dc.date.accessioned2019-08-17T22:05:05Z
dc.date.accessioned2023-05-31T19:05:11Z
dc.date.available2019-08-17T22:05:05Z
dc.date.available2023-05-31T19:05:11Z
dc.date.created2019-08-17T22:05:05Z
dc.date.issued2014-12
dc.identifierNieto 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.
dc.identifierhttp://repositorio.uch.edu.pe/handle/uch/322
dc.identifierhttp://dx.doi.org/10.1109/INDUSCON.2014.7059397
dc.identifierhttps://ieeexplore.ieee.org/document/7059397/citations#citations
dc.identifier10.1109/INDUSCON.2014.7059397
dc.identifierIEEE/IAS International Conference on Industry Applications, IEEE INDUSCON
dc.identifier2-s2.0-84946686073
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6495640
dc.description.abstractIn 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.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation11th IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON 2014
dc.relationinfo:eu-repo/semantics/article
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.sourceRepositorio Institucional - UCH
dc.sourceUniversidad de Ciencias y Humanidades
dc.subjectBall mills
dc.subjectMining
dc.subjectGrinding (machining)
dc.subjectModel predictive control
dc.subjectParticle size
dc.subjectPredictive control systems
dc.subjectStochastic control systems
dc.subjectStochastic systems
dc.subjectCirculants
dc.subjectControl system simulations
dc.subjectMill-grinding
dc.subjectQuantitative measurement
dc.subjectStochastic formulation
dc.subjectStochastic variable
dc.subjectStochastic models
dc.titleTesting a predictive control with stochastic model in a balls mill grinding circuit
dc.typeinfo:eu-repo/semantics/conferenceObject


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