Artificial neural network models for biomass gasification in fluidized bed gasifiers

b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

Available online at www.sciencedirect.com

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Artificial neural network models for biomass
gasification in fluidized bed gasifiers
Maria Puig-Arnavat a, J. Alfredo Herna´ndez b, Joan Carles Bruno a,*, Alberto Coronas a
a

Universitat Rovira i Virgili, Dept. Eng. Meca`nica, Av. Paı¨sos Catalans 26, 43007 Tarragona, Spain
Universidad Auto´noma del Estado de Morelos, Centro de Investigacio´n en Ingenierı´a y Ciencias Aplicadas (CIICAp), Av. Universidad No.
1001 Col. Chamilpa, 62209 Cuernavaca, Mexico
b

article info

abstract

Article history:

Artificial neural networks (ANNs) have been applied for modeling biomass gasification

Received 4 April 2012

process in fluidized bed reactors. Two architectures of ANNs models are presented; one for

Received in revised form

circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers

16 November 2012

(BFB). Both models determine the producer gas composition (CO, CO2, H2, CH4) and gas

Accepted 10 December 2012

yield. Published experimental data from other authors has been used to train the ANNs.

Available online 28 January 2013

The obtained results show that the percentage composition of the main four gas species in
producer gas (CO, CO2, H2, CH4) and producer gas yield for a biomass fluidized bed gasifier

Keywords:

can be successfully predicted by applying neural networks. ANNs models use in the input

Biomass

layer the biomass composition and few operating parameters, two neurons in the hidden

Gasification

layer and the backpropagation algorithm. The results obtained by these ANNs show high

Artificial neural network

agreement with published experimental data used R2 > 0.98. Furthermore a sensitivity

Simulation

analysis has been applied in each ANN model showing that all studied input variables are

Fluidized bed

important.
ª 2012 Elsevier Ltd. All rights reserved.

1.

Introduction

Biomass gasification is a highly efficient and clean conversion
process that converts different biomass feedstocks to a wide
variety of products for various applications. In this context,
modern use of biomass is considered a very promising clean
energy option for reducing energy dependency and greenhouse gas emissions; biomass is considered to be CO2-neutral.
Biomass gasification can be considered in advanced applications in developed countries, and also for rural electrification
in isolated installations or in developing countries. In addition, it is the only renewable energy source that can directly
replace fossil fuels as it is widely available and allows continuous power generation and synthesis of different fuels and
chemicals.

Gasification conversion process can be defined as a partial
thermal oxidation, which results in a great proportion of
gaseous products (carbon dioxide, hydrogen, carbon monoxide, water and other gaseous hydrocarbons), little quantities
of char, ash and several condensable compounds (tars and
oils). Air, steam or oxygen can be supplied to the reaction as
gasifying agents. The quality of gas produced varies according
to the gasifying agent used and the operating conditions
selected.

Consequently, it is necessary to simulate biomass gasification process for scale-up, industrial control strategies,
performance calculation after modifying the operating conditions, etc. Mathematical models aim to study the thermochemical processes during the gasification of the biomass and
to evaluate the influence of the main input variables on the

* Corresponding author. Tel.: þ34 977257068; fax: þ34 977559691.
E-mail addresses: (M. Puig-Arnavat), (J.C. Bruno).
0961-9534/$ e see front matter ª 2012 Elsevier Ltd. All rights reserved.
/>

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Input layer
( i)

Table 1 e Characteristics of input and output variables in
the ANN model for CFB gasifiers.
Range
Input variables for the ANNs
Ash content of dry biomass (g kgÀ1)
Moisture content of wet biomass (g kgÀ1)
Carbon content of dry biomass (g kgÀ1)
Oxygen content of dry biomass (g kgÀ1)
Hydrogen content of dry biomass (g kgÀ1)
Equivalence ratio (ER) (À)
Gasification temperature (Tg) ( C)
Output variables for the various ANNs
Producer gas yield (at 298 K, 103 kPa), (m3 kgÀ1)
Gas composition (volume fraction, dry basis)

H2 content (%)
CH4 content (%)
CO2 content (%)
CO content (%)

i=1

Ash

Hidden layer

Output layer

Weights

IWj,i

Moisture
4e33.4
35e220
476.6e529.9
383.8e435.5
54.3e78.6
0.19e0.64
701e861

LWk,j
C
j=1

ER

producer gas composition and calorific value. However, the
operation of a biomass gasifier depends on several complex
chemical reactions, including several steps like: pyrolysis,
thermal cracking of vapors to gas and char, gasification of
char, and partial oxidation of combustible gas, vapors and
char. Due to the complexity of the gasification process coupled
with the sensitivity of the product’s distribution to the operating conditions; many idealized assumptions have to be
made in the development of these models.
Different kinds of models have been implemented for
gasification systems, including equilibrium, kinetic and artificial neural networks. According to Villanueva et al. [1],
equilibrium models are considered a good approach when
simulating entrained-flow gasifiers in chemical process simulators or for downdraft fixed-bed gasifiers, as long as high
temperature and high gas residence time are achieved in the
throat. By contrast, updraft fixed-bed, dual fluidized-bed and
stand-alone fluidized-bed gasifiers should be modeled by
revised equilibrium models or, in some extreme cases, by
detailed rate-flow models. A detailed review of recent biomass
gasification models is available elsewhere [2,3].

(CO, CO2 ,H 2 ,CH 4
or Gas yield)

k=1

H

1.72e3.30
3.00e7.30

1.20e4.60
13.94e18.30
6.90e21.40

Output

O

i=7

Tg

j=2

b1j

b2k
biases

Fig. 1 e ANN model structure to predict producer gas
composition and gas yield from biomass gasification in
a CFB gasifier.

Artificial neural networks (ANNs) have been extensively
used in the field of pattern recognition; signal processing,
function approximation and process simulation. However,
they almost have not been used in the field of biomass gasification modeling. Only few references can be found in the
literature covering this field [4e6]. ANNs are useful when the
primary goal is outcome prediction and important interactions of complex nonlinearities exist in a data set like for
biomass gasification, because they can approximate arbitrary

nonlinear functions. One of the characteristics of modeling
based on artificial neural networks is that it does not require
the mathematical description of the phenomena involved in
the process, and might therefore prove useful in simulating
and up-scaling complex biomass gasification process. Guo
et al. [4] developed a hybrid neural network model to predict
the product yield and gas composition of biomass gasification
in an atmospheric pressure steam fluidized bed gasifier. They
used as input variables the bed temperature and the stock
residence time. Taking into account only these two input

Table 2 e Characteristics of input and output variables in
the ANN model for BFB gasifiers.
Range
Input variables for the ANNs
Ash content of dry biomass (g kgÀ1)
Moisture content of wet biomass (g kgÀ1)
Carbon content of dry biomass (g kgÀ1)
Oxygen content of dry biomass (g kgÀ1)
Hydrogen content of dry biomass (g kgÀ1)
Equivalence ratio (ER) (À)
Gasification temperature (Tg) ( C)
Steam to dry biomass ratio (VB) (kg kgÀ1)
Output variables for the various ANNs
Producer gas yield (at 298 K, 103 kPa), (m3 kgÀ1)
Gas composition (volume fraction, dry basis)
H2 content (%)
CH4 content (%)
CO2 content (%)
CO content (%)

5.5e11.0
62.8e250
458.9e505.4
411.1e471.8
56.4e70.8
0.19e0.47
700e900
0e0.04
1.17e3.42
4.97e26.17
2.40e6.07
9.82e18.60
10e29.47

Fig. 2 e ANN model structure to predict producer gas
composition and gas yield from biomass gasification in
a BFB gasifier.


b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

Fig. 3 e Comparison of the experimental results with the results calculated by ANN for CFB gasifiers.

281


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variables, forced the authors to develop four ANNs, one for
each biomass feedstock considered. Even the results showed
that the ANNs developed could reflect the real gasification
process; it would have been more interesting to develop just
one but more general model for the biomass gasifier in study
and accounting for different biomass feedstocks.
Brown et al. [5] developed a reaction model for computation of products compositions of biomass gasification in an
atmospheric air gasification fluidized bed reactor. They combined the use of an equilibrium model and ANN regressions
for modeling the biomass gasification process. Their objective
was to improve the accuracy of equilibrium calculations
and prevent the ANN model from learning mass and energy
balances, thereby minimizing the experimental data requirements. As a result, a complete stoichiometry was formulated, and corresponding reaction temperature difference
parameters computed under the constraint of the nonequilibrium distribution of gasification products determined
by mass balance and data reconciliation. The ANN regressions
related temperature differences to fuel composition and gasifier operating conditions. This combination of equilibrium
model and ANN was further investigated and improved by
the same authors [6]. Even though the model incorporates
ANNs, it cannot be considered a pure ANN model for biomass

gasification process because the most important part of the
model is a stoichiometric equilibrium model.
In this study, two feed-forward ANNs models have been
developed to simulate the biomass gasification process in
bubbling and circulating fluidized bed gasifiers, respectively.
The aim is to obtain two models that can predict the producer
gas composition and the gas yield from biomass composition
and few operating parameters, like thermodynamic equilibrium models do, but avoiding the high complexity of kinetic
models. The experimental data reported and published by
other authors has been used here to train the ANNs. The

resulting model predictions for different types of biomass,
given by the neural networks, are investigated in detail.

2.

Methods

2.1.

Experimental data selection

Since different kinds of biomass and different gasifiers have
different gasification behavior, two ANN models are presented
in this work. The first one applies for circulating fluidized bed
(CFB) gasifiers and the second one for bubbling fluidized bed
(BFB) gasifiers.

Table 3 e Weights and biases of the ANNs designed for the four major gas species of producer gas (CO, CO2, H2, CH4) and
producer gas yield for ANN model for CFB gasifiers.
CO
IWi,j
À3.2006
À1.1408
LW1,j
5.4159

0.0722
À1.8333

CO2

IWi,j
1.7859
9.8078
LW1,j
4.6685

3.1087
9.1839

CH4
IWi,j
1.1889
1.4276
LW1,j
1.2490

À0.5638
À0.3493

À10.0337

À3.7749
0.4085

4.0413
À12.4537

5.0279
À2.3948
b1j

À7.6506
2.1235

3.9112

À1.7017
2.6406

2.4613
3.9629
6.3563

H2
IWi,j
À1.7403
À16.8436
LW1,j
3.0137
Producer gas yield
IWi,j
À6.8841
À6.4443
À5.7169
À1.6951
LW1,j
À0.5083
0.3425

3.4878
24.2709

0.8185
1.2959

À1.9792

À2.3434
1.5775

À0.9014
3.8072
b2
12.9445

À0.9632
0.8495

1.5078
9.5719
b2
10.2800

0.6350
4.0890

À2.6040
À1.9226
b2
8.5215

2.7315
À2.5402

À4.0765
À6.6673

À7.8066
À20.5250
b2
7.8781

À0.7735
À18.7020

À3.7339
À3.6455

À9.9848
19.7080
b2
2.4497

À1.4279
À6.0075

5.3061
À0.1148
b1j
3.6063
0.0732

1.9819
À15.3984

4.4029
3.0161
b1j
À3.8959
À5.2290

0.2984
0.8706

À3.3632
À3.6059
b1j
3.9094
17.6810

À1.3813
3.2875
b1j
14.6688
À0.8342


b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

The selection of an appropriate set of variables for inclusion as inputs to the model is a crucial step in model development, as the performance of the final model is heavily
dependent on the input variables used.

In this study, an extensive literature review was done to
obtain experimental data that could be used to develop the
ANNs models. Due to the different properties and behavior of
different biomasses, and to have more homogeneous data,
only experimental data for wood gasification in atmospheric
pressure and inert bed reactors was considered. Data for circulating fluidized bed ANN model was obtained for air gasification of wood from Li et al. [7] (cypress, hemlock and mixed
sawdust) and van der Drift et al. [8] (mixed wood). Published
experimental data for bubbling fluidized bed reactors was
found in the studies of Narva´ez et al. [9] (pine sawdust),
Campoy [10] (pellets), Kaewluan and Pipatmanomai [11]
(rubber wood chips) and Lv et al. [12] (pine sawdust) for air
and airesteam gasification.
In both ANNs models, the data sets containing the information (the values of input and output variables) of different
biomass gasification tests are small. The data sets for CFB and
BFB gasifiers contain the results of 18 and 36 tests, respectively.
Due to the small size of the data sets and after some preliminary validation tests and results from the literature [5,6];

283

the number of input variables was reduced compared to the
initial available ones. Fixed carbon (FC) and volatile matter
(VM) were considered as dependent variables because the FC
ratio is proportional to both the H/C and O/C ratios [5,13,14].
Considering that the gas species to be determined are CO, CO2,
H2 and CH4; nitrogen and sulphur were not considered either as
input variables. In addition, their amount in wood is very low
and, in some cases, almost negligible compared with the content of carbon (C), hydrogen (H) and oxygen (O). For this reason,
the input layer for the CFB ANN model consists of seven variables: biomass moisture (MC), biomass content of ash, C, H and
O, gasification temperature (Tg) and equivalence ratio (ER). In
the case of BFB model, the operational variables considered for

the input layer were the same than those for CFB gasifier plus
another variable that stands for the ratio between the amount
of steam injected and the biomass flowrate (VB). The characteristics of these input and output variables, obtained from
published experimental data, are shown in Table 1 for CFB
gasifiers and in Table 2 for BFB gasifiers.

2.2.

Artificial neural networks topology

An artificial neural network is a system based on the operation
of biological neural networks, a computational model inspired

Fig. 4 e Relative impact (%) of input variables on the different outputs for the four main producer gas components and
producer gas yield of the ANN model for CFB gasifiers.


284

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The outputs of each ANN were compared with targets from
experimental data reported by other authors. To minimize the
error, the LavenbergeMarquardt backpropagation algorithm
was used. The system adjusted the weights of the internal
connections to minimize errors between the network output
and target output.
The performance of the different ANNs was statistically
measured by RMSE and regression coefficient (R2), which were
calculated with the experimental values and networks

predictions.

in the natural neurons. An ANN is composed of a large number of highly interconnected processing elements (neurons or
nodes) working in unison to solve specific problems. The
neurons are grouped into distinct layers and interconnected
according to a given architecture. Each layer has a weight
matrix, a bias vector and an output vector.
In this study, two ANNs models were developed in the
Matlab environment using the Neural Network Toolbox [15].
Fig. 1 and Fig. 2 illustrate the architecture of the models for
CFB and BFB gasifiers, respectively. Since there is no explicit
rule to determine either the number of neurons in the hidden
layer or the number of hidden layers, the trial and error
method was applied to find the best solution by minimizing
the Root Mean Square Error (RMSE). In this step of training,
a study was carried out to determine the number of neurons in
hidden layer which was considered to one and two neurons
for both ANNs models. The best obtained results (data not
show) were considering two neurons in hidden layer (see Figs.
1 and 2).
The ANNs models proposed in the present study consist in:

3.

3.1.
Proposed ANN model for circulating fluidized bed
gasifiers
Five neural networks with seven inputs, two neurons in the
hidden layer and one output each, was found to be efficient in
predicting producer gas composition as well as gas yield for

CFB gasifiers.
Experimental and simulated values for CO, CO2, H2, CH4,
and gas yield were compared satisfactorily through a linear
regression model ( y ¼ a$x þ b) for each. The obtained
regression coefficients (R2) are presented in Fig. 3. It can be
seen how all R2 values are higher than 0.99 except for the case
of H2 composition that it is 0.98.
According to Verma et al. [16] and El Hamzaoui et al. [17] to
satisfy the statistical test of intercept and slope; the interval
between the highest and lowest values of the intercept must
contain zero and the interval between the highest and lowest
values of the slope must contain one. The proposed ANNs
passed the test with 99.8% of confidence level. This test
guarantees that whole ANN model, containing five ANNs, has
a satisfactory level of confidence.
Table 3 gives the obtained parameters (IWj,i, LW1,j, b1j, b2)
of the best fit for 2 neurons in the hidden layer for each of the
five ANN developed in the CFB model. These parameters were
used in the proposed model to simulate the output values. In
consequence, the proposed ANN model follows Eq. (2):

- CFB gasifier model: five ANNs, one for each output (CO, CO2,
H2, CH4 and gas yield). Each ANN has one input layer with
seven variables (biomass moisture (MC), biomass content of
ash, C, H and O, gasification temperature (Tg) and equivalence ratio (ER)), one hidden layer with two neurons and
one output.
- BFB gasifier model: five ANNs with eight variables in the
input layer (biomass moisture (MC), biomass content of ash,
C, H and O, gasification temperature (Tg), equivalence ratio
(ER) and injected steam ratio (VB)), one hidden layer with

two neurons and one output each ANN.
To test the robustness and predict the ability of the models,
in both ANNs models, the data sets were divided into training
(80%) and validation-test subsets (20%), randomly selected
from the available database. Due to the small size of the
database, validation and test sets were the same.
In all models, a hyperbolic tangent sigmoid function (tansig) was used in the hidden layer and the linear transfer

2
aoutput ¼

0

function ( purelin) was used in the output layer. The input
parameters were normalized in the range of 0.2e0.8. So, any
samples from the training and validation-test sets ( pi) were
À
scaled to a new value ðpi Þ using Eq. (1) [19]:
À
À ÁÁ
0:6$ pi À min pi
À
À Á
À Á
(1)
pi ¼ 0:2 þ
max pi À min pi
À

13

C7
B
2
C7
B
6


 À 1C7 þ b2


6LW1;j $B
A5
@
4
P
À
i¼7
j¼1
þ b1j
1 þ exp À 2$
i¼1 IWj;i $pi

j¼2 6
X

where pi is the normalized input variable and pi is the input
variable.

Results and discussion

(2)

To assess the relative importance of the input variables,
the evaluation process based on the neural net weight matrix
and Garson equation [18] was used [17,19]. Garson proposed
an equation based on the partitioning of connection weights.
The numerator describes the sums of absolute products of
weights for each input while the denominator represents the
sum of all weights feeding into hidden unit, taking the absolute values. The proposed equation, adapted to the present
ANN topology, is as presented in Eq. (3):


b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

Fig. 5 e Comparison of the experimental results with the results calculated by ANN for BFB gasifiers.

285


286

b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

00

1
1


 C
B
B
C
Pj¼2 BB IWj;i  C 
C
C$ LW1;j C
j¼1 BBPi¼7 
A
@@
A
i¼1 IWj;i
Ii ¼

1
19
>
>


>
=
B
C
C
Pi¼7 Pj¼2 B
BB IWj;i  C 
C

LW

$


B
C
B
C
P
1;j
i¼1
j¼1
i¼7 

>
>
@
A
@
A
>
>
i¼1 IWj;i
>
>
;
:
8
>
>
>

<

00

in all cases (around 10%) except for CO2 where it is lower
(4.9%).

(3)

where Ii is the relative influence of the ith input variable on
the output variable. The relative importance of the different
input variables, for each ANN, calculated using Eq. (3) is
shown in Fig. 4. As it can be observed, all variables have
a strong effect on the different outputs (CO, CO2, H2, CH4
and producer gas yield). It can be seen how variables that
account for biomass composition (C, H, O) represent between 31.7% and 54.1% of the importance on CO, CO2, H2
and CH4 prediction. However, this importance is reduced to
25% for producer gas yield. On the other hand ER is the
most important variable for producer gas yield prediction
(37.6%) while it is also important for CO and H2 (31.2 and
30.2%) and less important for CO2 (11.5%) and CH4 (12.6%).
Gasification temperature has a relative constant importance

3.2.
Proposed ANN model for bubbling fluidized bed
gasifiers
In this model, the same procedure than that applied for
CFB gasifiers has been followed. The topology of the five
ANNs integrated in the model is the same than in the previous case. However, here, eight input variables are considered instead of seven because the model also accounts for
airesteam gasification and not only for air gasification like in

CFB gasifiers.
The obtained regression coefficients (R2) when comparing
experimental and simulated values for CO, CO2, H2, CH4,
and gas yield are presented in Fig. 5. All R2 values are higher
than 0.99 except for the case of CO2 composition that it is
0.98.
The limits for the statistical test of intercept and slope were
calculated. In all cases, the slope contained one and the
intercept contained zero. Consequently, the proposed ANNs
also passed the test with 99.8% of confidence level.

Table 4 e Weights and biases of the ANNs for the four major producer gas species (CO, CO2, H2, CH4) and producer gas yield
for the ANN model for BFB gasifiers.
CO
IWi,j
À0.9005
À4.0218
LW1,j
À33.7782

À22.8979
À2.0805

CO2
IWi,j
8.6144
À0.4782
LW1,j
3.5726

À1.1591
3.9688

CH4
IWi,j
À27.6038
À56.8348
LW1,j
À0.4665

30.0594
À245.3845

H2
IWi,j
À2.6766
1.0173
LW1,j
13.8413

3.3581
0.0697

Producer gas yield
IWi,j
À5.3707
À4.1585
LW1,j
À0.5422

À31.8927
À10.9772

0.3383
À0.6249

À10.2693
À1.9391
b1j
15.6788
3.6788

13.9051
À1.0988

À0.5125
0.6812
b2
12.3524

1.2177
À0.1740

À1.6145
0.5222

À9.1504
À5.2829

4.1321

1.2131
b1j
4.4389
À5.3372

À0.7413
18.4774

À12.6004
À3.4298
b2
13.4535

1.6067
À6.4298

4.8547
7.5909

À31.5068
194.6359

À31.9344
À29.1672
b1j
À28.9205
À79.8145

49.1297
243.0979

85.5683
158.8235
b2
4.2972

10.8387
À82.2433

1.0029
103.3151

À1.7070
3.1264

0.7123
1.8738
b1j
À1.3738
À0.7616

À1.0042
0.1026

À1.4738
1.6956
b2
13.6191

À0.0854

5.1339

2.3963
À6.0746

4.4783
2.1819

À23.2472
À5.8447
b1j
22.8709
6.0126

19.3959
6.7403

10.3177
4.6368
b2
1.7517

4.3555
4.3425

À12.2481
À1.4914

À39.6833

À2.6414

À1.0988

8.0323

1.2019


b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

producer gas yield). Variables that account for biomass
composition (C, H, O) always represent, like in CFB model,
more than 25% of the importance of all studied outputs. The
importance of ER is reduced in all cases. However, ER and VB
together represent around 20% of importance in all cases
except for CO.

Table 4 shows the obtained parameters (IWj,i, LW1,j, b1j, b2)
of the best fit for 2 neurons in the hidden layer for each of the
five ANN developed in the BFB model. The proposed ANN
model follows the same expression than the previous case
but it is necessary to take into account that in this case eight
inputs are considered as shown in Eq. (4):

2
aoutput

0

13

j¼2 6
C7
B
X
2
C7
B
6
¼


 À 1C7 þ b2


6LW1;j $B
A5
@
4
P
À
i¼8
j¼1
þ b1j
1 þ exp À 2$
i¼1 IWj;i $pi

The relative influence of the input variables was also
evaluated using Eq. (3) as in the CFB gasifiers’ model. The

relative importance of the different input variables for each
ANN is shown in Fig. 6. As can be seen in the previous
model, in this case, all of the variables also have a strong
effect on the different outputs (CO, CO2, H2, CH4 and

287

(4)

Results presented in this section and in Section 3.1 show
how the percentage composition of the main four gas species
in producer gas and producer gas yield for a biomass CFB or
BFB gasifier can be successfully predicted by applying a neural
network with two hidden neurons in the hidden layer and
using backpropagation algorithm. The results obtained by

Fig. 6 e Relative impact (%) of input variables on the different outputs for the four main producer gas components and
producer gas yield of the ANN model for BFB gasifiers.


288

b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9

these ANNs show high agreement with published experimental data used: very good correlations (R2 > 0.98) in almost
all cases and small RMSEs. However, it is necessary to have in
mind that ANN models are limited to a specified range of
operating conditions for which they have been trained. For
this reason, a larger experimental database would be desirable
to get improved models.

4.

Conclusions

Very few references can be found in the field of biomass
gasification modeling. The two ANN models developed in the
present study for CFB and BFB gasifiers have shown the possibility that ANN may offer some contribution to research in
this field.
Results presented show how the percentage composition
of the main four gas species in producer gas and producer gas
yield for a biomass CFB or BFB gasifier can be successfully
predicted by applying a neural network with two hidden
neurons in the hidden layer and using backpropagation
algorithm. The results obtained by these ANNs show high
agreement with published experimental data used: very good
correlations (R2 > 0.98) in almost all cases and small RMSEs.
According to analysis, all of the variables have a strong
effect on the different outputs (CO, CO2, H2, CH4 and producer
gas yield) for all ANN models. Biomass composition (C, H, O) in
CFB represents between 31.7% and 54.1% of the importance on
CO, CO2, H2 and CH4 prediction and in BFB between 28.9% and
52.3%. In the case of producer gas yield prediction, in CFB, the
ER input is the most important variable (37.6%) while in BFB
model decreases down to 10.8%.
This study is a first step and provides a good approach of
the great potential of this kind of models in this field. However, further additional experimental data to enlarge the
database would be useful for further ANN training and
improve the developed models. Finally, these proposed ANNs
models can be used to optimize and control the process.

Acknowledgments
The authors would like to thank the European Commission for
the financial support received as part of the European Project
Polycity (Energy networks in sustainable communities) (TREN/
05FP6EN/S07.43964/51381)

Nomenclature

ANN
BFB
b1, b2
CFB
ER
FC
IW, LW
MC
VM

artificial neural network
bubbling fluidized bed
biases
circulating fluidized bed
equivalence ratio (À)
mass fraction% of fixed carbon in dry biomass
matrix weight
mass fraction% of H2O
mass fraction% of volatile matter in dry biomass

H

I
O
C
p
À
p
R2
RMSE
Tg
VB

mass fraction% of hydrogen content in dry biomass
relative influence of an input variable on the output
variable (%)
mass fraction% of oxygen content in dry biomass
mass fraction% of carbon content in dry biomass
input to the ANN model
normalized input to the ANN model
correlation coefficient
root mean square error
gasification temperature ( C)
steam to dry biomass mass ratio (kg kgÀ1)

Subscripts
i
number of neurons in the input layer
j
number of neurons in the hidden layer
k
number of neurons in the output layer

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