Comparison of PBM and ANPM models for predicting grinding product size distributions

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dc.contributor.advisorBuiles Toro, Santiagospa
dc.contributor.authorLuján González, Juan Camilo
dc.contributor.authorRestrepo Lopera, Juan Pablo
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.degreeIngeniero de Procesosspa
dc.creator.emailjlujang2@eafit.edu.cospa
dc.creator.emailjurest82@eafit.edu.cospa
dc.date.accessioned2020-07-22T20:19:43Z
dc.date.available2020-07-22T20:19:43Z
dc.date.issued2020
dc.description.abstractGrinding is a very important industrial operation that draws up to 4% of the global electricity consumption. It is imperative to predict accurately the appropriate retention times necessary for a given size reduction to minimize the wasted energy invested in overgrinding. However, the most common models for scaling, such as Bond, could lead to a design risk on the order of ± 20% due to their assumption that a single particle size can describe the entire particle size distribution. Thus, different approaches (both phenomenological and non- phenomenological) need to be explored. In the present work, a population balance model is compared with an algebraic statistical model, to predict the evolution of particle size distribution over time, assessing them in terms of accuracy, robustness, and computational complexity. Even though the population balance model had a lower accuracy and higher mathematical complexity its predictions were physically coherent, which made it a more robust model for extrapolating to different initial conditions and milling times. It is important to note that due to the 2020 COVID-19 pandemic, experimental information was limited, which inhibited an independent validation of the models, and an overfitting analysis for the ANPM.spa
dc.identifier.ddc658.51 L953
dc.identifier.urihttp://hdl.handle.net/10784/17079
dc.language.isospaspa
dc.publisherUniversidad EAFITspa
dc.publisher.departmentEscuela de Ingeniería. Departamento de Ingeniería Procesosspa
dc.publisher.placeMedellínspa
dc.publisher.programIngeniería de Procesosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAcceso abiertospa
dc.subjectBalances poblacionalesspa
dc.subjectMoliendaspa
dc.subjectTrituraciónspa
dc.subjectDistribución de tamaño de partículaspa
dc.subject.lembPLANIFICACIÓN DE LA PRODUCCIÓNspa
dc.subject.lembMODELOS MATEMÁTICOSspa
dc.subject.lembESTADÍSTICA INDUSTRIALspa
dc.subject.lembCONSUMO DE ENERGÍAspa
dc.titleComparison of PBM and ANPM models for predicting grinding product size distributionsspa
dc.typebachelorThesiseng
dc.typeinfo:eu-repo/semantics/bachelorThesis
dc.type.hasVersionacceptedVersioneng
dc.type.localTrabajo de gradospa

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