Modelling the biomass updraft gasification process using the combination of a pyrolysis kinetic model and a thermodynamic equilibrium model

Autor: Damijan Cerinski, Ana Isabel Ferreiro, Jakov Baleta, Mário Costa, Francesco Zimbardi, Nadia Cerone, Jin Wang
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Energy Reports, Vol 7, Iss , Pp 8051-8061 (2021)
Druh dokumentu: article
ISSN: 2352-4847
DOI: 10.1016/j.egyr.2021.05.079
Popis: Conversion of biomass into gas suitable for further exploitation is one of the valuable renewable energy pathways due to the wide distribution and availability of raw materials. Biomass gasification is a thermochemical process of partial combustion in a reduced oxygen environment that aims to produce hydrogen-enriched syngas. Updraft gasifier design, with its advantages of high efficiency, produces syngas with higher hydrogen yield compared to other gasifier designs. The main drawback of the updraft gasifier is high yield of tars in the outflow gas decreasing its lower heating value. Recently, significant research efforts have focused on the optimization of the updraft gasifiers operating conditions, especially by developing numerical models as a complementary approach to experiments. The simplest modelling approach for predicting biomass gasification behaviour is the thermodynamic equilibrium model. When describing the behaviour of an updraft gasifier, special focus needs to be given to the pyrolysis stage, since in this type of reactor pyrolysis products directly outflow from the gasifier. In this work, a pilot-scale biomass gasifier was modelled using a combination of a pyrolysis kinetic model with a thermodynamic equilibrium model. To describe the pyrolysis behaviour, the CRECK-S-BIO and two secondary gas-phase mechanisms with distinct levels of complexity were used. The gasification and oxidation of char were modelled using a thermodynamic equilibrium model through the minimization of Gibbs free energy approach. The predicted results of dry clean syngas were compared to the experimental data considering eleven different operating conditions. The model combination that used the detailed secondary gas-phase mechanism achieved generally lower average prediction errors. Although some discrepancies were observed in the predictions, these preliminary results show that the model approach considered in this study represents a good basis for future development of the model.
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