Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process
Abstract The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. M...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2023-05-01
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| Series: | Engineering in Life Sciences |
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| Online Access: | https://doi.org/10.1002/elsc.202200058 |
| _version_ | 1797833455308898304 |
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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 | Abstract The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination (R2), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R2 values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process. |
| first_indexed | 2024-04-09T14:24:25Z |
| format | Article |
| id | doaj.art-e0a852f21fd3416993d0e8250abd1c19 |
| institution | Directory Open Access Journal |
| issn | 1618-0240 1618-2863 |
| language | English |
| last_indexed | 2024-04-09T14:24:25Z |
| publishDate | 2023-05-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Engineering in Life Sciences |
| spelling | doaj.art-e0a852f21fd3416993d0e8250abd1c192023-05-04T12:48:26ZengWiley-VCHEngineering in Life Sciences1618-02401618-28632023-05-01235n/an/a10.1002/elsc.202200058Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological processMpho Muloiwa0Megersa Dinka1Stephen Nyende‐Byakika2Department of Civil Engineering Tshwane University of Technology Pretoria South AfricaDepartment of Civil Engineering Science University of Johannesburg Johannesburg South AfricaDepartment of Civil Engineering Tshwane University of Technology Pretoria South AfricaAbstract The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination (R2), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R2 values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process.https://doi.org/10.1002/elsc.202200058biological treatment processDO concentrationArtificial Neural Networktemperatureorganic and inorganic matteroxygen uptake rate |
| spellingShingle | Mpho Muloiwa Megersa Dinka Stephen Nyende‐Byakika Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process Engineering in Life Sciences biological treatment process DO concentration Artificial Neural Network temperature organic and inorganic matter oxygen uptake rate |
| title | Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process |
| title_full | Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process |
| title_fullStr | Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process |
| title_full_unstemmed | Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process |
| title_short | Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process |
| title_sort | application of artificial neural network for predicting biomass growth during domestic wastewater treatment through a biological process |
| topic | biological treatment process DO concentration Artificial Neural Network temperature organic and inorganic matter oxygen uptake rate |
| url | https://doi.org/10.1002/elsc.202200058 |
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