Neural networks-based modeling applied to a process of heavy metals removal from wastewaters

Abstract

This article approaches the problem of environment pollution with heavy metals from disposal of industrial wastewaters, namely removal of these metals by means of biosorbents, particularly with Romanian peat (from Poiana Stampei). The study is carried out by simulation using feed-forward and modular neural networks with one or two hidden layers, pursuing the influence of certain operating parameters (metal nature, sorbent dose, pH, temperature, initial concentration of metal ion, contact time) on the amount of metal ions retained on the unit mass of sorbent. In neural network modeling, a consistent data set was used, including five metals: lead, mercury, cadmium, nickel and cobalt, the quantification of the metal nature being done by its electronegativity. Even if based on successive trials, the method of designing neural models was systematically conducted, recording and comparing the errors obtained with different types of neural networks, having various numbers of hidden layers and neurons, number of training epochs, or using various learning methods. The errors with values under 5% make clear the efficiency of the applied method.

Keywords:

• Heavy metals
• wastewaters
• neural networks
• modeling
• optimization

Acknowledgments

This work was supported by the “Partnership in priority areas-PN-II” program, financed by ANCS, CNDI-UEFISCDI, project PN-II-PT-PCCA-2011-3.2-0732, No. 23/2012.