Multi-objective Optimization of Coal-fired Boiler Combustion Based on NSGA-II

Abstract

NOx emission characteristics and overall heat loss model for a 300MW coal-fired boiler were established by Back Propagation (BP) neural network, by which the the functional relationship between outputs (NOx emissions & overall heat loss of the boiler) and inputs (operational parameters of the boiler) of a coal-fired boiler can be predicted. A number of field test data from a full-scale operating 300MWe boiler were used to train and verify the BP model. The NOx emissions & heat loss predicted by the BP neural network model showed good agreement with the measured. Then, BP model and the non-dominated sorting genetic algorithm II (NSGA-II) were combined to gain the optimal operating parameters which lead to lower NOx emissions and overall heat loss boiler. The optimization results showed that hybrid algorithm by combining BP neural network with NSGA-II can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which can reduce NOx emissions and overall heat loss effectively for the coal-fired boiler.