WO2021182041A1 - Procédé de détermination d'anomalie pour haut fourneau, procédé d'apprentissage pour modèle de période de stabilisation, procédé de fonctionnement pour haut-fourneau et dispositif de détermination d'anomalie pour haut-fourneau - Google Patents

Procédé de détermination d'anomalie pour haut fourneau, procédé d'apprentissage pour modèle de période de stabilisation, procédé de fonctionnement pour haut-fourneau et dispositif de détermination d'anomalie pour haut-fourneau Download PDF

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WO2021182041A1
WO2021182041A1 PCT/JP2021/005879 JP2021005879W WO2021182041A1 WO 2021182041 A1 WO2021182041 A1 WO 2021182041A1 JP 2021005879 W JP2021005879 W JP 2021005879W WO 2021182041 A1 WO2021182041 A1 WO 2021182041A1
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blast furnace
value
stable period
abnormality
abnormality determination
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PCT/JP2021/005879
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English (en)
Japanese (ja)
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島本　拓幸
啓史 小橋
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Ｊｆｅスチール株式会社
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Priority to JP2021533830A priority Critical patent/JP7192992B2/ja
Publication of WO2021182041A1 publication Critical patent/WO2021182041A1/fr

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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

Definitions

  • the present invention relates to a blast furnace abnormality determination method, a stable period model learning method, a blast furnace operation method, and a blast furnace abnormality determination device.
  • Patent Document 1 proposes a technique for grasping a sign of an operation abnormality by using a Q statistic.
  • the present invention has been made in view of the above, and learning of a blast furnace abnormality determination method and a stable period model capable of detecting an operation abnormality of a blast furnace at an early stage by simultaneously using various data having different characteristics. It is an object of the present invention to provide a method, a method of operating a blast furnace, and an abnormality determination device for a blast furnace.
  • the blast furnace abnormality determination method uses a plurality of operation data in the stable period of the blast furnace so that the input value and the output value are the same.
  • a plurality of operation data to be determined are input to the learned stable period model, and an abnormality determination step of determining an operation abnormality of the blast furnace based on the difference between the input value and the output value at that time is included. ..
  • the abnormality determination step is each input value and each output when a plurality of operation data to be determined are input to the stable period model.
  • the integrated value of the difference from the value is calculated, and when the integrated value of the difference exceeds a preset threshold value, it is determined that there is an operation abnormality.
  • each input value and each output when the abnormality determination step inputs a plurality of operation data to be determined for the stable period model is calculated for each positive and negative, and when the integrated value of the positive side difference exceeds the preset positive side threshold value, or the integrated value of the negative side difference is preset. When the threshold on the negative side is exceeded, it is determined that there is an operation abnormality.
  • each input value and each input value when a plurality of operation data to be determined are input to the stable period model after the abnormality determination step. It further includes a display step of displaying the integrated value of the difference from the output value for each positive or negative in a stacked graph.
  • the learning method of the stable period model according to the present invention includes input values and output values by inputting a plurality of operation data in the stable period of the blast furnace to the autoencoder. Includes learning steps to build a stable model trained to be the same.
  • the learning step selects a plurality of operation data in the stable period of the blast furnace based on the air flow rate, and constructs the stable period model. ..
  • the learning step is such that the blast flow rate is equal to or more than a preset threshold value among a plurality of operation data of the blast furnace, and the blast flow rate is the same.
  • the operation data excluding the predetermined time before and after the time when is equal to or more than the threshold value is selected, and the stable period model is constructed.
  • the blast furnace operation method changes the operation of the blast furnace based on the determination result of the above-mentioned blast furnace abnormality determination method.
  • the blast furnace abnormality determination device uses a plurality of operation data in the stable period of the blast furnace so that the input value and the output value are the same.
  • a plurality of operation data to be determined are input to the learned stable period model, and an abnormality determination means for determining an operation abnormality of the blast furnace is provided based on the difference between the input value and the output value at that time. ..
  • the present invention by performing anomaly determination using a stable period model trained so that the input value and the output value are the same, various data having different characteristics can be used simultaneously to determine the operation abnormality of the blast furnace. Detection can be performed early.
  • FIG. 1 is a diagram showing a schematic configuration of an abnormality determination device for a blast furnace according to an embodiment of the present invention.
  • FIG. 2 is a diagram showing an outline of an autoencoder used in the learning method of the stable period model according to the embodiment of the present invention.
  • FIG. 3 is a flowchart showing the flow of the learning method of the stable period model according to the embodiment of the present invention.
  • FIG. 4 is a flowchart showing the flow of the abnormality determination method according to the embodiment of the present invention.
  • FIG. 5 is an example of an abnormality determination method for a blast furnace according to the present invention, and is a diagram showing an example in which an abnormality determination is performed using verification data for a predetermined period.
  • the blast furnace abnormality determination method, the learning method of the stable period model, the blast furnace operation method, and the blast furnace abnormality determination device will be described with reference to the drawings.
  • the blast furnace abnormality determination device, the learning method of the stable period model, the blast furnace abnormality determination method, and the blast furnace operation method will be described in this order.
  • the present invention is not limited to the embodiments described below.
  • the abnormality determination device 1 is for determining an abnormality in a plant such as a blast furnace. As shown in FIG. 1, the abnormality determination device 1 includes a sensor group 11, a data acquisition unit 12, a storage unit 13, a calculation unit 14, and a display unit 15.
  • the sensor group 11 is composed of a plurality of sensors provided in the blast furnace, and outputs the detected sensor values to the data collection unit 12.
  • Examples of the sensor group 11 include a sensor group installed around the furnace body of the blast furnace.
  • the data collection unit 12 collects the sensor values detected by the sensor group 11 and stores them in the storage unit 13 as operation data. Further, the data collecting unit 12 calculates an index value based on the sensor value detected by the sensor group 11, and also stores the index value as operation data in the storage unit 13.
  • index value based on the sensor value examples include an index value related to the furnace heat of the blast furnace, an index value related to the ventilation resistance of the blast furnace, and the like. Further, as the above-mentioned "index value related to the furnace heat of the blast furnace", an index value calculated from the calorific value of the furnace body and the combustion heat at the tuyere can be mentioned.
  • the storage unit 13 is composed of a recording medium such as an EPROM (Erasable Programmable ROM), a hard disk drive (Hard Disk Drive: HDD), and a removable medium.
  • a recording medium such as an EPROM (Erasable Programmable ROM), a hard disk drive (Hard Disk Drive: HDD), and a removable medium.
  • removable media include disc recording media such as USB (Universal Serial Bus) memory, CD (Compact Disc), DVD (Digital Versatile Disc), and BD (Blu-ray (registered trademark) Disc).
  • the storage unit 13 stores the stable period model 131 constructed by the learning unit 141 and the operation data (sensor value and index value) collected by the data collection unit 12.
  • the operation data stored in the storage unit 13 includes, for example, data (learning data) used when constructing the stable period model 131 (see FIG. 3) and when performing an abnormality determination using the stable period model 131.
  • data (verification data) used for (see FIG. 4).
  • the stable period model 131 is a model constructed based on a plurality of operation data in the stable period of the blast furnace.
  • the stable period model 131 is constructed by the learning unit 141 based on the operation data collected by the data collecting unit 12. The method of constructing the stable period model 131 will be described later.
  • the arithmetic unit 14 is realized by, for example, a processor including a CPU (Central Processing Unit) and a memory (main storage unit) including a RAM (Random Access Memory) and a ROM (Read Only Memory).
  • a processor including a CPU (Central Processing Unit) and a memory (main storage unit) including a RAM (Random Access Memory) and a ROM (Read Only Memory).
  • the arithmetic unit 14 loads and executes the program in the work area of the main storage unit, and controls each component or the like through the execution of the program to realize a function that meets a predetermined purpose.
  • the calculation unit 14 functions as a learning unit (learning means) 141, a difference calculation unit (difference calculation means) 142, and an abnormality determination unit (abnormality determination means) 143 through the execution of the program.
  • learning unit 141, difference calculation unit 142, and abnormality determination unit 143 are realized by one calculation unit ( ⁇ computer), but a plurality of units are realized.
  • the functions of each unit may be realized by the arithmetic unit ( ⁇ computer).
  • the learning unit 141 uses a plurality of operation data in the stable period of the blast furnace (hereinafter referred to as “stable period data”) to perform learning so that the input value and the output value are the same, thereby performing the stable period model 131. To build. Specifically, the learning unit 141 constructs the stable period model 131 by using an autoencoder, which is a method of deep learning. Then, the learning unit 141 stores the constructed stable period model 131 in the storage unit 13. When constructing the stable period data, it is preferable to use the stable period data for at least the past six months.
  • An autoencoder is one of the mechanisms of a neural network, and is a method for reducing the dimension of input data and extracting features.
  • neurons having a “number of dimensions compressed input data” are provided in the intermediate layer. Further, in order to extract the feature amount of the input data, the number of dimensions of the intermediate layer is made smaller than the number of dimensions of the input layer. Then, the output value of the output layer is set to an output value that can reproduce the input value of the input data.
  • the input data is once embedded (encoded) in a small dimension, and the input data is reconstructed based on the encoded data. That is, by encoding with an autoencoder, data can be expressed with a smaller number of dimensions than originally intended.
  • the description of the configuration of the abnormality determination device 1 will be continued.
  • the difference calculation unit 142 inputs a plurality of operation data (verification data) to be determined to the stable period model 131, and calculates the difference between the input value and the output value at that time. Specifically, the difference calculation unit 142 calculates the integrated value of the difference between each input value and each output value when a plurality of operation data are input to the stable period model 131.
  • the above "integrated value of the difference between each input value and each output value” indicates, for example, the integrated value of the absolute value of the difference between each input value and each output value. Further, the difference calculation unit 142 may calculate, for example, the integrated value of the difference between each input value and each output value for each positive or negative, instead of the integrated value of the absolute value of the difference between each input value and each output value. good.
  • the abnormality determination unit 143 determines the operation abnormality of the blast furnace based on the difference between each input value and each output value when a plurality of operation data to be determined are input to the stable period model 131. Specifically, the abnormality determination unit 143 determines that there is an operation abnormality when the integrated value of the difference between each input value and each output value exceeds a preset threshold value, and when it is less than the threshold value, the operation abnormality Judge as none. In this way, by looking at the integrated value of the difference between each input value and each output value to the stable period model 131, small abnormalities of each item are amplified as stacked values, so it is possible to detect operational abnormalities at an early stage. Can be done.
  • the above threshold value can be calculated in advance empirically and experimentally.
  • the abnormality determination unit 143 determines that there is an operation abnormality when the integrated value of the difference on the positive side exceeds the preset threshold on the positive side. Further, the abnormality determination unit 143 determines that there is an operation abnormality when the integrated value of the difference on the negative side exceeds the preset negative threshold value. Further, the abnormality determination unit 143 determines that there is no operation abnormality when the integrated value of the difference on the positive side is less than the preset threshold value on the positive side.
  • the abnormality determination unit 143 determines that there is no operation abnormality when the integrated value of the difference on the negative side is less than the preset negative threshold value. In this way, the abnormality determination device 1 detects the operation abnormality at an early stage because the abnormality on the positive side and the abnormality on the negative side in each item are amplified as accumulated values by performing the abnormality determination for each positive or negative. be able to.
  • the display unit 15 is realized by a display device such as an LCD display or a CRT display, and displays various information based on a display signal input from the calculation unit 14. Examples of the information displayed on the display unit 15 include a stacked graph (see FIG. 5) showing the integrated value of the difference between each input value and each output value for each positive and negative, as described later.
  • model learning method The learning method of the stable period model (hereinafter, referred to as “model learning method”) executed by the abnormality determination device 1 will be described with reference to FIG.
  • the data selection step (step S1) and the model construction step (step S2) are performed in this order.
  • the construction of the stable period model 131 shown in the figure is performed in advance before the abnormality determination method (see FIG. 4) using the stable period model 131 is carried out.
  • the learning unit 141 selects stable period data, which is a plurality of operation data in the stable period of the blast furnace (step S1).
  • stable period data can be selected based on various indicators. For example, when the operating condition of the blast furnace deteriorates, the air flow rate can generally be changed to a small value (wind reduction). Therefore, it is preferable to select the stable period data based on this air flow rate.
  • the operation data excluding the predetermined time before and after the time when the blower flow rate is equal to or higher than the preset threshold value and the blower flow rate becomes equal to or higher than the threshold value is used as the stable period data. It is preferable to select. This is for the following two reasons. (1) It is not desirable as stable period data because there is a possibility that signs of abnormality may be included for several hours before and after the wind reduction. (2) On the contrary, when the air flow rate exceeds the threshold value, it is assumed that the unsteady state is very strong for several hours when the wind of the blast furnace is increased, which is not desirable as the stable period data.
  • the above "predetermined time” is preferably 8 hours, for example. This is because it takes about 8 hours for the raw material charged from the upper part of the blast furnace to descend to the lower part of the furnace, and if it exceeds 8 hours, it is less likely that a factor of deterioration of the operating condition exists in the furnace. Because.
  • the learning unit 141 inputs the stable period data selected in step S1 to the autoencoder to construct the stable period model 131 (step S2).
  • step S2 as shown in FIG. 2 described above, the data set of the stable period data is set in the input and the output by the number of data N, and the stable period model 131 is constructed.
  • step S2 in order to increase the expressivity of the stable period model 131, it is preferable to use stable period data having a large number of data N and having appropriate variations within the stable range. In this way, by using the autoencoder, it is possible to obtain the features of the stable state (that is, the features that express the normal state with reduced dimensions) of the data group of the input stable period data. can.
  • the abnormality determination method executed by the abnormality determination device 1 will be described with reference to FIG.
  • the data input step (step S11), the difference calculation step (step S12), and the abnormality determination step (steps S13 to S15) are performed in this order.
  • the model learning method (FIG. 3) and the abnormality determination method (FIG. 4) are described separately, but the model learning method may be followed by the abnormality determination method.
  • the difference calculation unit 142 inputs the operation data (verification data) to be determined to the stable period model 131 (step S11).
  • the difference calculation unit 142 calculates the integrated value of the difference (error) between each input value and each output value for each input operation data (step S12).
  • step S12 the integrated value of the absolute value of the difference between each input value and each output value may be calculated, or the integrated value of the difference between each input value and each output value is calculated for each positive or negative. You may.
  • calculating the integrated value of the difference for each positive or negative means adding each value of the item having a positive difference and adding each value of the item having a negative difference. This is because the deviation of the positive side and the negative side differs depending on the item, but the item on the positive side is added on the positive side and the item on the negative side is added on the negative side and evaluated respectively. As a result, when an abnormality occurs and the deviation from the normal state becomes large, the integrated value becomes cumulatively large (or small), so that the occurrence of the abnormality can be quickly grasped.
  • the abnormality determination unit 143 determines whether or not the integrated value of the difference between each input value and each output value exceeds a preset threshold value (step S13).
  • the integrated value of the absolute value of the difference between each input value and each output value is calculated in step S12
  • the integrated value and the threshold value are compared in step S13.
  • the integrated value of the difference between each input value and each output value is calculated for each positive or negative in step S12
  • the integrated value of the positive difference and the positive threshold are calculated.
  • the integrated value of the difference on the negative side and the threshold value on the negative side are compared.
  • step S13 If it is determined in step S13 that the integrated value of the difference exceeds a preset threshold value (Yes in step S13), the abnormality determination unit 143 determines that there is an operation abnormality (step S14), and ends this process. On the other hand, in step S13, when it is determined that the integrated value of the difference is less than the preset threshold value (No in step S13), the abnormality determination unit 143 determines that there is no operation abnormality (step S15), and this process is performed. finish.
  • a stacking graph in which the difference between each input value and each output value is stacked for each positive or negative is created, and a display step of displaying the difference on the display unit 15 is performed. May be good. By performing this display step, it is possible to visualize the degree of abnormality for each operation data (input item).
  • blast furnace operation method In the blast furnace operation method, the operation of the blast furnace is changed based on the determination result of the above-mentioned blast furnace abnormality determination method. This makes it possible to prevent serious abnormalities and troubles in the blast furnace.
  • the detection of the abnormality is earlier than in the case where the abnormality determination is performed using the data having the same characteristics. Can be done.
  • various data having different characteristics, which are expected to be related to the operation abnormality are not divided for each characteristic and a model is constructed for each characteristic, but one model is constructed to determine the abnormality. Therefore, the abnormality determination can be performed more easily.
  • the stable period data of the operation of the blast furnace is input to the autoencoder, a stable period model is constructed, and an abnormality determination is performed using the stable period model.
  • the stable period data the operation data when the air flow rate exceeds 75% of the steady operation was used. At that time, the operation data for 8 hours before and after the air flow rate became 75% or less of the steady operation was excluded.
  • FIG. 5 shows the result of inputting the operation data (verification data) before the occurrence of the operation abnormality to the time when the operation abnormality occurs for this stable period model.
  • the vertical axis is the index abnormal value indicating the abnormality of the index value
  • the horizontal axis is the time.
  • the sensor signals used include the measured values of blast furnace exhaust gas (N 2 , H 2 , CO, CO 2 ), the blast furnace gas utilization rate calculated based on them, and the ventilation resistance values of the blast furnace (ventilation of the entire furnace body). , Each ventilation of the lower part / middle part / upper part of the furnace), each index related to the heat of the furnace (heat blast, heat of tuyere combustion, solution reaction heat, heat of top gas, heat of blast moisture decomposition, heat radiated from the furnace body , The amount of heat generated by combustion of pulverized coal, the amount of heat generated by decomposition of pulverized coal, the amount of heat generated by slag, the amount of heat generated by charged raw materials, the heat generated by hot metal) The amount of PCI blown in), the processing value of the sensor group around the furnace body (average for each shaft pressure height, average for each temperature height of each part of the furnace body), etc. are included.
  • verification data we used operation data for about 1.5 months excluding wind breaks. Then, the verification data is input to the stable period model, the difference between the input value and the output value for each item is obtained, and the value obtained by integrating the difference between the input value and the output value for each positive or negative is divided into positive and negative, and FIG. It is represented by a stacked graph as shown in.
  • the blast furnace abnormality determination method, the stable period model learning method, the blast furnace operation method, and the blast furnace abnormality determination device according to the present invention have been specifically described with reference to the embodiments and examples for carrying out the invention.
  • the gist of the invention is not limited to these statements, and must be broadly interpreted based on the statements of the claims. Needless to say, various changes, modifications, etc. based on these descriptions are also included in the gist of the present invention.

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Abstract

L'invention concerne un procédé de détermination d'anomalie pour un haut fourneau qui comprend une étape de détermination d'anomalie pour entrer une pluralité d'éléments de données de fonctionnement à déterminer dans un modèle de période de stabilisation entraîné en utilisant une pluralité d'éléments de données de fonctionnement dans une période de stabilisation du haut fourneau pour rendre égales des valeurs d'entrée et des valeurs de sortie, et déterminer une anomalie de fonctionnement pour le haut-fourneau sur la base de la différence entre les valeurs d'entrée et les valeurs de sortie à ce moment.
PCT/JP2021/005879 2020-03-12 2021-02-17 Procédé de détermination d'anomalie pour haut fourneau, procédé d'apprentissage pour modèle de période de stabilisation, procédé de fonctionnement pour haut-fourneau et dispositif de détermination d'anomalie pour haut-fourneau WO2021182041A1 (fr)

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CN113836821A (zh) * 2021-10-26 2021-12-24 华电莱州发电有限公司 一种锅炉水冷壁拉裂在线预测方法

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CN113836821A (zh) * 2021-10-26 2021-12-24 华电莱州发电有限公司 一种锅炉水冷壁拉裂在线预测方法
CN113836821B (zh) * 2021-10-26 2023-11-28 华电莱州发电有限公司 一种锅炉水冷壁拉裂在线预测方法

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