Recursive estimation of selective catalyst reduction system parameters using modified Gauss-Newton method

Selective catalytic reduction (SCR) system is a complex chemical process which is used to treat exhaust gas in many applications, e.g. diesel engines in automobiles. An SCR model was constructed by General Motors (GM) and the problem considered was to estimate the unknown kinetic parameters on-line taking into account aging effects. There are a number of application constraints that must be taken into consideration, including inaccessible model structure, parametric identifiability difficulties and limited measurement data. In this work, the complexity and identifiability of this estimation problem is investigated through global sensitivity analysis and cost-function based analysis methods. One of the problems is to determine the parameters where the model is most sensitive and should thus be estimated onboard. A modified Gauss-Newton method was proposed and evaluated for this estimation problem. Simulation studies have confirmed that sensitive kinetic parameters of the SCR model can be estimated and tracked recursively using the proposed method.