Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion

被引：16

作者：

Zhou, H ^{[1
]}

Cen, KF ^{[1
]}

Mao, JB ^{[1
]}

机构：

[1] Zhejiang Univ, Inst Thermal Power Engn, Clean Energy & Environm Engn Key Lab MOE, Hangzhou 310027, Peoples R China

来源：

FUEL | 2001年 / 80卷 / 15期

关键词：

NOx emission; neural network; genetic algorithms; coal combustion;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The present work introduces a way of optimizing the low NOx combustion using the neural network and genetic algorithms for pulverized coal burned utility boiler. The NOx emission characteristic of a 600 MW capacity boiler operated under different conditions is experimentally investigated and on the basis of experimental results, the artificial 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 the neural network model, identifying appropriate setpoints for the current operating conditions and the low NOx emission of the pulverized coal burned boiler is achieved. (C) 2001 Elsevier Science Ltd. All rights reserved.

引用

页码：2163 / 2169

页数：7

共 27 条

[1] MEASUREMENTS OF NO(X) EMISSIONS FROM COAL BOILERS [J].