Proceedings of the International Conference on Management, Computer and Education Informatization

Research on Prediction Model of Basic Sintering Characteristics of Mixed Iron Ore and Sinter Quality

Authors
Song Liu, Fumin Li, Jianguang Lu, Qing Lu
Corresponding Author
Song Liu
Available Online June 2015.
DOI
https://doi.org/10.2991/mcei-15.2015.50How to use a DOI?
Keywords
Basic sintering characteristics; Sinter quality; Support vector machines; BP neural network; GRNN
Abstract
In order to solve the rapid decision of ore blending scheme in iron ore sintering process, the prediction model of the basic sintering characteristics of mixed iron ore and sinter quality has been established by three algorithms including the support vector machines, BP neural network and general regression neural network. The results show, the model based on support vector machine algorithm is better, which can accurately predict the basic sintering characteristics and sinter quality indexes; the accuracy of prediction for assimilation temperature, liquid fluidity and the binding phase strength are 90 , 93 and 91 respectively, based on the physical and chemical properties of raw material, and the accuracy of prediction for the drum strength and productivity are 89 and 88 , based on the basic sintering characteristics of mixed iron ore and technical parameters.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
International Conference on Management, Computer and Education Informatization
Part of series
Advances in Computer Science Research
Publication Date
June 2015
ISBN
978-94-6252-118-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/mcei-15.2015.50How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Song Liu
AU  - Fumin Li
AU  - Jianguang Lu
AU  - Qing Lu
PY  - 2015/06
DA  - 2015/06
TI  - Research on Prediction Model of Basic Sintering Characteristics of Mixed Iron Ore and Sinter Quality
BT  - International Conference on Management, Computer and Education Informatization
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-15.2015.50
DO  - https://doi.org/10.2991/mcei-15.2015.50
ID  - Liu2015/06
ER  -