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Quantifying the impact of temporal analysis of products reactor initial state uncertainties on kinetic parameters
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  • Adam Yonge,
  • M. Kunz,
  • Gabriel Gusmão,
  • Zongtang Fang,
  • Rakesh Batchu,
  • Rebecca Fushimi,
  • Andrew Medford
Adam Yonge
Georgia Institute of Technology

Corresponding Author:ayonge3@gatech.edu

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M. Kunz
Idaho National Laboratory
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Gabriel Gusmão
Georgia Institute of Technology
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Zongtang Fang
Idaho National Laboratory
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Rakesh Batchu
Idaho National Laboratory
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Rebecca Fushimi
Idaho National Laboratory
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Andrew Medford
Georgia Institute of Technology
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Abstract

The temporal analysis of products (TAP) reactor provides a route to extract intrinsic kinetics from transient measurements. Current TAP uncertainty quantification only considers the experimental noise present in the outlet flow signal. Additional sources of uncertainty such as initial surface coverages, catalyst zone location, inert void fraction, gas pulse intensity and pulse delay, are not included. For this reason, a framework for quantifying initial state uncertainties present in TAP experiments is presented and applied to a carbon monoxide oxidation case study. Two methods for quantifying these sources of uncertainty are introduced. The first utilizes initial state sensitivities to approximate the parameter variances and provide insights into the structural certainty of the model. The second generates parameter confidence distributions through an ensemble-based sampling algorithm. The initial state covariance matrix can ultimately be merged with the experimental noise covariance matrix, providing a unified description of the parameter uncertainties for a TAP experiment.
20 Sep 2021Submitted to AIChE Journal
21 Sep 2021Submission Checks Completed
21 Sep 2021Assigned to Editor
04 Dec 2021Reviewer(s) Assigned
16 Mar 2022Editorial Decision: Revise Minor
13 Apr 20221st Revision Received
13 Apr 2022Submission Checks Completed
13 Apr 2022Assigned to Editor
10 May 2022Editorial Decision: Accept
Sep 2022Published in AIChE Journal volume 68 issue 9. 10.1002/aic.17776