Modeling and Optimization of Unburned Carbon in Coal-Fired Boiler Using Artificial Neural Network and Genetic Algorithm

被引：25

作者：

Ilamathi, P. ^{[1
]}

Selladurai, V. ^{[2
]}

Balamurugan, K. ^{[3
]}

机构：

[1] Govt Coll Technol, Dept Prod Engn, Coimbatore 641013, Tamil Nadu, India

[2] Coimbatore Inst Technol, Coimbatore 641014, Tamil Nadu, India

[3] Inst Rd & Transport Technol, Dept Mech Engn, Erode 638316, India

来源：

JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME | 2013年 / 135卷 / 03期

关键词：

coal combustion process; unburned carbon; modeling; optimization; ANN; GA; COMBUSTION; PREDICTION; NOX;

D O I：

10.1115/1.4023328

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：

An approach to model coal combustion process to predict and minimize unburned carbon in bottom ash of a large-capacity pulverized coal-fired boiler used in thermal power plant is proposed. The unburned carbon characteristic is investigated by parametric field experiments. The effects of excess air, coal properties, boiler load, air distribution scheme, and nozzle tilt are studied. An artificial neural network (ANN) is used to model the unburned carbon in bottom ash. A genetic algorithm (GA) is employed to perform a search to determine the optimum level process parameters in ANN model which decreases the unburned carbon in bottom ash.

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