Elsevier

Fuel

Volume 107, May 2013, Pages 419-431
Fuel

Estimation of gas composition and char conversion in a fluidized bed biomass gasifier

https://doi.org/10.1016/j.fuel.2012.09.084Get rights and content

Abstract

A method is presented to predict the conversion of biomass in a fluidized bed gasifier. The model calculates the yields of CO, H2, CO2, N2, H2O, CH4, tar (represented by one single lump), and char, from fuel properties, reactor geometry and some kinetic data. The equilibrium approach is taken as a frame for the gas-phase calculation, corrected by kinetic models to estimate the deviation of the conversion processes from equilibrium. The yields of char, methane, and other gas species are estimated using devolatilization data from literature. The secondary conversion of methane and tar, as well as the approach to equilibrium of the water–gas-shift reaction, are taken into account by simple kinetic models. Char conversion is calculated accounting for chemical reaction, attrition and elutriation. The model is compared with measurements from a 100 kWth bubbling fluidized bed gasifier, operating with different gasification agents. A sensitivity analysis is conducted to establish the applicability of the model and to underline its advantages compared to existing quasi-equilibrium models.

Highlights

• The model predicts gas composition and carbon conversion in biomass FB gasifiers. • Correction of equilibrium is applied to improve the estimation of the gas composition. • Kinetics models are applied to predict char, tar and methane conversion. • Fluid-dynamics, entrainment and attrition are accounted for the calculation of char conversion. • The model has predictive capability in contrast to available pseudo-equilibrium models.

Introduction

Modeling and simulation of fluidized bed biomass gasifier (FBG) is a complex task. Advanced models have been developed for bubbling [1], [2], [3], [4], [5], [6], [7], [8] and circulating [9], [10], [11] FBG. These models usually require physical and kinetic input, which is difficult to estimate and it is sometimes not available to industrial practitioners. Simple and reliable tools to predict reactor performance with reasonable input are needed to support design and optimization. Besides purely empirical models only valid for specific units, more universal approaches presented up to date have been based on gas phase equilibrium [12].

Equilibrium models (EM) have been widely used because they are simple to apply and independent of gasifier design [13], [14], [15]. However, under practical operating conditions in biomass gasification, they overestimate the yields of H2 and CO, underestimate the yield of CO2, and predict a gas nearly free from CH4, tar, and char. Despite these limitations, EM are widely used for preliminary estimation of gas composition in a process flowsheet. However, EM are not accurate enough as tools for design, optimization, and scale-up of FBG units.

Quasi-equilibrium models (QEM) [16], [17], [18], [19], [20], [21], [22] improve the accuracy of the prediction of the gas composition. The foundation of the QE approach was given by Gumz [16], who introduced the “quasi-equilibrium temperature”, an approach where the equilibrium of the reactions is evaluated at a lower temperature than that of the actual process. The concept was applied for the simulation of a circulating FBG unit in the range of 740–910 °C [17] and for various pilot and commercial coal gasifiers [18]. The approach is still applied, although the method is far from predictive.

Another type of QEM has been developed [14], [20], [21], [22] for the simulation of biomass and coal gasifiers. The essential idea of this approach was to reduce the input amounts of carbon and hydrogen, fed to the control volume where the equilibrium is calculated. The underlying reason for the reduction of the C–H–O input is that, under practical operation conditions in a gasifier, the conversion of tar, light hydrocarbons, especially methane, and char are kinetically limited, and so they are controlled by non-equilibrium factors. The interaction between the main four species in the bulk gas is determined by the rate of the water–gas-shift reaction (WGSR). This reaction can also be far from equilibrium, although the existing QEM have assumed it to be in equilibrium. In the following, the main aspects of these conversion processes are discussed for biomass FBG:

  • The methane generated during devolatilization and primary conversion of gas and tar is very stable, and it is hardly affected by secondary conversion without Ni-based (or similar) catalysts at sufficiently high temperatures [22], [23]. Then, in intermediate-temperature gasification systems, i.e. the typical situation in FBG of biomass, the amount of methane in the exit stream of the gasifier is roughly that formed by devolatilization of the fuel [22], [24].

  • The attainment of equilibrium of WGSR has been analyzed in various gasification systems [15], [22], [23], [25], [26], [27], [28], [29]. The use of a synthetic catalyst allows the attainment of equilibrium above 750 °C [30]. However, such catalysts are rarely used as bed material. Mineral catalysts (dolomite, calcite, magnetite, olivine, etc.) are conventional bed materials, but their catalytic activity on WGSR (and also on tar reforming) is lower, and equilibrium is not generally attained at the usual temperature in biomass FBG, i.e. below 900 °C, with sand or similar (bauxite, alumina, ofite). The residence time of the gas also plays a substantial role, and this can differ between the units. Moreover, the real contact time with a catalyst in a FBG is usually lower than the residence time calculated using the superficial velocity of the gas. The reason is that fluid-dynamic factors affect the performance of FBG, such as poor contact of gas and solid caused by the bypass of gas through the bubbles or the plumes generated during devolatilization. These factors also affect other reactions in the bed, for instance, hydrocarbon reforming.

  • The conversion of char is the most decisive factor in FBG, because the main loss of efficiency is due to unconverted carbon in the ashes. The time for char conversion in an FBG is limited by entrainment and extraction of solids (if applied). Then the rate of char gasification has to be fast enough for the char to be converted during practical operation, mainly by reactions with H2O and CO2. The small amount of O2 added to the gasifier combines more rapidly with volatiles than with char. It is concluded that to determine the extent of char conversion in an FBG, all these processes have to be taken into account.

Due to the complications discussed, the QEM are usually applied together with experimental correlations obtained for the specific system under analysis [14], [20], [21]. Applied in this way, QEM refine the estimation of the gas phase composition compared to pure EM, but the prediction capability is limited. It was attempted to overcome this inconvenience by developing a general method for the estimation of the gas composition, based on three parameters: carbon conversion, methane yield during devolatilization, and conversion of methane by steam reforming [22]. Gross recommendations were given [22] for the values of the three parameters based on practical considerations: temperature, type of catalyst, and gasification agent. The recommendations are useful for the evaluation, for a given fuel, of the gas composition resulting from various gasification methods (air vs. steam-oxygen, catalyzed vs. non-catalyzed). However, the method is not generally useful to analyze the performance of a given FBG under different operating conditions, like the change of flowrates of biomass and gasification agent, topology of the gasifier, etc. The reason is that the three parameters are sensitive to the reactivity of fuel, gas velocity, and temperature in the gasifier. Moreover, the distribution of the main species in the gas, CO, H2, CO2, and H2O, is governed by the rate of WGSR, a reaction which rarely attains equilibrium in biomass FBG.

The objective of the present work is to develop a model, taking advantage of the simple framework of QEM, but expanding their predictive capability. There are three requisites: (i) to allow estimation of gas composition and solid fuel (char) conversion; (ii) to capture the effect of changes in operating conditions on the FBG performance, including velocity of the gas and the main geometry of the reactor, and therefore, to be useful for design, optimization and scale-up; and (iii) to be simple enough for implementation in flowsheet simulations, needing limited input, obtained by reasonable effort. Below, the validity of such a model compared to existing QEM is discussed, underlining the advantages of the present development.

Section snippets

Model approach

The process is simplified by decoupling primary (devolatilization) and secondary conversion, considering the different rates of these processes [31]. Volatiles and char are assumed to be well mixed in the isothermal reactor. Although sharp gradients in species concentration are observed in most FBGs [3], this occurs locally where the oxygen and fuel are injected (feed ports and gas distributor). As a result, most of the reactor remains with quasi-constant concentration, making the

Comparison of model with measurements

The model developed has been compared with experiments conducted in a bubbling FBG with different gasification agents: air, air–steam, and oxygen-enriched air–steam. The gasification agent was preheated to enter the reactor at 400 °C. The fuel was wood pellets with the empirical formula CH1.4O0.64, (dry and free of ash). The moisture and ash contents were 6.3% and 0.5% (mass basis), and the lower heating value of the fuel (as received) was 17.1 MJ/kg. The pellets were cylindrical with a mean

Summary and conclusions

A model has been developed to predict the performance of biomass fluidized bed gasifiers (FBG). The model uses an equilibrium submodel to calculate the gas-phase composition, corrected with kinetics sub-models to predict conversion processes that deviate from equilibrium. A carbon predictor is implemented as a submodel, accounting for chemical conversion, attrition, elutriation and mechanical removal of ash, allowing estimation of char conversion in the gasifier under different operating

Acknowledgements

The authors acknowledge the European Commission and Commission of Science and Technology (CICYT) of Spain and Junta de Andalucía for their financial support.

References (51)

  • S. Rapagnà et al.

    Steam-gasification of biomass in a fluidised-bed of olivine particles

    Biomass Bioenergy

    (2000)
  • S. Rapagnà et al.

    Development of catalysts suitable for hydrogen or syngas production from biomass gasification

    Biomass Bioenergy

    (2002)
  • A. Gómez-Barea et al.

    Devolatilization of biomass and waste in fluidized bed

    Fuel Proc Tech

    (2010)
  • S. Rapagnà et al.

    Catalytic gasification of biomass to produce hydrogen rich gas

    Int J Hydrogen Energy

    (1998)
  • A. Gómez-Barea et al.

    Modeling of biomass gasification in fluidized bed

    Progr Energy Combust Sci

    (2010)
  • D. Neves et al.

    Characterization and prediction of biomass pyrolysis products

    Progr Energy Combust Sci

    (2011)
  • S. Baumlin et al.

    The continuous self stirred tank reactor: measurement of the cracking kinetics of biomass pyrolysis vapours

    Chem Eng Sci

    (2005)
  • W.P. Jones et al.

    Global reaction schemes for hydrocarbon combustion

    Combust Flame

    (1988)
  • G. Donsi et al.

    Carbon fines production and elutriation from the bed of a fluidized coal combustor

    Combust Flame

    (1981)
  • Y. Wang et al.

    Kinetic model of biomass gasification

    Sol Energy

    (1993)
  • A. Gómez-Barea et al.

    Plant optimisation and ash recycling in fluidised bed waste gasification

    Chem Eng J

    (2009)
  • M. Campoy et al.

    Air-steam gasification of biomass in a fluidised bed: process optimisation by enriched air

    Fuel Proces Technol

    (2009)
  • P. Raman et al.

    Mathematical model for the fluid-bed gasification of biomass materials. Application to feedlot manure

    Ind Eng Chem Proc Des Dev

    (1981)
  • van den Aarsen FG. Fluidised bed wood gasifier. Performance and modelling, Ph.D. Dissertation. Twente University...
  • M.L. Souza-Santos

    Solid fuels combustion and gasification

    (2004)
  • Cited by (0)

    View full text