Artificial neural network models for biomass gasification in fluidized bed gasifiers

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.. Ev

Artificial neural network models for biomass

gasification in fluidized bed gasifiers

Maria Puig-Arnavata, J Alfredo Herna´ndezb, Joan Carles Brunoa,* , Alberto Coronasa

aUniversitat Rovira i Virgili, Dept Eng Meca`nica, Av Paı¨sos Catalans 26, 43007 Tarragona, Spain

bUniversidad 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

a r t i c l e i n f o

Article history:

Received 4 April 2012

Received in revised form

16 November 2012

Accepted 10 December 2012

Available online 28 January 2013

Keywords:

Biomass

Gasification

Artificial neural network

Simulation

Fluidized bed

a b s t r a c t Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB) Both models determine the producer gas composition (CO, CO2, H2, CH4) and gas yield Published experimental data from other authors has been used to train the ANNs 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 can be successfully predicted by applying neural networks ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm The results obtained by these ANNs show high agreement with published experimental data used R2> 0.98 Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are 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

green-house gas emissions; biomass is considered to be CO2-neutral

Biomass gasification can be considered in advanced

applica-tions in developed countries, and also for rural electrification

in isolated installations or in developing countries In

addi-tion, it is the only renewable energy source that can directly

replace fossil fuels as it is widely available and allows

con-tinuous 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 monox-ide, 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 gas-ification process for scale-up, industrial control strategies, performance calculation after modifying the operating con-ditions, etc Mathematical models aim to study the thermo-chemical 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:maria.puig@urv.cat(M Puig-Arnavat),juancarlos.bruno@urv.cat(J.C Bruno)

Available online at www.sciencedirect.com

http://www.elsevier.com/locate/biombioe

0961-9534/$e see front matter ª 2012 Elsevier Ltd All rights reserved

http://dx.doi.org/10.1016/j.biombioe.2012.12.012

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

oper-ating 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

arti-ficial neural networks According to Villanueva et al [1],

equilibrium models are considered a good approach when

simulating entrained-flow gasifiers in chemical process

sim-ulators 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]

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 gas-ification 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 in-teractions 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 1e 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 kg1) 4e33.4

Moisture content of wet biomass (g kg1) 35e220

Carbon content of dry biomass (g kg1) 476.6e529.9

Oxygen content of dry biomass (g kg1) 383.8e435.5

Hydrogen content of dry biomass (g kg1) 54.3e78.6

Equivalence ratio (ER) () 0.19e0.64

Gasification temperature (Tg) (C) 701e861

Output variables for the various ANNs

Producer gas yield (at 298 K, 103 kPa), (m3kg1) 1.72e3.30

Gas composition (volume fraction, dry basis)

H2content (%) 3.00e7.30

CH4content (%) 1.20e4.60

CO2content (%) 13.94e18.30

Table 2e 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 kg1) 5.5e11.0

Moisture content of wet biomass (g kg1) 62.8e250

Carbon content of dry biomass (g kg1) 458.9e505.4

Oxygen content of dry biomass (g kg1) 411.1e471.8

Hydrogen content of dry biomass (g kg1) 56.4e70.8

Equivalence ratio (ER) () 0.19e0.47

Gasification temperature (Tg) (C) 700e900

Steam to dry biomass ratio (VB) (kg kg1) 0e0.04

Output variables for the various ANNs

Producer gas yield (at 298 K, 103 kPa), (m3kg1) 1.17e3.42

Gas composition (volume fraction, dry basis)

H2content (%) 4.97e26.17

CH4content (%) 2.40e6.07

CO2content (%) 9.82e18.60

Moisture Ash

C O H ER

T g

Input layer ( i )

Hidden layer Output layer

i=1

i=7

j=1

j=2

k=1

IW j,i

LW k,j Weights

biases

Output (CO, CO 2 , H 2 , CH 4

or Gas yield)

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

a CFB gasifier

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

a BFB gasifier

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

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

computa-tion of products composicomputa-tions of biomass gasificacomputa-tion in an

atmospheric air gasification fluidized bed reactor They

com-bined 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

re-quirements As a result, a complete stoichiometry was

for-mulated, and corresponding reaction temperature difference

parameters computed under the constraint of the

non-equilibrium distribution of gasification products determined

by mass balance and data reconciliation The ANN regressions

related temperature differences to fuel composition and

gas-ifier 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 equilib-rium 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 3e 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

0.0732

CO2

IWi,j

2.1235

CH4

IWi,j

5.2290

H2

IWi,j

16.8436 24.2709 1.2959 3.6059 6.6673 20.5250 18.7020

17.6810 Producer gas yield

IWi,j

6.8841 6.4443 2.3434 1.3813 3.7339 9.9848 1.4279

0.8342

The selection of an appropriate set of variables for

inclu-sion as inputs to the model is a crucial step in model

devel-opment, 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

cir-culating fluidized bed ANN model was obtained for air

gas-ification 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

infor-mation (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

pre-liminary validation tests and results from the literature[5,6];

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,

H2and 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 con-tent of carbon (C), hydrogen (H) and oxygen (O) For this reason, the input layer for the CFB ANN model consists of seven vari-ables: 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 charac-teristics of these input and output variables, obtained from published experimental data, are shown in Table 1for CFB gasifiers and inTable 2for 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 4e 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

in the natural neurons An ANN is composed of a large

num-ber 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 1andFig 2illustrate 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 (seeFigs

1 and 2)

The ANNs models proposed in the present study consist in:

- CFB gasifier model: five ANNs, one for each output (CO, CO2,

H2, CH4and 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

equiv-alence 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

(tan-sig) was used in the hidden layer and the linear transfer

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

scaled to a new valueðpiÞ using Eq.(1) [19]:

p



i¼ 0:2 þ0:6$

pi min
pi



max

pi



 min
pi

where pi is the normalized input variable and piis the input

variable

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

3 Results and discussion

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 inFig 3 It can be seen how all R2values are higher than 0.99 except for the case

of H2composition 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 3gives 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):

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 abso-lute values The proposed equation, adapted to the present ANN topology, is as presented in Eq.(3):

aoutput¼Xj¼2

j¼1

2

6

6LW1;j$

0 B

1þ exp 2$Pi¼7

i¼1



IWj;i$pi

þ b1j

  1

1 C C

3 7

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

Pj¼2

j¼1

0

B

B

0

B

B IWj;i

Pi¼7

i¼1IWj;i

1 C C$LW1;j

1 C C

Pi¼7

i¼1

8

>

<

>

:

Pj¼2

j¼1

0

B

B

0

B

B IWj;i

Pi¼7 i¼1IWj;i

1 C C$LW1;j

1 C C

9

>

=

>

;

(3)

where Iiis 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

be-tween 31.7% and 54.1% of the importance on CO, CO2, H2

and CH4prediction 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

in all cases (around 10%) except for CO2where it is lower (4.9%)

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 pre-vious case However, here, eight input variables are consid-ered 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 inFig 5 All R2values are higher than 0.99 except for the case of CO2composition 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 4e 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

0.9005 22.8979 0.3383 10.2693 13.9051 0.5125 1.2177 1.6145

4.0218 2.0805 0.6249 1.9391 1.0988 0.6812 0.1740 0.5222

3.6788

CO2

IWi,j

8.6144 1.1591 9.1504 4.1321 0.7413 12.6004 1.6067 4.8547

0.4782 3.9688 5.2829 1.2131 18.4774 3.4298 6.4298 7.5909

5.3372

CH4

IWi,j

27.6038 30.0594 31.5068 31.9344 49.1297 85.5683 10.8387 1.0029

56.8348 245.3845 194.6359 29.1672 243.0979 158.8235 82.2433 103.3151

79.8145

H2

IWi,j

2.6766 3.3581 1.7070 0.7123 1.0042 1.4738 0.0854 2.3963 1.0173 0.0697 3.1264 1.8738 0.1026 1.6956 5.1339 6.0746

0.7616 Producer gas yield

IWi,j

5.3707 31.8927 4.4783 23.2472 19.3959 10.3177 4.3555 12.2481

4.1585 10.9772 2.1819 5.8447 6.7403 4.6368 4.3425 1.4914

6.0126

Table 4shows 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):

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

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

Results presented in this section and in Section3.1show 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 6e 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

aoutput¼X

j¼2

j¼1

2

6

6LW1;j$

0 B

1þ exp



 2$

P

i¼8 i¼1



IWj;i$pi

þ b1j

  1

1 C C

3 7

these ANNs show high agreement with published

exper-imental 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

pos-sibility 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, CH4and 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, H2and CH4prediction 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

How-ever, 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 artificial neural network

BFB bubbling fluidized bed

b1, b2 biases

CFB circulating fluidized bed

ER equivalence ratio ()

FC mass fraction% of fixed carbon in dry biomass

IW, LW matrix weight

MC mass fraction% of H2O

VM mass fraction% of volatile matter in dry biomass

H mass fraction% of hydrogen content in dry biomass

I relative influence of an input variable on the output

variable (%)

O mass fraction% of oxygen content in dry biomass

C mass fraction% of carbon content in dry biomass

p input to the ANN model p



normalized input to the ANN model

R2 correlation coefficient RMSE root mean square error

Tg gasification temperature (C)

VB steam to dry biomass mass ratio (kg kg1)

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

r e f e r e n c e s

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