Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
Inst Chemical Engineers
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
This work aims to analyze past publications on direct alcohol fuel cells (DAFC) in the literature using two data mining tools (artificial neural networks and decision trees) and to develop global models to predict the conditions leading to high performance of DAFC. The database constructed for this purpose contains 4682 data points over 271 polarization (IV) curves obtained from 36 publications in the literature. Decision tree classification models were used to develop heuristics to select the suitable fuel cell design and operational conditions to improve the maximum power density while artificial neural network models (ANN) were developed to test the predictability of IV curves at the conditions where experimental results were not available. The same ANN models were also used to determine the relative importance of design and operational variables to provide some insight to determine the variable to be manipulated. All these analyses were quite successful deducing some useful heuristics and models for the future studies from the continuously growing experience accumulated in the literature. (C) 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Description
Keywords
Direct Alcohol Fuel Cells, Data Mining, Knowledge Extraction, Artificial Neural Networks, Decision Trees, Artificial Neural-Network, Selective Co Oxidation, Noble-Metal Catalysts, Platinum-Based Anodes, Knowledge Extraction, Statistical-Analysis, Semiempirical Model, Methanol Crossover, Part I, Performance
Journal or Series
Chemical Engineering Research & Design
WoS Q Value
Q2
Scopus Q Value
Q2
Volume
105
