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《Power Engineering》 2003-05
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An On-Line Boiler Operating Optimization System Based on the Neural Network and the Genetic Algorithms

ZHOUHao1,ZHU Hongbo2,CEN Kefa1Institute of Thermal Power Engrg., Zhejiang Univ., Hangzhou 310027, China; 2. Guangdong Electric Power Group Corporation, Guangzhou 510600, China)  
The present work introduces an online low NOx combustion optimization system based on the neural network and genetic algorithms for the pulverized coal burned utility boiler. The NOx emission characteristic of a 600MW capacity boiler operated under different conditions is experimentally investigated and on the basis of experimental results, the artiticial neural network is used to describe its NOx emission property to develop a neural network based model. A genetic algorithm is employed to perform a search to determine the optimum solution of neural network model, identifying appropriate setpoints for the current operating conditions and the low NOx emission of the pulverized coal burned boiler is achieved. Figs 2, tables 4 and refs 8.
【Fund】: 国家重点基础研究专项经费资助
【CateGory Index】: TK223
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【References】
Chinese Journal Full-text Database 10 Hits
1 SUI Jin-xue1,YANG Li1,HE Yong-qiang1,DONG Xiao-feng2(1.School of Information and Electronics Engineering,Shandong Institute of Business and Technology,Yantai 264005,China;2.Department of Power Engineering,North China Electric Power University,Beijing 102206,China);Application of improved ART network in flame burning condition recognition[J];Transducer and Microsystem Technologies;2007-05
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