Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning
Abstract The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperatur...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50805-x |
| _version_ | 1797276617623470080 |
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| author | Rajshree Mathur Meena Kumari Sharma K. Loganathan Mohamed Abbas Shaik Hussain Gaurav Kataria Mohammed S. Alqahtani Koppula Srinivas Rao |
| author_facet | Rajshree Mathur Meena Kumari Sharma K. Loganathan Mohamed Abbas Shaik Hussain Gaurav Kataria Mohammed S. Alqahtani Koppula Srinivas Rao |
| author_sort | Rajshree Mathur |
| collection | DOAJ |
| description | Abstract The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment. |
| first_indexed | 2024-03-07T15:30:48Z |
| format | Article |
| id | doaj.art-8372c6da98f04a35b0bd80cac98540be |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:30:48Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8372c6da98f04a35b0bd80cac98540be2024-03-05T16:25:09ZengNature PortfolioScientific Reports2045-23222024-01-0114111410.1038/s41598-023-50805-xModeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learningRajshree Mathur0Meena Kumari Sharma1K. Loganathan2Mohamed Abbas3Shaik Hussain4Gaurav Kataria5Mohammed S. Alqahtani6Koppula Srinivas Rao7Department of Civil Engineering, Manipal University JaipurDepartment of Civil Engineering, Manipal University JaipurDepartment of Mathematics and Statistics, Manipal University JaipurElectrical Engineering Department, College of Engineering, King Khalid UniversityTrenchless Technology Center (TTC), Louisiana Tech UniversityDepartment of Chemical Engineering, Manipal University JaipurRadiological Sciences Department, College of Applied Medical Sciences, King Khalid UniversityDepartment of Computer Science and Engineering, MLR Institute of TechnologyAbstract The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.https://doi.org/10.1038/s41598-023-50805-x |
| spellingShingle | Rajshree Mathur Meena Kumari Sharma K. Loganathan Mohamed Abbas Shaik Hussain Gaurav Kataria Mohammed S. Alqahtani Koppula Srinivas Rao Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning Scientific Reports |
| title | Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning |
| title_full | Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning |
| title_fullStr | Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning |
| title_full_unstemmed | Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning |
| title_short | Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning |
| title_sort | modeling of two stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning |
| url | https://doi.org/10.1038/s41598-023-50805-x |
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