JP2737136B2 - Blast furnace heat control system - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a blast furnace heat control device for controlling the temperature of hot metal discharged from a blast furnace.

[Prior art] Conventionally, the temperature of hot metal in a blast furnace is estimated, and this is managed and controlled.
As a control method, a method is generally adopted in which a blast furnace operator qualitatively determines information from various sensors installed in the blast furnace, evaluates the condition of the blast furnace, and performs optimal adjustment of the operation factor. ing. However, there are individual differences in the results of the evaluation due to the capabilities and experience of operators, which makes it difficult to standardize the operation actions, and it is difficult to estimate the hot metal temperature because the evaluation is not quantitative. there were.

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

For this reason, a large wave change (change in a long cycle) that occurs when the blast furnace condition is calculated and controlled is corrected to realize an accurate operation.

[Problems to be Solved by the Invention] In the conventional process control method disclosed in Japanese Patent Publication No. 51-30007, information from a sensor is analyzed and input to a model to perform a predetermined calculation. ing. For this reason, a computer that executes the computation uses, for example, Fortran as a language, but the computation capacity is extremely large. Further, since the blast furnace changes over time, maintenance must be performed by changing the analysis model itself. However, since the analysis model itself is complicated, changing the conditions of the analysis model is extremely troublesome.

As means for solving the above problems, the maintainability can be improved by a computer system using an artificial intelligence language, for example, LISP. Here, sensor information (true / false data, various sensor data) and ,
In inferring the furnace heat situation using the knowledge base,
When the production rule is used, for example, IF (the temperature of the sensor i is in the range of T1 to T2) THEN for all the sensors concerned.
(The CF value that is the high heat level is C1)... (The CF value that is the low heat level is Cn); IF (The temperature of the sensor i is in the range of T1 to T3.) THEN
(CF value at high heat level C′1),... (CF value at low heat level is C′n);... In addition, the adjustment of the CF value is extremely complicated.

For this reason, there is a problem that the amount of numerical values indicating the degree of certainty described in the creation of the rule program or in the rules becomes enormous, and the input operation becomes complicated.

The present invention has been made in order to solve such problems, and can control furnace heat of a blast furnace with high accuracy. Improve capacity and calculation speed, and add rules for new situations such as aging of blast furnaces.
It is an object of the present invention to obtain a blast furnace heat control device that can be easily modified.

[Means for Solving the Problems] FIG. 1 is a block diagram showing the concept of a blast furnace heat control device according to the present invention. The blast furnace heat control device according to the present invention is a data input unit that captures data from various sensors installed in the blast furnace at a predetermined timing, and based on the data from the sensors, tuyere embedding temperature, unloading speed,
Create various data indicating the blast furnace status, such as pressure loss, furnace top temperature, gas utilization rate, solution loss amount, etc.
Processing data creating means for comparing the various data with the reference data and creating the difference data;

Further, storage means for temporarily storing the various data and difference data (hereinafter referred to as processing data), knowledge base storage means for storing various knowledge bases based on experiences, results, mathematical models, etc. of blast furnace operation, Inference means for inferring a furnace heat level and a furnace heat transition based on the processing data of the storage means and the knowledge base of the knowledge base storage means, and an inference means for 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 for estimating a furnace heat level, a "furnace heat transition determination knowledge base" used for estimating a furnace heat transition, "Action determination knowledge base" used to determine the amount of action from heat level and furnace heat transition, "action correction knowledge base" to determine the correction value of the action amount based on past actions and disturbances, and action It includes an “overall judgment knowledge base” for determining an actual action amount from the amount and the action correction amount.

The “furnace heat level judgment knowledge base” and the “furnace heat transition judgment knowledge base” are a confidence function in which the processing data and the furnace heat level are independent variables, and the confidence is a dependent variable, and a method of applying the confidence function. And the "action determination knowledge base" has a rule for determining an action amount from a furnace heat level and a furnace heat transition. The “comprehensive judgment knowledge base” has a rule for determining an actual action amount from an action amount and an action correction amount.

Also, in the present invention, the certainty factor of the certainty function is represented by a set of polygonal lines having different quadratic curves or slopes.

[Operation] In the present invention, the machining data creating means creates various data indicating the state of the blast furnace based on the blast furnace data from the data input means, and creates machining data based on the data. An inference operation as artificial intelligence is performed based on the processed data and the knowledge base stored in the knowledge base storage means to determine an action amount for the blast furnace.

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 certainty function and a group of rules that determine how to apply the function are used to obtain certainty for the inference result. Next, the action amount is determined by applying the furnace heat level and the furnace heat transition to the action determination knowledge base. The necessary correction amount is obtained using the action correction knowledge base.

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

Example An example of the present invention will be described below with reference to the drawings. Second
FIG. 1 is a block diagram showing a blast furnace heat control apparatus and related equipment according to one embodiment of the present invention.

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

The sensor data collection means (12) inputs data from various sensors, for example, a temperature sensor, a pressure sensor, a gas sensor, etc., in a time-series manner through data scanners (11a), (11b), and (11c).

The file means (14) fulfills a file data banking function. The arithmetic means (16) performs exponential smoothing processing on the data from the sensor data collecting means (12) stored in the file means (14), and then re-executes the file means (14).
To be stored. The average value and the difference data between the average value and the reference value are set as processing data every predetermined time, for example, every 20 minutes, and the common data buffer (2
Send out to 4).

The inference engine (26) has its data and knowledge base (2
Based on the knowledge of 2), a predetermined inference operation is performed, and the amount of action to be taken next is determined to obtain a common data buffer (24).
And again in the file means (14).

(30) is a CRT, and the inference result of the inference engine (26)
The information is transmitted and displayed via the file means (14).

(32) is a digital instrumentation device, which is a blast furnace heat control device (1
It controls the temperature of the blast furnace based on the command 0), and adjusts the operation amount of the operation type that can adjust the furnace heat such as the blast moisture, the blast temperature, and the liquid fuel such as heavy oil. At this time, the command of the blast furnace heat control device (10) is sent out to the digital instrumentation device (32) via the output interface (18).

(40) is a hot blast stove, and (42), (44) and (46) are control valves respectively.

An outline of the operation of the present embodiment having the above configuration will be described.

(1) First, data of various sensors are sequentially read at predetermined timings, for example, at one-minute intervals by a sensor data collecting means (12) via a data scanner (11) and stored in a file means (14). .

(2) The data stored in the file means (14) is subjected to exponential smoothing by the arithmetic means (16). Here, data relating to unloading, temperature, gas utilization rate, tapping slag, and the like are processed. The various data subjected to the arithmetic processing are stored again in the file means (14). Next, for these various data, an average value thereof and a difference between the average value and a predetermined reference value are obtained at predetermined time intervals, for example, at intervals of 20 minutes, and transferred to the common data buffer (24) as processed data. I do.

(3) The inference engine means (26) infers the situation in the blast furnace based on the knowledge data stored in the knowledge base (22) in advance and the processing data of the common data buffer (24).

Here, the knowledge base includes a furnace heat level determination KS group, a furnace heat transition determination KS group (KS; knowledge source), an action determination KS, an action correction determination KS, a comprehensive determination KS group, and an operation state determination as shown in FIG. Formed from each knowledge base unit of KS.

Furnace heat level determination The KS group is a knowledge base used by the inference engine (26) to determine the furnace heat level of the blast furnace, and determines the furnace heat level using the hot metal temperature as the main determining factor. "Hot metal temperature-furnace heat level KS", and "sensor-furnace heat level KS" for determining the furnace heat level based on the measured amount of other sensors.

Each of these KSs is an independent variable for each measurand and furnace heat level, and the probability of their combination occurring (confidence)
Confidence function (hereinafter referred to as CF function) with
It consists of a set of rules that determine the procedure for using the CF function.

The KS Group is a knowledge base used by the inference engine (26) to determine the level of the furnace heat transition of the blast furnace. Includes "hot metal temperature-furnace heat transition KS", which determines the transition of furnace heat, and "sensor-furnace heat transition KS", which determines the transition of furnace heat mainly based on the transition of measured values of other sensors. .

These KSs are also CF functions with each measured quantity and furnace heat transition level as independent variables, the probability of their combination occurring (confidence) as a dependent variable, and a set of rules that determine the procedure for using the CF function. It consists of.

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

Then, the operation state determination KS determines whether or not the furnace operation is normally performed 4 based on, for example, the furnace heat level or the like. If there is, it is made up of a group of rules that indicate so and provide guidance to the operator.

The inference engine means (26) executes each knowledge base. First, as shown in the flowchart of FIG. 3, the furnace heat level and the furnace heat transition are determined, and then the action amount is determined based on these determination results. Is determined. This action amount is subjected to a predetermined correction, and the result is temporarily stored in a common data buffer (24), and then sent to the digital instrumentation device (32) via the file means (14) and the output interface (18). Sent.

Then, the control valve (4
2), the opening degree of (44) and (46) is appropriately controlled, an action operation is performed, the temperature of the blast furnace (1) is controlled, and as a result, the hot metal temperature is controlled to a desired value.

Next, the configuration of the knowledge base and the outline of the specific inference will be described with reference to FIG.

(A) Furnace heat level determination KS (Knowledge Source) group: This furnace heat level determination KS group is a knowledge base that determines the state of the furnace heat at the inference start time, as described above. Temperature-furnace heat level KS "," sensor-furnace heat level
KS, etc., and as shown below, for each of the KS groups, the furnace heat level divided into seven levels from high to low level, the CF value distribution is determined by the method described below, and the maximum The confidence level is the furnace heat level at the current time.

Furnace heat level evaluation 7 Large heat 6 Medium heat 5 Normal 4 Small cold 3 Medium cold 2 Large cold 1 Extra large cold Here, an example of hot metal temperature-furnace heat level KS will be described.
The KS is composed of an IF section in which a condition is set and a THEN section in which the content of an instruction when the condition is satisfied is set. Examples are as follows.

Rule No.1 [IF section] Pot order = 1 NOT (many residues) NOT (elapsed time after wind reduction ≤ 180 minutes) Judgment of Si and S is "low" [THEN section] Melting by ordinary three-dimensional function Calculate CF value of pig 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 the hot metal temperature-furnace heat level is obtained by a normal three-dimensional function.

That is, the hot metal temperature-furnace heat level CF value function is usually ~ f
There are three types: _MTN , slightly higher ~ f _MTH , and higher ~ f _MTEH , and the inference engine (26) selects which function to use based on the above logic.

In other words, the hot metal temperature-furnace heat level KS includes the order of the ladle (the number indicating the ladle in which the ladle for which the hot metal temperature was measured hits the ladle used from the start of tapping) and the amount of residue after the end of wind reduction. A rule for determining which of the three types of functions is to be used is stored in accordance with the elapsed time from. Another example of this rule is as follows: Rule No. 2 [IF section] Pot order = 1 NOT (many residues) NOT (elapsed time after the end of wind reduction ≤ 180 minutes) Judgment of Si, S is "somewhat high" [THEN part] The hot metal level is calculated using a three-dimensional
Find the CF value.

Rule No.3 [IF section] Pot order = 1 NOT (many residues) NOT (elapsed time after wind reduction ≤ 180 minutes) Judgment of Si and S is "high" [THEN section] 3D when "high" The CF value of the hot metal level is determined by the function.

These rules No. 1 to No. 3 are that when there is not much residue and enough time has passed since the end of wind reduction, Si and S
Indicates that the type of function is selected.

The three CF functions of hot metal temperature-furnace heat level are all functions of ladle order LN, hot metal temperature MT, and furnace heat level FHL.

That is, f _MTN = f _MTN (LN, MT, FHL) f _MTH = f _MTH (LN, MT, FHL) fMTEH = fMTEH (LN, MT, FHL) The inference engine (26) was selected according to the above rule.
With respect to the CF value function, LN and MT are applied to actual measured values, and for FHL, the numerical values from 1 to 7 are applied to obtain CF values corresponding to respective furnace heat levels.

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

For example, if f _MTN is selected by the rules, when the hot metal temperature is 1400 ° C, the CF value is highest when the furnace heat level is 4
When CF = 0.2 and the hot metal temperature is 1480 ° C., the furnace value of 7 indicates that the CF value is the highest at 0.4 CF.

Next, the sensor-furnace heat level (tuyere embedded temperature-furnace heat level
KS, solution loss C amount-furnace heat level KS) will be described.

This KS is a rule for determining whether to use tuyere filling temperature-furnace heat level KS and solution loss C amount-furnace heat level KS, and tuyere filling temperature and furnace heat level, respectively.
F _HT = f _HT (HT, FHL) consisting of CF value function f _HT , f _SL with solution loss C amount and furnace heat level as two independent variables f _SL = f _SL (SL, FHL) Tuyere embedding temperature HT, used to calculate
As the solution loss C amount SL, an actual measurement value (exponential smoothed value or moving average value) is used.

The following are examples of rules that determine the use and non-use of these functions.

Rule No.1 [IF section] NOT (many residues) [THEN section] (1) Tuyere embedding temperature-furnace heat level according to tuyere embedding temperature
Find the CF value.

(2) The CF value of the solution loss C amount-furnace heat level is obtained from the solution loss C amount.

In this case, the inference engine (26) uses both functions.

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

(2) Set the CF value of the solution loss C amount-furnace heat level to “0”.

In this case, the tuyere embedding temperature-furnace heat level CF value and the solution loss C amount-furnace heat level CF value are constant regardless of the furnace heat level.

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

FIG. 5 is an explanatory diagram showing the operation of the inference engine (26). The CF value for each furnace heat level is obtained based on the hot metal temperature-furnace heat level KS. The CF value for each furnace heat level is determined based on the level KS and the solution loss C amount-furnace heat level KS. And the CF value of each level and the solution loss C amount by the tuyere embedding temperature KS
Add the CF value of each level by KS. The CF value of the sensor level obtained in this way and the CF value of each level based on the hot metal level KS are added. Thus, the CF value of each furnace heat level (7-1) is obtained.

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

Level Contents 5 Rapid rise 4 Up rise 3 Sideways 2 Down 1 Drop suddenly Here, the hot metal temperature-furnace heat transition KS will be described.

The KS is a CF function _fΔMT = _fΔMT (ΔMT, VFHL) with the difference ΔMT of the hot metal temperature between the preceding and following taps (one tapping) and the furnace heat transition level VFHL as two independent numbers.
Pre-processing and post-processing rules for the F function are stored.

 Hereinafter, the method of using the KS will be described.

Rule No. 0 [IF section] (Initial setting) [THEN section] The following values are set as CF values of hot metal transition.

Level 1 2 3 4 5 CF value 0 0 0 0 0 Rule No.1 [IF section] (1) NOT (Si, S judgment is "slightly high") (2) NOT (Si, S judgment is "high" (3) The stability flag is ON (the furnace condition is stable) [THEN part] (1) ΔMT = (hot metal temperature of current tap-hot metal temperature of previous tap) for each furnace heat transition level Obtain the CF value and use it as “Current hot metal temperature – furnace heat transition CF value”.

(2) Next, assuming that ΔMT = (hot metal temperature of front tap−hot metal temperature of front tap), a CF value is obtained for each furnace heat transition level,
It is assumed to be "last 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 factor and added to “hot metal temperature-furnace heat transition CF value”.

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

Rule No.2 [IF section] (1) (Si, S judgment is "slightly high") (2) NOT (Si, S judgment is "high") (3) Stability flag is OFF [THEN section] ( 1) Assuming that ΔMT = (pre-tap hot metal temperature-pre-tap hot metal temperature), a CF value is determined for each furnace heat transition level, and is defined as "previous hot metal temperature-furnace heat transition CF value".

(2) Multiply the “last hot metal temperature – furnace heat transition” by a weighting factor and add it to “hot metal temperature – smoothing furnace heat transition CF value” and set it as “hot metal temperature – smoothing furnace heat transition CF value” again. I do.

In this case, since the furnace condition is not stable, the “current hot metal temperature-furnace heat transition” is not considered.

 Next, the sensor-furnace heat transition KS will be described.

The sensor-furnace heat transition KS stores a CF value function that uses the transition of measured values of many sensors and the furnace heat transition as two independent variables, and a rule that determines how to use the function. It is provided in.

That is, f _ΔSi = f _ΔSi (ΔSi, VFHL) (i = 1 to n: for each corresponding sensor) An example in which the sensor has the tuyere embedded temperature will be described.

Rule No.1 [IF part] NOT (many residues) [THEN part] Assuming that ΔSi = (tuyere embedding temperature-60 min before tuyere embedding temperature), the tuyere embedding temperature-CF value of furnace heat transition Ask.

Rule No.2 [IF part] There are many residues. CF value of tuyere transition level 4> 0 [THEN part] Overwrite the CF value of tuyere transition level 4 with "0".

 Level 1 2 3 4 5 CF value * * * 0 * (* indicates that the value is the same as before.) Rule No. 3 Most residue Tuyere transition level 5 CF value> 0 [THEN part] "0" is overwritten to the CF value of the tuyere transition level 5.

FIG. 6 shows the tuyere embedding temperature-furnace heat CF function using the tuyere embedding temperature (difference from the reference value) and the furnace heat transition level as independent variables.

In addition, it is also possible to make rules for the hot metal temperature-furnace heat transition KS by dividing it into “short term transition” and “long term transition”.
Further, in addition to the tuyere embedded KS, other KSs such as unloading, blast pressure, gas utilization rate, solution loss amount, etc. are added to the sensor transition KS, and such information is also taken into consideration.

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

(C) Action determination KS; This action determination KS is a knowledge base for determining the furnace heat state at the current time on a matrix based on the furnace heat transition and the furnace heat level, and determining an 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 action determination KS, and writes the product in a matrix.

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

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.

When the action type action at a predetermined position is adopted, it is necessary that the CF value has reached a predetermined size. Further, the action amount is basically controlled automatically, but a part of the action amount can be controlled manually (for example, the action amount G in FIG. 9).

(D) Action correction amount determination KS group: In the action correction amount determination KS group, a past action or disturbance is determined, and a correction action amount is determined in consideration of the influence amount at the current time. Various KS to do is included. The contents are composed of rules such as changes in manipulated variables such as ventilation humidity, ventilation temperature, liquid fuel, and coke ratio, and detection and response to disturbances such as coke moisture and falling off deposits.

For example, when the ventilation humidity is changed, the change time and the change amount are automatically detected by the “operation amount change detection” rule,
Subsequent influence quantities are taken into account as a function of time according to the "ventilation humidity" rule. Also, when the deposits on the furnace wall fall off,
Based on the "wall drop" rule, the location of dropout, the amount of influence on the furnace heat, and the tuyere descent time are automatically determined, and the operation time and amount of the preliminary action are determined and incorporated into the correction calculation.

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

(E) Comprehensive judgment KS; This comprehensive judgment KS is a knowledge base for comprehensively judging an action amount to be taken based on the judgment results of the above (C) and (D). When the KS is inferred by the inference engine (26) and a determination result is obtained, the determination result is input to the operation state determination KS, and the operation state is determined,
The operator is instructed on the amount of action to be taken by displaying it on the CRT (30) and gives guidance, and at the same time, feeds back to the digital instrumentation device (32) to perform predetermined automatic control (see FIG. 2).

Next, a description will be given of a method of creating a multidimensional function used for obtaining a CF value in each of the above knowledge bases (A) and (B), for example, a CF function of hot metal temperature-furnace heat level.

FIG. 10 is a characteristic diagram showing transition of hot metal temperature in a tap (result of continuous measurement). As shown in the figure, even if the thermal state inside the furnace is stable,
Cooling in a tapping gutter, which is a flow path of the hot metal, causes the hot metal having a relatively low temperature to be discharged in the initial stage of tapping. With the passage of time, the tapping gutter rises in temperature due to the sensible heat of the hot metal, and since the effect of stagnation in the furnace decreases, the temperature of the discharged hot metal gradually increases and stabilizes, The information is well representative of the furnace heat that is the control target of the system.

Furthermore, since the tendency of the temperature rise is not always constant and varies depending on the operating conditions, the information of the hot metal temperature is information with ambiguity that the eligibility as information representing the control target of the system changes every moment. Becomes Therefore, in order to observe the hot metal temperature and link it to the estimation of the furnace heat state, keep in mind the elapsed time from the start of tapping, and further consider the process-specific ambiguity. It is necessary.

When the hot metal temperature during tapping is grasped from the above viewpoint, the temperature distribution is represented as shown in FIG.
In the figure, each axis represents X axis; dimensionless time Y axis; hot metal temperature Z axis; appearance frequency (occurrence frequency ratio).

Fig. 12 shows the hot metal temperature at tapping time X = Xi in Fig. 11.
It shows the relationship between Ti and the maximum tapping metal temperature Tmi. 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 based on this figure, the three-dimensional function shown in FIG. 13 is obtained. In addition, since the tapping temperature is measurement information depending on the elapsed time from the start of tapping and 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 by replacing the X-axis with the sensor data, it is expressed as shown in FIG. In the figure, the dots
The straight line connecting A1 and A4 and the straight line connecting points A3 and A6 each have a CF value of “0”, and the CF value increases toward the middle of both straight lines. Then, the straight line connecting points A2 and A5 indicates that the CF value has the maximum value "1", indicating that the reliability is the highest.

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 occurrence of errors is reduced as compared with the conventional operator method.

The three-dimensional function in FIG. 14 is formed by connecting the minimum value and the maximum value of a predetermined range of the sensor data with a straight line.
FIG. 16 expands the area of the sensor data and divides the area into a plurality according to the size of the sensor data (three divisions in this example)
A three-dimensional function is formed by connecting polygonal lines so that each divided area is continuous. Such 3 in FIG.
The dimensional function indicates the relationship between the actual sensor data, the furnace heat level, and the CF value (certainty factor) as compared with the three-dimensional function in FIG. The maximum value of the CF value indicates “1” in each of the divided areas, 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 is difference data from the reference value (= measured value−target value) is as follows.

B) The target value of the hot metal temperature changes due to component adjustment and the like.

B) The standard of the sensor data is changed daily depending on the operation policy, for example, whether or not the fuel data is low fuel ratio.

C) Further, depending on the degree of wear of the installation position of the brick or the like, the detected temperature of the thermometer or the like is different even if the conditions in the furnace are the same.

The three-dimensional function is adapted to cope with various changes based on the above difference data for the above reasons.

FIG. 17 shows the relationship between the action instruction, the furnace heat level and the hot metal temperature. The action instruction by the three-dimensional function in FIG. 14 is made at the timing indicated by the solid line in the tap at the tap, and the action instruction (dashed line) by the three-dimensional function in FIG. 16 is performed at the timing shown by A and B in the figure. . As a result, in the case of the three-dimensional function in FIG. 16, the hot metal temperature becomes the target value at the next tap. On the other hand, in the case of the three-dimensional function in FIG. 14, the value slightly deviates from the target value.

FIG. 18 shows the results for the hot metal temperature, Si and S when using the three-dimensional functions of FIGS. 14 and 16, and the results when using the three-dimensional functions of FIG. 16 are better. The characteristics superior to those in the case of FIG. 14 are obtained.

[Effects of the Invention] As described above, according to the present invention, processing data is created from various data installed in a blast furnace, and a predetermined inference as artificial intelligence is made based on the processing data and a knowledge base based on experience and the like. As a result, the existing experience is fully utilized, standardizing operation management, preventing human misjudgment, stable supply of high-quality hot metal with little temperature and component fluctuations to the next process, avoiding furnace cooling, Labor saving has been realized.

In addition, when the control device is implemented by computer, the knowledge base is used as a reference, so the calculation capacity and calculation speed are improved, and rules can be easily added and modified even in new situations such as aging of the blast furnace. It has become.

Furthermore, since the above-mentioned confidence function was used to obtain the CF value of each furnace heat level and each furnace heat transition, the number of rules,
The work of inputting the numerical value is reduced. Further, since the certainty factor of the certainty function is expressed using a quadratic curve or the like, the measurement accuracy is further improved.

[Brief description of the drawings]

FIG. 1 is a block diagram showing the configuration of the present invention, FIG. 2 is a block diagram of a blast furnace heat control apparatus 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 for explaining the inference operation. FIG. 6 is a characteristic diagram showing a three-dimensional function of a furnace heat transition level,
FIG. 7 is an explanatory diagram showing an example of the action matrix, FIG. 8 is an explanatory diagram showing an example of the type of the action matrix, and FIG. 9 is an explanatory diagram showing an example of the action amount. Fig. 10 is a characteristic diagram showing the transition of hot metal temperature in the tap, and Fig. 11
FIG. 13 to FIG. 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 a three-dimensional function, and FIG.
FIG. 14 is a characteristic diagram showing the results of the three-dimensional function, FIG. 16 is an explanatory diagram showing another example of the three-dimensional function, and FIG. 17 is the three-dimensional function of FIG. 14 and the three-dimensional function of FIG. FIG. 18 is a characteristic diagram showing the relationship between the action instruction of the control, the furnace heat level and the hot metal temperature, FIG. 18 is a three-dimensional function of FIG. FIG. 4 is a characteristic diagram showing distributions of Si and S. (11), (12); Data input means (16), (14); Processing data creation means (19), (24); Processing data storage means (22); Knowledge base storage means (26); Inference means

Claims (2)

(57) [Claims] 1. A data input means for taking in data from various sensors installed in a blast furnace at a predetermined timing; and a tuyere embedding temperature, based on data from the sensors.
Create various data indicating the condition of the blast furnace, such as unloading speed, pressure loss, furnace top temperature, gas utilization rate, solution loss amount, etc., compare the various data with the reference data, and create the difference data. Processing data creating means; the various data and difference data (hereinafter referred to as processing data)
Storage means for temporarily storing information, knowledge base storage means for storing various knowledge bases based on experiences, results, mathematical models, and the like regarding blast furnace operation; processing data of the storage means and knowledge of the knowledge base storage means. Inference means for inferring a furnace heat level and a furnace heat transition based on a base and determining an action amount for the blast furnace; and means for outputting an action amount, wherein the knowledge base storage means estimates the furnace heat level. A furnace heat level judgment knowledge base used for estimating a furnace heat transition judgment base used for estimating a furnace heat transition,
Action determination knowledge base used to determine the action amount from the furnace heat level and furnace heat transition, action correction knowledge base that determines the correction value of the action amount based on past actions and disturbances, action amount and action Comprehensive judgment knowledge base that determines the actual action amount from the correction amount, the furnace heat level judgment knowledge base and the furnace heat transition judgment knowledge base, the machining data and the furnace heat level as independent variables,
A confidence function having the likelihood of occurrence of these combinations (hereinafter referred to as confidence) as a dependent variable; and a rule group for determining a method of applying the confidence function. There is a rule for determining the action amount from the level and the furnace heat transition, and the comprehensive judgment knowledge base has a rule for determining the actual action amount from the action amount and the action correction amount. A blast furnace heat control device characterized by the above-mentioned. 2. The blast furnace heat control device according to claim 1, wherein the certainty factor of the certainty factor function is expressed by a combination of polygonal lines having different quadratic curves or slopes.