JPH039162B2 - - Google Patents

Description

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

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

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

As a method for predicting blast furnace furnace heat,
There is one disclosed in 39107. This method is based on the viewpoint that the charge temperature around the blast furnace belly has a strong correlation with the hot metal temperature. The relationship between the temperature of the hot metal and the temperature of the hot metal 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 belly.

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

Further, even if the hot metal temperature is the same, the furnace temperature may change due to changes in the production plan, raw material charging conditions, etc. Therefore, the linear equation based on the absolute value of the furnace wall temperature shown in FIG. 15 has the problem that accurate prediction cannot necessarily be made.

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

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

The amount of Sol loss C is determined by determining the proportion of CO, CO ₂ , N _2, etc. in the lamp top gas, ventilation conditions, and raw material charging every analysis cycle (about 3 minutes) of gas chromatography, which normally analyzes the composition of the furnace top gas. It is calculated based on the conditions, and conventionally, the decrease in furnace heat was managed by the average value of the Sol loss C amount every hour.

In Figures 16a and 16b, Figure a shows the change over time in the Sol loss C amount l1 every 3 minutes and the average Sol loss C amount l2 every hour, and Figure b shows the change over time in the hot metal temperature. It is. In the figure, time
Although the threshold value ε ₁ is exceeded at 17:00, no heating action is taken, and the hot metal temperature drops significantly after that. FIG. 17 shows the respective changes over time when the heating action A was initiated at time 13:00 when the threshold value ε ₂ was exceeded. Note that, similarly to FIG. 16, l1 in the figure indicates an instantaneous value every 3 minutes, and l2 indicates an hourly average Sol loss C amount. By comparing FIG. 16 and FIG. 17, it can be seen that the temperature increase of the hot metal as shown in FIG. 16b can be avoided to some extent by the heating action A, as shown in FIG. 17.

However, the problem with predicting the average value of Sol loss C amount every hour is that when there is a sudden increase in Sol loss C amount, in the worst case, there is a delay in predicting the decrease in furnace heat by approximately one hour. It was hot. For example, in the case of Fig. 17, the reason why the heating action A is taken due to the prediction delay is that the molten metal temperature is the control temperature T _c
It has become a problem since it has fallen below a certain level. Therefore, in order to avoid this problem, if the furnace heat drop is predicted using the instantaneous value of the Sol loss C amount measured at intervals of about 3 minutes, individual variations will be reduced as shown by l1 in Figs. 16 and 17. The data is not sustainable because it is large and the noise component is large. Therefore, it is almost impossible to predict the decrease in furnace heat using the instantaneous value of the Sol loss C amount.

(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 thermometer at a predetermined location of the blast furnace, and measuring the temperature at predetermined time intervals with the inner wall thermometer. a first prediction means that measures the inner wall temperature difference of and gives an evaluation point for a predetermined period when the total value of the portion showing a positive value of the inner wall temperature difference at a certain time exceeds a threshold; a second prediction means for giving an evaluation point for a predetermined period when the total value of the portion showing a negative value of the difference exceeds a threshold; and a moving average of the time width of the portion showing a positive value of the inner wall temperature difference at a certain time. and a third prediction means that gives an evaluation point for a predetermined period when the sum of the values exceeds a threshold, and calculates the amount of solution loss carbon at each predetermined time interval, and calculates the amount of solution loss carbon at each predetermined time interval. A fourth prediction means that gives an evaluation point when the moving average value in a predetermined time width exceeds a threshold value, and a fourth prediction means that calculates the amount of nitrogen in the furnace top gas component at each predetermined time interval and moves the determined value in the predetermined time width. Further comprising at least one of fifth prediction means for giving an evaluation point when the average value falls below the threshold,
The blast furnace heat drop is predicted according to the comprehensive evaluation of at least one evaluation point of the third prediction means and at least one evaluation point of the fourth and fifth prediction means.

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

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, the endothermic reaction of FeO+C→Fe+CO is promoted and the furnace heat decreases.

By the way, when a large amount of gas flows into the periphery of the furnace, the deposits and stagnation layer inside the furnace such as Na, K, and Pb peel off and fall off the wall, causing the temperature of the furnace wall in that area to rise 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.

When the unloading speed in the blast furnace increases for the same reason as A, the level of the cohesive zone in the blast furnace decreases due to so-called raw ore unloading, resulting in a decrease in furnace heat.

By the way, when the cohesive zone level decreases, the furnace wall temperature at the corresponding portion also decreases rapidly. If this rapid temperature drop is detected, a decrease in furnace heat can be predicted.

C. Third Reason for Decrease in Furnace Heat Furthermore, the following may be considered as a contributing factor to the decrease in furnace heat in the blast furnace.

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, for the same reasons as A and B, part of the gas flow may flow to the periphery of the furnace. If this state continues for a long time, heat dissipation from the gas flow from the blast furnace wall becomes greater than during normal operation, resulting in a decrease in furnace heat.

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

D First to Third Prediction Means FIGS. 1a and 1b 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. Inner wall thermometer 3 is shown in Figure a.
As shown in Figure b, seven pieces are installed in the height direction of the blast furnace 1 (three on the back, two on the abdomen, and two on the morning glory part), and four pieces are installed in the circumferential direction of the blast furnace 1, as shown in Figure b. In other words, a total of 28 inner wall thermometers 3 are installed at 7 levels in 4 directions.

For example, the inner wall thermometer is a
57-81531 and Utility Model Publication No. 59-16816 may be used, and Figure 2 shows the inner wall thermometer disclosed in the latter (hereinafter referred to as "FM sensor").
FIG.

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. 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 storing the sheath type temperature sensing element 4 in a sheath tube 9 while maintaining them in a non-contact manner with an insulating material 8.

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, 11 is a stave, 12 is a stamp, and 13 is an iron shell. As shown in the figure, the FM sensor 3 is connected to a packing 14 and a welded part 15.
It is installed inside the furnace wall by welding to.
Note that 16 is a filler, and 17 is a micro injection port into which the filler 16 is poured.

Furthermore, due to the installation and structure of the FM sensor 3 described here, the FM sensor 3 may deteriorate due to erosion of the furnace wall.
The sheath type temperature measuring element 4 may also be eroded and exposed inside the furnace near the furnace wall, meaning that it is actually measuring the ``furnace temperature near the furnace wall'' as well as the ``furnace wall temperature.'' Become. Hereinafter, a concept including both will be described as "furnace wall temperature." As mentioned above, the FM sensor 3 has a large number of measurement points compared to conventional thermometers such as sheathed thermocouples, satisfies rapid temperature measurement response, and is capable of long-term continuous temperature measurement.
Efforts are being made to improve reliability, durability, and workability.

Each FM sensor 3 measures the inner wall temperature of the blast furnace 1 at every predetermined sampling time Δt, as shown in FIG. Here, the i-th at time j
If the inner wall temperature of the FM sensor 3 is T _j,i and the inner wall temperature one sampling time Δt before time j is T _j-1,i , then the inner wall temperature difference between T _j,i and T _j-1,i is (difference value)
ΔT _j,i becomes 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 a weight w _i in consideration of the height, circumferential direction, etc. of each FM sensor 3. Furthermore, for negative difference values ΔT _j,i , v _i =
0 and other values, it is also multiplied by a positive/negative parameter v _i indicating v _i =1 to obtain a corrected difference value (positive difference value) CT _j,i at time j.

CT _j,i = w _i · v _i · ΔT _j,i (2) Next, the sum of the corrected difference values CT _j,i for all FM sensors 3 is calculated, and this is set as ST1 _j .

ST1 _j = ₂₈ 〓 ⁱ⁼¹ CT _j,i ...(3) Then, according to the following equation (4), if the value of this sum of difference values ST1 _j becomes larger than the predetermined threshold ε ₁ , there will be a rapid temperature rise. 1 evaluation point for a specified period of time
give.

ST1 _j ≧ε ₁ ...(4) The above is the first prediction means based on reason A.

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

In equation (2), the positive and negative parameters v _i are the difference values ΔT _j,i
For positive values, set v _i = 0; for other cases, set v _i = 1, and then set the corrected difference value CT _j,i
Take the sum of the absolute values of for all FM sensors 3,
Let this be ST2 _j .

ST2 _j = ₂₈ 〓 ⁱ⁼¹ | CT _j,i | ...(3)′ Then, according to the following equation (4)′, the value of the sum of difference values ST2 _j based on equation (3)′ is set to a predetermined threshold ε If it is greater than ₂ , it is assumed that there has been a sudden temperature drop due to raw ore descent, and a reserve point of 1 is given for a predetermined period of time.

ST2 _j ≧ε ₂ ...(4)' The third prediction means based on the reason C is as shown below.

Similar to the first prediction means, the positive/negative parameter v _i in equation (2) is v _i =0, when the difference value ΔT _j,i is negative.
For other cases, v _i =1. Also, k sampling times before time j (i.e., Δt×k
CT _jk,i is the corrected difference value (time before), and the sum of the moving average of this corrected difference value over a predetermined time width nΔt at time j for all FM sensors 3 is taken, and this is set as ST3 _j .

ST3 _j = ₂₈ 〓 ⁱ⁼¹ {(1/(n+1)) _o 〓 ^k=0 CT _jk,i } ...(3)'' Then, according to the following formula (4), this moving average sum ST3 _j
If the value of becomes larger than the predetermined threshold ε ₃ , then
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 specified period.

ST3 _j ≧ε ₃ ……(4)″ Since the first to third prediction methods described above are each performed based on the furnace wall temperature difference (difference value), they are independent of the upper and lower absolute values of the furnace wall temperature. , accurate predictions can be made.Furthermore, due to its ease of installation and good temperature measurement response, the FM sensor 3 can be placed to cover the entire circumference of the blast furnace, making it possible 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. Figure 6 is the first
3 is a flowchart showing the processing flow of the prediction means. In the figure, in step S1, the furnace wall temperature T _j,i of each FM sensor 3 is measured at every sampling time Δt. Next, in step S2, each FM sensor 3
Calculate the difference value based on equation (1).

Then, in step S3, the sum of positive difference values ST1 _j is determined based on equations (2) and (3). Furthermore, in step S4, this positive difference value sum ST1 _j is compared with a predetermined threshold value ε ₁ , and if equation (4) is satisfied, in step S5, the gas flow is rapidly shifted to the periphery of the furnace. 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, if equation (4) is not satisfied, it is assumed that there is no abnormality and the step
Returning to S1, the furnace heat reduction evaluation is performed by repeating steps S1 to S4.

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

Then, in step S13, the total absolute value _ST2j of the negative difference values is determined based on equations (2)' and (3)'.
Furthermore, in step S14, the absolute value sum ST2 _j of the negative difference values is compared with a predetermined threshold value ε ₂ , and if equation (4)' is satisfied, it is determined that the unloading speed has increased in step S15. It is assumed that a decrease in the furnace heat will occur due to the above, and an evaluation point of 1 will be given for the predetermined period. On the other hand, if formula (4)' is not satisfied, it is assumed that there is no abnormality and the process returns to step S11.
Furnace heat drop evaluation is performed by repeating steps S11 to S14.

FIG. 8 is a flowchart showing the processing flow of the third prediction means. In the figure, step S
At 21, the furnace wall temperature T _j,i of each FM sensor 3 is measured at every sampling time Δt. Next, in step S22, the difference value of each FM sensor 3 is calculated based on equation (1).

Then, in step S23, the moving average sum _ST3j of the positive difference values in the time width nΔt is determined based on equations (2)'' and (3)''. Furthermore, in step S24, this positive moving average sum ST3 _j is compared with a predetermined threshold value ε ₃ , and if the formula (4) is satisfied, then in step S25, the furnace heat decreases due to furnace heat dissipation. It is assumed that this will occur, and an evaluation point of 1 is given for the predetermined period.On the other hand, if formula (4) is not satisfied,
It is assumed that there is no abnormality, and the process returns to step S21, whereupon steps S21 to S24 are repeated to evaluate the decrease in furnace heat.

E Fourth and Fifth Prediction Means Sol loss C amount (Kg/t-p) is calculated for each sampling time Δt based on top gas component analysis by gas chromatography, ventilation conditions, raw material charging conditions, etc. Here, if the amount of Sol loss C at time j is x _j and the amount of Sol loss C at k sampling times before time j (that is, Δt×k hours before) is x _jk , then The moving average x _M can be calculated as x _M =1/n+1 _o 〓 ^k=0 x _jk (5).

x _M based on equation (5) is calculated for each sampling time Δt, and x _M is set to a predetermined threshold ε _xi (i=1 to n) (ε _x1 <ε _x2 ...< An evaluation point i is given based on the maximum threshold value ε _xi when ε _xo ) is exceeded, and evaluation is performed.

x _M > ε _xi (i=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”)
₂ amounts of N. ) has a strong negative correlation with the amount of Sol loss C, and instead of increasing the amount of Sol loss C, gas chromatography
A decrease in blast furnace heat can be predicted by decreasing the amount of _N2 .

As a result, the amount of gas chromatographic N ₂ at the current time j is set as y _j , and the amount of gas chromatic N ₂ at k sampling times before time j (that is, Δt × k hours before)
When the quantity is y _jk , the moving average y _M of the predetermined time width nΔt at the current time j can be calculated as follows: y _M =1/n+1 _o 〓 ^k=0 y _jk (7).

Calculate y _M based on equation (7) for each sampling time Δt, and use equation (8) below to calculate y _M to a predetermined threshold ε _yj (j=1 to m) (ε _y1 > ε _y2 >... An evaluation point j is given by the minimum threshold ε _yj when the value falls below ε _yn ),
Evaluate furnace heat drop.

y _M <ε _yj (j=1 to 1 m) (8) The above is the fifth prediction means.

Furthermore, when calculating the moving average of the Sol loss C amount, which is the fourth prediction means, the instantaneous value of the Sol loss C amount is an abnormal value due to noise etc. as shown in Figure 9a.
E1 and E2 may occur. Here, time j
When the Sol loss C amount is x _j and the Sol loss C amount one sampling time Δt before is x _j-1 , the absolute _value of the difference value of the Sol loss C amount Δx _j is Δx _{j =} | …(9) becomes. By comparing this Δx _j with the threshold value ε _z as shown in b in the same figure, abnormal values E1 and E2 are found, and
One possible method is to smooth the data by replacing it with the previous measured value, as shown in the figure below. By applying this method, a more accurate moving average of the Sol loss C amount can be obtained, and as a result, prediction with considerably high accuracy is possible.

A method of predicting a decrease in furnace heat by using a moving average of the Sol loss C amount, which includes such an 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, threshold values x1 to ε _xo are set in n stages with a magnitude of ε _x1 <ε _x2 . . . _{ε xo} . Then, in step S32, the instantaneous value x _j of the amount of Sol loss C is obtained for each sampling time Δt. Then, in step S33, the absolute value Δx _j of the difference value of the Sol loss C amount is determined. Next, in step S34, if the absolute value Δx _j of the difference value is larger than the threshold value ε _z , in step S35, this instantaneous value x _j is determined to be an abnormal value and replaced with the previous measured value x _j-1 , and the process moves to step S36. On the other hand, if it is smaller than the threshold value ε _z in step S34, the process moves to step S36 without changing the instantaneous value x _j . Step S36
Then, the moving average x _M of the time width nΔt is determined, and the evaluation point i is initially set to 0 in the next step S37.

Then, in step S38, the moving average value x _M of the Sol loss C amount is compared with the threshold value ε _x1 (from i=0), and if x _M ≥ε _x1 , the value of i is changed from 0 to 1 in step S39. 1, and it is determined that i=n in step S40, or x _M is determined in step S38.
The evaluation point i is calculated by repeating steps S38 to S40 while increasing the threshold value ε x( _{i +1) step by step until it is determined that <ε x(i+1)} , and the process proceeds to step S41. If x _M <ε _x1 , steps S39 and S40 are never executed, the evaluation point i is set to 0, and the process moves to step S41. Finally, in step S41, the step
The evaluation point i obtained in S38 to S40 is output.

Note that, as a matter of course, the above-described abnormal value processing can be applied to a computer in the same way in the case of predicting a decrease in furnace heat using the moving average value _yM of the amount of gas chromatin _N2 .

Since the fourth and fifth prediction means described above are based on a moving average for each sampling time Δt, predictions can be obtained quickly and the accuracy can be said to be sufficiently reliable.

F Comprehensive prediction means Comprehensive prediction is performed as described below by using the evaluation points of the first to fifth prediction means described in D and E.

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 S51, evaluation points f ₁ to _{f 3} of the first to third prediction means are determined. Evaluation points f ₁ to _{f 3} are 0 once →
If it becomes 1, that value is held during a data hold period, which will be described later. Therefore _, if _the hold time h _r (approximately 2 hours) is set at time t ₁ , then each of the evaluation points f ₁ to _{f 3 will} be , once it changes from 0 to 1, it will never change from 1 to 0 again. During this data hold period, the FM sensor total value F (=f ₁ + f ₂ + f ₃ ) is changed from F=0→F>
It is set at the same time as initializing the fluctuation time t _C when it changes to 0, and from then on, the time specified by h _r becomes the data hold period as described above.

Next, in step S52, by determining whether the FM sensor total value F is 0 or not, it is determined whether the data hold time h _r is set. If F = 0, the data hold time h _r Since there is no setting, the process moves to step S55. on the other hand,
If F≠0, the data hold time h _r has already been set, so the variable time is set in step S53.
By comparing t _c with the hold time h _r , it is checked whether the current data hold period is in progress. If t _c ≦ h _r , then the data hold period is in progress, so the FM sensor total value F is initialized. Since there is no need to convert, the process moves to step S55. However, if t _c > hr in step S53, it is determined that the data hold period has ended, and step
In S54, the FM sensor total value F is initialized to "0".

In this way, the FM sensor total value F is determined while taking into account the data hold period. This is because the first to third prediction means have higher foresight (predictions can be obtained faster) than the fourth and fifth prediction means, so in order to comprehensively evaluate the prediction results for the same point in the future, it is necessary to use the first to third prediction means. This is because it is necessary to hold the prediction result of the third prediction means for a certain period of time.

Then, in step S55, the evaluation score i by the fourth prediction means is calculated, and further step S55 is performed.
In S56, the evaluation score j by the fifth prediction means
Calculate. Next, in step S57, the overall evaluation C is determined according to the following formula.

C=w _A F+w _B i+w _C j ...(9) Here, w _A , w _B , w _C are the first to third, fourth,
It is a weight for each of the fifth prediction means. This comprehensive evaluation C is examined in step S58, and if C=0, it is assumed that there is no tendency for the furnace heat to decrease at all, and the process returns to step S51.
In step 57, the overall evaluation C is determined again. 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 S51, and the furnace heat drop prediction continues.

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 positive difference values of the FM sensor (first prediction means), and c is FM d is the sum of absolute values of negative sensor differences (second prediction means); d is the moving average sum of positive difference values of the FM sensor (third prediction means);
e represents the moving average value of the gas chromatography _N2 amount (fourth prediction means), f represents the moving average value of the Sol loss C amount (fifth prediction means), and g represents the change over time in the comprehensive evaluation C. The representative hot metal temperature value a is the average value of the temperatures actually measured approximately every 30 minutes during tapping into the hot metal ladle (one tap for approximately 2 hours). The overall evaluation C is given by equation (9),
w _A = w _B = w _C = 1, n = m = in equations (6) and (8)
3, and the range of the comprehensive evaluation C is 0 to 9.

In Fig. 12, the maximum value of the comprehensive evaluation C is 9, and it is necessary to output a serious alarm.
In fact, as shown in Figure a, representative hot metal temperatures have been measured that were considerably lower than the control temperature _Tc . 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 decreased only to the extent shown in the figure.

In this way, by changing the degree of alarm according to the value of the comprehensive evaluation C, the degree of heat raising action can be finely changed. As a result, it has become 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 actions, resulting in stable This also made it possible to operate a blast furnace economically.

G 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 with the service life. 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.

In addition, in the first to third prediction means in this embodiment, the FM sensor 3 is arranged in 7 levels and 28 directions in 4 directions.
Of course, it is sufficient to install them appropriately depending on the characteristics of the blast furnace.

Furthermore, as mentioned in F, the comprehensive prediction is
Although it is desirable to use all of the fifth prediction means,
At least one of the first to third prediction means
By using at least one of the first, fourth, and fifth prediction means, substantially the same effect as in the example described in F can be expected. Furthermore, the data hold period can also be set independently by the first to third prediction means, and modifications such as providing a plurality of threshold values as in the fourth and fifth prediction means are also conceivable.

(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. The corresponding heating action can be taken.

[Brief explanation of the drawing]

1a and 1b are a side sectional view and a plan sectional view showing the arrangement of an FM sensor used in an embodiment of the present invention in the blast furnace wall, respectively; FIGS. 2 and 3 are sectional views, respectively.
Conceptual diagram of FM sensor, installation explanation diagram, Figure 4 is FM
A graph showing changes over time in the furnace wall temperature measured by the sensor, Figure 5 is a graph showing changes over time in the difference value of the furnace wall temperature measured by the FM sensor, and Figure 6 is a flowchart showing the processing flow of the first prediction means. Chaat, 7th
The figure is a flowchart showing the process flow of the second prediction means, FIG. 8 is a flowchart showing the process flow of the third prediction means, and FIGS. 9a, b, and c are Sol losses including abnormal values. Instantaneous value of C amount, Sol loss C
A graph showing the absolute value of the difference value of the quantity and the instantaneous value of the Sol loss C quantity after removing abnormal values. Fig. 10 is a flowchart showing the processing flow of the fourth prediction means. Fig. 11 is the processing of the comprehensive prediction means. Flowcharts showing the flow of Fig. 12, Fig. 13 a, b, c, d,
e, f, and g are the representative value of hot metal temperature, the total sum of positive difference values of the FM sensor, the sum of absolute negative difference values, the moving average sum of positive difference values, the moving average value of gas chromatin _N2 amount,
Figure 14 is a graph showing the moving average value of the Sol loss C amount and changes over time in the comprehensive evaluation C.
Figure 5 is a graph showing the correlation between hot metal temperature and temperature around the furnace abdomen, Figure 16 is a graph showing the hourly average value of Sol loss C amount and temporal change in hot metal temperature, and Figure 17 is a graph showing the correlation over time. It is a graph showing the temporal correspondence between the one-hour average value of the Sol loss amount when the heating action occurs and the change over time in the hot metal temperature. 1... Blast furnace, 3... FM sensor.

Claims (1)

[Claims] 1. An inner wall thermometer is installed at a predetermined location in the blast furnace, and the inner wall temperature difference is measured at predetermined time intervals with the inner wall thermometer, and the positive value of the inner wall temperature difference at a certain time is determined. a first prediction means for giving an evaluation point for a predetermined period when the total value of the indicated portion exceeds a threshold; and a first prediction means for giving an evaluation point for a predetermined period when the total value of the portion indicating a negative value of the inner wall temperature difference at a certain time exceeds the threshold. a second prediction means for giving a point; and a third prediction means for giving an evaluation point for a predetermined period when the sum of 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. and a fourth means for determining the amount of solution loss carbon at each predetermined time interval and giving an evaluation point when the moving average value of the determined value over a predetermined time width exceeds a threshold value. a prediction means;
further comprising at least one of the prediction means, and the evaluation score of at least one of the first to third prediction means and the evaluation score of 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 comprehensive evaluation.