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Artificial Neural Networks for Modelling the Degradation of Emerging Contaminants Process

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A Correction to this article was published on 23 August 2022

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Abstract

Diclofenac sodium is an emerging contaminant that can be harmful for ecology and human health. This substance can be degraded by a heterogeneous Photo-Fenton process, CoFe2O4 as catalyst, H2O2 as oxidant and UV radiation. The aims of the work are the comparison of different artificial neural networks to characterize the relationship between diclofenac degradation and H2O2 consumption, with the Total Organic Carbon achieved in the mineralization of the drug and the testing of the selected model capacity to predict the Total Organic Carbon concentration, by employing the reused catalyst. The best performing backpropagation neural network was constituted with a ten neurons hidden layer with sigmoid transfer function and one linear neuron, as output. It was determined that the model can approximate the trend between the input data (Absorbance and H2O2 concentration) and output ones (Total Organic Carbon concentration) when it was validated with data from reactions employing CoFe2O4 for second and third time. The development of these models is of interest due to the consequent reduction of time and costs in experimental work. It represents a study of the evolution of chemical indicators in the treatment of emerging contaminants.

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Acknowledgements

The authors would like to acknowledge the financial support received from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Universidad Tecnológica Nacional of Argentina. To Dr. Labuckas Diana of the Instituto de Ciencia y Tecnología de Los Alimentos (ICTA), Facultad de Cs. Exactas, Físicas y Naturales and Instituto Multidisciplinario de Biología Vegetal (IMBIV)-CONICET, Universidad Nacional de Córdoba. And to Eng. Anabella Ortega.

Funding

Universidad Tecnológica Nacional, ASUTICO0007656TC, Dolores Marí­a Eugenia Álvarez, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET).

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Authors and Affiliations

  1. Centro de Investigación y Tecnología Química, Universidad Tecnológica Nacional-CONICET-Facultad Regional Córdoba, Maestro Marcelo López esq. Cruz Roja Argentina, Ciudad Universitaria, 5016, Córdoba, CP, Argentina

    Dolores M. E. Álvarez, María V. Gerbaldo, Silvia N. Mendieta & Mónica E. Crivello

  2. Laboratorio de Sensores e Instrumentación (LabSen), Universidad Tecnológica Nacional - Facultad Regional Córdoba, Córdoba, Argentina

    Mario R. Modesti

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  1. Dolores M. E. Álvarez
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  2. María V. Gerbaldo
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  3. Mario R. Modesti
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  4. Silvia N. Mendieta
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  5. Mónica E. Crivello
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Correspondence to Dolores M. E. Álvarez.

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The original online version of this article was revised: Figure 8 has been updated

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Álvarez, D.M.E., Gerbaldo, M.V., Modesti, M.R. et al. Artificial Neural Networks for Modelling the Degradation of Emerging Contaminants Process. Top Catal 65, 1440–1446 (2022). https://doi.org/10.1007/s11244-022-01674-7

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