Article ID Journal Published Year Pages File Type
172267 Computers & Chemical Engineering 2015 14 Pages PDF
Abstract

•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.

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Physical Sciences and Engineering Chemical Engineering Chemical Engineering (General)
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