Model NOx, SO 2 Emissions Concentration and Thermal Efficiency of CFBB Based on a Hyper-Parameter Self-Optimized Broad Learning System

Yunpeng Ma, Chenheng Xu (), Hua Wang, Ran Wang, Shilin Liu and Xiaoying Gu
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Yunpeng Ma: School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China
Chenheng Xu: School of Economics, Tianjin University of Commerce, Beichen, Tianjin 300134, China
Hua Wang: School of Artificial Intelligence, Hebei University of Technology, Hongqiao, Tianjin 300132, China
Ran Wang: School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China
Shilin Liu: School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China
Xiaoying Gu: School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China

Energies, 2022, vol. 15, issue 20, 1-19

Abstract: At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO 2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO 2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method.

Keywords: broad learning system; sparrow search algorithm; hyper-parameter optimization; circulating fluidized bed boiler; complex system modeling (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: 2022
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