Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions

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dc.contributor.authorMaússe, Celestino Fernandoen_ZA
dc.date.accessioned2014-07-31T11:07:36Z
dc.date.available2014-07-31T11:07:36Z
dc.date.issued2006en_ZA
dc.descriptionIncludes bibliographical references (leaves 121-123).
dc.description.abstractThe 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.en_ZA
dc.identifier.apacitationMaússe, C. F. (2006). <i>Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Chemical Engineering. Retrieved from http://hdl.handle.net/11427/5296en_ZA
dc.identifier.chicagocitationMaússe, Celestino Fernando. <i>"Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Chemical Engineering, 2006. http://hdl.handle.net/11427/5296en_ZA
dc.identifier.citationMaússe, C. 2006. Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Maússe, Celestino Fernando AB - 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. DA - 2006 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2006 T1 - Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions TI - Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions UR - http://hdl.handle.net/11427/5296 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5296
dc.identifier.vancouvercitationMaússe CF. Use of artificial neural network models to derive particle size distributions and their moments from chord length distributions. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Chemical Engineering, 2006 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5296en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Chemical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherChemical Engineeringen_ZA
dc.titleUse of artificial neural network models to derive particle size distributions and their moments from chord length distributionsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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