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Simulation-based investigation of tar formation in after-treatment systems for biomass gasification

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Even though biomass gasification remains a promising technology regarding de-centralized sustainable energy supply, its main limitations, namely the issues of unsteady operation, tar formation in after-treatment systems, and consequential high maintenance requirements, have never been fully overcome. In order to tackle the latter two deficiencies and to increase the understanding of thermodynamic and thermokinetic producer gas phase phenomena within the after-treatment zones, a numerical system dynamic model has been created. Thereby, naphthalene has been chosen to represent the behavior of tars. The model has been validated against a wide variety of measured and simulated producer gas compositions. This work particularly focuses on the investigation and minimization of tar formation within after-treatment systems at low pressures and decreasing temperatures. Model-based analysis has led to a range of recommended measures, which could reduce the formation tendency and thus the condensation of tars in those zones. These recommendations are (i) to decrease gas residence time within pipes and producer gas purification devices, (ii) to increase temperatures in low-pressure zones, (iii) to increase hydrogen to carbon ratio, and (iv) to increase oxygen to carbon ratio in the producer gas. Furthermore, the numerical model has been included in the cloud-computing platform KaleidoSim. Thus, a wider range of process parameter combinations could be investigated in reasonable time. Consequentially, a simulation-based sensitivity analysis of producer gas composition with respect to process parameter changes was conducted and the validity basis of the above recommendations was enlarged.
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ORIGINAL ARTICLE
Simulation-based investigation of tar formation in after-treatment
systems for biomass gasification
G. Boiger
1
&V. Buff
1
&D. Sharman
1
&M. Boldrini
1
&V. Lienhard
1
&D. Drew
2
Received: 15 April 2020 /Revised: 11 July 2020 /Accepted: 23 July 2020
#The Author(s) 2020
Abstract
Even though biomass gasification remains a promising technology regarding de-centralized sustainable energy supply, its main
limitations, namely the issues of unsteady operation, tar formation in after-treatment systems, and consequential high mainte-
nance requirements, have never been fully overcome. In order to tackle the latter two deficiencies and to increase the under-
standing of thermodynamic and thermokinetic producer gas phase phenomena within the after-treatment zones, a numerical
system dynamic model has been created. Thereby, naphthalene has been chosen to represent the behavior of tars. The model has
been validated against a wide variety of measured and simulated producer gas compositions. This work particularly focuses on
the investigation and minimization of tar formation within after-treatment systems at low pressures and decreasing temperatures.
Model-based analysis has led to a range of recommended measures, which could reduce the formation tendency and thus the
condensation of tars in those zones. These recommendations are (i) to decrease gas residence time within pipes and producer gas
purification devices, (ii) to increase temperatures in low-pressure zones, (iii) to increase hydrogen to carbon ratio, and (iv) to
increase oxygen to carbon ratio in the producer gas. Furthermore, the numerical model has been included in the cloud-computing
platform KaleidoSim. Thus, a wider range of process parameter combinations could be investigated in reasonable time.
Consequentially, a simulation-based sensitivity analysis of producer gas composition with respect to process parameter changes
was conducted and the validity basis of the above recommendations was enlarged.
Keywords Gasification .Tar formation .Naphthalene .Thermodynamic .System dynamic .Simulation
1 Introduction
The method of biomass gasification via drying, pyrolysis, re-
duction, consequential partial oxidation to combustible pro-
ducer gas, gas purification, feeding a gas motor, and produc-
ing electricity via a generator has been applied for decades. It
remains a promising technology regarding future,
decentralized, sustainable energy supply (e.g., Najser et al.
[1], French et al. [2], Reed et al. [3], Sansaniwal et al. [4]).
However, the main limitations of wood gasification systems,
namely the issue of unsteady operation, excessive tar forma-
tion and condensation within after-treatment systems and en-
gines, and consequential high maintenance requirements,
have never been fully overcome. In this context, Groenier
et al. [5] report that an exemplary 50 kW biomass gasification
system requires not less than 30-min maintenance time per
day. Reed et al. [3] point out that tar depositions in after-
treatment systems and gas engines are a cause of failure for
valves and moving engine parts due to sticking. Asadullah [6]
remarks that tar condenses in the low-temperature area in
downstream applications, resulting in plugging and fouling
of pipes, tubes, and other equipment. Palma [7]highlightsthat
tars frequently cause operational problems to equipment.
Furthermore, Palma [7] states that heavy tars (e.g., naphtha-
lene) may condense on cooler surfaces downstream, leading
to blockage of particle filters and fuel lines. The author even
goes as far as linking the efficient removal of tars from the
product gas to the attractiveness of gasification as a whole.
And finally, Ruiz et al. [8] declare that the presence of tars in
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s13399-020-00915-7) contains supplementary
material, which is available to authorized users.
*G. Boiger
gernot.boiger@zhaw.ch
1
Institute of Computational Physics, Zurich University of Applied
Sciences, Wildbachstrasse 21, 8409 Winterthur, Switzerland
2
Kaleidosim Technologies AG, Englischviertelstrasse 33,
8032 Zurich, Switzerland
https://doi.org/10.1007/s13399-020-00915-7
/ Published online: 11 August 2020
Biomass Conversion and Biorefinery (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
the syngas is one of the main technology barriers to the devel-
opment of gasification.
In order to tackle the challenge of minimizing tar formation
as well as to further increase the understanding of thermody-
namic and thermokinetic gas phase and tar-related phenome-
na, this work turns to the development of a system dynamic
numerical gas phase model.
Numerical modelling efforts concerning biomass gasifica-
tion have been undertaken on a wide international basis and
for many years. As summarized by Sharma [9], commonly
used gasification modelling techniques include the application
of thermodynamic equilibrium, chemical kinetics, diffusion-
controlled and diffusion-kinetic approaches, and CFD tools.
Boiger [10] extended the range of applied simulation method-
ology by a system dynamic modelling scheme. The said ap-
proach is further elaborated within the current work. With
regard to thermodynamic equilibriarelated models,
Jarungthammachote et al. [11], Ramanan et al. [12], and
Roy et al. [13] developed software focusing on downdraft
gasifiers. While the former two predicted the composition of
syngas in a downdraft waste gasifier for solid waste as well as
for cashew shells respectively, the latter highlighted the pyro-
oxidation zone and kinetically controlled reduction reactions
in the reduction zone. Babu et al. [14] developed another
equilibrium model in which the effect of oxygen enrichment
of air, preheating of air and steam to air ratio on gas compo-
sition, reaction temperature, and calorific values were
investigated.
By combining the chemical equilibrium and the thermody-
namic equilibrium of the global reaction, Melgar et al. [15]
predicted the final composition of the producer gas as well as
its reaction temperature in a downdraft biomass gasifier. In
addition, Sharma [16] used global reduction reactions apply-
ing thermodynamic principles based on the stoichiometric
approach.
In terms of numerical reaction zone simulation for down-
draft gasifiers, Giltrap et al. [17] developed a model for the
reduction zone. Babu et al. [18] extended that model by in-
cluding variable char reactivity. Jayah et al. [19] on the other
hand developed a model, which incorporates a flaming pyrol-
ysis sub-model by Milligan et al. [20] along with a gasification
zone sub-model. Tinaut et al. [21] described a one-
dimensional stationary model of biomass gasification in a
fixed bed downdraft gasifier. Reed et al. [22] developed a
predictive model for stratified downdraft gasification. And
Murugan et al. [23] as well as Ngamsidhiphongsa et al. [24]
established numerical simulationmodels for downdraft Imbert
gasifiers.
Neither of these models considers tar within the product
gas phase. This is either because tar was excluded for the
reason of simplification or because it was assumed that in
downdraft gasifiers tars are cracked as the producer gas passes
through high-temperature zones of the reactor (see, e.g., Sheth
et al. [25] and Giltrap et al. [17]). However, Asadullah [6]
points out that, even in downdraft gasifiers, tars form during
secondary gas phase reactions of devolatilized organic com-
pounds with gasifying agents and generally exist in the pro-
ducer gas stream. Jaojaruek et al. [26] confirm a lessened but
still existing presence of tars within downstream systems of
downdraft gasifiers. Authors such as Bhattacharva et al. [27]
and Strassen et al. [28] furthermore point to the sensitivity of
internal combustion engines towards tar content. While
Bhattacharva et al. [27] state that producer gas tar content
must be lower than 50 mg/Nm
3
, Strassen et al. [28]and
Karuppaswamy et al. [29] mention that up to 100 mg/Nm
3
can be tolerated.
In any case, tars within the producer gas remain an issue to
be dealt with, especially if an internal combustion engine is
applied further downstream. Thus, any biomass gasification
device feeding an internal combustion engine would generally
require an appropriate after-treatment system to lower unsat-
isfactory tar content (see, e.g., Jaojaruek et al. [26]). As a
consequence, any simulation model with a focus on producer
gas within after-treatment systems should account for the
presence of tars as well as for secondary gas phase reactions
involving tars. In this context, Barman et al. [30]comment
that as different tars are produced in a gasification reaction
through complex set of reactions, predicting tar species in the
product gas using any numerical technique is very difficult.
As a consequence, the equilibrium-based simulation model
presented by Barman et al. [30] involves a representative,
rather than a calculated amount of tar, namely 4.5% (mass
percentage) of total biomass yield. This value had been chosen
on the basis of Yamazaki et al. [31] who investigated the
changes in the amount and composition of tar with superficial
velocity in a downdraft biomass gasifier.
Palma [7] provides an overview of modelling techniques
used to investigate tar formation mechanisms within the reac-
tor. As such, the author distinguishes between single com-
pound models, lumped models, and detailed kinetic models.
The model developed within the current work has the goal
to better explain, predict, and thus understand naphthalene and
tar formation phenomena within after-treatment systems of
biomass gasification plants. It advances a system dynamic
scheme presented in Boiger [10] by including tars.
According to Palma [7], the tar-related aspects within the cur-
rent model are classified as a single compound approach.
This is because these aspects focus on the tendency of forma-
tion and decomposition of one representative tar component:
naphthalene.
Being a rather large, aromatic, tertiary tar compound, naph-
thalene features one of the highest condensation points of all
tars to be expected in biomass gasification (see, e.g., Morf
et al. [32]). Other authors such as van Paasen et al. [33]also
use naphthalene in a similar manner, namely to represent the
behavior of un-polar tar species. Additionally, Aldèn et al.
40 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
[34] found naphthalene to be the predominant condensable tar
component even after catalytic cracking. The authors also
state that [...] these residual components may cause fouling
problems in the downstream equipment of a gasifier.
Morf et al. [32] propose a two-step process to model the
formation kinetics of naphthalene out of gravimetric tar via
intermediate products such as phenols. In contrast, the hereby-
presented work omits the demand for accurately modelling the
formation and decomposition kineticsof naphthalene. It rather
focuses on relative comparisons of thermodynamic tendencies
and kinetic rates. Thereby the overall thermodynamic tenden-
cy within summed naphthalene formation and decomposition
reactions (see, Eqs. 1519) is interpreted as an indicator for
the likelihood of tar occurrence.Despite these limitations,
the chosen approach features the following decisive advan-
tage: while other modelling approaches involving tar can only
assume tar content as a fixed value (e.g., Barman et al. [30]),
the choice of using naphthalene allows for a dynamic consid-
eration of (representative) tars within the full chemical
reacting system.
While a previous work by Boiger [35] has focused on
modelling conditions within the biomass reactor (similar to
Giltrap et al. [17], Jayjah et al. [19], Miligan et al. [20],
Murugan et al. [23], and Ngamsidhiphongsa [24]), this work
aims at phenomena within the producer gas phase and more
specifically within downstream after-treatment systems.
Being based on system dynamic principles, the current
model includes a combination of calculating (i) divergences
in molar Gibbs free energy, (ii) their overall tendency for
minimization, (iii) the consequential drive towards chemical
equilibrium, and (iv) modelling relative chemical kinetics.
The model thus allows for the investigation of both dynamic
non-equilibrium and equilibrium states. Thereby, the imple-
mentation of kinetic effects relies on Arrhenius laws (as in,
e.g., Prakash et al. [36] and Murugan [23]), but ultimately on
relative rather than absolute reaction rates (see Boiger [10]).
In order to account for typical process conditions within
after-treatment systems, the numerical investigation assumes
boundary conditions such as gradually reducing temperatures
as well as sudden pressure drops.
Model-based analysis of thermodynamic naphthalene for-
mation tendencies in low-temperature, low-pressure zones has
led to a series of results. Namely (i) the statement that system
dynamic simulations can qualitatively account for the increase
in tar formation at low pressures, which is reported in real-life
after-treatment systems (e.g., Palma [7]); (ii) a better general
understanding of such effects and ultimately; (iii) the recom-
mendation of a range of constructive and process-based mea-
sures to reduce the thermodynamic tendency of occurrence of
tars in after-treatment systems.
Furthermore, the latest software version of the system dy-
namic solver has been adapted to a cloud-based computation
environment within the platform KaleidoSim (see Boiger et al.
[37]). Thus, massive simultaneous computation of multiple
process scenarios has been enabled. On this basis, computa-
tion time consumption to simulate a considerably wider range
of process parameter settings was strongly reduced. As a con-
sequence, the hereby-presented recommendations could be
put into a broader perspective as compared with the status
shown already in Boiger et al. [38].
2 Concept and methodology
2.1 System dynamic modelling concept
Based on system dynamic principles, the newly developed gas
phase model is inspired by a unifying perspective on general
physical phenomena. The concept thus basically leads back to
Gibbs fundamental equation (Eq. 1). Thereby, a change in
global energy of any system dE is attributed to the sum of
any physical potentials φ
j
times the change of attributed con-
servative quantities dΨ
j
(see, e.g., Job et al. [39]). In the face
of thermo-chemical investigation, the global energy change
becomes a change of total Gibbs free energy per chemical
species ΔG
i
, the driving potential specializes to the species-
specific molar Gibbs free energy G
i
, and the conservative
quantity becomes the total amount of moles per chemical spe-
cies N
i
(Eq. 1).
dE ¼
j
φjdψj¼dGi¼−∑
i
Gi
dNið1Þ
In addition, the local temperature T, the molar gas phase
composition x
i
, the local pressure p, and the standard states
Θ
of the latter two parameters have an impact on the molar Gibbs
free energy of each species as seen in Eq. 2.
ΔGi
T;p;xi
ðÞ¼ΔGi
ΘT;pΘ

þTRIn pxi=pΘ
ð2Þ
The calculation of molar Gibbs free energies of formation
in standard state ΔG
i
Θ
calls for the knowledge of molar stan-
dard entropies and enthalpies of formation, which in turn re-
quires the implementation of the proper thermodynamic coef-
ficients for each species. The latter were extracted from
McBride et al. [40].
A balance of species-specific molar Gibbs free energies
with respect to stoichiometric constants γ
ji
for species iand
reaction jis the basis for calculating prevailing molar Gibbs
free energies of reaction ΔG
R,j
according to Eq. 3.
ΔG
R;j
T;p;xi
ðÞ¼ΔG
Θ
R;j
T;pΘ

þTR
i
ln pxγji
i=pΘ

ð3Þ
Finally, any modelled chemical reaction jis driven by a
non-zero molar Gibbs free energy of reaction.
41Biomass Conv. Bioref. (2021) 11:39–56
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2.2 Thermokinetic modelling concept
The rate of chemical reactions I
Nj
(mol j/s) is modelled as seen
in Eq. 4. It is composed of a combination of a reaction-specific
relative rate constant f
j
(mol
i
2
/J), the prevailing Gibbs free
energy of reaction, and a reaction rate coefficient k
j
(1/s). The
rate coefficient in turn is based on Arrhenius kinetics as pro-
vided in Eq. 5.ThereA
j
(1/s) is the reaction-specific pre-expo-
nential factor and Ea
j
(J/Kmol) the reaction-specific activation
energy as retrieved from Murugan et al. [23].
INj ¼fjkjΔG
R;j
ð4Þ
kj¼AjeEa j
R*T ð5Þ
The chosen kinetic modelling approach rather relies on
relative inter-reaction-comparison than on absolute reaction
rates. Thus, a spectrum of varying f
j
can be investigated, ac-
counting for the reality within a complex heterogeneous
thermo-chemical reactor where the speed of individual reac-
tions can vary dramatically in space and time. A related kinetic
study on pseudo-equilibria states in wood gas systems caused
by a relative variation of f
j
was published in Boiger [10].
2.3 System dynamic modelling scheme
Figure 1presents a graphical interpretation as well as a very
condensed and generalized version of the underlying system
dynamic modelling scheme. It combines thermodynamic and
thermokinetic aspects. Hereby, the rectangular containers
represent conservative quantities Ψ
j
, the filling heights of these
containers are driving potentials φ
j
and their cross-sectional
areas represent system capacities κ
j
. In addition, the arrows
with solid dots are fluxes of the conservative quantities I
Ψj
,
single dots are information processing units (e.g., equations),
and the dashed arcs are conveyors of information.
Furthermore, N
tot
and N
i
are total and species-specific
amounts of molecules respectively.
A mathematical interpretation of the graphic solver concept
within Fig. 1amounts to a series of coupled ordinary 1st-order
differential equations according to Eqs. 6and 7, which com-
bine with the coupling relations within Eqs. 2and 3.
dNi
dt ¼
j
INi;jγji;ΔG
R;j
 ð6Þ
dGi
dt ¼
j
IGi;j¼
j
ΔGi
INij ð7Þ
The differential equations are discretized with respect to
time. A linear system of equations is assembled and numeri-
cally solved by a four-step Runge-Kutta scheme according
Hazewinkel et al. [41]. The prototype model was implemented
graphically within the system dynamic simulation software
Berkeley Madonna. This platform is well suited to obtain an
overall visually aided understanding of complex interdepen-
dencies within 0D and 1D multiphysics systems. However,
Berkeley Madonna has certain limitations regarding overview
and usability when it comes to handling larger more complex
models. Thus, as the system dynamic solver evolved its code-
basis was transferred to a Matlab environment. Finally, in
Fig. 1 Graphic interpretation of
the essentials of the underlying
modelling scheme where iis any
chemical species and jany
chemical reaction
42 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
order to establish compatibility with the online cloud platform
KaleidoSim, the whole software was again transferred into C-
code.
2.4 Chemical species and reactions
The simulation model considers the following interacting
chemical species: coal C(s), water H
2
O(g), methane CH
4
(g),
carbon-dioxide CO
2
(g), carbon-monoxide CO(g), hydrogen
H
2
(g), and naphthalene C
10
H
8
(g). Thereby, naphthalene is
used to represent any tar components. The reason for choosing
naphthalene as a representative for a wide range of tar-
molecules is its relatively high condensation temperature
(see also Section 1). If a gasification system is designed such
that occurring naphthalene will not condensate, then it is likely
that other tar components with lower condensation tempera-
tures will not condensate either.
Within the simulation model, the above-mentioned chem-
ical species interact in terms of the following chemical reac-
tions: heterogeneous (i) Boudouard (Eq. 8), (ii) methanation
(Eq. 9), and (iii) steam-carbon (Eq. 10)reactions.
CsðÞþCO22CO ð8Þ
CsðÞþ2H2CH4ð9Þ
CsðÞþH2OCO þH2ð10Þ
Those mechanisms occur in combination with (iv) a variety
of possible oxidation reactions, which can be summarized as
seen in Eq. 11. There, k,n,andmare the relative stoichiomet-
ric amounts of carbon, oxygen, and hydrogen atoms respec-
tively.
CkOnHmþm2n
4þk

O2kCO2þm
2H2Oð11Þ
While the gasification reactions cited in Eqs. 811 corre-
spond to approaches for modelling wood gas equilibria found
in standard literature (e.g., Reed et al. [22]), the hereby-
presented model considerably extends this spectrum by a se-
ries of gas phase reactions (according to, e.g., Murugan et al.
[23]andGajetal.[42]), as well as naphthalene sum reactions,
namely homogeneous (v) steam-carbon (Eq. 12), (vi) metha-
nation (Eq. 13),and (vii) Boudouard (Eq. 14) reactions as well
as naphthalene-based (viii) methanation (Eq. 15), (ix) steam-
carbon (Eqs. 16 and 17), and (x) CO
2
/CO conversion (Eq. 18
and 19)reactions.
CO þH2OH2þCO2ð12Þ
CH4þH2O3H2þCO ð13Þ
2CO þ2H2CH4þCO2ð14Þ
C10H8þ16H210CH4ð15Þ
C10H8þ10H2O10CO þ14H2ð16Þ
C10H8þ20H2O10CO2þ24H2ð17Þ
C10H8þ10CO224CO2þ4H2Oð18Þ
C10H8þ10CO220CO þ4H2ð19Þ
2.5 Solver validation
The solver has been validated in two phases:
(i) Validation phase I considering the comparison to another
in-house solver based on the method of Lagrangian
multipliers
(ii) Validation phase II considering the comparison to pub-
lished experimental and alternative model results
2.5.1 Validation phase I
Within the first validation phase, the system dynamic solver
was compared with another in-house solver, which is based on
amorecommonapproach to find chemical equilibria by
minimization of global Gibbs free energy. The said model
thus only considers the equilibrium state which is calculated
by using the method Lagrangian multipliers as seen in
Shabbar et al. [43]. There, the global Gibbs free energy and
deviations in atomic species balances are minimized by the
introduction and consequential minimization of a Lagrange
function L(see Eq. 20).
L¼
i
ΔGiþ
j
λj
i
ai;jNib0
j
 ð20Þ
The Lagrange function consists of two components: one
expresses the sum of all Gibbs free energies of formation
ΔG
i
of all molecular species i, while the other stands for the
jatomic species balances. Thereby, λ
j
and b
0
j
are the
Lagrangian multiplier and the input rate of atomic species j
respectively, a
i,j
is the number of atoms jper molecular spe-
cies i,andN
i
is the total number of molecules per molecular
species.
Figure 2compares the results of the Lagrange approach to
the hereby-presented system dynamic solver in terms of cal-
culated tar-free homogeneous producer gas equilibria compo-
sitions at p=10
5
Pa,anoxygentohydrogenratioofR
O/H
=1,
and a temperature range of 375 K T1350 K. It clearly
shows very good correspondence of the results. Discounting
gas compounds with molar fractions below 10
3
, the maxi-
mum encountered relative deviation over the entire relevant
temperature spectrum amounts to 3.1% of calculated molar
fractions. This particular deviation occurred at T=1025K,
where the system dynamic model yielded x
CH4
= 0.02990
while the Lagrangian equilibrium solver yielded x
CH4
=
0.02897.
43Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Additional aspects of the validation procedure within vali-
dation phase I have already been published in Boiger [10].
2.5.2 Validation phase II
Within the second validation phase, the system dynamic
solver was compared with a rather extensive series of
published results regarding producer gas composition,
based on either experimental data or on alternative
models. Experimental data used for validation came from
Jayah et al. [44] who measured the producer gas compo-
sition of rubber wood at varying moistures and varying
air-fuel ratios (mole air per mole biomass, short: A/F);
Pratik et al. [45] who gasified wood waste in a downdraft
gasifier at varying air-fuel ratios; Simone et al. [46]who
investigated the gasification of wood-saw-dust pellets and
sunflower seed pressings; Pedroso et al. [47] who present-
ed the measured gas composition of olive wood, peach
wood, and pine wood at varying air-fuel ratios; and
Dogru et al. [48] who analyzed the gasification output
of sewage slug at specific moistures and at varying air-
fuel ratios. Calculated gas compositions from alternative
models used for validation came from
Jarungthammachote et al. [49] who modelled the producer
gas composition of rubber wood at varying moistures and
varying air-fuel ratios; Ptasinski et al. [50] who simulated
the gasification of straw and treated wood at varying air-
fuel ratios; Roy et al. [13] who modelled the gasification
of olive wood, peach wood, and pine wood at varying air-
fuel ratios; and Barman et al. [30] who compared his
model to Jayah et al. [44], Dogru et al. [48], Ptasinski
et al. [50], Pedroso et al. [47], and Roy et al. [13].
Tables 1,2,3,4,5,6,7,and8sum up all essential results in
regard to the validation procedure. In order to recreate indi-
vidual gasification scenarios presented within the validation
cases, boundary conditions of system dynamic solver runs
were set such that R
O/H
and R
C/H
of a wet gas scenario were
always in line with the validation data. Since cited literature in
general does not clearly state exact temperatures at which gas
compositions were measured or calculated, they had to be
approximated in most cases. Assuming gas sampling locations
towards the entrance of typical gas purification systems, tem-
peratures would range between 700 and 750 K (see, e.g.,
Martinez et al. [51]).
In the following, the deviations between the system dy-
namic solver and results from literature are presented twofold:
(i) as maximum absolute deviation (Max. Dev.) in mol% and
(ii) in terms of the root-mean-square error (RMS) as defined in
Eq. 21. Thereby, x
i,SD
represents calculated results from the
system dynamic solver, x
i,lit
stands for either experimental or
model-based results from literature and Nis the number of gas
compounds.
Fig. 2 Wood gas equilibria compositions x
j
()atp=10
5
Pa, R
O/H
= 1 versus process temperature 375 K T
p
1350 K, calculated by Lagrangian
equilibrium solver (Equ x
i
) and system dynamic solver (SD x
i
).
44 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
RMS ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
N
ixi;SDxi;lit

2
N
sð21Þ
The presented comparisons in Tables 1,2,3,4,5,6,7,and
8have to be evaluated bearing in mind that the system dy-
namic solver does not include a full reactor model. Instead it
mainly focuses on the producer gas phase in interaction with
the coal bed as well as on gas-phase reactions in cool down
zones of after-treatment systems. Also, consider the fact that
R
O/H
and R
C/H
values of published experiments and alternative
models have been provided as boundary conditions for com-
parison runs. In addition, temperatures have been chosen such
that realistic conditions at probable producer gas analysis lo-
cations within after-treatment systems (namely in the entry
Table 1 Results of validation phase II for the gasification of rubber
wood at varying moistures and air-fuel ratios (A/F). Producer gas com-
positions as predicted by the system dynamic solver (SD) are compared
with experimental results from Jayah et al. [44] and modelled results from
Jarungthammachote et al. [49]. Results do not consider nitrogen.
Literature values do not consider water either
Rubber wood; moisture: 16%; A/F = 2.20 Rubber wood; moisture: 14.7%; A/F = 2.00
SD [44][49]SD [44][49]
Mod Exp Mod Mod Exp Mod
X
CH4
() 0.041 0.027 0.002 0.042 0.029 0.002
X
CO
() 0.398 0.389 0.373 0.464 0.462 0.385
X
CO2
() 0.218 0.224 0.247 0.207 0.208 0.238
X
H2O
()0.024--0.017- -
X
H2
() 0.319 0.359 0.377 0.270 0.306 0.375
T(K) 730 - - 730 - -
R
H/C
() 1.293 1.293 1.225 1.042 1.042 1.215
R
O/C
() 1.307 1.307 1.393 1.255 1.255 1.377
Max. Dev. (%) - 4.07 5.83 - 3.53 10.47
RMS (%) - 2.21 3.99 - 1.88 7.01
Table 3 Results of validation phase II for the gasification of wood-saw-
dust pellets (WSP) and sunflower seed pressing (SMP) as well as a 50/50
mixture of WSP and SMP (MIX) in three consecutive test runs at varying
feedstock (F-S) and char-bed (C-B) compositions. Producer gas compo-
sitions as predicted by the system dynamic solver (SD) are compared with
experimental results from Simone et al. [46]. Results do not consider
nitrogen. Literature values do not consider water either
Wood waste in downdraft gasifier
F-S: WSP F-S: MIX F-S: MIX
C-B: WSP C-B: WSP C-B: MIX
SD [46]SD [46]SD [46]
Mod Exp Mod Exp Mod Exp
X
CH4
() 0.050 0.040 0.058 0.046 0.061 0.058
X
CO
() 0.428 0.420 0.381 0.370 0.411 0.394
X
CO2
() 0.245 0.250 0.233 0.241 0.225 0.240
X
H2O
() 0.019 - 0.024 - 0.020 -
X
H2
() 0.258 0.420 0.305 0.370 0.284 0.308
T(K) 720 - 720 - -
R
H/C
() 1.043 1.042 1.324 1.324 1.222 1.222
R
O/C
(-) 1.296 1.296 1.296 1.296 1.263 1.264
Max. Dev. (%) - 3.20 - 3.76 - 2.37
RMS (%) - 4.27 - 4.76 - 3.69
Table 2 Results of validation phase II for the gasification of wood
waste in a downdraft gasifier at varying air-fuel equivalence ratios
(ER). Producer gas compositions as predicted by the system dynamic
solver (SD) are compared with experimental results from Pratik et al.
[45]. Results do not consider nitrogen. Literature values do not consider
water either
Wood waste in downdraft gasifier
ER = 0.16 ER = 0.20 ER = 0.35
SD [45]SD [45]SD [45]
Mod Exp Mod Exp Mod Exp
X
CH4
() 0.068 0.062 0.047 0.037 0.033 0.030
X
CO
() 0.483 0.477 0.538 0.537 0.476 0.463
X
CO2
() 0.243 0.246 0.124 0.122 0.182 0.194
X
H2O
() 0.012 - 0.010 - 0.017 -
X
H2
() 0.195 0.215 0.281 0.305 0.292 0.313
T(K) 710 - 745 - 740 -
R
H/C
() 0.863 0.863 1.088 1.088 1.087 1.087
R
O/C
() 1.235 1.235 1.123 1.123 1.239 1.239
Max. Dev. (%) - 2.00 - 2.35 - 2.13
RMS (%) - 1.10 - 1.29 - 1.41
45Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
area and always between 700 and 750 K) are reflected.
Despite these facts, the level of correspondence between final
gas compositions predicted by the system dynamic solver and
published values covering a multitude of application cases is
still quite remarkable. Maximum encountered absolute devia-
tions to comparable experimental runs amount to 5.05%
mol
(see Table 4, gasification of sewage slug, predicted amount
of hydrogen, according Dogru et al. [48]). Maximum encoun-
tered absolute deviations to comparable simulations amount to
10.47%
mol
(see Table 1, gasification of rubber wood, predict-
ed hydrogen content, according Jarungthammachote et al.
[49]). The issue that 10.47%
mol
might appear as a relatively
large value can be discarded since a comparison with the cor-
responding experiments (see Jayah et al. [44]), yields a much
smaller maximum deviation of 3.53%
mol
.
3 Results and discussion
Based on the, thus validated, thermo-chemical model, a di-
mensionless timeline of gas being produced, purified, and
sucked towards the engine can be simulated. The solver
Table 4 Results of validation phase II for the gasification of sewage
slug at specific moisture and varying air-fuel ratios (A/F). Producer gas
compositions as predicted by the system dynamic solver (SD) are
compared with experimental results from Dogru et al. [48] and modelled
results from Barman et al. [30]. Results do not consider nitrogen
Sewage slug; moisture: 11.75%; A/F = 2.28 Sewage slug; moisture: 11.75%; A/F = 2.69
SD [48][30]SD [48][30]
Mod Exp Mod Mod Exp Mod
X
CH4
() 0.032 0.041 0.024 0.036 0.055 0.037
X
CO
() 0.243 0.270 0.268 0.194 0.192 0.220
X
CO2
() 0.350 0.324 0.342 0.379 0.384 0.390
X
H2O
() 0.054 0.095 0.098 0.067 0.096 0.098
X
H2
() 0.321 0.270 0.268 0.324 0.274 0.256
T(K)710--700--
R
H/C
() 1.404 1.404 1.308 1.521 1.522 1.321
R
O/C
() 1.596 1.596 1.654 1.673 1.674 1.698
Max. Dev. (%) - 5.05 5.25 - 5.00 6.79
RMS (%) - 3.38 3.30 - 2.73 3.56
Table 6 Results of validation phase II for the gasification of olive wood
at a specific air-fuel ratio (A/F). Producer gas compositions as predicted
by the system dynamic solver (SD) are compared with experimental
results from Pedroso et al. [47] and modelled results from Roy et al.
[13]aswellasfromBarmanetal.[30]. Results do not consider nitrogen.
Literature values do not consider water either
Olive wood; A/F = 2.03
SD [47][13][30]
Mod Exp Mod Mod
X
CH4
() 0.017 0.017 0.006 0.022
X
CO
() 0.447 0.424 0.442 0.425
X
CO2
() 0.254 0.277 0.245 0.274
X
H2O
() 0.022---
X
H2
() 0.26 0.282 0.306 0.279
T(K) 740---
R
H/C
() 0.880 0.882 0.917 0.899
R
O/C
() 1.361 1.362 1.345 1.349
Max. Dev. (%) - 2.33 4.61 2.24
RMS (%) - 1.98 2.43 1.80
Table 5 Results of validation phase II for the gasification of straw and
treated wood at varying air-fuel ratios (A/F). Producer gas compositions
as predicted by the system dynamic solver (SD) are compared with
modelled results from Ptasinski et al. [50] and modelled results from
Barman et al. [30]. Results do not consider nitrogen
Straw; A/F = 1.40 Treated wood; A/F = 1.63
SD [50][30]SD [50][30]
Mod Mod Mod Mod Mod Mod
X
CH4
() 0.016 0.017 0.033 0.020 0.017 0.037
X
CO
() 0.291 0.339 0.328 0.295 0.339 0.316
X
CO2
() 0.221 0.174 0.189 0.227 0.183 0.199
X
H2O
() 0.050 0.099 0.090 0.048 0.087 0.108
X
H2
() 0.421 0.372 0.361 0.410 0.374 0.341
T(K) 745 - - 740 - -
R
H/C
() 1.906 1.906 1.881 1.837 1.835 1.892
R
O/C
() 1.484 1.484 1.448 1.470 1.470 1.491
Max. Dev. (%) - 4.92 6.04 - 4.44 6.92
RMS (%) - 4.33 3.99 - 3.67 4.44
46 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
qualitatively predicts shifts in species concentration as tem-
perature and pressure decrease along the flow path of the
producer gas. Naphthalene content is investigated in particular
and is used to draw conclusions about tar content in general.
On this basis, the following measures are recommended to
minimize the tendency for tar occurrence in low-pressure
zones: (i) decrease gas residence time, (ii) increase tempera-
tures within the gas after-treatment system; (iii) increase
hydrogen to carbon ratio R
H/C
, and (iv) oxygen to carbon ratio
R
O/C
in the producer gas. While measures (i) and (ii) require
modifications to the plant and process design itself (e.g.,
smaller pipes in combination with stronger suction or re-
circulation of thermal energy via heat exchangers), measures
(iii) and (iv)can be implemented by either removingcoal from
the reaction zone or by adding either water or process air to the
process. Note that these recommendations can only hold if it
can be assured that no other essential process parameters (e.g.,
temperatures, feedstock bulk parameters) are affected by the
implemented changes.
3.1 Measures to decrease tendency of tar formation
and condensation
In the following, simulation results are presented, which lead
to the recommended measures formulated above.
3.1.1 Measure (i): decrease gas residence time
The model does not depict chemical reaction kinetics quanti-
tatively but qualitatively and in relative relation between oth-
erwise comparable cases. Thus, simulations are able to show
that lower pressure can enhance the tendency towards naph-
thalene production according to Eqs. 1519.Figure3demon-
strates an exemplary simulation run, where lower pressures
lead to faster creation of naphthalene as compared with a
high-pressure case. In addition, the simulation run behind
Fig. 3also demonstrates that pressure differences do not nec-
essarily have to yield a change in the final equilibrium com-
position of naphthalene.
In the low-pressure case (Fig. 3, case ii, red), naphthalene is
more readily produced along the gas flow path than in the
high-pressure case (Fig. 3, case i, green), while the final equi-
librium state remains almost the same in terms of naphthalene
content. The exemplary result shown in Fig. 3is not represen-
tative of wider process parameter windows. Still, on the basis
of being able to simulate individual outcomes like this, it is
fair to assume that process condition windows exist in real life,
which lead to similar effects in actual gasification systems.
The obvious countermeasure to avoid tar condensation within
piping systems in the context of this phenomenon is decreased
gas residence time in critical low-pressure zones.
3.1.2 Measure (ii): increase temperatures
The recommendation to reduce tar condensation in low-
pressure zones by increasing local temperatures is a relatively
trivial measure and does not require simulation but is an ob-
vious and effective method to prevent any condensation. As
such, the measure is stated here for the sake of completeness.
However, increasing temperatures is just a remedy to prevent
Table 7 Results of validation phase II for the gasification of peach
wood at a specific air-fuel ratio (A/F). Producer gas compositions as
predicted by the system dynamic solver (SD) are compared with experi-
mental results from Pedroso et al. [47] and modelled results from Roy
et al. [13]aswellasfromBarmanetal.[30]. Results do not consider
nitrogen. Literature values do not consider water either
Peach wood; A/F = 2.03
SD [47][13][30]
Mod Exp Mod Mod
X
CH4
() 0.018 0.021 0.016 0.027
X
CO
() 0.415 0.385 0.420 0.392
X
CO2
() 0.251 0.281 0.255 0.258
X
H2O
() 0.026---
X
H2
() 0.290 0.313 0.309 0.323
T(K) 740---
R
H/C
() 1.029 1.030 0.985 1.111
R
O/C
() 1.379 1.379 1.346 1.341
Max. Dev. (%) - 3.03 2.60 3.26
RMS (%) - 2.40 0.99 2.06
Table 8 Results of validation phase II for the gasification of pine wood
at a specific air-fuel ratio (A/F). Producer gas compositions as predicted
by the system dynamic solver (SD) are compared with experimental
results from Pedroso et al. [47] and modelled results from Roy et al.
[13]aswellasfromBarmanetal.[30]. Results do not consider nitrogen.
Literature values do not consider water either
Pine wood; A/F = 2.58
SD [47][13][30]
Mod Exp Mod Mod
X
CH4
() 0.015 0.007 0.012 0.025
X
CO
() 0.446 0.432 0.402 0.425
X
CO2
() 0.266 0.277 0.293 0.263
X
H2O
() 0.023---
X
H2
() 0.250 0.284 0.293 0.288
T(K) 740---
R
H/C
() 0.834 0.830 0.897 0.947
R
O/C
() 1.377 1.377 1.397 1.333
Max. Dev. (%) - 3.38 4.36 3.75
RMS (%) - 1.95 3.33 2.21
47Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
existing tars from condensation, while measures (i), (iii), and
(iv) are meant to prevent or at least reduce tar formation itself.
3.1.3 Measure (iii): increase hydrogen to carbon ratio R
H/C
Selected case studies were made to simulate the effect of vary-
ing hydrogen to carbon ratios within the fuel input (e.g., cel-
lulose plus additives) on the naphthalene content of the wood
gas. Figure 4shows the comparison of three simulation runs
where producer gas with varying R
H/C
ratios was exposed to
temperature and pressure drop. According to this prediction,
the thermodynamic tendency of the formation of naphthalene
in equilibrium decreases with increasing R
H/C
. The case with
the lowest R
H/C
yields the highest amounts of naphthalene in
the final equilibrium state.
Furthermore, the pressure drop causes a clearly discernable
spike in naphthalene content (see t
*
4), which points to the
correspondence between calculated results and experimental-
ly observed effects (see, e.g., Asadullah [6]).
Figure 5depicts a broader perspective on expected gas
phase compositions including naphthalene content, with in-
creasing R
H/C
. The study reveals that naphthalene content in
equilibrium shows a peak at R
H/C
0.49 and decreases con-
tinuously with a further increase of the hydrogen to carbon
ratio R
H/C
> 0.49. The said peak shall hereby be referred to as
methane-emergence-threshold since it corresponds with the
appearance of methane, which is only present in equilibria
compositions featuring R
H/C
> 0.49. Above this threshold,
additional hydrogen is apparently used rather for the forma-
tion of additional methane, than for naphthalene. Below the
methane-emergence-threshold however, additional hydrogen
will favor naphthalene formation.
Based on these results, the authors recommend the increase
of R
H/C
in the gasification reactor well beyond 0.49 in order to
reduce the thermodynamic tendency for tar formation in low-
temperature, low-pressure zones. This could be achieved, e.g.,
by either removing coal, which would otherwise continue to
participate in gasification reactions (see Eqs. 810)fromthe
reaction zone, or by adding controlled amounts of water. Note
that this recommendation can only hold if R
H/C
can be modi-
fied without impacting any other relevant process parameters.
3.1.4 Measure (iv): increase oxygen to carbon ratio R
O/C
Selected case studies were also made to simulate the effect of
varying oxygen to carbon ratios within the feedstock on the
naphthalene content of the producer gas. Figure 6shows the
comparison of three simulation runs where gas with varying
Fig. 3 Simulated absolute amount of naphthalene N
C10H8
(mol) for
atomic species ratios R
O/C
=0.706andR
H/C
= 0.44. Comparison of two
cases versus dimensionless time t*(), where in case i (green) pressure
(purple) remains at 10
5
Pa and in case ii (red) pressure (yellow) drops
from 10
5
Pa to 10
3
Pa within dimensionless residence time window 3 t
*
4. In both cases, temperature drops from T=1200KtoT= 500 K within
dimensionless residence time window 1.5 t
*
2.
48 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
R
O/C
ratios is exposed to temperature and pressure drop.
According to this prediction, the thermodynamic tendency
of naphthalene formation in equilibrium decreases with in-
creasing R
O/C
. As with R
H/C
, the case with the lowest R
O/C
yields the highest amounts of naphthalene in the final equilib-
rium state.
Again the pressure drop causes a clearly discernable spike
in naphthalene content, just as observed in real-life gasifica-
tion systems.
Figure 7depicts a broader perspective on expected
gas phase compositions including naphthalene content,
with increasing R
O/C
. The study shows that naphthalene
content in equilibrium continuously decreases with in-
creasing R
O/C
.AtR
O/C
0.55, methane content reaches
a minimum, causing a steeper decline in naphthalene
content for R
O/C
> 0.55.
Based on these results, the authors recommend the increase
of R
O/C
in the gasification reactor as a measure to reduce tar
content in low-pressure, low-temperature zones. This could be
achieved, e.g., by either removing coal from the reaction zone,
by adding more process air, or by adding controlled amounts
of water. Note that this recommendation can only hold if R
O/C
can be modified without impacting any other relevant process
parameters.
3.1.5 Discussion of recommendations
At first sight, the recommendations to increase R
H/C
and
R
O/C
factors cannot be backed up by experimental studies
that relate fuel input to tar formation (e.g., Walander
et al. [52], Kaupp et al. [53], Reed et al. [3]). For exam-
ple, data out of said literature does not show a clearly
discernable tendency for tar reduction in dry output gas
with respect to an increasing water supply. However, at
closer inspection, it becomes clear that neither these ex-
periments nor any other published experimental works
known to the authors are directly comparable to the find-
ings within this work. The reason is that in real-life sce-
narios it is hard to vary neither R
H/C
nor R
O/C
without
impacting other essential process parameters. Without an
extensive amount of implemented process control, any
shift in atomic species ratios will also cause changes in
temperature, pressure drop, consistency of the bulk feed-
stock, and/or charcoal bed as well as exposure of indi-
vidual particles to reactive oxygen from process air, etc.
In contrast, this work isolates the impact on thermody-
namic and thermokinetic gas phase reaction tendencies
by means of simulation. As such, the above recommen-
dations to reduce the tendency for tar formation and/or
Fig. 4 Simulated relative amount of naphthalene x
C10H8
(mol) in produc-
er gas versus dimensionless time t*(), for three cases where R
H/C
is set to
0.44 (yellow/red), 0.88 (green), and 1.32 (black) respectively, R
O/C
=
0.706, prescribed pressure (blue) drops from p=10
5
Pa to p = 10
3
Pa
within dimensionless residence time window 3 t
*
4 and temperature
drops from T=1200KtoT=500Kwithin1.5t
*
2. The higher R
H/C
,
the lower the naphthalene content in the equilibrium state.
49Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
condensation within after-treatment systems can only
hold, as long as only the very parameters in question
are modified. It is the authorsopinion that, even though
this is hard to achieve in actual gasification/after-
treatment systems, these findings constitute a gain in
knowledge and understanding in themselves.
3.2 Applying KaleidoSim cloud computation to
increase perspective on process parameters
As indicated in Boiger et al. [38], the system dynamic
model has been converted into a process optimization
tool for after-treatment systems of biomass gasification
plants. In this context, the computational requirements
of the system dynamic solver constituted a certain issue.
One single simulation run requires the computation of
202 unknowns. Because of the stiffness of the numeri-
cal problem, time-stepping needs to be extremely fine.
Thus, one single simulation run would require between
6.67e6 and 1.00e7 time steps. As a consequence, a total
amount of up to 202 × 1.00e7 = 2.02e9 unknowns have
to be computed in order to reach thermodynamic equi-
librium for one set of simulated process parameters.
Considering single-precision floating-point numbers
according to the IEEE standard, this equals approxi-
mately 8.08 Gb of data per simulation run. On this
basis, RAM becomes a limiting factor for the solver
since at least 10 Gb should be available for each simu-
lation run. Table 9shows a brief overview of the
amount of produced data as well as the number of in-
dividual simulation runs required to complete the stud-
ies underlying Figs. 2,3,4,5,6,7,8,and9.
By integration into the cloud-based HPC platform
KaleidoSim (see Boiger et al. [37]), any issues regarding
hardware-limitations could be overcome. In addition, massive
simultaneous cloud computing (MSCC) was enabled. Thus,
dozens of simulation runs could be executed and evaluated
simultaneously and within relatively short time periods. On
this basis, a larger perspective on tar minimization measures
and their relation to wider process parameter ranges could be
gained.
3.2.1 Varying oxygen and hydrogen to carbon ratios
over wider process parameter windows
Section 3.1 investigated the effect on naphthalene content
within the product gas by varying R
H/C
and R
O/C
at R
O/C
=
0.706 and R
H/C
= 0.706 respectively. In order to build up
Fig. 5 Simulated producer gas equilibrium composition x
j
()withthe
molar fraction of naphthalene x
C10H8
() being highlighted on the right
axes and with x
H2
()andx
H2O
() being omitted because they are below
1e3, versus ratio of hydrogen to carbon R
H/C
. In all cases, ratio of
oxygen to carbon is R
O/C
= 0.706, prescribed pressure drops from p=
10
5
Pa to p=10
3
Pa within dimensionless residence time window 3 t
*
4 and temperature drops from T=1200KtoT=500Kwithin1.5t
*
2
before gas equilibrium is reached.
50 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
a more general basis for conclusions as well as recom-
mendations for how to run the gasification process with
respect to adjusting R
H/C
and R
O/C
, the investigated pro-
cess parameter window was increased. Simulation runs
for 0.11 < R
O/C
< 1.65 were performed where R
H/C
was
parameterized between 0.55 and 1.87. Figure 8shows the
results in terms of predicted naphthalene content within
the product gas. This extended investigation confirms that
for R
H/C
> 0.55 the tendency to produce naphthalene de-
creases for increasing R
H/C
. This seems to hold true
throughout the entire R
O/C
process parameter spectrum.
Thus measure (iii) to reduce tar formation in producer
gas systems (see Section 3.1) can be confirmed as stated.
On this basis, the statement that increasing R
O/C
will al-
ways lead to a reduced tendency for naphthalene forma-
tion (see measure (iv), Section 3.1), must be refined.
AccordingtoFig.8, certain process parameter constella-
tions can be found where a positive gradient of naphtha-
lene content with respect to R
O/C
appears to exist. Thus,
for R
H/C
1.30 and R
O/C
< 0.8 an increase in R
O/C
would
actually lead to more tars. Outside this relatively narrow
process parameter window, higher R
O/C
would always
lead to less tar in the final output gas.
3.2.2 Sensitivity analysis of wood gas composition
In order to compare the relative effect of process pa-
rameter variations on producer gas composition in after-
treatment systems, a simulation-based sensitivity analy-
sis has been performed. Condensing the results, a nu-
merically derived, normalized wood gas sensitivity ma-
trix J has been introduced. Basically related to a stan-
dard Jacobian,its entries J
ji
consist of normalized gas
equilibrium compounds x
j
(P)/x
j
(P
0
) being successively
derived with respect to process parameters P
i
. Thereby,
x
j
are the components of the gas composition vector x
defined as x
CH4
,x
CO
,x
CO2
,x
H2O
,x
H2
,andx
C10H8
re-
spectively and P
i
are the components of the process
parameter vector P. The latter components are hereby
defined as reactor temperature T
R
,productgastempera-
ture T
P
, product gas pressure p,R
O/C
,andR
H/C
respec-
tively. On this basis, Eq. 22 shows the full, normalized
producer gas sensitivity matrix J. Thereby, P
0
is the
process parameter vector at which the sensitivity matrix
is to be evaluated. Its components P
i,0
are defined as
T
R,0
=1200K,T
P,0
=500K,p
0
=10
3
Pa, R
O/C,0
=
0.706, R
H/C,0
= 0.706 respectively. The gas compounds
Fig. 6 Simulated relative amount of naphthalene x
C10H8
()inproducer
gas versus dimensionless time t*(), for three cases where R
O/C
is set to
0.44 (yellow/red), 0.88 (green), and 1.32 (black) respectively, R
H/C
=
0.706, prescribed pressure (blue) drops from p=10
5
Pa to p=10
3
Pa
within dimensionless residence time window 3 t
*
4 and temperature
drops from T=1200KtoT= 500 K within 1.5 t
*
2. The higher R
O/C
is, the lower the naphthalene content in the equilibrium state.
51Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
at those process parameters are x
j,0
such that x
j,0
=
x
j
(P
0
).
J P0

¼
xCH4
xCH4;0TR
xCH4
xCH4;0TP
xCH4
xCH4;0p
xCH4
xCH4;0RO=C
xCH4
xCH4;0RH=C
xCO
xCO;0TR
xCO
xCO;0TP
xCO
xCO;0p
xCO
xCO;0RO=C
xCO
xCO;0RH=C
xCO2
xCO2;0TR
xCO2
xCO2;0TP
xCO2
xCO2;0p
xCO2
xCO2;0RO=C
xCO2
xCO2;0RH=C
xH2O
xH2O;0TR
xH2O
xH2O;0TP
xH2O
xH2O;0p
xH2O
xH2O;0TO=C
xH2O
xH2O;0RH=C
xH2
xH2;0TR
xH2
xH2;0TP
xH2
xH2;0p
xH2
xH2;0TO=C
xH2
xH2;0RH=C
xC10H8
xC10;H8;0TR
xC10H8
xC10;H8;0TP
xC10H8
xC10;H8;0p
xC10H8
xC10;H8;0RO=C
xC10H8
xC10;H8;0RH=C
0
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
@
1
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
A
P0
ð22Þ
The individual matrix entries J
ji
in Eq. 22 can be
approximated by numerical difference terms ΔJ
ji
apply-
ing a central differencing scheme according to Eq. 23.ΔJji ¼1
xjp0

xj
pip0
¼xjpi;oþΔpi

xjpi;oΔpi

2Δpixip0
 pki;0
ð23Þ
Fig. 7 Simulated producer gas equilibrium composition x
j
()withthe
molar fraction of naphthalene x
C10H8
() being highlighted on the right
axes and with x
H2
()andx
H2O
() being omitted because they are below
1e3, versus ratio of oxygen to carbon R
O/C
. In all cases, the ratio of
hydrogen to carbon is R
H/C
= 0.706, prescribed pressure drops from p=
10
5
Pa to p=10
3
Pa within dimensionless residence time window 3 t
*
4 and temperature drops from T=1200KtoT=500Kwithin1.5t
*
2
before gas equilibrium is reached.
52 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
In Eq. 23,Δp
i
are process parameter variations
which are hereby defined as Δp
i
=5×10
°4
×p
i,0
.
Executing the central differencing scheme, each entry
ΔJ
ji
requires two separate simulation runs yielding x
j
(p
i,0
+Δp
i
)andx
j
(p
i,0
Δp
i
).
Figure 9depicts a graphic evaluation of the results of
the producer gas sensitivity analysis in terms of J(P
0
).
Entries relating to x
H2O
and x
H2
are thereby omitted
because of their extremely low values in the vicinity
of P
0
. The full dataset of the entire sensitivity matrix
is compiled in Table 10.
The analysis shows that for the process parameter vec-
tor P
0
output gas composition generally features the
highest relative thermodynamic sensitivity with respect
to changes in R
O/C
and R
H/C
. The sensitivity towards
changes in temperatures and pressure is comparably low.
Furthermore, the evaluation of J(P
0
) shows that naphtha-
lene content is primarily sensitive to changes in R
O/C
followed by sensitivity to changes in R
H/C
. Extrapolating
naphthalene behavior to tar formation tendencies, this
means that shifting oxygen to carbon ratio is the most
effective thermodynamic measure to influence tar forma-
tion in biomass gasification systems.
4 Conclusion and outlook
This work has presented insights into the physical, me-
thodical, and numerical principles behind a system dy-
namic simulation model for the prediction of naphthalene
formation as well as producer gas compositions under
Table 9 Stats concerning the amount of computation
Fig. # of simulation runs Data produced in Gb
236 290.9
3 2 16.7
4 3 24.2
531 250.5
6 3 24.2
714 113.1
842 339.4
910 80.8
Total 141 1139.8 k
Fig. 8 Simulated molar fraction of naphthalene x
C10H8
()atproducergas
equilibrium versus the ratio of oxygen to carbon R
O/C
. Results are
parameterized with respect to the ratio of oxygen to carbon R
H/C
=0.55,
0.66, 0.88, 1.10, 1.32, 1.54, and 1.87 respectively. Simulating conditions
in the wood gas after-treatment system, the prescribed pressure drops
from p=10
5
Pa to p=10
3
Pa within dimensionless residence time
window 3 t
*
4. The prescribed temperature drops from T=1200K
to T= 500 K within 1.5 t
*
2, before producer gas equilibrium is
reached.
53Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
varying temperatures, pressures, and atomic species ratios.
Besides smaller molecular species, the model includes
naphthalene in order to represent the thermodynamic ten-
dency of formation and decomposition of tars with high-
temperature condensation points. The models ability to
predict producer gas phase composition was validated
against a broad spectrum of published modelled as well
as experimental results. Bearing in mind certain limita-
tions, the software was used to derive four concrete mea-
sures, which would help to minimize the thermodynamic
tendency of tar formation and condensation in low-pres-
sure, low-temperature zones of after-treatment systems.
Several exemplary simulation case studies were presented
to demonstrate the existenceofprocessparameterwin-
dows, which would require those very measures to ensure
longer maintenance intervals and smoother, tar-reduced
operation of any after-treatment system. Furthermore, an
instance of the model was implemented within the cloud-
based high-performance computation platform
KaleidoSim (see Boiger et al. [37]). This enabled the con-
sideration of wider process parameter windows in reason-
able computation time. Thus, the general validity of the
proposed naphthalene/tar reduction measures could be re-
fined and the relative effectiveness of process parameter
variations could be compared by means of a sensitivity
analysis.
Under the name BiogasSim and in combination with
KaleidoSim, the hereby-presented system dynamic model
will be free to use and accessible via internet browser any-
where in the world by mid-2021.
Code availability Code of system dynamic simulation software is not
open source. But source code documentation can be provided upon per-
sonal request to the corresponding author.
Fig. 9 Visualization of J(P
0
)withoutentriesforx
H2O
and x
H2
.Results
of sensitivity analysis of producer gas composition towards process
parameter changes. Changes in the composition of gas components
with respect to process parameter changes normalized by gas
composition at base process parametersP
0
Table 10 Full dataset of producer gas sensitivity matrix J accordingEq.
22, evaluated at P
0
.WhereP
1,0
=T
R,0
= 1200 K, P
2,0
=T
P,0
=500K,P
3,0
=p
0
=10
3
Pa, P
4,0
=R
O/C,0
=0.706,andP
5,0
=R
H/C,0
=0.706
T
R
T
P
PR
O/C
R
H/C
X
CH4
7.493E04 7.550E04 1.304E05 5.016E01 9.540E02
X
CO
1.562E04 3.068E02 5.269E04 7.635E02 8.854E01
X
CO2
4.793E05 3.559E03 6.112E05 2.657E01 5.611E01
X
H2O
6.153E04 1.399E02 1.386E04 8.023E01 2.984E00
X
H2
5.071E04 2.909E02 4.491E04 4.605E01 2.661E00
X
C10H8
6.172E04 3.145E03 5.476E05 2.343E00 1.457E00
54 Biomass Conv. Bioref. (2021) 11:39–56
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Funding information Open access funding provided by ZHAW Zurich
University of Applied Sciences. Klimafonds Stadtwerke Winterthur
granted to Kaleidosim Technologies AG for pro-bono project
Biogassim:https://stadtwerk.winterthur.ch/privatkundschaft/
nachhaltigkeit/klimafonds
Data availability Presented studies: Full numerical data available upon
personal request to the corresponding author.
Software: System dynamic simulation software to be freely accessible
via www.woodgassim.ch by end of 2020/mid 2021 within pro-bono-
project Biogassimby Kaleidosim Technologies AG. Software is not
open source though.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this article
are included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in the
article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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... cm in length, and the reduction zone ends with a 50 cm diameter. In addition, the geometry of the downdraft gasifier of Jayah et al. [57] is considered for validation, whose results have been commonly used for validation in previous gasification model studies [10,49,[58][59][60][61]. This gasifier treats rubber wood, the throat diameter is 10 cm and the diameter at the end of reduction zone is 34 cm with a height difference of 22 cm [57]. ...
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Biomass gasification technology is evolving and more research through modelling alongside the experimental work needs to be performed. In the past, all the attention has been concentrated on the combustion and reduction stages to be the controlling reactions while the pyrolysis is modelled as an instantaneous process. In this study, a new enhanced model for the gasification process in the downdraft reactor is proposed with a more realistic representation of the pyrolysis stage as a temperature-dependent sequential release of gases. The evolution of the pyrolysis gas, followed by the combustion and reduction reactions, are kinetically controlled in the proposed model which is developed within the Aspen Plus software package. The simulation of the reactor temperature profile and the evolution of the pyrolysis gas is carried out in an integrated MATLAB and Aspen Plus model. The proposed model has been validated against experimental data obtained from the gasification of different woody biomass types and considering a range of scale reactor and power loads. The predicted results are in very good agreement with the experimental data, and therefore the model can be used with confidence to perform a sensitivity analysis to predict the performance of a gasifier at different load levels corresponding to the air flow rate range of 3–10 L/s. As the supplied air flow rate increases, the LHV decreases but the gas yield behaves conversely, and in turn the cold gas efficiency is maintained at a good level of energy conversion at ≥ 70%. Furthermore, the variation in the biomass moisture content, which is commonly in the range of 5–25 % has a significant effect on the gasification efficiency. Such that biomass that has a high moisture content substantially reduces the CO content and consequently the LHV of the produced gas. Hence, it is important to maintain the moisture content at the lowest level.
... The biomass pyro-gasification process can be divided into the following steps: drying, devolatilization or pyrolysis, partial oxidation, combustion, and gasification of the devolatilization products [24,25]. Pyrolysis occurs in the total absence of an external oxidizing agent, and only the heat is used to degrade feedstock. ...
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This study focuses on the energetic valorization of agave bagasse (AB) waste. The pyro-gasification tests with air were conducted in a thermogravimetric analyzer (TGA) coupled with a micro-gas chromatograph (μ-GC) analyzer using AB as feedstocks and α-cellulose (CEL) as biomass model. The effects of heating rate values (10 to 80 °C/min) and stoichiometric air-to-biomass ratio values (ABR) on the performance of the process were investigated. The evolution of CO, CO2, CH4, and H2 was monitored through online and offline TGA/μ-GC measurements. It was shown that CO and CO2 were mainly released at a temperature below 450 °C and followed almost the same pattern. The CH4 started to evolve at a temperature above 300 °C, while the maximum H2 production was obtained between 600 and 700 °C. In addition, an appreciable decrease in the combustible gas composition (CO, CO2, CH4, and H2) was achieved with an increment in ABR, while the fraction of non-combustible gases (CO2 and N2) increased. On the other hand, the increase in heating rate positively influences combustible gas yields. In fact, the H2 production increased from 70.75 to 128.89 mL/(g of feedstock) with the increase in heating rate from 10 to 80 °C/min, thus improving in the process efficiency from 33 to 60. The average lower heating value of the fuel was about 5.5 MJ/Nm³. The results suggest that air pyro-gasification for hydrogen-rich gas production could be a promising route for the energetic valorization of AB waste. Graphical abstract
... Among different available technologies, gasification is one of the most promising technologies that produce biofuel with less toxic emission and enhance the H 2 /CO ratio in the produced gas [21][22][23]. Therefore, it remains a promising future and sustainable energy supply [24]. It is a thermochemical process that converts carbonaceous fuel into combustible gases called syngas which primarily consists of hydrogen (H 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), and methane (CH 4 ) along with other gases and impurities. ...
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... For this, proper design should consider fulfilling this arrangement with the downdraft gasifier [12,14]. Application in ICE or gas turbine to assurance lifetime performance, gas turbine including an engine generator needs producer gas where producer gas can have tar content at a maximum of 50 mg/Nm 3 [5,[15][16][17] and tar content level of 5 mg/Nm 3 can reach fully meeting the application in solid oxide fuel cell (SOFC) and chemical synthesis [18]. ...
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