Paper Info
Title
Neuro-evolutionary modelling of the electrodeposition stage of a polymer-supported ultrafiltration-electrodeposition process for the recovery of heavy metals.
Abstract
This paper presents a neuro-evolutionary modelling methodology applied to an electrodeposition process for the recovery of copper and zinc. This technique consists in designing the optimal neural network model using an algorithm obtained through the combination of a multi-objective evolutionary algorithm (NSGA-II) and a local search algorithm (Quasi-Newton). Parametric and structural optimization for feed-forward neural networks are performed determining the optimum number of hidden layers and hidden neurons, the optimum weights and the most appropriate activation functions for the hidden and output layers. Accurate results are obtained in the modelling procedure, with the possibility to choose the adequate model, representing a compromise between performance and complexity. Significant information is obtained by simulation, related to the rate and quality of the electrodeposition process depending of the working conditions. The highest accuracy of the model is obtained for the prediction of copper and zinc concentrations (the most important output variables), a promising result to use the proposed model for the future optimization of the process. Moreover, due to the very different behaviour of copper and zinc in the electrodeposition process, the proposed model could be also successfully used for a wide variety of heavy metal ions.
Year
DOI
Venue
2013
10.1016/j.envsoft.2013.01.001
Environmental Modelling and Software
Keywords
Field
DocType
neuro-evolutionary modelling,electrodeposition process,hidden layer,optimal neural network model,feed-forward neural network,heavy metal,zinc concentration,hidden neuron,adequate model,polymer-supported ultrafiltration-electrodeposition process,multi-objective evolutionary algorithm,local search algorithm,electrodeposition stage,evolutionary algorithm,neural network,water treatment,ultrafiltration
Evolutionary algorithm,Biological system,Computer science,Artificial intelligence,Artificial neural network,Zinc,Management science,Copper,Polymer,Ultrafiltration,Parametric statistics,Local search (optimization),Machine learning
Journal
Volume
ISSN
Citations 
42
1364-8152
0
PageRank 
References 
Authors
0.34
26
5
Name
Order
Citations
PageRank
Javier Llanos100.34
Manuel A. Rodrigo200.34
Pablo CañIzares363.48
Renata Popa Furtuna400.34
Silvia Curteanu5636.26