@inbook{e945238959ab47f68cfa97785d43cbf8,

title = "Dimensionality reduction in feasibility analysis by latent variable modeling",

abstract = "We propose a systematic methodology to exploit partial least-squares (PLS) regression modeling to reduce the dimensionality of a feasibility analysis problem. PLS is used to project the original multidimensional space of input factors onto a lower dimensional latent space. We then apply a radial basis function (RBF) adaptive sampling feasibility analysis on this lower dimensional space to identify the feasible region of the process. A simple low-dimensional representation of the feasible region is thus obtained with this combined PLS-RBF approach. The performance of the methodology is tested on a mathematical example involving six inputs. We show the ability of this PLS-RBF approach to reduce the computational burden of the feasibility analysis while maintaining an accurate and robust identification of the feasible region.",

keywords = "adaptive sampling, feasibility analysis, model reduction, partial least-squares regression, radial basis function",

author = "Gabriele Bano and Zilong Wang and Pierantonio Facco and Fabrizio Bezzo and Massimiliano Barolo and Marianthi Ierapetritou",

year = "2018",

month = jan,

day = "1",

doi = "10.1016/B978-0-444-64241-7.50241-X",

language = "English (US)",

series = "Computer Aided Chemical Engineering",

publisher = "Elsevier B.V.",

pages = "1477--1482",

booktitle = "Computer Aided Chemical Engineering",

}