Assessment of syngas composition variability in a pilot-scale downdraft biomass gasifier by an extended equilibrium model
Introduction
The prediction of the composition of the syngas produced by biomass gasifiers is an important part in the development of modeling tools, which can support the understanding of the gasifier phenomenology as well as the assessment of the gasification performance.
This task requires an accurate representation of several phenomena involved in a gasification system, such as devolatilization, heterogeneous and homogeneous reactions, heat and mass transfer. In most cases, the development of complex models may result inapplicable, due to the lack of information and data needed to run the simulations.
As far as fixed bed gasifiers are concerned, a simple approach to compute the syngas composition relies on equilibrium models (Zainal et al., 2002, Altafini et al., 2003, Jarungthammachote and Dutta, 2007, Balu and Chung, 2012, Barman et al., 2012). These models often consider a stoichiometric combustion of the biomass in the oxidation zone followed by an equilibrium stage. As observed by Balu and Chung (2012) these models often underestimate the methane content in the syngas and to this purpose Barman et al. (2012) introduced a deviation from the equilibrium in the methane formation reaction to fit its prediction with the experimental values.
Equilibrium models cannot account for the presence of ethylene in the syngas. Although its concentration is usually low compared to other major species, ethylene is usually more abundant than other hydrocarbons (Wander et al., 2004, Simone et al., 2012a) and it is considered an indicator of biomass devolatilization (Boroson et al., 1989, Fagbemi et al., 2001).
Model predictions are usually compared with average syngas composition, which is clearly satisfactory when the model is used to evaluate gasification performance. However, experimental tests at pilot scale (Simone et al., 2012a, Simone et al., 2012b) show that the syngas composition is characterized by a dynamic evolution, affected by operating parameters and bed properties.
To be reliable, a simplified gasification model should take into account at least the variation of the syngas composition and be able to describe the presence of the most important hydrocarbons in the syngas.
In this work a new simplified approach based on equilibrium modeling is proposed which reproduces the variation of the syngas composition observed in a pilot scale downdraft gasifier operated with different feedstock.
Section snippets
Experimental setup
The experimental tests are carried out with a pilot scale downdraft biomass gasifier (described in detail in Simone et al. (2012a)). The plant is operated slightly below atmospheric conditions due to a fan-blower positioned at the end of the gas clean-up line, which drives air to enter the gasifier through four nozzles positioned in the throated section of the gasifier. This oxygen rich section is called oxidation zone, since combustion reactions occur there. The section above the oxidation
Experimental results
Although the syngas composition appears to randomly fluctuate during stationary operation of the gasifier (see Fig. 1), clear trends and correlations among chemical species can be identified. For instance, Fig. 3 shows some composition plots for the gasification of pelletized biomass obtained by microGC.
An inverse proportionality between the carbon dioxide and carbon monoxide volume fractions can be observed in Fig. 3, which can be related to both variation of the oxygen availability and
Conclusions
The extended equilibrium model presented in this paper allows the prediction of syngas composition, including devolatilization products (methane and ethylene), in downdraft biomass gasification. The simulations, validated using pilot-scale experimental data, suggest that degree of by-pass and bed permeability are key macroscopic factors determining the syngas composition and therefore the gasifier performance. More research work is required to assess the effects of operating conditions and
Acknowledgements
This work was carried out with the facilities of the Centro di Ricerca Interuniversitario Biomasse da Energia (CRIBE) and benefited of the financial support provided by the Fondazione Cassa di Risparmio di Pisa.
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