Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN)
The objective of this research is to develop a neural network model to predict the pore size of ultrafiltration membrane. Usually, the pore size of ultrafiltration membrane was determined experimentally using permeation and rejection rate experiments, followed by empirical equations. Therefore, in t...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2008
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/8721/ http://eprints.utm.my/8721/1/UTMjurnalTEK_49F_DIS%5B23%5D.pdf |
| _version_ | 1848891751414104064 |
|---|---|
| author | Razali, Nur Myra Rahayu Idris, Ani Mohd Yusof, Khairiyah |
| author_facet | Razali, Nur Myra Rahayu Idris, Ani Mohd Yusof, Khairiyah |
| author_sort | Razali, Nur Myra Rahayu |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | The objective of this research is to develop a neural network model to predict the pore size of ultrafiltration membrane. Usually, the pore size of ultrafiltration membrane was determined experimentally using permeation and rejection rate experiments, followed by empirical equations. Therefore, in this study, Artificial Neural Network (ANN) has been proposed as an alternative method to predict the pore size of flat sheet ultrafiltration membranes. Experimental data were collected from the previous research whereby the polyethersulfone (PES) polymeric membranes were fabricated with lithium bromide (LiBr) additive. The membranes were tested by using various polyethylene glycol PEG molecular weights solution. The neural network has a pyramidal architecture with three different layers which consists of an input layer, hidden layer and output layer. Feed-forward Backpropagation (FFBP) network was constructed in MATLAB version 7.2 environment by using Levenberg-Marquardt algorithm (trainlm) training method. In addition, Bayesian regularization method was introduced to improve the neural network generalization. The simulated results obtained from this study were then compared to the experiment results so as to obtain the best model with the smallest Root-Mean Square (RMS) error. The results revealed that the constructed networks were able to accurately estimate the pore size of ultrafiltration membrane. |
| first_indexed | 2025-11-15T21:02:57Z |
| format | Article |
| id | utm-8721 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:02:57Z |
| publishDate | 2008 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-87212010-06-02T01:57:22Z http://eprints.utm.my/8721/ Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) Razali, Nur Myra Rahayu Idris, Ani Mohd Yusof, Khairiyah TP Chemical technology The objective of this research is to develop a neural network model to predict the pore size of ultrafiltration membrane. Usually, the pore size of ultrafiltration membrane was determined experimentally using permeation and rejection rate experiments, followed by empirical equations. Therefore, in this study, Artificial Neural Network (ANN) has been proposed as an alternative method to predict the pore size of flat sheet ultrafiltration membranes. Experimental data were collected from the previous research whereby the polyethersulfone (PES) polymeric membranes were fabricated with lithium bromide (LiBr) additive. The membranes were tested by using various polyethylene glycol PEG molecular weights solution. The neural network has a pyramidal architecture with three different layers which consists of an input layer, hidden layer and output layer. Feed-forward Backpropagation (FFBP) network was constructed in MATLAB version 7.2 environment by using Levenberg-Marquardt algorithm (trainlm) training method. In addition, Bayesian regularization method was introduced to improve the neural network generalization. The simulated results obtained from this study were then compared to the experiment results so as to obtain the best model with the smallest Root-Mean Square (RMS) error. The results revealed that the constructed networks were able to accurately estimate the pore size of ultrafiltration membrane. Penerbit UTM Press 2008-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8721/1/UTMjurnalTEK_49F_DIS%5B23%5D.pdf Razali, Nur Myra Rahayu and Idris, Ani and Mohd Yusof, Khairiyah (2008) Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN). Jurnal Teknologi, F (49). pp. 229-235. ISSN 0127-9696 |
| spellingShingle | TP Chemical technology Razali, Nur Myra Rahayu Idris, Ani Mohd Yusof, Khairiyah Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) |
| title | Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) |
| title_full | Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) |
| title_fullStr | Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) |
| title_full_unstemmed | Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) |
| title_short | Prediction of pore size of ultrafiltration membrane by using artificial neural network (ANN) |
| title_sort | prediction of pore size of ultrafiltration membrane by using artificial neural network (ann) |
| topic | TP Chemical technology |
| url | http://eprints.utm.my/8721/ http://eprints.utm.my/8721/1/UTMjurnalTEK_49F_DIS%5B23%5D.pdf |