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The flame state recognition during the combustion stage of municipal solid waste is an important component of intelligent optimization control in the process of municipal solid waste incineration (MSWI). A single feature quantity cannot fully represent all combustion states. Using too many feature quantities will consume a lot of computer resources, and make timeliness worse. In response to the above issues, this article first based on the experience of domain experts and research achievements in related fields, divides the flame combustion state into four categories based on global information: normal burning, Channelling burning, partial burning, and Smothering; Then the flame image is de-fogged and denoted using pre-processing means such as artificial multi- exposure image fusion de-fogging algorithm, feature normalization, trap filtering, median filtering, etc., to obtain a clear flame image; Next, multiple features such as brightness, flame, color, and principal components are extracted from the flame image to represent the image from multiple views, and the multi-view features are reduced based on mutual information; Finally, a combustion state recognition model is constructed based on reduced image features and deep forest models © 2024 IEEE.
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年份: 2024
页码: 2374-2379
语种: 英文
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