Practical identifiability of ASM2d parameters—systematic selection and tuning of parameter subsets

In many applications, some parameters of the Activated Sludge Model No. 2d (ASM2d) need calibration. Since ASM2d usually is overparameterized with respect to the available data, the subset of calibration parameters is not unique. In practice, calibration of ASM2d (and other ASMs) is often addressed by ad hoc selecting and tuning procedures. In this paper, a more systematic approach based on parameter identifiability analysis of parameter subsets is applied. The approach consists of a preliminary prior parameter analysis and a subsequent iterative parameter subset selection and tuning procedure. The former includes the choice of suitable prior parameter values and uncertainties and a pre-selection of parameters which are reasonably estimated from the data available. The latter is based on three diagnostic measures which are simple to calculate and easy to interpret. It is demonstrated as to how these measures can be used to identify the most important model parameters and to analyze their interdependencies. In addition, it is shown how these measures facilitate the analysis of the influence of fixed parameter values on parameter estimates.