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Mathematical Problems in Engineering
Volume 2019, Article ID 6287291, 13 pages
Research Article

Artificial Bee Colony Optimization of NOx Emission and Reheat Steam Temperature in a 1000 MW Boiler

School of Energy and Environment, Southeast University, Nanjing, Jiangsu 210096, China

Correspondence should be addressed to Zhi-gang Su; nc.ude.ues@usgnagihz

Received 3 July 2019; Revised 10 September 2019; Accepted 30 September 2019; Published 11 November 2019

Academic Editor: Kauko Leiviskä

Copyright © 2019 Xian-hua Gao and Zhi-gang Su. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper puts forward a new viewpoint on optimization of boiler combustion, namely, reducing NOx emission while maintaining higher reheat steam temperature rather than reducing NOx emission while improving boiler efficiency like traditional practices. Firstly, a set of multioutputs nonlinear partial least squares (MO-NPLS) models are established as predictors to predict these two indicators. To guarantee better predictive performance, repeated double cross-validation (rdCV) strategy is proposed to identify the structure as well as parameters of the predictors. Afterward, some controllable process variables, taken as inputs of the predictors, are then optimized by minimizing NOx emission and maximizing reheat steam temperature via multiobjective artificial bee colony (MO-ABC). Results show that our rdCV-MO-NPLS model with MO-ABC optimization methods can reduce NOx emission synchronously and improve reheat steam temperature effectively compared with nondominated sorting genetic algorithm II (NSGA-II) and combustion adjustment experimental data on a real 1000 MW boiler.