Method for predicting converter terminal point using artificial nurve network technology

An artificial neural network and technology prediction technology, which is applied in the field of predicting the end point of small and medium-sized converters, can solve the problems of many human factors, the inability to accurately and objectively reflect the actual situation of the end point of converter smelting, and the low hit rate of the end point.
CN1588346AInactive Publication Date: 2005-03-02XINGTAI IRON & STEEL
0 Cites 21 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XINGTAI IRON & STEEL
Publication Date
2005-03-02
Estimated Expiration
Not applicable · inactive patent

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The present invention discloses artificial neural network technological method of forecasting the terminal of medium-sized and small converter. For steel-making converter system, there are many factors, such as furnace life, gun position, sputtering, etc. to affect the terminal carbon content and temperature in non-linear relation hard to describe mathematically. The present invention applies neural network technology in the control system, and can monitor and forecast the non-linearity, non-determinacy and complexity effectively to forecast the terminal temperature and terminal carbon content of converter accurately.
Need to check novelty before this filing date? Find Prior Art

Description

Technical field:

[0001] The invention relates to a method for predicting the end point of a small and medium-sized converter. Background technique:

[0002] Since the 1960s, people began to study trying to control the end point of the converter with a computer, and successfully developed a static model and a dynamic model of converter smelting. Because the static model only considers the initial and final state conditions of smelting, blowing according to the track calculated in advance, the complex blowing process factors cannot be considered, and no correction is made in the middle, so the hit rate of the end point is low. The dynamic model requires a more complex furnace information detection system, most of which use temperature measurement and carbon determination sub-lances to continuously feed back information in the furnace to the computer, and correct the blowing track until the blowing end, so the hit rate is relatively high. However, in order to obtain accurate m...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More