A real-time model based on least squares support vector machines and output bias update for the prediction of NO˂inf˃ x˂/inf˃ 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 NO˂inf˃ x˂/inf˃ emission from coal-fired power plant
- Author
- 김진국
- Keywords
- NOx Prediction; Real-time Model; Least Squares Support Vector Machine; Partial Least Squares; Output Bias Update
- Issue Date
- 2015-07
- Publisher
- Korean Institute of 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/20.500.11754/26292
- ISSN
- 0256-1115; 1975-7220
- DOI
- 10.1007/s11814-014-0301-2
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > CHEMICAL ENGINEERING(화학공학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML