Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells
Küçük Resim Yok
Tarih
2016
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inst Chemical Engineers
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
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
Kaynak
Chemical Engineering Research & Design
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
105
