EP3733876A1 - System and method for evaluating operational conditions of blast furnace - Google Patents
System and method for evaluating operational conditions of blast furnace Download PDFInfo
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- EP3733876A1 EP3733876A1 EP18893418.6A EP18893418A EP3733876A1 EP 3733876 A1 EP3733876 A1 EP 3733876A1 EP 18893418 A EP18893418 A EP 18893418A EP 3733876 A1 EP3733876 A1 EP 3733876A1
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- combustion state
- tuyeres
- blast furnace
- tuyere
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000002485 combustion reaction Methods 0.000 claims abstract description 164
- 238000011156 evaluation Methods 0.000 claims abstract description 10
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 19
- 239000003245 coal Substances 0.000 claims description 14
- 238000013135 deep learning Methods 0.000 claims description 14
- 238000002347 injection Methods 0.000 claims description 10
- 239000007924 injection Substances 0.000 claims description 10
- 239000000571 coke Substances 0.000 claims description 7
- 239000012768 molten material Substances 0.000 claims description 7
- 230000007547 defect Effects 0.000 claims description 3
- 238000010926 purge Methods 0.000 claims description 2
- 230000007704 transition Effects 0.000 claims description 2
- 230000005856 abnormality Effects 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000036760 body temperature Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000000498 cooling water Substances 0.000 description 1
- 238000013481 data capture Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
Images
Classifications
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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
- F27D19/00—Arrangements of controlling devices
-
- 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
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/16—Tuyéres
-
- 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
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B1/00—Shaft or like vertical or substantially vertical furnaces
- F27B1/10—Details, accessories, or equipment peculiar to furnaces of these types
- F27B1/28—Arrangements of monitoring devices, of indicators, of alarm devices
-
- 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
- F27D21/02—Observation or illuminating devices
-
- 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
-
- 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
- F27D21/02—Observation or illuminating devices
- F27D2021/026—Observation or illuminating devices using a video installation
Definitions
- the present disclosure relates to a system and a method for evaluating operational conditions of a blast furnace.
- Japanese Patent Publication No. 2015-52148 discloses a control method based on determination of operational conditions of a furnace.
- an embodiment of the present disclosure is to provide a system for evaluating operational conditions of a blast furnace.
- a system and a method for evaluating operational conditions of a blast furnace includes: an image capturing unit for capturing image data according to each of a plurality of tuyeres disposed in a blast furnace; an image collection unit for collecting the image data captured according to each of the tuyeres by the image capturing unit; a tuyere combustion state determination unit for classifying, on the basis of artificial intelligence, combustion states according to each of the tuyeres by using the image data according to each of the tuyeres; a tuyere combustion state index generation unit for generating combustion state indices according to each of the tuyeres by using the result of classifying the combustion states according to each of the tuyeres by the tuyere combustion state determination unit; and an integrated evaluation unit for generating an integrated combustion state index on the basis of the combustion state indices according to each of the tuyeres.
- another embodiment of the present disclosure is to provide a method for evaluating operating conditions a blast furnace.
- the method for evaluating operating conditions a blast furnace includes operations of: collecting image data according to a plurality of tuyeres provided in a blast furnace; classifying combustion states according to each of the tuyeres on the basis of artificial intelligence, by using the image data according to each of the tuyeres; generating combustion state indices according to each of the tuyeres by using the result of classifying the combustion state according to a plurality of tuyeres; and generating an integrated combustion state index on the basis of the combustion state indices according to each of the tuyeres.
- a tuyere combustion state based on deep learning using the tuyere image data, in addition to the result of classifying the result of classifying the tuyere combustion state, and a result of analyzing the tuyere image data and a result of analyzing the blast furnace operational data may be additionally used to extract the tuyere combustion state indices according to each of the tuyeres, and an operational condition of a blast furnace may be integrally evaluated and controlled.
- the blast furnace combustibility and the blast furnace condition may be quantitatively evaluated to enable stable blast furnace operations, and productivity may be improved.
- FIG. 1 is a configuration diagram of a system for evaluating operational conditions of a blast furnace according to an embodiment of the present disclosure.
- a system 100 for evaluating operational conditions of a blast furnace may be configured to include an image capturing unit 110, an image collection unit 120, a tuyere combustion state determination unit 130, a tuyere combustion state index generation unit 140, an operational information collection unit 150, an integrated evaluation unit 160, and a blast furnace condition control unit 170.
- the image capturing unit 110 may acquire image data according to each of the tuyeres 11 provided in the blast furnace 10.
- the image capturing unit 110 may include a plurality of cameras installed in each tuyere 11, and may acquire the image data according to each of the tuyeres in real time (e.g., in ms units) through each camera.
- the image collection unit 120 may collect image data according to each of the tuyeres captured by the image capturing unit 110.
- the image collection unit 120 may collect image data obtained in real time according to each of the tuyeres from a plurality of cameras included in the image capturing unit 110.
- the image collection unit 120 may map the collected image data with collection environment information including a tuyere number, data capture time, and the like.
- the image data which has been mapped by the image collection unit 120, may be stored in a data storage (not shown) provided in a system for evaluating operational conditions of a blast furnace 100, or may be transmitted in real time to the tuyere combustion state determination unit 130.
- the tuyere combustion state determination unit 130 is for classifying a combustion state according to each of the tuyeres using the image data according to each of the tuyeres transmitted from the image collection unit 120, and may be configured to include an AI-based determination unit 131 and an image processing-based determination unit 132.
- the AI-based determination unit 1311 may classify the combustion state according to each of the tuyeres based on artificial intelligence using the image data according to each of the tuyeres.
- the AI-based determination unit 131 may classify the combustion state according to each of the tuyeres based on deep learning.
- the AI-based determination unit 131 may primarily classify the combustion state according to each of the tuyeres based on a convolutional neural network (CNN) using image data according to each of the tuyeres.
- CNN convolutional neural network
- the AI-based determination unit 131 may determine the tuyere combustion state classification based on results of accumulating the results of classifying the combustion states according to each of the tuyeres in time series, primarily classified, thereby further improving consistency of the combustion state classification.
- FIG. 2 is a view illustrating the concept of primarily classifying a tuyere combustion state based on deep learning according to an embodiment of the present disclosure.
- the AI-based determination unit 131 may classify the combustion states based on the image deep learning, for example, CNN, for first tuyere image to the Nth tuyere image data (21 to 2N) captured according to each of the tuyeres, thereby obtaining the results of the first tuyere combustion state classification to the Nth tuyere combustion state classification (21'to 2N').
- N means the number of tuyere.
- FIGS. 3 and 4 are diagrams illustrating a concept of determining a tuyere combustion state classification based on a result of accumulating a result primarily classified based on deep learning in time series according to an embodiment of the present disclosure.
- the AI-based determination unit 131 may determine a tuyere combustion state classification according to each of the tuyeres based on first tuyere combustion state classifications 31-1, 31-2, and 31-3, second tuyere combustion state classifications 32-1, 32-2, and 32-3, and Nth tuyere combustion state classifications 3N-1, 3N -2, and 3N-3, and may obtain determined tuyere combustion state classification results 31' to 33'.
- a result of classifying the plurality of combustion states primarily classified for an arbitrary time period (t-1 to t+1) to determine the tuyere combustion state classification may be determined as the corresponding combustion state classification.
- the AI-based determination unit 131 may determine tuyere combustion state classification according to each of the tuyeres based on deep learning in time series on first tuyere combustion state classifications 41-1, 41-2, and 41-3, second tuyere combustion state classifications 42-1, 42-2, and 42-3, and Nth tuyere combustion state classifications 4N-1, 4N-2, and 4N-3, and may obtain determined tuyere combustion state classification results 41' to 43'.
- the AI-based determination unit 131 determine the tuyere combustion state classification according to each of the tuyeres based on a recurrent neural network (RNN) or a recurrent convolutional neural network (RCNN) by using the result of classifying a plurality of combustion states primarily classified according to each of the tuyeres for an arbitrary time period (t-1 to t+1).
- RNN recurrent neural network
- RCNN recurrent convolutional neural network
- the accuracy may be deteriorated to determine the combustion state of the tuyere only at a certain point in time.
- an image time-series deep learning may be applied to further improve the accuracy of the tuyere combustion state classification.
- the accuracy of classification may be affected according to the time period (for example, t-1 to t+1) for accumulating the results primarily classified and a start time (t-1) of the corresponding time period.
- the tuyere combustion state classification may be determined by accumulating the results primarily classified for a time period set by a user.
- the above-described time period is adjusted according to the elapsed time information from the time at which the tuyere combustion state classification is first detected to the time at which the tuyere combustion state classification transitions to another state, such that the accuracy may be further improved.
- the tuyere combustion state classified by the AI-based determination unit 131 may include, for example, a normal combustion state, a poor combustion state, pulverized coal non-injection, unreduced molten material falling(raw ore falling), coke turning, and the like.
- pulverized coal non-injection means that it is determined whether or not pulverized coal is injected, unreduced molten material falling(raw ore falling)means that it is determined whether or not an unreduced raw material in a molten state in which raw materials that need to be reduced in an upper part of the furnace are unreduced and fall, and coke turning means whether coke turns in a middle part of the coke.
- the image processing-based determination unit 132 may diagnose a tuyere facility through image processing for image data according to each of the tuyeres, and determine the tuyere combustion state.
- the image processing-based determination unit 132 may determine a tuyere facility abnormal condition including presence or absence of a curvature of a tuyere, presence or absence of a tuyere attachment, clogging or a tuyere, lance banding or burning, or the like, through image processing of the image data according to each of the tuyeres.
- the image processing-based determination unit 132 may extract a combustion area and combustion brightness (i.e., luminance) through image processing of image data according to each of the tuyeres.
- the image processing-based determination unit 132 may determine a pulverized coal flow rate through image processing of the image data according to each of the tuyeres.
- the determination by the AI-based determination unit 131 and the image processing-based determination unit 132 described above may be performed in parallel.
- the combustion condition classification result according to each of the tuyeres classified by the tuyere combustion state determination unit 130 and the tuyere facility diagnosis result may be mapped and stored and managed together with image data and collection environment information according to each of the tuyeres.
- the tuyere combustion state index generation unit 140 may generate a combustion state index according to each of the tuyeres by using the combustion state classification result according to each of the tuyeres classified by the tuyere combustion state determination unit 130.
- the combustion state index according to each of the tuyeres generated by the tuyere combustion state index generation unit 140 may include a combustion state defect index, a pulverized coal non-injection index, an unreduced molten material falling(raw ore falling) index, a coke turning index, a combustion state level index, a pulverized coal flow rate index, a tuyere raceway index, and the like.
- the tuyere combustion state index generation unit 140 may count the number of times that an arbitrary classification result has occurred based on the combustion state classification results according to each of the tuyeres by the tuyere combustion state determination unit 130 for every predetermined period, and generate a related index by scoring it according to the number of times counted for each corresponding period.
- the tuyere combustion state index generation unit 150 may score the combustion state level index according to a combustion area and combustion brightness (i.e., luminance) extracted by the tuyere combustion state determination unit 130, combine the calculated scores for a predetermined period to generate a combustion state level index.
- reference information used to generate the combustion state level index can be updated according to the input signal by the administrator. Accordingly, the updated reference information may be reflected in real time to generate index information reflecting the blast furnace condition.
- the tuyere combustion state index generation unit 140 may generate a tuyere facility abnormality index by scoring the results of the tuyere facility diagnosis determined by the tuyere combustion state determination unit 130.
- the tuyere facility abnormality index may include a tuyere curvature index, a tuyere attachment index, a tuyere blockage index, a lance damage index, and the like.
- An operational information collection unit 150 is for collecting operational information generated during a blast furnace operation in real time.
- the operational information may include, for example, a blast furnace body temperature, pressure, a cooling water flow rate, and the like.
- the operational information collected in real time by the operational information collection unit 150 may be mapped with the tuyere combustion state index information generated by the tuyere combustion state index information unit 140 described above and stored and managed.
- An integrated evaluation unit 160 may be integrally evaluated in a circumferential direction of the blast furnace based on the tuyere operational state index information generated according to each of the tuyeres by the tuyere combustion state index generation unit 140 and operational information collected by an operational information collection unit 150.
- the integrated evaluation unit 160 may generate an integrated combustion state index by comprehensively considering the tuyere combustion state index information generated according to each of the tuyeres by the tuyere combustion state index generation unit 140.
- the integrated combustion state index may include an integrated combustion state index, matched 1:1 to the integrated combustion state index generated according to each of the tuyeres such as an integrated combustion state defect index, an integrated pulverized coal non-inj ection index, an integrated unreduced molten material falling(raw ore falling)index, and the like.
- the integrated evaluation unit 160 may generate a circumferential balance index based on tuyere raceway indices generated according to each of the tuyeres.
- the integrated evaluation unit 160 may generate an integrated tuyere facility abnormality index based on the tuyere facility abnormality index generated according to each of the tuyeres.
- a blast furnace condition control unit 170 may perform at least one of pulverized coal injection control, N2 purge control, and blast furnace charge control, based on the tuyere combustion state index information generated according to each of the tuyeres by the tuyere combustion state index generation unit 140 or the integrated combustion state index generated by the integrated evaluation unit 160 to control the blast furnace condition.
- the blast furnace condition control unit 170 may perform pulverized coal injection control when a pulverized coal non-injection index for an arbitrary tuyere exceeds a predetermined reference value.
- the blast furnace condition control unit 170 may perform blast furnace charging control when a unreduced molten material falling (raw ore falling)index exceeds a predetermined reference value due to occurrence of raw ore falling in any tuyere region.
- the blast furnace condition control unit 170 may integrally control a plurality of tuyeres based on information of an integrated combustion state index or a circumferential balance index.
- the blast furnace control unit 170 may control a blast furnace charging, for example, by changing distribution of charges to change a direction in which the charges fall, when raw ore falling occurs in only one direction.
- the system for evaluating operational conditions of a blast furnace 100 described above with reference to FIG. 1 applies an artificial intelligence algorithm to input data and performs image processing, and may be implemented by combination of a processing device capable of calculating various indices, and a control device capable of performing blast furnace control.
- FIG. 5 is a flowchart of a method for evaluating operational conditions of a blast furnace according to another embodiment of the present disclosure.
- image data according to each of the tuyeres provided in a blast furnace may be collected in real time by an image capturing unit 110 and an image collection unit 120 (S510).
- a combustion state according to each of the tuyeres may be classified using the image data according to each of the tuyeres (S520) .
- an AI-based determination unit 131 after primarily classifying the tuyere combustion state based on artificial intelligence using the image data according to each of the tuyeres (S521), the classification of the tuyere combustion state may be determined based on the result of classifying the combustion states (S522).
- an image processing-based determination unit 132 in addition to classifying the combustion state according to each of the tuyeres through image processing for the image data according to each of the tuyeres, a tuyere facility can be diagnosed (S525).
- a combustion state index is generated based on the result of classifying the combustion state according to each of the tuyeres (S530) , and by an integrated evaluation unit 160, an operational condition of a blast furnace may be integrally evaluated in a circumferential direction based on the generated combustion state index according to each of the tuyeres (S540) .
- a blast furnace condition may be controlled based on the integrally evaluated operational condition (S550).
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Abstract
Description
- The present disclosure relates to a system and a method for evaluating operational conditions of a blast furnace.
- In order to evaluate operational conditions of a blast furnace, attempts have been made to determine a situation inside a furnace by analyzing image data captured through a tuyere of the blast furnace, or the like, or to determine a situation inside a furnace by monitoring operational data.
- However, in the related art, an operator has merely qualitatively judged the blast furnace combustibility or the blast furnace condition simply through image data, or merely judged the situation inside the furnace by analyzing luminance of the image data.
- In this regard, Japanese Patent Publication No.
2015-52148 (published date: March 19, 2015 - In the technical field, a method for quantitatively evaluating a combustion state of a tuyere based on the tuyere image data, and based thereon, a method for integrally evaluating operational conditions of a blast furnace is required.
- In order to solve the above problems, an embodiment of the present disclosure is to provide a system for evaluating operational conditions of a blast furnace.
- According to an embodiment of the present disclosure, a system and a method for evaluating operational conditions of a blast furnace includes: an image capturing unit for capturing image data according to each of a plurality of tuyeres disposed in a blast furnace; an image collection unit for collecting the image data captured according to each of the tuyeres by the image capturing unit; a tuyere combustion state determination unit for classifying, on the basis of artificial intelligence, combustion states according to each of the tuyeres by using the image data according to each of the tuyeres; a tuyere combustion state index generation unit for generating combustion state indices according to each of the tuyeres by using the result of classifying the combustion states according to each of the tuyeres by the tuyere combustion state determination unit; and an integrated evaluation unit for generating an integrated combustion state index on the basis of the combustion state indices according to each of the tuyeres.
- Meanwhile, another embodiment of the present disclosure is to provide a method for evaluating operating conditions a blast furnace.
- According to another embodiment of the present disclosure, the method for evaluating operating conditions a blast furnace includes operations of: collecting image data according to a plurality of tuyeres provided in a blast furnace; classifying combustion states according to each of the tuyeres on the basis of artificial intelligence, by using the image data according to each of the tuyeres; generating combustion state indices according to each of the tuyeres by using the result of classifying the combustion state according to a plurality of tuyeres; and generating an integrated combustion state index on the basis of the combustion state indices according to each of the tuyeres.
- In addition, not all features of the present disclosure are listed in the solution means of the above-mentioned problem. Various features of the present disclosure and the advantages and effects thereof may be understood in more detail with reference to specific embodiments below.
- According to an embodiment in the present disclosure, it is possible to classify a tuyere combustion state based on deep learning using the tuyere image data, in addition to the result of classifying the result of classifying the tuyere combustion state, and a result of analyzing the tuyere image data and a result of analyzing the blast furnace operational data may be additionally used to extract the tuyere combustion state indices according to each of the tuyeres, and an operational condition of a blast furnace may be integrally evaluated and controlled.
- Accordingly, the blast furnace combustibility and the blast furnace condition may be quantitatively evaluated to enable stable blast furnace operations, and productivity may be improved.
-
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FIG. 1 is a configuration diagram of a system for evaluating operational conditions of a blast furnace according to an embodiment of the present disclosure. -
FIG. 2 is a view illustrating a concept for primarily classifying a tuyere combustion state on the basis of deep learning according to an embodiment of the present disclosure. -
FIGS. 3 and4 are views diagrams illustrating a concept of determining classification of a tuyere combustion state on the basis of accumulating results primarily classified in time series based on deep learning according to an embodiment of the present disclosure. -
FIG. 5 is a flowchart of a method for evaluating operating conditions according to another embodiment of the present disclosure. - Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The disclosure may, however, be exemplified in many different forms and should not be construed as being limited to the specific embodiments set forth herein, and those skilled in the art and understanding the present disclosure can easily accomplish retrogressive inventions or other embodiments included in the scope of the present disclosure by the addition, modification, and removal of components within the same scope, but those are construed as being included in the scope of the present disclosure. Like reference numerals will be used to designate like components having similar functions throughout the drawings within the scope of the present disclosure.
- Throughout the specification, it will be understood that when an element is referred to as being "on," "connected to," or "coupled to" another element, it can be directly "on," "connected to, " or "coupled to" the other element or indirectly "on", "connected to", or "coupled to" the other elements intervening therebetween may be present. In addition, when a component is referred to as "comprise" or "comprising," it means that it may include other components as well, rather than excluding other components, unless specifically stated otherwise.
-
FIG. 1 is a configuration diagram of a system for evaluating operational conditions of a blast furnace according to an embodiment of the present disclosure. - Referring to
FIG. 1 , asystem 100 for evaluating operational conditions of a blast furnace according to an embodiment of the present disclosure may be configured to include animage capturing unit 110, animage collection unit 120, a tuyere combustionstate determination unit 130, a tuyere combustion stateindex generation unit 140, an operationalinformation collection unit 150, an integratedevaluation unit 160, and a blast furnacecondition control unit 170. - The
image capturing unit 110 may acquire image data according to each of the tuyeres 11 provided in theblast furnace 10. - For example, the
image capturing unit 110 may include a plurality of cameras installed in each tuyere 11, and may acquire the image data according to each of the tuyeres in real time (e.g., in ms units) through each camera. - The
image collection unit 120 may collect image data according to each of the tuyeres captured by theimage capturing unit 110. - For example, the
image collection unit 120 may collect image data obtained in real time according to each of the tuyeres from a plurality of cameras included in theimage capturing unit 110. - In addition, the
image collection unit 120 may map the collected image data with collection environment information including a tuyere number, data capture time, and the like. - In addition, the image data, which has been mapped by the
image collection unit 120, may be stored in a data storage (not shown) provided in a system for evaluating operational conditions of ablast furnace 100, or may be transmitted in real time to the tuyere combustionstate determination unit 130. - The tuyere combustion
state determination unit 130 is for classifying a combustion state according to each of the tuyeres using the image data according to each of the tuyeres transmitted from theimage collection unit 120, and may be configured to include an AI-baseddetermination unit 131 and an image processing-baseddetermination unit 132. - The AI-based determination unit 1311 may classify the combustion state according to each of the tuyeres based on artificial intelligence using the image data according to each of the tuyeres. For example, the AI-based
determination unit 131 may classify the combustion state according to each of the tuyeres based on deep learning. - According to an embodiment, the AI-based
determination unit 131 may primarily classify the combustion state according to each of the tuyeres based on a convolutional neural network (CNN) using image data according to each of the tuyeres. - If necessary, the AI-based
determination unit 131 may determine the tuyere combustion state classification based on results of accumulating the results of classifying the combustion states according to each of the tuyeres in time series, primarily classified, thereby further improving consistency of the combustion state classification. - The concept of classifying and determining the tuyere combustion state by the AI-based
determination unit 131 will be described in more detail with reference toFIGS. 2 to 4 . -
FIG. 2 is a view illustrating the concept of primarily classifying a tuyere combustion state based on deep learning according to an embodiment of the present disclosure. - Referring to
FIG. 2 , the AI-baseddetermination unit 131 may classify the combustion states based on the image deep learning, for example, CNN, for first tuyere image to the Nth tuyere image data (21 to 2N) captured according to each of the tuyeres, thereby obtaining the results of the first tuyere combustion state classification to the Nth tuyere combustion state classification (21'to 2N'). Here, N means the number of tuyere. -
FIGS. 3 and4 are diagrams illustrating a concept of determining a tuyere combustion state classification based on a result of accumulating a result primarily classified based on deep learning in time series according to an embodiment of the present disclosure. - First, referring to
FIG. 3 , as a result of accumulating the result of classifying the combustion states in time series, primarily classified according to each of the tuyeres, that is, the AI-baseddetermination unit 131 may determine a tuyere combustion state classification according to each of the tuyeres based on first tuyere combustion state classifications 31-1, 31-2, and 31-3, second tuyere combustion state classifications 32-1, 32-2, and 32-3, and Nth tuyerecombustion state classifications 3N-1, 3N -2, and 3N-3, and may obtain determined tuyere combustion state classification results 31' to 33'. - In the present embodiment, if any combustion state classification occurs more than a predetermined number of times, a result of classifying the plurality of combustion states primarily classified for an arbitrary time period (t-1 to t+1) to determine the tuyere combustion state classification, may be determined as the corresponding combustion state classification. Thereby, it is possible to further improve the accuracy of the tuyere combustion state classification.
- Next, referring to
FIG. 4 , as a result of accumulating the result of classifying the combustion states in time series, primarily classified according to each of the tuyeres, that is, the AI-baseddetermination unit 131 may determine tuyere combustion state classification according to each of the tuyeres based on deep learning in time series on first tuyere combustion state classifications 41-1, 41-2, and 41-3, second tuyere combustion state classifications 42-1, 42-2, and 42-3, and Nth tuyerecombustion state classifications 4N-1, 4N-2, and 4N-3, and may obtain determined tuyere combustion state classification results 41' to 43'. - For example, the AI-based
determination unit 131 determine the tuyere combustion state classification according to each of the tuyeres based on a recurrent neural network (RNN) or a recurrent convolutional neural network (RCNN) by using the result of classifying a plurality of combustion states primarily classified according to each of the tuyeres for an arbitrary time period (t-1 to t+1). - Since the combustion state of the tuyere changes with continuity over time, the accuracy may be deteriorated to determine the combustion state of the tuyere only at a certain point in time.
- Therefore, according to the present embodiment, in order to determine the combustion state classification of the tuyere by comprehensively considering the changes in the combustion state of the tuyere according to the time flow, an image time-series deep learning may be applied to further improve the accuracy of the tuyere combustion state classification.
- Meanwhile, as illustrated in
FIGS. 3 and4 , in determining the tuyere combustion state classification based on the results accumulated in time series, the accuracy of classification may be affected according to the time period (for example, t-1 to t+1) for accumulating the results primarily classified and a start time (t-1) of the corresponding time period. - According to an embodiment, at the time of initial performance, starting from the time at which the classification of the combustion state of the corresponding tuyere is first detected, the tuyere combustion state classification may be determined by accumulating the results primarily classified for a time period set by a user.
- In addition, when the determination result of the tuyere combustion state classification is accumulated, the above-described time period is adjusted according to the elapsed time information from the time at which the tuyere combustion state classification is first detected to the time at which the tuyere combustion state classification transitions to another state, such that the accuracy may be further improved.
- The tuyere combustion state classified by the AI-based
determination unit 131 may include, for example, a normal combustion state, a poor combustion state, pulverized coal non-injection, unreduced molten material falling(raw ore falling), coke turning, and the like. - Here, pulverized coal non-injection means that it is determined whether or not pulverized coal is injected, unreduced molten material falling(raw ore falling)means that it is determined whether or not an unreduced raw material in a molten state in which raw materials that need to be reduced in an upper part of the furnace are unreduced and fall, and coke turning means whether coke turns in a middle part of the coke.
- The image processing-based
determination unit 132 may diagnose a tuyere facility through image processing for image data according to each of the tuyeres, and determine the tuyere combustion state. - According to an embodiment, the image processing-based
determination unit 132 may determine a tuyere facility abnormal condition including presence or absence of a curvature of a tuyere, presence or absence of a tuyere attachment, clogging or a tuyere, lance banding or burning, or the like, through image processing of the image data according to each of the tuyeres. - In addition, the image processing-based
determination unit 132 may extract a combustion area and combustion brightness (i.e., luminance) through image processing of image data according to each of the tuyeres. - In addition, when the combustion state is normal, the image processing-based
determination unit 132 may determine a pulverized coal flow rate through image processing of the image data according to each of the tuyeres. - Various image processing techniques known to a person skilled in the art may be applied to the image processing-based
determination unit 132 for image processing of image data according to each of the tuyeres, and detailed description thereof will be omitted. - The determination by the AI-based
determination unit 131 and the image processing-baseddetermination unit 132 described above may be performed in parallel. - The combustion condition classification result according to each of the tuyeres classified by the tuyere combustion
state determination unit 130 and the tuyere facility diagnosis result may be mapped and stored and managed together with image data and collection environment information according to each of the tuyeres. - The tuyere combustion state
index generation unit 140 may generate a combustion state index according to each of the tuyeres by using the combustion state classification result according to each of the tuyeres classified by the tuyere combustionstate determination unit 130. - According to an embodiment, the combustion state index according to each of the tuyeres generated by the tuyere combustion state
index generation unit 140 may include a combustion state defect index, a pulverized coal non-injection index, an unreduced molten material falling(raw ore falling) index, a coke turning index, a combustion state level index, a pulverized coal flow rate index, a tuyere raceway index, and the like. - For example, the tuyere combustion state
index generation unit 140 may count the number of times that an arbitrary classification result has occurred based on the combustion state classification results according to each of the tuyeres by the tuyere combustionstate determination unit 130 for every predetermined period, and generate a related index by scoring it according to the number of times counted for each corresponding period. - In addition, the tuyere combustion state
index generation unit 150 may score the combustion state level index according to a combustion area and combustion brightness (i.e., luminance) extracted by the tuyere combustionstate determination unit 130, combine the calculated scores for a predetermined period to generate a combustion state level index. Here, reference information used to generate the combustion state level index can be updated according to the input signal by the administrator. Accordingly, the updated reference information may be reflected in real time to generate index information reflecting the blast furnace condition. - In addition, the tuyere combustion state
index generation unit 140 may generate a tuyere facility abnormality index by scoring the results of the tuyere facility diagnosis determined by the tuyere combustionstate determination unit 130. Here, the tuyere facility abnormality index may include a tuyere curvature index, a tuyere attachment index, a tuyere blockage index, a lance damage index, and the like. - An operational
information collection unit 150 is for collecting operational information generated during a blast furnace operation in real time. Here, the operational information may include, for example, a blast furnace body temperature, pressure, a cooling water flow rate, and the like. - The operational information collected in real time by the operational
information collection unit 150 may be mapped with the tuyere combustion state index information generated by the tuyere combustion stateindex information unit 140 described above and stored and managed. - An
integrated evaluation unit 160 may be integrally evaluated in a circumferential direction of the blast furnace based on the tuyere operational state index information generated according to each of the tuyeres by the tuyere combustion stateindex generation unit 140 and operational information collected by an operationalinformation collection unit 150. - According to an embodiment, the integrated
evaluation unit 160 may generate an integrated combustion state index by comprehensively considering the tuyere combustion state index information generated according to each of the tuyeres by the tuyere combustion stateindex generation unit 140. - For example, the integrated combustion state index may include an integrated combustion state index, matched 1:1 to the integrated combustion state index generated according to each of the tuyeres such as an integrated combustion state defect index, an integrated pulverized coal non-inj ection index, an integrated unreduced molten material falling(raw ore falling)index, and the like.
- In addition, the integrated
evaluation unit 160 may generate a circumferential balance index based on tuyere raceway indices generated according to each of the tuyeres. - In addition, the integrated
evaluation unit 160 may generate an integrated tuyere facility abnormality index based on the tuyere facility abnormality index generated according to each of the tuyeres. - A blast furnace
condition control unit 170 may perform at least one of pulverized coal injection control, N2 purge control, and blast furnace charge control, based on the tuyere combustion state index information generated according to each of the tuyeres by the tuyere combustion stateindex generation unit 140 or the integrated combustion state index generated by the integratedevaluation unit 160 to control the blast furnace condition. - According to an embodiment, the blast furnace
condition control unit 170 may perform pulverized coal injection control when a pulverized coal non-injection index for an arbitrary tuyere exceeds a predetermined reference value. - In addition, the blast furnace
condition control unit 170 may perform blast furnace charging control when a unreduced molten material falling (raw ore falling)index exceeds a predetermined reference value due to occurrence of raw ore falling in any tuyere region. - According to another embodiment, the blast furnace
condition control unit 170 may integrally control a plurality of tuyeres based on information of an integrated combustion state index or a circumferential balance index. - For example, the blast
furnace control unit 170 may control a blast furnace charging, for example, by changing distribution of charges to change a direction in which the charges fall, when raw ore falling occurs in only one direction. - The system for evaluating operational conditions of a
blast furnace 100 described above with reference toFIG. 1 applies an artificial intelligence algorithm to input data and performs image processing, and may be implemented by combination of a processing device capable of calculating various indices, and a control device capable of performing blast furnace control. -
FIG. 5 is a flowchart of a method for evaluating operational conditions of a blast furnace according to another embodiment of the present disclosure. - Referring to
FIG. 5 , according to a method for evaluating operational conditions of a blast furnace, image data according to each of the tuyeres provided in a blast furnace may be collected in real time by animage capturing unit 110 and an image collection unit 120 (S510). - Thereafter, by a tuyere combustion
state determination unit 130, a combustion state according to each of the tuyeres may be classified using the image data according to each of the tuyeres (S520) . - Specifically, by an AI-based
determination unit 131, after primarily classifying the tuyere combustion state based on artificial intelligence using the image data according to each of the tuyeres (S521), the classification of the tuyere combustion state may be determined based on the result of classifying the combustion states (S522). - In addition, in parallel therewith, by an image processing-based
determination unit 132, in addition to classifying the combustion state according to each of the tuyeres through image processing for the image data according to each of the tuyeres, a tuyere facility can be diagnosed (S525). - Thereafter, by a tuyere combustion state
index generation unit 140, a combustion state index is generated based on the result of classifying the combustion state according to each of the tuyeres (S530) , and by an integratedevaluation unit 160, an operational condition of a blast furnace may be integrally evaluated in a circumferential direction based on the generated combustion state index according to each of the tuyeres (S540) . - Thereafter, by a blast furnace
condition control unit 170, a blast furnace condition may be controlled based on the integrally evaluated operational condition (S550). - Since the detailed method of performing each operation described above with reference to
FIG. 5 is the same as described above with reference toFIGS. 1 to 4 , redundant description thereof will be omitted. - While embodiments have been shown and described above, it will be apparent to those skilled in the art that modifications and variations could be made without departing from the scope of the present disclosure as defined by the appended claims.
Claims (12)
- A system for evaluating operational conditions of a blast furnace, comprising:an image capturing unit for capturing image data according to each of a plurality of tuyeres provided in a blast furnace;an image collection unit for collecting the image data captured according to each of the tuyeres by the image capturing unit;a tuyere combustion state determination unit for classifying, on the basis of artificial intelligence, combustion states according to each of the tuyeres, by using the image data according to each of the tuyeres;a tuyere combustion state index generation unit for generating a combustion state index according to each of the tuyeres by using the result of classifying the combustion states according to each of the tuyeres by the tuyere combustion state determination unit; andan integrated evaluation unit for generating an integrated combustion state index on the basis of the combustion state index according to each of the tuyeres.
- The system for evaluating operational conditions of a blast furnace of claim 1, wherein the tuyere combustion state determination unit comprises an AI-based determination unit for classifying combustion states according to each of the tuyeres based on deep learning using the image data according to each of the tuyeres.
- The system for evaluating operational conditions of a blast furnace of claim 2, wherein the AI-based determination unit determines the combustion state classification according to each of the tuyeres based on a result of accumulating a result of classifying the combustion states according to each of the tuyeres classified based on deep learning in time series for a predetermined time period.
- The system for evaluating operational conditions of a blast furnace of claim 3, wherein the AI-based determination unit determines the combustion state classification if any combustion state classification occurs more than a predetermined number of times during the time period.
- The system for evaluating operational conditions of a blast furnace of claim 3, wherein the AI-based determination unit determines the combustion state classification based on time-series deep learning on the results of classifying the combustion states according to each of the tuyeres accumulated during the time period.
- The system for evaluating operational conditions of a blast furnace of claim 3, wherein the time period is adjusted according to elapsed time information from a time at which the tuyere combustion state classification is first detected to a time at which the tuyere combustion state classification transitions to another state.
- The system for evaluating operational conditions of a blast furnace of claim 2, wherein the tuyere combustion state determination unit further comprises an image processing-based determination unit for diagnosing a tuyere facility through image processing on the image data according to each of the tuyeres and determining the tuyere combustion state.
- The system for evaluating operational conditions of a blast furnace of claim 1, wherein the combustion state classification comprises a normal combustion state, a poor combustion state, pulverized coal non-injection, unreduced molten material falling, and coke turning.
- The system for evaluating operational conditions of a blast furnace of claim 1, further comprising a blast furnace condition control unit for performing at least one of pulverized coal injection control, N2 purge control, and blast furnace charge control, based on the combustion state index according to each of the tuyeres or the integrated combustion state index.
- The system for evaluating operational conditions of a blast furnace of claim 1, wherein the combustion state index according to each of the tuyeres comprises at least one of a combustion state defect index, a pulverized coal non-injection index, an unreduced molten material falling index, a coke turning index, a combustion state level index, a pulverized coal flow rate index, and a tuyere raceway index.
- A method for evaluating operational conditions of a blast furnace comprising operations of:collecting image data according to a plurality of tuyeres provided in a blast furnace;classifying a combustion state according to each of the tuyeres based on artificial intelligence using the image data according to each of the tuyeres;generating a combustion state index according to each of the tuyeres using the result of classifying the combustion state according to each of the tuyeres; andgenerating an integrated combustion state index based on the combustion state index according to each of the tuyeres.
- The method for evaluating operational conditions of a blast furnace of claim 11, wherein the operation of classifying the combustion state according to each of the tuyeres comprises operations of:classifying the combustion state according to each of the tuyeres based on deep learning using the image data according to each of the tuyeres; anddetermining a combustion state classification according to each of the tuyeres based on a result of accumulating the result of classifying the combustion state according to each of the tuyeres classified based on the deep learning for a predetermined time period.
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PCT/KR2018/016580 WO2019132476A1 (en) | 2017-12-26 | 2018-12-24 | System and method for evaluating operational conditions of blast furnace |
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CN112094974A (en) * | 2020-08-21 | 2020-12-18 | 广东韶钢松山股份有限公司 | Blast furnace taphole spray control method, system and computer readable storage medium thereof |
CN112501368B (en) * | 2020-11-17 | 2022-07-08 | 中冶南方工程技术有限公司 | Blast furnace smelting method and computer equipment |
JP7380604B2 (en) * | 2021-01-12 | 2023-11-15 | Jfeスチール株式会社 | Learning model generation method, learning model generation device, blast furnace control guidance method, and hot metal manufacturing method |
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JPH07116527B2 (en) * | 1990-04-20 | 1995-12-13 | 新日本製鐵株式会社 | Sintering process controller |
KR950014631B1 (en) * | 1993-12-28 | 1995-12-11 | 포항종합제철주식회사 | Apparatus of temperature pre-estinate and action control guide with molten metal |
JPH07305105A (en) * | 1994-05-02 | 1995-11-21 | Nippon Steel Corp | Method for evaluating raceway condition of blast furnace |
KR100376525B1 (en) * | 1996-12-20 | 2003-06-09 | 주식회사 포스코 | Apparatus and method for monitoring blast furnace race way |
KR19990080341A (en) * | 1998-04-15 | 1999-11-05 | 이해규 | Combustion diagnosis device of boiler burner |
CN2589484Y (en) * | 2002-12-12 | 2003-12-03 | 武汉钢铁(集团)公司 | Blast furnace tuyere combustion status monitoring device |
CN2913380Y (en) * | 2006-06-22 | 2007-06-20 | 重庆大学 | On-line monitoring device for blast furnace tuyere operating mode |
KR101747591B1 (en) * | 2013-06-19 | 2017-06-14 | 제이에프이 스틸 가부시키가이샤 | Method for detecting abnormality at blast furnace and method for operating blast furnace |
JP5644910B1 (en) * | 2013-07-29 | 2014-12-24 | Jfeスチール株式会社 | Abnormality detection method and blast furnace operation method |
EP3029160B1 (en) * | 2013-07-29 | 2018-04-25 | JFE Steel Corporation | Abnormality detection method and blast-furnace operation method |
JP6119515B2 (en) * | 2013-09-02 | 2017-04-26 | 新日鐵住金株式会社 | Blast furnace tuyere state observation method and blast furnace tuyere state observation apparatus |
JP2015052148A (en) | 2013-09-06 | 2015-03-19 | 新日鐵住金株式会社 | Method for controlling blast furnace by determining operational situation thereof |
JP6350159B2 (en) * | 2014-09-17 | 2018-07-04 | 新日鐵住金株式会社 | Blast furnace tuyere state observation method and blast furnace tuyere state observation apparatus |
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