(573f) Production of Syngas from Biomass Using a Moving Bed Downdraft Reactor | AIChE

(573f) Production of Syngas from Biomass Using a Moving Bed Downdraft Reactor

Authors 

Golpour, H. - Presenter, Missouri University of Science and Technology
Alembath, A. - Presenter, Missouri University of Science and Technology
Boravelli, T. - Presenter, Missouri University of Science and Tech

The role of biomass in energy and fuel production as an alternative to fossil fuel becomes vital especially considering the concern of carbon dioxide production vs. energy use. Sustainable, renewable and reliable resources of domestically produced biomass comparing to wind and solar energy is a sensible motivation to establish a small-scale power plant using biomass as feed to supply electricity demand and heat for rural development.

The present work focuses on:

  1. Design and operation of a vertical downdraft reactor,
  2. Establishing an optimum operating methodology and parameters to maximize syngas production through process testing.

The down draft reactor design is based on previous work completed at Brigham Young University-Idaho and subsequent design optimization to enhance the operating flexibility for biomass at a one ton per day rate. The reactor is equipped with internal heat transfer surfaces to enhance intra-bed heat and mass transfer inside the reactor.  A cyclone separator is used to remove solids carried over from the reactor and to pre-clean the produced gas which is then burnt in a closed combustion chamber. Three different woody biomass feedstocks have been examined in this work.

Specific work described in this paper focuses on identifying and characterizing the key operating factors (i.e., temperature profile, feed stock carbon/hydrogen mass ratio, particle size, residence time) required to optimize yield from this reactor system.   To achieve the maximum production yield, experiments were developed and carried out based on classical experimental design methodology.  Important factor effects and their two-way interactions were identified using the statistical software package SAS.  This approach successfully analyzed experimental error to establish the operating factors for subsequent process modeling and scaling of the technology