Flow regime identification using neural network based electrodynamic tomography system
Process tomography is a low cost, efficient and non-invasive industrial process imaging technique. It is used in many industries for process imaging and measuring. Provided that appropriate sensing mechanism is used, process tomography can be used in processes involving solids, liquids, gases, and a...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2004
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/12798/1/MohdFuaadHjRahmat2004_FlowRegimeIdentificationUsingNeural.pdf |
| _version_ | 1796855140999757824 |
|---|---|
| author | Rahmat, Mohd. fua’ad Hakilo, Ahmed sabit |
| author_facet | Rahmat, Mohd. fua’ad Hakilo, Ahmed sabit |
| author_sort | Rahmat, Mohd. fua’ad |
| collection | ePrints |
| description | Process tomography is a low cost, efficient and non-invasive industrial process imaging technique. It is used in many industries for process imaging and measuring. Provided that appropriate sensing mechanism is used, process tomography can be used in processes involving solids, liquids, gases, and any of their mixtures. In this paper, the process to be imaged and measured involves solid particles flow in gravity drop system. Electrical charge tomography or electrodynamic tomography is a tomographic technique using electrodynamic sensors. This paper presents the flow regime identification using neural network. |
| first_indexed | 2024-03-05T18:24:16Z |
| format | Article |
| id | utm.eprints-12798 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:24:16Z |
| publishDate | 2004 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-127982017-11-01T04:17:38Z http://eprints.utm.my/12798/ Flow regime identification using neural network based electrodynamic tomography system Rahmat, Mohd. fua’ad Hakilo, Ahmed sabit TA Engineering (General). Civil engineering (General) Process tomography is a low cost, efficient and non-invasive industrial process imaging technique. It is used in many industries for process imaging and measuring. Provided that appropriate sensing mechanism is used, process tomography can be used in processes involving solids, liquids, gases, and any of their mixtures. In this paper, the process to be imaged and measured involves solid particles flow in gravity drop system. Electrical charge tomography or electrodynamic tomography is a tomographic technique using electrodynamic sensors. This paper presents the flow regime identification using neural network. Penerbit UTM Press 2004 Article PeerReviewed application/pdf en http://eprints.utm.my/12798/1/MohdFuaadHjRahmat2004_FlowRegimeIdentificationUsingNeural.pdf Rahmat, Mohd. fua’ad and Hakilo, Ahmed sabit (2004) Flow regime identification using neural network based electrodynamic tomography system. Jurnal Teknologi, 40 . pp. 109-118. http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/408/398 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Rahmat, Mohd. fua’ad Hakilo, Ahmed sabit Flow regime identification using neural network based electrodynamic tomography system |
| title | Flow regime identification using neural network based electrodynamic tomography system |
| title_full | Flow regime identification using neural network based electrodynamic tomography system |
| title_fullStr | Flow regime identification using neural network based electrodynamic tomography system |
| title_full_unstemmed | Flow regime identification using neural network based electrodynamic tomography system |
| title_short | Flow regime identification using neural network based electrodynamic tomography system |
| title_sort | flow regime identification using neural network based electrodynamic tomography system |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/12798/1/MohdFuaadHjRahmat2004_FlowRegimeIdentificationUsingNeural.pdf |
| work_keys_str_mv | AT rahmatmohdfuaad flowregimeidentificationusingneuralnetworkbasedelectrodynamictomographysystem AT hakiloahmedsabit flowregimeidentificationusingneuralnetworkbasedelectrodynamictomographysystem |