Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling
In this study, ozone combination with a sequencing batch reactor was tested in laboratory scale for treating a soft drink wastewater characterized by high concentrations of chemical oxygen demand (COD). A bench scale aerobic sequencing batch reactor (SBR) is carried out by two stages. The system was...
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| Format: | Article |
| Language: | English |
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Razi University
2015-12-01
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| Series: | Journal of Applied Research in Water and Wastewater |
| Subjects: | |
| Online Access: | https://arww.razi.ac.ir/article_395_6613d4cc79d487edaaf54497f341fb97.pdf |
| _version_ | 1811316675628236800 |
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| author | Negar Amiri Mojtaba Ahmadi Meghdad Pirsaheb Yasser Vasseghian Pegah Amiri |
| author_facet | Negar Amiri Mojtaba Ahmadi Meghdad Pirsaheb Yasser Vasseghian Pegah Amiri |
| author_sort | Negar Amiri |
| collection | DOAJ |
| description | In this study, ozone combination with a sequencing batch reactor was tested in laboratory scale for treating a soft drink wastewater characterized by high concentrations of chemical oxygen demand (COD). A bench scale aerobic sequencing batch reactor (SBR) is carried out by two stages. The system was operated under three different mixed liquid suspended solids (MLSS) concentrations (3000, 4500, 6000 mg/l). The results show that the integrated ozonation with biological process was able to achieve high removal efficiencies for chemical oxygen demand (COD), with residual concentrations much lower than the current discharge limits. Also, the process was characterized by a very low MLSS concentration. Hence, the ratio between ozone dose and the COD removal was 0.72, indicating that the removed COD was higher than the dosed ozone. Artificial neural networks (ANN) was also employed to model the COD data obtained. A network consisting of two layers of five neurons in the hidden layer was considered. Regression coefficient between experimental data and data predicted by neural networks and root mean square error (R<sup>2</sup>, RMSE) obtained 0.991, 80.36, respectively. Very low error in the network estimation confirmed validity of the obtained networks for further analysis and optimization. |
| first_indexed | 2024-04-13T11:53:36Z |
| format | Article |
| id | doaj.art-3f066a2c002a4aa090e735299d72b2cf |
| institution | Directory Open Access Journal |
| issn | 2476-6283 2476-6283 |
| language | English |
| last_indexed | 2024-04-13T11:53:36Z |
| publishDate | 2015-12-01 |
| publisher | Razi University |
| record_format | Article |
| series | Journal of Applied Research in Water and Wastewater |
| spelling | doaj.art-3f066a2c002a4aa090e735299d72b2cf2022-12-22T02:47:59ZengRazi UniversityJournal of Applied Research in Water and Wastewater2476-62832476-62832015-12-0122156163395Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modelingNegar Amiri0Mojtaba Ahmadi1Meghdad Pirsaheb2Yasser Vasseghian3Pegah Amiri4Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, IranChemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, IranDepartment of Environmental Health Engineering-Kermanshah Health Research Center (KHRC), Kermanshah University of Medical Sciences, Iran.Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, IranChemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran.In this study, ozone combination with a sequencing batch reactor was tested in laboratory scale for treating a soft drink wastewater characterized by high concentrations of chemical oxygen demand (COD). A bench scale aerobic sequencing batch reactor (SBR) is carried out by two stages. The system was operated under three different mixed liquid suspended solids (MLSS) concentrations (3000, 4500, 6000 mg/l). The results show that the integrated ozonation with biological process was able to achieve high removal efficiencies for chemical oxygen demand (COD), with residual concentrations much lower than the current discharge limits. Also, the process was characterized by a very low MLSS concentration. Hence, the ratio between ozone dose and the COD removal was 0.72, indicating that the removed COD was higher than the dosed ozone. Artificial neural networks (ANN) was also employed to model the COD data obtained. A network consisting of two layers of five neurons in the hidden layer was considered. Regression coefficient between experimental data and data predicted by neural networks and root mean square error (R<sup>2</sup>, RMSE) obtained 0.991, 80.36, respectively. Very low error in the network estimation confirmed validity of the obtained networks for further analysis and optimization.https://arww.razi.ac.ir/article_395_6613d4cc79d487edaaf54497f341fb97.pdfSoft drink Wastewater SBR Ozone Artificial neural network |
| spellingShingle | Negar Amiri Mojtaba Ahmadi Meghdad Pirsaheb Yasser Vasseghian Pegah Amiri Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling Journal of Applied Research in Water and Wastewater Soft drink Wastewater SBR Ozone Artificial neural network |
| title | Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling |
| title_full | Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling |
| title_fullStr | Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling |
| title_full_unstemmed | Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling |
| title_short | Combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment: experiments and neural network modeling |
| title_sort | combination of ozonation with aerobic sequencing batch reactor for soft drink wastewater treatment experiments and neural network modeling |
| topic | Soft drink Wastewater SBR Ozone Artificial neural network |
| url | https://arww.razi.ac.ir/article_395_6613d4cc79d487edaaf54497f341fb97.pdf |
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