Boiler combustion optimization based on ANN and PSO-Powell algorithm
DAI Wei-bao1, ZOU Ping-hua1, FENG Ming-hua2, DONG Zhan-shuang3
Author NameAffiliation
DAI Wei-bao School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China,dwb750424@126.com
ZOU Ping-hua School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China,dwb750424@126.com
FENG Ming-hua Heilongjiang Electric Power Research Institute, Harbin 150030, China
DONG Zhan-shuang Heilongjiang Asia Power Xinbao Heating and Power Co., Ltd., Qiqihar 161041, China
Abstract:
To improve the thermal efficiency and reduce nitrogen oxides (NOx) emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function (RBF) artificial neural network (ANN) models for forecasting unburned carbon in fly ash and NOx emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell (PSO-Powell) algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal (PC) fired boiler is presented as well.
Key words: boiler combustion ANN PSO-Powell algorithm multi-objective optimization section temperature field
DOI：10.11916/j.issn.1005-9113.2009.02.010
Clc Number:TK311
Fund:
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Boiler combustion optimization based on ANN and PSO-Powell algorithm
本站小编 哈尔滨工业大学/2019-10-23
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