Seminar Details
2012-01-24 (14.00) : Statistical Methods for Developing Reliable Fundamental Models
At Euler building
Organized by Mathematical Engineering
Speaker :
Kim McAuley (Department of Chemical Engineering, Queen's University, Canada)
Abstract :
Fundamental models are used to design, debottleneck, optimize and control chemical processes. Obtaining reliable model predictions requires an appropriate balance between simplicity and complexity of model equations, as well as appropriate values for model parameters. This talk will focus on statistical tools that can assist modellers when they develop model equations, estimate parameters and select conditions for new experiments. One common problem that arises when modeling chemical processes is the large number of parameters that appear in equations describing rates of chemical reactions and transport of species between phases. A modeller with a large and informative data set will be able to reliably estimate a large number of model parameters. When there is insufficient information in the data to reliably estimate all of the model parameters, only a subset of the parameters should be estimated. This talk will introduce easy-to-use parameter ranking and selection techniques (based on mean-squared error) that can help modellers to decide which parameters to estimate to get the best possible model predictions. Use of models to select operating conditions for new dynamic and steady-state experiments will also be discussed. These methods will be illustrated using models and data from a steam-methane reformer, bioreactors, and industrial polymerization processes.