Fuel cells have lately received growing attention since they allow the use of non-precious metals as catalysts, which reduce the cost per kilowatt of power in fuel cell devices to some extent. Until recent years, the major barrier in the development of fuel cells was the obtainability of highly conductive anion exchange membranes (AEMs). On the other hand, improvements show that newly enhanced anion exchange membranes have already reached high conductivity levels, leading to the suitable presentation of the cell. Currently, an increasing number of studies have described the performance results of fuel cells. Much of the literature reporting cell performance is founded on hydrogen‒anion exchange membrane fuel cells (AEMFCs), though a growing number of studies have also reported utilizing fuels other than hydrogen—such as alcohols, non-alcohol C-based fuels, and N-based fuels. This article reviews the types, performance, utilized membranes, and operational conditions of anion exchange membranes for fuel cells.
In this study, an artificial neural network (ANN) model was developed and compared with a rigorous mathematical model (RMM) to estimate the performance of an industrial heavy naphtha reforming process. The ANN model, represented by a multilayer feed forward neural network (MFFNN), had (36-10-10-10-34) topology, while the RMM involved solving 34 ordinary differential equations (ODEs) (32 mass balance, 1 heat balance and 1 momentum balance) to predict compositions, temperature, and pressure distributions within the reforming process. All computations and predictions were performed using MATLAB® software version 2015a. The ANN topology had minimum MSE when the number of hidden layers, number of neurons in the hidden layer, and the number of training epochs were 3, 10, and 100,000, respectively. Extensive error analysis between the experimental data and the predicted values were conducted using the following error functions: coefficient of determination (R2), mean absolute error (MAE), mean relative error (MRE), and mean square error (MSE). The results revealed that the ANN (R2 = 0.9403, MAE = 0.0062) simulated the industrial heavy naphtha reforming process slightly better than the rigorous mathematical model (R2 = 0.9318, MAE = 0.007). Moreover, the computational time was obviously reduced from 120 s for the RMM to 18.3 s for the ANN. However, one disadvantage of the ANN model is that it cannot be used to predict the process performance in the internal points of reactors, while the RMM predicted the internal temperatures, pressures and weight fractions very well.
The liquid-phase hydrogenation of cinnamaldehyde over a Pt/SiO2 catalyst was investigated experimentally and theoretically. The experiments were conducted in a 300 cm3 stainless steel stirred batch reactor supplied with hydrogen gas and ethanol as a solvent. Five Langmuir–Hinshelwood kinetic models were investigated to fit the experimental data. The predictions from the bulk model were compared with predictions from the intraparticle diffusion model. Competitive and non-competitive mechanisms were applied to produce the main intermediate compound, cinnamyl alcohol. Reaction rate parameters for the different reaction steps were calculated by comparing between the experimental and mathematical models. All rate data utilized in the present study were obtained in the kinetic regime. The kinetic parameters were obtained by applying a nonlinear dynamic optimization algorithm. Nevertheless, the comparison between the methodology of the present model and these five models indicated that the non-competitive mechanism is more acceptable and identical with the single-site Langmuir–Hinshelwood kinetic model including mass transfer effects and it mimicked the reactant behavior better than the other models. In addition, the observed mean absolute error (MAE) for the non-competitive mechanism of the present model was 2.3022 mol/m3; however, the MAE for the competitive mechanism was 2.8233 mol/m3, which is an increase of approximately 18%. The prediction of the intraparticle diffusion model was found to be very close to that of the bulk model owing to the use of a catalyst with a very small particle size (<40 microns). Employing a commercial 5% Pt/SiO2 catalyst showed a result consistent with previous research using different catalysts, with an activation energy of ≈24 kJ/mol.
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