Combustion Optimization Based on RBF Neural Network and Multi-objective Genetic Algorithms

Coal-fired boiler operation is confronted with two requirements to reduce its operation cost and to lower its emission. In this paper, a model for boiler efficiency and a model for NOx emission are set up respectively by RBF neural network. In order to obtain more accurate models without trying repe...

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Bibliographic Details
Published in2009 Third International Conference on Genetic and Evolutionary Computing pp. 496 - 501
Main Authors Wang, Dong Feng, Li, Qin Dao, Meng, Li, Han, Pu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Artificial neural networks
boiler efficiency
Boilers
Combustion
Feedforward neural networks
Function approximation
Genetic algorithms
Neural networks
NOx emission
NSGA-II
Power generation
Radial basis function networks
RBF neural network
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Summary:Coal-fired boiler operation is confronted with two requirements to reduce its operation cost and to lower its emission. In this paper, a model for boiler efficiency and a model for NOx emission are set up respectively by RBF neural network. In order to obtain more accurate models without trying repeatedly, GA is introduced to optimize the parameter of RBF network. Then Non-Dominated Sorting Genetic Algorithm-II is employed to perform a search to determine the optimum solution of boiler operation after we obtain boiler combustion model. Experimental results prove that the method proposed in this paper can improve boiler efficiency and reduce NOx emission obviously. Through analysis, we can see this method is better than the traditional method which uses weights to combine boiler efficiency and NOx emission in one objective function.
ISBN:9781424452453
1424452457
9780769538990
0769538991
DOI:10.1109/WGEC.2009.47