Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network
Vanillin adsorption onto resin H103 was modelled using artificial neural network (ANN) approach and the best ANN algorithm was determined in this work. The first step of ANN modeling was ANN set up, followed by the optimization of ANN. The parameters for the input layers are contact time, initial va...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IOP Publishing Ltd
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/35839/ http://umpir.ump.edu.my/id/eprint/35839/1/Modeling%20of%20vanillin%20adsorption%20from%20aqueous%20solution%20using%20resin%20H103%20by%20artificial%20neural%20network.pdf |
| _version_ | 1848824883865190400 |
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| author | Chan, W. S. Rozaimi, Abu Samah Norazwina, Zainol Abdul Sahli, Fakharudin Suraini, Abd-Aziz Phang, Lai Yee |
| author_facet | Chan, W. S. Rozaimi, Abu Samah Norazwina, Zainol Abdul Sahli, Fakharudin Suraini, Abd-Aziz Phang, Lai Yee |
| author_sort | Chan, W. S. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Vanillin adsorption onto resin H103 was modelled using artificial neural network (ANN) approach and the best ANN algorithm was determined in this work. The first step of ANN modeling was ANN set up, followed by the optimization of ANN. The parameters for the input layers are contact time, initial vanillin concentration, resin dosage, pH, and temperature while the response is residual vanillin concentration. The neural network was trained using backpropagation (BP) algorithm. The result shows that the Levenberg-Marquardt algorithm was best suited the training function and the optimized ANN involved seven neurons at the hidden layer. This model can produce a correlation of determination value of 0.9999 with the mean square error (MSE) value of 0.0277. The best adsorption efficiencies for each factor were 98.11%, 96.03%, 98.14%, 98.2%, and 98.10% at 2.0 g of adsorbent dosage, 30 min of contact time, 100 mg/L of initial vanillin concentration, pH 5, and 25 °C, respectively. The outcomes of this work proved that ANN is excellent in predicting experimental data of vanillin adsorption by resin H103. |
| first_indexed | 2025-11-15T03:20:07Z |
| format | Conference or Workshop Item |
| id | ump-35839 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:20:07Z |
| publishDate | 2019 |
| publisher | IOP Publishing Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-358392022-12-27T04:09:36Z http://umpir.ump.edu.my/id/eprint/35839/ Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network Chan, W. S. Rozaimi, Abu Samah Norazwina, Zainol Abdul Sahli, Fakharudin Suraini, Abd-Aziz Phang, Lai Yee QA75 Electronic computers. Computer science QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Vanillin adsorption onto resin H103 was modelled using artificial neural network (ANN) approach and the best ANN algorithm was determined in this work. The first step of ANN modeling was ANN set up, followed by the optimization of ANN. The parameters for the input layers are contact time, initial vanillin concentration, resin dosage, pH, and temperature while the response is residual vanillin concentration. The neural network was trained using backpropagation (BP) algorithm. The result shows that the Levenberg-Marquardt algorithm was best suited the training function and the optimized ANN involved seven neurons at the hidden layer. This model can produce a correlation of determination value of 0.9999 with the mean square error (MSE) value of 0.0277. The best adsorption efficiencies for each factor were 98.11%, 96.03%, 98.14%, 98.2%, and 98.10% at 2.0 g of adsorbent dosage, 30 min of contact time, 100 mg/L of initial vanillin concentration, pH 5, and 25 °C, respectively. The outcomes of this work proved that ANN is excellent in predicting experimental data of vanillin adsorption by resin H103. IOP Publishing Ltd 2019-12-09 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/35839/1/Modeling%20of%20vanillin%20adsorption%20from%20aqueous%20solution%20using%20resin%20H103%20by%20artificial%20neural%20network.pdf Chan, W. S. and Rozaimi, Abu Samah and Norazwina, Zainol and Abdul Sahli, Fakharudin and Suraini, Abd-Aziz and Phang, Lai Yee (2019) Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network. In: IOP Conference Series: Materials Science and Engineering; 1st Process Systems Engineering and Safety Symposium 2019, ProSES 2019 , 4 September 2019 , Kuantan, Pahang, Malaysia. pp. 1-13., 702 (012048). ISSN 1757-8981 (Published) https://doi.org/10.1088/1757-899X/702/1/012048 |
| spellingShingle | QA75 Electronic computers. Computer science QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Chan, W. S. Rozaimi, Abu Samah Norazwina, Zainol Abdul Sahli, Fakharudin Suraini, Abd-Aziz Phang, Lai Yee Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network |
| title | Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network |
| title_full | Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network |
| title_fullStr | Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network |
| title_full_unstemmed | Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network |
| title_short | Modeling of vanillin adsorption from aqueous solution using resin H103 by artificial neural network |
| title_sort | modeling of vanillin adsorption from aqueous solution using resin h103 by artificial neural network |
| topic | QA75 Electronic computers. Computer science QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/35839/ http://umpir.ump.edu.my/id/eprint/35839/ http://umpir.ump.edu.my/id/eprint/35839/1/Modeling%20of%20vanillin%20adsorption%20from%20aqueous%20solution%20using%20resin%20H103%20by%20artificial%20neural%20network.pdf |