Model-based experimental screening for DOC parameter estimation

• Parameter estimation with data reduction by Multivariate Data Analysis was evaluated.
• Data reduction based on parameter sensitivity improved the parameter estimation.
• The method achieved 32% lower final residual sum of squares compared to reference.
• The method also displayed reduced tendencies to converge to a local minima.
• The computational time was significantly longer for the evaluated method.

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.