2021
DOI: 10.1155/2021/1082834
|View full text |Cite|
|
Sign up to set email alerts
|

[Retracted] Effect of Basicity on the Microstructure of Sinter and Its Application Based on Deep Learning

Abstract: The influence of the evolution rule of basicity (0.6∼2.4) on the mineral composition and microstructure of sinter is studied by using a polarizing microscope, and the comprehensive application analysis of the drum index, vertical sintering speed, and yield of sinter shows that, over the course of an increase in basicity (0.6∼1.0), the mineral structure changed from the original porphyritic-granular structure to a porphyritic structure. At the same time, there was no calcium ferrite phase in the bonding phase a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
6
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5

Relationship

1
4

Authors

Journals

citations
Cited by 7 publications
(6 citation statements)
references
References 23 publications
0
6
0
Order By: Relevance
“…To cope with this problem, many researchers explore data-driven methods to timely and accurately monitor the FeO content. To begin with, Xie et al developed an intelligent framework combining adaptive particle swarm optimization (APSO) algorithm and least-squares support vector machine (LSSVM) algorithm to predict FeO content. On the basis of this study, a knowledge and data fusion model was designed to realize online prediction of FeO content according to the temperature distribution mechanism .…”
Section: Review Of Soft Sensing Methods In Ironmaking Processmentioning
confidence: 99%
“…To cope with this problem, many researchers explore data-driven methods to timely and accurately monitor the FeO content. To begin with, Xie et al developed an intelligent framework combining adaptive particle swarm optimization (APSO) algorithm and least-squares support vector machine (LSSVM) algorithm to predict FeO content. On the basis of this study, a knowledge and data fusion model was designed to realize online prediction of FeO content according to the temperature distribution mechanism .…”
Section: Review Of Soft Sensing Methods In Ironmaking Processmentioning
confidence: 99%
“…Further, a CNN based on the visual geometry group network (VGG16) model was developed to predict the basicity of an ore phase image. [ 56 ]…”
Section: Review Of Data‐driven Methods In the Sintering Processmentioning
confidence: 99%
“…Further, a CNN based on the visual geometry group network (VGG16) model was developed to predict the basicity of an ore phase image. [56] For the prediction of sinter ore quality indicators, Gao et al [32] built an integrated model based on PCA and the genetic algorithm (GA) to predict the TS of sinter ore. Qiang et al [57] combined the artificial fish swarm algorithm and back propagation (BP) network to predict the TS. In the actual production process, the labelled samples are often not enough.…”
Section: Prediction Of Quality Parametersmentioning
confidence: 99%
“…In practice, the sinter basicity should be controlled around 2.0, when both the calcium ferrite content and the drum index reach their maximum. Based on the ore-phase datasets, Zhi et al [106] conducted experiments on a CNN-based basicity prediction model for orephase photographs and achieved more accurate prediction results. The predominant transport method in sintering production is belt transport.…”
Section: Convolutional Neural Network Model Structure and Its Applica...mentioning
confidence: 99%