Pulse response analysis using the Y-procedure: A data science approach

Kunz, M. Ross; Borders, Tammie; Redekop, Evgeniy; Yablonsky, Gregory S.; Constales, Denis; Wang, Lucun; Fushimi, Rebecca

doi:10.1016/j.ces.2018.06.078

Title: Pulse response analysis using the Y-procedure: A data science approach

Journal Article · Sat Jun 30 00:00:00 EDT 2018 · Chemical Engineering Science

DOI:https://doi.org/10.1016/j.ces.2018.06.078· OSTI ID:1478468

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Idaho National Lab. (INL), Idaho Falls, ID (United States). Energy & Environment Science and Technology; Center for Advanced Energy Studies, Idaho Falls, ID (United States)
Idaho National Lab. (INL), Idaho Falls, ID (United States). Energy & Environment Science and Technology
Univ. of Oslo (Norway). Centre for Materials Science and Nanotechnology (SMN), Department of Chemistry
Washington Univ., St. Louis, MO (United States). Department of Chemical Engineering
Ghent University (Belgium). Department of Mathematical Analysis

We report that pulse response experiments can provide a wealth of data for transient kinetic studies of gas phase heterogeneous reactions, with the ability to paint a detailed picture of intrinsic reaction kinetics pertaining to complex catalytic mechanisms. A novel framework called the Y-Procedure was developed by Yablonsky et al. to extract information from non-steady-state kinetic data (Yablonsky et al., 2007). The Y-Procedure reconstructs the chemical transformation rate and gas composition in the catalytic zone with no a priori assumptions on the kinetic model and is used to relate changes in catalytic reaction rates and kinetic parameters to changes in a catalyst’s surface composition. This paper provides a detailed description of the data science work flow implemented in the Y-procedure through an experimental example of oxygen adsorption on platinum. Lastly, this powerful method, along with a robust list of pre-processing methodology, will enable deeper understanding of kinetic processes and mechanism to be derived from the TAP pulse response technique.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1478468

Alternate ID(s):: OSTI ID: 1495270

Report Number(s):: INL/JOU-18-44371-Rev001

Journal Information:: Chemical Engineering Science, Vol. 192, Issue C; ISSN 0009-2509

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning Kunz, M. Ross; Yonge, Adam; Fang, Zongtang arXiv https://doi.org/10.48550/arxiv.2011.08810	preprint	January 2020

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