Image segmentation and CNN-based deep learning architectures for the modelling on particulate matter formation during solid fuels combustion

Image segmentation and CNN-based deep learning architectures for the modelling on particulate matter formation during solid fuels combustion

Three typical solid fuels (coal, biomass, and refuse-derived fuel) were individually combusted in a lab-scale drop tube furnace. The computer Vision method was employed to extract the morphological characteristics of particulate matter (PM) and establish a dataset of 12,637 particle features. Six co...

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Bibliographic Details
Main Authors: Yanchi Jiang, Lanting Zhuo, Xiaojiang Wu, Zhongxiao Zhang, Xinwei Guo, Wei Wang, Cunjiang Fan
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Fuel Processing Technology
Subjects:
Deep learning
PM formation
Morphology transformation
Object detection
Transfer learning
Online Access:http://www.sciencedirect.com/science/article/pii/S0378382024001462
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