www.elsevier.com/locate/micromeso

Microporous and Mesoporous Materials 78 (2005) 73–81

Application of artificial neural networks to high-throughputsynthesis of zeolites

M. Moliner a, J.M. Serra a, A. Corma a,*, E. Argente b, S. Valero b, V. Botti b

a Instituto de Tecnologıa Quımica, UPV-CSIC, Universidad Politecnica de Valencia, Avda. de los Naranjos s/n, E-46022 Valencia, Spainb Dep. Sistemas Informaticos y Computacion, Universidad Politecnica de Valencia, Camino Vera s/n, E-46022 Valencia, Spain

Received 5 July 2004; received in revised form 21 September 2004; accepted 21 September 2004

Available online 24 November 2004

Abstract

It is shown that multi-phase crystalline systems in zeolite synthesis can be modelled by using artificial neural networks (ANNs)

and considering as input variables the molar compositions of the starting synthesis gel. Experimental data were obtained using high-

throughput tools for synthesis of solid materials under hydrothermal conditions and following a multi-level factorial experimental

design of the system TEA:SiO2:Na2O:Al2O3:H2O. The study of several neural networks resulted in a non-linear model able to pre-

dict the occurrence and crystallinity of zeolite beta and competing phases, being the predictions much better than those obtained by

classical quadratic models.

� 2004 Elsevier Inc. All rights reserved.

Keywords: Neural networks; Zeolites; Beta; High-throughput synthesis; Data mining; Factorial design

1. Introduction

There are a large number of variables that can influ-

ence the crystallisation kinetics and the final crystalline

phase or phases produced during the hydrothermal syn-thesis of microporous materials. These factors include,

among others, gel composition, type of reagents and

precursors, crystallisation time and temperature. De-

spite remarkable efforts to rationalise the synthesis of

zeolites [1–4], the relationship between these synthesis

factors and the type of zeolite formed is not always

clearly understood. This is due to the metastable nature

of zeolites and the complexity of the synthesis mecha-nism. Combinatorial techniques can be applied in this

field in order to explore simultaneously different vari-

ables, by applying high-throughput synthesis and char-

acterisation, and computational techniques for data

1387-1811/$ - see front matter � 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.micromeso.2004.09.018

* Corresponding author. Tel.: +34 96 387 7800; fax: +34 96 387

7809.

E-mail address: acorma@itq.upv.es (A. Corma).

analysis (data mining). This approach has been applied

successfully in the development of new materials with

enhanced properties [5–7] and in the development of

new solid catalysts for industrial processes [8–10]. The

parallel synthesis under hydrothermal conditions [11–14] of large libraries of solid materials, i.e., zeolites

[15–19] or aluminophosphates [20], has been accom-

plished with high reproducibility and scalability, result-

ing in the discovery of new crystalline materials [21].

There is not doubt that experimental data analysis

plays an important role in the discovery and optimisa-

tion of solid materials when exploring high-dimensional

spaces. In fact, experimental relationships between syn-thesis variables and hidden patterns can be found using

data analysis, and this experimental ‘‘knowledge’’ accel-

erates the optimisation by reducing the required number

of experiments. However, the analysis of data derived

from combinatorial experimentation cannot be carried

out by classical statistical treatment due to the large

population of data and their complexity. Data mining

techniques are suitable for the analysis of these

Fig. 1. Picture of the high-throughput system employed for parallel gel

preparation.

74 M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81

experimental results and, more specifically, artificial

neural networks (ANNs) have been shown as useful

analysis tools to model data derived from combinatorial

experimentation in heterogeneous catalysis [22]. Indeed,

ANNs are complex mathematical systems [23–25] able

to find non-linear relationships in high-dimensionalexperimental spaces, giving as a result an empirical

model that can predict the experimental performance

corresponding to an input data vector.

Up to now, ANNs have not been employed for com-

prehensive modelling of multi-phase crystalline systems

considering the synthesis conditions or gel molar com-

positions as input variables. Only theoretical calcula-

tions [26] have been reported for the prediction of Si/Al ratio from batch compositions, giving poor predic-

tions and showing the limitations of theoretical methods

in this field.

In the present work, we have explored, as an example

to test the methodology, the system TEA:SiO2:Na2O:

Al2O3:H2O with crystallinity and yield of zeolite beta

as objective functions. For this study, high-throughput

experimentation tools are employed for automated syn-thesis and characterisation of a multi-level factorial de-

sign of experiments. In the second part of the work,

the capabilities of ANNs in modelling the synthesis

experimental data are investigated, considering the influ-

ence of network topology, activation functions and

training algorithms. Finally, the ANN prediction per-

formance is compared with the performance obtained

by means of a classical statistical treatment of results.

2. Experimental

2.1. Synthesis procedure

The molar gel composition was explored by varying

the following molar ratios: Na/(Si + Al), TEA/(Si + Al),OH/(Si + Al) and H2O/(Si + Al), while Si/Al ratio was

fixed. Synthesis gels were prepared using an in-house

developed robotic system (Fig. 1) composed of: (i) ro-

botic arm (SCORBOT-ER 4pc), for handling of vials

and solid weighting (range 20–1000mg), (ii) stirring sta-

tion for gel stirring and solvent evaporation by IR heat-

ing and Air/N2 sweep flow and (iii) liquid dosing station

equipped with seven calibrated syringe pumps (range of10–5000mg) and an analytical balance.

The reagents in the synthesis were tretraethylammo-

nium hydroxide (aq. solution 35% Aldrich), tretraethy-

lammonium bromide (98% Aldrich), sodium hydroxide

(99.9% Scharlau), sodium chloride (99.9% Fluka),

LUDOX (40wt% Aldrich) and sodium aluminate (pure

Carlo Erba). Gel synthesis was done inside Teflon vials

(3ml), which were finally inserted in a multi-autoclave of15 positions and sealed with a Teflon-lined stainless steel

tip. Crystallisation is carried out at 140 �C during 24h

under agitation by rotation. The samples were washed

and centrifuged at 6000rpm and finally dried at 100 �Covernight. Finally, the samples were weighted and char-

acterised by XRD using a multi-sample Phillips X�Pertdiffractometer employing CuKa radiation. Crystallinityof beta zeolite was calculated integrating the area of

the diffractograms between 22� and 23�, and normalisingthis value with respect to a reference sample (CP806

from PQ).

2.2. Reproducibility

Reproducibility of the automated synthesis of betazeolite was carried out following the experimental proce-

dure and tools depicted above. A single gel composition

(SiO2:0.08NaAlO2:0.19NaCl:0.43TEAOH:0.037TEABr:

10.8H2O) was prepared 15 times in parallel and was let

to crystallise simultaneously inside the multi-autoclave

for 24h. The values of crystallinity of beta zeolite sam-

ples obtained have a standard deviation of ±4%. This

value includes the deviations produced during gel syn-thesis, crystallisation and XRD characterisation.

3. High-throughput synthesis results

In order to optimise the preparation conditions for

the synthesis of beta zeolite, a factorial design (32 · 42)was employed for exploring different molar gel composi-tion (Table 1). This experimental design considers the

following four molar ratios (level): Na/(Si + Al) (3)

ranging from 0 to 0.5; TEA/(Si + Al) (4) from 0.1 to

Table 1

Molar rates between gel components and variation range explored by

the factorial design

Factor Lower level Upper level Number of levels

Na/(Si + Al) 0 0.5 3

TEA/(Si + Al) 0.1 0.6 4

OH�/(Si + Al) 0.15 0.52 4

H2O/(Si + Al) 5 15 3

Si/Al 12.5 1

M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81 75

0.6; OH�/(Si + Al) (4) from 0.15 to 0.52; and H2O/

(Si + Al) (3) from 5 to 15. The total number of samples

synthesised considering this factorial design was 144.

The synthesis were carried out following a randomised

sequence in order to provide protection against the ef-fects of lurking variables. In the explored space, only

two types of crystalline materials were obtained corre-

sponding with two different XRD pattern. These materi-

als were zeolite beta and another dense material (named

as UDM). Fig. 2 shows all the measured XRD for amor-

phous samples, beta phase, UDM phase and the

mixtures of both phases. A phase diagram for the exper-

imental space explored is shown in Fig. 3. The influenceof the different variables on the final crystallinity of the

zeolite beta can be observed in Fig. 4, being possible to

identify the compositional ranges that give highly crys-

Fig. 2. XRD diagrams obtained by hi

talline beta zeolite. They are: (a) range at low H2O/

(Si + Al) ratios, which corresponds to gels with high

concentration of nuclei leading to higher crystallisation

rates, (b) range with intermediate OH�/(Si + Al) ratio,

in where the concentration of OH� allows the solution

of the silica precursors and the crystallisation of sili-ceous nuclei that induce the formation of the beta struc-

ture, (c) range with medium-high TEA/(Si + Al) ratio, in

which the SDA concentration allows the nucleation of

beta zeolite and (d) zone with medium-low Na/(Si + Al)

ratio, in where this sodium cation concentration allows

the proper nucleation maintaining high growth rates.

The influence of the different factors on beta crystallinity

and the second order interactions between factors aresummarised in the Pareto diagram of Fig. 5. Pareto

diagram displays a frequency histogram where the

length of each bar on the chart is proportional to the

absolute value of its associated estimated effect or

the standardised effect. This factorial analysis was done

using Statgraphics Plus 5.1.

Regarding the formation of the UDM phase, this is

favoured at low OH� /(Si + Al) and high TEA/(Si + Al)ratios, independent of the water and sodium concentra-

tions. This material is formed when there are not enough

mineral agents to dissolve silica and aluminium, and

there is an excess of organic in the gel.

gh-throughput experimentation.

LOW Na/(Si+Al)

MEDIUM Na/(Si+Al)

HIGH Na/(Si+Al)

0.52

0.40

0.27

5

5

2

0

7

5

10

2

0.40

0.27

0.15

15

0.10 0.27 0.43 0.60 0.10 0.27 0.43 0.60 0.10 0.27 0.43 0.60

TEA/(Si+Al)

OH

— /(Si+

Al)

H2O

/(Si+Al)

Amorphous Beta UDM Mixture

0.1

0.5

0.4

0.2

0.1

0.5

Fig. 3. Phase diagram for the experimental space explored.

76 M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81

4. Neural network modelling

4.1. Neural network fundamentals

Artificial neural networks consist of a number of sim-

ple interconnected processing units, also called neurons,

which are analogous to the biological neurons. Thoseunits can be organised by different ways, defining the

neural network topology [27].

The basic unit of a neural network is composed of: (i)

a set of connections or inputs xj(t), each of which is char-

acterised by a synaptic weight wij, that represents the

intensity of interactions between each neuron j of a pre-

vious level and the actual neuron i; (ii) a propagation-

rule, which determines the effective input of neuron k

from all individual inputs to this neuron; (iii) an activa-

tion function Fk, that determines the output yk of neuron

k by means of its level of excitation [28].

Two important features of neural networks are the

ability of supplying fast answers to a problem and

the capability of generalising their answers, providing

acceptable results for unknown pattern. In this way,

they need to learn about the problem under study andthis learning is commonly named training process. Dur-

ing this training process, neural networks are supplied

with a set of samples belonging to the problem domain.

Then they establish mathematical correlations between

the samples [29] and large quantity of information and

time are required for analysis and processing.

One of the most well known structures of neuronal

networks for supervised learning is the multi-layer per-

ceptron (Fig. 6), which is generally used for classification

and prediction problems. In the multi-layer perceptron,

neurons are grouped into layers or levels, so each input

of a neuron is composed of the outputs of the neurons of

the previous level. And the neurons in the input layertake as input all the problem data. The number of nodes

at the input and output layers are determined by the

problem features.

4.2. Neural network model optimisation

Artificial neural networks are applied here for pre-

dicting formation and crystallinity of zeolite beta andcompeting phases on the bases of molar gel composi-

tions. Input variables to the neural networks were:

Na/(Si + Al), TEA/(Si + Al), OH�/(Si + Al) and H2O/

(Si + Al), while output variables were crystallinity of

beta zeolite and UDM material.

A total number of 144 samples were available for this

study. In order to minimise the effect of experimental

error in theANN training process and testing, experimen-tal data were randomly divided three times (groups A, B

and C) into three data sets: training data (100 samples),

validation data (20 samples) and test data (24 samples).

The prediction performance for each new ANN was cal-

culated considering the independent training and testing

using each data group (A, B, C).

Fig. 4. Influence of the four factors in the final crystallinity of the zeolite beta.

Fig. 5. Pareto diagram showing the influence of the different factors (A, B, C, D) in beta crystallinity and the second-order interactions between

factors (AB, BC, . . .).

M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81 77

Table 2

Neural network topologies employed for the optimisation of the neural

network model

Name ANN topology

Input

layer

1st hidden

layer

2nd hidden

layer

Output

layer

4_2_2 4 2 – 2

4_3_2 4 3 – 2

4_4_2 4 4 – 2

4_6_2 4 6 – 2

4_10_2 4 10 – 2

4_4_2_2 4 4 2 2

4_8_4_2 4 8 4 2

All those topologies were studied with logistic and tangential activa-

tion functions in hidden nodes, respectively.

Fig. 6. Selected neural network with a topology composed of four

nodes in the input layer, 10 nodes in the hidden layer and two nodes in

the output layer.

78 M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81

Using supervised learning, an incremental method

was applied and different neural network topologies

based on the multi-layer perceptron were tested. Start-

ing with one single hidden layer and few neurons, the

topology was modified by increasing the number of neu-

rons and the number of hidden layers. The inputs of the

network were the gel composition (see molar ratios

given above), and the outputs were the crystallinity ofeach phase. Different experiments were also carried

out with the algorithms more suitable for the multi-

layer perceptron. Specifically, neural networks were

trained with backpropagation (learning factor a = 0.2;

0.5; 0.8) and backpropagation with momentum

(a = 0.2; 0.5; 0.8 and momentum term l = 0.2; 0.5;

0.8, respectively). Moreover, logistic and tangential sig-

moidal activation functions were independently used inneurons of hidden layers. In Table 2, the neural network

topologies used in the study are summarised. A total

number of 14 ANN topologies was employed, each of

them trained with 12 different training algorithms and

using the three data groups (A, B, C). Therefore, the

total number of models obtained by training was 504

(14 · 12 · 3).Through the training process, an analysis of the mean

square error (MSE) for the validation samples is taken

each 1000 cycles. If MSE gets worse, the training process

is stopped otherwise the process continues up to 5000cycles.

4.3. Modelling results

In Table 3, the five best and five worst average results

of the training and test made with the three groups of

samples are displayed. It can be seen that neural net-

works using logistic activation functions provide betterresults than when using tangential activation functions.

Moreover, when comparing the mean absolute values

provided for test and training samples, the best pre-

dictions are obtained with a neural network with

logistic activation function in hidden nodes, trained with

backpropagation algorithm with momentum (a = 0.5,

l = 0.8), and a topology composed of four nodes in

the input layer, 10 nodes in the hidden layer and twonodes in the output layer (Fig. 6). This ANN provides

good prediction results for training and test samples

and, therefore, is able to predict properly the crystallin-

ity of the two competing phases with low deviations.

This can directly be observed in Fig. 7, where the predic-

tions for training, validation and test samples (group A)

are plotted.

The prediction goodness of the ANN model wascompared with that obtained using a multi-variable

quadratic model, with the following quadratic equation:

Crystallinity ¼ aþX4

i¼1bi � factori

þX4

i¼1

X4

j¼1ci;j � factori � factorj:

In Fig. 8, the predicted and experimental values for the

crystallinity of beta and UDM obtained with the ANNmodel and the multi-variable quadratic model are dis-

played. There the goodness of the ANN prediction can

again be observed for both variables, whereas the qua-

dratic model exhibits a poor prediction performance in

both cases.

Using the ANN model, the molar gel composition

that maximises the crystallinity of beta zeolite was calcu-

lated: Na/(Si + Al) 0.214, TEA/(Si + Al) 0.600, OH�/(Si + Al) 0.388 and H2O/(Si + Al) 12.8. When constrain-

ing the beta optimisation to values that minimise the re-

quired concentration of TEA, the results were: Na/

(Si + Al) 0.25, TEA/(Si + Al) 0.243, OH�/(Si + Al)

0.414 and H2O/(Si + Al) 5.

BETA

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Pre

dict

ed

UDM

0

20

40

60

80

100

0 20 40 60 80

Pre

dict

ed

100Experimental

Experimental

Fig. 7. Beta and UDM prediction performance of selected neural network for training data (squares) and validation and test data (triangles). NN

with four input nodes, 10 nodes in hidden layer and two output nodes, using logistic function in hidden nodes. Trained with 100 samples, employing

backpropagation algorithm with momentum (a = 0.5, l = 0.8) and validated with 20 samples and tested with 24 samples.

Table 3

Mean absolute prediction errors for different ANN topologies and different training algorithms

Network Neural network training process Cycles Mean absolute errors

Test data Train data

Act. function Training algorithm BETA UDM Mean BETA UDM Mean

4_8_4_2 Logistic BackMom a = 0.8, l = 0.5 1000 11.86 5.93 8.89 3.52 3.83 3.68

4_8_4_2 Logistic BackMom a = 0.8, l = 0.2 5000 11.17 6.67 8.92 3.01 3.25 3.13

4_10_2 Logistic BackMom a = 0.5, l = 0.8 5000 9.67 8.31 8.99 2.21 2.17 2.19

4_4_2 Logistic BackMom a = 0.5, l = 0.5 1000 12.81 5.35 9.08 7.17 4.90 6.03

4_10_2 Logistic Backpropagation a = 0.5 5000 12.40 5.76 9.08 3.23 4.49 3.86

4_4_2 Tangential BackMom a = 0.5, l = 0.8 4000 21.06 10.91 15.99 4.15 4.21 4.18

4_8_4_2 Tangential BackMom a = 0.8, l = 0.5 3000 24.12 8.86 16.49 13.26 3.72 8.49

4_10_2 Tangential BackMom a = 0.8, l = 0.8 1000 24.60 9.34 16.97 9.91 3.54 6.73

4_4_2_2 Tangential BackMom a = 0.8, l = 0.8 2000 25.32 9.95 17.64 20.36 9.36 14.86

4_8_4_2 Tangential BackMom a = 0.8, l = 0.8 2000 25.45 10.07 17.76 18.23 8.58 13.40

Five best and five worst prediction errors for test data are shown. Prediction errors in training data for those same ANN are also shown.

M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81 79

5. Conclusions

This work illustrates the integration of high-

throughput tools for synthesis of solid materials and

data mining techniques in the discovery and optimisa-

tion of new microporous materials. As an example,

the crystallinity of zeolite beta was optimised by

exploring simultaneously synthesis gels with different

molar compositions. The experimental work has been

accomplished using automated systems for synthesis

and characterisation. The final crystalline phase and

its crystallinity can be successfully modelled using arti-

ficial neural networks, obtaining high-quality predic-

tions.

BETA

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

BETA

experimental NN prediction

BETA

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

Samples

BETA

experimental multi-variable quadratic model

UDM

0.00

20 .00

40 .00

60 .00

80 .00

100 .00

120 .00

1 112 1 31 41 51 61 71 81 91 101 111 121 131 141

Samples

UD M

exper iment al NN prediction

UDM

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

120.00

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141Samples

UDM

experimental multi-variable quadratic model

Samples

Fig. 8. Crystallinity predictions for 144 samples obtained with the selected ANN model (squares) and the multi-variable quadratic model (triangles)

for beta and UDM crystallinity.

80 M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81

M. Moliner et al. / Microporous and Mesoporous Materials 78 (2005) 73–81 81

Acknowledgment

Financial support from Spanish government

(MAT2003-07945-C02-01 and grants TIC2003-07369-

C02-01, CICYT DPI2002-04434-C04-02, FPU AP2001-

1516) is gratefully acknowledged.

References

[1] A. Corma, M.E. Davis, Chem. Phys. Chem. 5 (2004) 304.

[2] A. Corma, M.J. Diaz-Cabanas, J. Martinez-Triguero, F. Rey,

J. Rius, Nature 418 (6897) (2002) 514.

[3] M.E. Davis, S.I. Zones, Chemical Industries (Dekker) 69 (1997) 1.

[4] G. Sastre, A. Pulido, R. Castaneda, A. Corma, J. Phys. Chem. B

108 (2004) 8830.

[5] B. Jandeleit, D.J. Schaefer, T.S. Powers, H.W. Turner, W.H.

Weinberg, Angew. Chem., Int. Ed. 38 (1999) 2495.

[6] X.D. Xiang, X.D. Sun, G. Briceno, Y.L. Lou, K.A. Wang, H.Y.

Chang, W.G. Wallacefreedman, S.W. Chen, P.G. Schultz, Science

265 (1995) 1738.

[7] P. Atienzar, A. Corma, H. Garcia, J.M. Serra, High throughput

characterisation of materials by photoluminescence spectroscopy,

Chem. Euro. J. (in press).

[8] C. Mirodatos, in: E. Gaigneaux, D.E. De Vos, P. Grange, P.A.

Jacobs, J.A. Martens, P. Ruiz, G. Poncelet (Eds.), Proceedings of

the Eighth International Symposium on Deactivation, Louvain-

la-Neuve, Belgium, September 9–12, 2002, Stud. Surf. Sci. Catal.

143 (2002) 89.

[9] J.M. Serra, A. Chica, A. Corma, Appl. Catal. A. 239 (2003) 35.

[10] S. Senkan, Angew. Chem., Int. Ed. 40 (2001) 312.

[11] A. Moini, G.S. Koermer, C. Castellano, US Pat. Appl.

20,040,028,815, 2004, Engelhard Corporation.

[12] D.E. Akporiaye, I.M. Dahl, A. Karlsson, R. Wendelbo, US Pat.

Appl. 20,030,206,830, 2003, UOP LLC.

[13] J. Klein, C.W. Lettmann, H.W. Schmidt, W.F. Maier, Angew.

Chem., Int. Ed. 37 (1999) 3369.

[14] R. Lai, B.S. Kang, G.R. Garvalas, Angew. Chem., Int. Ed. 40

(2001) 408.

[15] D.E. Akporiaye, I.M. Dahl, A. Karlsson, R. Wendelbo, Angew.

Chem., Int. Ed. 37 (1998) 609.

[16] J. Holmgren, D. Bem, M.L. Bricker, R.D. Gillespie, G. Lewis, D.

Akporiaye, I. Dahl, A. Karlsson, M. Plassen, R. Wendelbo, in: A.

Galarneau, F. Di Renzo, F. Fajula, J. Vedrine (Eds.), Proceedings

of the 13th International Zeolite Conference, Montpellier, France,

July 8–13, 2001, Stud. Surf. Sci. Cat. 135 (2001) 461.

[17] M.L. Bricker, J.W.A. Sachtler, R.D. Gillespie, C.P. McGoneral,

H. Vega, D.S. Bem, J.S. Holmgren, Appl. Surf. Sci. 223 (2004) 109.

[18] P.P. Pescarmona, J.J.T. Rops, J.C. van der Waal, J.C. Jansen,

T. Maschmeyer, J. Mol. Chem. A 182–183 (2002) 319.

[19] M. Tagliabue, L.C. Carluccio, D. Ghisletti, C. Perego, Catal.

Today 81 (2003) 405.

[20] L. Zhang, J. Yao, V. Zeng, N. Xu, Chem. Commun. 17 (2003)

2232.

[21] N. Stock, T. Bein, Angew. Chem. 43 (2004) 749.

[22] A. Corma, J.M. Serra, E. Argente, S. Valero, V. Botti, Chem.

Phys. Chem. 3 (2002) 939.

[23] J. Hertz, A. Krogh, R. Palmer, Introduction to The Theory of

Neural Computation, Addison Weslye, Redwood City, Califor-

nia, 1991.

[24] A.B. Bulsari (Ed.), Neural Networks for Chemical Engineers,

Elsevier, Amsterdam, 1995.

[25] J.M. Serra, A. Corma, E. Argente, S. Valero, V. Botti, Appl.

Catal. 254 (2003) 133.

[26] H. Lerchert, Micropor. Mesopor. Mater. 40 (2000) 181.

[27] J.A. Freeman, D.M. Skapura, Neural networks: Algorithms,

Applications and Programming Techniques, Addison-Wesley,

1992.

[28] C.M. Bishop, Neural Networks for Pattern Recognition, Oxford

Clarendon Press, 1996, XVII.

[29] B.D. Ripley, Pattern Recognition and Neural Networks, Cam-

bridge University Press, 1996.