Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment
The freshwater is a challenge as the world’s population grows. The largest sources of water in this planet are brackish water and sea water. So, water purification process is very important during this water crisis using desalination and various water treatment techniques. In this paper, we have dev...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Cleaner Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772782322000377 |
| _version_ | 1828177328585310208 |
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| author | Dipak Kumar Jana Prajna Bhunia Sirsendu Das Adhikary Barnali Bej |
| author_facet | Dipak Kumar Jana Prajna Bhunia Sirsendu Das Adhikary Barnali Bej |
| author_sort | Dipak Kumar Jana |
| collection | DOAJ |
| description | The freshwater is a challenge as the world’s population grows. The largest sources of water in this planet are brackish water and sea water. So, water purification process is very important during this water crisis using desalination and various water treatment techniques. In this paper, we have developed some machine learning approaches for a detergent industry in India. The whole effluent and waste disposal in the detergent industry were treated by different treatment process like air flotation, chemical coagulation, sedimentation and biological treatment through completely mixed activated sludge process. The soft computing techniques (i) a five-layered feed forward ANN (ii) a five-layered cascade forward neural network and (iii) support vector regression have been applied to optimize the proposed models. Training function are considered as Feed-Forward BP(MLP), Cascade Forward BP and SVR where as Training algorithm Levenberg Marquardt and Sequential minimal optimization have been used. Graphical representation has been given for different types of pollutants, effluent treatment plant flow, and Change of color of wastewater after treatment and mathematical operations for Mechanism on Support Vector Regression has been established. To get the best number of neurons for the hidden layer, the network was trained for varied numbers of iterations (Nbest). The data was statistically examined as well. The Nbest value was found to be 10, with the lowest root mean square error (0.066), mean square error (0.0043), and greatest R2 value (0.996); these values show that the predicted and experimental responses are similar, and plant performance was adequately predicted using the backpropagation ANN model thus ANN may be used to describe the process. |
| first_indexed | 2024-04-12T04:57:54Z |
| format | Article |
| id | doaj.art-4efb2c82f3d944bc90d95bf87dafc3c6 |
| institution | Directory Open Access Journal |
| issn | 2772-7823 |
| language | English |
| last_indexed | 2024-04-12T04:57:54Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Chemical Engineering |
| spelling | doaj.art-4efb2c82f3d944bc90d95bf87dafc3c62022-12-22T03:47:03ZengElsevierCleaner Chemical Engineering2772-78232022-09-013100039Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater TreatmentDipak Kumar Jana0Prajna Bhunia1Sirsendu Das Adhikary2Barnali Bej3School of Applied Science & Humanities, Haldia Institute of Technology, Haldia, Purba Midnapur, 721657, West Bengal, IndiaSchool of Applied Science & Humanities, Haldia Institute of Technology, Haldia, Purba Midnapur, 721657, West Bengal, IndiaSchool of Applied Science & Humanities, Haldia Institute of Technology, Haldia, Purba Midnapur, 721657, West Bengal, IndiaCorresponding author.; Department of Chemical Engineering, Haldia Institute of Technology Haldia, Purba Midnapur, 721657, West Bengal, IndiaThe freshwater is a challenge as the world’s population grows. The largest sources of water in this planet are brackish water and sea water. So, water purification process is very important during this water crisis using desalination and various water treatment techniques. In this paper, we have developed some machine learning approaches for a detergent industry in India. The whole effluent and waste disposal in the detergent industry were treated by different treatment process like air flotation, chemical coagulation, sedimentation and biological treatment through completely mixed activated sludge process. The soft computing techniques (i) a five-layered feed forward ANN (ii) a five-layered cascade forward neural network and (iii) support vector regression have been applied to optimize the proposed models. Training function are considered as Feed-Forward BP(MLP), Cascade Forward BP and SVR where as Training algorithm Levenberg Marquardt and Sequential minimal optimization have been used. Graphical representation has been given for different types of pollutants, effluent treatment plant flow, and Change of color of wastewater after treatment and mathematical operations for Mechanism on Support Vector Regression has been established. To get the best number of neurons for the hidden layer, the network was trained for varied numbers of iterations (Nbest). The data was statistically examined as well. The Nbest value was found to be 10, with the lowest root mean square error (0.066), mean square error (0.0043), and greatest R2 value (0.996); these values show that the predicted and experimental responses are similar, and plant performance was adequately predicted using the backpropagation ANN model thus ANN may be used to describe the process.http://www.sciencedirect.com/science/article/pii/S2772782322000377Artificial Neural NetworkSupport Vector RegressionWastewater treatment plant |
| spellingShingle | Dipak Kumar Jana Prajna Bhunia Sirsendu Das Adhikary Barnali Bej Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment Cleaner Chemical Engineering Artificial Neural Network Support Vector Regression Wastewater treatment plant |
| title | Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment |
| title_full | Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment |
| title_fullStr | Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment |
| title_full_unstemmed | Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment |
| title_short | Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment |
| title_sort | optimization of effluents using artificial neural network and support vector regression in detergent industrial wastewater treatment |
| topic | Artificial Neural Network Support Vector Regression Wastewater treatment plant |
| url | http://www.sciencedirect.com/science/article/pii/S2772782322000377 |
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