When measuring the furnace wall temperature of the blast furnace, the temperature measuring point P of the FM sensor 3 which is the furnace wall thermometer changes with the erosion of the furnace wall as described in D. Therefore, not only the measured furnace wall temperature itself, but also the peak value (maximum value) of the furnace wall temperature difference may change depending on the erosion of the furnace wall. Since the first to third predicting means make predictions based on the furnace wall temperature difference, the prediction accuracy also fluctuates in such a case.

When the relationship between the measurement distance 1 of the FM sensor 3 and the furnace thermal temperature variation Δθ is treated by the one-dimensional heat conduction equation, it is expressed by the following equation (5).

Λ: furnace wall thermal conductivity ρ: furnace wall density conductivity c: furnace wall specific heat conductivity On the other hand, assuming that the inner wall surface temperature changes periodically with A cos (ωt), The solution of the conduction equation is Becomes

From equation (6), the amplitude change of the furnace wall temperature fluctuation Δθ | Δθ |
Is determined by the following equation (7).

From equation (7), it can be said that the furnace wall temperature amplitude change | Δθ | decays exponentially as the measurement distance 1 increases. This indicates that the sensitivity of the FM sensor 3 deteriorates as the measurement distance 1 increases.

FIG. 9 is a graph showing the difference in sensitivity due to the difference in the measurement distance l of the FM sensor 3, where FIG. 9A is the inner wall temperature, FIG. 9B is the difference value ΔT _{j, i of} the FM sensor 3, The same figure (c) has shown the time-dependent change of the hot metal temperature T _pig .

As described above, since the sensitivity of the FM sensor 3 becomes worse as the measurement distance 1 becomes larger _, the amplitude of the difference value ΔT _{j, i} becomes smaller as shown in FIGS. 9 (a) and 9 (b). As a result, the amplitudes of the total sums ST1 _j , ST2 _j , and ST3 _j also become smaller, so that similar furnace heat reduction phenomena that occur after similar changes in inner wall temperature as shown in FIGS. It becomes difficult to accurately predict the above by the first to third blast furnace heat drop prediction means with the threshold ε ^* kept constant.

This is because, for example, in the first predicting means, as shown in FIG.
This is because the positive difference value sum ST1 _j differs depending on the magnitude of. This also applies to the second and third predicting means.

For the above reason, it is necessary to correct the threshold value ε ^* according to the measurement distance l.

FIG. 11 is an installation explanatory diagram of the FM sensor 3 showing an example where the measurement distance 1 is short and an example where it is long. When the measurement distance l was short, the furnace wall was eroded as shown in Fig. 3 (a) in addition to immediately after the FM sensor was installed (see Fig. 3).
The temperature measuring point P ₁ of the sensor 3 is not eroded and is operating normally, or the furnace wall and the FM sensor 3 are both eroded as shown in FIG. There are cases where it has reached near P ₂ .

On the other hand, when the measurement distance 1 is long, as shown in FIG. 3C, when the deposit 20 is formed on the furnace wall where the FM sensor 3 is installed, or as shown in FIG. There is a case where the temperature measuring point P ₂ is used because the furnace wall is eroded and the temperature measuring point P ₁ of the FM sensor 3 becomes unusable due to disconnection, deterioration and the like. As a method of diagnosing the deterioration of the temperature measurement point P, a method of comparing the resistance value at the temperature measurement point P or the amount of change in the temperature measurement data with a threshold value can be considered.

From the above, it can be seen that the measurement distance l cannot be grasped at all from the usage status of the temperature measuring point P.

On the other hand, as the measuring means of the measuring distance l itself, there is only batch-like measuring means such as boring, and it is difficult to always grasp the accurate value of the measuring distance l.

Therefore, in order to solve the above problem, the following processing is performed. First, up backwards from the current time t to the time width h _s past, the standard deviation σ ₁ ~σ ₃ of the sum ST1 _j ~ST3 _j in the interval h _s calculated from these standard deviations σ ₁ ~σ ₃ following The thresholds ε _{1 to} ε ₃ are determined by the equations (14) to (16).

ε ₁ = a ₁ × σ ₁ (8) ε ₂ = a ₂ × σ ₂ (9) ε ₃ = a ₃ × σ ₃ (10) Note that a _{1 to} a ₃ are coefficients.

The standard deviations σ _{1 to} σ ₃ of the total sums ST1 _{j to} ST3 _j were used for learning the threshold value because the measured values of the FM sensor 3 when the sensitivity becomes poor.
It is based on the fact that the amplitude (that is, the variance) of (ST1 _j , ST2 _j , ST3 _j ) becomes small.

FIG. 12 is a flowchart showing a threshold learning processing procedure. In the figure, first, in step S31, the positive difference value sum ST _j , the negative difference value absolute value sum ST2 _j , and the positive difference value moving average sum in the predetermined time width h _s that has been reversed in the past from the current time t. obtaining ST3 _j of the standard deviation σ ₁ ~σ ₃ respectively.

Then, using these standard deviations σ _{1 to} σ ₃ , step S3
In 2, the thresholds ε _{1 to} ε ₃ are determined from the equations (8) to (10).

Thereafter, the process returns to step S31, and the threshold is determined at any time by repeating the above processing.

FIG. 13 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 predicting means. Period t _a
Indicates a period in which the detection sensitivity of the FM sensor 3 is low due to a wall drop or the like, and a period t _b is a period in which the detection sensitivity of the FM sensor 3 is high. Therefore, to keep fixed the threshold epsilon ^* to a constant value, so that missed in the period t _a lot, the over-detection in the period t _b. Given this fact, the period t in _a threshold ε
It is desirable that ^* is low and the threshold ε ^* is set high in the period t _b . The present learning method, the period t _a the lower threshold epsilon ^*, and can be set high period t _b in the threshold epsilon ^*, it can be seen that accurately detect walls fall like.

As described above, the threshold value ε ^* is automatically determined based on the change of each sampling data in the first to third predicting means at a proper time, so that it is constant regardless of the change of the measurement distance l. It is possible to make predictions with the accuracy of.

G. Supplement Note that the FM sensor is used as the inner wall thermometer in the first to third predicting means in this embodiment, but a normal temperature measuring sensor (for example, a sheath thermocouple) has a problem in terms of life, but it is a substitute. It is possible. In addition, even if a stave thermometer or a brick-embedded thermometer is used, the prediction accuracy is slightly lowered due to its low reliability and low temperature response, but it can be used instead.

In addition, in the first to third predicting means in this embodiment,
28 FM sensors 3 were installed in 7 levels and 4 directions, but it goes without saying that they may be installed appropriately depending on the characteristics of the blast furnace.

(Effects of the Invention) As described above, according to the blast furnace heat drop prediction method of the present invention, the threshold value is changed momentarily according to the change in the sensitivity of the inner wall thermometer during the operation of the blast furnace. Since the furnace heat decrease prediction is performed by at least one of the third prediction means, the decrease in the hot metal temperature can be accurately predicted.

[Brief description of drawings]

1 (a) and 1 (b) are side sectional views, plan sectional views, FIG. 2 and FIG. 3, respectively, showing the arrangement of the FM sensor used in one embodiment of the present invention in the blast furnace wall. Conceptual diagram of FM sensor, installation explanatory diagram, FIG. 4 is a graph showing the time-dependent change of the furnace wall temperature measured by the FM sensor, FIG. 5 is a graph showing the time-dependent change of the measured furnace wall temperature by the FM sensor, FIG. 6 is a flowchart showing a processing flow of the first prediction means,
FIG. 8 is a flow chart showing the flow of processing of the second predicting means, FIG. 8 is a flow chart showing the flow of processing of the third predicting means, and FIG. 9 is the FM sensor 3 due to changes in the measurement distance l.
10 is a graph showing the change in sensitivity, FIG. 10 is a graph showing the change in the positive difference value due to the change in the measurement distance 1, and FIG.
(D) is an installation explanatory diagram showing the relationship between the FM sensor 3 and the measured distance l, FIG. 12 is a flowchart showing the flow of processing of the threshold learning method, and FIG. 13 is a graph showing the advantages of the threshold learning method. FIG. 14 is a side sectional view showing the arrangement of the furnace belly sonde in the blast furnace in the prior art, and FIG. 15 is a graph showing the correlation between the hot metal temperature and the furnace belly peripheral temperature. 1 ... Blast furnace, 3 ... FM sensor

Claims (1)

[Claims] 1. A portion in which an inner wall thermometer is installed at a predetermined location of a blast furnace, the inner wall temperature difference is measured at predetermined time intervals by the inner wall thermometer, and a positive value of the inner wall temperature difference at a certain time is shown. And a first predicting means for predicting the furnace heat decrease when the total value of the above exceeds a threshold value, and a furnace heat decrease prediction when the total value of the portion showing the negative value of the inner wall temperature difference at a certain time exceeds the threshold value. A second predicting means to perform, and a third predicting means to predict the furnace heat drop when the sum of the moving average values of the portion showing the positive value of the inner wall temperature difference at a certain time in a predetermined time width exceeds a threshold value. When performing the blast furnace heat drop prediction using at least one of the prediction means, the threshold value of the first to third prediction means is set to the total value or the standard deviation of the total sum for each correspondence within a certain period in the past. Based on the fact that it changes momentarily based on Blast furnace heat reduced prediction method that.