Mathematical Modeling of Alkaline Methanol Oxidation for Design of Efficient Fuel Cells
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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.
KeywordsComplex systems modeling and simulation Non-linear optimization Parameter identification Application in electrochemistry
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.
- 1.Clees, T., Nikitin, I., Nikitina, L., Pott, S., Krewer, U., Haisch, T.: Parameter identification in cyclic voltammetry of alkaline methanol oxidation. In: Proceedings of the SIMULTECH 2018, Porto, Portugal, 29–31 July 2018, pp. 279–288 (2018)Google Scholar
- 6.Gamry Instruments, Basics of Electrochemical Impedance Spectroscopy, online tutorial. http://www.gamry.com/application-notes/EIS/basics-of-electrochemical-impedance-spectroscopy
- 7.Clees, T., Nikitin, I., Nikitina, L., Steffes-lai, D., Pott, S., Krewer, U., Windorfer, T.: Electrochemical impedance spectroscopy of alkaline methanol oxidation. In: Proceedings of the INFOCOMP 2017, The Seventh International Conference on Advanced Communications and Computation, pp. 46–51, IARIA (2017)Google Scholar
- 8.Bard, A.J., Faulkner, L.R.: Electrochemical Methods: Fundamentals and Applications. Wiley, Hoboken (2000)Google Scholar
- 10.Strutz, T.: Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond. Springer (2016)Google Scholar
- 12.Nikitin, I., Nikitina, L., Clees, T.: Stochastic analysis and nonlinear metamodeling of crash test simulations and their application in automotive design. In: Browning, J.E. (ed.) Computational Engineering: Design, Development, and Applications, pp. 51–74. Nova Science Publishers, New York (2012)zbMATHGoogle Scholar
- 14.Clees, T., Nikitin, I., Nikitina, L., Pott, S.: Quasi-Monte Carlo and RBF metamodeling for quantile estimation in river bed morphodynamics. In: Obaidat, M.S., et al. (eds.) Simulation and Modeling Methodologies, Technologies and Applications. Advances in Intelligent Systems and Computing, vol. 319, pp. 211–222. Springer, Cham (2014)CrossRefGoogle Scholar
- 17.Otten, R.H.J.M., van Ginneken, L.P.P.P.: The Annealing Algorithm. Kluwer (1989)Google Scholar
- 19.Mathematica 11, Reference Manual. http://reference.wolfram.com