Premium
Mathematical modelling of chemical processes—obtaining the best model predictions and parameter estimates using identifiability and estimability procedures
Author(s) -
McLean Kevin A. P.,
McAuley Kim B.
Publication year - 2012
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20660
Subject(s) - identifiability , estimation theory , mathematics , model selection , model parameter , mean squared error , selection (genetic algorithm) , statistics , computer science , mathematical optimization , machine learning
Abstract Chemical engineers who develop fundamental models often have difficulties estimating all model parameters due to problems with parameter identifiability and estimability. These two concepts are reviewed, as are techniques for assessing identifiability and estimability. When some parameters are not estimable from the data, modellers must decide whether to conduct new experiments, change the model structure, or to estimate only a subset of the parameters and leave the others at fixed values. Estimating a reduced number of parameters can lead to better model predictions with lower mean squared error (MSE). MSE‐based techniques for parameter subset selection are discussed and compared. © 2011 Canadian Society for Chemical Engineering