DOI: 10.3303/CET2292114 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 3 February 2022; Revised: 18 March 2022; Accepted: 13 May 2022 
Please cite this article as: Catalanotti E., Porter R.T., Mahgerefteh H., 2022, An Aspen Plus Kinetic Model for the Gasification of Biomass in a 
Downdraft Gasifier, Chemical Engineering Transactions, 92, 679-684  DOI:10.3303/CET2292114 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 92, 2022 

A publication of 

 

The Italian Association 

of Chemical Engineering 

Online at www.cetjournal.it 

Guest Editors: Rubens Maciel Filho, Eliseo Ranzi, Leonardo Tognotti 

Copyright © 2022, AIDIC Servizi S.r.l. 

ISBN 978-88-95608-90-7; ISSN 2283-9216 

An Aspen Plus Kinetic Model for the Gasification of Biomass 

in a Downdraft Gasifier 

Elena Catalanotti*, Richard T.J. Porter, Haroun Mahgerefteh 

Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK 

e.catalanotti@ucl.ac.uk 

Gasification is a useful technology to recover energy from renewable biomass by producing a versatile syngas 

which can be converted into useful chemicals or fuels, or used directly for energy generation. The quality and 

composition of the syngas is highly dependent on the biomass feedstock, design parameters and process 

conditions, such as temperature, gasifying agent and Equivalence Ratio (ER). Downdraft gasifiers are 

considered to be a good option for low tar syngas production. In this work, a kinetic model for a downdraft 

gasifier is assembled and incorporated into a flowsheet using Aspen Plus with the aim of performing detailed 

process analysis. The model is organised according to the assumption that in a downdraft gasifier pyrolysis, 

oxidation and reduction occur almost as separate consecutive processes, with the pyrolysis considered as an 

instantaneously occurring process while oxidation and reduction are governed by chemical kinetics. The model 

has been validated against experimental data for different conditions of ER ranging from 0.2 to 0.35. The results 

show an overall agreement of the main species, with slight discrepancies in the prediction of CH4, which is over-

predicted at lower ERs and under predicted at ER 0.345. This has an effect on the calculated Lower Heating 

Value (LHV) of the syngas which is generally higher than the experimental value. A set of sensitivity analyses 

were performed to investigate the impact of the value of the Char Reactivity Factor (CRF) on the composition 

of the producer gas and the kinetic parameters used in the model on the production of CH4. Sensitivity analyses 

show that a CRF of 14 gives the best prediction of the syngas composition and that the kinetics of the reactions 

in the reduction zone do not have a large impact on the final levels of methane in the syngas. More important is 

the sensitivity to variation of the kinetic parameters in the oxidation stage. By doubling the rate of oxidation of 

CH4 in the oxidation zone, the final levels of CH4 in the syngas are reduced by almost 20%. 

1. Introduction 

Biomass is plant or animal material that can be used as a fuel-based source of energy and it has attracted 

increasing interest due to its characteristics; it is renewable and considered to be ‘carbon neutral’, meaning that 

the CO2 released when treating the biomass had previously been taken up by the organisms during 

photosynthesis, so no net additional carbon is added to the atmosphere when burnt as a fuel. Biomass 

gasification has been the focus of many studies in the past decades because of its ability to produce a versatile 

syngas which can be converted into useful chemicals or fuels or used directly for energy generation. With Lower 

Heating Value (LHV) typically ranging from about 4 to 18 MJ/Nm3 (Gallucci et al., 2020), biomass-derived syngas 

is mainly composed of H2, CO, CO2 and small amounts of CH4, in concentrations which vary according to the 

type of biomass used, the gasifying agent (such as air, O2-enriched air, steam), ER and the temperature of the 

process. The type of gasifier used and its design also have an impact on syngas composition and level of by-

products produced, such as tar and unconverted char. Given the large interest attracted by the topic and the 

large amount of work available, several review articles have been published recently which help to identify the 

areas needing further development, which are summarised below. 

Although limited to small-scale application, biomass gasification in a downdraft gasifier has the benefit of 

producing low levels of tar (Puig-Arnavat et al., 2010) due to the high temperature oxidation and cracking 

reactions happening in a specific section of the gasifier immediately after tar is formed during pyrolysis. Baruah 

& Baruah (2014) have reviewed numerous biomass gasification studies and analysed the influence of different 

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parameters on syngas quality and offered an overview of the types of models published for different overall 

process configurations. They identified that equilibrium models have been widely used to study the process in 

downdraft gasifiers with satisfying results in predicting the composition of the syngas. The limitation of this type 

of modelling approach is, however, the inability to predict tar and to work in conditions far from equilibrium, for 

example for low temperature processes. A similar conclusion was drawn in the review from Mutlu & Zeng (2020), 

which focuses on process simulation models developed using Aspen Plus. Smith et al. (2019) developed and 

compared an equilibrium model based on the Gibbs energy minimisation method with a kinetic model for the 

gasification of biomass in a downdraft gasifier and found that the kinetic model was more accurate in predicting 

the experimental data used for validation. Despite the large number of models developed, experimental data on 

downdraft gasifiers are less available and sometimes design details are not adequately described. This presents 

an obstacle for the validation of a model over a large number of conditions, including feedstock types and 

gasifying agents. 

In this work, a kinetic mechanism previously developed by the authors (Catalanotti et al., 2020) was incorporated 

into Aspen Plus with the aim of reproducing the sequence of three thermochemical stages for design of a 

downdraft gasifier, which are described here. Biomass is fed at the top of the gasifier and first it encounters a 

hot region, typically with temperatures between 500 and 700 °C. In this condition, the complex structure of the 

feedstock is broken down to form a mixture of gases, comprising mainly H2, CO, CO2, CH4 and H2O, along with 

char, mainly composed of carbon, and tar, a liquid by-product composed of a mixture of aromatic hydrocarbons. 

This process, called pyrolysis, occurs very quickly and is therefore often simulated using equilibrium models. As 

the hot pyrolysis products travel downwards, they enter a second zone, in the throat of the gasifier, where air or 

O2-enriched air is injected and oxidation reactions take place. This section of the gasifier reaches temperatures 

of around 1000 °C (Zainal et al., 2002) and provides the necessary energy for the entire process. In order to 

obtain a high energy syngas, oxidation must be incomplete and a large amount of char must be left 

uncombusted. Typically, the optimal amount of O2 fed to the system is just under a third of the stoichiometric 

value for a given feedstock. In this stage, part of the tar formed is also combusted and a kinetic approach can 

be used to assess the impact of variation of ER and temperature on the production rate of tar. Once the O2 has 

been consumed completely, the gases, char and tar left, keep travelling downwards and enter the reduction 

zone. Here the temperature reaches values of about 700 – 800 °C (Zainal et al., 2002) and the endothermic 

gasification reactions take place, along with steam methane reforming and water gas shift reactions, resulting 

in the production of the final syngas product. A schematic of the process and the reactions considered in this 

study are presented in Figure 1. 

 

Figure 1: Schematic of the consecutive stages occurring during biomass gasification in a downdraft gasifier. 

2. The Aspen Plus model 

Aspen Plus is a process simulation software used to model, simulate, and optimise complex processes such as 

biomass gasification. Biomass has a complicated structure made of large polymeric molecules such as lignin, 

which can vary drastically for different feedstocks. It cannot therefore be found as a pure chemical compound 

in any database but it must be defined in Aspen Plus as a nonconventional fuel, characterised by its proximate 

and ultimate analyses, which are used to generate material balance calculations in later stages. The model 

proposed here follows the rationale described above for a downdraft gasifier. Figure 2 shows the flowsheet built 

to simulate the sequence of thermochemical stages biomass undergoes, from wet biomass to the produced 

syngas. The red streams represent the biomass and the species it converts into, while the blue streams 

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represent external inputs. Table 1 presents the list of units employed in the flowsheet with a short description of 

each block. 

 
Figure 2: Aspen Plus flowsheet constructed for the present work. Red streams represent the motion of the 

biomass material and derivatives.  

Table 1: List of units used in the Aspen Plus flowsheet. 

Block name Type Description 

WATER-CALCULATOR Fortran Calculator Controls the fraction of water removed during the drying step. 

DRY-REAC RStoic Simulates the drying process. 

DRY-FLSH Flash 2 Separates the solid dry biomass from the hot gas and water vapour. 

SEP2 SSOLID Separates the hot gas from the water extracted from the biomass. 

HEATER HEATER Heats the biomass to the temperature chosen for pyrolysis. 

DECO-CAL Fortran Calculator Controls the conversion of nonconventional component into tar, char and 

conventional components. 

DECOMP RYield Converts the nonconventional component into tar, char and conventional 

components. 

SEP SSOLID Separates char, tar and mixture of conventional components. 

PYRO RGibbs Simulates the reactions between conventional components through Gibbs 

free energy minimisation. 

MIX-PYRO Mixer Mixes char, tar and gas producing the pyrolysis products stream. 

MIX-OXI Mixer Mixes the pyrolysis products with air or oxygen. 

OXI RPlug Computes the kinetics of the oxidation zone.  

MIX-STEAM Mix Optional: Mixes oxidation products with additional steam. 

RED RPlug Computes the kinetics of the reduction zone. 

2.1 Physical Properties Model and Model Assumptions 

Aspen Plus allows the user to select several sets of property calculation methods. The choice depends on the 

specific process studied, the conditions used (temperature and pressure ranges) and the nature of the 

components involved. In this work, the IDEAL property method has been chosen as the process involves low 

pressure and relatively small quantities of condensable components (tars). Since biomass and ash were defined 

as nonconventional components, the density and enthalpy calculation methods must be specified, which were 

DCOALIGT and HCOALGEN, respectively. The stream class was defined as MCINCPSD, for the presence of 

both conventional and nonconventional streams with solid particles. Given the complexity of the process, a set 

of assumptions was adopted: the process described is steady-state, pyrolysis is considered instantaneous while 

oxidation and reduction are governed by chemical kinetics and each block is modelled as isothermal. Char is 

modelled as carbon (graphite) and tars are represented by a surrogate mixture of benzene and naphthalene, in 

a ratio of 3:7. The chemistry of minor species, such as sulfur compounds, is neglected. 

2.2 Model development 

A first step is sometimes included for drying biomass. This step is not needed if dry biomass is fed into the 

system instead of the wet biomass. Hot air or nitrogen is mixed with biomass to remove a portion of the moisture. 

The fraction removed depends on the temperature and the amount of hot gas used. Since specifications for 

drying were not available in the set of experimental data employed in this work for model validation, a general 

approach was used when modelling this step (Aspen Technology, Inc., 2013). The drying step produces a water 

stream which can potentially be recycled back into the reduction zone for steam gasification. The dry biomass 

is heated to the temperature at which pyrolysis occurs.   

Since biomass is defined as a nonconventional component, before modelling a chemical process, it needs to 

be ‘decomposed’ into its elemental constituents. This can be done in an RYield block (DECOMP) where the 

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output is defined through a Fortran calculator block as derived from the ultimate analysis. The standard 

subroutine described in Aspen Technology, Inc. (2013) was modified to account for the formation of specified 

yields of tar and char, according to the empirical formula reported by Gomez-Barea et al. (2010). The DECOMP 

stream output was then separated into three sub-streams, CHAR, TAR and a mixture of elemental species (C, 

H2, O2 and S) and ash. The latter stream was inputted in an RGibbs reactor unit to calculate the composition at 

equilibrium of the expected mixture of pyrolysis gases, H2, CO, CO2 and CH4, based on the minimisation of the 

Gibbs free energy of the system. The product streams from the pyrolysis stage, TAR, CHAR and GAS, were 

mixed before entering the oxidation zone. The pyrolysis products were mixed with air (or oxygen) before entering 

the oxidation reactor (named OXI). In this work, oxidation is assumed to be governed by chemical kinetics. An 

RPlug reactor was chosen for this section of the model and the reaction scheme is that proposed by Gerun et 

al. (2008) and includes oxidation of the tar species benzene and naphthalene. The reactor was modelled as 

isothermal. Details of the size of the oxidation zone are difficult to retrieve in literature. The actual length of the 

zone is quite small in comparison to the reduction zone as the oxygen is consumed relatively quickly by reacting 

with H2, CO, CH4 and tar. In this work, the experimental data from Sheth and Babu (2009) were used to validate 

the model and the sizes of the reactors are based on their findings as will be discussed in Section 2.3. Oxidation 

products exit the OXI block and can be mixed with steam for steam enhanced gasification before entering the 

reduction zone, modelled with a second Rplug reactor (named RED). The heterogeneous reactions in the 

reduction zone were modelled according to a Langmuir-Hinshelwood mechanism as shown in equations 1. 

 𝑟𝑎𝑡𝑒 =  
[𝐾𝑖𝑛𝑒𝑡𝑖𝑐 𝐹𝑎𝑐𝑡𝑜𝑟][𝐷𝑟𝑖𝑣𝑖𝑛𝑔 𝐹𝑜𝑟𝑐𝑒]

[𝐴𝑑𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛]
 (1) 

where the Kinetic Factor is defined by the Arrhenius parameters, i.e. the pre-exponential factor k and the 

activation energy Ea, the Driving Force is a function of the concentration of the species and the Adsorption 

Factor takes into account competitive adsorption on the char surface due to the co-presence of several gases. 

The reaction scheme, along with the kinetic parameters and adsorption constants, were based on the work 

published by the authors (Catalanotti et al., 2019). The parameters were recast for compatibility with the Aspen 

Plus format and are reported in Table 2.  The factor k is a combination of the frequency factor of the reaction 

(Wang and Kinoshita, 1993) and a Char Reactivity Factor (CRF), which is a function of char density and particle 

size, for which a reference value of about 15 was calculated based on the expressions and values of char 

characteristics used in Catalanotti et al. (2019) and extensively described in Wang and Kinoshita (1993). 

Table 2: Kinetic parameters used in reduction reactor (RED).  

Reaction  Frequency factor (s-1) CRF k (s-1)  E (kJ/kmol) 

C+CO2 → 2  CO 36.16 14.8 535 77,390 

C+H2O →  CO + H2 15,170 14.8 224,705 121,620 

C+2H2 → CH4 0.004 14.8 0.062 19,210 

2.3 Model validation 

The model was validated against experimental data provided by Sheth and Babu (2009) which investigated the 

syngas composition and heating value obtained from biomass gasification in a downdraft gasifier for different 

values of ER, ranging 0.2 to 0.35. The composition of the biomass was the same reported in Sheth and Babu 

(2009), with the proximate analysis (% by wt. dry basis) reporting fixed carbon 15.70%, volatile matter 80.40% 

and ash 3.90% and the ultimate analysis (% by wt. dry basis) reporting carbon 48.6% hydrogen 6.2% oxygen 

44.87% and nitrogen 0.33%. A scale-up of the whole system was necessary to avoid computational errors 

encountered when the flow rates of the feed were as low as those employed in the experiments. The sizes of 

the reactors were enlarged accordingly to retain equivalent residence time in each section. Details of the 

conditions and the calculations used for the scale-up are listed in Table 3 for ER = 0.2533.  

Table 3: Experimental and model gasifier design parameters. 

Parameter  Sheth & Babu (2009) Current Model  

Biomass Flowrate (kg/hr) 2.1 21  

Air (kg/hr) 4.9 49  

ER 0.253 0.253  

Oxidation zone Volume (mm3) 937 4,683  

Reduction zone Volume (mm3) 1,767 22,443  

Overall Calculations for Scale-up    

Tot Vol m3 2.7 27  

Biomass/Air 0.43 0.43  

Air/Volume 1.81 1.81  

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The same approach was used to run the cases with different flowrates and ERs. A comparison of the model 

results with the experimental data is reported in Figure 3. The results in Figure 3a show the composition of the 

syngas at the exit of the gasifier. The model is able to satisfactorily predict the relative levels of the main species 

responsible for the energy content of the syngas, H2 and CO. However, an accurate comparison reveals slight 

discrepancies in the prediction of CH4, which is over predicted at lower ERs and under predicted at ER 0.345, 

while N2 levels are overall under predicted by the model. The LHV for the gas was calculated according to the 

composition and the values published in Waldheim and Nilsson (2001) for H2, CO and CH4 (Figure 3b). The 

discrepancies described above have a direct impact on the LHV of the producer gas which is higher than the 

experimental value for ER = 0.2054 and 0.2533 as CH4 has a relatively high heating value for unit of volume in 

comparison to H2 and CO, so small variations in the composition have a visible effect.  
 

 
Figure 3: Comparison of model predictions with experimental data from Sheth and Babu (2009); a) presents the 

syngas composition for different values of ER; b) presents the LHV calculated for the gas at different ER values. 

3. Sensitivity Analyses 

In order to investigate the effect of the kinetic parameter variation on the syngas composition and, in particular, 

on the concentration of CH4, sensitivity analyses were performed and the results are shown in Figures 4 and 5. 

Since information on the char was not given in the experiments, the main uncertainty was the CRF. The impact 

of varying the CRF from 1 to 45 on all the species compared with the experimental data is shown in Figure 4 for 

ER = 0.2533. The CFR initially chosen as a reference value of roughly 15 gives already a good representation 

of the main species (Figure 4a) with deviations from the experimental data of CO and H2 equal to 3.7% and 

2.3% respectively, with the best results obtained for CRF 14 where the deviations are 1.4% and 1%, while there 

is negligible effect on methane concentration. Moreover, while reducing the CRF to about 7-8 improves the 

prediction of N2, as less char is converted to gas, it also negatively affects the accuracy of predicting the 

concentration of most important components H2 and CO.  

The effect of the variation of the pre-exponential factor for each reaction on the final concentration of methane 

in the syngas was also investigated with the most influential reactions plotted in Figure 5. The oxidation reaction 

of CH4 was found to be highly sensitive, with doubling the reaction rate leading to final levels of methane in the 

syngas being reduced by almost 20% (Figure 5a). For the reduction zone the pre-exponential factor of each 

reaction was varied in the range from 1 to 100. The results showed that the Water-Gas reaction (i.e. the reaction 

of water with char) has the greatest effect on methane formation (Figure 5b). This is mostly due to the increase 

of H2 which in turn shifts the Steam-Methane-Reforming reaction towards the production of more CH4. The value 

used for the base-case scenario gives, however, the minimum CH4 levels already. The kinetics of the other 

reactions in the reduction zone do not have a large impact on the final levels of methane in the syngas. 
 

 
Figure 4: Sensitivity of syngas composition on value of CRF for the heterogeneous reactions. The dotted lines 

represent the experimental values while the plain lines are the model predictions for different CRF values.  

683



 
Figure 5: Sensitivity of pre-exponential parameter value for selected chemical reactions on CH4 concentration 

vol% in syngas, i.e.; a) CH4 oxidation; b) three heterogeneous reactions. The dotted line represents the nominal 

parameter value. 

4. Conclusions 

A kinetic scheme was incorporated into Aspen Plus to simulate and predict the syngas product composition of 

biomass gasification in a downdraft gasifier. In the model, the layout of the gasifier is taken into consideration 

and pyrolysis, oxidation and reduction are modelled as consecutive stages. Pyrolysis is considered to occur 

instantaneously and is modelled through a combination of temperature dependent empirical relationships to 

generate tar and char. An RGibbs reactor is used to define the composition of the pyrolysis gas, while oxidation 

and reduction are modelled in two consecutive RPlug reactors and are governed by chemical kinetic reactions. 

A comparison with experimental data reveals that the performance is satisfactory in regards to the main species 

with minor deviation in the prediction of CH4. Sensitivity analyses performed on kinetic parameters identified 

that the rate of methane oxidation is the most influential factor in the model governing the predicted CH4 syngas 

concentration. 

Acknowledgements: This work was supported by a Daphne Jackson Trust Postdoctoral Fellowship, funded 

by EPSRC and University College London. 

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