Subset measurement selection for globally self-optimizing control of Tennessee Eastman process

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dc.contributor.authorYe, Lingjian
dc.contributor.authorCao, Yi
dc.contributor.authorYuan, Xiaofeng
dc.contributor.authorSong, Zhihuan
dc.date.accessioned2016-08-23T11:30:49Z
dc.date.available2016-08-23T11:30:49Z
dc.date.issued2016-08-09
dc.description.abstractThe concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selection problem has not be studied. In this paper, we consider the measurement subset selection problem for globally self-optimizing control (gSOC) of Tennessee Eastman (TE) process. The TE process contains substantial measurements and had been studied for SOC with controlled variables selected from individual measurements through exhaustive search. This process has been revisited with improved performance recently through a retrofit approach of gSOC. To extend the improvement further, the measurement subset selection problem for gSOC is considered in this work and solved through a modification of an existing partially bidirectional branch and bound (PB3) algorithm originally developed for local SOC. The modified PB3 algorithm efficiently identifies the best measurement candidates among the full set which obtains the globally minimal economic loss. Dynamic simulations are conducted to demonstrate the optimality of proposed results.en_UK
dc.identifier.citationLingjian Ye, Yi Cao, Xiaofeng Yuan, Zhihuan Song, Subset Measurement Selection for Globally Self-Optimizing Control of Tennessee Eastman Process*, IFAC-PapersOnLine, Volume 49, Issue 7, 2016, Pages 121-126en_UK
dc.identifier.issn2405-8963
dc.identifier.urihttp://dx.doi.org/10.1016/j.ifacol.2016.07.227
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/10391
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTennessee Eastmanen_UK
dc.subjectSelf-optimizing controlen_UK
dc.subjectControlled variableen_UK
dc.subjectPlant-wide controlen_UK
dc.titleSubset measurement selection for globally self-optimizing control of Tennessee Eastman processen_UK
dc.typeArticleen_UK

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