Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural NetworkSource: Journal of Electrochemical Energy Conversion and Storage:;2017:;volume( 014 ):;issue: 003::page 31008DOI: 10.1115/1.4036811Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: A direct methanol fuel cell (DMFC) converts liquid fuel into electricity to power devices, while operating at relatively low temperatures and producing virtually no greenhouse gases. Since DMFC performance characteristics are inherently complex, it can be postulated that artificial neural networks (NN) represent a marked improvement in prediction capabilities. In this work, an artificial NN is employed to predict the performance of a DMFC under various operating conditions. Input variables for the analysis consist of methanol concentration, temperature, current density, number of cells, and anode flow rate. The addition of the two latter variables allows for a more distinctive model when compared to prior NN models. The key performance indicator of our NN model is cell voltage, which is an average voltage across the stack and ranges from 0 to 0.8 V. Experimental studies were conducted using DMFC stacks with membrane electrode assemblies consisting of an additional unique liquid barrier layer to minimize water loss to atmosphere. To determine the best fit to the experimental data, the model is trained using two second-order training algorithms: OWO-Newton and Levenberg–Marquardt (LM). The topology of OWO-Newton algorithm is slightly different from that of LM algorithm by employing bypass weights. The application of NN shows rapid construction of a predictive model of cell voltage for varying operating conditions with an accuracy on the order of 10−4, which can be comparable to literature. The coefficient of determination of the optimal model results using either algorithm were greater than 0.998.
|
Show full item record
contributor author | Rafe Biswas, M. A. | |
contributor author | Robinson, Melvin D. | |
date accessioned | 2017-11-25T07:20:59Z | |
date available | 2017-11-25T07:20:59Z | |
date copyright | 2017/21/6 | |
date issued | 2017 | |
identifier issn | 2381-6872 | |
identifier other | jeecs_014_03_031008.pdf | |
identifier uri | http://138.201.223.254:8080/yetl1/handle/yetl/4236812 | |
description abstract | A direct methanol fuel cell (DMFC) converts liquid fuel into electricity to power devices, while operating at relatively low temperatures and producing virtually no greenhouse gases. Since DMFC performance characteristics are inherently complex, it can be postulated that artificial neural networks (NN) represent a marked improvement in prediction capabilities. In this work, an artificial NN is employed to predict the performance of a DMFC under various operating conditions. Input variables for the analysis consist of methanol concentration, temperature, current density, number of cells, and anode flow rate. The addition of the two latter variables allows for a more distinctive model when compared to prior NN models. The key performance indicator of our NN model is cell voltage, which is an average voltage across the stack and ranges from 0 to 0.8 V. Experimental studies were conducted using DMFC stacks with membrane electrode assemblies consisting of an additional unique liquid barrier layer to minimize water loss to atmosphere. To determine the best fit to the experimental data, the model is trained using two second-order training algorithms: OWO-Newton and Levenberg–Marquardt (LM). The topology of OWO-Newton algorithm is slightly different from that of LM algorithm by employing bypass weights. The application of NN shows rapid construction of a predictive model of cell voltage for varying operating conditions with an accuracy on the order of 10−4, which can be comparable to literature. The coefficient of determination of the optimal model results using either algorithm were greater than 0.998. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network | |
type | Journal Paper | |
journal volume | 14 | |
journal issue | 3 | |
journal title | Journal of Electrochemical Energy Conversion and Storage | |
identifier doi | 10.1115/1.4036811 | |
journal fristpage | 31008 | |
journal lastpage | 031008-7 | |
tree | Journal of Electrochemical Energy Conversion and Storage:;2017:;volume( 014 ):;issue: 003 | |
contenttype | Fulltext |