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A compact optimization strategy for combustion in a 125 MW tangentially anthracite‐fired boiler by an artificial neural network
Author(s) -
Sun DanPing,
Fang QingYan,
Wang HuaJian,
Zhou HuaiChun
Publication year - 2008
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.153
Subject(s) - flue gas , boiler (water heating) , combustion , anthracite , artificial neural network , pulverized coal fired boiler , coal , process engineering , engineering , heat of combustion , waste management , computer science , chemistry , artificial intelligence , organic chemistry
In the past decade, artificial neural networks (ANNs) have been widely utilized to model, control, and optimize combustion processes in utility boilers. Since many highly correlated factors influence the combustion process in a furnace, it is necessary to select several important parameters to form a more simplified ANN model, which can be easily put into real‐time operation. In this paper, a parameter representing the secondary air assignment mode was quantified by the ratio of the damper opening values between the upper and lower secondary air. This parameter, together with the oxygen concentration in flue gas, the volatile matter and the heating value of coal, constitute the four input parameters of the ANN. Its output is the efficiency of the boiler. The ANN model was trained from 34 samples obtained from experiments on a 125 MW pulverized‐coal‐fired boiler, and the results predicted by the network model agreed well with the experimental results. A compact optimization strategy for combustion was proposed, by which the best combination of the oxygen concentration and the secondary air assignment mode can be determined directly according to the volatile matter and the heating value of coal. For two coals, the combustion in the furnace was obviously improved with the help of the optimization strategy, and its applicability was validated. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd.