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Predicting the silo discharge behavior of wood chips - a choice of method

Predicting the silo discharge behavior of wood chips - a choice of method

Salehi, Hamid, Barletta, Diego, Poletto, Massimo and Larsson, Sylvia (2018) Predicting the silo discharge behavior of wood chips - a choice of method. Biomass and Bioenergy, 120. pp. 211-218. ISSN 0961-9534 (doi:https://doi.org/10.1016/j.biombioe.2018.11.023)

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Abstract

Storage, transfer and feeding of biomass particulate solids often cause problems at biobased production sites. Irregular flow and blockage in storage units create supply uncertainties and production stops with economically detrimental consequences. Despite the industrial awareness of these problems, reliable analysis methods for prediction of larger particle size biomass bulk solid flow properties are lacking.

In this study, the silo discharge and arching behavior of different size fractions of wood chips from beech and pine forest residues were analyzed, utilizing a parallelepiped bin and a wedge-shaped hopper. The wood chip assortments were analyzed by Schulze shear testing, angle of repose, Hausner ratio, and bulk density. Obtained material data was utilized to build regression models versus the experimentally obtained critical hopper outlet size for material flow.

The best fitted regression model for explaining the critical hopper outlet size was obtained with an equation with linear proportionality to the hopper half opening angle and to the square of the angle of repose. This regression model was well fitted to both assortments and over the whole particle size range, indicating robustness and that the proposed model can be useful to diagnose the silo discharge behavior for broad ranges of wood chip materials. The extension of the results of this paper to larger scale silos requires further investigation.

Item Type: Article
Uncontrolled Keywords: biomass, arching, flowability, biomass bulk solids
Subjects: Q Science > Q Science (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 17 Oct 2019 09:57
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/25494

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