Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.

Gas-assisted magnetic separation for the purification of proteins in batch systems

In this paper, gas-assisted magnetic separation （GAMS）, a technique that combines magnetic separation with flotation, was investigated for the potential large-scale separation of proteins, The GAMS process includes adsorption of target proteins and magnetic separation to recover protein-loaded magnetic particles from the dilute biosuspension with the assistance of bubbles, Microsized ethylenediamine- functionalized poly（glycidyl methacrylate） superparamagnetic microspheres （MPMs） and bovine serum albumin （BSA） were used as a model system. The feasibility of GAMS for capturing BSA-loaded MPMs from an appropriate medium was shown, High recovery of BSA-loaded MPMs was obtained by simple adjustment of the initial solution pH without extra detergents and antifoaming agents. The GAMS con- ditions were consistent with the adsorption conditions, and no proteins were desorbed from the MPMs during this process. Under the optimal conditions, the separation rate and recovery percentage reached 410 mL/min and 98% in 0.61 min, respectively. Conformational changes of BSA during the GAMS process were investigated by fluorescence spectroscopy and circular dichroism spectrometry,