Abstract
In this chapter, the background and the key problems are presented, like the emission of nitrogen oxides (NO x ) and the level of unburned carbon. Selective Catalytic Reduction (SCR) and Selective Non-Catalytic Reduction (SNCR) are conducted to reduce the NO x emissions. Artificial intelligence methods are used to solve the complexity of boiler system. The characteristic of coal combustion and the parameter of unburned carbon content are discussed in this chapter. Later, coal combustion optimization is proposed. The outline of the book is recommended at last.
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References
Lam PM, Shiu A. Efficiency and productivity of China’s thermal power generation. Rev Ind Organ. 2004;24:73–93.
Zhou H, Cen KF, Fan JR. Modeling and optimization of the NO x emission characteristics of a tangentially fired boiler with artificial neural networks. Energy. 2004;29:167–83.
Zheng LG, Zhou H, Cen KF, Wang CL. A comparative study of optimization algorithms for low NO x combustion modification at a coal-fired utility boiler. Expert Syst Appl. 2009;36(2):2780–93.
Zhou H, Cen KF, Mao JB. Combining neural network and genetic algorithms to optimize low NO x pulverized coal combustion. Fuel. 2001;80(15):21633–2169.
Wu F, Zhou H, Tao R, Zheng LG, Cen KF. Combining support vector regression and cellular genetic algorithm for multi-objective optimization of coal-fired utility boilers. Fuel. 2009;88:1864–70.
Bloch G, Lauer F, Colin G, Chamaillard Y. Support vector regression from simulation data and few experimental samples. Inf Sci. 2008;178(20):3813–27.
Li R, Cui YM, He H, Wang HN. Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting. Adv Space Res. 2008;42(9):1469–74.
Fan Y. Guoluyuanli. Beijing: China electric power press; 2008.
Styszko-Grochowiak K, Gołaś J, Jankowski H, Koziński S. Characterization of the coal fly ash for the purpose of improvement of industrial on-line measurement of unburned carbon content. Fuel. 2004;83(13):1847–53.
Helle S, Gordon A, Alfaro G, et al. Coal blend combustion: link between unburnt carbon in fly ashes and maceral composition. Fuel Process Technol. 2003;80(3):209–23.
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© 2018 Springer Nature Singapore Pte Ltd. and Zhejiang University Press, Hangzhou
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Zhou, H., Cen, K. (2018). Introduction. In: Combustion Optimization Based on Computational Intelligence. Advanced Topics in Science and Technology in China. Springer, Singapore. https://doi.org/10.1007/978-981-10-7875-0_1
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DOI: https://doi.org/10.1007/978-981-10-7875-0_1
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