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Testing a predictive control with stochastic model in a balls mill grinding circuit

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dc.contributor.author Nieto Chaupis, Huber
dc.coverage.temporal 7 December 2014 through 10 December 2014
dc.date.accessioned 2019-08-17T22:05:05Z
dc.date.available 2019-08-17T22:05:05Z
dc.date.issued 2014-12
dc.identifier.citation Nieto Chaupis, H. (Diciembre, 2014). Testing a predictive control with stochastic model in a balls mill grinding circuit. En 11th IEEE/IAS International Conference on Industry Applications, Brazil. en_PE
dc.identifier.uri http://repositorio.uch.edu.pe/handle/uch/322
dc.identifier.uri http://dx.doi.org/10.1109/INDUSCON.2014.7059397
dc.identifier.uri https://ieeexplore.ieee.org/document/7059397/citations#citations
dc.description.abstract In this paper, the formulation of a stochastic model and its subsequent incorporation into a predictive control of a balls mill grinding circuit, is presented. The apparition of stochastic variables is a consequence of variables interaction by which is impossible to know a well-defined determinist mathematical methodology. Thus, the perceived dynamics is simulated by emphasizing those possible scenarios of alarm situations in where overloading might collapse the system. Under this perception, the system identification is based on probabilities. Once the model is built, it enters in a based-model predictive control by taking into account the hypothesis that the circulant load and water are under interaction each other. Although the quantitative measurement of this interaction might be speculative, it is not discarded that this interaction might be actually the main source of disturbs on the the particle size evolution. The results have shown positive prospects of the proposed methodology as seen in the control system simulations in where the particle size acquires stability. Furthermore the dramatic reduction of alarms events supports the idea that the MPC is still robust even with stochastic formulations. en
dc.description.sponsorship Axxiom;CEMIG;et al.;Governo de Minas;Ohmini;Yokogawa
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 11th IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON 2014
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 Ball mills en
dc.subject Mining en
dc.subject Grinding (machining) en
dc.subject Model predictive control en
dc.subject Particle size en
dc.subject Predictive control systems en
dc.subject Stochastic control systems en
dc.subject Stochastic systems en
dc.subject Circulants en
dc.subject Control system simulations en
dc.subject Mill-grinding en
dc.subject Quantitative measurement en
dc.subject Stochastic formulation en
dc.subject Stochastic variable en
dc.subject Stochastic models en
dc.title Testing a predictive control with stochastic model in a balls mill grinding circuit en_PE
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi 10.1109/INDUSCON.2014.7059397 en_PE
dc.identifier.journal IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON en_PE
dc.identifier.scopus 2-s2.0-84946686073


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