首页> 外国专利> Learning method of prediction model of load down speed of blast furnace, method for predicting down load rate of blast furnace, operation guidance method for blast furnace, method for controlling down load rate of blast furnace, method for producing molten iron, method for operating blast furnace, and learning device for predicting load drop rate of blast furnace

Learning method of prediction model of load down speed of blast furnace, method for predicting down load rate of blast furnace, operation guidance method for blast furnace, method for controlling down load rate of blast furnace, method for producing molten iron, method for operating blast furnace, and learning device for predicting load drop rate of blast furnace

机译:高炉负荷速度预测模型的学习方法,预测高炉载荷率的方法,高炉运转指导方法,控制高炉载荷率,生产铁水生产方法,运行爆炸方法。 用于预测高炉负荷降速的炉子和学习装置

摘要

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
机译:为了提供高炉水平降低速度预测模型的学习方法,高炉水平降低速度预测模型,高炉,高炉操作引导方法,水平降低速度控制方法的水平降低速度预测方法对于高炉,铁水生产方法,高炉操作方法和用于高炉水平降低速度预测模型的学习装置,可以准确地预测高炉中的水平降低速度。落实:学习根据本发明的高炉水平降低速度预测模型的方法包括至少一个由高炉操作中的流速组成的操作变量,富集氧气流,粉煤吹送量;通风含水量;和炉子顶部的焦炭比,作为输入变量。学习方法包括:构建用于高炉的水平降低速度预测模型的模型的步骤,该高炉使用时步前的高炉水平降低速度的一个时间步骤作为输出变量作为输出变量递归神经网络模型;以及使用基于高炉原料停留时间的学习数据确定高炉水平降低速度预测模型的学习参数的步骤。选择图:图1

著录项

  • 公开/公告号JP6933196B2

    专利类型

  • 公开/公告日2021-09-08

    原文格式PDF

  • 申请/专利权人 JFEスチール株式会社;

    申请/专利号JP20180145017

  • 发明设计人 橋本 佳也;海瀬 達哉;

    申请日2018-08-01

  • 分类号C21B5;C21B7/24;F27D21;F27B1/28;

  • 国家 JP

  • 入库时间 2022-08-24 20:53:41

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