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Method for performance index prediction and coverall quality evaluation of sinter

A technology for quality evaluation and sintering ore, applied in neural learning methods, special data processing applications, instruments, etc., can solve the problems of not fully considering the impact, lack of prediction models, etc.

Active Publication Date: 2017-06-06
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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  • Method for performance index prediction and coverall quality evaluation of sinter
  • Method for performance index prediction and coverall quality evaluation of sinter
  • Method for performance index prediction and coverall 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 an important influence parameter corresponding to the corresponding performance index, and the output of the BP neural networ...

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Abstract

The invention relates to a method for performance index prediction and coverall quality evaluation of sinter. The method includes the steps that 1, all performance indexes of overall quality evaluation of the sinter are determined, and an important influence parameter corresponding to each performance index is determined according to a grey relational degree method; 2, two independent prediction models are established for each performance index, wherein the prediction models are used for predicting performance index values; 3, for each performance index, weights of predicted values obtained through the two independent prediction models are determined based on an information entropy method, and then a predicted value of each performance index, integrating the two prediction models, of the sinter is obtained; 4, the obtained predicted value of each performance index, integrating the two prediction models, of the sinter is overall evaluated to obtain the quality level of the sinter. Compared with the prior art, the predicted values are accurate, and the evaluation result is reliable.

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 Applications(China)
IPC IPC(8): G06F17/50G06N3/04G06N3/08
CPCG06N3/084G06F30/20G06N3/045
Inventor 乔非卢凯璐
Owner TONGJI UNIV
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