dc.contributor.advisor | Skogestad, Sigurd | nb_NO |
dc.contributor.advisor | Ghardan, Maryam | nb_NO |
dc.contributor.author | Grimholt, Chriss | nb_NO |
dc.date.accessioned | 2014-12-19T13:23:46Z | |
dc.date.available | 2014-12-19T13:23:46Z | |
dc.date.created | 2013-06-16 | nb_NO |
dc.date.issued | 2011 | nb_NO |
dc.identifier | 629178 | nb_NO |
dc.identifier | ntnudaim:6442 | nb_NO |
dc.identifier.uri | http://hdl.handle.net/11250/248358 | |
dc.description.abstract | This thesis compares the performance of the new static model based esti- mator proposed by Skogestad et al. (2011) with least squares (LS), principal component regression (PCR), and partial least squares (PLS) estimators on a linear, binary, and multicomponent distillation example. The performance is classified into two categories: “open-loop” performance (estimator used for monitoring) and “closed-loop” performance (estimator used for control). The new estimator is derived from a regression point of view, and it is shown that this estimator is optimal for “closed-loop” estimation. Skogestad et al. (2011) also presented a method called loss regression for applying the new estima- tor on data. This thesis shows that this estimator is sensitive to noise and collinearity, and a new improved method called the truncated "closed-loop" method (truncated CLM) is proposed. It is found that the new estimator and the truncated CLM have better “closed-loop” performance, but worse “open- loop” performance than LS, PCR and PLS. | nb_NO |
dc.language | eng | nb_NO |
dc.publisher | Institutt for kjemisk prosessteknologi | nb_NO |
dc.title | A New Static Estimator Based on Self-Optimizing Theory | nb_NO |
dc.type | Master thesis | nb_NO |
dc.source.pagenumber | 95 | nb_NO |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for naturvitenskap og teknologi, Institutt for kjemisk prosessteknologi | nb_NO |