Abstract：For the problem that the conventional generalized predictive method are not capable to accurately predict the NOx emission generated by boiler, a new approach with combining the advantages of quantum adaptation bird swarm algorithm (QBSA) and fast learning network (FLN) is put forward to get boiler NOx emission concentration model. Compared with bird swarm algorithm (BSA), differential evolution algorithm and particle swarm optimization, QBSA algorithm has presented its advantage on optimization accuracy and convergence speed. Finally, the accuracy of the prediction by the way of QBSA-FLN and BSA-FLN are tested by using the sample data under different working conditions. It turns out that QBSA-FLN model can offer better generalization capability and more accurate forecasting on predicting the mass concentration of NOx emission.
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2017, Vol. 38 
