Realistic Assessment of Parameter Uncertainty in Dynamic Parameter Estimation

Mordechai Shacham, Neima Brauner

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Assessment of the uncertainty in parameter estimation is essential for confidence in subsequent use of the dynamic model with the associated parameters. The assessment of the parameter uncertainty in highly nonlinear kinetic models is often a very difficult task. In this paper a new method for parameter uncertainty assessment is presented and its use is demonstrated for a cellulose hydrolysis kinetic model. The new method involves generation of pseudo-experimental data using a known set of “reference” parameter values. Stepwise regression is used in an attempt to generate alternative sets of parameter values that yield results with precision similar to the reference set. The difference between the individual parameter values in the separate sets represents the uncertainty of these values. High uncertainty level indicates that no physical meaning can be attributed to the predicted parameter values. Application of the proposed method is therefore recommended prior to applying the individual parameter values in other models.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Number of pages6
StatePublished - 1 Oct 2017

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946


  • model identification
  • parameter estimation
  • stepwise regression

ASJC Scopus subject areas

  • General Chemical Engineering
  • Computer Science Applications


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