JPH01205009A - Apparatus for controlling furnace heat in blast furnace - Google Patents

Abstract

PURPOSE:To control furnace heat in a blast furnace at high accuracy in accordance with the furnace condition and to facilitate addition and revision of rules by executing the prescribed inference as artificial intelligence from the processed data prepared from the data and the knowledge data based on the experiences, etc., in the blast furnace. CONSTITUTION:The various data in the blast furnace 1 are stored in filing means 14 from data scanner 11 through collecting function 12. These data operate the average value and the difference with the reference value, etc., with operating means 16 and the obtd. processed data are stored in a common data buffer 24. Based on the processed data and the knowledge bases from the knowledge base storing means 22, the furnace heat level and the furnace heat shifting are inferred with an inference engine 26 to decide an actin rate to the blast furnace 1. This action rate is outputted into a digital measuring instrument 32 through output means 18 to execute control of the furnace heat in the blast furnace. Further, the above knowledge data 22 contain the various rules deciding the various judging knowledge data and action rates, and the alteration and abolition are facilitated. By this method, the control corresponding to the actual furnace condition containing variation of furnace bottom part, etc., can be executed.

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial Application Field] The present invention relates to a blast furnace furnace heat control device that controls the temperature of hot metal tapped from a blast furnace.

[Prior Art] Conventionally, as a method for estimating, managing and controlling the temperature of hot metal in a blast furnace, a blast furnace operator qualitatively judges information from various sensors installed in the blast furnace. The method used is to evaluate the blast furnace situation and make optimal adjustments to operating factors.

However, there are individual differences in the evaluation results depending on the ability and experience of the operator, which makes it difficult to standardize operational actions, and the evaluation is not quantitative, making it difficult to estimate the hot metal temperature. there were.

For this reason, a process control method for a blast furnace has been proposed, for example, as disclosed in Japanese Patent Publication No. 51-30007. This process control method maintains the blast temperature constant, determines the amount of direct reduction in the furnace from measurements that can be obtained continuously during operation, and calculates the target Si content in the pig iron and an exponentially smoothed value that represents the actual value. The blast humidity is determined by an equation with a correction term added to prevent long-term fluctuations in the Si content in the pig iron, and the heat balance in the furnace is controlled using this blast moisture determined value. ing.

For this reason, accurate operation has been achieved by correcting the large wave changes (long-period changes) that occur when the blast furnace status is controlled by calculation.

[Problems to be Solved by the Invention] In the conventional process control method disclosed in the above-mentioned Japanese Patent Publication No. 51-30007, information from sensors is analyzed and predetermined calculations are performed manually on a model. ing. For this reason, the computer that executes the calculation uses, for example, Fortran as the language, but the calculation capacity is extremely large. Furthermore, since blast furnaces change over time, the analysis model itself must be changed for maintenance, but since the analysis modem itself is complex, changing the conditions of the analysis model becomes an extremely troublesome task.

In addition, as a means to solve the above-mentioned problems, it is possible to improve maintainability by using a computer system that uses an artificial intelligence language such as LISP, but here, sensor information (authenticity data, various sensor data) ,
In using the knowledge base to reason about the furnace thermal situation,
When using the production rule, for all related sensors, for example, (1) IF (the temperature of sensor i is in the range of T1 to T2).
THEN (CF value, which is a high fever level, is C1)...
..., (CF value, which is a low heat level, is Cn); ■IF (Temperature of sensor i is in the range of T1 to T3.) THEN
(High fever level +7) CF value C'l).

・・・・・・(CF value of low fever level is C'n);■・・・
. . . It is necessary to express the rules as follows, which results in a huge number of rules, which increases the inference time and makes the adjustment of the CF value extremely complicated.

For this reason, there is a problem in that the creation of a program for the above-mentioned rules or the amount of numerical values indicating the reliability described in the rules becomes enormous, making the input work complicated.

Additionally, when the bottom of a blast furnace becomes eroded, the tapping temperature pattern changes, but there has been no control in the past to deal with this erosion, and from this point of view, sufficient furnace heat control is not possible. There was a problem.

The present invention was made to solve these problems, and it is possible to control the furnace heat of a blast furnace with high precision, improve the calculation capacity and calculation speed when realized by a computer, Another object of the present invention is to provide a blast furnace furnace heat control device that allows rules to be easily added and modified in response to new situations such as aging of the blast furnace, and also takes into consideration the situation at the bottom of the furnace.

[Means for Solving the Problems] FIG. 1 is a block diagram showing the concept of a blast furnace furnace heat control device according to the present invention. The blast furnace furnace heat control device according to the present invention includes:
A data input means that takes in data from various sensors installed in the blast furnace at a predetermined timing, and based on the data from the sensors, determines the tuyere embedding temperature, unloading speed, pressure loss, furnace top temperature, gas utilization rate, The apparatus is provided with processing data creation means that creates various data indicating the status of the blast furnace, such as the amount of solution loss, and compares the various data with reference data to create difference data.

Furthermore, a storage means for temporarily storing the various data and difference data (processed data), a knowledge base storage means for storing various knowledge bases based on experience, results, mathematical models, etc. regarding blast furnace operation, and the storage means. The apparatus includes inference means for inferring the furnace heat level and furnace heat transition based on the processed data of the means and the knowledge base of the knowledge base storage means and determining an action amount for the blast furnace, and an output means for outputting the action amount.

The knowledge base storage means includes a "furnace heat level determination knowledge base" used to estimate the furnace heat level, a "furnace heat transition determination knowledge base" used to estimate the furnace heat transition, and a "furnace heat level determination knowledge base" used to estimate the furnace heat transition. "Action Judgment Knowledge Base" used to determine the amount of action based on heat level and furnace heat transition
, an "action correction knowledge base" that determines the correction value of the action amount based on past actions and disturbances, and a "comprehensive judgment knowledge base" that determines the actual action amount from the action amount and the action correction amount. There is.

The "furnace heat level determination knowledge base" and the "furnace heat transition determination knowledge base" are based on processing data and the furnace heat level as independent variables, and are based on the output of a heat measurement sensor installed at the bottom of the furnace. Each of them has a confidence function group and a rule group that determines how to apply this confidence function group. The "action determination knowledge base" has rules for determining the amount of action based on the furnace heat level and furnace heat transition. The "comprehensive judgment knowledge base" has rules for determining the actual amount of action from the amount of action and the amount of action correction.

[Operation] In the present invention, after the processed data creating means creates various data indicating the status of the blast furnace based on the blast furnace data from the data input means, the processed data is created based on the data. Based on the processed data and the knowledge base stored in the knowledge base storage means, inference calculations are performed as artificial intelligence, and the amount of action for the blast furnace is determined.

In the inference calculation, the furnace heat level is estimated using the furnace heat level determination knowledge base, and the furnace heat transition is estimated using the furnace heat transition determination knowledge base.

When inferring these furnace heat levels and furnace heat transitions, a confidence function and a set of rules that determine how to apply the confidence function are used to obtain confidence in the inference results.

When making inferences at this time, multiple confidence function groups are prepared in advance to be selected according to the temperature at the bottom of the hearth.
A confidence function is selected based on the output of the thermal measurement sensor. Then, the action amount is determined by applying the above-mentioned furnace heat level and furnace heat transition to the action determination knowledge base, and when there is a disturbance, the action correction amount is determined using the action correction knowledge base.

Next, the actual amount of action is determined from the amount of action and the amount of action correction using the comprehensive judgment knowledge base. Then, the blast furnace furnace heat is controlled by adjusting the operating factors based on the action amount.

[Example] Embodiments of the present invention will be described below based on the drawings.

FIG. 2 is a block diagram showing a blast furnace furnace heat control device and related equipment according to an embodiment of the present invention.

In the figure, (1) is a blast furnace to be controlled, (10) is a blast furnace furnace heat control device according to the present invention, and a data scanner (
11), sensor data collection means (12), file means (14), calculation means (16), output interface (18), knowledge base (22), inference engine (26)
), and a common data buffer (24).

The sensor data collection means (12) collects data from various sensors, such as temperature sensors, pressure sensors, gas sensors, etc., using data scanners (lla), (flb), (l).
lc) and is manually processed in chronological order.

The file means (14) performs a file data banking function. The calculation means (16) performs exponential smoothing on the data from the sensor data collection means (12) stored in the file means (14), and then outputs the data again to the file means (14).
14). Then, at predetermined intervals, for example, every 20 minutes, the average value and the difference data between the average value and the reference value are sent as processed data to a common data buffer (24) which is processed data storage means.

The inference engine (26) uses its data and knowledge base (2
2) performs a predetermined inference calculation based on the knowledge of 2), calculates the amount of action to be taken next, and stores it in the common data buffer (24).
), and also stored in the file means (14).

(30) is a CRT on which the inference results of the inference engine (26) are transmitted and displayed via the file means (14).

(32) is a digital instrumentation device, and the blast furnace furnace thermal control device (1
It controls the temperature of the blast furnace based on the command of 0), and adjusts the manipulated variables that can adjust the furnace heat, such as air humidity, air temperature, and liquid fuel such as heavy oil. At this time, the command of the blast furnace furnace heat control device (10) is sent to the digital instrumentation device (32) via the output means (18).

(40) is a hot air stove, (42), (44), (4E
i) are each control valves.

An overview of the operation of this embodiment having the above configuration will be explained.

(1) First, data from various sensors are sequentially read by the sensor data collection means (12) via the data scanner (11) at predetermined timings, for example, at one-minute intervals;
It is stored in the file means (14).

(2) The data stored in the file means (14) is subjected to exponential smoothing processing by the calculation means (16). here,
Data regarding unloading, temperature, gas utilization rate, tap slag, etc. are processed. The various data subjected to the arithmetic processing are stored again in the file means (14). Next, the average value of these various data at intervals of a predetermined period of time, for example, 200 minutes, and the difference between the average value and a predetermined reference value are calculated and transferred to the common data buffer (24) as processed data. .

(3) The inference engine means (26) comprises a knowledge base (22
) and the processed data in the common data buffer (24), the situation inside the blast furnace is inferred and calculated.

Here, as shown in Figure 3, the knowledge base is the furnace heat level judgment KS group, the furnace heat transition judgment KS group (KS;
knowledge source), action judgment KS, action correction judgment K
It is formed from the knowledge base units of S, comprehensive judgment KS group, and operating state judgment KS.

Furnace heat level determination KS group uses an inference engine (2B) to determine at what level the furnace heat level of the blast furnace is.
"Hot metal temperature - Furnace heat level KS", which determines the furnace heat level using the hot metal temperature as the main determining factor, and "Hot metal temperature - furnace heat level KS", which determines the furnace heat level using the measured quantities of other sensors as the main determining factor [ Includes sensor furnace heating bell KSJ, etc.

Each of these KS uses each measured quantity and the furnace heat level as independent variables, and the probability (confidence) that a combination of these will occur.
It is made up of a confidence function (hereinafter referred to as CF function) whose dependent variable is , and a set of rules that determine the procedure for using the CF function.

Furnace heat transition determination KS Group uses an inference engine (
26) is a knowledge base used by ``Hot Metal Temperature - Furnace Heat Transition KSJ'' which determines the transition of furnace heat using the transition of hot metal temperature as the main determining factor, It includes the sensor furnace heat transition KSJ, etc. that determines the thermal transition.

These KSs also include a CF function that has each measured quantity and furnace heat transition level as independent variables and the probability (confidence) that a combination of these will occur as a dependent variable, and a set of rules that determine the procedure for using the CF function. It is established from

The action determination KS is made up of a group of rules for determining the amount of action based on a combination of the furnace heat level and the furnace heat transition level. The action correction amount KS group is made up of a group of rules for correcting the current action amount based on information on actions taken in the past and disturbances that occurred in the past. The comprehensive judgment KS is made up of a group of rules for determining the final action amount based on the action judgment result and the action correction amount judgment result.

Then, the operating state determination KS determines whether or not the furnace is operating normally based on, for example, the furnace heat level,
It consists of a set of rules that sends the above action amount as is to the control system if it is normal, and if it is abnormal, it is displayed and provides guidance to the operator.

The inference engine means (26) executes each knowledge base, and as shown in the flowchart of FIG. 3, first determines the furnace heat level and furnace heat transition, and then determines the amount of action based on these determination results. Determine. This amount of action is corrected in a predetermined manner, and the result is temporarily stored in the common data buffer (24) and then stored in the file means (14).
) and the digital instrumentation device (3) via the output means (18).
2).

Then, the control valve (42) is controlled by the digital instrumentation device (32).
), (44), and (4B) are appropriately controlled to perform an action operation, and the temperature of the blast furnace (1) is controlled.
As a result, the hot metal temperature is controlled to a desired value.

Next, an outline of the structure of the knowledge base and its specific inference will be explained based on FIG. 3.

(A) Furnace heat level determination K S (Knowl
This furnace heat level determination KS group is a knowledge base that determines the state of furnace heat at the inference start time. It consists of KS groups such as Bell KSJ, and as shown below,
The CF value distribution is determined by the method described later for the furnace heat level, which is divided into seven levels from high to low for each KS group, and the level of maximum certainty is determined as the furnace heat level at the current time.

Furnace heat level evaluation 7 Large fever 6 Medium fever 5 Normal 4 Small cold 3 Medium Cold 2 Large Cold 1 Extra large cold Here, an example of the hot metal temperature-furnace heat level KS will be explained.

The KS is composed of an IF section in which conditions are set and a THEN section in which instruction contents are set when the conditions are met. Examples are as follows.

Rule No. 1 [IF section] Fully satisfied -1 NOT (Many residues) NOT (Time elapsed after wind reduction ended: 5180 minutes) Si, 5 judgment is "low" [THE N section] Hot metal is removed by normal three-dimensional function Find the CF value of temperature-furnace heat level.

This rule No. 1 indicates that when the operating state of the blast furnace is in a steady state, the CF value of molten metal temperature - furnace heat level is determined by a normal three-dimensional function.

That is, the CF value function of hot metal temperature - furnace heat level usually has ~f
, Slightly high ~ f 1 high ~' MTEIIM T
There are three types: N M T H, and the inference engine (2B) selects which function to use based on the logic described above.

In other words, the hot metal temperature - furnace heat level KS is determined by the number of degrees (the number indicating the position of the ladle where the hot metal temperature was measured compared to the ladle used since the start of tapping) and the amount of slag after the end of wind reduction. A rule is stored for determining which of the BtTi-type functions to adopt in accordance with the elapsed time of . Other examples of this rule are as follows: Rule No. 2 [IF section] Full application-I N0T (many residue) NOT (6180 minutes elapsed after wind reduction ended) St and S judgments are "slightly high" CTHEN section ] CF of hot metal level by three-dimensional function when “slightly high”
Find the value.

Rule N (L 3 [IF section pot head - 1 NOT (There is a lot of residue) NOT (Elapsed time 6180 minutes after the end of wind reduction) Si, S judgment is "high" [T HE When N section is "high" The CF value of the hot metal level is determined using a three-dimensional function.

These rules No. 1 to No. 3 indicate that when there is not much residue and sufficient time has elapsed since the end of wind reduction, the type of function should be selected based on the determination of St and S in the hot metal. .

Further, the three CF functions of hot metal temperature-furnace heat level are all functions of pot head LN, melt temperature MT, and furnace heat level FHL.

That is, f - f (LN, MT, FHL) MTN
MTN t -t (LN, MT, FHL) MTII
MTll f -f (LN, MT, FHL) MTEI
! MTE)I The inference engine (26) applies the actual measured values to LN and MT for the CF value function selected by the rule,
For FHL, apply the numbers 1 to 7 above,
Find the CF value corresponding to each furnace heat level.

FIG. 4 shows the CF value function for one pot head. Since the pot head is fixed, the CF value is a function of the hot metal temperature and the furnace heat level.

For example, if f is selected according to the rule, then when the hot metal temperature is 1400℃, the highest CF value is CF-0.2 at furnace heat level 4, and when the hot metal temperature is 1480℃, the highest CF value is CF-0,2, and when the hot metal temperature is 1480℃, the furnace heat level is 7. The highest CF value is CFo, 4
It shows that.

Next, the sensor furnace heat level (tuyere embedding temperature - furnace heat level K
S, solution loss C'm-furnace heat level KS) will be explained.

This KS includes rules for determining whether to use tuyere embedding temperature-furnace heat level KS and solution loss Cff1-furnace heat level KS, as well as tuyere embedding temperature, furnace heat level, and solution loss C amount, respectively. HT 5L fHT-fH7 (HT, FHL) fsL-fsL (SL, FHL) The blades used to calculate this function are: Mouth embedding temperature HT,
For the solution loss Cf1SL, an actual measured value (its exponential smoothed value or moving average value) is used.

Examples of rules for determining the use or non-use of these functions are shown below.

Rule No. 1 [IF part] NOT (Many residues) [THEN part] (1) Calculate the CF value of tuyere embedding temperature - furnace heat level from tuyere embedding temperature.

(2) Calculate the CF value of solution loss Cfu-furnace heat level from the solution loss C amount.

In this case, the inference engine (2B) uses both functions.

Rule No. 2 [IF section] (Many residues) [THE N section] (1) Set the CF value of tuyere embedding temperature-furnace heat level to "0".

(2) Solution loss Cff1 - CF of furnace heat level
Set the value to "0".

In this case, the tuyere embedding temperature−furnace heat level CF valueMosolution loss C′m−furnace heat level CF value also becomes a constant value regardless of the furnace heat level.

This means that these factors do not contribute to the determination of the furnace heat level, which will be described later, and the furnace heat level is determined only by the above-mentioned molten metal temperature - furnace heat level KS.

FIG. 5 is an explanatory diagram showing the operation of the inference engine (26), which calculates the CF value for each furnace heat level based on the hot metal temperature - furnace heat level KS, and also calculates the CF value for each furnace heat level. K
The CF value for each furnace heat level is determined based on S and solution loss C amount−furnace heat level KS. Then, the CF value of each level based on the tuyere embedding temperature KS and the CF value of each level based on the solution loss Cfa K S are added.

The CF value of the sensor level thus obtained and the CF value of each level based on the hot metal level KS described above are added.

In this way, the CF value for each furnace heat level (7 to 1) is determined.

(B) Furnace heat transition determination KS group; This furnace heat transition determination KS group includes hot metal temperature-furnace heat transition KS and sensor furnace heat transition KS. Depending on the degree, the CF value is determined for each rank in five stages from rapid rise to constant rise to sudden decline as shown below, and the stage position of the maximum value is taken as the furnace heat transition state at the current time.

5 Rapid rise ４　　　　　　Up　Up 3. Staying the same. 2　　　　　　Down　Downward 1. Sudden descent Here, the hot metal temperature-furnace heat transition KS will be explained.

This KS is a CF function f 8. which has two independent numbers as the difference ΔMT in hot metal temperature between successive taps (one tap) and the furnace heat transition level ■FHL. −f (ΔMT
, VFHL) and 1. :(7) CF function ΔMT Pre-processing and post-processing rules are stored.

The method of using this KS will be explained below.

Rule No. 0 [IF section] (Initial setting) [THE N section] The following values are set as the CF value of the molten metal transition.

Level 12345 CF value　oooo.

Rule No. 1 [IF section] (L) NOT (Judgment of S i, S is "slightly high") (
2) NOT (Judgment of Si, S is "high") (3) Stability flag is ON (furnace condition is stable) [THE N part] (1>ΔMT-(current tap hot metal temperature - hot metal temperature of the previous tub), the CF value is determined for each furnace heat transition level, and is defined as "current hot metal temperature - furnace heat transition CF value".

(2) Next, the CF value is determined for each furnace heat transition level as ΔMT-(hot metal temperature of the previous tap - hot metal temperature of the front tap), and is defined as "previous hot metal temperature - furnace heat transition CF value".

(3) "Current hot metal temperature - Furnace heat transition CF value" and "Previous hot metal temperature - Furnace heat transition CF value" are each multiplied by a weighting coefficient to
Add to "Hot metal temperature - Smooth furnace heat transition CF value".

(These calculations are performed separately for each level of the furnace heat transition.) In other words, in a stable furnace condition, up to the most recent data is used to estimate the furnace heat transition.

Rule No. 2 [IF section] (1) (Si, s judgment is "slightly high") (2) NO
T (S t, 5 (7) Judgment is "High") (3) Stability flag is OFF [T HE N section] (1) Each furnace heat is calculated as ΔMT - (previous tap hot metal temperature - pre-previous hot metal temperature) The CF value is determined for each transition level and is defined as "previous hot metal temperature - furnace heat transition CF value".

(2) Multiply the "previous hot metal temperature - furnace heat transition" by a weighting coefficient, add it to the "hot metal temperature - smooth furnace heat transition CF value", and set it again as the "hot metal temperature - smooth furnace heat transition CF value".

Here, since the furnace conditions are not stable, "current hot metal temperature - furnace heat transition" is not considered.

Next, the sensor furnace heat transition KS will be explained.

The sensor furnace heat transition KS stores the number of CF values with the transition of measured values of a large number of sensors and the furnace heat transition as two independent variables, and the rules that determine how to use them. It is set in.

ASI ASI (ΔS t, VFHL) or f
-f (i-1 to n; for each corresponding sensor) An example will be described below in which the sensor measures the tuyere embedding temperature.

Rule No. 1 [IF part] NOT (There is a lot of residue) [TlEN part CF value of tuyere embedding temperature - furnace heat transition is calculated as Δ5t - (tuyere embedding temperature - 60 minutes front blade embedding temperature) demand.

Rule No. 2 [IF section] There is a lot of residue CF value of tuyere transition level 4〉0 [THE N section] Overwrite the CF value of tuyere transition level 4 with "0".

Level 12345 CF value ***0* (* indicates that the value remains as it was) Rule No.
3 CF value of tuyere transition level 5 with a lot of residue > 0 [THE N section] Overwrite the CF value of tuyere transition level 5 with "0".

Level 12345 CF value ***0 Figure 6 shows the tuyere embedding temperature-furnace heat CF function with the tuyere embedding temperature (difference from the reference value) and the furnace heat transition level as independent variables.

In addition, it is possible to divide the hot metal temperature-furnace heat transition KS into rules for "short-term transition" and "long-term transition," and for the sensor transition KS, there are other rules in addition to the tuyere-embedded KS. K.S.
, for example, each KS such as unloading, blowing pressure, gas utilization rate, solution loss amount, etc. is also taken into consideration.

The inference engine (26) calculates the CF value for each transition based on each rule of the hot metal temperature-furnace heat transition KS, and calculates the CF value for each sensor based on the rule of the sensor furnace heat transition KS. Find the CF value for each transition. Then, the CF values of these KSs are added for each furnace heat transition level to obtain the CF value for each furnace heat transition level.

(C) Action judgment KS.

This action determination KS is a knowledge base for determining the current furnace heat state on a matrix centered on the furnace heat transition and furnace heat level, and determining the action to be taken.

The inference engine (26) calculates the product of the CF value of the furnace heat level and the CF value of the furnace heat transition based on the above-mentioned action determination KS, and writes it into the matrix.

FIG. 7 shows an example of this, and this example shows that the apex (maximum value) of the CF value is a furnace heat level of -4 and a furnace heat transition of -3. Note that the action type and amount of action at each position on the matrix are stored in advance as knowledge in the frame.

FIG. 8 is a diagram showing an example of an action type, and FIG. 9 is a diagram showing an example of an action amount.

Note that when adopting an action-type action at a predetermined position, it is necessary that the CF value reaches a predetermined value. In principle, all action amounts are automatically controlled, but it is also possible to manually control some of them (for example, action amount G in FIG. 9).

(D) Action correction amount determination KS group; This action correction amount determination KS group determines the amount of corrected action by determining actions taken in the past or disturbances, and taking into account their influence at the current time. Contains various KS for The contents include changes in manipulated variables such as air humidity, air temperature, liquid fuel, coke ratio, etc.
It also consists of rules for detecting and responding to disturbances such as coke moisture and falling deposits.

For example, when the ventilation humidity is changed, the change time and amount are automatically detected by the "operation amount change detection" rule, and the subsequent influence amount is considered as a function of time by the "ventilation humidity" rule. In addition, when something attached to the furnace wall falls off, the "wall fall" rule automatically determines the falling place, the amount of influence on the furnace heat, and the time for the tuyere tip to fall, and the operation time and amount of the preliminary action are determined and correction calculations are made. be incorporated into.

The inference engine (26) executes each of the above rules to obtain the necessary correction action amount and operation time.

(E) Comprehensive judgment KS; This comprehensive judgment KS is a knowledge base for comprehensively determining the amount of action to be taken based on the judgment results of (C) and (D) above. Then, when this KS is inferred by the inference engine (26) and a judgment result is obtained, the judgment result is input to the operating state judgment KS, the operating state is judged, and the action to be taken is displayed on the CRT (30). The quantity is instructed and guided to the operator, and at the same time, feedback is given to the digital instrumentation device (32) for predetermined automatic control (see Fig. 2).

Next, a method of creating a multidimensional function used when determining the CF value using each of the above knowledge bases (A) and (B), such as a CF function of hot metal temperature-furnace heat level, will be explained.

FIG. 10 is a characteristic diagram showing the change in hot metal temperature within the tap (continuous measurement results). As shown in the figure, even if the thermal condition inside the furnace is stable, the influence of bottom cooling during the residence in the lower part of the furnace,
During the early stage of tapping, the hot metal is discharged at a relatively low temperature due to cooling in the tap trough, which is the flow path for the hot metal. As time passes, the temperature of the tap runner increases as it receives the sensible heat of the hot metal, and the influence of retention in the furnace decreases, so the temperature of the discharged hot metal gradually rises and stabilizes. This information is representative of the furnace heat that the system is controlling.

Furthermore, the tendency of this temperature rise is not always constant (it also changes depending on the operating conditions, so the suitability of the information on the hot metal temperature as information representative of the system's control target changes from moment to moment, and it has ambiguity). It becomes information.Therefore,
Check the hot metal temperature? #1 In order to link this to the estimation of the furnace heat state, it is necessary to keep in mind how much time has passed since the start of tapping, and also to take into account the ambiguity peculiar to the process. Become.

If we understand the temperature of hot metal during tapping from the above perspective,
The temperature distribution is expressed as shown in FIG. In the figure, each axis represents the following: X-axis; dimensionless time Y; hot metal temperature Z-axis; appearance frequency (occurrence frequency rate).

FIG. 12 shows the relationship between the hot metal temperature Ti and the tap maximum hot metal temperature Tmi at the tapping time X-X1 in FIG. 11. Based on this figure, the three-dimensional function shown in FIG. 13 is obtained by adjusting the relationship between the tap maximum hot metal temperature and the furnace heat, and the relationship between the appearance frequency and the CF value. Since the tapping temperature is measurement information that depends on the elapsed time from the start of tapping and the operating conditions, more than 30 types are prepared so that they can be used properly, and are automatically selected according to the conditions.

When the characteristic diagram of FIG. 13 is illustrated with the X axis replaced with sensor data, it is expressed as shown in FIG. 14.

In the figure, the straight line connecting points A1 and A4 and the straight line connecting points A3 and A6 each have a CF value of "0",
The CF value increases toward the middle of the two-plate line. The straight line connecting points A2 and A5 has the maximum CF value of "1", indicating that it has the highest reliability.

As a result of controlling the furnace temperature based on such a three-dimensional function,
As shown in FIG. 15, it can be seen that the frequency of errors occurring is reduced compared to the conventional operator method.

The three-dimensional function shown in Fig. 14 is constructed by connecting the minimum and maximum values of a predetermined range of sensor data with a straight line.
In Figure 16, the sensor data area is expanded and divided into multiple parts depending on the size of the sensor data (in this example, it is divided into three parts).
, a three-dimensional function is constructed by connecting polygonal lines so that each divided region is continuous. The three-dimensional function shown in FIG. 16 shows a more realistic relationship between sensor data, furnace heat level, and CF value (confidence factor) than the three-dimensional function shown in FIG. 14. The maximum value of this CF value is "1" in each divided area, but it goes without saying that the maximum value may be different for each area.

The reason why the sensor data of the three-dimensional function described above is difference data from the reference value (-measured value - target value) is as follows.

b) The target value of hot metal temperature changes due to component adjustment, etc.

Regarding sensor data, the standards are also changed daily depending on the operational policy, for example, whether or not the system is oriented toward low fuel ratios.

c) Furthermore, depending on the degree of wear of the installed location of the bricks, etc., the temperature detected by the thermometer etc. will differ even if the situation inside the furnace is the same.

For the above reasons, the three-dimensional function uses the above difference data as 2!
standard, so that it can respond to various changes.

FIG. 17 shows the relationship between action instructions, furnace heat level, and hot metal temperature. The action instruction using the 3-dimensional function in Figure 14 is performed at the tap of ■ at the timing shown by the solid line in the figure, and the action instruction using the 3-dimensional function (broken line) in Figure 16 is performed at the timing shown in A and B in the figure. be exposed.

As a result, in the case of using the three-dimensional function shown in FIG. 16, the molten metal temperature becomes the target value at the next tap. In comparison, the three-dimensional function shown in FIG. 14 deviates slightly from the target value.

Figure 18 shows the results regarding hot metal temperature, Si, and S when the three-dimensional functions in Figures 14 and 16 are used, and the results are better when the three-dimensional functions in Figure 16 are used. Characteristics superior to those shown in FIG. 14 are obtained.

By the way, since the above-mentioned three-dimensional function corresponds to the condition of the furnace, its contents must be changed when the condition of the furnace changes. For example, if the bottom of the furnace is eroded as shown by the broken line in FIG. 19 and that part solidifies or melts, it is presumed that the tapping temperature pattern will change accordingly.

Therefore, as shown in FIG. 19, a sensor is attached to a predetermined position on the hearth bottom to grasp the situation at the hearth bottom. Specifically, as shown in Figure 20 (A), furnace bottom side plate thermometers (52) are installed in nine directions near the taphole (51) on the side plates of the third and first stages. There is.

Furthermore, as shown in Figure 20 (B), the side plate of the second stage has 4
Furnace bottom side plate thermometers (52) are installed at equal intervals in two directions. Furnace bottom side plate thermometer (5
3) are arranged at different positions in the diametrical direction as shown in FIG.

Regarding the hearth bottom thermometer (54), its upper stage is shown in Figure 22 (
The middle row is installed in four directions as shown in A), the middle row is installed in five directions as shown in FIG. 22(B), and the lower row is installed in two directions as shown in FIG. 22(C).

Regarding the bottom heat flow meter (55), the lower part is shown in Figure 23 (A
) as shown in the figure, and in the upper and middle rows, as shown in FIG.

Figure 24 shows, for example, the change in furnace bottom temperature over time obtained based on the measured temperature of the second stage and the temperature of the side plate of the third stage. means a change in

FIGS. 25(A), 25(B), and 25(C) show changes in the tapping temperature pattern depending on the erosion condition at the bottom of the furnace. For example, (A) in the same figure shows a temperature rise pattern when the temperature is stable, (C) in the same figure shows a pattern when the temperature rises rapidly, and (B) in the same figure shows a pattern in between.

Therefore, one of the temperature increase patterns shown in FIG. 25 is selected based on the outputs of the sensors shown in FIGS. 19 to 23 (A) and (B). Selection of this temperature increase pattern is performed as follows.

The following α is determined using the daily average value of hearth bottom temperature.

α - Average temperature of the previous day - Average temperature of the day before the previous day Next, temperature rise patterns A and B corresponding to Fig. 24 (A), (B), and (C)
, C are determined by the following calculation.

Temperature increase pattern A, T2≧α Temperature increase pattern B; Tl≧α≧T2 Temperature increase pattern C; α>Tl However, Tl and T2 are constants, and there is a relationship of TI > T2.

Figure 26 shows an example of a three-dimensional function in the format shown in Figure 14, and the solid line is 1 corresponding to temperature rise pattern A (stable period).
The broken lines indicate one three-dimensional function corresponding to the temperature rise pattern C, respectively. In addition, point A
A famous function composed by connecting l-A3, or points 81 to B6
The functions formed by connecting these correspond to the three-dimensional functions (fMTE) I'f, f) in Fig. 4, and for example, when temperature pattern A is selected, the corresponding The three-dimensional functions shown in FIG. 4 are called to execute each of the above rules.

Figure 27 shows the molten metal in the case where three-dimensional functions prepared in advance are selected and controlled according to the situation at the hearth bottom (example) and in the case where such consideration is not taken. The temperature and furnace heat level are shown respectively. According to this example, it can be seen that the furnace heat level changes appropriately in response to changes in the hot metal temperature, and as a result, the target hot metal temperature is obtained.

Figure 28 shows the incidence of temperature difference with respect to the target value in this example and when the situation at the bottom of the hearth is not considered.
It can also be seen from this figure that according to this example, the control of the hot metal temperature is sufficiently effective.

Although the three-dimensional function in FIG. 26 has the format shown in FIG. 14, a three-dimensional function in the format shown in FIG. 16 may also be used, and in that case, the control precision can be further improved.

[Effects of the Invention] As described above, according to the present invention, processed data is created from various data installed in a blast furnace, and predetermined inferences as artificial intelligence are made using the processed data and a knowledge base based on experience. As a result, conventional experience is fully utilized, standardizing operational management, preventing human errors in judgment, stably supplying high-quality hot metal to the next process with little temperature and composition fluctuations, avoiding furnace cooling, and saving labor. has been realized.

In addition, when the control device is implemented using a computer, it is based on a knowledge base, which improves calculation capacity and speed, and makes it easy to add and modify rules to accommodate new situations such as aging of the blast furnace. It has become. Furthermore, since the above-mentioned confidence function is used to obtain the CF value of each furnace heat level and each furnace heat transition, the number of rules and the work of inputting their numerical values are reduced.

In addition, the next action amount is determined by selecting a confidence function according to the situation at the bottom of the furnace, so control can be performed in accordance with the actual furnace condition, and this allows extremely high-precision furnace temperature control. It's now possible.

[Brief explanation of the drawing]

Fig. 1 is a block diagram showing the configuration of the present invention, Fig. 2 is a block diagram of a blast furnace furnace heat control device and related equipment according to an embodiment of the present invention, and Fig. 3 shows a knowledge base and its inference order. FIG. 4 is a characteristic diagram showing a three-dimensional function of the furnace heat level, and FIG. 5 is an explanatory diagram illustrating the inference operation. Figure 6 is a characteristic diagram showing the three-dimensional function of the furnace heat transition level.
FIG. 7 is an explanatory diagram showing an example of an action matrix, FIG. 8 is an explanatory diagram showing an example of an action matrix type, and FIG. 9 is an explanatory diagram showing an example of an action amount. FIG. 10 is a characteristic diagram showing the transition of hot metal temperature in the tap, and FIGS. 11 to 13 are explanatory diagrams showing a method of creating a three-dimensional function of the furnace heat level. Fig. 14 is an explanatory diagram showing an example of the three-dimensional function, Fig. 15 is a characteristic diagram showing the results of the three-dimensional function in Fig. 14, and Fig. 16 is an explanatory diagram showing another example of the three-dimensional function. , Fig. 17 is a characteristic diagram showing the relationship between control action instructions, furnace heat level, and hot metal temperature using the three-dimensional function shown in Fig. 14 and the three-dimensional function shown in Fig. 16, and Fig. 18 is the three-dimensional function shown in Fig. 14. FIG. 17 is a characteristic diagram showing a temperature difference with respect to a target temperature, and a distribution of Si and S, showing control performance using the function and the three-dimensional function of FIG. 16; Figure 19 is a cross-sectional diagram of the bottom of the furnace, Figure 20 (A) (B)
is an explanatory diagram showing the installation position of the furnace bottom side plate thermometer, Figure 21 is an explanatory diagram showing the installation position of the furnace bottom erosion thermometer, and Figure 22 (A), (B), and (C) are the furnace bottom thermometers. FIGS. 23(A) and 23(B) are explanatory diagrams showing the installation position of the furnace heat flow meter. Figure 24 shows a measured by the thermometers on the third-stage side plate and the second-stage side plate.
Figure 25 (A) (B) (C) Characteristic diagram showing constant performance
) is an explanatory diagram showing various patterns of hot metal temperature, FIG. 26 is an explanatory diagram showing two types of three-dimensional patterns, FIG. 27 is a characteristic diagram showing hot metal temperature and furnace heat level according to this example, FIG. 28 is a characteristic diagram showing the rate of occurrence of temperature difference with respect to the target temperature according to this embodiment. (11), (12): Data human power means (te), (14); Processed data creation means (19)
, (24) ; Processed data storage means (22) : Knowledge base storage means (26); Reasoning means Agent Patent attorney Souji Sasaki Figure 9 Figure 10 Time 1 rninl Diagonal ■ Poetry Figure 11 Figure 12 Figure 17 -Ichichu Ichigame! '1 Yo. ■ 昂 2 瘭子Meng H* Old Figure 5 1-/ml Figure 20 (A) Figure 23 Ko1 1ri; 1 Figure 24 (A) (8)

Claims (1)

[Claims] (1) A data input means that takes in data from various sensors installed in the blast furnace at a predetermined timing; A processing data creation means that creates various data indicating the status of the blast furnace, such as utilization rate, solution loss amount, etc., and compares the various data with reference data to create difference data; and the various data and difference data. (hereinafter referred to as processed data)
a knowledge base storage means that stores various knowledge bases based on experience, results, mathematical models, etc. regarding blast furnace operation; and processed data in the storage means and knowledge in the knowledge base storage means. an inference means for inferring a furnace heat level and a furnace heat transition based on the base and determining an action amount for the blast furnace; and a means for outputting the action amount, and the knowledge base storage means estimates the furnace heat level. A furnace heat level determination knowledge base used for estimating the furnace heat level transition; a furnace heat transition determination knowledge base used for estimating the furnace heat transition;
An action determination knowledge base that is used to determine the amount of action based on the furnace heat level and furnace heat transition, an action correction knowledge base that determines the correction value of the amount of action based on past actions and disturbances, and the amount of action and action. a comprehensive judgment knowledge base that determines the actual action amount from the correction amount, and the furnace heat level judgment knowledge base and the furnace heat transition judgment knowledge base have processing data and the furnace heat level as independent variables, and a combination thereof. The probability of occurrence (hereinafter referred to as "certainty") as the dependent variable determines a group of confidence functions selected based on the output of a thermal measurement sensor installed at the bottom of the reactor, and a method of applying this group of confidence functions. The action judgment knowledge base has a rule for determining the action amount from the furnace heat level and the furnace heat transition, and the comprehensive judgment knowledge base determines the actual action from the action amount and the action correction amount. A blast furnace furnace heat control device characterized by having a rule for determining the amount.