Optimizing the Controlling Parameters of a Biomass Boiler Based on Big Data

Jiaxin He, Junjiao Zhang (), Lezhong Wang, Xiaoying Hu, Junjie Xue, Ying Zhao, Xiaoqiang Wang and Changqing Dong
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Jiaxin He: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Junjiao Zhang: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Lezhong Wang: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Xiaoying Hu: School of New Energy, North China Electric Power University, Beijing 102206, China
Junjie Xue: School of New Energy, North China Electric Power University, Beijing 102206, China
Ying Zhao: School of New Energy, North China Electric Power University, Beijing 102206, China
Xiaoqiang Wang: School of New Energy, North China Electric Power University, Beijing 102206, China
Changqing Dong: School of New Energy, North China Electric Power University, Beijing 102206, China

Energies, 2023, vol. 16, issue 23, 1-16

Abstract: This paper presents a comprehensive method for optimizing the controlling parameters of a biomass boiler. The historical data are preprocessed and classified into different conditions with the k-means clustering algorithm. The first-order derivative (FOD) method is used to compensate for the lag of controlling parameters, the backpropagation (BP) neural network is used to map the controlling parameters with the boiler efficiency and unit load, and the ant colony optimization (ACO) algorithm is used to search the opening of air dampers. The results of the FOD-BP-ACO model show an improvement in the boiler efficiency compared to the predicted values of FOD-BP and the data compared to the historical true values were observed. The results suggest that this FOD-BP-ACO method can also be used to search and optimize other controlling parameters.

Keywords: biomass; boiler; big data; machine learning (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: 2023
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