Review of Kinetic and Equilibrium Concepts for Biomass Tar Modeling by Using Aspen Plus

Review of kinetic and equilibrium concepts for biomass tar modelingby using Aspen Plus

A.M.A Ahmed, A. Salmiaton n, T.S.Y Choong, W.A.K.G. Wan AzlinaDepartment of Chemical & Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

a r t i c l e i n f o

Article history:Received 26 November 2014Received in revised form16 May 2015Accepted 27 July 2015

Keywords:Biomass tarModelingAspen PlusKineticThermodynamic equilibrium

a b s t r a c t

Biomass tar has attracted attention in recent years to be modeled or represented by a specific formula orcompound. It is a complex material, and its composition varies according to the process operatingconditions such as gasification or pyrolysis. This paper reviews different tar models in which tar isrepresented as different components such as naphthalene, toluene and even as a bulk tar, based onoperating temperatures range and their thermal stability or assumptions that have been made to modelthe process. All these models are done by Aspen Plus simulator based on kinetic and thermodynamicequilibrium, whereby different reactor models are used to represent processes in relevant with tarproduction or cracking. Results for the operation of combined heat and power (CHP) biomass bubblingfluidized bed gasification, which integrated with solid fuel cell (SOFC) or coupled with an internalcombustion engine (ICE), show different accuracy in terms of cold gas or electrical efficiencies,depending on how tar is approximated (either as one hydrocarbon compound or mixture ofhydrocarbons). Likewise, for three-stage and one fluidized bed unit, the performance is predictedthrough estimation of the cold gas efficiency and high heating value (HHV) of the produced gas, wherethe tar representation has also an impact on the accuracy of the predictions.

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Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16252. Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16253. Principles of chemical equilibrium and kinetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16264. Biomass tar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1628

4.1. Tar definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16284.2. Tar composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629

4.2.1. Primary tar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16294.2.2. Secondary tar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16294.2.3. Tertiary tar products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1629

4.3. Sampling and characterization of tar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16304.3.1. Tar sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16304.3.2. Characterization of tar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1631

4.4. Tar reaction mechanism and kinetic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16315. Aspen Plus tar models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1631

5.1. Combined heat and power (CHP) biomass bubbling fluidized bed gasification unit coupled with an internal combustion engine (ICE) 16315.2. Integrated atmospheric pressure solid oxide fuel cell (SOFC) with biomass steam gasification process and combined heat and power (CHP)

system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16335.3. Biomass gasification fuel cell (BGFC) and biomass gasification combined cycle (BGCC) systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16345.4. Biomass integrated gasification combined cycle (BIGCC) technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16355.5. Complete life cycle inventory of a biomass gasification integrated with heat and power (CHP) plant . . . . . . . . . . . . . . . . . . . . . . . . . . 16355.6. Pressurised steam/O2-blown fluidized-BEd gasification for biomass. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1638

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n Corresponding author. Tel.: þ60 3 89466297; fax: þ60 3 86567120.E-mail address: [email protected] (A. Salmiaton).

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Nomenclature

ΔrG reaction Gibbs energy (kJ/mol)A, B reactants in chemical equationa, b, c, d stoichiometric coefficient of components a, b, c, dA, k0, k0,app pre-exponential factor (1/s)C carbonC, D products in chemical equationC10H8 naphthaleneC12H8 acenaphtyleneC14H10a anthraceneC14H10p phenanthreneC16H10 pyreneC2H4 ethyleneC2H4O2 acetic acidC2H6 ethaneC3H3 2-cyclopropenyliumC3H8 propaneC6H6 benzeneC6H6O phenolC7H8 tolueneC7H8O benzyl alcoholC8H8 styreneC9H8 indeneCH4 methaneCi concentration of component iCmHy hydrocarbon with smaller carbon numberCnHx tarCO carbon monoxideCO2 carbon dioxideE, Ea, Eapp activation energy (kJ/mol)fact activity factorG Gibbs free energy (kJ/mol)GS/MS gas chromatography–mass spectrometryH2 hydrogenH2O waterH2S hydrogen sulfidehcg cold gas efficiencyHCl hydrogen chlorideHCN hydrogen cyanideHHV high heating valueht thermal efficiencyk rate constantLHV lower heating valuen stoichiometric coefficient of components CO, CO2, Cn stoichiometric coefficient of H2On, x, r stoichiometric coefficient of H2

N2O nitrous oxideNH3 ammoniaNO nitrogen oxideNOx, SOx Gaseous emissionsO2 oxygenp stoichiometric coefficient of tarpi partial pressure of component iq stoichiometric coefficient of light hydrocarbonsR universal gas constantri rate of reaction of component iSOx sulphur oxidesT temperatureTsK solid structure temperature (K)v0 volumetric flowrate (m3/h)

W weight (kg)X conversion

Subscripts

app apparentcg cold gasm number of atoms of C in light hydrocarbonn number of atoms of Ct thermaltar tarx number of atoms of H2

y number of atoms of H2 in light hydrocarbon

Greek letters

μa, μb, μc, μd chemical potential of components a, b, c, dτ W/v0

Abbreviations

BFB bubbling fluidized bedBGCC biomass gasification combined cycleBGFC biomass gasification fuel cellBIGCC biomass integrated gasification combined cycleB-IGFC biomass-integrated gasification fuel cell systemsCHP combined heat and powerCR char converterDDGS distiller dried grains with solublesDSS dried sewage sludgeDWG distiller’s wet grainsEFFLUSEP flash separator effluent stream name in Aspen PlusER equivalence ratioFB fluidized bedFBD fluidized bed devolatizerGE general electricICE internal combustion engineIEA International Energy AgencyIGCC Integrated gasification combined cycleIPA iso-propanol solutionLS sealNCGR non-catalytic gas reformerNREL National Renewable Energy LaboratoryPAH polyaromatic hydrocarbonsPTR pneumatic transported riserRCSTR continuous stirred tank reactor model in Aspen PlusREQUIL equilibrium reactor model in Aspen PlusRGIBBS Gibbs reactor model in Aspen PlusRPLUG plug flow reactor in Aspen PlusRSTOIC stoichiometric reactor model in Aspen PlusRYIELD yield reactor model in Aspen PlusSOFC solid oxide fuel cellSOR steam to oxygen ratioSTAGSIFYrecycle stream name in Aspen PlusSTBR steam to biomass ratioSTR steam reforming reactionsTARIN stream name for tar in Aspen PlusTES thermal energy storageTNEE Tunzini Nessi Entreprises d’Equipements process

A.M.A Ahmed et al. / Renewable and Sustainable Energy Reviews 52 (2015) 1623–16441624

5.7. Staged-gasification system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16385.8. Different systems and different tar representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1640

6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1642Appendix A. Supplementary information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643

1. Introduction

The continual increase of energy demand and the impact of fossilfuel on the environment have made heavy stress on the developedcountries to look for another alternative for energy supply. Manyresearches have reviewed and discussed alternatives technologiessuch as thermal energy storage (TES), which considered as a friendlyenergy source, utilizing biomass [1–5]. The availability of biomass andits clean relationship with environment, has given it the priority to beunder intensive research. The lignocelluloses polymers such as cellu-lose, hemicelluloses and lignin represent the major portion of theenormous amount of biomass annually produced on land [6].

Cellulose is basically consist of β-glucosidic units in the polymerchain. It forms approximately of 50 wt% on a dry basis of biomasscomponents [7]. The other constituent is hemicelluloses which takeplace a lower percent than celluloses [7]. It contains backbones of 1,4-β linked major sugar units which make its chemical composition closeto cellulose [6]. The morphology of hemicellulose and cellulose arestrongly associated with each other in the plant cell walls [6]. On otherhand, the last constituent lignin can mainly be found in woodybiomass and cellulosic [7]. Moreover, the lignin is also found interrestrial biomass as aromatic polymers that contains phenyl propaneunits, and those units contain benzene rings which substituted bymethoxyl and hydroxyl groups [7].

Thermal degradation of biomass under severe temperature condi-tions leads to produce many different types of condensable hydro-carbons (tar) and depends on that severe temperature. The tarremoval technologies such as reformer or cracking are the main targetby the industrial gasification units since its existence causes severeproblems to the facilities that utilize the produced gases fromgasification or pyrolysis units. Hence, many mathematical modelshave been created and developed to estimate or to optimize theoperating conditions which predict the tar condensation temperature,and to evaluate the performance of gasification unit. Other models aredeveloped by using simulation programs, and Aspen Plus is one ofthem. This simulation software consists of many physical relationshipssuch as material and energy balance, thermodynamic equilibrium andrate equations which enable the designer to predict process behavior.This paper reviews many models of gasification and pyrolysis pro-cesses involving tar formation during the process and its removalusing either reforming or cracking. These reviewed models aredesigned by Aspen Plus by which the tar is assumed to be one ofthe stable constituents, like naphthalene, toluene and benzene incomparison with the other constituents that forming the tar.

2. Gasification

It is a chemical process that occurs in a deficient oxygen environ-ment to convert carbonaceous wastes to useful gaseous with a highheating value or chemical feedstock. In contrast, combustion takesplace in oxidizing environment [8]. Through many years, this processhad been utilized as an energy source for industry and transportation.In recent years, owing to the global warming issues, the seeking forindependence in energy production and the oscillation in prices ofcrude oil motivated some countries to develop integrated gasificationcombined cycle (IGCC) plants.

The typical gasification process follows the scheme illustrated inFig. 1, and consist of many steps [8]. These steps overlap and there areno clear boundaries between them, however in modeling purposesthese steps are done in series for simplification, and they are asfollows.

� Preheating and drying� Pyrolysis� Char gasification� Combustion

nitially, gasification begins with drying step in order to remove themoisture from the feedstock and followed by thermal decompositionor thermal degradation which produces gases, tar and char. Theseproducts react with each other and with gasifying agent such asoxygen, air and steam to form the final gasification products. Thechemical reactions of gasification are listed in Table 1 [7–10].

Many reactors can be used to perform gasification process. Thetype of gas–solid contacting reactors used shows different gasificationreactions sequence [6]. The main reactors for gasification are asfollows:

� Moving-bed reactor: The simple type that explains this kind ofthe reactors is the updraft gasifier reactor, Fig. 2. In this type,the fuel is fed from the top and the produced gases leave fromthe top also [6]. The gasifying agents (oxygen, air, steam or theirmixtures) are preheated and enter the reactor from the bottomthrough a grid. The gases will then rise through the reactormeeting the descending fuel or ash.

Fig. 3 shows the reaction steps in downdraft gasifiers will differfrom those in updraft gasifiers [6]. The gasifying agents andbiomass are both fed into the gasifier lower part. The pyrolysis

Fig. 1. Potential paths for gasification.

A.M.A Ahmed et al. / Renewable and Sustainable Energy Reviews 52 (2015) 1623–1644 1625

and combustion products flow downward, and the hot gases alsomove downward meeting the remaining hot char, where gasifica-tion takes place.

� Fluidized-bed reactor: In a bubbling fluidized bed, the solid fuelis fed either from the top or the sides, and the gasifying agentsare served from the bottom of the reactor [9], Fig. 4. The directcontact of the solid fuel and the hot bed material brings the fuelquickly to the bed temperature which enhances the drying andpyrolysis steps. The gasification reactions occur as the gas rises.

� Entrained-flow reactor: This kind of reactors is commonlyoperated at 1400 1C and 20 to 70 bar. The solid fuel is in apowder condition and can be entrained by gasifying agent. Thistype of reactors is preferred to be used with IGCC plants [6].There are two types of the reactor; in the first one, side feedingfor both biomass and gasifying agent is used [6], Fig. 5a,whereas in the second one, the feeding is from the top forboth biomass and gasifying agent [6], Fig. 5b.

3. Principles of chemical equilibrium and kinetic

Chemical thermodynamics mainly deals with prediction of aspontaneity tendency of the reactants mixture change into products,to predict the reaction mixture composition at equilibrium, and topredict the mechanism involved during the modification of thecomposition by changing the process conditions. Only one thing toremember, thermodynamic never considers the rate of the reaction.The spontaneous change in thermodynamic at constant temperatureand pressure is ΔGo0. Up to these conditions, a reaction mixtureproceeds setting its composition to attain minimum Gibbs energy. InFig. 6(a), we see very small variation in the Gibbs energy of the mixturemeans a very small conversion extent of the reactants before G hasattained its minimum value and the reaction cannot be proceeded. If Gchanges as shown in Fig. 6(c), then a high percent of products mustform before G attains its minimum and the reaction proceeds. In somecases, no significant amount of reactants or products can be found inthe equilibriummixture. However, other reactions have a Gibbs energyvaries as shown in Fig. 6(b), and means a considerable amount of bothreactants and products exist at equilibrium [11].

The reaction Gibbs energy (ΔrG) can be interpreted in two way[11]. First, at specific composition of the reaction mixture, it is thedifference of the chemical potentials of the products and reactants.Secondly, we can consider of ΔrG is the slope of the graph of Gplotted versus the change in composition of the system since ΔrGis the change in G divided by the change in composition, Fig. 7.

For the reaction:

aAþbB-cCþdD ð1ÞIn Gibbs energy of a reaction equation, each reactant multiplied

by their stoichiometric coefficient and subtracted from the pro-ducts that also multiplied by their stoichiometric coefficients

Δr G¼ cμCþ dμD

� �� aμAþ bμB

� � ð2ÞThe chemical composition of a substance is decided by the mixture

composition that contains this substance, and becomes high when itsconcentration or partial pressure is high. So, ΔrG is changed as thecomposition changes, Fig. 8. For the chemical reaction in Eq. (1), wenotice ΔrGo0 which means the slope of G is negative when themixture is rich in the reactants A and B because μA and μB are thenhigh. In contrast, ΔrG40 and the slope of G is positive when themixture is rich in the products C and D because μC and μD are alsohigh. When ΔrGo0 the mixture composition forms more products;while ΔrG40, the reverse reaction is promoted to form the reactants

Fig. 2. Gasification reactions illustrated in stages in an updraft gasifier.

Table 1Gasification Reactions at Standard Temp. (25 1C).

Reaction type Reaction

Carbon reactionsR1 (Boudouard) CþCO222COþ172 kJ/mola

R2 (water–gas or steam) CþH2O2COþH2þ131 kJ/molb

R3 (hydrogasification) Cþ2H22CH4�74.8 kJ/molb

R4 Cþ0.5 O2-CO�111 kJ/mola

Oxidation reactionsR5 CþO2-CO2�394 kJ/molb

R6 COþ0.5O2-CO2�284 kJ/mold

R7 CH4þ2O22CO2þ2H2O�803 kJ/molc

R8 H2þ0.5 O2-H2O�242 kJ/mold

Shift reactionR9 COþH2O2CO2þH2�41.2 kJ/mold

Methanation reactionsR10 2COþ2H2-CH4þCO2�247 kJ/mold

R11 COþ3H22CH4þH2O�206 kJ/mold

R14 CO2þ4H2-CH4þ2H2O�165 kJ/molb

Steam-reforming reactionsR12 CH4þH2O2COþ3H2þ206 kJ/molc

R13 CH4þ0.5 O2-COþ2H2�36 kJ/molc

a Source: [9].b Source: [7].c Source: [9].d Source: [10].


again, means products decomposition occur [11]. At ΔrG¼0 (the pointat which the slope is zero), the reaction will not take place to formeither products or reactants, Fig. 8. That is, the reaction is atequilibrium at constant temperature and pressure [11]. RGIBSS andREQUIL (Aspen Plus reactors models) are based on previously men-tioned criteria.

On contrary, chemical kinetic deals with how fast reactants andproducts are consumed and formed, the response of reaction ratesto change with the presence of a catalyst, and identify the steps bywhich a reaction happens. The reason behind studying the rates ofreactions is the practical importance of being able to estimate howrapidly the equilibrium of the reaction achieved. The factors thatthe rate depends on (under our control) are pressure, temperature,and presence of a catalyst. Choosing the suitable conditions has agreat role in optimizing the rate of the reaction. One more reasonfor studying the reaction rates is to realize the mechanism of thereaction and the sequence of its elementary steps [11].

A kinetic of any reaction which are represented by the rateequation should be well understood, by studying and investigatingthe details of a rate equation, and those are: the order of thereaction, rate constant and kinetic parameters such as A (preexponential factor) and E (activation energy). The rate equation isused to describe the rate of a reaction quantitatively, and toexpress the functional dependence of the rate on temperatureand on the species concentrations. In symbolic form, the rateequation can be written as follows [12],

rA ¼ rA ðT ; all CiÞ ð3ÞFrom Eq. (3) two independent variables are seen; T is the

temperature and the term “all Ci” is present to remind us that thereaction rate can be affected by the concentrations of the reactant

(s), the product(s), and any other compounds that are present,even if they do not participate in the reaction [12]. Another form ofrate equation may include partial pressure of a substance pi,especially for gases.

The coefficient k, which appears in the reaction rate equation, iscalled the rate constant (as in Eq. (4)) [11]. This rate constant dependson the temperature but it is independent of the concentration of thespecies participates in the reaction.

Rate¼ k A½ �½B� ð4Þ

The measuring units of k are always used as a conversion factorfor the product of concentrations of species, which appear in therate equation, to units expressed as a change in concentrationdivided by time. For example, if the rate law is the one shownabove, with concentrations expressed in mol/dm3, so the units of kwill be dm3/mol/s as follows:

dm3= mol= s�mol=dm3 �mol=dm3 ¼mol =dm3= s ð5Þ

The three terms of the left hand side of Eq. (5) are k,concentrations of [A] and [B], respectively, and at the same timethe right hand of the equation denotes the rate. In gas-phasestudies, concentrations are commonly expressed in moleculescm�3, so the rate constant for the reaction above would beexpressed in cm3/molecules/s. We can use the approach justdeveloped to determine the units of the rate constant from ratelaws of any form. For example, the rate constant for a reactionwithrate law of the form k [A] is commonly expressed in s�1 [11].

As mentioned above in the symbolic form of rate equation, it is alsocan be written as a function of temperature and composition [13], or

ri ¼ f 1 temperatureð ÞU f 2 compositionð Þ ð6Þ

¼ kU f 2 compositionÞ ð7Þ

Fig. 3. Downdraft gasifier and gasification reactions.

Fig. 4. Scheme of a bubbling fluidized-bed gasifier.

Fig. 5. Entrained-flow gasifier types: (a) side-fed reactor, and (b) top-fed reactor.


The temperature-dependent term or the reaction rate constantis well defined by Arrhenius’ law:

k¼ k0 U exp �E=RT� � ð8Þ

The parameters of Arrhenius’ law are k0 and E which representfrequency or pre-exponential factor and activation energy, respec-tively. These parameters also called the Arrhenius parameters. Thecalculations of these parameters are done by plotting lnk (rateconstant) versus 1/T (absolute temperature), fromwhich a straightline obtained, Fig. 9. Experiments approved that Arrhenius’ lawshows a good fitting over a wide range of temperature [13].

The mathematical expression of this conclusion is that the rateconstant varies with temperature as mentioned below [11].

ln k¼ interceptþslope � 1=T ð9ÞThis expression is normally written as the Arrhenius equation:

ln k¼ ln A� Ea=RT ð10ÞSince the rate shows dependence on the temperature, so we

can estimate the rate of a reaction at different temperature butwith same concentration [7], suggesting E constant, Arrhenius’ lawexpressed by Eq. (11)

lnr2r1

¼ lnk2k1

¼ ER

1T1

� 1T2

� �ð11Þ

4. Biomass tar

Biomass gasification mainly produces different products: gases,condensable tars, and solids (char and ash). Tars are condensableorganic compounds formed in thermochemical processes such asgasification and pyrolysis. The tar species is a wide range of differentboiling points hydrocarbons that is mainly consist of single-ring tofive-ring aromatic hydrocarbons. Due to difficulty of sampling and

analysis of tar and also gases are the predominant product ofgasification, few previous studies on biomass gasification include theanalysis of tars were done [14].

Most of the data nowadays available on biomass-derived tars wasobtained from pyrolysis studies at low reaction temperatures [14].Nonetheless, the amount and make-up of tar species evolved in thepyrolysis of biomass at temperatures below 600 1C may usually havelittle correspondence to tars extracted in the gasification of biomass/oxidant at temperatures above 700 1C. To conclude, one way to solidengineering information, an ability of operating of gasification systemsand controlling emissions from the process is understanding thefundamentals of tar formation in biomass gasification [14].

4.1. Tar definition

One of the definitions of the tar regards it to be a complexmixture of condensable fraction of the organic products whichproduced from gasification and predominately aromatic hydro-carbons. Due to variable product gas composition that needed for aspecific end-use application and different procedures for samplingand analyzing the tar collected, many operational definitions fortar could be found [15].

biomassþO or H2Oð Þ -CO;CO2; H2O; H2; CH4þother hydrocarbons

-tarþcharþash ð12Þ

-HCNþNH3þHClþH2Sþother sulfur gas

In Eq. (12), many different products from biomass gasificationcan be seen and range from light gases such as CO, H2, CO2, CH4,H2O and N2 to organic (tars). Besides that, inorganic (H2S, HCl,NH3, alkali metals) impurities and particulates also found in theproducts. The organic impurities extend from low molecularweight hydrocarbons to high molecular weight polynuclear

Fig. 9. Arrhenius plot.

Fig. 6. Variation of Gibbs energy of a reaction mixture during reaction course.

Fig. 7. Variation of Gibbs energy at a specific composition of the system.

Fig. 8. Reaction tendency related to ΔrG.


aromatic hydrocarbons. Although the lower molecular weighthydrocarbons are undesirable products in fuel cell applicationsand methanol synthesis, it can be used as fuel in gas turbine orengine applications. The higher molecular weight hydrocarbonscan be termed as “tar”. The specifications of tar are beingrefractory and difficult to remove by neither thermal, catalyticnor physical processes. Moreover, tar can be condensed or poly-merized into more complex structures causing problems (chockingand attrition) in downstream applications such as heat exchan-gers, pipes or on particulate filters. Besides that, the total efficiencycould be reduced and the cost of the process is raised as a result ofthe generation of tar [15].

Formation of tar happens by a series of complex reactionsduring gasification process, and is highly dependent on thereaction conditions. Tar secondary reactions happen in the gasphase after increasing in temperature of the reaction that convertoxygenated tar compounds to light hydrocarbons, aromatics,oxygenates and olefins and subsequently form higher hydrocar-bons and larger PAH (polyaromatic hydrocarbons) in tertiaryprocesses [16].

A scheme proposed by Elliott and summarized by Milne [16]that explains the tar formation is presented in Fig. 10. This schemeshows the transformation of tar over different temperature start-ing frommixed oxygenates compounds (primary tar) to larger PAH[17]. Table 2 shows various types of chemical components corre-sponding each tar class based on Gas chromatography–massspectrometry (GC/MS) analysis of tar [15]. A review study for the

composition of biomass pyrolysis products and gasifier tar fromvarious processes also done by Elliott [17].

4.2. Tar composition

Tar is a complex mixture of various hydrocarbons as shown inTable 3. It consists of oxygen-containing compounds, derivatives ofphenol, guaiacol, veratrol, syringol, free fatty acids, and esters offatty acids [18]. In addition, Table 3 shows that benzene has amaximum percent among other constituents of a typical tar [8,16].The reaction temperature, the type of reactor and the feedstock arethe factors that affect the yield and the composition of tar.

There is another classification for tar and this would be:primary, secondary, alkyl tertiary and condensed tertiary [16].Short descriptions of these sorts of tar are as follow [8]:

4.2.1. Primary tarPrimary tar is a product of pyrolysis at temperatures 673–973 K

[19]. It contains oxygenated, primary organic and condensablemolecules. The main constituents of biomass: cellulose, hemicel-lulose and lignin are responsible for formation of the primaryproducts by direct breakdown of the aforementioned constituentsof biomass. Many compounds such as acids, sugars, alcohols,ketones, aldehydes, phenols, guaiacols, syringols, furans, andmixed oxygenates in this group are listed by Milne et al. [16].

4.2.2. Secondary tarPrimary tar at temperature above 500 1C starts rearranging,

forming more non-condensable gases and heavier moleculescalled secondary tar. Phenols and olefins are considered to beimportant constituents of this kind of tar [8].

4.2.3. Tertiary tar productsThe contents of this kind of tar are: alkyl tertiary product which

mainly contains methyl derivatives of aromatics, such as methylacenaphthylene, methylnaphthalene, toluene, and indene [16];and the other content is condensed tertiary aromatics that makeup a polynuclear aromatic hydrocarbon (PAH) series. This series isnot branched with atoms as a substitute of hydrogen in the mainchain. Benzene, naphthalene, acenaphthylene, anthracene/phe-nanthrene, and pyrene are the constituent of this series. Bothsecondary and tertiary tar are produced from the primary tar.When tertiary products appear, no primary tar can be found [16].

Table 2Tar compounds for different tar classes.

Tarclass

Class name Property Representative compounds

1 GC-undetectable Very heavy tars, cannot be detected by GC Determined by subtracting the GC-detectable tar fraction from thetotal gravimetric tar

2 Heterocyclic aromatics Tars containing hetero atoms; highly water soluble compounds Pyridine, phenol, cresols, quinoline, isoquinoline, dibenzophenol3 Light aromatic (1 ring) Usually light hydrocarbons with single ring; do not pose a problem

regarding condensability and solubilityToluene, ethylbenzene, xylenes, styrene

4 Light PAH compounds(2–3 rings)

2 and 3 Rings compounds; condense at low temperature even atvery low concentration

Indene, naphthalene, methylnaphthalene, biphenyl,acenaphthalene, fluorene, phenanthrene, anthracene

5 Heavy PAH compounds(4–7 rings)

Larger than 3-ring, these components condense at high-temperatures at low concentrations

Fluoranthene, pyrene, chrysene, perylene, coronene

Table 3Composition of Tar.Source: Adopted from Milne et al. [16].

Component Weight(%)

Benzene 37.9Toluene 14.3Other 1-ring aromatichydrocarbons

13.9

Naphthalene 9.6Other 2-ring aromatichydrocarbons

7.8

3-Ring aromatic hydrocarbons 3.64-Ring aromatic hydrocarbons 0.8Phenolic compounds 4.6Heterocyclic compounds 6.5Others 1.0

Fig. 10. Tar compounds transformation scheme proposed by Elliott.


4.3. Sampling and characterization of tar

Sampling and analysis of tars and particulates (gasificationproducts) were done by many different techniques. Therefore, aworking group from the Biomass Gasification Task of the IEABioenergy Agreement was delegated to develop a criterion formeasuring produced gas quality [20]. The new procedure “TarProtocol” adopted different air or oxygen-blown gasifier types(updraft, downdraft/fixed bed or fluidized bed gasifier), operatingconditions (0–900 1C and 0.6–60 bar), and concentration ranges(1–300 mg/N m3) [15].

Moreover, a lot of development has been made for on-linemethods, which is concerned in collecting, identifying and quan-tifying tars in syngas (product gas). Three types for these methodsare available: gas conditioning, sampling and analytical phases. Ascheme proposed for the tar sampling and analysis procedure isshown in Fig. 11 [20]. Several methods were adopted for samplingof tars like trapping of compounds onto cold surfaces and filters,absorption of tars into cold organic solvents or adsorption ontosuitable adsorbents [20]. Gas chromatography (GC) or gravime-trically is used in analytical phase analysis of tar compounds [20].

4.3.1. Tar sampling“European Tar Protocol” has presented a sampling unit for

collecting and analyzing tar from the produced gas in gasification[20]. In this unit, six impinger bottles are ordered in a series way,the first five bottles are filled with iso-propanol (IPA) solution,while the remaining one is left empty, Fig. 12 [20]. For the bottles(1, 2 and 4) are heated to 35 1C, while the rest for the other threebottles (3, 5 and 6) are cooled down to �20 1C. For the sake ofobtaining better gas dispersion, the bottles 2, 3 and 5 are equippedwith glass-sinters. The sixth bottle is needed as a droplet collector,that’s why it is kept empty. A particular filter is prepared to receivethe sample gas and followed by six impinger bottles to collect tarand moisture into the IPA solution, Fig. 12. The order of the bottlesare 1, 2, 3, 4, 5, and 6 to guarantee heat circulation from hot to hot,cold, hot, cold and cold [20]. All these bottles are placed in a styroxbox with a thick styro-foam wall between the warm and coldbaths [20]. Flow rate of gases that pass through the bottles iscontrolled by a regulator which in turn is connected to a pump.After the process of tar collection is finished, the contents ofbottles are collected then tubing and glass parts washed with IPAsolution and the collected solution is made to volume [20].

Fig. 12. Tar sampling unit.

Fig. 13. Characterisation and analysis of tars.

Fig. 11. Tar sampling and analysis procedure.


4.3.2. Characterization of tarTwo kinds of tar are detected from biomass gasification: heavy

tar which condenses and trapped onto cold surface and filters(isopropanol is used to collect it), and light tar which gatheredfrom impinger bottles, Fig. 13 [20].

The collected light tar is analyzed by using two methods, ofwhich the gravimetric analysis is the simplest [20]. The procedureof this analysis implies recording the weight of the tar left in aceramic dish after a 50 ml of isopropanol tar mixture initiallyevaporated in a fume hood for a whole night and then heating upthe mixture in a chamber at 105 1C for 60 min [20]. Later, the tarconcentration in the produced gas is calculated from the total gasflow rate [20]. The second method shows a different technique,where the 50 ml of isopropanol tar mixture is separated in adistillation process that consist of a water bath of 75 1C for 30 min[20]. The products of this process are two fractions of hydrocar-bons: the fraction dissolved in the isopropanol which is called“light hydrocarbons” and a distillation residue [20]. Moreover, athird fraction of hydrocarbons is available in the decanted waterwhich called” water soluble hydrocarbons” [20]. For tar analysispurposes, gas chromatography with mass detection (GC–MS) is themajor analysis technique [20]. Standard gas chromatographyparameters are: column temperature program: 50 1C for 5 min to325 1C at 8 1C/min, stop for 5 min; injector:split, 1:75; injectortemperature: 275 1C; detector temperature: 300 1C; injectionvolume: 1–2 μl; carrier gas: hydrogen or helium, column pressureadjusted so that the linear velocity of hydrogen is 30–55 cm/s andhelium 20–40 cm/s [20].

4.4. Tar reaction mechanism and kinetic

Cracking, steam and dry reforming are reactions that respon-sible for tar decomposition, as shown below [21].

Cracking : pCnHx -qCmHyþrH2 ð13Þ

Steamreforming : CnHxþnH2O- nþ x=2� �

H2þnCO ð14Þ

Dryreforming : CnHxþnCO2 - x=2� �

H2þ2nCO ð15Þ

Carbonformation : CnHx -nCþðx=2ÞH2 ð16Þ

where CnHx and CmHy stand for tar and hydrocarbon with smallercarbon number than CnHx, respectively.

At typical gasification temperatures, the tar composition ismainly consist of polycyclic aromatic hydrocarbons (PAHs—ben-zene, naphthalene, phenanthrene, anthracene, etc.) and somemethylated aromatics [22–25]. Tar with such composition meanspotential problems will appear, and that for two reasons: (1) theyare very stable, refractory, and difficult to crack further and (2) theycause catalyst deactivation by formation of coke on catalyst surface[26]. For these reasons, many authors choose these compounds torepresent tar in tar modeling in order to calculate the kineticparameters of tar as done by Devi et al. [21] and Jess [27] whochoose naphthalene as tar component model, while Simell andHirvensalo [28] used benzene as a component model of tar. Inother research, 1-methylnaphthalene was used by Dou et al. [29]for modeling. The common reaction kinetic equations used in tarkinetic models are shown in the following [15]:

�rtar ¼ kappCtar ; kapp ¼½� lnð1�XÞ�

τ; τ¼ W

v0; kapp ¼ k0;app Uexp �Eapp

RT

� �

ð17Þ

5. Aspen Plus tar models

Aspen Plus broadly can be defined as a process simulationprogramwhich provides an estimation for parameters of a process(pressure, temperature, composition and sizing) under specificconditions by using mass and energy balances relations and phaseequilibrium database. It consists of many built-in unit operationssuch as reactors, separators, heat exchangers, distillation andphysical properties of the materials. In addition, this simulationcan handle unconventional materials such as solids which areconsidered the main constituent in many processes such asgasification. Adopting suitable thermodynamic data, realistic oper-ating conditions and precise equipment models, real processbehavior approached. Process simulation program is a useful toolin planning new process and developing existing one.

Additional characteristics of Aspen Plus are following:

� Rigorous Electrolyte Simulation.� Petroleum Handling.� Data Regression.� Data Fit.� Optimization.� User Routines (This property can be done by writing external

subroutines using Fortran Programming Language).

Aspen Plus has been used in different aspects of modeling suchas steam power plant [30], predicting emissions of NO and N2Ofrom coal combustion [31], catalytic coal gasification in fixed beds[32], biomass gasification in fluidized bed reactor [33], and incombined heat and power (CHP) biomass bubbling fluidized bedgasification unit coupled with an internal combustion engine (ICE)[34].

In spite of the biomass tar is a complex mixture, many authorsrepresented it in Aspen Plus in many ways.

5.1. Combined heat and power (CHP) biomass bubbling fluidized bedgasification unit coupled with an internal combustion engine (ICE)

Damartzis et al. [34] assessed a combined heat and power(CHP) biomass bubbling fluidized bed gasification unit coupledwith an internal combustion engine (ICE), as represented in Fig. 14.A comprehensive mathematical model based on the Aspen Plusprocess simulator was adopted by the author. This model wasbased on the kinetic and equilibrium criteria along with energyand mass balances. For the kinetic principles, a RCSTR (Aspen Plusreactor model) was used to represent the behavior of a fluidizedbed since the characteristics of the bed is similar to the features ofthe RCSTR reactor by assuming a perfect gas mixture. WhereasRGIBBS (Aspen Plus reactor model) reactor block was used tosimulate the volatiles release during biomass decompositionwhich was already occurred in RYIELD (Aspen Plus reactor model)block, and this RGIBBS follows the chemical equilibrium. The tarproduced from pyrolysis was modeled as a mixture of threecomponents: benzene, toluene and naphthalene. The quantitativemeasurement of this tar mixture was assumed to be a 20% w/wconversion of the original biomass to tar and a composition of 60%,20% and 20% for benzene, toluene and naphthalene, respectively[35]. The kinetic data for the formation of the 3 tars compoundsand their decomposition to CO2, H2 and CO was taken from theliteratures, and the tar reactions considered in this model arelisted in Table 4 [36–38].

A sensitivity analysis was accomplished for two main para-meters in the gasification process: equivalence ratio (ER) andtemperature. Syngas (H2þCO) yield was assessed according tovarious values of ER (0.2, 0.3 and 0.4), and three temperatures


values 750, 800 and 850 1C, see Fig. 15. The yield showed highsignificant flat peak at low temperature (750 1C) and ER (between0.2 and 0.3). This is because of low oxygen rate supplied to theunit. In another word, the complete combustion reaction is notfavored at these conditions. Moreover, at low temperatures, theproduced gas residence time is low, which means high conversionof H2 and CO. However, the yield at (850 1C) had higher end value(59.6%) than (800 1C) (58.6%), due to rate of endothermic reactionsincrease at high temperatures.

Fig. 14. Process flow diagram.

Table 4Kinetic parameters considered in the model for the tar oxidation reactions.

Reactions k0 (s�1) E (kJ/mol) Ref.

Tar oxidation reactionsC6H6þ7.5 O2-6CO2þ3H2O 9.55�108 1.25�102 [36]C6H6þ4.5 O2-6COþ3H2O 1.35�109 1.25�102 [36]C7H8þ3.5 O2-7COþ4H2 2.08�109 1.65�102 [37]C10H8þ5 O2-10COþ4H2 2.07�104 80.2 [38]

Fig. 15. Syngas yield variations with air equivalence ratio.

Fig. 16. Syngas yield variations with temperature.

Fig. 17. Gas composition as a function of operating temperature: validation ofexperimental results and simulation predictions. Symbols: experiment (□: CO, ○:H2, Δ: CO2, ◊: CH4), simulation: lines.


Otherwise, the impact of the temperature on the yield of thegas was examined for three equivalence ratios (0.2, 0.3 and 0.4), asshown in Fig. 16. In case of high temperatures and ER equals to0.3 and 0.4, the yield was decreased because of high oxygencontent, where the complete oxidation of char is favored to

produce CO2 than CO. Conversely, at equivalence ratio (0.2), moresyngas is predicted, where pyrolysis is the major controlling step.Furthermore, endothermic Boudouard reaction is favored at lowoxygen rate, and more CO produced from CO2.

The experimental data at the Aristotle University pilot reactorfor olive kernel gasification was used to validate the model. Fig. 17shows the gas composition for different temperatures and specificair ratio (ER¼0.2), whereas Fig. 18 depicts the dependence of gascomposition on different air ratios but one temperature(T¼750 1C) is fixed. It can be observed that the model well fittedthe experimental data, which had the same trends under variousoperating conditions. The deviations (between 2.1% and 8%) wasexplained by using a bed of olivine in the experimental work,which act as a catalyst.

In addition, energetic assessment of the integrated system wasestimated through two indices: the cold gas (hcg) and thermalefficiency (ht), see Fig. 19. The two indices justified both experi-mental results and model predictions in Figs. 15 and 16. In Fig. 19,the behavior of the system was explained by defining two verticalaxes, left axis denotes to the cold gas efficiency and represented bya straight line, while right axis denotes to the thermal efficiencyand represented by a dashed line. Due to high temperatures andhigh values of ER, both efficiencies decreased in the same way asthe syngas yield showed previously in Figs. 15 and 16. Thisdecrease has a direct impact on gas low heating value (LHV) sincethis parameter is proportional to the mole fractions of the syngas.Nevertheless, the total output power that estimated by the modelwas inaccurate. It may be attributed to the approximation of thetar yield and composition by considering it as a mixture of3 components.

5.2. Integrated atmospheric pressure solid oxide fuel cell (SOFC) withbiomass steam gasification process and combined heat and power(CHP) system

Panopoulos et al. [39] investigated the integration of a nearatmospheric pressure solid oxide fuel cell (SOFC) with a novelallothermal biomass steam gasification process together with acombined heat and power (CHP) system of nominal output rangeless than MWe by using Aspen Plus, Fig. 20. The developed steadystate model used thermodynamic equilibrium criteria for repre-senting the process, where it was divided into four subsections:gasification, heat pipes, gas cleaning and SOFC. The subsectionsrelated to SOFC and heat pipe models were incorporated withFORTRAN calculator block. This block was used for calculating theelectrical output power from SOFC, and to estimate: total thermal

Fig. 18. Gas composition as a function of ER: validation of experimental results andsimulation predictions. Symbols: experiment (□: CO, ○: H2, Δ: CO2, ◊: CH4),simulation: lines.

Fig. 19. Cold gas and thermal efficiencies.

Fig. 20. Aspen Plus biomass gasification modeling flow sheet.


resistance, temperature drop and heat transfer rate for heat pipes.This facility of calculations in Aspen Plus is used whenever there isneed to calculate parameters that could not be possible tocalculate them by equations built in the simulator. However, themost important subsection that should be reviewed is that relatedto gasification since gasification is the process which concerned inproduction of tar and syngases. In real gasification process,thermodynamically unstable products such as tar might beformed. The equilibrium models are not able to predict tarformation. So, the model of gasification subsection used correc-tions apart from thermodynamic principles. This was done byusing a split stream that contains some of carbon, which isconsidered one of the constituents of biomass, see Fig. 20. More-over, tar amount was set in order to allow �1–5 g/mn

3 in dry basisproduced gas, where RSTOIC (Aspen Plus reactor model) was usedto represent tar formation. The tar value that selected previouslywas according to the literatures of steam gasification experimentswith catalytic in situ FB (fluidized bed) tar reduction [40]. The nextreactor model RYIELD, was used to convert the non-conventionalbiomass to its elemental composition. The simulation started bychoosing the property estimation model in ASPEN PLUS, whichwas the Redlich–Kwong–Soave cubic equation of state method.Later, tar was modeled as naphthalene because it is the moststable compound in the biomass tar. The elemental compositionrather than tar and methane (CH4) was fed to RGIBSS reactormodel, where equilibrium calculations are done. In this subsection,the parameter steam to biomass ratio (STBR) was studied. Theminimum STBR was thermodynamically estimated to be 0.4 andthis value was to guarantee the complete conversion of the carboninto the gaseous products. It was suggested that the model was

better to run with STBR¼0.6 to get high production of gases andexcess of heat demand is not required.

Tar problems start when it condenses, hence the gas cleaningstage was examined to ensure no lose in product gas sensible heatand water vapor condensation, and no tar condensation in down-stream equipment. Therefore, the dew point of tar was adopted tobe 523 K instead of 473 K (dew point temperature of presentlyidentified tar species), to guarantee that the process will be on thesafe side and to account the heavier tar species. Another reactormodel (REQUIL) was used to estimate the temperature at whichthe impurities (HCl and H2S) reach their lower levels withoutcondensation of tar. This temperature was calculated according tothe equilibrium principles. This model also discussed the relationbetween tar formation issues and the gas cleaning subsection. Thissubsection favored lower water vapor (i.e. lower STBR, steam tobiomass ratio) and temperature to reach 1 ppm of HCl concentra-tion, which can be done at 773 K even for high water vapor andthat means no tar condensation will occur, as represented inFig. 21. While for H2S removal, to reach less than 0.1 ppm requiresa temperature about 573 K for low STBR¼0.6, Fig. 22. As a result,tar condensation can be avoided. For STBR¼2 the cleaning processhave to be done at less than 523 K, which means some heavier tarcondensation might be predicted. Eventually, the model of gascleaning subsection failed to reach the required H2S withoutconcentration of tar condensation at low temperatures.

5.3. Biomass gasification fuel cell (BGFC) and biomass gasificationcombined cycle (BGCC) systems

In this study, two systems were under comprehensive investi-gation and comparison by Jhuma et al.: biomass gasification fuelcell (BGFC) and biomass gasification combined cycle (BGCC)systems [41]. This model compared the energetic and emissionsperformance of the two systems. A detailed process simulationwas done by Aspen Plus in order to develop systematic site-wideprocess integration strategies. For BGFC, Fig. 23, the tar in thisstudy (TARIN, as represented in Aspen flow sheet) was modeled asphenol (the major constituent of tar) as revealed in numerousstudies [42,43], and it was fed into RGIBBS (the reactor model forthe fluidized bed reactors). In another word, equilibrium modelwas adopted instead of kinetic model. The stream of the tar whichenters the RGIBBS was taken from primary pyrolysis of a biomassfeedstock, as provided in Table 5a [44] and b [43]. The separationof tar from syngas was done by flash separator (EFFLUSEP), wherethe cooling was below its dew point, but nothing mentionedwhich temperature was specified as a dew point temperature.

While for BGCC system, the simulation showed that upstreamprocesses (gasification, gas cooling and cleaning processes) wereidentical to the BGFC system which means that tar representationshould also be the same, see Fig. 24. Unlike BGCC system, the BGFCsystem implied a recycle stream (STGASIFY) which compensatedthe reforming reactions of tar by the heat required for steamgasification, while unreacted hydrogen and carbon monoxide inthis stream can guarantee the balance between endothermicsteam gasification and exothermic char combustion. This meansa neutral gasification process can occur.

Eventually, no specific formula of tar was adopted, but tar wasrepresented as a lump component. It was reported that the netpower generation of 601.89 kW and 295.60 kW for BGFC and BGCCsystems, respectively, indicates the powerful mechanism used inBGFC system that implicitly included the effects of tar conversionto syngas (reforming reactions) rather than BGCC system. Whilefor emission performance of a BGFC process in comparison withBGCC process, the simulation of BGFC showed a result of less than0.1 ppm by volume emissions such as H2S, COS, HCN, NH3, nickeland iron carbonyls, mercaptans, naphthalene and organic

Fig. 21. Prediction of HCl concentration for different STBR.

Fig. 22. Prediction of H2S concentration for different STBR.


sulphides, as for BGCC, the results was less than 1 ppm by volumeof emissions.

5.4. Biomass integrated gasification combined cycle (BIGCC)technology

Another Biomass Integrated Gasification Combined Cycle(BIGCC) technology is discussed, but this time by De Kam et al.[45] who explained how to use this process in generation of processheat and considerable amounts of electricity from dry-grind ethanolfacilities. The aim of this study was to estimate the renewableenergy ratio of ethanol production according to the new configura-tion of the system. The ethanol process co-products and other

biomass sources are utilized by this technology. An example ofthese co-products is “Distiller’s Dried Grains with Solubles (DDGS)”.The DDGS is produced from mixing of two process streams:Distiller’s Wet Grains (DWG) and concentrated distiller’s solubles(also known as “syrup”), while the biomass sources is corn cobs.The property analysis of this fuel is taken from Morey [46] andderived from data of five dry-grind ethanol plants. Table 6 showsproperty data of the biomass used in this model [46–48]. TheSilvaGas process was adopted for use in an ethanol plant whichconsist of one gasifier and one combustor. The representation of thisprocess in the Aspen Plus simulation was accomplished by usingequilibrium criteria instead of kinetic, as shown in Fig. 25. Thekinetic parameters of gasification reactions are still under investiga-tion because of their detailed reaction mechanisms is complex [45].Therefore, the two main equipment (gasifier and combustor) weremodeled according to the Gibbs free energy minimization principlesand represented as the RGIBBS reactor model of the Aspen Plussimulator. Tar was modeled as phenol and nothing mentionedabout the reactions that tar can undergo. Moreover, nothingmentioned about the effects of representing the tar as phenol onthe process and on the final products distribution. All the attentionwas about how much heat and electricity were needed for suchprocess. Finally, it was suggested that this configuration showed ahigh value (5.1) of the renewable energy ratio of ethanol productionrather than a value of (1.7).

5.5. Complete life cycle inventory of a biomass gasification integratedwith heat and power (CHP) plant

The first detailed process simulation of wood gasification “byFrancois et al.” revealed a complete life cycle inventory of abiomass gasification integrated with heat and power (CHP) plant[49]. The model was able to predict:

� Emissions (NOx, SOx, and aromatics).� Gasification products (water, permanent gases, inorganics,

particles and tars).

Fig. 23. Simulation sheet in Aspen of material and heat integrated BGFC system.

Table 5(a) Straw ultimate analysis in wt%; (b) composition of (gas, tar and char) afterprimary pyrolysis or devolatilization.

wt% Straw

(a)C 36.57H 4.91N 0.57O 40.70S 0.14Ash 8.61Moisture 8.50LHV (MJ/kg) 14.60Component kg/kg biomass(b)Total devolatilization 0.9600Total gas 0.4760H2 0.0016CH4 0.0241C2 0.1227CO 0.2164CO2 0.0308H2O 0.0804Tar Total devolatilization–total gasChar 1-Total devolatilization


� Thermal and electrical efficiencies.

Aspen Plus modules in combination with FORTRAN sub-modelsand FORTRAN calculators were used to model this process. Kineticprinciples were adopted for char oxidation and pyrolysis second-ary reactions of tar by using external FORTRAN subroutines, whichsupply kinetic laws for these reactions. The composition of thesyngas was validated with experimental results of Tunzini NesssiEntreprises d’Equipements (TNEE) process.

The property methods in this simulation were set as RK-Aspenequation-of-state property model, which able to predict thethermo-physical properties of hydrocarbon mixtures and lightgases (tar mixture properties can be estimated by this method)[50]. The gasifier in this simulation was modeled as three reactionzones based on TNEE technology, as represented in Fig. 26 [51].The three zones are wood gasification, wood pyrolysis and charcombustion.

The first stage in the gasification is pyrolysis, as shown inFig. 27. The pyrolysis was represented as RYIELD block coupledwith FORTRAN sub-routine. This sub-routine includes empiricalcorrelations used in the pyrolysis module to estimate the gascomposition for a biomass which assumed to contain only C, H andO [52]. At the outlet of the pyrolyzer, tar would be one of thegaseous mixture components, which consist of benzene (C6H6),phenol (C6H6O), toluene (C7H8) and naphthalene (C10H8). Inaddition, non-condensable gases and light hydrocarbons could

be found at the outlet of the pyrolyzer. The modeling of tar as avariety of hydrocarbons should improve the prediction of the dewpoint temperature of heavy tars. Gas phase reactions of tar intolighter hydrocarbons and catalytic cracking of tar (chars wasconsidered as a catalyst) occurred in the Freeboard section of thegasifier, which was modeled as RPLUG reactor model, Fig. 27.Kinetic details of these reactions were included in this model [52].Due to lack of information of exact tar composition after gasifier,especially class 4 and 5, and accurate measurement of tar compo-sition for TNEE pilot plant is not available, pushed the author toadjust the gasifier outlet according to the tar composition of theGüssing gasifier, which composed of C6H6, C7H8 and C10H8 [53].User blockþFortran sub-routine was used after Freeboard block tosupply split fractions of C6H6, C7H8 and C10H8, where naphthalene(C10H8) mass flow split in styrene (C8H8)—class 3; in indene (C9H8),acenaphtylene (C12H8), phenanthrene (C14H10p) and anthracene(C14H10a)—class 4; and in pyrene (C16H10)—class 5, Fig. 27. The splitfractions are given in Appendix A (Fig. S1, Supplementarymaterials). Table 7 shows wood and char characteristics andcorrelations [52,54].

Tar sludge, which produced from water treatment unit, wasforwarded to the combustor, where its combustion in RSTOICblock occurred, Fig. 27. The outlet gaseous from the combustorwere containing PAH, which is considered one of the tar consti-tuent, and that consist of naphthalene, acephthylene, anthracene,phenanthrene and pyrene. An assumption was set which impliesthat 2% of PAH in the fuel are not burned.

The catalytic tar reformer was utilizing olivine as a catalyst andrepresented as REQUIL reactor model, as shown in Fig. 28. Thesteam reforming reactions of all hydrocarbons, water gas shiftreactions and ammonia decomposition were included in theREQUIL. The model of the reformer was adopted from NREL(National Renewable Energy Laboratory) model [55]. The purposeof use of FORTRAN calculator was to control different componentconversion efficiencies which are based on Devi empirical resultsand tar conversion is one of them, see Table 8 [21,56,57]. More-over, tar species absorption efficiencies by water scrubber werebased on Rabou experimental results, as represented in Table 8[55,58–60]. The Aspen Plus module for the scrubber was modeledas Flash2 block.

It was suggested that representing tar as a mixture of manycompounds, which belong to many different classes, enabled theauthor to predict the total amount of tar produced from gasifica-tion (50 g/m3 on dry basis and normal condition), as shown inFig. 29. The tar prediction from the model revealed the consistency

Fig. 24. Simulation sheet in Aspen of material and heat integrated BGCC system.

Table 6Property data of biomassa.

DDGS Syrup Corn stover Corn cobsb

Moisture (wt%, wet) 10.1 66.8c 13.0d 13.0d

HHV (dry, MJ/kg) 21.75 19.73 17.93 18.30e

Ultimate (wt%, dry)Carbon 50.15 42.97 45.44 46.58Hydrogen 6.87 7.04 5.52 5.87Nitrogen 4.78 2.62 0.69 0.47Oxygen 33.36 39.07 41.49 45.46Sulfur 0.77 0.96 0.04 0.01Chlorine 0.18 0.35 0.1 0.21Ash 3.89 6.99 6.72 1.4

a Data adapted from Morey [46] unless otherwise noted.b Corn Cob data from Brown [47] unless otherwise noted.c Calculated from ARS Aspen Plus ethanol plant model.d Estimated moisture content necessary for storage.e Butuk and Morey [48].


of the model results with TNEE plant (56 g/m3, including ben-zene). However, the assessment of the conventional gas cleaningconfiguration (cyclone, bag filter, olivine catalytic tar reformer anda water scrubber) showed a failure to reach appropriate dew pointof the tar to less than 35 1C instead of 70 1C. The 35 1C tar dewpoint is one of the requirements of clean syngas to be used in ICengine, according to the GE’s Jenbacher recommendations [61].

The sensitivity analysis was not accomplished to see the responseof the model, especially the catalytic reactions of tar since thesereactions are sensitive of temperature, which was held constant(900 1C) in the model of catalytic reformer. Moreover, gaseousresidence time (1.4) and temperature of the free board (960 1C) ofthe gasifier also fixed, which they are considered the mostimportant parameters that could affect the kinetic of the tar.

Fig. 25. Twin fluidized bed steam gasification model in Aspen Plus.

Fig. 26. Scheme of the TNEE dual fluidized bed gasifier.


5.6. Pressurised steam/O2-blown fluidized-BEd gasification forbiomass

Hannula and Kurkela developed a model of pressurised steam/O2-blown fluidized-bed gasification for wood as a biomass [62].Themodeling of catalytic reforming of hydrocarbons and tar formationwas done by Aspen Plus simulation software, where the main gasescomposition predicted. Moreover, a parametric study submitted inorder to study the main parameters affecting the gasification.

The two main blocks of this model (block 5 and 9) are mainlybased on equilibrium criteria (RGIBBS represents these blocks), asshown in Fig. 30. In another words, they are based on theminimization of Gibbs-free energy. Other blocks were simulatedfar from equilibrium, but not kinetic, using conversion correlationsas in Table 9.

The model calculated tar conversion, which was modeled asnaphthalene (C10H8), and other hydrocarbons conversions (CH4,C2H2, C2H4, C2H6, C3H8, and C6H6) in block 4 (RSTOIC) from theexperimental data. Table 9 shows the conversion equations for thehydrocarbons as a function of temperature of the freeboard. This

temperature has a strong relation with the total tar concentration.Moreover, these equations were included in FORTRAN subroutine forcalculations purposes. The tar and the hydrocarbons were consideredinert compounds in block 5 (equilibrium block) in order to preventtheir decomposition.

The model validated with experimental data and showed areasonably agreement for the main gases composition H2, CO, CO2

and H2O with average error about 12%. Later, in parametricanalysis, it was suggested that the syngas efficiency increasesfrom 70 to 78% due to high conversion in the reformer andfiltration temperature. In addition, the temperature impact of thereformer on the conversion at 950 1C instead of 850 1C causes thedecrease of syngas efficiency by 2%. Another important parameteris the moisture content of the fuel, 9% increase in the efficiencywhen drying occurred from 50% to 10 wt%.

5.7. Staged-gasification system

A new system of staged-gasification based on a fluidized-beddesign presented by Nilsson et al. [63]. This system consists of

Fig. 27. Scheme of the gasification unit model.

Table 7Properties of wood feedstock and char.

Moisture % on wet basis Ash (%) C (%) H (%) O (%) N (%) S (%) Cl (%) LHVanhy. basis (MJ/kg)

Wood 40 0.9 48.6 6.0 44.3 0.14 0.02 0.005 18.1Char – 5.3 88.7 1.0 4.7 0.26 0.05 0.008 31.3� Correlations for calculating the Χ, Η, Ο composition of char in Pyrolyzer [52], with T in KC/100¼�2.4977.10�7� T2þ0.000660002� TH/100¼1.6601.10�7� T2-4.0765.10�4� Tþ0.260630036O/100¼1�(C/100þH/100)� High heating value (HHV) and low heating value (LHV) of wood and char [54], with HHV and LHV in MJ/kg and Ash, C, H, O, N, S, mass fraction in %HHV¼0.3491�Cþ1.1783�Hþ 0.1005� S-0.1034�O�0.0151�N�0.0211�AshLHV¼HHV�2.442�8.936�H/100


three stages: devolatilization of the fuel, homogeneous gasreforming/oxidation of volatiles and heterogeneous reformingreactions of gas over in situ generated char. Kinetic approachwas used to model each stage. The data required for the kineticmodel was obtained either from experiments by using laboratory-scale fluidized bed reactor or taken from literatures. The model

predicted the performance of the new system for gasification ofDried Sewage Sludge (DSS). The main parameters that studied inthis model: The equivalence ratio (ER), steam to oxygen ratio(SOR) and reactor temperature.

The model was divided into four sub-models: fluidized beddevolatilizer (FBD), seal (LS), non-catalytic gas reformer (NCGR)

Fig. 28. Scheme of the syngas cleaning unit model.

Table 8Cleaning equipment modules and their operating conditions, efficiencies and correlations.

Cyclone Catalytic tar reformer Bag filter Wet scrubber

Temperature (1C) 960 960–840 120 120–30Pressure drop (kPa) 0.5 [59] 0.2 [55] 1.5 [59] 0.6 [59]Efficiencies (% on weight basis)Particlesdo5 mm 50–80 [59] – 80 [60] 1005odo20 mm 80–95 [59] – 80 [60] 100d420 mm 95–99 [59] – 80 [60] 100Light hydrocarbonsCH4 – 20 [55] – –

C2H4 – 50 [55] – –

C2H6 – 90 [55] – –

Tars–

Unclass C6H6 – 89.4 [56,57] – 35 [58]Class 2 C6H6O 100 [56,57] – 72 [58]Class 3 C7H8, C8H8 – 93.6 [56,57] – 28 [58]Class 4 C9H8,C10H8,C12H8,C14H10 – 85.1 [56,57] 25 [60] 69 [58]Class 5 C16H10 – 99.9 [56,57] 25 [60] 50 [58]ImpuritiesNH3 – 70 [55] – 99a

H2S – – – 1.7a

H2S – – – 100a

� Efficiencies are defined as follow, where _mCxHy is the mass flow stream in kg/s of the CxHy component

X %ð Þ ¼ CxHy�CxHy

CxHy� 100

� Reforming reactions implemented in the catalytic tar reformer [55]Steam reforming: CxHyþxH2O-xCOþ xþ y=2

� �H2

NH3-1=2N2þ3=2H2

Water gas shift: COþH2O-CO2þH2

a Water scrubber efficiencies for NH3, HCl and H2S are calculated from ELECNRTL property model in Aspen.


and char converter (CR), Fig. 31. In the first sub-model (FBD), asshown in Fig. 31, the RYIELD Aspen Plus block reactor model in thispaper modeled the devolatilization process by using a calculatorblock. This calculator was considered to be the way to estimate theyield of the devolatilization as a function of the FBD temperatureby using experimental correlations, which were mentioned in the

same paper. The yield distribution for this process consist of tar,which was represented as toluene (C7H8), and called a light tar inorder to distinguish it from the heavy tar which is a product ofsecondary tar reactions. Tar yield was calculated in order to fulfillthe carbon and hydrogen balances. The oxidation of tar (toluene,R8-Table 10) was among many oxidation reactions which wasmodeled as a RPLUG (Aspen Plus reactor model) in the sub model(NCGR) [64]. Its conversion reactions were considered a homo-genous by using high temperature and heterogeneous by usingchar as a catalyst. For the homogenous reactions, two sequentialreactions were adopted for the tar reactions:

� Cracking of light tar into heavy tar and light gas (R10-Table 10)[27].

� Reforming of heavy tar into light gas and coke (R11-Table 10)[27].

Both reactions were represented as RPLUG, and for the reform-ing reactions were being found in both sub-models (FBD andNCGR). The other tar conversion reaction was also modeled asRPLUG reactor model, where reforming reaction may occur over abed of char (R12—Table 10) [65], and this reaction was a part of CRsub-model.

According to the tar content in the produced gas, the modelrevealed a good estimation of both light and heavy tar, asrepresented in Fig. 32. Their reduction or conversion was corre-lated with temperature and catalyst existing in each part of theabove mentioned sub-models. In addition, the results showed thatthe impact of the temperature of less than 900 1C would not affectthe reforming of tar inside the gasifier. This is due to thetemperature decreased during the increasing of the steam tooxygen ratio, so reforming of tar was reduced, which elevatedthe concentration of tar in the produced gas. In conclusion, moresteam added to the gasification is not favored because of the tarreforming reaction is not enhanced.

5.8. Different systems and different tar representation

Other papers are found in the literatures but they do notmention which reactor block model was used by Aspen Plus tosimulate the biomass tar reactions or what compound was chosento represent the tar. These papers were prepared by Nagel [66],Heijden [67] and Murakami [68].

Fig. 29. Composition of raw syngas according to (a) permanent gases on wet basis,and (b) contaminants.

Fig. 30. Scheme of the model.

Table 9Conversion equations used to model the non-equilibrium criteria.

Conversions related to gasificationCarbon 0:0155 nGþ86:068 %

CH4 �0:003 nGþ7:074 mol=kgC2H2 �0:00004 nGþ0:06454 mol=kgC2H4 �0:002 nGþ2:987 mol=kgC2H6 �0:001 nGþ1:196 mol=kgC3H8 �0:000155 nGþ0:150921 mol=kgC6H6 0:27 mol=kgC10H8 0:3 mol=kgNH3 0:04154 mol=kgConversions related to reformingCH4 0:2247nR�127:36 %

C2H2 0:8439nR�634:66 %

C2H4 0:3818nR�237:31 %

C2H6 0:2753nR�143:5 %

C3H3 100 %C6H6 0:1875nR�76:532 %

C10H3 94.6 %NH3 1:0679nR�899:25 %

G¼Gasifier freeboard temperature [1C]R¼Reformer outlet temperature [1C]


In a paper that done by Nagel et al., the gas processing of theBiomass-Integrated gasification fuel cell systems (B-IGFC) was modeledby using Aspen Plus and tar was represented as a variety of differentcomponents. These components were reduced to three species:

� Acetic acid is the tar originating from thermal decomposition ofcellulose and hemicellulose.

� Anisole denotes tar originating from the thermal decomposi-tion of lignin such as m-cresol and syringol [69].

� Toluene refers to tar formed in the secondary tar reactions suchas xylene and naphthalene. The steam reforming reactions(STR) for these compounds are as follows:

C2H4þ2H2O-2COþ4H2 ð18Þ

C7H8þ7H2O-7COþ11H2 ð19Þ

Fig. 31. Scheme of the three-stage gasification model.

Table 10Different tar reforming reactions considered in the model and related stoichiometry and kinetic expressions.

Reaction Stoichiometry Kinetic expression Reference

R8 C7H8þ5:5 O2- 4H2O þ7 CO rR8 ¼ kR8 C0:5C7H8

CO2[64]

R10 3C7H8þH2 -C10H8þ 3CH4 þ C2H6þ 6C rR10 ¼ kR10 CC7H8 C0:5H2

[27]

R11 C10H8þ2H2O- 3CH4 þ2 CO þ 5C rR11 ¼ kR11 C1:6C10H8

C�0:5H2

[27]

R12 C10H8þ10H2O- 10CO þ 14H2 rR12 ¼ kR12 CC10H8[65]

Fig. 32. Gas content of light and heavy tar: at the exit of the devolatilizer (FBD), gasreformer (NCGR) and char converter (CR).

Fig. 33. Gasification plant in Aspen Plus.


C2H4O2-2COþ2H2 ð20Þ

C7H8þ6H2O-7COþ10H2 ð21ÞNo specific kinetic models were found for the STR of these

compounds, instead the power law type kinetic model of Achen-bach was assumed [70], and represented by the following equa-tion:

rCx HY STR ¼ f act U4274:0 mol=m2=s=barUpCx HYUexp �82;000 J=mol

RUTsK

� �

ð22Þwith CxHy¼C2H4, C7H8, C7H8O and C2H4O2 and f act¼2.5, 1.5, 1.75and 2.0.

In addition, no Aspen Plus reactor model was specified for thereforming reaction of the tar. The air to fuel ratio was a studied as aparameter, by which keeping the temperature of the cell at itsvalue when reforming reactions potential decreased. It was con-cluded that cell design can fit with variable temperature, gascomposition.

Murakami et al. revealed in his paper substantial engineeringfundamentals relative to chemical reactions and heat/mass balancesfor the development of a bubbling fluidized bed (BFB) biomass gasifiercoupled to a pneumatic transported riser (PTR) char combustor. First, abatch fluidized bed experiment was conducted to estimate C and Hconversion, products characteristic and gas composition. These datawere the input of the simulation that done by ASPEN PLUS. In thissimulation also no specific ASPEN PLUS’s reactor block models werementioned such as the type of the reactors (RYIELD, RGIBBS or others),but just a sketch demonstrated the simulated process, Fig. 33. Inaddition, tar was not represented by any previous models mentionedabove. The tar composition was based on elemental analysis of theproduct tar from a pilot plant, Table 11.

Tar reforming reaction was also considered, as shown in Fig. 34,and the necessary tar formation enthalpy was roughly calculatedaccording to the tar elemental analysis. The model predicted that

all tar generated in fuel pyrolysis was transformed via reformingreaction, and this was one of the reasons that kept the dualfluidized bed working with cold gas efficiency higher than 75% andtemperature of 1073 K.

Heijden and Ptasinski presented an exergy analysis for thermo-chemical ethanol production from biomass. The exergy analysiswas tested against different types of ethanol catalysts such as Rh-based and MoS2-based (target) and different gasification tempera-tures. This process was modeled according to thermodynamicprinciples. In his paper, tar reforming process was also includedbut without specifying any formula for the tar and nothingmentioned about its reactions as was done in other papers. Thetar was modeled based on assumed conversion. Moreover, no clearrepresentation for the tar reforming process in Aspen Plus hasbeen found. In another word, the ASPEN PLUS’s reactor models(RYIELD, RGIBBS, RPLUG and others) are not exactly explained andhow they represent the reforming process. Related to the reform-ing reactions, it was predicted that lowmass flowrate of steamwasneeded with MoS2-based catalyst (target) rather than Rh-based,because the former needs a ratio of H2/CO (1.2) while the latterneeds (2.0). The performance of the reformer was related to thegasification temperature, when an increase in the temperaturecorresponds to high steam is required. Thus MoS2-based catalystshowed high exergetic efficiency (44.4%) than Rh-based catalystwith value of (43.5%).

6. Conclusions

1- Most of papers that reviewed modeled gasifier by using(RGIBBS reactor model), where calculations are done accordingto the minimization of Gibbs free energy, assuming completeconversion of char, and hydrocarbons formation such as tarcannot predicted by equilibrium models. Representing pyroly-sis or gasification reactions according to thermodynamic equi-librium need to be reviewed again since this principle does notcontribute in reactor design. However, some correlations usedtogether with equilibrium models to predict char and tarformation, but still valid to specific conditions under whichthe correlation was established. So, it is suggested to use kineticfor both char reactivity and reforming or oxidations reactions oftar, where more syngas produced especially for tar reactionsand eventually the efficiency of the desired process shall giveaccurate results. In addition, kinetic reactor models is advisedto be used such as (RPLUG and RCSTR) in order to improve thepredictions.

2- The strength of this software in modeling of gasification andpyrolysis is lied in existing of FORTRAN calculator block andFORTRAN subroutine, where complex kinetic issues and designspecification can be programmed and compiled using FORTRANprogramming language. However, there are some weakness inusing this software especially hydrodynamic problems that

Table 11Pyrolysis of fuel at 1073 K in a steam-blown fluidized bed and expected C and H conversions, product distribution and composition at the endof the process.

Conversion to gas (%) C: 63.0, H: 88.7

Product distribution (wt%)a Gas: 75, Char: 22.1, Tars: 2.9Molar composition (vol%) (free of tracer gas) 19:3H2þ38:2COþ9:5CO2þ17:5CH4þ9:9C2H4þ5:2C2H6þ0:4C3H6

Char composition (wt%)b 63:82Cþ0:0Hþ11:57Nþ14:33Oþ0:003Sþ0:36Clþ9:90ashTar composition (wt%)c 64:40Cþ6:23Hþ11:10Nþ15:10Oþ3:01Sþ0:11Clþ9:90ash

a The fraction of tars was from pilot gasification test.b Derived from element mass balance.c From measuring the tar sample taken in pilot gasification test.

Fig. 34. Reactions considered in the model.


cannot be modeled, and kinetic related reactor blocks (RPLUGand RCSTR) are based on ideal mixing behavior.

3- Tar is a complex mixture of many components and its removalis one of the most important factors that employed in assessingof gasification process besides reforming processes and pyro-lysis. The approximation of tar as a narrow range of compo-nents leads to inaccurate results since tar represents a widerange of hydrocarbons. The use of wide variety of tar compo-nents in any model would accurately estimate the efficiency ofthat process and the possibility to predict the tar dew pointtemperature. This temperature is considered to be anotherimportant factor that leads to estimate the condensation oftar and eventually to avoid formation of tar in downstreamfacilities

Appendix A. Supplementary information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.rser.2015.07.125.

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Review of Kinetic and Equilibrium Concepts for Biomass Tar Modeling by Using Aspen Plus

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