A method for performance index prediction and comprehensive quality evaluation of sinter

A technology for quality evaluation and sintering, which is applied in neural learning methods, special data processing applications, biological neural network models, etc.

Active Publication Date: 2020-10-02
TONGJI UNIV
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Problems solved by technology

However, the existing research has not fully considered the influence of various parameters of sinter production on the performance of sinter, and lacks a prediction model that can be applied to different performance indicators of sinter

Method used

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  • A method for performance index prediction and comprehensive quality evaluation of sinter
  • A method for performance index prediction and comprehensive quality evaluation of sinter
  • A method for performance index prediction and comprehensive quality evaluation of sinter

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Embodiment

[0100] Such as figure 1 As shown, a method for sinter performance index prediction and comprehensive quality evaluation, the method includes the following steps:

[0101] Step (1): Determine all the performance indicators used for the comprehensive quality evaluation of sinter, and determine the important influencing parameters corresponding to each performance indicator according to the gray correlation degree method;

[0102]Step (2): Establish two independent prediction models for each performance index respectively, and described prediction model is used for predicting each performance index value, two independent prediction models comprise gray prediction model and BP in the present embodiment Neural network prediction model, the gray prediction model is a prediction model based on time series, the input of the BP neural network prediction model is the important influencing parameters corresponding to the corresponding performance indicators, and the output of the BP neur...

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Abstract

The invention relates to a method for predicting and comprehensive quality evaluation of sinter performance indicators, comprising the steps of: (1) determining all performance indicators for sinter comprehensive quality evaluation, and determining the important influencing parameters corresponding to each performance indicator according to the gray correlation degree method (2) establish two independent prediction models respectively for each performance index, and described prediction model is used for predicting each performance index value; (3) for each performance index, determine two based on the method of information entropy The weight of the predicted value obtained by the independent prediction model, and then obtain the predicted value of each performance index of the sintered ore combining the two prediction models; (4) the prediction of each performance index of the sintered ore obtained by combining the two prediction models The value is comprehensively evaluated to obtain the quality grade of sinter. Compared with the prior art, the invention has accurate prediction value and reliable evaluation result.

Description

technical field [0001] The invention relates to a sinter performance prediction and evaluation method, in particular to a sinter performance index prediction and comprehensive quality evaluation method. Background technique [0002] Iron and steel production is a complex process industrial production process. The core of the production process is blast furnace ironmaking. As the pre-process of blast furnace ironmaking production, sintering production is the raw material preparation link of ironmaking production. The quality of sintered ore directly affects blast furnace ironmaking The accurate prediction of sinter production performance is the premise of optimizing steel production and has important guiding significance for steel production. [0003] The process mechanism of the sintering process is complex, including multiple processes, and the processes are interrelated and affect each other. The basic principle of the sintering process is to mix useful mineral powders (i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06N3/084G06F30/20G06N3/045
Inventor 乔非卢凯璐
Owner TONGJI UNIV
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