Neuro-evolutionary modelling of the electrodeposition stage of a polymer-supported ultrafiltration-electrodeposition process for the recovery of heavy metals

Authors:
Javier Llanos;Manuel A. Rodrigo;Pablo CañIzares;Renata Popa Furtuna;Silvia Curteanu
Affiliations:
Chemical Engineering Department, Faculty of Chemical Sciences, University of Castilla-La Mancha, Edificio Enrique Costa Novella, Avda. Camilo José Cela 12, 13071 Ciudad Real, Spain;Chemical Engineering Department, Faculty of Chemical Sciences, University of Castilla-La Mancha, Edificio Enrique Costa Novella, Avda. Camilo José Cela 12, 13071 Ciudad Real, Spain;Chemical Engineering Department, Faculty of Chemical Sciences, University of Castilla-La Mancha, Edificio Enrique Costa Novella, Avda. Camilo José Cela 12, 13071 Ciudad Real, Spain;Department of Chemical Engineering, "Gheorghe Asachi" Technical University of Iasi, Str. Prof. Dr. Doc. Dimitrie Mageron, No. 73, 700050 Iasi, Romania;Department of Chemical Engineering, "Gheorghe Asachi" Technical University of Iasi, Str. Prof. Dr. Doc. Dimitrie Mageron, No. 73, 700050 Iasi, Romania
Venue:
Environmental Modelling & Software
Year:
2013

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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.