| Date: |
2019 |
| Abstract: |
Char morphologies produced during pulverised coal combustion may determine coal reactivity which affects the combustion efficiency and the emissions of CO2 in power plants. Commonly, char samples are characterised manually, but this process is subjective and time-consuming. This work proposes methods to automate the char reactivity characterisation using microscopy images and computer vision techniques. These methods are summarised in three contributions: the localisation of char particles based on candidate regions and deep learning methods; the classification of particles into char reactivity groups using morphological and texture features; and the integration in a system of the two previous proposals to characterise char sample reactivity. The proposed system successfully estimate char reactivity in a fast and accurate way. |
| Rights: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Language: |
Anglès |
| Document: |
Altres ; altres ; Versió publicada |
| Subject: |
Computer vision ;
Machine learning ;
Object detection ;
Image processing ;
Char morphology ;
Coal combustion ;
Coal reactivity |
| Published in: |
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 18 Núm. 2 (2019) , p. 16-17 (Special Issue on Recent PhD Thesis Dissemination (2018 - 2019)) , ISSN 1577-5097 |
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