Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane
Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation spe...
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my.utm.289692022-01-31T08:38:09Z http://eprints.utm.my/id/eprint/28969/ Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane Lau, Yien Jun Karri, Rama Rao Lau, Sie Yon Mubarak, N. M. Chua, Han Bing Mohammad, Khalid Jagadish, Priyanka Abdullah, E. C. TP Chemical technology Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H2O2), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R2 values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H2O2/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent. Elsevier Inc 2020-04 Article PeerReviewed Lau, Yien Jun and Karri, Rama Rao and Lau, Sie Yon and Mubarak, N. M. and Chua, Han Bing and Mohammad, Khalid and Jagadish, Priyanka and Abdullah, E. C. (2020) Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane. Environmental Research, 183 . ISSN 0013-9351 http://dx.doi.org/10.1016/j.envres.2020.109158 DOI:10.1016/j.envres.2020.109158 |
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TP Chemical technology Lau, Yien Jun Karri, Rama Rao Lau, Sie Yon Mubarak, N. M. Chua, Han Bing Mohammad, Khalid Jagadish, Priyanka Abdullah, E. C. Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
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Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H2O2), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R2 values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H2O2/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent. |
format |
Article |
author |
Lau, Yien Jun Karri, Rama Rao Lau, Sie Yon Mubarak, N. M. Chua, Han Bing Mohammad, Khalid Jagadish, Priyanka Abdullah, E. C. |
author_facet |
Lau, Yien Jun Karri, Rama Rao Lau, Sie Yon Mubarak, N. M. Chua, Han Bing Mohammad, Khalid Jagadish, Priyanka Abdullah, E. C. |
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Lau, Yien Jun |
title |
Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
title_short |
Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
title_full |
Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
title_fullStr |
Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
title_full_unstemmed |
Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
title_sort |
modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane |
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Elsevier Inc |
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2020 |
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http://eprints.utm.my/id/eprint/28969/ http://dx.doi.org/10.1016/j.envres.2020.109158 |
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1724073258922278912 |
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13.19449 |