Temperature Prediction Model of Cement Rotary Kiln Based on MPGA-LSSVM

DOI

10.2991/icence-16.2016.45How to use a DOI?

Keywords

Cement rotary kiln; Temperature prediction; Least square support vector machine; Multiple population genetic algorithm; MPGA-LSSVM.

Abstract

The burning zone temperature (BZT) in cement rotary kiln is a mostly important index which affects the quality of produced cement. In rotary kiln, the bilateral movements of air and complex chemical reactions take place. Thus, traditional modeling methods can not predict the BZT effectively. In order to improve the prediction accuracy, the temperature prediction model was established by least square support vector machine (LSSVM). The input variables of this model were selected by the gray correlation analysis method and mechanism analysis. The parameters of the LSSVM were optimized by multi population genetic algorithm (MPGA). The final simulation results show that the prediction accuracy is improved obviously. The effectiveness of the proposed method is verified.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hui Zhao
AU  - Yu Wang
AU  - Hongjun Wang
AU  - Youjun Yue
PY  - 2016/09
DA  - 2016/09
TI  - Temperature Prediction Model of Cement Rotary Kiln Based on MPGA-LSSVM
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 213
EP  - 218
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.45
DO  - 10.2991/icence-16.2016.45
ID  - Zhao2016/09
ER  -