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Blast furnace molten iron silicon content feature analysis and prediction method

A blast furnace molten iron and characteristic analysis technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as subcooling, furnace condition failure, overheating, etc.

Inactive Publication Date: 2008-07-02
ZHEJIANG UNIV
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Problems solved by technology

If the furnace temperature control fluctuates, resulting in "overheating" or "overcooling", it is easy to induce furnace condition failure

Method used

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  • Blast furnace molten iron silicon content feature analysis and prediction method
  • Blast furnace molten iron silicon content feature analysis and prediction method
  • Blast furnace molten iron silicon content feature analysis and prediction method

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Embodiment Construction

[0046] Aiming at the characteristics of high nonlinearity of the blast furnace ironmaking process and strong coupling between various blast furnace production parameters, the present invention combines the support vector machine algorithm and the independent component analysis algorithm in the blast furnace ironmaking production process, and establishes a blast furnace based on the support vector machine Furnace temperature forecast model. The invention has universal applicability to the prediction of the silicon content of molten iron in the blast furnace smelting process, improves the accuracy and hit rate of the prediction of the silicon content of the molten iron of the blast furnace, and provides technical guarantee for the smooth and reliable operation of the blast furnace.

[0047] The characteristic analysis and prediction method of silicon content in blast furnace hot metal includes the following steps:

[0048] 1) Determine the model input variables for the predictio...

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Abstract

The invention discloses a characteristics analysis and forecast method for blast furnace molten iron silicon content. Blast furnace technological parameters in a forecast module for blast furnace molten iron silicon content are deemed as input variables; after exponential weight mobile average filtration and normalized pre-process for sample data of the input variables, the invention can use an improved dynamic separate composition analysis method for conducting characteristic extraction for the sample data of the input variables, so as to eliminate relevance between production technological parameters; a dynamic recurrence module for forecast of the blast furnace molten iron silicon content is established using the least square support vector machine arithmetic, so as to bring in a genetic arithmetic to optimize module parameters. The invention has common universality for molten iron silicon content forecast in blast furnace smelting process, so as to gain rather good forecast accuracy and improve the forecast hit ratio for blast furnace molten iron silicon content.

Description

technical field [0001] The invention relates to a characteristic analysis and prediction method of the silicon content of blast furnace molten iron. Background technique [0002] Blast furnace production is a complex chemical, kinetic, and thermodynamic process under closed conditions, and is a complex, highly coupled nonlinear system. Maintaining a reasonable furnace temperature is one of the key factors for the stable production of blast furnaces. During the smelting process, if the furnace temperature is controlled within the normal range, the blast furnace will run smoothly. If the furnace temperature control fluctuates, forming "overheating" or "overcooling", it is easy to induce furnace condition failure. The good ring of furnace temperature control directly affects the fluctuation of furnace conditions, and the state of furnace conditions determines the control mode of furnace temperature. Therefore, the reason for the technical difficulty of comprehensive automati...

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Application Information

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IPC IPC(8): G06F17/50G06N3/12C21B7/24
Inventor 吴铁军于玲李艳君吴毅平陶钧孙丽丽杜方周微崔承刚
Owner ZHEJIANG UNIV
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