Support vector regression modelling of an aerobic granular sludge in sequential batch reactor
Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The predictio...
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Online Access: | http://eprints.utm.my/id/eprint/95309/1/NurSakinahAhmadYasmin2021_SupportVectorRegressionModelling.pdf http://eprints.utm.my/id/eprint/95309/ http://dx.doi.org/10.3390/membranes11080554 |
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my.utm.953092022-04-29T22:03:24Z http://eprints.utm.my/id/eprint/95309/ Support vector regression modelling of an aerobic granular sludge in sequential batch reactor Yasmin, N. S. A. Wahab, N. A. Ismail, F. S. Musa, M. J. Ab. Halim, M. H. Anuar, A. N. TK Electrical engineering. Electronics Nuclear engineering Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The prediction becomes harder when dealing with a limited dataset due to the limitation of the experimental works. A radial basis function algorithm with selected kernel parameters of cost and gamma was used to developed SVR models. The kernel parameters were selected by using a grid search method and were further optimized by using particle swarm optimization and genetic algorithm. The SVR models were then compared with an artificial neural network. The prediction results R2 were within >90% for all predicted concentration of COD. The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment. MDPI AG 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95309/1/NurSakinahAhmadYasmin2021_SupportVectorRegressionModelling.pdf Yasmin, N. S. A. and Wahab, N. A. and Ismail, F. S. and Musa, M. J. and Ab. Halim, M. H. and Anuar, A. N. (2021) Support vector regression modelling of an aerobic granular sludge in sequential batch reactor. Membranes, 11 (8). ISSN 2077-0375 http://dx.doi.org/10.3390/membranes11080554 DOI: 10.3390/membranes11080554 |
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TK Electrical engineering. Electronics Nuclear engineering Yasmin, N. S. A. Wahab, N. A. Ismail, F. S. Musa, M. J. Ab. Halim, M. H. Anuar, A. N. Support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
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Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The prediction becomes harder when dealing with a limited dataset due to the limitation of the experimental works. A radial basis function algorithm with selected kernel parameters of cost and gamma was used to developed SVR models. The kernel parameters were selected by using a grid search method and were further optimized by using particle swarm optimization and genetic algorithm. The SVR models were then compared with an artificial neural network. The prediction results R2 were within >90% for all predicted concentration of COD. The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment. |
format |
Article |
author |
Yasmin, N. S. A. Wahab, N. A. Ismail, F. S. Musa, M. J. Ab. Halim, M. H. Anuar, A. N. |
author_facet |
Yasmin, N. S. A. Wahab, N. A. Ismail, F. S. Musa, M. J. Ab. Halim, M. H. Anuar, A. N. |
author_sort |
Yasmin, N. S. A. |
title |
Support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
title_short |
Support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
title_full |
Support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
title_fullStr |
Support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
title_full_unstemmed |
Support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
title_sort |
support vector regression modelling of an aerobic granular sludge in sequential batch reactor |
publisher |
MDPI AG |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/95309/1/NurSakinahAhmadYasmin2021_SupportVectorRegressionModelling.pdf http://eprints.utm.my/id/eprint/95309/ http://dx.doi.org/10.3390/membranes11080554 |
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13.160551 |