Model-based experimental screening for DOC parameter estimation
Artikel i vetenskaplig tidskrift, 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.

Parameter estimation

Multivariate Data Analysis

Engine rig experiments

D-optimal design

Diesel Oxidation Catalyst

Författare

Björn Lundberg

Chalmers, Kemi och kemiteknik, Kemiteknik, Kemisk reaktionsteknik

Jonas Sjöblom

Chalmers, Tillämpad mekanik, Förbränning

Åsa Johansson

Johnson Matthey AB

Björn Westerberg

Scania AB

Derek Creaser

Chalmers, Kemi och kemiteknik, Kemiteknik, Kemisk reaktionsteknik

Computers and Chemical Engineering

0098-1354 (ISSN)

Vol. 74 144-157

Styrkeområden

Transport

Ämneskategorier

Kemiska processer

DOI

10.1016/j.compchemeng.2015.01.004