Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Xinying Xu, Qi Chen, Mifeng Ren, Lan Cheng and Jun Xie
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Xinying Xu: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Qi Chen: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Mifeng Ren: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Lan Cheng: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Jun Xie: College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China

Energies, 2019, vol. 12, issue 6, 1-24

Abstract: Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NO x emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NO x emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NO x emissions by combining different weight coefficients as needed.

Keywords: boiler combustion model; MapReduce; distributed extreme learning machine; distributed particle swarm optimization; weight coefficient method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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