摘要
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.
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 NO, 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.
