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Prediction of fast pyrolysis products yields using lignocellulosic compounds and ash contents
School of Engineering and Ryan Institute, National University of Ireland Galway, Galway, Ireland.
Chemical Engineering Department, Worcester Polytechnic Institute, Worcester, MA, USA.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-6081-5736
2020 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 257, article id 113897Article in journal (Refereed) Published
Abstract [en]

The effects of lignocellulosic biomass composition on product yields and distributions were studied under high-temperature pyrolysis conditions (800–1250 °" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">°°C) in a drop tube reactor. Several types of biomass were studied along with xylan, cellulose, and two types of lignin as model feeds. Among the model feeds, soot yields obtained from lignin pyrolysis were greater than those obtained from cellulose or xylan. Cellulose pyrolysis produced mostly gaseous products, along with small amounts of tars. Impregnation of lignin with alkali metals greatly reduced tar and soot formation, simultaneously increasing the hydrogen content of the syngas product. An empirical model predicted with reasonable accuracy trends in the product yields obtained from pyrolysis of whole biomass samples using as input data obtained from model feeds composition data and the pyrolysis temperature. Reaction temperature and ash content both have a strong influences on char yield, whereas gas yields were mostly affected by the reaction temperature.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 257, article id 113897
Keywords [en]
Fast pyrolysis, Lignin, Potassium, Residence time, Modeling
National Category
Energy Engineering
Research subject
Energy Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-76607DOI: 10.1016/j.apenergy.2019.113897Scopus ID: 2-s2.0-85073997898OAI: oai:DiVA.org:ltu-76607DiVA, id: diva2:1367646
Note

Validerad;2019;Nivå 2;2019-11-04 (johcin)

Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-19Bibliographically approved

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Umeki, Kentaro

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