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Ultraviolet-activated persulfate oxidation of methyl orange: a comparison between artificial neural networks and factorial design for process modelling

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Abstract

In this work, the degradation of the azo dye methyl orange in model aqueous solutions by UVC light-induced persulfate oxidation was studied. Five operating parameters that may influence the decolorization kinetics were evaluated, namely, methyl orange (MO) (5–50 mg L−1) and sodium persulfate (SPS) (50–150 mg L−1) concentration, reaction time (up to 60 min), (un-buffered) solution pH (3–9) and the addition of NaCl (0–500 mg L−1). The process was simulated, applying and comparing two methodologies, namely two-level factorial design and an artificial neural network (ANN). It was found that MO concentration is the most influential parameter, followed by the reaction time and SPS concentration, while the effects of solution pH and the addition of sodium chloride are statistically less significant; this order of significance was predicted by both methodologies. The ANN can simulate the process more accurately (i.e. in terms of R2, mean square error (MSE) and residuals) than factorial design, although it needs significantly larger sets of data and longer computational time.

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Authors and Affiliations

  1. Department of Chemical Engineering, University of Patras, Caratheodory 1, University Campus, GR-26504, Patras, Greece

    Zacharias Frontistis & Dionissios Mantzavinos

  2. Civil and Environmental Engineering Department and Nireas, International Water Research Center, University of Cyprus, P.O. Box 20537, 1678, Nicosia, Cyprus

    Zacharias Frontistis, Evroula Hapeshi & Despo Fatta-Kassinos

Authors
  1. Zacharias Frontistis
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  2. Evroula Hapeshi
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  3. Despo Fatta-Kassinos
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  4. Dionissios Mantzavinos
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Correspondence to Zacharias Frontistis or Dionissios Mantzavinos.

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Frontistis, Z., Hapeshi, E., Fatta-Kassinos, D. et al. Ultraviolet-activated persulfate oxidation of methyl orange: a comparison between artificial neural networks and factorial design for process modelling. Photochem Photobiol Sci 14, 528–535 (2015). https://doi.org/10.1039/c4pp00277f

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  • DOI: https://doi.org/10.1039/c4pp00277f

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