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

Luján González, Juan Camilo; Restrepo Lopera, Juan Pablo

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

dc.contributor.advisor	Builes Toro, Santiago
dc.contributor.author	Luján González, Juan Camilo
dc.contributor.author	Restrepo Lopera, Juan Pablo
dc.coverage.spatial	Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees	eng
dc.creator.email	jlujang2@eafit.edu.co	spa
dc.creator.email	jurest82@eafit.edu.co	spa
dc.date.accessioned	2020-07-22T20:19:43Z
dc.date.available	2020-07-22T20:19:43Z
dc.date.issued	2020
dc.description.abstract	Grinding 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.description.degreelevel	Trabajo	spa
dc.description.degreename	Ingeniero(a) de Procesos	spa
dc.format.mimetype	application/pdf
dc.identifier.ddc	658.51 L953
dc.identifier.instname	instname:Universidad EAFIT
dc.identifier.reponame	reponame:Repositorio Institucional Universidad EAFIT
dc.identifier.repourl	repourl:https://repository.eafit.edu.co
dc.identifier.uri	https://hdl.handle.net/10784/17079
dc.language.iso	spa
dc.publisher	Universidad EAFIT	spa
dc.publisher.department	Departamento de Ingeniería Procesos	spa
dc.publisher.faculty	Escuela de Ingeniería	spa
dc.publisher.place	Medellín	spa
dc.publisher.program	Ingeniería de Procesos	spa
dc.rights.accessrights	info:eu-repo/semantics/openAccess
dc.rights.coar	http://purl.org/coar/access_right/c_abf2
dc.rights.local	Acceso abierto	spa
dc.subject	Balances poblacionales	spa
dc.subject	Molienda	spa
dc.subject	Trituración	spa
dc.subject	Distribución de tamaño de partícula	spa
dc.subject.lemb	PLANIFICACIÓN DE LA PRODUCCIÓN	spa
dc.subject.lemb	MODELOS MATEMÁTICOS	spa
dc.subject.lemb	ESTADÍSTICA INDUSTRIAL	spa
dc.subject.lemb	CONSUMO DE ENERGÍA	spa
dc.title	Comparison of PBM and ANPM models for predicting grinding product size distributions
dc.type	info:eu-repo/semantics/bachelorThesis
dc.type.coar	http://purl.org/coar/resource_type/c_7a1f
dc.type.coarversion	http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.local	Trabajo de grado	spa
dc.type.redcol	http://purl.org/redcol/resource_type/TP
dc.type.version	info:eu-repo/semantics/acceptedVersion
dspace.entity.type	Publication