A New Feasible Opportunity for Recycling Lead and Silver from Zinc Plant Residues by Flotation.

Millions of tons of zinc plant leach residues (ZPLR) have been stockpiled in Iranian hydrometallurgical zinc plants during the last few decades. Due to the low grades of zinc, lead, and silver in these residues, these residues have been abandoned without treatment. The authors of this paper studied zinc plant leach residues (ZPLR) to propose a flotation process for separating and producing lead and silver concentrate.

Effect of hydrodynamic parameters on nickel removal rate from wastewater by ion flotation.

This study investigated the effect of hydrodynamic parameters on the nickel ion removal in an oscillating grid flotation cell (OGC) with near ideal hydrodynamic environments. Nickel ion was removed in the OGC at various energy inputs (0-2 W/kg), using two bubble sizes (130 and 820 μm) at three surfactant concentrations (SDS/Ni(II) ratio of 1-3) and three air flow rates (1-3 L/min). The results indicated that the energy input has a considerable effect on the ion flotation kinetics and recovery and it is strongly dependent on the cell types (contact environment) and bubble size.

The main factors effecting the efficiency of Zn(II) flotation: Optimum conditions and separation mechanism.

In this study, the effects of chemical conditions on the recovery of Zn(II) and water during the ion flotation process were evaluated using a central composite design (CCD) of response surface methodology. The optimum effective parameters including pH, collector and frother concentration were determined. The results showed that the pH and collector concentration were effective factors for the efficiency of Zn(II) flotation.

The nickel ion removal prediction model from aqueous solutions using a hybrid neural genetic algorithm.

Prediction of Ni(II) removal during ion flotation is necessary for increasing the process efficiency by suitable modeling and simulation. In this regard, a new predictive model based on the hybrid neural genetic algorithm (GANN) was developed to predict the Ni(II) ion removal and water removal during the process from aqueous solutions using ion flotation. A multi-layer GANN model was trained to develop a predictive model based on the important effective variables on the Ni(II) ion flotation.