Branch and bound method for regression-based controlled variable selection

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dc.contributor.authorKariwala, Vinay
dc.contributor.authorYe, Lingjian
dc.contributor.authorCao, Yi
dc.date.accessioned2016-10-25T14:13:48Z
dc.date.available2016-10-25T14:13:48Z
dc.date.issued2013-03-27
dc.description.abstractSelf-optimizing control is a promising method for selection of controlled variables (CVs) from available measurements. Recently, Ye, Cao, Li, and Song (2012) have proposed a globally optimal method for selection of self-optimizing CVs by converting the CV selection problem into a regression problem. In this approach, the necessary conditions of optimality (NCO) are approximated by linear combinations of available measurements over the entire operation region. In practice, it is desired that a subset of available measurements be combined as CVs to obtain a good trade-off between the economic performance and the complexity of control system. The subset selection problem, however, is combinatorial in nature, which makes the application of the globally optimal CV selection method to large-scale processes difficult. In this work, an efficient branch and bound (BAB) algorithm is developed to handle the computational complexity associated with the selection of globally optimal CVs. The proposed BAB algorithm identifies the best measurement subset such that the regression error in approximating NCO is minimized and is also applicable to the general regression problem. Numerical tests using randomly generated matrices and a binary distillation column case study demonstrate the computational efficiency of the proposed BAB algorithm.en_UK
dc.identifier.citationKariwala V, Ye L, Cao Y. (2013) Branch and bound method for regression-based controlled variable selection. Computers & Chemical Engineering, Volume 54, July 2013, pp. 1-7en_UK
dc.identifier.issn0098-1354
dc.identifier.urihttp://dx.doi.org/10.1016/j.compchemeng.2013.03.006
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10862
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsThis is the author’s version of a work that was accepted for publication in Computers & Chemical Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Chemical Engineering, Volume 54, 11 July 2013, pp1-7. DOI:10.1016/j.compchemeng.2013.03.006
dc.subjectBranch and bounden_UK
dc.subjectControl structure designen_UK
dc.subjectControlled variablesen_UK
dc.subjectCombinatorial optimizationen_UK
dc.subjectDistillationen_UK
dc.subjectSelf-optimizing controlen_UK
dc.titleBranch and bound method for regression-based controlled variable selectionen_UK
dc.typeArticleen_UK

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