Research on Prediction Model of Basic Sintering Characteristics of Mixed Iron Ore and Sinter Quality
Song Liu, Fumin Li, Jianguang Lu, Qing Lu
Available Online June 2015.
- https://doi.org/10.2991/mcei-15.2015.50How to use a DOI?
- Basic sintering characteristics; Sinter quality; Support vector machines; BP neural network; GRNN
- 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.
- 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 PB - Atlantis Press SP - 187 EP - 190 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 -