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Korean Journal of Chemical Engineering, Vol.32, No.6, 1029-1036, 2015
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
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.
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[Cited By]
- Ammi Y, Khaouane L, Hanini S, Korean Journal of Chemical Engineering, 32(11), 2300, 2015
- Ko JH, Park RS, Jeon JK, Kim DH, Jung SC, Kim SC, Park YK, Journal of Industrial and Engineering Chemistry, 32, 109, 2015
- Lee J, Edgar TF, Korean Journal of Chemical Engineering, 33(2), 416, 2016
- Zheng Y, Gao X, Sheng C, Korean Journal of Chemical Engineering, 34(4), 1273, 2017
- Kim TY, Kim BS, Park C, Yeo YK, Korean Journal of Chemical Engineering, 34(7), 1952, 2017
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