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Parameter estimation and prediction uncertainties for multi-response kinetic models with uncertain inputs
  • Kaveh Abdi,
  • Benoit Celse,
  • Kimberley McAuley
Kaveh Abdi
Queen's University Faculty of Engineering and Applied Science

Corresponding Author:[email protected]

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Benoit Celse
IFP Energies nouvelles
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Kimberley McAuley
Queen's University
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Error-in-variables model (EVM) methods are used for parameter estimation when independent variables are uncertain. During EVM parameter estimation, output measurement variances are required as weighting factors in the objective function. These variances can be estimated based on data from replicate experiments. However, conducting replicates is complicated when independent variables are uncertain. Instead, pseudo-replicate runs may be performed where the target values of inputs for repeated runs are the same, but the true input values may be different. Here, we propose a method to estimate output-measurement variances for use in multivariate EVM estimation problems, based on pseudo-replicate data. We also propose a bootstrap technique for quantifying uncertainties in resulting parameter estimates and model predictions. The methods are illustrated using a case study involving n-hexane hydroisomerization in a well-mixed reactor. Case-study results reveal that assumptions about input uncertainties can have important influences on parameter estimates, model predictions and their confidence intervals.
21 Oct 2022Submitted to AIChE Journal
24 Oct 2022Submission Checks Completed
24 Oct 2022Assigned to Editor
24 Oct 2022Review(s) Completed, Editorial Evaluation Pending
27 Oct 2022Reviewer(s) Assigned
02 Dec 2022Editorial Decision: Revise Major
09 Dec 20221st Revision Received
09 Dec 2022Submission Checks Completed
09 Dec 2022Assigned to Editor
09 Dec 2022Review(s) Completed, Editorial Evaluation Pending
11 Dec 2022Reviewer(s) Assigned
14 Jan 2023Editorial Decision: Accept