Combustion State Identification of Municipal Solid Waste Incineration Process Based on VGG19 Depth Feature Migration

The combustion stability of solid waste in the incinerator determines the operating efficiency and pollutant emission concentration of the municipal solid waste incineration (MSWI) process. At present, domain experts identify the combustion state and manually control the MSWI process has the problem of unstable identification results and low intelligence. A combustion state recognition method for the MSWI process based on VGG19 depth feature migration is proposed to address the abovementioned problems. First, the original flame image is enhanced by rotation, adding noise, and other data enhancement methods to expand the size of labeling samples to overcome the high cost of manual labeling. Second, the VGG19 model based on ImageNet pre-training is used as the base model, and the output of the last layer of the middle layer is used as model the output to realize feature transfer learning by enhancing the flame image dataset and fine-tuning the model parameters. Finally, the flame feature extracted by VGG19 is used as the input of the improved cascade forest to build the combustion slate recognition model. The experimental results show that the recognition rate is 99.72%, which proves the effectiveness of the method.