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Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide

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Abstract

Based on an experimental database consisting of 194 daily cases, artificial neural networks were used to model the removal efficiency of a biofilter for treating hydrogen sulphide (H2S). In this work, the removal efficiency of the reactor was considered as a function of the changes in the air flow and concentration of H2S entering the biofilter. In order to obtain true representative values, the removal efficiencies (outputs) were measured 24 h after each input was changed. A MLP (multilayer perceptron 2-2-1) model with two input variables (unit flow and concentration of the contaminant fed into the biofilter) rendered good prediction values with a determination coefficient of 0.92 for the removal efficiency within the range studied. This means that the MLP model can explain 92% of the overall variability detected in the biofilter corresponding to a wide range of operating conditions.

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Abbreviations

ANN:

Artificial neural network

b 1 and b 0 :

Slope and intercept, respectively, of a least-squares regression between the predicted and observed data

C :

Concentration of H2S fed to the reactor

d :

Index of agreement

RE:

Removal efficiency

FA2:

Fraction of two

MLR:

Multiple linear regression

MLP:

Multilayer perceptron

Q u :

Unit flow

R :

Pearson correlation coefficient

R 2 :

Determination coefficient

R m :

Ratio of means

RMSE:

Total root mean squared error

R st :

The ratio of the systematic error component to total RMSE

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Acknowledgements

The authors wish to thank the Basque Government (BERRILUR project No. IE03-110), the Spanish Government (MCYT PPQ2002-01088 with FEDER funding) and the University of the Basque Country (UPV 00149.345-E-15398/2003 project) for the financial support to develop this research. Special thanks are given to the EUVE for the lab-scale pilot plant simulation.

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Authors and Affiliations

  1. Department of Chemical and Environmental Engineering, Engineering Faculty, University of the Basque Country, Alda Urkijo s/n., 48013, Bilbao, Spain

    A. Elías, R. Arias & A. Barona

  2. Department of Nuclear Engineering and Fluid Mechanics, Engineering Faculty, University of the Basque Country, Alda Urkijo s/n., 48013, Bilbao, Spain

    G. Ibarra-Berastegi

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  1. A. Elías
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  2. G. Ibarra-Berastegi
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  3. R. Arias
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  4. A. Barona
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Correspondence to A. Barona.

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Elías, A., Ibarra-Berastegi, G., Arias, R. et al. Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide. Bioprocess Biosyst Eng 29, 129–136 (2006). https://doi.org/10.1007/s00449-006-0062-3

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  • DOI: https://doi.org/10.1007/s00449-006-0062-3

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