NEAR-INFRARED SPECTROSCOPY FOR REFUSE DERIVED FUEL : Classification of waste material components using hyperspectral imaging and feasibility study of inorganic chlorine content quantification

NEAR-INFRARED SPECTROSCOPY FOR REFUSE DERIVED FUEL : Classification of waste material components using hyperspectral imaging and feasibility study of inorganic chlorine content quantification

This degree project focused on examining new possible application of near-infrared (NIR) spectroscopy for quantitative and qualitative characterization of refuse derived fuel (RDF). Particularly, two possible applications were examined as part of the project. Firstly, use of NIR hyperspectral imagin...

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Bibliographic Details
Main Author: Ševčík, Martin
Format: Others
Language:English
Published: Mälardalens högskola, Akademin för ekonomi, samhälle och teknik 2019
Subjects:
near-infrared spectroscopy
NIR
municipal solid waste
MSW
refuse derived fuel
RDF
classification
chemometrics
machine learning
artificial neural network
radial basis neural network
high-temperature corrosion
chlorine
waste to energy
Energy Engineering
Energiteknik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42376

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42376

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