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Water, Air, & Soil Pollution

, 229:379 | Cite as

Photo-Assisted Degradation, Toxicological Assessment, and Modeling Using Artificial Neural Networks of Reactive Gray BF-2R Dye

  • Graziele Elisandra do Nascimento
  • Daniella Carla Napoleão
  • Polyana Karynne de Aguiar Silva
  • Rayany Magali da Rocha Santana
  • Ana Maria Ribeiro Bastos
  • Léa Elias Mendes Carneiro Zaidan
  • Maiara Celine de Moura
  • Luana Cassandra Breitenbach Barroso Coelho
  • Marta Maria Menezes Bezerra Duarte
Article
  • 62 Downloads

Abstract

This work investigates the degradation of Reactive Gray BF-2R dye (a blend of reactive yellow 145, reactive orange 122 and reactive black 5 dyes) using UV/H2O2, Fenton, and photo-Fenton-advanced oxidative processes, with artificial sunlight and UV-C radiations. The photo-Fenton process employing UV-C radiation was the most efficient under the conditions studied. The ideal conditions for the degradation of the dye, determined using a factorial design 23 and a study of the concentration of hydrogen peroxide ([H2O2]), were [H2O2] equal to 40 mg L−1, iron concentration [Fe] of 1 mg L−1, and pH between 3 and 4. The Chan and Chu non-linear kinetic model predicted the kinetic data with a degradation of over 98% for color and 68% for aromatics after 60 min. The behavior of the chemical oxygen demand fitted the first-order kinetic model well, with a degradation of 64% after 60 min. The Multilayer Perceptron 7-11-2 artificial neural network model enabled to model the degradation process of the aromatics and accurately predict the experimental data. Toxicity tests indicated that the post-treatment samples were non-toxic for Escherichia coli bacteria, and Portulaca grandiflora and Basil sabory seeds. However, they inhibited the growth of Lactuca sativa seeds and Salmonella enteritidis bacteria. The photo-Fenton process with UV-C radiation degraded the dye studied efficiently and the degradation percentages were, on average, 7% and 5% higher for color than those observed when employing the Fenton and UV/H2O2 processes, respectively. With the aromatic, however, they were 84% and 62% higher, thus justifying the use of this process.

Keywords

Artificial neural networks Kinetic Textile dyes Toxicity 

Notes

Acknowledgments

Authors would like to thank the Texpal company for providing the dye, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Apoio ao Desenvolvimento da Universidade Federal de Pernambuco (FADE/UFPE), and Núcleo de Química Analítica Avançada de Pernambuco da Fundação de Amparo a Ciência e Tecnologia de Pernambuco (NUQAAPE/FACEPE).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Graziele Elisandra do Nascimento
    • 1
  • Daniella Carla Napoleão
    • 1
  • Polyana Karynne de Aguiar Silva
    • 1
  • Rayany Magali da Rocha Santana
    • 1
  • Ana Maria Ribeiro Bastos
    • 1
  • Léa Elias Mendes Carneiro Zaidan
    • 1
  • Maiara Celine de Moura
    • 2
  • Luana Cassandra Breitenbach Barroso Coelho
    • 2
  • Marta Maria Menezes Bezerra Duarte
    • 1
  1. 1.Chemical Engineering DepartmentFederal University of PernambucoRecifeBrazil
  2. 2.Biochemistry DepartmentFederal University of PernambucoRecifeBrazil

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