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| IGSA-LSSVM Soft Sensing Model for Predicting NOx Emission of Coal-fired Boiler |
| DING Zhi-ping1,LIU Chao2,NIU Pei-feng3 |
1. Institute of Information Technology and Creative Design, Qingyuan Polytechnic, Qingyuan, Guangdong 511510, China
2. Guizhou Aerospace Electronics Co. Ltd., Guiyang, Guizhou 550009, China
3. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract Based on least squares support vector machine optimized by improved gravitational search algorithm (IGSA-LSSVM), an intelligent soft sensing method to accurately measure the NOx emission of the coal-fired boiler is presented. Firstly, the GSA have the drawbacks of easy to fall into local minimum and poor global search ability, so an improved version of GSA is proposed to improve global optimal performance, using the grid algorithm that is employed to initialize the population and using the adaptive decreasing inertia weight based on fitness value of optimization problems that is introduced into position update. Secondly, IGSA is developed to find the optimal parameters of LSSVM to improve the regression accuracy and generalization ability for predicting NOx emission. Finally, a soft computing method based on IGSA-LSSVM is established to forecast NOx emission of a 330 MW coal-fired boiler.The simulation results show that the IGSA-LSSVM model demonstrates better regression precision and generalization capability, it can accurately measure NOx emission.
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Received: 27 July 2017
Published: 12 April 2018
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2018, Vol. 39 
