Neuro-evolutionary approach applied for optimizing the PEMFC performance

A multi-objective optimization strategy, based on stacked neural network–genetic algorithm (SNN–GA) hybrid approach, was applied to study the C/PBI content on a high temperature PEMFC performance. The operating conditions of PEMFC were correlated with power density and electrochemical active surface area for electrodes. The structure of the stack was determined in an optimal form related to the contribution of individual neural networks, after applying an interpolation based procedure. Multi-objective optimization using SNN as model and GA as solving procedure provides optimal working conditions which lead to a high PEMFC performance. Simulation results were in agreement with experimental data, both for model validation and system optimization (the C/PBI content in the range of 17–21%).     

Photodegradation process of Eosin Y using ZnO/SnO2 nanocomposites as photocatalysts: experimental study and neural network modeling

A series of coupled ZnO/SnO₂ nanocomposites were prepared with different molar ratios (1:10, 1:2, 2:1, and 10:1), using a homogeneous co-precipitation method. The structural properties were evaluated by different techniques: XRD, UVDR, SEM, N₂ adsorption, and IR. The photocatalytic activity of the samples was tested with the main goal of Eosin Y degradation from wastewaters. The prepared nanocomposites/systems exhibit higher photocatalytic activity than a single semiconductor photocatalyst and ZnO can effectively improve the photocatalytic efficiency of SnO₂ under UV illumination. A direct neural network modeling methodology, based on feed-forward neural networks, was performed in order to evaluate the efficiency of the photodegradation process of Eosin Y, depending of the reaction conditions. The developed model considered the following parameters with significant influence on the approached process: crystallite size, surface area, absorbtion edge, TOC values, time of reaction, and catalyst concentration as inputs and the final dye concentration as output. Accurate results were obtained in the validation phase of the neural model: relative average error under 4 % and a correlation between experimental and simulation data of 0.999.     

Ten steps modeling of electrolysis processes by using neural networks

Neural networks have been developed to model the electrolysis of wastes polluted with phenolic compounds, including phenol, 4-chlorophenol, 2,4-dichlorophenol, 2,4,6-trichlorophenol, 4-nitrophenol and 2,4-dinitrophenol. They enable the prediction the Chemical Oxygen Demand of a treated waste as a function of the initial characteristics (pollutant concentration, pH), operation conditions (temperature, current density) and current charge passed. A consistent set of experimental data was obtained by electrochemical oxidation with conductive diamond electrodes, used to treat synthetic aqueous wastes.Several modeling strategies based on simple and stacked neural networks, with different transfer functions into the hidden and output layers, have been considered to obtain a good accuracy of the model. Global errors during the training stage were under 3% and those of the validation stage were under 4%, demonstrating that the neural network based technique is appropriate for modeling the system.The generalization capability of the neural networks was also tested in realistic conditions where Chemical Oxygen Demand was predicted with errors around 5%. Therefore, the developed neural models can be used in industry to determine the required treatment period, to obtain the discharge limits in batch electrolysis processes, and it is a first step in the development of process control strategies.The ten step methodology was applied to the neural network based process modeling.     

Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties.