Prediction of doxycycline removal by photo-fenton process using an artificial neural network - multilayer perceptron model

Original scientific paper

Authors

  • Nabila Boucherit Biomaterials and Transport Phenomena Laboratory (LBMPT), Yahia Fares University, Medea (26000), Algeria https://orcid.org/0000-0002-0984-3616
  • Salah Hanini Biomaterials and Transport Phenomena Laboratory (LBMPT), Yahia Fares University, Medea (26000), Algeria
  • Abdellah Ibrir Biomaterials and Transport Phenomena Laboratory (LBMPT), Yahia Fares University, Medea (26000), Algeria. 2 Materials and Environment Laboratory (LME), Faculty of Technology, Yahia Fares University, Medea (26000), Algeria https://orcid.org/0000-0003-0332-1398
  • Maamar Laidi Biomaterials and Transport Phenomena Laboratory (LBMPT), Yahia Fares University, Medea (26000), Algeria https://orcid.org/0000-0002-8977-9895
  • Mohamed Roubehie Fissa Biomaterials and Transport Phenomena Laboratory (LBMPT), Yahia Fares University, Medea (26000), Algeria https://orcid.org/0000-0002-9154-6409

DOI:

https://doi.org/10.2298/CICEQ230824009B

Keywords:

Doxycycline hydrate, modelling, photo-fenton, optimized artificial neural network, removal

Abstract

This paper presents a study on the effectiveness of the Photo-Fenton Process (PF) for removing the doxycycline hyclate (DXC) antibiotic. The experiment showed that the best removal efficiency was achieved (79%) at pH 3 for 2.5 mg/L of DXC, 76.53 mg/L of H2O2, and 86.8 mg/L of Fe2+. The degradation mechanism of DXC by hydroxyl radicals was confirmed by FTIR and HPLC.  To model the oxidation reaction of DXC by PF, an multilayer perceptron (MLP) based optimized artificial neural network (OANN) was used, taking into account experimental data such as pH and initial concentrations of DXC, H2O2, and Fe2+. The OANNN predicted removal efficiency results were in close agreement with experimental results, with an RMSE of 0.0661 and an R2 value of 0.99998. The sensitivity analysis revealed that all studied inputs significantly impacted the transformation of DXC.

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13.03.2024

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Copyright (c) 2023 Nabila Boucherit, Salah Hanini, Abdellah Ibrir, Maamar Laidi, Mohamed Roubehie Fissa

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How to Cite

Prediction of doxycycline removal by photo-fenton process using an artificial neural network - multilayer perceptron model: Original scientific paper. (2024). Chemical Industry & Chemical Engineering Quarterly. https://doi.org/10.2298/CICEQ230824009B

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