PREDICTION OF DOXYCYCLINE REMOVAL BY PHOTO-FENTON PROCESS USING AN ARTIFICIAL NEURAL NETWORK - MULTILAYER PERCEPTRON MODELtificial neural network - multilayer perceptron model Original scientific paper

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Published: 03.03.2025
Updated: 03.03.2025
DOI: https://doi.org/10.2298/CICEQ230824009B
Keywords:
Doxycycline hydrate, modelling, photo-fenton, optimized artificial neural network, removal

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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

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, a 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 OANN 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.

Article Details

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Vol. 31 No. 1 (2025)

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How to Cite

PREDICTION OF DOXYCYCLINE REMOVAL BY PHOTO-FENTON PROCESS USING AN ARTIFICIAL NEURAL NETWORK - MULTILAYER PERCEPTRON MODELtificial neural network - multilayer perceptron model: Original scientific paper. (2025). Chemical Industry & Chemical Engineering Quarterly, 31(1), 13-21. https://doi.org/10.2298/CICEQ230824009B

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