Estimation Of The Lower Heating Value Of Solid Recovered Fuel Based On Swir Hyper-Spectral Images And Machine Learning

In this work, we apply Machine Learning techniques to Hyper-Spectral Images acquired by a Short Wave Infra-Red (SWIR) Camera, to classify the materials composing the Solid Recovered Fuel (SRF). This classification, enabled by data pre-processing techniques, is used to estimate the Lower Heat Value (...

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Bibliographic Details
Published in2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) pp. 1 - 5
Main Authors Verga, S., Compare, M., Zio, E., Carra, G., Farina, M., Righetto, I., Sala, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.09.2022
Subjects
Conferences
Data models
Estimation
Fuels
Lower Heat Value
LR-Classifier
Machine learning
Signal processing
Solid Recovered Fuel
Solids
SWIR
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Summary:In this work, we apply Machine Learning techniques to Hyper-Spectral Images acquired by a Short Wave Infra-Red (SWIR) Camera, to classify the materials composing the Solid Recovered Fuel (SRF). This classification, enabled by data pre-processing techniques, is used to estimate the Lower Heat Value (LHV) of SRF samples, building on models of the literature. The accurate and timely estimates of SRF LHVs yield significant benefits to SRF consumers.
ISSN:2158-6276
DOI:10.1109/WHISPERS56178.2022.9955135