Published Date
February 2015, Vol.130:87–95, doi:10.1016/j.fuproc.2014.09.039
Author
Screw conveyor
Residence time distribution
Axial dispersion
Biomass
For further details log on website :
http://www.sciencedirect.com/science/article/pii/S0196890414010711
February 2015, Vol.130:87–95, doi:10.1016/j.fuproc.2014.09.039
aDepartment of Biosystems Engineering, Ghent University, Coupure Links 653, Ghent 9000, Belgium
Received 14 March 2014. Revised 29 September 2014. Accepted 29 September 2014. Available online 14 October 2014.
Highlights
- Residence time distributions for coarse biomass in a screw conveyor are determined.
- •A model for the prediction of mean residence times is presented.
- •The measured axial dispersion is highly variable and sensitive to process variability and measurement errors.
Abstract
Rotating screw conveyors can be utilized in the application of heat to granular materials: the mechanical motion of the rotating screw provides a controlled means of transferring the product through different thermal sections. This is particularly interesting for thermochemical biomass reactor applications such as slow pyrolysis and torrefaction. To understand the transfer of heat to the biomass, it is necessary to predict the residence time distribution of the material within the rotating screw reactor. Experiments were performed using a bench-scale (2.5 kg/h) rotating screw conveyor. In this work, it has been shown that an empirical mathematical model is sufficient for predicting the mean residence time of various materials (pine chips of different sizes, rice and sand) if the rotational frequency of the screw conveyor and the volumetric throughput rate are both known. The model parameters obtained in this work were found to be independent of material selection but are specific to the geometry of the screw reactor. The form of the mathematical model proposed in this work may be applied to similar applications. Unlike the mean residence times, the axial dispersion calculated from the experimental data showed greater variability and this precluded any predictive modeling.
Keywords
For further details log on website :
http://www.sciencedirect.com/science/article/pii/S0196890414010711
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