Automatic reactivity characterisation of char particles from pulverised coal combustion using computer vision
- 1 Universidad del León | Universidad del Valle
ISSN: 1577-5097
Año de publicación: 2019
Título del ejemplar: Special Issue on Recent PhD Thesis Dissemination (2019)
Volumen: 18
Número: 2
Páginas: 16-17
Tipo: Artículo
Otras publicaciones en: ELCVIA. Electronic letters on computer vision and image analysis
Resumen
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.