Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/8185
Title: Integration of data uncertainty in linear regression and process optimization
Authors: Reis, Marco S. 
Saraiva, Pedro M. 
Issue Date: 2005
Issue Date: 2005
Citation: AIChE Journal. 51:11 (2005) 3007-3019
Abstract: Data uncertainties provide important information that should be taken into account along with the actual data. In fact, with the development of measurement instrumentation methods and metrology, one is very often able to rigorously specify the uncertainty associated with each measured value. The use of this piece of information, together with raw measurements, should - in principle - lead to more sound ways of performing data analysis, empirical modeling, and subsequent decision making. In this paper, we address the issues of using data uncertainty in the task of model estimation and, when it is already available, we show how the integration of measurement and actuation uncertainty can be achieved in the context of process optimization. Within the scope of the first task (model estimation), we make reference to several methods designed to take into account data uncertainties in linear multivariate regression (multivariate least squares, maximum likelihood principal component regression), and others whose potential to deal with noisy data is well known (partial least squares, principal component regression, and ridge regression), as well as modifications of previous methods that we developed, and compare their performance. MLPCR2 tends to achieve better predictive performance than all the other tested methods. The potential benefits of including measurement and actuation uncertainties in process optimization are also illustrated. © 2005 American Institute of Chemical Engineers AIChE J, 2005
URI: http://hdl.handle.net/10316/8185
DOI: 10.1002/aic.10540
Rights: openAccess
Appears in Collections:FCTUC Eng.Química - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat
obra.pdf258.69 kBAdobe PDFView/Open
Show full item record

Page view(s)

96
checked on Aug 14, 2019

Download(s) 50

152
checked on Aug 14, 2019

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.