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Evaluation of the Degree of Rate Control via Automatic Differentiation
  • Yilin Yang,
  • Siddarth Achar,
  • John Kitchin
Yilin Yang
Carnegie Mellon University

Corresponding Author:[email protected]

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Siddarth Achar
Carnegie Mellon University
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John Kitchin
Carnegie Mellon University
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The degree of rate control quantitatively identifies the kinetically relevant (sometimes known as rate-limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, e.g. with finite differences. Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this work, we demonstrate the use of automatic differentiation in the evaluation of the degree of rate control. Automatic differentiation libraries are increasingly available through modern machine learning frameworks. Compared to the finite differences, automatic differentiation provides solutions with higher accuracy with lower computational cost. Furthermore, we illustrate a hybrid local-global sensitivity analysis method, the distributed evaluation of local sensitivity analysis (DELSA), to assess the importance of kinetic parameters over an uncertain space. This method also benefits from automatic differentiation to obtain high-quality results efficiently.

27 Jul 2021Submitted to AIChE Journal
29 Jul 2021Submission Checks Completed
29 Jul 2021Assigned to Editor
09 Aug 2021Reviewer(s) Assigned
04 Nov 2021Editorial Decision: Revise Minor
26 Nov 20211st Revision Received
03 Dec 2021Submission Checks Completed
03 Dec 2021Assigned to Editor
22 Dec 2021Reviewer(s) Assigned
09 Jan 2022Editorial Decision: Revise Minor
25 Jan 20222nd Revision Received
29 Jan 2022Submission Checks Completed
29 Jan 2022Assigned to Editor
10 Feb 2022Editorial Decision: Accept
Jun 2022Published in AIChE Journal volume 68 issue 6. 10.1002/aic.17653