A review of biomass gasification modelling

• Published in: Renewable and Sustainable Energy Reviews
• Main Authors: Safarian, Sahar, Unnthorsson, Runar, Richter, Christiaan
• Other Authors: Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild (HÍ), Faculty of Industrial Eng., Mechanical Eng. and Computer Science (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier BV 2019
• Subjects: Biomass Gasification; Equilibrium model; Stoichiometric model; Kinetic model; Tar; Lífmassi; Gas (eldsneyti); Úrgangur; Líkön; Arctic
• Online Access: https://hdl.handle.net/20.500.11815/1544; https://doi.org/10.1016/j.rser.2019.05.003

Post-print (lokagerð höfundar) Currently around 10% of all energy generated worldwide comes from biomass. Most of this 10% is biofuel energy from the fermentation of corn and sugarcane. Fermentation of corn competes with the global food supply, and fermentation of sugarcane drives deforestation. Therefore, the renewable and sustainable growth of these two bio-based energy sources may not be desirable even if it is economically feasible. Biomass gasification by contrast is significantly more flexible in terms of the bio-feedstock or waste that can be processed to either produce biofuels or to co-generate electricity and heat on demand. Fluidized bed and entrained flow gasifiers already achieve promising economy-of-scale for fuel production whilst downdraft gasifiers are well-suited for small-scale heat and power co-generation. This superior flexibility of gasification both in terms of the feedstock type and also the energy generation or fuel production options, is what drives expanding research and implementation opportunities for biomass gasification. Research progress is accelerated by modelling work. This review is the first review in the biomass gasification modelling field to collect and analyze statistics on the growing number of gasification modelling studies and approaches used. The frequency of the various modelling choices made, and the trends this data reveals, is reported. For new researchers this review provides a succinct guide to the modelling choices that needs to made early on in a modelling study or project. A detailed methodology characterization is introduced that includes consequential modelling choices not explicitly addressed by prior reviews. To seasoned researchers this study provides the first statistical (as opposed to ad hoc or anecdotal) picture of what their fellow researchers are doing. Rannís Technology Development Fund (project 175326-0611), the Icelandic Research Fund (grant 196458-051) and the Northern Periphery and Arctic program (project H-CHP 176) Peer Reviewed