Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole : Insights into mechanistic approach
Copyright © 2024 Elsevier Ltd. All rights reserved..
Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of H2O2, Fe2+ concentration, pH, and temperature using statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical metrics, the ANN model demonstrated superior predictive accuracy compared to its counterparts, with lowest RMSE values of 0.986 and 1.173 for SMX and TOC removal, respectively. Sensitivity showcased H2O2/Fe2+ ratio, time and pH as pivotal for SMX degradation, while in simultaneous SMX and TOC reduction, fine tuning the time, pH, and temperature was essential. Leveraging a Hybrid Genetic Algorithm-Desirability Optimization approach, the trained ANN model revealed an optimal desirability of 0.941 out of 1000 solutions which yielded a 91.18% SMX degradation and 87.90% TOC removal under following specific conditions: treatment time of 48.5 min, Fe2+: 7.05 mg L-1, H2O2: 128.82 mg L-1, pH: 5.1, initial SMX: 97.6 mg L-1, and a temperature: 29.8 °C. LC/MS analysis reveals multiple intermediates with higher m/z (242, 270 and 288) and lower m/z (98, 108, 156 and 173) values identified, however no aliphatic hydrocarbon was isolated, because of the low mineralization performance of Fenton process. Furthermore, some inorganic fragments like NH4+ and NO3- were also determined in solution. This comprehensive research enriches AI modeling for intricate Fenton-based contaminant degradation, advancing sustainable antibiotic removal strategies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:357 |
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| Enthalten in: |
Chemosphere - 357(2024) vom: 07. Apr., Seite 141868 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gul, Saima [VerfasserIn] |
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| Links: |
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| Themen: |
Antibiotic |
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| Anmerkungen: |
Date Revised 24.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.chemosphere.2024.141868 |
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| Förderinstitution / Projekttitel: |
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NLM370832930 |
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| 520 | |a Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of H2O2, Fe2+ concentration, pH, and temperature using statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical metrics, the ANN model demonstrated superior predictive accuracy compared to its counterparts, with lowest RMSE values of 0.986 and 1.173 for SMX and TOC removal, respectively. Sensitivity showcased H2O2/Fe2+ ratio, time and pH as pivotal for SMX degradation, while in simultaneous SMX and TOC reduction, fine tuning the time, pH, and temperature was essential. Leveraging a Hybrid Genetic Algorithm-Desirability Optimization approach, the trained ANN model revealed an optimal desirability of 0.941 out of 1000 solutions which yielded a 91.18% SMX degradation and 87.90% TOC removal under following specific conditions: treatment time of 48.5 min, Fe2+: 7.05 mg L-1, H2O2: 128.82 mg L-1, pH: 5.1, initial SMX: 97.6 mg L-1, and a temperature: 29.8 °C. LC/MS analysis reveals multiple intermediates with higher m/z (242, 270 and 288) and lower m/z (98, 108, 156 and 173) values identified, however no aliphatic hydrocarbon was isolated, because of the low mineralization performance of Fenton process. Furthermore, some inorganic fragments like NH4+ and NO3- were also determined in solution. This comprehensive research enriches AI modeling for intricate Fenton-based contaminant degradation, advancing sustainable antibiotic removal strategies | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Antibiotic | |
| 650 | 4 | |a Fenton | |
| 650 | 4 | |a Multiplle regression | |
| 650 | 4 | |a Neural network | |
| 650 | 4 | |a Process optimisation | |
| 650 | 4 | |a Sulfamethoxazole | |
| 650 | 4 | |a Support vector | |
| 700 | 1 | |a Hussain, Sajjad |e verfasserin |4 aut | |
| 700 | 1 | |a Khan, Hammad |e verfasserin |4 aut | |
| 700 | 1 | |a Arshad, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Khan, Javaid Rabbani |e verfasserin |4 aut | |
| 700 | 1 | |a Motheo, Artur de Jesus |e verfasserin |4 aut | |
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