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Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks
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  • Journal title : Korean Chemical Engineering Research
  • Volume 53, Issue 2,  2015, pp.236-242
  • Publisher : The Korean Institute of Chemical Engineers
  • DOI : 10.9713/kcer.2015.53.2.236
 Title & Authors
Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks
Han, In-Su; Shin, Hyun Khil;
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We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.
Fuel Cell;Polymer Electrolyte Membrane;Back-box Modeling;Partial Least Squares;Artificial Neural Networks;
 Cited by
PCBs 독성 예측을 위한 주요 분자표현자 선택 기법 및 계산독성학 기반 QSAR 모델 개발,김동우;이승철;김민정;이은지;유창규;

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