The modeling for pellets induration process based on Bagging method

Author

Xiaoke Fang ; Jun Peng ; Jianhui Wang ; Xiao Wang

Author_Institution

Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

777

Lastpage

781

Abstract

The grate-kiln system for iron ore pellet induration is a nonlinear, high coupling and large delay process. Considering the deviation between assumption and actual results, it is hard to build accurate kinetic models. Besides, the pure kinetic model also has the limitations to describe the induration process. In this paper, based on kinetic modeling, a hybrid model is built via neural network ensemble. The Bagging method is applied for training sample set with BP network as its network. The result shows that the hybrid model is more accurate and better than the kinetic model.

Keywords

backpropagation; kilns; mineral processing; minerals; neural nets; production engineering computing; BP network; bagging method; grate-kiln system; high coupling process; hybrid model; iron ore pellet induration; kinetic models; large delay process; neural network ensemble; nonlinear process; pellet induration process modeling; sample set training; Bagging; Iron; Kinetic theory; Mathematical model; Neural networks; Predictive models; Training; Bagging; Ensemble Learning; Hybrid Modeling; Iron Ore Pellet;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6852270

Filename

6852270