scholarly journals Radiolytic Synthesis of Vinyl Polymer-Clay Nanocomposite Membranes for Direct Methanol Fuel Cell

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Yoon-Seob Kim ◽  
Yun Ok Kang ◽  
Seong-Ho Choi
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Exchange Capacity ◽  
Nanocomposite Membrane ◽  
Nanocomposite Membranes ◽  
Polymer Clay Nanocomposite ◽  
Direct Methanol ◽  
Vinyl Polymer ◽  
Methanol Fuel Cell

The three-type vinyl polymer-clay nanocomposite membranes with sulfonate (–SO3Na) are prepared by the solvent casting method after radiation-induced copolymerization for application of the direct methanol fuel cell (DMFC) membrane. The three-type vinyl polymers in polymer-clay nanocomposite membranes are included in poly(styrene-co-sodium styrene sulfonate), poly(St-co-NaSS), poly(2-hydroxyethyl methacrylate-co-NaSS), poly(HEMA-co-NaSS), and poly(acrylic acid-co-NaSS), and poly(AAc-co-NaSS). The proton conductivity (S/cm), water uptake (%), and ion-exchange capacity (meq/g) of the poly(St-co-NaSS)-clay nanocomposite membrane are 0.0779, 32.4, 3.63, respectively. The MeOH permeability of the poly(St-co-NaSS)-clay nanocomposite membrane is exhibited as7.7×10−9 mmol·cm−2·s−1. DMFC performance for poly(St-co-NaSS)-clay nanocomposite membrane is also measured in cell voltage and power density verses current density. As results, the poly(St-co-NaSS)-clay nanocomposite membrane can be used as a DMFC membrane on behalf of the commercial Nafion membrane.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL

2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG
Keyword(s):  
Fuel Cell ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.

scholarly journals A Review on the Fabrication of Electrospun Polymer Electrolyte Membrane for Direct Methanol Fuel Cell

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Hazlina Junoh ◽  
Juhana Jaafar ◽  
Muhammad Noorul Anam Mohd Norddin ◽  
Ahmad Fauzi Ismail ◽  
Mohd Hafiz Dzarfan Othman ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
Polymer Matrix ◽  
Direct Methanol Fuel Cell ◽  
Proton Exchange ◽  
Nanocomposite Membrane ◽  
Inorganic Filler ◽  
Important Indicator ◽  
Direct Methanol ◽  
Methanol Fuel Cell

Proton exchange membrane (PEM) is an electrolyte which behaves as important indicator for fuel cell’s performance. Research and development (R&D) on fabrication of desirable PEM have burgeoned year by year, especially for direct methanol fuel cell (DMFC). However, most of the R&Ds only focus on the parent polymer electrolyte rather than polymer inorganic composites. This might be due to the difficulties faced in producing good dispersion of inorganic filler within the polymer matrix, which would consequently reduce the DMFC’s performance. Electrospinning is a promising technique to cater for this arising problem owing to its more widespread dispersion of inorganic filler within the polymer matrix, which can reduce the size of the filler up to nanoscale. There has been a huge development on fabricating electrolyte nanocomposite membrane, regardless of the effect of electrospun nanocomposite membrane on the fuel cell’s performance. In this present paper, issues regarding the R&D on electrospun sulfonated poly (ether ether ketone) (SPEEK)/inorganic nanocomposite fiber are addressed.

Effect of Anode and Cathode Current-Collector Structure on Performance of a Passive Direct Methanol Fuel Cell

2011 ◽  
Vol 347-353 ◽  
pp. 3275-3280 ◽  
Author(s):  
Xian Qi Cao ◽  
Ji Tian Han ◽  
Ze Ting Yu ◽  
Pei Pei Chen
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Current Collector ◽  
Cell Voltage ◽  
Methanol Concentration ◽  
Membrane Area ◽  
Fuel Cell Performance ◽  
Direct Methanol ◽  
Cathode Current ◽  
Methanol Fuel Cell

In this work, the effect of the current-collector structure on the performance of a passive direct methanol fuel cell (DMFC) was investigated. Parallel current-collector (PACC) and other two kinds of perforated current collectors (PECC) were designed, fabricated and tested. The studies were conducted in a passive DMFC with active membrane area of 9 cm2, working at ambient temperature and pressure. Two kinds of methanol solution of 2 M and 4 M were used. Results showed that the PACC as anode current-collector has a positive effect on cell voltage and power. For the cathode current-collector structure, the methanol concentration of 2 M for PECC-2 (higher open ratio 50.27 %) increased performance of DMFC. But the methanol concentration of 4 M led to an enhancement of fuel cell performance that used PACC or PECC-2 as cathode current-collector.

Porous layered oxide/Nafion® nanocomposite membranes for direct methanol fuel cell applications

2009 ◽  
Vol 118 (1-3) ◽  
pp. 427-434 ◽  
Author(s):  
Yeny Hudiono ◽  
Sunho Choi ◽  
Shu Shu ◽  
William J. Koros ◽  
Michael Tsapatsis ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Nanocomposite Membranes ◽  
Layered Oxide ◽  
Direct Methanol ◽  
Methanol Fuel Cell
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