LEARNING METHOD OF LEVEL LOWERING SPEED PREDICTION MODEL FOR BLAST FURNACE, LEVEL LOWERING SPEED PREDICTION MODEL FOR BLAST FURNACE, PREDICTION METHOD OF LEVEL LOWERING SPEED FOR BLAST FURNACE, BLAST FURNACE OPERATION GUIDANCE METHOD, CONTROL METHOD OF LEVEL LOWERING SPEED FOR BLAST FURNACE, MOLTEN IRON PRODUCTION METHOD, BLAST FURNACE OPERATION METHOD, AND A LEARNING DEVICE FOR LEVEL LOWERING SPEED PREDICTION MODEL FOR BLAST FURNACE

• Main Authors: HASHIMOTO YOSHIYA, KAISE TATSUYA
• Format: Patent
• Language: English; Japanese
• Published: 06.02.2020
• Subjects: BLASTING; CHEMISTRY; DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS,IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KINDOF FURNACE; FURNACES; FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; HEATING; KILNS; LIGHTING; MANUFACTURE OF IRON OR STEEL; MECHANICAL ENGINEERING; METALLURGY; METALLURGY OF IRON; OPEN SINTERING OR LIKE APPARATUS; OVENS; RETORTS; WEAPONS

To provide a learning method of a level lowering speed prediction model for a blast furnace, level lowering speed prediction model for a blast furnace, prediction method of level lowering speed for a blast furnace, blast furnace operation guidance method, control method of level lowering speed for a blast furnace, molten iron production method, blast furnace operation method, and a learning device for a level lowering speed prediction model for a blast furnace, by which a level lowering speed in a blast furnace can be accurately predicted.SOLUTION: The learning method of a level lowering speed prediction model for a blast furnace according to the present invention includes at least one operation variables consisting of a flow rate in blast furnace operation, enrichment oxygen flow, pulverized coal blowing amount; ventilation moisture content; and a coke ratio at the top of the furnace, as an input variable. The learning method includes: a step of constructing a model of a level lowering speed prediction model for a blast furnace which uses a level lowering speed of a blast furnace at the time step one time step ahead of the current time as an output variable as a recursive neural network model; and a step of determining a learning parameter of a level lowering speed prediction model of a blast furnace using learning data based on the raw material residence time in the blast furnace.SELECTED DRAWING: Figure 1 【課題】高炉の荷下り速度を精度よく予測可能な高炉の荷下り速度予測モデルの学習方法、高炉の荷下り速度予測モデル、高炉の荷下り速度予測方法、高炉の操業ガイダンス方法、高炉の荷下り速度制御方法、溶銑の製造方法、高炉の操業方法、及び高炉の荷下り速度予測モデルの学習装置を提供すること。【解決手段】本発明に係る高炉の荷下り速度予測モデルの学習方法は、高炉操業における送風流量、富化酸素流量、微粉炭吹込み量、送風湿分、及び炉頂におけるコークス比のうち、少なくとも１つ以上の操作変数を入力変数として含み、現在時刻の１つ先のタイムステップの高炉の荷下り速度を出力変数とする高炉の荷下り速度予測モデルを再帰型ニューラルネットワークモデルとして構築するステップと、高炉内の原料滞留時間を基準とした学習データを用いて、高炉の荷下り速度予測モデルの学習パラメータを決定するステップと、を含むことを特徴とする。【選択図】図１