Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA

Marjan Salari; Mohammad Reza Nikoo; Abdullah Al-Mamun; Gholam Reza Rakhshandehroo; Mehrdad Ghorbani Mooselu

doi:10.1016/j.jenvman.2022.115469

Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA

J Environ Manage. 2022 Sep 1:317:115469. doi: 10.1016/j.jenvman.2022.115469. Epub 2022 Jun 8.

Authors

Marjan Salari¹, Mohammad Reza Nikoo², Abdullah Al-Mamun³, Gholam Reza Rakhshandehroo⁴, Mehrdad Ghorbani Mooselu⁵

Affiliations

¹ Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
² Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman. Electronic address: m.reza@squ.edu.om.
³ Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
⁴ Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran.
⁵ Department of Engineering and Science, University of Agder, Norway.

PMID: 35751268
DOI: 10.1016/j.jenvman.2022.115469

Abstract

Antibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH ≅ 7, optimal conditions for initial CIP concentration, Fe²⁺ concentration, [H₂O₂]/[Fe²⁺] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.

Keywords: Ciprofloxacin antibiotic; Homogeneous processes; Multi-objective optimization; Pharmaceutical compounds.

MeSH terms

Anti-Bacterial Agents
Ciprofloxacin*
Edetic Acid
Hydrogen Peroxide*
Hydrogen-Ion Concentration
Neural Networks, Computer
Sewage

Substances

Anti-Bacterial Agents
Sewage
Ciprofloxacin
Edetic Acid
Hydrogen Peroxide