Repositorio Dspace

Predictive Control of the mineral particle size with kernel-reduced volterra models in a balls mill grinding circuit

Mostrar el registro sencillo del ítem

dc.contributor.author Nieto Chaupis, Huber
dc.coverage.temporal 3 June 2015 through 5 June 2015
dc.date.accessioned 2019-08-26T02:27:19Z
dc.date.available 2019-08-26T02:27:19Z
dc.date.issued 2015-06
dc.identifier.citation Nieto Chaupis, H. (Junio, 2015). Predictive Control of the mineral particle size with kernel-reduced Volterra models in a balls mill grinding circuit. En 24th International Symposium on Industrial Electronics (ISIE), Brazil. en_PE
dc.identifier.uri http://repositorio.uch.edu.pe/handle/uch/376
dc.identifier.uri http://dx.doi.org/10.1109/ISIE.2015.7281453
dc.identifier.uri https://ieeexplore.ieee.org/document/7281453
dc.description.abstract We report the results of the application of the Model-based Predictive Control (MPC) algorithm for a 3×3 MIMO balls mill grinding system by using computational simulation and Monte Carlo data generation. For this purpose, the system has been identified through a reduced scheme of Volterra formalism by which the proposed methodology has required to employ up to 20 parameters. Subsequently, the model enters in a framework of MPC which targets to control the particle size, one of the most important output variables in this study. According to the simulation results the system identification error is of order of 3%, whereas the MPC scheme applied to control a desired set-point namely 75 %-200mesh is accompanied by a deviation of ±5%. Since the balls mill grinding circuit is a nonlinear system, it is expected that the system might collapse as consequence of the accumulated circulant load. The simulations have predicted that the MPC algorithm running with a Volterra-based model might surpass situations of stops and alarms system, even in those cases where the system is attacked by unexpected disturbs and random events. en
dc.description.sponsorship Federal University of Mato Grosso do Sul (UFMS);Federal University of Rio de Janeiro (UFRJ);State University of Rio de Janeiro (UERJ);The Institute of Electrical and Electronics Engineers Industrial Electronics Society (IEEE IES)
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_PE
dc.relation info:eu-repo/semantics/article en_PE
dc.relation.isPartOf IEEE International Symposium on Industrial Electronics
dc.rights info:eu-repo/semantics/embargoedAccess en_PE
dc.source Repositorio Institucional - UCH en_PE
dc.source Universidad de Ciencias y Humanidades en_PE
dc.subject Algorithms en
dc.subject Grinding (machining) en
dc.subject Industrial electronics en
dc.subject Model predictive control en
dc.subject Monte Carlo methods en
dc.subject Particle size en
dc.subject Predictive control systems en
dc.subject Computational simulation en
dc.subject Mill-grinding en
dc.subject Mineral particles en
dc.subject Model based predictive control en
dc.subject Monte Carlo data en
dc.subject Output variables en
dc.subject Predictive control en
dc.subject Volterra model en_
dc.subject Ball mills en
dc.title Predictive Control of the mineral particle size with kernel-reduced volterra models in a balls mill grinding circuit en_PE
dc.type info:eu-repo/semantics/conferenceObject en_PE
dc.identifier.doi 10.1109/ISIE.2015.7281453 en_PE
dc.identifier.journal IEEE International Symposium on Industrial Electronics, ISIE en_PE
dc.identifier.scopus 2-s2.0-84947230914


Ficheros en el ítem

Ficheros Tamaño Formato Ver

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

REALIZA TU BUSQUEDA


Búsqueda avanzada

Listar

Mi cuenta


© 2021 Repositorio Institucional - Universidad de Ciencias y Humanidades (UCH)