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

• Published in: Water research (Oxford) Vol. 36; no. 16; pp. 4113 - 4127
• Main Authors: Brun, Roland, Kühni, Martin, Siegrist, Hansruedi, Gujer, Willi, Reichert, Peter
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.09.2002; Elsevier Science
• Subjects: Activated sludge models (ASM); Activated Sludge Process; Applied sciences; Bias; Biological and medical sciences; Biological treatment of waters; Biotechnology; Calibration; Collinearity index; Data processing; Environment and pollution; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General purification processes; Identifiability analysis; Industrial applications and implications. Economical aspects; Models, Chemical; Parameter estimation; Pollution; Sensitivity analysis; Sensitivity and Specificity; Sewage - chemistry; Wastewaters; Water Purification - methods; Water treatment and pollution; Parameter estimation; Sensitivity analysis; Collinearity index; Activated sludge models (ASM); Identifiability analysis; Calibration; Activated sludge; Identification; Waste water purification; Modeling; Biological purification
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/S0043-1354(02)00104-5

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.