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Statistical and Principal Component Analysis in the Design of Alkaline Methanol Fuel Cells

  • In this paper, the electrochemical alkaline methanol oxidation process, which is relevant for the design of efficient fuel cells, is considered. An algorithm for reconstructing the reaction constants for this process from the experimentally measured polarization curve is presented. The approach combines statistical and principal component analysis and determination of the trust region for a linearized model. It is shown that this experiment does not allow one to determine accurately the reaction constants, but only some of their linear combinations. The possibilities of extending the method to additional experiments, including dynamic cyclic voltammetry and variations in the concentration of the main reagents, are discussed.

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Metadaten
Document Type:Conference Object
Language:English
Author:Tanja Clees, Bernhard Klaassen, Igor Nikitin, Lialia Nikitina, Sabine Pott
Parent Title (English):Dini, Pyshkin et al. (Eds.): ADVCOMP 2021. Fifteenth International Conference on Advanced Engineering Computing and Applications in Sciences, October 3-7, 2021, Barcelona, Spain
Number of pages:5
First Page:1
Last Page:5
ISBN:978-1-61208-887-7
URL:https://www.thinkmind.org/index.php?view=article&articleid=advcomp_2021_1_10_20008
Publisher:IARIA
Date of first publication:2021/10/03
Copyright:(c) IARIA, 2021
Keyword:advanced applications; mathematical chemistry; modeling of complex systems; observational data and simulations
Departments, institutes and facilities:Fachbereich Ingenieurwissenschaften und Kommunikation
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Entry in this database:2023/01/03