Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network
The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wa...
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PeerJ Inc.
2023-09-01
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| author | Atef El Jery Hayder Mahmood Salman Nadhir Al-Ansari Saad Sh. Sammen Mohammed Abdul Jaleel Maktoof Hussein A. Z. AL-bonsrulah |
| author_facet | Atef El Jery Hayder Mahmood Salman Nadhir Al-Ansari Saad Sh. Sammen Mohammed Abdul Jaleel Maktoof Hussein A. Z. AL-bonsrulah |
| author_sort | Atef El Jery |
| collection | DOAJ |
| description | The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques. |
| first_indexed | 2024-03-09T07:00:08Z |
| format | Article |
| id | doaj.art-75d777afbb0d4192bb6165e2648707d4 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:00:08Z |
| publishDate | 2023-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-75d777afbb0d4192bb6165e2648707d42023-12-03T09:55:01ZengPeerJ Inc.PeerJ2167-83592023-09-0111e1585210.7717/peerj.15852Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural networkAtef El Jery0Hayder Mahmood Salman1Nadhir Al-Ansari2Saad Sh. Sammen3Mohammed Abdul Jaleel Maktoof4Hussein A. Z. AL-bonsrulah5Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, King Saudi ArabiaDepartment of Computer Science, Al-Turath University College Al Mansour, Baghdad, IraqCivil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, SwedenDepartment of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, IraqDepartment of Computer Science, Al-Turath University College Al Mansour, Baghdad, IraqMechanical Power Technical Engineering Department, Al-Amarah University College, Maysan, Iraq., Maysan, IraqThe alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques.https://peerj.com/articles/15852.pdfElectrochemicalElectrode surface areaCurrent densityCODMachine learningArtificial neural network. |
| spellingShingle | Atef El Jery Hayder Mahmood Salman Nadhir Al-Ansari Saad Sh. Sammen Mohammed Abdul Jaleel Maktoof Hussein A. Z. AL-bonsrulah Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network PeerJ Electrochemical Electrode surface area Current density COD Machine learning Artificial neural network. |
| title | Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network |
| title_full | Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network |
| title_fullStr | Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network |
| title_full_unstemmed | Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network |
| title_short | Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system’s performance by artificial neural network |
| title_sort | optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system s performance by artificial neural network |
| topic | Electrochemical Electrode surface area Current density COD Machine learning Artificial neural network. |
| url | https://peerj.com/articles/15852.pdf |
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