Feeding system for wood chips

A DEM study to improve equipment performance

Journal Article (2017)
Author(s)

M. Rackl (Technische Universität München)

F. Top (Technische Universität München)

C.P. Molhoek

Dingena L. Schott (TU Delft - Transport Engineering and Logistics)

Research Group
Transport Engineering and Logistics
DOI related publication
https://doi.org/10.1016/j.biombioe.2017.01.003
More Info
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Publication Year
2017
Language
English
Research Group
Transport Engineering and Logistics
Volume number
98
Pages (from-to)
43-52

Abstract

Discrete element method (DEM) is useful for simulating the behavior of bulk solids and corresponding interaction with handling equipment. Although DEM is widely used for simulation of bulk solids, there are no applications involving biomass handling. This study presents the first published attempt at DEM modeling of wood chips in a feeding system. The DEM model of a wood chip feeding system was first validated with experimental data. Thereafter, the validated DEM model from this study was applied to study the effect of the mounting position as well as design aspects of a horizontal agitator within a wood chip feeding system. The validation outcome demonstrates that DEM is a suitable method to compute relevant values of a wood chip system and that it can be applied to predict trends with regard to more efficient operation of the system. Results show that a sensibly chosen agitator mounting position can decrease the mass-related energy consumption by as much as 30%. In terms of design aspects, the agitator's rotational speed and the blade design are the most influential factors governing the feeding system's mass-related energy consumption. Altering the number of agitator blades and blade design can also decrease energy use by 30%. In conclusion, even simple design changes can have considerable influence when feeding wood chips, while maintaining the system's mass flow. Besides, future studies could focus on more elaborate particle shape representation in the DEM model to enable to prediction of critical flow situations, such as blocking.

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