Combustion state identification of MSWI processes using ViT-IDFC

Abstract

Municipal solid waste (MSW) incineration processes often experience fluctuations in the physical and chemical properties of waste materials, leading to unstable combustion and increased flue-gas emissions. This hinders the development of greener waste-treatment methods. Currently, experts rely on their experience in observing flames during field operations to determine the combustion state. However, this process is energy consuming, subjective, and not optimal for controlling MSW incineration (MSWI). To address this issue, this study proposes a dataset of MSWI flame images for identifying combustion states using a vision-transformer-improved deep forest classification (ViT-IDFC) algorithm. The ViT-IDFC algorithm extracts multilayer visual transformation features from flame images based on a pre-trained ViT model’s transformer coding layer. The experience of domain experts was used to select the depth features. The algorithm then constructed an IDFC model by combining the selected ViT visual transformation features and the original flame image as inputs for the cascade forest. The proposed method was evaluated using a dataset of flame images, and the results demonstrated accuracies of 98.15% and 96.43% in identifying the left and right grate flame images, respectively, which are acceptable in the industry.