Model-based experimental screening for DOC parameter estimation
Journal article, 2015

In the current study a parameter estimation method based on data screening by sensitivity analysis is presented. The method applied Multivariate Data Analysis (MVDA) on a large transient data set to select different subsets on which parameters estimation was performed. The subset was continuously updated as the parameter values developed using Principal Component Analysis (PCA) and D-optimal onion design. The measurement data was taken from a Diesel Oxidation Catalyst (DOC) connected to a full scale engine rig and both kinetic and mass transport parameters were estimated. The methodology was compared to a conventional parameter estimation method and it was concluded that the proposed method achieved a 32% lower residual sum of squares but also that it displayed less tendencies to converge to a local minima. The computational time was however significantly longer for the evaluated method.

Engine rig experiments

Parameter estimation

Multivariate Data Analysis

Diesel Oxidation Catalyst

D-optimal design


Björn Lundberg

Chalmers, Chemistry and Chemical Engineering, Chemical Technology

Jonas Sjöblom

Chalmers, Applied Mechanics, Combustion and Propulsion Systems

Åsa Johansson

Johnson Matthey AB

Björn Westerberg

Scania CV AB

Derek Creaser

Chalmers, Chemistry and Chemical Engineering, Chemical Technology

Computers and Chemical Engineering

0098-1354 (ISSN)

Vol. 74 144-157

Areas of Advance


Subject Categories

Chemical Process Engineering



More information

Latest update