Evaluation of the effects of oxygen enrichment on combustion stability of biodiesel through a PSO-EMD-RBF model: An experimental study

Shengyang Gao; Fashe Li; Hua Wang; Shengyang Gao; Fashe Li; Hua Wang

doi:10.3934/math.2024235

AIMS Mathematics

2024, Volume 9, Issue 2: 4844-4862. doi: 10.3934/math.2024235

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Evaluation of the effects of oxygen enrichment on combustion stability of biodiesel through a PSO-EMD-RBF model: An experimental study

1.
State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming 650093, China
2.
School of Mechanical and Electrical Engineering, Yunnan Agricultural University Kunming 650201, China
3.
Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China

Received: 22 November 2023 Revised: 05 January 2024 Accepted: 08 January 2024 Published: 23 January 2024
MSC : 62P30

In this study, we processed the flame images of biodiesel combustion in industrial furnaces, classified and evaluated flame states using digital image processing techniques, and proposed a combustion stability index (CSI) using the particle swarm optimization (PSO) algorithm. In order to more accurately predict the combustion stability under different oxygen concentrations, we proposed a method that combines the Multi-Input Radial basis function neural network (RBF-NN) with empirical mode decomposition (EMD). Initially, the EMD method was employed to decompose the original time series of CSI. Subsequently, a decomposition model incorporating initial parameters and CSI was established using the radial basis function. The results of the computations indicate that the EMD-RBF-NN model significantly outperforms existing models in enhancing the accuracy of CSI.
- industrial furnaces,
- combustion stability,
- empirical mode decomposition,
- radial basis function neural network
Citation: Shengyang Gao, Fashe Li, Hua Wang. Evaluation of the effects of oxygen enrichment on combustion stability of biodiesel through a PSO-EMD-RBF model: An experimental study[J]. AIMS Mathematics, 2024, 9(2): 4844-4862. doi: 10.3934/math.2024235

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Abstract

In this study, we processed the flame images of biodiesel combustion in industrial furnaces, classified and evaluated flame states using digital image processing techniques, and proposed a combustion stability index (CSI) using the particle swarm optimization (PSO) algorithm. In order to more accurately predict the combustion stability under different oxygen concentrations, we proposed a method that combines the Multi-Input Radial basis function neural network (RBF-NN) with empirical mode decomposition (EMD). Initially, the EMD method was employed to decompose the original time series of CSI. Subsequently, a decomposition model incorporating initial parameters and CSI was established using the radial basis function. The results of the computations indicate that the EMD-RBF-NN model significantly outperforms existing models in enhancing the accuracy of CSI.

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