@article{Gao2024, 
author = {Shengyang Gao and Fashe Li and Hua Wang},
title = {Evaluation of the effects of oxygen enrichment on combustion stability of biodiesel through a PSO-EMD-RBF model: An experimental study},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {2},
pages = {4844-4862},
keywords = {industrial furnaces, combustion stability, empirical mode decomposition, radial basis function neural network},
url = {https://www.sciopen.com/article/10.3934/math.2024235},
doi = {10.3934/math.2024235},
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.}
}