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Computational Thermal Sciences: An International Journal
ESCI SJR: 0.249 SNIP: 0.434 CiteScore™: 0.7

ISSN Imprimer: 1940-2503
ISSN En ligne: 1940-2554

Computational Thermal Sciences: An International Journal

DOI: 10.1615/ComputThermalScien.2018020948
pages 375-388

BIOMASS FAST PYROLYSIS IN A SHAFTLESS SCREW REACTOR: RESIDENCE TIME DISTRIBUTION AND HEATING EVALUATION BY MEANS OF A DEM APPROACH

Stefano Cordiner
Department of Industrial Engineering, University of Rome Tor Vergata, Via Del Politecnico 1, Roma, 00133, Italy
Alessandro Manni
Department of Industrial Engineering, University of Rome Tor Vergata, Via Del Politecnico 1, Roma, 00133, Italy
Vincenzo Mulone
Department of Industrial Engineering, University of Rome Tor Vergata, Via Del Politecnico 1, Roma, 00133, Italy
Vittorio Rocco
Department of Industrial Engineering, University of Rome Tor Vergata, Via Del Politecnico 1, Roma, 00133, Italy

RÉSUMÉ

Screw reactors may be designed for small-scale fast pyrolysis processes, but few models have been available so far for design and optimization. In this work, an analysis of specific aspects such as solid residence time and heating processes is proposed for this type of reactor, studying the system via a 3D computational fluid dynamics model. The screw reactor where the pyrolysis process takes place has been modeled with a discrete element model (DEM) approach based on the multiphase code MFiX. The interactions between the gaseous phase and particles are taken into account, while the evaporation process and chemical reactions are neglected. This approach allows for investigating in more detail some characteristic effects related to the perturbation of operating parameters to compare numerical and experimental data. The numerical heat flux is in line with experimental results at an average value of about 100 W, corresponding to 1 kg/h biomass flow rate with a 7% moisture content wet basis (w.b.). The residence time of the solid particles in the pyrolysis region (less than half of the whole reactor length) is on the order of 6 seconds, which is typical for such applications. A 1D model based on the 3D data evaluation has then been set up with the aim of calibrating such effects by a simplified axial diffusion coefficient evaluated at 1 × 10-5 m2/s.