WO2021033721A1 - 高炉の異常判定装置、高炉の異常判定方法、高炉の操業方法および溶銑の製造方法 - Google Patents
高炉の異常判定装置、高炉の異常判定方法、高炉の操業方法および溶銑の製造方法 Download PDFInfo
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- WO2021033721A1 WO2021033721A1 PCT/JP2020/031287 JP2020031287W WO2021033721A1 WO 2021033721 A1 WO2021033721 A1 WO 2021033721A1 JP 2020031287 W JP2020031287 W JP 2020031287W WO 2021033721 A1 WO2021033721 A1 WO 2021033721A1
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/24—Test rods or other checking devices
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/0028—Devices for monitoring the level of the melt
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B2300/00—Process aspects
- C21B2300/04—Modeling of the process, e.g. for control purposes; CII
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D2021/0007—Monitoring the pressure
Definitions
- the present invention relates to a blast furnace abnormality determination device for detecting an abnormality such as a shelf suspension or a stairwell due to poor ventilation, a blast furnace abnormality determination method, a blast furnace operation method using the blast furnace abnormality determination device, and a hot metal manufacturing method.
- a blast furnace that produces pig iron, iron ore and coke, which are raw materials, are usually charged alternately from the top of the furnace, and the ore layer and coke layer are laminated in layers. Then, the gas flow in the furnace is controlled by adjusting the distribution of the ore layer and the coke layer after deposition in the furnace.
- the furnace condition abnormality means a state in which the state deviates greatly from the steady state, and examples thereof include the following (1) to (3).
- Patent Document 1 proposes a method of detecting an abnormality in a blast furnace from shaft pressure data based on principal component analysis.
- Patent Document 1 discloses that a Q statistic or the like is calculated by principal component analysis from a plurality of shaft pressures at different positions of a blast furnace, and an abnormality determination is performed based on the Q statistic.
- the raw material input from the top of the furnace descends to the bottom of the furnace over a long period of time (for example, about 8 hours), and the state inside the furnace changes slowly accordingly. Therefore, the ventilation condition may not deteriorate rapidly but gradually deteriorate. Such slow deterioration of the condition can also cause troubles later, so it is desirable to take measures such as wind reduction at an early stage.
- an object of the present invention is to provide an abnormality determination device and a method capable of detecting not only the detection of abnormal conditions but also the signs of abnormal conditions of the blast furnace.
- An abnormality determination device for detecting an abnormality in a blast furnace using a plurality of sensors installed at different positions in the blast furnace, and an evaluation value for calculating an evaluation value from a plurality of measurement data detected by the plurality of sensors.
- An abnormality detection unit that detects an abnormality in the blast furnace by using an abnormality threshold value and a predictive threshold value smaller than the abnormality threshold value based on the evaluation value calculated by the calculation unit and the evaluation value calculation unit.
- the evaluation value is larger than the abnormality threshold value
- the abnormality detection unit determines that the abnormality is present, and the period in which the evaluation value is larger than the predictive threshold value is equal to or longer than the set period.
- An abnormality judgment device for a blast furnace that determines that there is a sign of abnormality when [2]
- the abnormality detection unit determines, for each predetermined determination period, whether the integrated value for a period in which the evaluation value is larger than the predictive threshold value exceeds the set period, and the integrated value exceeds the set period.
- [3] The abnormality determination device for a blast furnace according to [1] or [2], wherein the abnormality detection unit determines that there is a sign of abnormality when the time integration value of the evaluation value is larger than the integration threshold value.
- the evaluation value calculation unit calculates a Q statistic or a T 2 statistic by performing principal component analysis of a plurality of the measurement data, and calculates the evaluation value based on the calculated Q statistic or the T 2 statistic.
- the abnormality determination device for a blast furnace according to any one of [1] to [3], which is calculated.
- the blast furnace abnormality determination device according to any one of [1] to [4], wherein the plurality of sensors are shaft pressure sensors installed at different height positions and different circumferential positions of the blast furnace. ..
- a blast furnace abnormality determination method in which an abnormality in a blast furnace is detected using a plurality of sensors installed at different positions in the blast furnace, and an evaluation value is calculated from a plurality of measurement data detected by the plurality of sensors.
- the predictive threshold value is the plurality of measurements calculated when the fluctuation of a part of the pressure values of the plurality of measurement data during normal operation exceeds a predetermined range from the fluctuation of the pressure value during normal operation.
- a method for operating a blast furnace wherein the blast furnace is operated while determining the abnormality of the blast furnace using the blast furnace abnormality determination device according to any one of [1] to [5].
- a method for producing hot metal wherein the hot metal is produced by the operating method of the blast furnace according to [8].
- the occurrence of an abnormality is utilized before the occurrence of an abnormality, and the occurrence of an abnormality is not only detected when the evaluation value exceeds the abnormality threshold value.
- a sign of abnormality is detected when the evaluation value exceeds the sign threshold for a set period or longer.
- FIG. 1 is a block diagram showing a preferred embodiment of the abnormality determination device of the present invention.
- FIG. 2 is a graph illustrating two measurement data measured by the different sensors of FIG.
- FIG. 3 is a graph illustrating two measurement data measured by the different sensors of FIG.
- FIG. 4 is a graph showing an example of the evaluation value calculated by the evaluation value calculation unit of FIG.
- FIG. 5 is a graph showing how a sign of abnormality is detected in the abnormality detection unit of FIG.
- FIG. 6 is a graph showing how the evaluation values are time-integrated in the abnormality detection unit of FIG.
- FIG. 7 is a flowchart showing a preferred embodiment of the blast furnace abnormality determination method of the present invention.
- FIG. 1 is a block diagram showing a preferred embodiment of the abnormality determination device for a blast furnace of the present invention.
- the configuration of the abnormality determination device 10 as shown in FIG. 1 is constructed on the computer by executing the program stored in the computer.
- the abnormality determination device 10 of the blast furnace of FIG. 1 detects an abnormality of the blast furnace 1 by using a plurality of sensors S1 to Sn installed at different positions of the blast furnace.
- the plurality of sensors S1 to Sn are, for example, shaft pressure sensors, and a plurality of (for example, 30) sensors are installed at different positions in the height direction and the circumferential direction of the blast furnace 1.
- the plurality of measurement data D1 to Dn measured by the plurality of sensors S1 to Sn are stored in the database DB of the abnormality determination device 10.
- the abnormality determination device 10 of the blast furnace detects an abnormality of the blast furnace and a sign of the abnormality based on a plurality of measurement data D1 to Dn.
- the abnormality determination device 10 of the blast furnace includes an evaluation value calculation unit 11, an abnormality detection unit 12, and an information output unit 13.
- the evaluation value calculation unit 11 calculates the evaluation value EV from the plurality of measurement data D1 to Dn detected by the plurality of sensors S1 to Sn. For example, the evaluation value calculation unit 11 calculates the evaluation value EV by applying the principal component analysis to the plurality of measurement data D1 to Dn.
- Principal component analysis (PCA: Principal Component Analysis) is a variable that reflects the characteristics of the original data while reducing the loss of the amount of information that the original data group has for multiple data groups. It means mathematical processing to be done. By monitoring a small number of variables reduced in dimension by principal component analysis instead of monitoring all data groups, it is possible to more easily monitor the state inside the furnace.
- FIG. 2 and 3 are graphs illustrating two measurement data measured by the different sensors of FIG.
- the measurement data D1 and D2 tend to change synchronously within a range of predetermined signal values as shown in FIG.
- Synchronization means that the behavior of measured data (variables) in operation is coordinated with respect to the time transition or operation action in the process.
- the evaluation value calculation unit 11 obtains one Q statistic or T 2 statistic from n measurement data.
- This T 2 statistic is an index indicating whether or not the signal is within a predetermined fluctuation range.
- the Q statistic is an index orthogonal to the T 2 statistic and is an index showing asynchrony.
- This Q statistic or T 2 statistic can be calculated using a known technique. Although the case of using the second principal component value is illustrated, those values may be used when an abnormal phenomenon appears significantly after the third principal component.
- the evaluation value calculation unit 11 stores in advance the maximum value of the Q statistic when the Q statistic of the second principal component is calculated using the measurement data during normal operation.
- the data of the stability limit that can be judged as normal is included.
- To find the maximum value of the second principal component for a normal operation section determine the fluctuation range of the measured data and the maximum value of the deviation amount from the normal operation range (stability limit value) during normal operation. Means to ask.
- the evaluation value calculation unit 11 calculates the Q statistic index obtained by dividing the Q statistic calculated from the measurement data D1 to Dn by the maximum stored value as the evaluation value EV.
- the evaluation value calculation unit 11 calculates the evaluation value EV using the Q statistic. Even in this case, the evaluation value calculation unit 11, the maximum value of T 2 statistic when the calculated T 2 statistic using the measurement data during normal operation are stored in advance. The evaluation value calculation unit 11 calculates the T 2 statistic from the measurement data, divides the calculated T 2 statistic by the stored maximum value, and obtains the T 2 statistic index as the evaluation value EV.
- FIG. 4 is a graph showing an example of the evaluation value EV calculated by the evaluation value calculation unit of FIG.
- the abnormality detection unit 12 detects an abnormality in the blast furnace 1 based on the evaluation value EV calculated by the evaluation value calculation unit 11.
- the abnormality detection unit 12 stores an abnormality threshold value EVref1 and a predictive threshold value EVref2 smaller than the abnormality threshold value EVref1.
- the evaluation value EV is larger than the abnormality threshold value EVref1
- the abnormality detection unit 12 determines that the abnormality is present.
- the abnormality detection unit 12 determines that there is a sign of abnormality when the evaluation value EV is equal to or less than the abnormality threshold value EVref1 and the period larger than the predictive threshold value EVref2 becomes the set period PT or more.
- the evaluation value EV consists of the Q statistic index
- the abnormal threshold value EVref1 is set in the range of, for example, 0.5 to 1.0
- the predictive threshold value EVref2 is set, for example, in the range of 0.5 or less. .. EVref1 may be determined, for example, in correspondence with the evaluation value EV value immediately before (several minutes before) when a stairwell or the like is actually reached in the past.
- the state in which a sign of abnormality occurs is considered to be a state in which a small pressure fluctuation is locally generated in the blast furnace 1. This is a pressure fluctuation caused by local disturbance of the raw material layer, accumulation of powder such as coke powder, and local fluctuation of unloading (raw material drop).
- the pressure fluctuation propagates in various directions in the furnace from the place where the small pressure fluctuation occurs, and the pressure fluctuation may occur in other places as well.
- the passing gas flows from the lower part to the upper part in the blast furnace 1, a small disturbance of the raw material affects and propagates the state in the vicinity and the upper part.
- small disturbances in the raw material affect and propagate to the lower state. In this way, small disturbances in the local raw material affect and propagate upwards and downwards, resulting in large disturbances (abnormalities).
- the predictive threshold EVref2 for detecting the sign due to the occurrence of the above-mentioned small local pressure fluctuation is set.
- the sign threshold value EVref2 may be determined by using the evaluation value EV at the time of occurrence of the sign in the operation in which the sign has occurred among the operations of the blast furnace 1 in which the abnormality has occurred.
- the predictive threshold value EVref2 may be determined as follows. Considering the case where the local pressure fluctuation propagates into the blast furnace 1, the local pressure fluctuation is considered to be about several m ⁇ several m as the area in contact with the furnace body. The number of pressure gauges affected by this is about 4 in the example shown in FIG. Therefore, the evaluation value EV when the fluctuation of the pressure value of the pressure gauge affected by this exceeds 2 ⁇ when the standard deviation of the fluctuation of the pressure value during normal operation (normal time) is ⁇ is used. , The predictive threshold EVref2 may be determined.
- the abnormality detection unit 12 determines for each predetermined determination period (for example, 45 minutes) whether the integrated value for the period in which the evaluation value EV is larger than the predictive threshold value EVref2 becomes the set period PT (for example, 40 minutes) or more. To do. Then, when the integrated value does not exceed the set period PT within the determination period, the abnormality detection unit 12 resets the counting period and starts measuring the new period. This is because the evaluation value EV may decrease for a short time due to noise, and if it is not determined that there is a sign of abnormality unless the evaluation value EV continuously exceeds the sign threshold value EVref2 for the set period PT or longer, it is abnormal. This is because there are cases where the sign of is not detected.
- the set period PT for example, 40 minutes
- the abnormality detection unit 12 determines that there is a sign of abnormality if the integrated value is equal to or longer than the set period PT within the predetermined determination period even if the period of the sign threshold value EVref2 or higher is not continuous. I am trying to judge.
- the set period PT is preferably set to a period shorter than the period from the occurrence of the sign to the abnormality in the operation in which the sign is confirmed among the operations of the blast furnace 1 in which the abnormality has occurred. As a result, it is possible to reduce the wind before the abnormality occurs and prevent the occurrence of the abnormality.
- the predetermined determination period is set to 45 minutes and the set period PT is set to 40 so that a full-scale abnormality can be dealt with with a margin.
- This period is set to a period in which the probability that the local abnormal region propagates and expands and becomes abnormal such as a stairwell can be reduced in consideration of the unloading speed and the rising speed. Since the unloading speed in the blast furnace 1 is about 4 m / h, the determination period was set to 45 minutes in order to keep the area expansion in the height direction due to the unloading within 3 m.
- the set period PT For example, if the loading is discontinuous due to catching due to wear of bricks inside the furnace body, it may become abnormal after a short sign. In this case, shorten the predetermined judgment period and set period PT. Is preferable. However, even when the predetermined determination period and the set period PT are shortened, it is preferable to set the predetermined determination period to 10 minutes or more and the set period PT to 8 minutes or more in order to prevent false detection.
- FIG. 5 is a graph showing how a sign of abnormality is detected in the abnormality detection unit of FIG.
- the abnormality detection unit 12 may determine that there is a sign of abnormality when the time integration value I of the evaluation value EV exceeds the integration threshold value Iref, instead of performing the threshold value processing.
- the information output unit 13 of FIG. 1 is composed of, for example, a display device or a speaker, and outputs a sign of abnormality when a sign of abnormality is detected to notify the operator.
- the operator who knows that a sign of an abnormality has been detected prevents the occurrence of an abnormal phenomenon by adjusting the operating conditions of the blast furnace, such as reducing the amount of air blown into the blast furnace or stopping the air blowing. As a result, it is possible to prevent the occurrence of abnormal phenomena such as shelving, slipping, and stairwell caused by poor ventilation, that is, abnormal furnace conditions.
- a control device may automatically reduce the amount of air blown or stop the air blowing.
- FIG. 7 is a flowchart showing a preferred embodiment of the abnormality determination method of the present invention, and the abnormality determination method will be described with reference to FIG. 7.
- the measurement data D1 to Dn are acquired from the plurality of sensors S1 to Sn (step ST1), and the evaluation value EV is calculated by the evaluation value calculation unit 11 (evaluation value calculation step, step ST2).
- the abnormality detection unit 12 determines whether the evaluation value EV is larger than the abnormality threshold value EVref1 (abnormality detection step, step ST3).
- step ST3 When the evaluation value EV is larger than the abnormality threshold value EVref1 (YES in step ST3), it is determined that an abnormality has occurred in the blast furnace, and a warning is output from the information output unit 13 (step ST4).
- the evaluation value EV is equal to or less than the abnormal threshold value EVref1 (NO in step ST3), it is determined whether the period in which the evaluation value EV is larger than the predictive threshold value EVref2 exceeds the set period PT (abnormality detection step, Step ST5).
- step ST5 it may be determined whether or not the time integration value I of the evaluation value EV is larger than the integration threshold value.
- step ST5 when the period in which the evaluation value EV is larger than the predictive threshold value EVref2 becomes the set period PT (YES in step ST5), it is output that there is a sign of abnormality (step ST6).
- step ST6 when the period in which the evaluation value EV is larger than the predictive threshold value EVref2 is shorter than the set period PT, it is determined that there is no sign of abnormality (NO in step ST5), and the monitoring of abnormality is continued (steps ST1 to ST5). ..
- the occurrence of an abnormality is detected when the evaluation value EV exceeds the abnormality threshold value EVref1 by utilizing the fact that a sign of an abnormality appears before the occurrence of an abnormality.
- the operation of the blast furnace can be carried out while determining the abnormality of the blast furnace, and the hot metal can be produced by carrying out the operation.
- the sign of abnormality is detected when the evaluation value EV exceeds the sign threshold value EVref2 for the set period PT or more. As a result, it is possible to reduce the wind at an early stage so that an abnormality does not occur, and it is possible to prevent operational troubles.
- the bleeder valve at the top of the furnace opens and the bleeder valve at the top of the furnace is opened to allow the bleeder valve to escape.
- the evaluation value EV then returns to the normal value.
- the furnace heat decreases due to the increase in heat loss, and the raw material layer collapses, which adversely affects the blast furnace. Therefore, it is preferable to detect a sign of abnormality before the abnormality occurs.
- the evaluation value EV tends to be larger than that in the steady state before the occurrence of the abnormality, it is conceivable to detect the sign of the abnormality by using the predictive threshold EVref2 lower than the abnormal threshold EVref1. ..
- the abnormality detection unit 12 did the abnormality detection unit 12 exceed the set period PT (for example, 40 minutes) in the integrated value during the period when the evaluation value EV became larger than the predictive threshold value EVref2 within the predetermined determination period (for example, 45 minutes)?
- the sign of abnormality is determined by determining. Then, even if there is a sign of abnormality, it is possible to prevent it from being determined that the sign of abnormality has disappeared by temporarily lowering the evaluation value EV below the sign threshold value EVref2. Alternatively, even if there is no sign of abnormality, it is possible to prevent it from being determined that there is a sign of abnormality by temporarily setting the evaluation value EV to the sign threshold value EVref2 or higher. As a result, it is possible to detect an abnormality sign with higher accuracy.
- the abnormality detection unit 12 may determine that there is a sign of abnormality when the time integration value I of the evaluation value EV is larger than the integration threshold value Iref. As a result, the period until it is determined that there is a sign of abnormality can be adjusted according to the degree of deterioration of the condition inside the furnace reflected in the evaluation value EV.
- the embodiment of the present invention is not limited to the above embodiment, and various modifications can be made.
- the case where the plurality of sensors S1 to Sn are shaft pressure sensors has been illustrated, but if an abnormality can be detected, it may be another type of sensor installed in a blast furnace such as a temperature sensor. You may.
- the evaluation value calculation unit 11 exemplifies the case where either the Q statistic index or the T 2 statistic index is calculated as the evaluation value EV, but both may be calculated as the evaluation value EV and an abnormality may be detected. In this case, a warning may be output when an abnormality or a sign of an abnormality is detected in both evaluation values EV, or a warning may be output when an abnormality or the like is detected in either one of them.
- the case of calculating the statistic as the evaluation value EV is illustrated, but any method can be used as long as it is a method of unifying a plurality of input data and indexing them as an abnormality. A known technique such as one index using the above may be used.
- the evaluation value calculation unit 11 illustrates the case of calculating one evaluation value EV, but for example, two evaluation value EVs of the upper stage and the lower stage depending on the installation height of the sensors S1 to Sn. May be calculated and an abnormality may be detected for each evaluation value EV.
- the abnormality detection unit 12 exemplifies the case where it is determined whether the integrated value of the evaluation value EV is larger than the predictive threshold value EVref2 within the determination period is equal to or more than the set period PT, but it is simply a continuous predictor. When the period exceeding the threshold value EVref2 becomes the set period PT or more, it may be determined that there is a sign of abnormality.
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Abstract
Description
(1)炉上部から順次降下する鉱石及びコークスの降下が停止してしまう「棚吊り」。
(2)停止している鉱石及びコークスが突如降下する「スリップ」。
(3)炉下部から供給された高温のガスが急激に炉上部へと噴出する「吹き抜け」。
[1]高炉の異なる位置に設置された複数のセンサを用いて高炉の異常を検出する異常判定装置であって、複数の前記センサにより検知された複数の測定データから評価値を算出する評価値算出部と、前記評価値算出部において算出された評価値に基づき、異常しきい値と、前記異常しきい値より小さい予兆しきい値とを用いて、前記高炉の異常を検出する異常検出部と、を有し、前記異常検出部は、前記評価値が前記異常しきい値より大きい場合には異常であると判定し、前記評価値が前記予兆しきい値よりも大きい期間が設定期間以上になった場合には異常の予兆があると判定する、高炉の異常判定装置。
[2]前記異常検出部は、所定の判定期間毎に、前記評価値が前記予兆しきい値よりも大きい期間の積算値が設定期間以上になったかを判定し、前記積算値が設定期間以上になった場合には異常の予兆があると判定する、[1]に記載の高炉の異常判定装置。
[3]前記異常検出部は、前記評価値の時間積分値が積分しきい値より大きい場合に異常の予兆があると判定する、[1]または[2]に記載の高炉の異常判定装置。
[4]前記評価値算出部は、複数の前記測定データを主成分分析してQ統計量またはT2統計量を算出し、算出したQ統計量またはT2統計量に基づいて前記評価値を算出する、[1]から[3]のいずれか1つに記載の高炉の異常判定装置。
[5]複数の前記センサは、高炉の異なる高さ位置及び異なる円周位置に設置されたシャフト圧センサからなる、[1]から[4]のいずれか1つに記載の高炉の異常判定装置。
[6]高炉の異なる位置に設置された複数のセンサを用いて高炉の異常を検出する高炉の異常判定方法であって、複数の前記センサにより検知された複数の測定データから評価値を算出する評価値算出ステップと、算出した前記評価値に基づき、異常しきい値と、前記異常しきい値より小さい予兆しきい値とを用いて、前記高炉の異常を検出する異常検出ステップと、を有し、前記異常検出ステップにおいて、前記評価値が前記異常しきい値より大きい場合には異常であると判定し、前記評価値が前記予兆しきい値よりも大きい期間が設定期間以上になった場合には異常の予兆があると判定する、高炉の異常判定方法。
[7]前記予兆しきい値は、通常操業時における前記複数の測定データの一部の圧力値の変動が正常時の圧力値の変動から所定の範囲を超える場合に算出される前記複数の測定データの評価値を用いて決定される、[6]に記載の高炉の異常判定方法。
[8][1]から[5]のいずれか1つに記載の高炉の異常判定装置を用いて高炉の異常を判定しながら高炉を操業する、高炉の操業方法。
[9][8]に記載の高炉の操業方法により溶銑を製造する、溶銑の製造方法。
10 異常判定装置
11 評価値算出部
12 異常検出部
13 情報出力部
D1~Dn 測定データ
DB データベース
EV 評価値
EVref1 異常しきい値
EVref2 予兆しきい値
I 時間積分値
Iref 積分しきい値
PT 設定期間
S1~Sn センサ
Claims (9)
- 高炉の異なる位置に設置された複数のセンサを用いて高炉の異常を検出する異常判定装置であって、
複数の前記センサにより検知された複数の測定データから評価値を算出する評価値算出部と、
前記評価値算出部において算出された評価値に基づき、異常しきい値と、前記異常しきい値より小さい予兆しきい値とを用いて、前記高炉の異常を検出する異常検出部と、
を有し、
前記異常検出部は、前記評価値が前記異常しきい値より大きい場合には異常であると判定し、前記評価値が前記予兆しきい値よりも大きい期間が設定期間以上になった場合には異常の予兆があると判定する、高炉の異常判定装置。 - 前記異常検出部は、所定の判定期間毎に、前記評価値が前記予兆しきい値よりも大きい期間の積算値が設定期間以上になったかを判定し、前記積算値が設定期間以上になった場合には異常の予兆があると判定する、請求項1に記載の高炉の異常判定装置。
- 前記異常検出部は、前記評価値の時間積分値が積分しきい値より大きい場合に異常の予兆があると判定する、請求項1または請求項2に記載の高炉の異常判定装置。
- 前記評価値算出部は、複数の前記測定データを主成分分析してQ統計量またはT2統計量を算出し、算出したQ統計量またはT2統計量に基づいて前記評価値を算出する、請求項1から請求項3のいずれか1項に記載の高炉の異常判定装置。
- 複数の前記センサは、高炉の異なる高さ位置及び異なる円周位置に設置されたシャフト圧センサからなる、請求項1から請求項4のいずれか1項に記載の高炉の異常判定装置。
- 高炉の異なる位置に設置された複数のセンサを用いて高炉の異常を検出する高炉の異常判定方法であって、
複数の前記センサにより検知された複数の測定データから評価値を算出する評価値算出ステップと、
算出した前記評価値に基づき、異常しきい値と、前記異常しきい値より小さい予兆しきい値とを用いて、前記高炉の異常を検出する異常検出ステップと、
を有し、
前記異常検出ステップにおいて、前記評価値が前記異常しきい値より大きい場合には異常であると判定し、前記評価値が前記予兆しきい値よりも大きい期間が設定期間以上になった場合には異常の予兆があると判定する、高炉の異常判定方法。 - 前記予兆しきい値は、通常操業時における前記複数の測定データの一部の圧力値の変動が正常時の圧力値の変動から所定の範囲を超える場合に算出される前記複数の測定データの評価値を用いて決定される、請求項6に記載の高炉の異常判定方法。
- 請求項1から請求項5のいずれか1項に記載の高炉の異常判定装置を用いて高炉の異常を判定しながら高炉を操業する、高炉の操業方法。
- 請求項8に記載の高炉の操業方法により溶銑を製造する、溶銑の製造方法。
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