Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network
A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each...
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| Language: | en |
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EDP Sciences
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/23251/1/Parametric%20Optimization%20of%20the%20Poly%20%28Nvinylcaprolactam%29%20%28PNVCL%29.pdf http://umpir.ump.edu.my/id/eprint/23251/ https://doi.org/10.1051/matecconf/201822502023 |
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| author | Marwah Noori, Mohammed Kamal, Yusoh Jun Haslinda, Haji Shariffuddin |
| author_facet | Marwah Noori, Mohammed Kamal, Yusoh Jun Haslinda, Haji Shariffuddin |
| author_sort | Marwah Noori, Mohammed |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each parameter and their interactions on the conversion rate. The influence of the time, polymerization temperature, initiator concentration and concentration of the monomer were studied. The results obtained in this study indicate that the RBFNN was an effective method for predicting the conversion rate. The time of the PNVCL polymerization as well as the concentration of the monomer show the maximum effect on the conversion rate. In addition, compared with the RSM method, the RBFNN showed better conversion rate comparing with the experimental data. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.23251 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2018 |
| publisher | EDP Sciences |
| record_format | eprints |
| spelling | my.ump.umpir.232512018-12-14T09:06:43Z http://umpir.ump.edu.my/id/eprint/23251/ Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network Marwah Noori, Mohammed Kamal, Yusoh Jun Haslinda, Haji Shariffuddin TP Chemical technology A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each parameter and their interactions on the conversion rate. The influence of the time, polymerization temperature, initiator concentration and concentration of the monomer were studied. The results obtained in this study indicate that the RBFNN was an effective method for predicting the conversion rate. The time of the PNVCL polymerization as well as the concentration of the monomer show the maximum effect on the conversion rate. In addition, compared with the RSM method, the RBFNN showed better conversion rate comparing with the experimental data. EDP Sciences 2018 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/23251/1/Parametric%20Optimization%20of%20the%20Poly%20%28Nvinylcaprolactam%29%20%28PNVCL%29.pdf Marwah Noori, Mohammed and Kamal, Yusoh and Jun Haslinda, Haji Shariffuddin (2018) Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network. In: MATEC Web of Conferences: UTP-UMP-VIT Symposium on Energy Systems 2018 (SES 2018) , September 18-19, 2018 , Universiti Malaysia Pahang. pp. 1-7., 225 (02023). ISSN 2261-236X (Published) https://doi.org/10.1051/matecconf/201822502023 |
| spellingShingle | TP Chemical technology Marwah Noori, Mohammed Kamal, Yusoh Jun Haslinda, Haji Shariffuddin Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network |
| title | Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network |
| title_full | Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network |
| title_fullStr | Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network |
| title_full_unstemmed | Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network |
| title_short | Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network |
| title_sort | parametric optimization of the poly (nvinylcaprolactam) (pnvcl) thermoresponsive polymers synthesis by the response surface methodology and radial basis function neural network |
| topic | TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/23251/1/Parametric%20Optimization%20of%20the%20Poly%20%28Nvinylcaprolactam%29%20%28PNVCL%29.pdf http://umpir.ump.edu.my/id/eprint/23251/ https://doi.org/10.1051/matecconf/201822502023 |
| url_provider | http://umpir.ump.edu.my/ |