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Particle size distribution reconstruction using a finite number of its moments through artificial neural networks: a practical application

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dc.contributor.author Cogoni, Giuseppi
dc.contributor.author Frawley, Patrick J.
dc.date.accessioned 2015-09-10T11:27:12Z
dc.date.available 2015-09-10T11:27:12Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/10344/4628
dc.description peer-reviewed en_US
dc.description.abstract An artificial neural network (ANN) approach to reconstruct the particle size distribution (PSD) is proposed in this paper. This novel technique has been applied for acetaminophen crystallization in ethanol. Several experimental PSDs taken in different operating conditions, such as temperature and agitation degree, at different stages of the process have been considered, in order to ensure a wide range of different distributions for the system taken into account. The first stage of the ANN modeling is represented by the structure definition and the network training through a backpropagation algorithm in which the experimental PSDs and their associated vector of moments have been used. The second stage is represented by the feedforward application and validations of the proposed model estimating the PSDs using a finite set of experimental moments compared with their associated PSDs and then, using a set of time-dependent moments, obtaining the transitory PSD. The proposed approach represents a more suitable way to reconstruct the PSD in full, for the first time without assuming any reference distribution or knowing in advance the shape of the experimental PSD, leading to a generalized characterization of the PSDs with possible implementations in other multiphase unit operations. en_US
dc.language.iso eng en_US
dc.publisher American Chemical Society en_US
dc.relation.ispartofseries Crystal Growth and Deisgn;15, pp. 239-246
dc.relation.uri http://dx.doi.org/10.1021/cg501288z
dc.rights "© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Crystal Growth and Design, 2015, 15, pp. 239-246, http://dx.doi.org/10.1021/cg501288z en_US
dc.subject population balance-equations en_US
dc.subject quadrature method en_US
dc.subject nucleation en_US
dc.subject algorithm en_US
dc.subject growth en_US
dc.title Particle size distribution reconstruction using a finite number of its moments through artificial neural networks: a practical application en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2015-09-10T11:17:32Z
dc.description.version ACCEPTED
dc.identifier.doi 10.1021/cg501288z
dc.contributor.sponsor SFI en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1579156
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed


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