Flame Combustion State Identification Based on CNN in Municipal Solid Waste Incineration Process

Municipal solid waste (MSW) has characteristics such as large component differences and unstable calorific value. Flame combustion automatic state identification is beneficial for achieving stable control of the MSW incineration (MSWI) process in terms of feedback control. The industrial site usually recognizes the combustion state based on the visual perception mechanism of domain experts' experience. Based on the widely application of convolutional neural network (CNN) in the field of image recognition, this article chooses Lenet-5 as the recognition model of flame combustion state recognition. In order to obtain an appropriate CNN model to automatically extract flame image features, the influence of multiple CNN networks on flame image feature is designed and tested. The optimal structure setting of the CNN network is obtained. This study can provide support for practical applications in industrial sites.