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Smaali A, Berkani M, Benmatti H, Lakhdari N, Al Obaid S, Alharbi SA, Fakhreddine B, Ines A, Marouane F, Rezania S, Lakhdari N. Degradation of Azithromycin from aqueous solution using Chlorine-ferrous- oxidation: ANN-GA modeling and Daphnia magna biotoxicity test assessment. ENVIRONMENTAL RESEARCH 2022; 214:114026. [PMID: 35977588 DOI: 10.1016/j.envres.2022.114026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/24/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
Azithromycin (AZM), an antibacterial considered one of the most consumed drugs, especially during the period against the Covid 19 pandemic, and it is one of the persistent contaminants that can be released into aquatic ecosystems. The purpose of this study is to determine the efficacy of a Fenton-like process (chlorine/iron) for the degradation of AZM in an aqueous medium by determining the impact of several factors (the initial concentration of (FeSO4, NaClO, pollutant), and the initial pH) on the degradation rate. The Response Surface Methodology (RSM) based on the Box-Wilson design as well as the Artificial Neural Network (ANN) modeling combined with a genetic algorithm (GA) approaches were used to determine the optimal levels of the selected variables and the optimal rate of degradation. The quadratic model of multi-linear regression developed indicated that the optimal conditions were a concentration of chlorine of 600 μM, the concentration of AZM is 32.8 mg/L, the mass of the catalyst FeSO4 is 3.5 mg and a pH of 2.5, these optimal values gave a predicted and experimental yield of 64.05% and 70% respectively, the lack of fit test in RSM modeling (F0 = 3.31 which is inferior to Fcritic (0.05, 10.4) = 5.96) indicates that the true regression function is not linear therefore, the ANN-GA modeling as non-linear regression indicated that the optimal conditions were a concentration of chlorine of 256 μM, the concentration of AZM is 5 mg/L, the mass of the catalyst FeSO4 is 9.5 mg and a pH of 2.8, these optimal values gave a predicted and experimental yield of 79.69% and close to 80% respectively, Furthermore, biotoxicity tests were conducted to confirm the performance of our process using bio-indicators called daphnia (Daphnia magna), which demonstrated the efficacy of the like-Fenton process after 4 h of degradation.
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Affiliation(s)
- Anfel Smaali
- Laboratoire Biotechnologies, Ecole Nationale Supérieure de Biotechnologie, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Mohammed Berkani
- Laboratoire Biotechnologies, Ecole Nationale Supérieure de Biotechnologie, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria.
| | - Hadjer Benmatti
- Laboratoire Biotechnologies, Ecole Nationale Supérieure de Biotechnologie, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Nadjem Lakhdari
- Laboratoire Biotechnologies, Ecole Nationale Supérieure de Biotechnologie, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Sami Al Obaid
- Department of Botany and Microbiology, College of Science, King Saud University, PO Box -2455, Riyadh, 11451, Saudi Arabia
| | - Sulaiman Ali Alharbi
- Department of Botany and Microbiology, College of Science, King Saud University, PO Box -2455, Riyadh, 11451, Saudi Arabia
| | - Belhadef Fakhreddine
- Laboratoire de Biologie et Environnement, Campus Chaab-Erssas, Biopole université des frères Mentouri Constantine 1, Ain Bey, 25000, Constantine, Algeria
| | - Amri Ines
- Laboratoire SARL HupPharma 25100, Constantine, Algeria
| | - Fateh Marouane
- Laboratoire Biotechnologies, Ecole Nationale Supérieure de Biotechnologie, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Shahabaldin Rezania
- Department of Environment and Energy, Sejong University, Seoul, 05006, South Korea
| | - Nadjem Lakhdari
- Laboratoire Biotechnologies, Ecole Nationale Supérieure de Biotechnologie, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
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Taoufik N, Boumya W, Achak M, Chennouk H, Dewil R, Barka N. The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 807:150554. [PMID: 34597573 DOI: 10.1016/j.scitotenv.2021.150554] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/02/2021] [Accepted: 09/20/2021] [Indexed: 06/13/2023]
Abstract
During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.
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Affiliation(s)
- Nawal Taoufik
- Sultan Moulay Slimane University of Beni Mellal, Research Group in Environmental Sciences and Applied Materials (SEMA), FP Khouribga, Morocco.
| | - Wafaa Boumya
- Sultan Moulay Slimane University of Beni Mellal, Research Group in Environmental Sciences and Applied Materials (SEMA), FP Khouribga, Morocco
| | - Mounia Achak
- Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaïb Doukkali University, El Jadida, Morocco; Chemical & Biochemical Sciences, Green Process Engineering, CBS, Mohammed VI Polytechnic University, Ben Guerir, Morocco
| | - Hamid Chennouk
- RITM Laboratory, Computer Science and Networks Team ENSEM - ESTC - UH2C, Casablanca, Morocco
| | - Raf Dewil
- KU Leuven, Department of Chemical Engineering, Process and Environmental Technology Lab, J. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium
| | - Noureddine Barka
- Sultan Moulay Slimane University of Beni Mellal, Research Group in Environmental Sciences and Applied Materials (SEMA), FP Khouribga, Morocco.
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Kodavatiganti S, Bhat AP, Gogate PR. Intensified degradation of Acid Violet 7 dye using ultrasound combined with hydrogen peroxide, Fenton, and persulfate. Sep Purif Technol 2021. [DOI: 10.1016/j.seppur.2021.119673] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Investigation and optimization of anaerobic system for treatment of seafood processing wastewater. CHEMICAL PAPERS 2021. [DOI: 10.1007/s11696-021-01675-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Gomes RKM, Santana RMR, de Moraes NFS, Júnior SGS, de Lucena ALA, Zaidan LEMC, Elihimas DRM, Napoleão DC. Treatment of direct black 22 azo dye in led reactor using ferrous sulfate and iron waste for Fenton process: reaction kinetics, toxicity and degradation prediction by artificial neural networks. CHEMICAL PAPERS 2021. [DOI: 10.1007/s11696-020-01451-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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