Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems
In this work, the effects of operative parameters on CH<sub>4</sub>, CO<sub>2</sub>, O<sub>2</sub>, and N<sub>2</sub> membrane gas separation for poly (4-methyl-1-pentane) (PMP) membrane modified by adding nanoparticles of TiO<sub>2</sub>,...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Iran Polymer and Petrochemical Institute
2020-07-01
|
| Series: | Polyolefins Journal |
| Subjects: | |
| Online Access: | http://poj.ippi.ac.ir/article_1697_da2254e6dde46764f2570e708e05db8d.pdf |
| _version_ | 1818456708765188096 |
|---|---|
| author | Afshar Alihosseini Amin Hedayati Moghaddam |
| author_facet | Afshar Alihosseini Amin Hedayati Moghaddam |
| author_sort | Afshar Alihosseini |
| collection | DOAJ |
| description | In this work, the effects of operative parameters on CH<sub>4</sub>, CO<sub>2</sub>, O<sub>2</sub>, and N<sub>2</sub> membrane gas separation for poly (4-methyl-1-pentane) (PMP) membrane modified by adding nanoparticles of TiO<sub>2</sub>, ZnO, and Al<sub>2</sub>O<sub>3</sub> are assessed and investigated. The operative parameters were type and percentage of nanoparticles, and cross membrane pressure. The membrane permeability and selectivity were selected as the responses and indexes of separation process performance. To design the experimental layout, design of experiment methodology (DoE) techniques were used. Further, the separation process was modeled and simulated using artificial intelligence (AI) methods. So, a robust black-box model based on radial basis function (RBF) network was developed and trained with the ability for predicting the performance of membrane process. The developed model could simulate the process and predict the permeability with R<sup>2</sup>-validation of 0.9. Finally, it was found that addition of nanoparticles and increasing the operative pressure had positive effects on membrane performance. Maximum permeability values for O<sub>2</sub>, N<sub>2</sub>, CO<sub>2 </sub>and CH<sub>4</sub> were 181.58, 52.09, 550.85, and 54.26, respectively. The maximum values of validation-R<sup>2</sup> of optimum structure for CO<sub>2</sub>/N<sub>2</sub> and CO<sub>2</sub>/CH<sub>4</sub> selectivity were 0.8697 and 0.7028, respectively. |
| first_indexed | 2024-12-14T22:30:58Z |
| format | Article |
| id | doaj.art-44c01f565dfa4cb89dd345df803ea98f |
| institution | Directory Open Access Journal |
| issn | 2322-2212 2345-6868 |
| language | English |
| last_indexed | 2024-12-14T22:30:58Z |
| publishDate | 2020-07-01 |
| publisher | Iran Polymer and Petrochemical Institute |
| record_format | Article |
| series | Polyolefins Journal |
| spelling | doaj.art-44c01f565dfa4cb89dd345df803ea98f2022-12-21T22:45:15ZengIran Polymer and Petrochemical InstitutePolyolefins Journal2322-22122345-68682020-07-0172919810.22063/poj.2020.2638.11501697Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systemsAfshar Alihosseini0Amin Hedayati Moghaddam1Department of Chemical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Chemical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, IranIn this work, the effects of operative parameters on CH<sub>4</sub>, CO<sub>2</sub>, O<sub>2</sub>, and N<sub>2</sub> membrane gas separation for poly (4-methyl-1-pentane) (PMP) membrane modified by adding nanoparticles of TiO<sub>2</sub>, ZnO, and Al<sub>2</sub>O<sub>3</sub> are assessed and investigated. The operative parameters were type and percentage of nanoparticles, and cross membrane pressure. The membrane permeability and selectivity were selected as the responses and indexes of separation process performance. To design the experimental layout, design of experiment methodology (DoE) techniques were used. Further, the separation process was modeled and simulated using artificial intelligence (AI) methods. So, a robust black-box model based on radial basis function (RBF) network was developed and trained with the ability for predicting the performance of membrane process. The developed model could simulate the process and predict the permeability with R<sup>2</sup>-validation of 0.9. Finally, it was found that addition of nanoparticles and increasing the operative pressure had positive effects on membrane performance. Maximum permeability values for O<sub>2</sub>, N<sub>2</sub>, CO<sub>2 </sub>and CH<sub>4</sub> were 181.58, 52.09, 550.85, and 54.26, respectively. The maximum values of validation-R<sup>2</sup> of optimum structure for CO<sub>2</sub>/N<sub>2</sub> and CO<sub>2</sub>/CH<sub>4</sub> selectivity were 0.8697 and 0.7028, respectively.http://poj.ippi.ac.ir/article_1697_da2254e6dde46764f2570e708e05db8d.pdfpoly (4-methyl 1-pentane)aimembrane gas separationnanoparticle |
| spellingShingle | Afshar Alihosseini Amin Hedayati Moghaddam Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems Polyolefins Journal poly (4-methyl 1-pentane) ai membrane gas separation nanoparticle |
| title | Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems |
| title_full | Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems |
| title_fullStr | Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems |
| title_full_unstemmed | Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems |
| title_short | Permeability and selectivity prediction of poly (4-methyl 1-pentane) membrane modified by nanoparticles in gas separation through artificial intelligent systems |
| title_sort | permeability and selectivity prediction of poly 4 methyl 1 pentane membrane modified by nanoparticles in gas separation through artificial intelligent systems |
| topic | poly (4-methyl 1-pentane) ai membrane gas separation nanoparticle |
| url | http://poj.ippi.ac.ir/article_1697_da2254e6dde46764f2570e708e05db8d.pdf |
| work_keys_str_mv | AT afsharalihosseini permeabilityandselectivitypredictionofpoly4methyl1pentanemembranemodifiedbynanoparticlesingasseparationthroughartificialintelligentsystems AT aminhedayatimoghaddam permeabilityandselectivitypredictionofpoly4methyl1pentanemembranemodifiedbynanoparticlesingasseparationthroughartificialintelligentsystems |