Biomass pyrolysis - A review of modelling, process parameters and catalytic studies

Abstract

© 2015 Elsevier Ltd. All rights reserved. Biomass as a form of energy source may be utilized in two different ways: directly by burning the biomass and indirectly by converting it into solid, liquid or gaseous fuels. Pyrolysis is an indirect conversion method, and can be described in simpler terms as a thermal decomposition of biomass under oxygen-depleted conditions to an array of solid, liquid and gaseous products, namely biochar, bio-oil and fuel gas. However, pyrolysis of biomass is a complex chemical process with several operational and environmental challenges. Consequently, this process has been widely investigated in order to understand the mechanisms and kinetics of pyrolysis at different scales, viz. particle level, multi-phase reacting flow, product distribution and reactor performance, process integration and control. However, there are a number of uncertainties in current biomass pyrolysis models, especially in their ability to optimize process conditions to achieve desired product yields and distribution. The present contribution provides a critical review of the current status of mathematical modelling studies of biomass pyrolysis with the aim to identify knowledge gaps for further research and opportunities for integration of biomass pyrolysis models of disparate scales. Models for the hydrodynamic behaviour of particles in pyrolysis, and their interaction with the reactive flow and the effect on the performance of the reactors have also been critically analyzed. From this analysis it becomes apparent that feedstock characteristics, evolving physical and chemical properties of biomass particles and residence times of both solid and gas phases in reactors hold the key to the desired performance of the pyrolysis process. Finally, the importance of catalytic effects in pyrolysis has also been critically analyzed, resulting in recommendations for further research in this area especially on selection of catalysts for optimal product yields under varying operating conditions.