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Neural network modeling of the light profile in a novel photobioreactor

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Abstract

An artificial neural network (ANN) was implemented to model the light profile pattern inside a photobioreactor (PBR) that uses a toroidal light arrangement. The PBR uses Tequila vinasses as culture medium and purple non-sulfur bacteria Rhodopseudomonas palustris as biocatalyzer. The performance of the ANN was tested for a number of conditions and compared to those obtained by using deterministic models. Both ANN and deterministic models were validated experimentally. In all cases, at low biomass concentration, model predictions yielded determination coefficients greater than 0.9. Nevertheless, ANN yielded the more accurate predictions of the light pattern, at both low and high biomass concentration, when the bioreactor radius, the depth, the rotational speed of the stirrer and the biomass concentration were incorporated in the ANN structure. In comparison, most of the deterministic models failed to correlate the empirical data at high biomass concentration. These results show the usefulness of ANNs in the modeling of the light profile pattern in photobioreactors.

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Acknowledgments

The support of Consejo Nacional de Ciencia y Tecnología (CONACyT) Mexico, Project (CB-2008-01/101971) and the DAAD “Exceed” program at the TU Braunschweig, Germany, to this project are gratefully acknowledged.

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Authors and Affiliations

  1. Chemical Engineering Department, Universidad de Guadalajara, CUCEI , Calz. Marcelino García Barragán 1421, 44430, Guadalajara, Jalisco, Mexico

    R. Salazar-Peña, V. Alcaraz-González, V. González-Álvarez, R. Snell-Castro & H. O. Méndez-Acosta

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  1. R. Salazar-Peña
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  2. V. Alcaraz-González
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  3. V. González-Álvarez
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  5. H. O. Méndez-Acosta
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Correspondence to V. Alcaraz-González.

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Salazar-Peña, R., Alcaraz-González, V., González-Álvarez, V. et al. Neural network modeling of the light profile in a novel photobioreactor. Bioprocess Biosyst Eng 37, 1031–1042 (2014). https://doi.org/10.1007/s00449-013-1073-5

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  • DOI: https://doi.org/10.1007/s00449-013-1073-5

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