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

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Résumé

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.
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Dates et versions

hal-03908568 , version 1 (20-12-2022)

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Citer

S. Verga, M. Compare, E. Zio, G. Carra, M. Farina, et al.. Estimation Of The Lower Heating Value Of Solid Recovered Fuel Based On Swir Hyper-Spectral Images And Machine Learning. 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Sep 2022, Rome, Italy. pp.1-5, ⟨10.1109/WHISPERS56178.2022.9955135⟩. ⟨hal-03908568⟩
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