Abstract
Thermochemical gasification is a potential energy extraction pathway that converts biomass to gaseous fuel and chemical feedstock. Modelling of biomass gasification is an efficient and economical method to predict the gaseous fuel characteristics compared to experimentation, which incurs additional time and effort. The present work deals with kinetic modelling and analysis of CaO enabled biomass gasification to analyse the effect of steam to biomass ratio, sorbent addition and residence time on syngas composition in MATLAB platform. The model is developed by applying Arrhenius reaction kinetics for homogeneous, heterogeneous and tar cracking reactions associated with gasification. Rate of gasification, tar cracking and carbonation reactions are incorporated in the model to predict the effect of residence time of solid and gaseous reaction species in the final syngas constitution. Rate of generation of each chemical species is determined by considering the cumulative effect of reaction kinetics of the reactions considered. The developed model is validated for product gas composition with the experimental results of air–steam gasification. An RMS error of 3.66 is reflected when the model estimated syngas composition for air–steam gasification is compared with that of the experimental results. The uniqueness of the model is that it can predict the temporal variation of syngas species for steam-assisted air gasification in sorbent enabled environment for CO2 capture. It is also found that at a temperature of 1000 K, equivalence ratio of 0.25 and steam to biomass ratio of unity, H2 mole fraction has no appreciable increase beyond a sorbent to biomass ratio of 1. The developed model can be used to predict the effect of residence time on syngas composition for air–steam biomass gasification (with and without sorbent) for a steam to biomass ratio and sorbent to biomass ratio range of 0–2.
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S, R., C, M. & P, A. Influence of Residence Time on Syngas Composition in CaO Enhanced Air–Steam Gasification of Biomass. Environ Dev Sustain 24, 8363–8377 (2022). https://doi.org/10.1007/s10668-021-01787-1
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DOI: https://doi.org/10.1007/s10668-021-01787-1