A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae
Abstract By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, $${\mathrm{COD}}_{\mathrm{inlet}}$$ COD inlet , and air flowra...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-06-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13201-023-01957-8 |
| _version_ | 1797784469248147456 |
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| author | Atef El Jery Ayesha Noreen Mubeen Isam José Luis Arias-Gonzáles Tasaddaq Younas Nadhir Al-Ansari Saad Sh. Sammen |
| author_facet | Atef El Jery Ayesha Noreen Mubeen Isam José Luis Arias-Gonzáles Tasaddaq Younas Nadhir Al-Ansari Saad Sh. Sammen |
| author_sort | Atef El Jery |
| collection | DOAJ |
| description | Abstract By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, $${\mathrm{COD}}_{\mathrm{inlet}}$$ COD inlet , and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, $${\mathrm{COD}}_{\mathrm{inlet}}=600\, \mathrm{mg}/\mathrm{l}$$ COD inlet = 600 mg / l , and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 $$\mathrm{mg}/\mathrm{l}$$ mg / l , showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient ( $${R}^{2}$$ R 2 ) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively. |
| first_indexed | 2024-03-13T00:40:19Z |
| format | Article |
| id | doaj.art-18e5d4b5146d4f55b33b221a2cb4cf6c |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-03-13T00:40:19Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-18e5d4b5146d4f55b33b221a2cb4cf6c2023-07-09T11:20:41ZengSpringerOpenApplied Water Science2190-54872190-54952023-06-0113711410.1007/s13201-023-01957-8A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgaeAtef El Jery0Ayesha Noreen1Mubeen Isam2José Luis Arias-Gonzáles3Tasaddaq Younas4Nadhir Al-Ansari5Saad Sh. Sammen6Department of Chemical Engineering, College of Engineering, King Khalid UniversityDepartment of Social Environmental Sciences, Faculty of Language History and Geography, Ankara UniversityBuilding and Construction Techniques Engineering, Al-Mustaqbal University CollegeDepartment of Social Sciences, Faculty of Social Studies, Pontifical University of PeruHassan Al Amir Soil AnalysisCivil, Environmental and Natural Resources Engineering, Lulea University of TechnologyDepartment of Civil Engineering, College of Engineering, University of DiyalaAbstract By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, $${\mathrm{COD}}_{\mathrm{inlet}}$$ COD inlet , and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, $${\mathrm{COD}}_{\mathrm{inlet}}=600\, \mathrm{mg}/\mathrm{l}$$ COD inlet = 600 mg / l , and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 $$\mathrm{mg}/\mathrm{l}$$ mg / l , showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient ( $${R}^{2}$$ R 2 ) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively.https://doi.org/10.1007/s13201-023-01957-8Sequential batch reactor (SBR)Wastewater treatmentMicroalgaeChlorella vulgarisArtificial neural network |
| spellingShingle | Atef El Jery Ayesha Noreen Mubeen Isam José Luis Arias-Gonzáles Tasaddaq Younas Nadhir Al-Ansari Saad Sh. Sammen A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae Applied Water Science Sequential batch reactor (SBR) Wastewater treatment Microalgae Chlorella vulgaris Artificial neural network |
| title | A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae |
| title_full | A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae |
| title_fullStr | A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae |
| title_full_unstemmed | A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae |
| title_short | A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae |
| title_sort | novel experimental and machine learning model to remove cod in a batch reactor equipped with microalgae |
| topic | Sequential batch reactor (SBR) Wastewater treatment Microalgae Chlorella vulgaris Artificial neural network |
| url | https://doi.org/10.1007/s13201-023-01957-8 |
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