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Combustion optimization model for NO x reduction with an improved particle swarm optimization

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Abstract

Abstract: This paper focuses on the combustion optimization to cut down NO x emission with a new strategy. Firstly, orthogonal experimental design (OED) and chaotic sequences are introduced to improve the performance of particle swarm optimization (PSO). Then, a predicting model for NO x emission is established on support vector machine (SVM) whose parameters are optimized by the improved PSO. Afterwards, a new optimization model considering coal quantity and air quantity along with the traditional optimization variables is established. At last, the operating parameters are optimized by the improved PSO to cut down the NO x emission. An application on 600MW unit shows that the new optimization model can cut down NO x emission effectively and maintain the load balance well. The NO x emission optimized by the improved PSO is lowest among some state-of-the-art intelligent algorithms. This study can provide important guides for the low NO x combustion in the power plant.

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Authors and Affiliations

  1. School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China

    Qingwei Li  (李庆伟)

  2. School of Energy and Environment, Southeast University, Nanjing, 210096, China

    Keyi Zhou  (周克毅)

  3. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211800, China

    Guihuan Yao  (姚桂焕)

Authors
  1. Qingwei Li  (李庆伟)
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  2. Keyi Zhou  (周克毅)
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  3. Guihuan Yao  (姚桂焕)
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Corresponding author

Correspondence to Qingwei Li  (李庆伟).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 51406077), and the Natural Science Foundation of Jiangsu Province (No. 12KJB470008)

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Li, Q., Zhou, K. & Yao, G. Combustion optimization model for NO x reduction with an improved particle swarm optimization. J. Shanghai Jiaotong Univ. (Sci.) 21, 569–575 (2016). https://doi.org/10.1007/s12204-016-1764-6

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  • DOI: https://doi.org/10.1007/s12204-016-1764-6

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