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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
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Siddarth Achar
Carnegie Mellon University
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John Kitchin
Carnegie Mellon University
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Abstract

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.

Peer review status:UNDER REVIEW

27 Jul 2021Submitted to AIChE Journal
29 Jul 2021Assigned to Editor
29 Jul 2021Submission Checks Completed
09 Aug 2021Reviewer(s) Assigned