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Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions
Includes bibliographical references (leaves 121-123).
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Department of Chemical Engineering
2014
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| _version_ | 1867613858002108416 |
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| access_status_str | Open Access |
| author | Maússe, Celestino Fernando |
| author_browse | Maússe, Celestino Fernando |
| author_facet | Maússe, Celestino Fernando |
| author_sort | Maússe, Celestino Fernando |
| collection | Thesis |
| description | Includes bibliographical references (leaves 121-123). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/5296 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:42:49.341Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Chemical Engineering |
| publisherStr | Department of Chemical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/5296 Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions Maússe, Celestino Fernando Chemical Engineering Includes bibliographical references (leaves 121-123). The objective of this study was to develop a method for in-lie measurement of a particle size distribution (PSD) of suspended solids and its moments. This was part of a wider study, the aim of which was to develop a system for controlling a crystallisation process. The control strategy to be used is dependent on kinetic models of the process. These are in turn dependent on the zeroth to fifth moments of the particle size distribution and the supersaturation levels of the solution. In order to apply advanced control to a process, continuous monitoring of the process to provice real time information for the process model is required. 2014-07-31T11:07:36Z 2014-07-31T11:07:36Z 2006 Master Thesis Masters MSc http://hdl.handle.net/11427/5296 eng application/pdf Department of Chemical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Chemical Engineering Maússe, Celestino Fernando Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| thesis_degree_str | Master's |
| title | Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| title_full | Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| title_fullStr | Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| title_full_unstemmed | Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| title_short | Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| title_sort | use of artificial neural network models to derive particle size distributions and their moments from chord length distributions |
| topic | Chemical Engineering |
| url | http://hdl.handle.net/11427/5296 |
| work_keys_str_mv | AT maussecelestinofernando useofartificialneuralnetworkmodelstoderiveparticlesizedistributionsandtheirmomentsfromchordlengthdistributions |