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An automated computational approach to kinetic model discrimination and parameter estimation.

Connor J Taylor1, Hikaru Seki2, Friederike M Dannheim2

  • 1Institute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds Leeds LS2 9JT UK t.w.chamberlain@leeds.ac.uk R.A.Bourne@leeds.ac.uk.

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This study introduces an automated computational method for identifying chemical reaction networks (CRNs). The autonomous tool determines kinetic models and parameters from species and concentration data with minimal human input.

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Area of Science:

  • Chemical kinetics
  • Computational chemistry
  • Reaction network analysis

Background:

  • Chemical reaction network (CRN) identification is crucial for understanding chemical processes.
  • Accurate kinetic models and parameters are essential for process optimization and development.
  • Current methods often require significant human intervention and expertise.

Purpose of the Study:

  • To present a novel, automated computational approach for CRN identification.
  • To demonstrate the first chemical applications of an autonomous tool for kinetic model and parameter determination.
  • To showcase the method's ability to handle complex rate laws, including non-integer orders and catalytic species.

Main Methods:

  • Inputting chemical species and time-series concentration data into the autonomous tool.
  • Utilizing computational algorithms to identify the kinetic model and rate law parameters.
  • Performing minimal human interaction during the kinetic analysis process.

Main Results:

  • Successful identification of kinetic models and parameters for various chemical systems.
  • Demonstrated applicability across diverse chemical scenarios, including those with catalytic species and complex rate orders.
  • Validation of the automated approach using experimental data from multiple sources.

Conclusions:

  • The developed automated computational approach provides an efficient and robust method for CRN identification.
  • This tool significantly reduces human interaction, accelerating process development.
  • The open-source availability of the code promotes wider adoption and further research in chemical kinetics.