BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Statistical Methods for Developing Reliable Fundamental Models.
DTSTART:20120203T101500
DTSTAMP:20260428T183533Z
UID:1069dc027d7763b7de895169e5f9c7258b062b934eced40d4000ed43
CATEGORIES:Conferences - Seminars
DESCRIPTION:Pr. K. McAuley\, Department of Chemical Engineering Queen's Un
 iversity Kingston\, Canada.\nFundamental models are used to design\, debot
 tleneck\, optimize and control chemical processes to ensure safe and econo
 mical production of high quality products.  Obtaining reliable model predi
 ctions requires an appropriate balance between simplicity and complexity o
 f model equations\, as well as appropriate values for model parameters.   
 This talk will focus on statistical tools that can assist modellers when t
 hey develop model equations\, estimate parameters and selecting conditions
  for new experiments.  \n One common problem that arises when modeling che
 mical processes is the large number of parameters that appear in equations
  describing rates of chemical reactions and transport of species between p
 hases.  A modeller with a large and informative data set will be able to e
 stimate a large number of model parameters.  When there is insufficient in
 formation in the data to reliably estimate all of the model parameters\, o
 nly a subset of the parameters should be estimated.  This talk will introd
 uce easy-to-use parameter ranking and selection techniques that can help m
 odellers to decide which parameters to estimate to get the best possible m
 odel predictions.  Use of models to select operating conditions for dynami
 c and steady-state experiments will also be discussed.  These methods will
  be illustrated using models and data from a steam-methane reformer\, bior
 eactors\, and industrial polymerization processes.
LOCATION:ME C2 405
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR