A real-time model based on least squares support vector machines and output bias update for the prediction of NOx emission from coal-fired power plant

Title
A real-time model based on least squares support vector machines and output bias update for the prediction of NOx emission from coal-fired power plant
Authors
여영구
Keywords
NOx Prediction; Real-time Model; Least Squares Support Vector Machine; Partial Least Squares; Output Bias Update
Issue Date
2015-06
Publisher
KOREAN INSTITUTE CHEMICAL ENGINEERS
Citation
KOREAN JOURNAL OF CHEMICAL ENGINEERING, v. 32, NO 6, Page. 1029-1036
Abstract
The accurate and reliable real-time estimation of NOx emission is indispensable for the implementation of successful control and optimization of NOx emission from a coal-fired power plant. We apply a real-time update scheme to least squares support vector machines (LSSVM) to build a real-time version for real-time prediction of NOx. Incorporation of LSSVM in the update scheme enhances its generalization ability for long-term predictions. The proposed real-time model based on LSSVM (LSSVM-scheme) is applied to NOx emission process data from a coal-fired power plant in Korea to compare the prediction performance of NOx emission with real-time model based on partial least squares (PLS-scheme). Prediction results show that LSSVM-scheme predicts robustly for a long passage of time with higher accuracy in comparison with PLS-scheme. We also present a user friendly and sophisticated graphical user interface to enhance the convenience to approach the features of real-time LSSVM-scheme.
URI
http://link.springer.com/article/10.1007/s11814-014-0301-2http://hdl.handle.net/20.500.11754/25738
ISSN
0256-1115; 1975-7220
DOI
http://dx.doi.org/10.1007/s11814-014-0301-2
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > CHEMICAL ENGINEERING(화학공학과) > Articles
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