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Mathematical Modeling of Alkaline Methanol Oxidation for Design of Efficient Fuel Cells

  • Tanja Clees
  • Igor Nikitin
  • Lialia NikitinaEmail author
  • Sabine Pott
  • Ulrike Krewer
  • Theresa Haisch
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 947)

Abstract

This paper considers the electrochemical kinetic model of alkaline methanol oxidation, the process, relevant for the design of efficient fuel cells. Fuel cells of direct methanol type have a great advantage in safety and storage compared to hydrogen-oxygen fuel cells. They possess high energy density and are especially suitable for portable applications. The oxidation of fuel in an alkaline medium allows the use of affordable electrodes.

The mathematical model of the oxidation process includes a system of non-linear differential equations of high order, describing elementary reactions. The model also possesses 14 unknown reaction constants and 6 dynamic variables describing surface coverages of the intermediates. These variables cannot be measured directly, but can be reconstructed by the parameter identification procedure. The procedure comprises numerical integration of the system of differential equations, automatic global minimization of the distance between measured and modeled cyclic voltammograms, iterative Monte Carlo search and interactive parameter study.

The developed methods have been applied to 9 cyclic voltammograms of cells with different concentrations of alkaline and fuel. The reaction constants have been reconstructed, their dependence on concentrations has been discussed. Dynamic behavior of the system in form of the reconstructed evolution of the intermediates has been presented.

Keywords

Complex systems modeling and simulation Non-linear optimization Parameter identification Application in electrochemistry 

Notes

Acknowledgment

The authors are grateful to the organizers and participants of SIMULTECH 2018 conference for fruitful discussions. The authors thank also Kira Konich and Kevin Reinartz for proofreading the paper. The work has been partially supported by German Federal Ministry for Economic Affairs and Energy, project BMWI-0324019A, MathEnergy: Mathematical Key Technologies for Evolving Energy Grids and by the German Bundesland North Rhine-Westphalia using fundings from the European Regional Development Fund, grant Nr. EFRE-0800063, project ES-FLEX-INFRA.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tanja Clees
    • 1
    • 2
  • Igor Nikitin
    • 2
  • Lialia Nikitina
    • 2
    Email author
  • Sabine Pott
    • 2
  • Ulrike Krewer
    • 3
  • Theresa Haisch
    • 3
  1. 1.University of Applied SciencesSankt AugustinGermany
  2. 2.Fraunhofer Institute for Algorithms and Scientific ComputingSankt AugustinGermany
  3. 3.Institute of Energy and Process Systems EngineeringTechnical UniversityBraunschweigGermany

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