Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm
The biological treatment process (BTP) is responsible for removing chemical oxygen demand (COD) and ammonia using microorganisms present in wastewater. The BTP consumes large quantities of energy due to the transfer of oxygen using air pumps/blowers. Energy consumption in the BTP is due to low solub...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-12-01
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| Series: | Water Science and Technology |
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| Online Access: | http://wst.iwaponline.com/content/86/11/2912 |
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| author | Mpho Muloiwa Megersa Dinka Stephen Nyende-Byakika |
| author_facet | Mpho Muloiwa Megersa Dinka Stephen Nyende-Byakika |
| author_sort | Mpho Muloiwa |
| collection | DOAJ |
| description | The biological treatment process (BTP) is responsible for removing chemical oxygen demand (COD) and ammonia using microorganisms present in wastewater. The BTP consumes large quantities of energy due to the transfer of oxygen using air pumps/blowers. Energy consumption in the BTP is due to low solubility of oxygen, which results in low aeration efficiency (AE). The aim of the study was to develop an AE model that can be used to monitor the performance of the BTP. Multilayer perceptron artificial neural network (MLP ANN) algorithm was used to model AE in the BTP. The performance of the AE model was evaluated using R2, mean square error (MSE), and root mean square error (RMSE). Sensitivity analysis was performed on the AE model to determine variables that drive AE. The results of the study showed that MLP ANN algorithm was able to model AE. R2, MSE, and RMSE results were 0.939, 0.0025, and 0.05, respectively, during testing phase. Sensitivity analysis results showed that temperature (34.6%), COD (21%), airflow rate (19.1%), and OTR/KLa (15.7%) drive AE. At high temperatures, the viscosity of wastewater is low which enables oxygen to penetrate the wastewater, resulting in high AE. The AE model can be used to predict the performance of the BTP, which will assist in minimizing energy consumption.
HIGHLIGHTS
Aeration efficiency increases with an increase in temperature.;
Aeration efficiency decreases with an increase in airflow rate.;
Temperature and airflow rate are the biggest driver of energy consumption.;
Temperature, COD, and airflow rate control aeration efficiency in the biological treatment process.;
High temperatures and airflow rates improve volumetric mass transfer coefficient.; |
| first_indexed | 2024-04-12T02:06:27Z |
| format | Article |
| id | doaj.art-10d279f3115e4df8a95edb036a01442e |
| institution | Directory Open Access Journal |
| issn | 0273-1223 1996-9732 |
| language | English |
| last_indexed | 2024-04-12T02:06:27Z |
| publishDate | 2022-12-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Science and Technology |
| spelling | doaj.art-10d279f3115e4df8a95edb036a01442e2022-12-22T03:52:31ZengIWA PublishingWater Science and Technology0273-12231996-97322022-12-0186112912292710.2166/wst.2022.388388Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithmMpho Muloiwa0Megersa Dinka1Stephen Nyende-Byakika2 Department of Civil Engineering, Tshwane University of Technology, Private Bag X680, Pretoria 0001 Staatsartillerie Road, Pretoria West, South Africa Department of Civil Engineering Science, University of Johannesburg, Auckland Park Campus 2006, Box 524, Johannesburg, South Africa Department of Civil Engineering, Tshwane University of Technology, Private Bag X680, Pretoria 0001 Staatsartillerie Road, Pretoria West, South Africa The biological treatment process (BTP) is responsible for removing chemical oxygen demand (COD) and ammonia using microorganisms present in wastewater. The BTP consumes large quantities of energy due to the transfer of oxygen using air pumps/blowers. Energy consumption in the BTP is due to low solubility of oxygen, which results in low aeration efficiency (AE). The aim of the study was to develop an AE model that can be used to monitor the performance of the BTP. Multilayer perceptron artificial neural network (MLP ANN) algorithm was used to model AE in the BTP. The performance of the AE model was evaluated using R2, mean square error (MSE), and root mean square error (RMSE). Sensitivity analysis was performed on the AE model to determine variables that drive AE. The results of the study showed that MLP ANN algorithm was able to model AE. R2, MSE, and RMSE results were 0.939, 0.0025, and 0.05, respectively, during testing phase. Sensitivity analysis results showed that temperature (34.6%), COD (21%), airflow rate (19.1%), and OTR/KLa (15.7%) drive AE. At high temperatures, the viscosity of wastewater is low which enables oxygen to penetrate the wastewater, resulting in high AE. The AE model can be used to predict the performance of the BTP, which will assist in minimizing energy consumption. HIGHLIGHTS Aeration efficiency increases with an increase in temperature.; Aeration efficiency decreases with an increase in airflow rate.; Temperature and airflow rate are the biggest driver of energy consumption.; Temperature, COD, and airflow rate control aeration efficiency in the biological treatment process.; High temperatures and airflow rates improve volumetric mass transfer coefficient.;http://wst.iwaponline.com/content/86/11/2912aeration efficiencyairflow ratecod concentrationoxygen uptake ratetemperaturevolumetric mass transfer coefficient |
| spellingShingle | Mpho Muloiwa Megersa Dinka Stephen Nyende-Byakika Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm Water Science and Technology aeration efficiency airflow rate cod concentration oxygen uptake rate temperature volumetric mass transfer coefficient |
| title | Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm |
| title_full | Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm |
| title_fullStr | Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm |
| title_full_unstemmed | Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm |
| title_short | Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm |
| title_sort | modelling the biological treatment process aeration efficiency application of the artificial neural network algorithm |
| topic | aeration efficiency airflow rate cod concentration oxygen uptake rate temperature volumetric mass transfer coefficient |
| url | http://wst.iwaponline.com/content/86/11/2912 |
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