Development of a novel computational tool for optimizing the operation of fuel cells systems: Application for phosphoric acid fuel cells

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dc.contributor.author Zervas, PL en
dc.contributor.author Tatsis, A en
dc.contributor.author Sarimveis, H en
dc.contributor.author Markatos, NCG en
dc.date.accessioned 2014-03-01T01:28:09Z
dc.date.available 2014-03-01T01:28:09Z
dc.date.issued 2008 en
dc.identifier.issn 0378-7753 en
dc.identifier.uri http://hdl.handle.net/123456789/18731
dc.subject CFD en
dc.subject Fuel cells en
dc.subject Modeling en
dc.subject Neural networks en
dc.subject Optimization en
dc.subject.classification Electrochemistry en
dc.subject.classification Energy & Fuels en
dc.subject.other Acids en
dc.subject.other Applications en
dc.subject.other Computational fluid dynamics en
dc.subject.other Computer networks en
dc.subject.other Cytology en
dc.subject.other Database systems en
dc.subject.other Dynamics en
dc.subject.other Electric batteries en
dc.subject.other Electrochemistry en
dc.subject.other Feedforward neural networks en
dc.subject.other Fluid dynamics en
dc.subject.other Fluid mechanics en
dc.subject.other Fuel cells en
dc.subject.other Linear programming en
dc.subject.other Linearization en
dc.subject.other Multiobjective optimization en
dc.subject.other Neural networks en
dc.subject.other Nonlinear programming en
dc.subject.other Optimization en
dc.subject.other Oxidants en
dc.subject.other Phosphoric acid en
dc.subject.other Phosphoric acid fuel cells (PAFC) en
dc.subject.other Radial basis function networks en
dc.subject.other Regression analysis en
dc.subject.other Solid oxide fuel cells (SOFC) en
dc.subject.other Statistical tests en
dc.subject.other Three dimensional en
dc.subject.other CFD en
dc.subject.other Modeling en
dc.subject.other Volumetric rates en
dc.subject.other Fuels en
dc.title Development of a novel computational tool for optimizing the operation of fuel cells systems: Application for phosphoric acid fuel cells en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.jpowsour.2008.06.081 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.jpowsour.2008.06.081 en
heal.language English en
heal.publicationDate 2008 en
heal.abstract Fuel cells offer a significant and promising clean technology for portable, automotive and stationary applications and, thus, optimization of their performance is of particular interest. In this study, a novel optimization tool is developed that realistically describes and optimizes the performance of fuel cell systems. First, a 3D steady-state detailed model is produced based on computational fluid dynamics (CFD) techniques. Simulated results obtained from the CFD model are used in a second step, to generate a database that contains the fuel and oxidant volumetric rates and utilizations and the corresponding cell voltages. In the third step mathematical relationships are developed between the input and output variables, using the database that has been generated in the previous step. In particular, the linear regression methodology and the radial basis function (RBF) neural network architecture are utilized for producing the input-output ""meta-models"". Several statistical tests are used to validate the proposed models. Finally, a multi-objective hierarchical Non-Linear Programming (NLP) problem is formulated that takes into account the constraints and limitations of the system. The multi-objective hierarchical approach is built upon two steps: first, the fuel volumetric rate is minimized, recognizing the fact that our first concern is to reduce consumption of the expensive fuel. In the second step, optimization is performed with respect to the oxidant volumetric rate. The proposed method is illustrated through its application for phosphoric acid fuel cell (PAFC) systems. © 2008 Elsevier B.V. All rights reserved. en
heal.publisher ELSEVIER SCIENCE BV en
heal.journalName Journal of Power Sources en
dc.identifier.doi 10.1016/j.jpowsour.2008.06.081 en
dc.identifier.isi ISI:000259906600048 en
dc.identifier.volume 185 en
dc.identifier.issue 1 en
dc.identifier.spage 345 en
dc.identifier.epage 355 en

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