Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network
Abstract Serious health issues can result from exposure to the nitrogenous pollutant like 2,4,6-trinitrotoluene (TNT), which is emitted into the environment by the munitions and military industries, as well as from TNT-contaminated wastewater. The TNT removal by extended aeration activated sludge (E...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34657-z |
| _version_ | 1797811551835521024 |
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| author | Hossein Karimi Farzaneh Mohammadi Saeed Rajabi Amir Hossein Mahvi Ghader Ghanizadeh |
| author_facet | Hossein Karimi Farzaneh Mohammadi Saeed Rajabi Amir Hossein Mahvi Ghader Ghanizadeh |
| author_sort | Hossein Karimi |
| collection | DOAJ |
| description | Abstract Serious health issues can result from exposure to the nitrogenous pollutant like 2,4,6-trinitrotoluene (TNT), which is emitted into the environment by the munitions and military industries, as well as from TNT-contaminated wastewater. The TNT removal by extended aeration activated sludge (EAAS) was optimized in the current study using artificial neural network modeling. In order to achieve the best removal efficiency, 500 mg/L of chemical oxygen demand (COD), 4 and 6 h of hydraulic retention time (HRT), and 1–30 mg/L of TNT were used in this study. The kinetics of TNT removal by the EAAS system were described by the calculation of the kinetic coefficients K, Ks, Kd, max, MLSS, MLVSS, F/M, and SVI. Adaptive neuro fuzzy inference system (ANFIS) and genetic algorithms (GA) were used to optimize the data obtained through TNT elimination. ANFIS approach was used to analyze and interpret the given data, and its accuracy was around 97.93%. The most effective removal efficiency was determined using the GA method. Under ideal circumstances (10 mg/L TNT concentration and 6 h), the TNT removal effectiveness of the EAAS system was 84.25%. Our findings demonstrated that the artificial neural network system (ANFIS)-based EAAS optimization could enhance the effectiveness of TNT removal. Additionally, it can be claimed that the enhanced EAAS system has the ability to extract wastewaters with larger concentrations of TNT as compared to earlier experiments. |
| first_indexed | 2024-03-13T07:24:25Z |
| format | Article |
| id | doaj.art-7e9b1185ba9c480eb26df2b0b883bf2e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T07:24:25Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7e9b1185ba9c480eb26df2b0b883bf2e2023-06-04T11:26:48ZengNature PortfolioScientific Reports2045-23222023-06-0113111110.1038/s41598-023-34657-zBiological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural networkHossein Karimi0Farzaneh Mohammadi1Saeed Rajabi2Amir Hossein Mahvi3Ghader Ghanizadeh4Health Research Center, Baqiyatallah University of Medical SciencesDepartment of Environmental Health Engineering, School of Health, Isfahan University of Medical SciencesStudent Research Committee, School of Health, Shiraz University of Medical SciencesCenter for Solid Waste Research, Institute for Environmental Research, Tehran University of Medical SciencesHealth Research Center, Baqiyatallah University of Medical SciencesAbstract Serious health issues can result from exposure to the nitrogenous pollutant like 2,4,6-trinitrotoluene (TNT), which is emitted into the environment by the munitions and military industries, as well as from TNT-contaminated wastewater. The TNT removal by extended aeration activated sludge (EAAS) was optimized in the current study using artificial neural network modeling. In order to achieve the best removal efficiency, 500 mg/L of chemical oxygen demand (COD), 4 and 6 h of hydraulic retention time (HRT), and 1–30 mg/L of TNT were used in this study. The kinetics of TNT removal by the EAAS system were described by the calculation of the kinetic coefficients K, Ks, Kd, max, MLSS, MLVSS, F/M, and SVI. Adaptive neuro fuzzy inference system (ANFIS) and genetic algorithms (GA) were used to optimize the data obtained through TNT elimination. ANFIS approach was used to analyze and interpret the given data, and its accuracy was around 97.93%. The most effective removal efficiency was determined using the GA method. Under ideal circumstances (10 mg/L TNT concentration and 6 h), the TNT removal effectiveness of the EAAS system was 84.25%. Our findings demonstrated that the artificial neural network system (ANFIS)-based EAAS optimization could enhance the effectiveness of TNT removal. Additionally, it can be claimed that the enhanced EAAS system has the ability to extract wastewaters with larger concentrations of TNT as compared to earlier experiments.https://doi.org/10.1038/s41598-023-34657-z |
| spellingShingle | Hossein Karimi Farzaneh Mohammadi Saeed Rajabi Amir Hossein Mahvi Ghader Ghanizadeh Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network Scientific Reports |
| title | Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network |
| title_full | Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network |
| title_fullStr | Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network |
| title_full_unstemmed | Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network |
| title_short | Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network |
| title_sort | biological 2 4 6 trinitrotoluene removal by extended aeration activated sludge optimization using artificial neural network |
| url | https://doi.org/10.1038/s41598-023-34657-z |
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