Explicit stochastic predictive control of combustion plants based on Gaussian process models

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creativework.keywordsModel predictive control, Multi-parametric nonlinear programming, Power plants, Probabilistic models, Stochastic systems
dc.contributor.authorGrancharova A.
dc.contributor.authorKocijan J.
dc.contributor.authorJohansen T.A.
dc.date.accessioned2024-07-10T14:27:03Z
dc.date.accessioned2024-07-10T14:47:03Z
dc.date.available2024-07-10T14:27:03Z
dc.date.available2024-07-10T14:47:03Z
dc.date.issued2008-06-01
dc.description.abstractEnergy production is one of the largest sources of air pollution. A feasible method to reduce the harmful flue gases emissions and to increase the efficiency is to improve the control strategies of the existing thermoelectric power plants. This makes the Nonlinear Model Predictive Control (NMPC) method very suitable for achieving an efficient combustion control. Recently, an explicit approximate approach for stochastic NMPC based on a Gaussian process model was proposed. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation, which is an essential issue in safety-critical applications. This paper considers the application of an explicit approximate approach for stochastic NMPC to the design of an explicit reference tracking NMPC controller for a combustion plant based on its Gaussian process model. The controller brings the air factor (respectively the concentration of oxygen in the flue gases) on its optimal value with every change of the load factor and thus an optimal operation of the combustion plant is achieved. © 2008 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.automatica.2008.04.002
dc.identifier.issn0005-1098
dc.identifier.scopusSCOPUS_ID:44549084178en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/136
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=44549084178&origin=inward
dc.titleExplicit stochastic predictive control of combustion plants based on Gaussian process models
dc.typeArticle
oaire.citation.issue6
oaire.citation.volume44
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