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

The underlying reason for the reduction of the C–H–O in-put is that, under practical operation conditions in a gasifier, the conversion of tar, light hydrocarbons, especially methane, and

Estimation of gas composition and char conversion in a fluidized bed

biomass gasifier

A Gómez-Bareaa,⇑, B Lecknerb

a

Bioenergy Group, Chemical and Environmental Engineering Department, Escuela Superior de Ingenieros, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain b

Department of Energy and Environment, Chalmers University of Technology, S-412 96 Göteborg, Sweden

h i g h l i g h t s

"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

a r t i c l e i n f o

Article history:

Received 14 August 2012

Received in revised form 17 September 2012

Accepted 27 September 2012

Available online 22 October 2012

Keywords:

Gasification

Fluidized-bed

Biomass

Model

Char

a b s t r a c t

A method is presented to predict the conversion of biomass in a fluidized bed gasifier The model calcu-lates 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 pro-cesses from equilibrium The yields of char, methane, and other gas species are estimated using devola-tilization 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 con-version is calculated accounting for chemical reaction, attrition and elutriation The model is compared with measurements from a 100 kWthbubbling 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

Ó 2012 Elsevier Ltd All rights reserved

1 Introduction

Modeling and simulation of fluidized bed biomass gasifier (FBG)

is a complex task Advanced models have been developed for

bub-bling[1–8]and circulating[9–11]FBG These models usually

re-quire 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

univer-sal 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[13–15]

However, under practical operating conditions in biomass

gasifica-tion, they overestimate the yields of H2and 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 esti-mation 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)[16–22]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 ‘‘quasi-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 var-ious 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[14,20–22]for the simulation of biomass and coal gasifiers The essential idea of this approach was to reduce the input amounts of carbon and 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd All rights reserved.

⇑ Corresponding author Tel.: +34 95 4487223; fax: +34 95 4461775.

E-mail address: agomezbarea@esi.us.es (A Gómez-Barea).

Contents lists available atSciVerse ScienceDirect

Fuel

j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / f u e l

hydrogen, fed to the control volume where the equilibrium is

cal-culated The underlying reason for the reduction of the C–H–O

in-put is that, under practical operation conditions in a gasifier, the conversion of tar, light hydrocarbons, especially methane, and char

Nomenclature

A pre-exponential factor, 1/s

a decay coefficient, –

cp specific heat, J K1kg1

c gas concentration, mol m3

CkHlOm tar component

dch average char particle diameter in the reactor, m

fWGSR coefficient of approach to WSGR equilibrium, –

E activation energy, kJ/mol

Fgp gas yield, molgp/kgfuel(daf)

Ff,daf flowrate of fuel, dry and ash-free (daf), kg/s

h, hf specific enthalpy and enthalpy of formation, J/kg,

k kinetic coefficient, various units

K equilibrium constant, –

Katt attrition constant, –

Lb, Lfb bed and freeboard heights, m

madd,b mass of additive/inert in the reactor, kg

mc,p mass of carbon in a char particle, kg

mc,b mass of carbon in the reactor, kg

mch,b mass of char (carbon and fuel ash) in the reactor, kg

mch,b,crit critical value of mass of char in the reactor, kg

mT,b mass of total inventory (additive and char) in the

reac-tor, kg

M molecular mass, kg kmol1

k, l, m atoms in equivalent tar, C, H and O, –

n1, n2,m fragmentation coefficients in Eq.(29)

p pressure, Pa

Ql specific rate of heat loss, W/kgfuel(daf)

R reaction rate, kmol m3s1

Rg universal constant of gases, J K mol1

rc,ch overall reactivity of the char, s1

rCþH2O intrinsic reactivity of carbon in char with H2O, s1

rCþCO2 intrinsic reactivity of carbon in char with CO2, s1

T temperature, K

Th Throughput, kg/(m2h)

u0 superficial gas velocity, m s1

xi,j mass of compound i in stream j per kgfuel(daf), kg/kg

Xtar conversion of tar

XCH4 conversion of methane

Xch conversion of carbon in the char through the reactor

xadd mass of additive fed to the reactor per kgfuel(daf), kg/kg

xash,da ash (non-carbon) in discharged ash (fly + bottom) per

kgfuel(daf), kg/kg

xch,d mass of char per kgfuel(daf)produced during fuel

devola-tilization, kg/kg

xch,2 mass of char in the bottom ash discharge (stream 2) per

kgfuel(daf), kg/kg

xch,3 mass of char in the bottom fly ash (stream 3) per

kgfuel(daf), kg/kg

xc,da mass of carbon in discharged ash (fly + bottom) per

kgfuel(daf), kg/kg

xtar,d mass tar per kgfuel(daf)produced during fuel

devolatiliza-tion, kg/kg

xCH 4;d mass of methane per kgfuel(daf) produced during fuel

devolatilization, kg/kg

xH2O;f moisture (in fuel) per kgfuel(daf), kg/kg

xi,ga mass of i (i=O2, H2O, N2) in the gasification agent per

kgfuel(daf), kg/kg

wi,f mass fraction of the i-component (i = C, H, O, N, ash,

m(iosture)) in the fuel, kg/kg

wc,b mass fraction of carbon in the reactor, kg/kg

wc,ch,b mass fraction of carbon in the char of the reactor, kg/kg

wc,ch,d mass fraction of carbon in the char after

devolatiliza-tion, kg/kg

wc,ch,2 mass fraction of carbon in the char of bottom ash

dis-charge (stream 2), kg/kg

wc,ch,3 mass fraction of carbon in the char of fly ash (stream 3),

kg/kg

wch,b,crit critical value of the char mass fraction in the reactor, kg/

kg

yi molar fractions of i in the produced gas, kmol/kmolgp

Greek symbols

r coefficient in Eq.(29), –

s residence time, s

s2 rate constant of bottom ash discharged, s

s3 rate constant of fly ash, s

sR time constant of reaction (the inverse of reactivity of

charsR= 1/rc,char), s

u coefficient in Eq.(29), – Subscripts

0 standard conditions superficial (velocity)

2, 3 bottom discharge, fly ash

att attrition

b bed, reactor

C, H, O, N carbon, hydrogen, oxygen, nitrogen

daf dry and ash-free

coar coarse particle fraction crit critical value

d devolatilization

da discharged ash

fin fine particle fraction

ga gasification agent

gp gas produced

i, j indices

mf minimum fluidization

k, l, m atoms in equivalent (heavy) lumped tar

Abbreviations

daf based on dry and ash-free substance CSTR continuous stirred tank reactor

EM equilibrium model

ER fuel equivalence ratio, – FBG fluidized biomass gasification (gasifier) LHV lower heating value (lower), J kg1

na not available QEM quasi equilibrium model

RZ reduction zone SBR steam to biomass ratio SRMR steam reforming of methane reaction WGSR water–gas-shift reaction

are kinetically limited, and so they are controlled by

non-equilib-rium factors The interaction between the main four species in

the bulk gas is determined by the rate of the water–gas-shift

reac-tion (WGSR) This reacreac-tion 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

 The attainment of equilibrium of WGSR has been analyzed in

various gasification systems[15,22,23,25–29] The use of a

syn-thetic catalyst allows the attainment of equilibrium above

750 °C [30] However, such catalysts are rarely used as bed

material Mineral catalysts (dolomite, calcite, magnetite,

oliv-ine, etc.) are conventional bed materials, but their catalytic

activity on WGSR (and also on tar reforming) is lower, and

equi-librium 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

sub-stantial 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 O2added to the gasifier

combines more rapidly with volatiles than with char It is

con-cluded 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

ap-plied together with experimental correlations obtained for the

spe-cific 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

parame-ters: carbon conversion, methane yield during devolatilization, and

conversion of methane by steam reforming[22] Gross

recommen-dations 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, H, CO, and HO, is governed by the

rate of WGSR, a reaction which rarely attains equilibrium in bio-mass 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 esti-mation 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 geom-etry of the reactor, and therefore, to be useful for design, optimiza-tion 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

2 Model development 2.1 Model approach The process is simplified by decoupling primary (devolatiliza-tion) 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 qua-si-constant concentration, making the simplification of constant temperature and concentration reasonable The residence time of volatiles depends on the flows of the biomass and gasification agent and the geometry of the reactor, whereas the residence time

of char particles also depends on the rate of removal by entrain-ment (mainly governed by gas velocity) and bed extraction applied

to maintain smooth operation

released where the fuel is devolatilized The yield of species from devolatilization depends on fuel, temperature, and heating rate and can be estimated empirically [28,32] The main yields con-cerned in the present model are methane, tar and char, xCH 4 ;d, xtar,d

and xch,d, (seeFig 1) Other species (CO, H2O, H2and CO2) are also considered for the estimation of WGSR conversion, but only a

GASIFICATION AGENT FUEL

CHAR

Produced gas Discharged ashes

DEVOLATILIZATION

METHANE CONVERSION

4 ,d

CH

,gp

tar x

,gp

char x

OVERALL MASS AND HEAT BALANCE, PSEUDO-EQUILIBRIUM IN THE GAS PHASE

4

( XCH ) ( Xchar) ( Xtar)

WGSR FACTOR

( fWGSR)

4 ,gp

CH x

Fig 1 Scheme of the model.

rough estimate is sufficient for the present development, as

ex-plained below The devolatilization yield is the source for the

sub-sequent conversion in the reduction zone (RZ), represented by the

dashed line inFig 1, where there is no oxygen left In fact, the

dev-olatilization box in Fig.1 also includes the reactions of volatiles

(mainly H2and CO) with oxygen Therefore this zone is sometimes

called flaming pyrolysis zone[31] In the RZ H2O and CO2react

with the char, the methane is converted by steam reforming, and

the tar by reforming/cracking The main compounds in the gas

phase react through the WGSR The conversions of the tar,

meth-ane, and char in RZ are Xtar, XCH 4, and Xch The factor fWGSRis the

ratio of the actual coefficient Kexp¼ yCO2yH2=ðyCOyH2O) and that of

equilibrium KWGSR, y being molar fraction in the gas The kinetics

of WGSR are taken into account to calculate Kexp

Once the four parameters Xtar, XCH 4, Xch, and fWGSRare estimated,

the gas composition is evaluated by a pseudo-equilibrium model

(thick solid line in Fig 1) The composition of the final (outlet)

gas is obtained by the overall atomic mass and heat balance over

the entire gasifier

2.2 Model formulation

2.2.1 Overall atomic balances

The models for the estimation of the parameters (Xtar, XCH 4, Xch

and fWGSR), as well as the yields xCH 4 ;dxch,d,and xtar,dand other

spe-cies from fuel devolatilization are presented in the following

The fuel conversion in the gasifier related to 1 kg of dry, ash-free

fuel (daf) (1 kgdaf= wC,f+ wH,f+ wO,f+ wN,f) can be written as

(fuel + gasification agent + additive = gas produced + discharged

ash):

wC;f þ wH;f þ wO;f þ wN;f þ wash;f þ wH2O;f

|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

1 kg fuel daf

|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

þ xadd

|{z}

Additive

! MCOyCO þ MH2yH2þ MH2OyH2Oþ MCO2yCO2þ MCH4yCH4þ MN2yN2þ MCkHlO m yCkH l O m

Fgp

|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

Gas produced

þxash;da þ xc;da

|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}

discharged ash ð1Þ All quantities in Eq.(1)are in kg/kgfuel(daf) wi,frepresents the mass

fraction of the ith component in the dry ash-free fuel (daf), whereas

xi,jis the mass of i flowing in or out in stream j of the system per kg

fuel (daf) The gasification agent (‘ga’) is in general composed of

oxygen, xO 2 ;ga, steam, xH 2 O;ga, and nitrogen, xN 2 ;ga yiand Miare the

molar fraction of the species i in the produced gas (molei/molegp)

and its molecular mass The gas yield (molegp/kgfuel(daf)) is Fgp The

additive, xadd, can be a catalyst, sand or any material fed to the

sys-tem for the improvement of the gasification performance The char

is assumed to comprise the inorganic material from the fuel and

unconverted carbon; small contents of hydrogen and oxygen in

the char are neglected Then, discharged ash (‘da’) contains

uncon-verted carbon in the char xc,daand ash xash,da, this latter consists of

the ash from the fuel and bed material removed from the bed The

discharged ash in Eq.(1)includes both fly and bottom ash The tar

component is given by the species CkHlOm, which can be estimated

flu-idisation agent and the biomass) is assumed to be released as N2

The oxygen demand will be characterized by the oxygen

equiv-alence ratio, ER, defined as the amount of oxygen supplied to the

gasifier over the oxygen required for stoichiometric combustion

The atomic CHON balances applied to Eq.(1)are:

wC;f¼ ðyCOþ yCO2þ yCH4þ kytarÞMCFgpþ xc;da ð3Þ

wH 2 ;fþMH2

MH 2 O

wH 2 O;fþ xH2O;ga

¼ yH2Oþ yH2þ 2yCH4þ‘

2 tar

MH2Fgp

ð4Þ

wO 2 ;fþ1 2

MO2

MH 2 O

wH 2 O;fþ xH 2 O;ga

þ xO 2 ;ga

2yCOþ yCO2þ1

2yH2Oþk

2 tar

whereas the ash balance is:

2.2.2 Equilibrium of the modified gas-phase (QEM) The composition of a gas in pseudo-equilibrium is calculated according to Jand et al.[22] Three quantities (Fig 1) are subtracted from the product gas to attain the equilibrium: methane, tar, and carbon (xCH 4 ;gp, xtar,gp, and xc,da) calculated as

Methane removed ¼ unconverted methane ¼ xCH 4 ;gp

Tar removed ¼ unconverted carbon in tar ¼ xtar;gp

Carbon in char removed ¼ unconverted carbon in char

¼ xc;da¼ xc;ch;dð1  XchÞ ð10Þ where XCH 4, Xtar, and Xchare the methane, tar, and char conversions

in the RZ and xCH4;d, xtar,d, and xc,ch,dthe corresponding yields of these compounds after devolatilization of the fuel Then, the CHON bal-ances for the pseudo-equilibrium calculation of the gas phase, cor-responding to Eqs.(3)–(6), are:

wC;f xc;da xtar;gpkMC

Mtar

 xCH 4 ;gp

MC

MCH 4

wH 2 ;fþ MH2

MH 2 O

wH 2 O;fþ xH2O;ga

 xtar;gp

‘ 2

MH 2

Mtar xCH4;gp2MH2

MCH 4

¼ yH 2 Oþ yH 2þ 2yCH 4

wO 2 ;fþ1 2

MO 2

MH2O

wH 2 O;fþ xH 2 O;ga

þ xO 2 ;ga xtar;gp

m 2

MO 2

Mtar

2yCOþ yCO 2þ1

2yH 2 O

The equilibrium equations for the WGSR and SRMR (steam reform-ing of methane) are:

yH2yCO2

yH2OyCO¼ fWGSR 0:029 exp

4094 T

ð15Þ

y3

H 2yCO

yCH4yH2O¼ 6:14  10

13exp 28116

T

ð16Þ where the terms within brackets on the right-hand side of Eqs.(15)

fWGSRis the factor that measures the approach to equilibrium of the WGSR, obtained by taking into account the kinetics as explained below To replace the contribution of methane and tar removed from the gas (Eqs.(8) and (9)) in the pseudo-equilibrium calcula-tions, a fictitious inert gaseous compound is considered[22], given

by x ð1  X Þ=M þ x ð1  X Þ=M (kmol inert/kg )

Eqs.(11)–(16)are solved for a given temperature and

parame-ters (Xtar, XCH 4, Xch, xCH 4 ;d, xtar,d, and xc,ch,dand fWGSR) yielding the

composition of the pseudo-gas (yi): yCO, yH2;yCO2;yN2;yH2O and

yCH

4 Then, the composition of the final outlet gas is obtained by

restoring the amount of methane (xCH 4 ;gp calculated from Eq.(8)),

and tar (xtar,gpcalculated from Eq.(9)) previously subtracted

2.2.3 Overall heat balance

Once the gas composition of the outlet gas and the amount of

unconverted fuel (xc,daand xash,da) have been calculated by the

ki-netic model described below, an energy balance over the gasifier

yields for 1 kg fuel:

hf ;dfþ

Z Tf ;in

T0

cp;dfdT þ wH2O;fhf ;H2OðlÞþ xH 2 O;gahf ;H2OðgÞþ xN 2 ;gahf ;N2

þ xO 2 ;gahf ;O 2¼ Fgp

X7

i¼1

yihf ;gp;iþ xc;dahf ;cþ xash;da

Z Tb

T0

cp;ash;dadT þ Ql ð17Þ

The enthalpy of formation of the dry fuel hf,df, char hf,c, and tar

hf,gp,tar, are calculated from their heating values The heating value

of the fuel is the input from an analysis, while the heating value

of char and tar are estimated from Ref.[34]

2.2.4 Kinetic models for secondary conversion of gas

Methane and tar conversions are calculated assuming perfect

mixing of the gas in the bed and freeboard (CSTR) and first order

kinetics

Xi¼ kisi

1 þ kisi

The kinetics of the methane and tar reactions have been discussed

in[31] The selected kinetic parameters for the two reactions are

presented inTable 1 The kinetics for the methane is that for

homo-geneous conversion and it has been considered pseudo-first order

reaction by lumping a typical steam concentration into the kinetics

coefficient The methane conversion below 1000 °C is very low so

this simplification is quite insignificant If a catalyst is added to

the bed, the rate should be modified to account for its influence

The conversion of tar compounds is a complex process, still to

be addressed in its details The objective of modeling tar

decompo-sition in the present work is to give rough estimates of tar

concen-tration in the gas, with the purpose of capturing the effects in the

change of operation conditions of FBG The tar concentration in the

outlet gas of an FBG is small compared to other components CO,

and CH4, etc Although tar in the gas is a decisive issue for the

utilization of the gas, its effect on the mass balance is not signifi-cant The effect of tar concentration on the heat balance could have some significance due to its high energy density Here the kinetics

of Baumlin et al.[35]are taken to represent the overall tar decom-position of an lumped tar in a CSTR If an active catalyst is present this kinetics should be changed to account for the impact of the bed material on tar decomposition

The kinetics of WGSR have been measured[36]both for the homogeneous case and for various bed materials used in FBG The kinetics obtained were similar to those usually applied in mod-eling gasifiers[37], but they differ from others[38] The kinetic expressions and related parameters are presented inTable 1 Note that for the estimation of methane and tar conversion (Eq

(18)), the initial yields of methane and tar from devolatilization do not need to be known as a consequence of the 1st order reactions For WGSR, however, the amounts of CO, H2O, H2and CO2entering

RZ are needed, since the kinetics correspond to a reversible reac-tion (Table 1)

The gas residence time is that of the total flow of gas in the bed and freeboard of the specific geometry considered (diameter and height)

2.2.5 Char conversion model

(using quantities x, which are mass flowrate per kilogram of daf) The control volume is represented by the dashed line The fuel decomposes into char and volatiles during devolatilization, and these are the inputs to the control volume together with added material xadd The volatiles, fluidization agent, and produced gas interact with the solids, resulting in a temperature, a gas composi-tion, and a gas velocity in the reactor The normalized flowrates of solids x are those of the additives (add) and char (ch) The solids en-ter the control volume (d) and leave as bottom ash(2)and fly ash

(3) wc,ch,dand wc,ch,bare the carbon (c) contents in the entering char (ch) stream and in the char found in the bed (b) The solids are as-sumed to be perfectly mixed in the reactor so wc,ch,2= wc,ch,3= wc,ch,b

as indicated inFig 2 The normalized mass flowrate of char leaving the reactor is then xch,2+ xch,3= xch,da= xc,da+ xash,da, and the corre-sponding normalized flowrate of carbon is xc,da=(xch,2+ xch,3)wc,ch,b

(carbon is exiting the system in the solids of streams 2 and 3, where the char particles have the same composition as the bed, wc,ch,b) Similarly, the ash balance is xash,da=(xch,2+ xch,3)(1  wc,ch,b) + xadd Under steady state conditions a constant mass of char inventory

mch,b(fuel ash and carbon) and carbon mc,b= mch,bwc,ch,bremain in the bed, constituting the char and carbon load Note the difference

Table 1

Kinetics of gas (methane reforming, tar thermal decomposition and WGSR) and char reactions (with H 2 O and CO 2 ).

Methane reforming CH 4 þ H 2 O ! CO þ 3H 2 R CH 4 ¼ kc CH 4 c H 2 O (kmol m 3

s 1

A = 3.00  10 8

m 3 kmol 1

s 1

E = 125 kJ mol 1 Thermal decomposition Tar ? lighter gas R tar ¼ k c tar (kmol m 3

s 1

A = 1.93  10 3

s 1

E = 59 kJ mol 1 WGSR

CO þ H 2 O ¢kd

k i

CO 2 H 2 R CO ¼ k i ðc CO 2 c H 2  K e c CO c H 2 O Þ (kmol m 3

s 1

A = 1.41  10 5 m 3 kmol 1 s 1

E = 54.2 kJ mol 1

K e ¼ 0:029 expð4094=TÞ Gasification C þ CO 2 ! 2CO r C—CO 2 ¼ k p 0:38

A = 3.1  10 6

s 1 bar 0.38

E = 215 kJ mol 1 Gasification C þ H 2 O ! CO þ H 2 r C—H 2 O ¼ k p 0:57

H 2 O (s 1

A = 2.6  10 8 s 1 bar 0.57

E = 237 kJ mol 1

between the mass fraction of carbon in the char remaining in the

bed, wc,ch,b, and that in the whole bed, which includes also the inert

additives, wc,b= mc,b/mT,b= wch,bwc,ch,b The total mass of bed material

(inert/additive and char) in the reactor is mT,b= mch,b+ madd,b The

char load at steady state depends on the char reactivity and the

residence time of the char particles in the bed The main operation

variables in the reactor are indicated in the figure: temperature T,

superficial velocity u0, and the partial pressures pCO2 and pH2O of

CO2and H2O

A balance of char and carbon in the control volume ofFig 2b

gives (in = out + reacted):

xch;dwc;ch;d¼ ðxch;2þ xch;3Þwc;ch;bþ xR ð20Þ

where xR=(rc,chmc,b)/Ff,dafis the normalized rate of reaction of the

carbon in the char (kg carbon reacted in the char/kgfuel,daf) By

defin-ing the residence time of char in the reactor ass= mch,b/(xch,dFf,daf)

ands2ands3as the time constants of removal of solids material

from the reactor by extraction s2= mT,b/(x2Ff,daf) and elutriation,

s3= mT,b/(x3Ff,daf) (1/s2and 1/s3are the constant rates of solids

re-moval), Eqs.(19) and (20)can be solved for the two unknownss

and wc,ch,b:

ð1=s2þ 1=s3Þ 1 

wc;ch;d=sR

ð1=s2þ 1=s3þ 1=sRÞ

ð21Þ

wc;ch;b¼ ð1=s2þ 1=s3Þwc;ch;d

where the char conversion timesRis the inverse of the reactivity of

the charsR= 1/rc,ch, the latter defined as rc,ch=(1/mc,p)dmc,p/dt where

mc,pis the mass of carbon in a char particle in the bed The intrinsic

reactivities of biomass char with CO2and H2O, used for the

simula-tion presented in Secsimula-tion3, are given inTable 1 For other chars

(from other fuels) the intrinsic reactivity has to be changed

consis-tently The reactivity of a single particle, taking into account

diffu-sion, is obtained by a simple model[31]where two effectiveness

factors are calculated for a char particle of size dchin a gas at a

tem-perature T and pressures pCO

2 and pH

2 O (with two coupled equa-tions) The effective diffusivity and mass transfer coefficient,

necessary for the char-particle model, are estimated by a

correla-tion sensitive to the operating condicorrela-tions (velocity, temperature,

size of char, etc.)[31] The size of the average char particle in the

reactor dch, is estimated from the fuel size, taking into account shrinkage, fragmentation and reaction as detailed below

gi-vens2,s3andsR The overall carbon (char) conversion in the reac-tor is obtained by applying a balance of carbon, i.e (carbon in – carbon out)/carbon in) to give:

Xch¼ 1 wc;ch;b

wc;ch;d

s

s2

s3

ð23Þ

wc,ch,dis given as input (or estimated as explained below), and the reactivity of the char (and sosR) is calculated as a function of the conditions in the reactor (T, pCO2and pH2O) with the expressions gi-ven inTable 1.s3is calculated by the elutriation model presented below

Various operation modes can be applied to remove bed material

by bed extraction: (i) the extraction of material is adjusted to a gi-ven rate (an overflow or bottom pipe continuously removes mate-rial from the bed, or it is adjusted by a given sequence) Thens2is known, and Eqs.(21)–(23)can be directly applied to obtain the solution (ii) the char content in the bed mch,bis maintained to some prescribed value mch,b,crit in relation to the total inventory

of the bed, mT,b, for instance to avoid accumulation of ash in the bed; Thens2has to be calculated to control the bed at wch,b,crit=

mch,b,crit/mT,b In such a casesis known (s=scrit= mch,b,crit/(xch,dFf,daf)) and Eqs.(21)–(23)can be solved for wc,ch,bands2to give:

wc;ch;b¼ ð1 þsR=scritÞ=2 þ ð1=4ð1 þsR=scritÞ2

In some cases the system can operate safely ats<scritbecauses3is high enough to entrain the ash at sufficient rate to maintain the re-quired condition In such a case 1/s2= 0, and Eqs.(24) and (25)are used Alternatively, Eqs (21)–(23) can be used directly with 1/s2= 0 Finally, the flowrate of the inert solids, necessary to main-tain the inventory during operation, can be calculated by an overall mass balance, Eq.(7):

xadd¼mT;b

Ff ;daf

wc;ch;b

wc;b

sR 1 s

s2þ1

s3

ð26Þ

In operation with low-ash fuel, such as wood, there is no bed extraction (1/s2= 0) during a long time, so the material in the reac-tor is not strictly maintained steady (there is only a single value of

s3that makes xaddin Eq.(26)zero for given input), but the accumu-lation (or loss) is slow and the system is operated in a quasi-steady manner

To calculates3(i.e., x3) an elutriation model is applied[42] Two types of char particle are elutriated: coarse particles (xch,coar,3) gen-erated after devolatilization and fragmentation, and fines (xch,fin,3) produced by abrasion of char in the bed Particles are carried away

in the fly-ash stream x3= xch,3+ xadd,3with xch,3= xch,coar,3+ xch,fin,3 xadd,3

is calculated similar to char but applying the density, size and attrition constant of the additive In the following, the case of char

is explained, since the impact of inert material in x3is negligible in steady state operation of bubbling FBG provided that the size of additive particles are large enough, i.e xadd,3 xch,3

Fines are assumed to be produced by attrition at a rate[43,44],

xch;fin;3¼Kattmch;bðu0 umfÞ

Ff ;dafdch

ð27Þ

Kattis the dimensionless attrition constant, determined experimen-tally for a variety of chars and solids, ranging from 1  107and

1  108for various biomasses[43] dchis the average particle size

of the coarse char in the reactor, calculated below, and u is the

Fig 2 (a) Control volume for the char conversion model with the main gas and

solids streams (b) Solids balance in the control volume: Inlet solids stream (index

d) comprises char generated after devolatilization and additive; Outlet solids

streams are: extraction or bottom ash (index 2) and elutriation or fly ash (index 3).

minimum fluidization velocity corresponding to that average size.

The fines are assumed to be elutriated immediately as they are

pro-duced, and their conversion along the reactor is small; it takes just a

few seconds for the gas to carry them away The coarse particles can

be converted or elutriated depending on their size, dch, and

operat-ing conditions (mainly velocity)

To calculate Fch,coar,3a fluid-dynamic model of the FBG is solved

to give

xch;coar;3¼ xch;1þ ðxch;b xch;1Þ expðaðLfb LbÞÞ ð28Þ

Lband Lfbare the height of the dense bed and the freeboard, xch,bis

the entrainment flux of coarse char particles at the surface of the

dense bed[45], xch,1is the particle flux of coarse char in an

imagi-nary long column, whose height is higher than the transport

disen-gaging height, calculated by applying the correlation in[46]and a is

the decay coefficient[47]

In general, population balances on the particle sizes in the bed

are necessary for precise evaluation of the processes In this work,

however, an approximate method estimates dch as a function of

fuel particle size df[42,48],

n1, n2m,ubeing parameters related with shrinkage and

fragmenta-tion of the fuel in a fluidized bed[48]

In summary, x3 in s3 is calculated by x3= xch,3+ xadd,3 where

xch,3= xch,coar,3+ xch,fin,3.xch,coar,3is calculated by Eq.(28)and xch,fin,3

by Eq.(27) Similar equations are used for the additive Most of

the fluid-dynamics correlations used for the solution of Eqs.(27)

two types only need to be distinguished in some details[31]

2.2.6 Estimation of devolatilization yields

The yield of devolatilization (xch,d, xCH 4 ;d, xtar,d, xCO,d,

xCO 2 ;d;xH 2 ;d;xH 2 O;d) is ideally estimated for fluidized bed conditions

(fuel and temperature) similar to that to be modeled Such data

have been obtained for different fuels, providing correlations as a

function of temperature [28] When the yield cannot be

deter-mined experimentally, data from compilations can be taken,

searching for similar fuel and operating conditions[32] The

fol-lowing can be generally applied: The char yield xch,dcan be taken

from the proximate analysis of the fuel with reasonable accuracy,

since its variation with temperature and heating rate is small

[28] The yield of methane xCH 4 ;ddepends on fuel but is in the range

of 50–80 g/kgdaffor most biomass species devolatilized in FB even

under quite different conditions[22] The yield of tar xtar,dcan be

assumed to be between 0.15 and 0.20 kgtar/kgdafa range which is

consistent with the model of tar conversion presented above This

treatment is enough for the present model because the solution is

not much sensitive to the actual tar concentration in the gas due to

its low concentration in the produced gas of FBG, but the value

chosen may be sensitive to different operating conditions

For the calculation of WGSR, the yields of CO, CO2, H2and H2O

(xCO,d, xCO 2 ;d;xH 2 ;d;xH 2 O;d) have to be estimated (see the kinetic

expression inTable 1) The yield of devolatilization, followed by

partial combustion of the fuel gas with the O2fed to the gasifier,

is considered, following the treatment of Wang[49] This is made,

knowing that O2will be consumed rapidly, mainly by H2and CO

yielding CO2and H2O[49]and very little char and methane are

burned owing to the low combustion rates in the gasifier Instead

of calculating the competition between H2and CO for the O2, it is

assumed that H2is consumed first, because of its higher

combus-tion rate compared to CO[31], yielding H2O, and the remaining

O2combines with CO to give CO2 The yields of CO, CO2, O2and

H2O from devolatilization of a given fuel can be estimated from

correlations as a function of temperature[28], by simple

pseudo-empirical models[32,34], or by rough estimation like the one given

in the following Here we have demonstrated that the exact evalu-ation of these yields does not significantly improve the estimevalu-ation

of the gas phase composition, so the following typical values can be assigned for the yields (kg/kgdaf): 0.15 for CO, 0.20 for CO2, 0.02 for

H2and 0.10 for H2O

2.3 Solution procedure The main steps to solve the model are summarized in the following:

1 Introduction of inputs:

– Geometry: internal diameter and height of bed and free-board Total bed inventory in the reactor, or pressure drop

– Fuel: Composition (elemental and ultimate analyses) and calorific value

– Flow rates of biomass and gasification agent Alterna-tively, the model can be specified with gas velocity and temperature, from which the flowrate of the gasification agent is obtained once the iteration is finished

– In Step 3 (see below) some dedicated inputs are required

2 The reactor temperature, flowrate and gas composition of the produced gas, gas velocity in the bed and freeboard, are assumed as guess for the first iteration (Steps 3–7, below)

3 The conversions of methane, tar, and char are calculated, as well as the factor of approaching the equilibrium of WGSR

by applying the kinetic models indicated in Sections2.2.4

(tar, methane, and WGSR) and Section2.2.5(char) For the kinetic models various additional inputs are required: – The devolatilization yields (xch,d, xCH 4 ;d, xtar,d, xCO,d,

xCO 2 ;d;xH 2 ;d;xH2O;d): these are taken as input in the form

of correlations (for instance as a function of temperature)

or by estimations from literature for the same fuel and operating conditions, or by the gross recommendation made in Section2.2.6

– Size and density of fuel and additive

– The kinetics of steam reforming, tar conversion, WGSR, and reactivity of char (Table 1) The former may depend

on the additive used in the bed and the reactivity has to

be selected for the fuel/char to be modeled

– Input to the char conversion model (attrition constant,

Katt, fragmentation parameters, n1 and n2m) Other parameters for the fluid-dynamic model, such as decay factor in the freeboard a, umf, are taken from the spec-ified correlations for the properties of bed material and calculated char diameter in the bed, dch

4 Calculation of CHO to be subtracted from QEM (Eqs.(8)– (10))

5 Solution of QEM (Eqs.(11)–(16)) for the calculation of the pseudo-gas phase (yi)

6 Calculation of char xch,d(1  Xch) and the bed material removed from the bed (entrainment or extraction) Then

xc,da and xash,da are calculated Determination of the amount of methane (xCH 4 ;gp in Eq.(8)) and tar (xtar,gpin

Eq.(9)) to be restored to the gas phase

7 With the gas and solids compositions in the outlet streams, the atomic balances (Eqs (3)–(6)) and the energy balance over the gasifier (Eq.(17)) are solved to yield the actual gas phase composition (yi) and its tem-perature (the thermal losses are given as input, although this value can be determined easily) From this, the flow-rate of gas produced and gas velocities in the bed and

freeboard are calculated The assumed values in Step 2

are corrected, and the iterative process is repeated until

convergence

3 Results and discussion

3.1 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 diameter of

6 mm and a height between 5 and 10 mm The apparent density

of a pellet was 1300 kg/m3, whereas the bulk density was

600 kg/m3 The bed material used in the FBG was ofite with density

of 2650 kg/m3and an average size of 290lm Ultimate and

ele-mental analyses as well as particle size distribution of the ofite

are reported in[50] The main geometrical parameters of the FBG

unit are: bed and freeboard diameters 150 and 250 mm; bed and

freeboard heights 1500 and 3500 m The initial bed inventory in

all tests was 12 kg, which was kept roughly constant by controlling

the pressure drop across the bed The rig, test procedure, and

anal-ysis of results have been reported in detail in Refs[29,51]

Other inputs needed for the simulations are the following:

attri-tion and fragmentaattri-tion parameters obtained from measurements

in a lab-scale FB with wood pellets[28]and literature data for

var-ious biomasses [43] The inputs chosen for the simulations are:

Katt= 1  107, n1= 2, n2m= 3, andr= 0.8 The calculated shrinkage

factor isu= 1.22, and the resulting average char particle size in

the bed is dch= 2 mm The tar component is given by the species

CkHlOm with k = 6; l = 6.2; m = 0.2, estimated from [33,34] The estimated heating values of char and tar are 33 and 37 MJ/kg The char reactivity and other kinetics are given inTable 1

parameters together with those calculated by the model The

mod-el was run at the same temperatures as those measured in the experiments by adjusting the heat loss, which varied between 2% and 14% of the heating value of the fuel In all the tests, except in the first one, there was no mechanical extraction of material from the bed The factor fWGSRwas evaluated by setting the temperature

in the freeboard about 100 °C lower than that of the bed to repre-sent the temperature drop measured in the experiments

The gas composition was generally well predicted Especially, the model confirmed that the methane in the outlet gas is nearly that produced during devolatilization and calculated methane con-version was below 0.01% in all the tests This fact underlines the importance for the model of a good estimation of the methane yield from devolatilization The carbon in the gas phase is reason-ably well predicted, although the distribution between CO and CO2

calculated by the model varies to some extent More importantly, the main changes during variations in the operating conditions were captured by the model The char conversion does not seem

to agree too well with the two tests where it was measured Nevertheless, the measured values of char conversion reported in

[29]have to be taken as rough estimates, since they were the average values calculated from the material collected in the cyclones after two or three tests conducted under different operating conditions The simulations show that the WGSR is far from equilibrium, with

fWGSRranging from 0.40 to 0.65 In all simulations, the rate of entrain-ment of coarse char was much smaller than that of attrition, i.e

xch,coar,3 xch,fin,3and x3 xch,fin,3 The model was capable of predicting the distribution of the four main species (CO, CO2, and H2 and H2O) reasonably well, as as-sessed by inspection of CO, CO2, and H2 in the table (H2O was

Table 2

Comparison of model results with FB gasification tests from [29,51]

Flowrates

Air (Nm 3

Representative parameters

Throughput ðkg biomass=m 2

Temperature and wall-heat loss

Rate of ash removal

Bottom discharged (kg/h) 

Gas phase outputs

Tar (g/Nm 3

LHV (MJ/Nm 3

Solid conversion outputs

not measured in the experiments) Test 1 was simulated with a bed

discharge of 6 kg/h Although the reported test is said to be without

significant bed removal, the authors detected an accumulation of

material in the bed during the tests, noted by the increase of

pres-sure in the bed, so steady state conditions were not reached This is

reasonable, since the throughput of Test 1 was higher than in the

rest of tests and higher than that usually attained in bubbling

FBG (between 300 and 800 kg/(m2h)

The comparison is a positive proof of the prediction ability of

the model However, detailed conclusions should be taken with

caution because some key information from the measurements is

not clearly reported, such as: char accumulation during the

start-up period, the run time of each test after steady state (in fact,

stea-dy state of the char load has to be reached, and in some test this is

doubtful, as discussed above in relation to Test 1), the sequence of

opening the bottom ash pipe for ash removal sometimes applied,

the amount of fly ash collected, and its composition An attempt

was made in[29,51]to minimize the heat loss during the tests in

order to simulate industrial autothermal units, where the heat loss

is small However, the simulation showed that the heat loss was

significant and varied between the tests The most likely

explana-tion is that steady-state operaexplana-tion was not completely achieved in

some tests In such a case, the additional heat requirement is not a

heat loss through external surfaces but additional heat required for

heating up the material Due to the large size of the pilot gasifier

and the limited gasification runtime, this amount of heat could

be significant This aspect is common to most pilot and laboratory

units As we demonstrate below by means of a sensitivity analysis

of the model, this information is of concern for the simulation of

the real experimental operation point

3.2 Sensitivity analysis and comparison with other QEM

The model is used to analyze the performance of an FBG under

different conditions Simulations are made for the same fuel as in

the previous section The flowrate of biomass per unit of cross

sec-tion of bed, the throughput Th kg/(m2h) characterizes the

opera-tion of the unit, allowing scale-up of results to geometrically

similar FBGs (freeboard height to bed diameter ratio and mass

inventory/bed diameter) The pilot FBG analyzed in the previous

section is taken as reference geometry The same fuel (wood pel-lets) and char reactivity and fragmentation parameters are chosen The gasification agent enters the reactor at 400 °C and the wall heat loss is 3% of LHV of the input fuel in all simulations below

3.2.1 Effect of oxygen equivalence ratio (ER) and comparison with existing QEM

The main reason for the development of the present model was the uncertainty caused by the assignment of an arbitrary value of char conversion Xchin the existing QEM[22] To study this aspect,

gas as a function of equivalence ratio (ER) calculated with a QEM

at various pre-assigned (not calculated) values of Xch Actually, the model developed here becomes a QEM by setting char, meth-ane and tar conversion, as well as the convergence factor fWGSR,

to prefixed values (not calculated as a function of process condi-tions) Under such conditions the model was used with XCH 4¼ 0,

Xtar= 1, and fWGSR= 1 for various Xch, as shown inFig 3 The results reveal that the value assigned for Xchhas a major effect on the tem-perature and the gas heating value, and therefore on other param-eters like gas composition and cold gas efficiency The higher the char conversion, the lower the temperature of the reactor and the higher the heating value of the gas produced, because at higher conversion more heat is consumed due to the endothermicity of the char gasification reactions In other words, sensible energy from the gas is transformed into chemical energy in the gas (mainly into H2and CO)

The circle line inFig 3shows the result from the present model without pre-assignment of char conversion, but keeping the other parameters at the same fixed values as used in the QEM: XCH 4¼ 0,

Xtar= 1, and fWGSR= 1) It is demonstrated that the char conversion varies significantly with the operating conditions (from about 0.5

at ER = 0.2 with a temperature around 730 °C, up to nearly 1 at

ER = 0.3, where the temperature is roughly 905 °C)

The dashed–dotted line inFig 3shows the result of the com-plete model, i.e with the calculation of all parameters Xch, XCH 4, Xtar

and fWGSRas a function of operating conditions XCH 4, Xtarand fWGSR

calculated with the model for ER varying from 0.2 to 0.3, range from 0.001 to 0.03, from 0.89 to 0.97, and from 0.7 to 0.8, respec-tively By comparison of dashed-dotted and circle lines inFig 3it is

(b) (a)

600 650 700 750 800 850 900 950 1000 1050 1100

Equivalence ratio (ER)

0.50 0.75

1.00

Xch calculated with complete model oooXchcalculated with simplified model

5 5.5 6 6.5 7 7.5 8

Equivalence ratio (ER)

1.00

0.75 0.50

Fig 3 Temperature and lower heating value of the gas produced as a function of equivalence ratio Solid lines were calculated with a QEM with X CH 4 ¼ 0; X tar = 1 and

f WGSR = 1, for various values of X ch , 0.25, 0.5, 0.75 and 1 (each of the solid curves) The circle line curve was made with the present model calculating X ch as a function of process conditions, but with pre-assigned values of X CH4, X tar , and f WGSR , equal to those used in the QEM to calculate the solid lines (X CH4¼ 0; X tar = 1 and f WGSR = 1); the dashed–dotted curve is calculated by the complete model, i.e calculating all parameters X , X , X and f as a function of process conditions.

concluded that the influence of XCH 4 and Xtaris not so significant,

whereas the calculated fWGSRleads to visible differences in

temper-ature (comparison between dashed line and circle line inFig 3a),

and, especially, in the heating value of the gas (Fig 3b) InFig 4

the corresponding char conversion and gas composition are shown,

using the complete model The char conversion rises from 0.6 at

ER = 0.2 (730 °C) to nearly 1 at ER = 0.3 (905 °C) The molar fraction

of CO in the gas presents a maximum at ER = 0.28 where the char is

almost converted (carbon boundary point), whereas hydrogen

de-creases monotonically with ER While the available char dede-creases,

the additional oxygen tends to burn the fuel gas, increasing the

temperature and diminishing the heating value of the gas, as seen

in the circle and dashed–dotted lines inFig 3

It is concluded that the most important parameter to be

esti-mated is the char conversion Tar concentration in the gas is small

and methane is not converted, so very little influence on the

com-position and thermal efficiency of the process is expected from the

variation of these parameters around the pre-assigned values, 1

and 0, respectively The approach to WGSR equilibrium changes

the gas composition significantly, but its impact is less significant

than that of char conversion Therefore QEM having a given char

conversion has no prediction ability A char predictor inside the QEM is needed to capture the change of performance with operat-ing conditions

3.2.2 Effect of throughput (Th) For the calculation of Fig 4 (and the dashed–dotted lines in

m2h (in QEM the throughput does not influence the results, since QEM is not sensitive to the gas flow and other fluid-dynamic pro-cesses that affect the performance of the gasifier) The effect of the throughput is studied with the present model inFigs 5–7 The rise

in Th (a higher fuel flowrate in the given gasifier) increases the gas velocity and so the rate of entrainment; the temperature and the reactivity increase, resulting in lower char load in the bed The gas velocity (in fact, u0 umf) is roughly proportional to Th, so

xch,3,finincreases through the term (u0 umf) but decreases because

mch,bis lower (see Eq.(27)) Therefore, there are two competing ef-fects The char conversion as a function of Th is plotted inFig 5, where a weak minimum is observed in Xchat around 900 kg/m2h

at ER = 0.25 In contrast, the conversion decreases continuously with Th for ER = 0.30, but in both cases the change in Xchwith Th

(b) (a)

0 0.05 0.1 0.15 0.2 0.25

Equivalence ratio (ER)

CO

CO2

H2O

CH4

0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Equivalence ratio, ER

Fig 4 Char conversion (a) and gas composition (b) calculated with the complete model as a function of ER for Th = 500 kg/m 2

h (the corresponding temperature and LHV as a function of ER are those drawn as dashed-dotted lines of Fig 3 ).

(b)

400 600 800 1000 1200 1400 0.886

0.888 0.89 0.892 0.894 0.896 0.898 0.9 0.902 0.904 0.906

Throughput (Th), kg/(m 2h)

ER=0.25

300 400 500 600 700 800 900 1000 0.88

0.9 0.92 0.94 0.96 0.98 1

Throughput (Th), kg/(m 2h)

(a)

ER=0.30

ER=0.25