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

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.

► A general methodology for developing optimal neural networks is presented.
► The proposed neuro-evolutive method combines NSGA-II and Quasi-Newton algorithms.
► The case study is an electrodeposition process for the recovery of heavy metals.
► A data set with experimental data obtained in our laboratory is used for modelling.
► Experimental data validated the modelling results with a correlation of 0.99.