Modeling of filtration process using PSO-neural network
Modeling of membrane filtration process is a challenging task because it involves many interactions from biological and physical operation behavior. Membrane fouling in filtration process is too complex to understand and to derive a robust model become very difficult. The aim of this paper is to stu...
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Universiti Teknikal Malaysia Melaka
2017
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my.utm.765622018-04-30T13:32:51Z http://eprints.utm.my/id/eprint/76562/ Modeling of filtration process using PSO-neural network Yusuf, Z. Abdul Wahab, N. Sahlan, S. TK Electrical engineering. Electronics Nuclear engineering Modeling of membrane filtration process is a challenging task because it involves many interactions from biological and physical operation behavior. Membrane fouling in filtration process is too complex to understand and to derive a robust model become very difficult. The aim of this paper is to study the potential of neural network based dynamic model for submerged membrane filtration process. The purpose of the model is to represent the dynamic behavior of the filtration process therefore the model can be utilized in the prediction and control. The neural network model was trained using particle swarm optimization (PSO) technique. Three methods of PSO are compared to obtained an optimal model which are random PSO (RPSO), constriction factor PSO (CPSO) and inertia weight PSO (IW-PSO). In the data collection, a random step was applied to the suction pump in order to obtained the permeate flux and transmembrane pressure (TMP) dynamic. The model was evaluated in term of %R2, root mean square error (RMSE,) and mean absolute deviation (MAD). The result of proposed modeling technique showed that the neural network with PSO is capable to model the dynamic behavior of the filtration process. Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed Yusuf, Z. and Abdul Wahab, N. and Sahlan, S. (2017) Modeling of filtration process using PSO-neural network. Journal of Telecommunication, Electronic and Computer Engineering, 9 (3). pp. 15-19. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031287139&partnerID=40&md5=499054bfece5d3e5cdf46f581205ba61 |
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TK Electrical engineering. Electronics Nuclear engineering Yusuf, Z. Abdul Wahab, N. Sahlan, S. Modeling of filtration process using PSO-neural network |
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Modeling of membrane filtration process is a challenging task because it involves many interactions from biological and physical operation behavior. Membrane fouling in filtration process is too complex to understand and to derive a robust model become very difficult. The aim of this paper is to study the potential of neural network based dynamic model for submerged membrane filtration process. The purpose of the model is to represent the dynamic behavior of the filtration process therefore the model can be utilized in the prediction and control. The neural network model was trained using particle swarm optimization (PSO) technique. Three methods of PSO are compared to obtained an optimal model which are random PSO (RPSO), constriction factor PSO (CPSO) and inertia weight PSO (IW-PSO). In the data collection, a random step was applied to the suction pump in order to obtained the permeate flux and transmembrane pressure (TMP) dynamic. The model was evaluated in term of %R2, root mean square error (RMSE,) and mean absolute deviation (MAD). The result of proposed modeling technique showed that the neural network with PSO is capable to model the dynamic behavior of the filtration process. |
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Article |
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Yusuf, Z. Abdul Wahab, N. Sahlan, S. |
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Yusuf, Z. Abdul Wahab, N. Sahlan, S. |
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Yusuf, Z. |
title |
Modeling of filtration process using PSO-neural network |
title_short |
Modeling of filtration process using PSO-neural network |
title_full |
Modeling of filtration process using PSO-neural network |
title_fullStr |
Modeling of filtration process using PSO-neural network |
title_full_unstemmed |
Modeling of filtration process using PSO-neural network |
title_sort |
modeling of filtration process using pso-neural network |
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Universiti Teknikal Malaysia Melaka |
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2017 |
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http://eprints.utm.my/id/eprint/76562/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031287139&partnerID=40&md5=499054bfece5d3e5cdf46f581205ba61 |
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13.160551 |