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Near-infrared spectral measurements and multivariate analysis for predicting glass contamination of refuse-derived fuel
Mälardalen University.
Mälardalen University.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-5341-3656
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates how glass contamination in refuse-derived fuel can be quantitatively detected using near-infrared spectroscopy. Near-infrared spectral data of glass in four different background materials were collected, each material chosen to represent a main component in municipal solid waste; actual refuse-derived fuel was not tested. The resulting spectra were pre- processed and used to develop multi-variate predictive models using partial least squares regression. It was shown that predictive models for coloured glass content are reasonably accurate, while models for mixed glass or clear glass content are not; the validated model for coloured glass content had a coefficient of determination of 0.83 between the predicted and reference data, and a root- mean-square error of validation of 0.64. The methods investigated in this paper show potential in predicting coloured glass content in different types of background material, but a different approach would be needed for predicting mixed type glass contamination in refuse-derived fuel. 

Place, publisher, year, edition, pages
2017.
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-37507OAI: oai:DiVA.org:mdh-37507DiVA: diva2:1168060
Conference
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2017-12-21Bibliographically approved

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Aslanidou, IoannaSkvaril, JanKyprianidis, Konstantinos

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Winn, OliviaSivaram, Kiran ThekkemadathilAslanidou, IoannaSkvaril, JanKyprianidis, Konstantinos
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