Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

Dynamic Data Modeling of SCR Denitration System Based on Mutual Information

Authors
Wenjie Zhao, Luyao Zhang
Corresponding Author
Wenjie Zhao
Available Online February 2018.
DOI
https://doi.org/10.2991/csece-18.2018.69How to use a DOI?
Keywords
coal-fired unit; SCR de-NOx system; mutual information; least squares support vector machine
Abstract
The establishment of accurate models is vital in parametric optimization of control systems, and the choice of input variables can directly affect the accuracy and complexity of the model. Therefore, this paper proposed a modeling method based on mutual information (MI) and least squares support vector machine (LSSVM). On the basis of MI, the problem of delay, the correlation and redundancy among variables were considered synthetically. The optimal ones were screened through field measured variables by MI, and chosen as the input of LSSVM for predictions of output NOx concentration. The results proved the method can decrease complexity, improve approximation and generalization capability.
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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
February 2018
ISBN
978-94-6252-487-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/csece-18.2018.69How 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  - Wenjie Zhao
AU  - Luyao Zhang
PY  - 2018/02
DA  - 2018/02
TI  - Dynamic Data Modeling of SCR Denitration System Based on Mutual Information
PB  - Atlantis Press
SP  - 321
EP  - 324
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.69
DO  - https://doi.org/10.2991/csece-18.2018.69
ID  - Zhao2018/02
ER  -