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Blast furnace molten iron silicon content four-classification trend prediction model establishing method and application

A blast furnace molten iron and forecasting model technology, which is applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of inability to comprehensively forecast, only to forecast numerical values, and unable to predict in advance when abnormal furnace conditions will occur.

Active Publication Date: 2015-09-09
CENT SOUTH UNIV
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Problems solved by technology

Although this method comprehensively considers both stable and abnormal furnace conditions, in practical applications, it is impossible to predict in advance when abnormal furnace conditions will occur, and then it is impossible to replace the prediction model in time, which has a certain impact on the hit rate of the results
[0011] To sum up, the existing methods for predicting the furnace temperature either cannot fully predict, or can only predict the value, and there are few methods for predicting the trend of the furnace temperature

Method used

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  • Blast furnace molten iron silicon content four-classification trend prediction model establishing method and application
  • Blast furnace molten iron silicon content four-classification trend prediction model establishing method and application
  • Blast furnace molten iron silicon content four-classification trend prediction model establishing method and application

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Embodiment 1

[0159] This embodiment is in a steel factory 2650m 3 Blast furnace for experimental testing.

[0160] A method for establishing a four-category trend forecasting model for silicon content in molten iron of a blast furnace, specifically comprising the following steps:

[0161] 1) Collect historical data. The ironmaking process control and data collection are realized through the configuration software of the automation system. The automation system includes a blast furnace body, a feeding system, a hot blast stove system, and a coal injection system. Among them, the data from the blast furnace body mainly consist of relevant data such as furnace top pressure, hot air pressure, and furnace top temperature. The data from the hot blast stove system mainly include: blast furnace gas volume, air supply, furnace top temperature, flue temperature and other air supply related data. The data from the coal injection system mainly include: injection pressure, injection flow and other ...

Embodiment 2

[0174] This embodiment relates to a four-category trend forecasting method for blast furnace silicon content in molten iron using the four-category trend forecast model established in Example 1. Specifically, a set of variable data is selected as input variables and input to the forecast model. The output results of the forecast model are decoded to obtain the final silicon content change trend (the four-category trend forecast result of the silicon content in blast furnace hot metal).

[0175] Specifically, a total of 1166 sets of data were selected from 21:00 on January 9, 2013 to 10:00 on February 27, 2013, and processed using the data processing method described in Example 1. Among them, the change trend of the actual molten iron silicon content corresponding to 200 groups of test samples is as follows: Figure 5 shown by Figure 5 It can be seen that most of the change trends fall in the range of slight rise and slight decline, and only a small number of samples fall in ...

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Abstract

The invention relates to a blast furnace molten iron silicon content four-classification trend prediction model establishing method and application and belongs to the technical field of automatic detection. The method comprises acquiring historical data; performing data pre-processing; determining model input variables and output variables; clustering molten iron silicon content samples through a fuzzy mean clustering method effectively to obtain model output variable four-classification trend change block division standards; establishing the four-classification trend prediction model through an extreme learning machine. By means of the model, the silicon content change trend can be predicted, the trend change amplitude can be obtained, that is, four-classification trend change conditions of sharp rise, small rise, sharp decrease and small decrease of the molten iron silicon content of the next furnace can be predicted. Guidance for blast furnace operators to determine furnace conditions in advance, take measures of small adjustment, early adjustment and the like and prevent rapid changes of the furnace conditions.

Description

technical field [0001] The invention relates to a method for establishing a trend forecasting model of molten iron silicon content in a blast furnace smelting process and an application thereof, belonging to the technical field of automatic detection. Background technique [0002] The silicon content of molten iron is the key information to characterize the furnace temperature and its changing trend in the blast furnace ironmaking process, and it is also an important physical quantity that reflects the quality of molten iron, energy consumption and other indicators. However, the silicon content of molten iron and its changing trend cannot be directly detected online, resulting in untimely or blind control of furnace conditions, resulting in large fluctuations in furnace temperature and unsatisfactory furnace conditions. If the furnace temperature is too low, the physical heat of molten iron will be insufficient and the hearth heat reserve Not enough, not only the quality of ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 蒋朝辉尹菊萍桂卫华阳春华谢永芳
Owner CENT SOUTH UNIV
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