Pyrite flotation in the presence of galena. Study on galvanic interaction

In this investigation, galvanic interaction between galena and pyrite in flotation and its effect on floatability of pyrite were studied. Rest and mixed potential studies in the presence and absence of a collector indicated that pyrite was nobler than galena under all investigated conditions. Therefore, pyrite served as a cathode in galvanic interactions with galena. Floatability of pyrite was performed alone and as a mixture with galena in the ratios of pyrite to galena equal to 1:4, 1:1 and 4:1. The experiments were conducted with air and nitrogen. In any galvanic contact between pyrite and galena, anodic oxidation occurred on the galena surface, and hydrolysed lead species adsorbed on the pyrite surface. The investigation of the various reactions occurring on the sample surface was investigated by ethylene diamine-tetra acetic acid disodium salt (EDTA) extraction and X-ray photoelectron spectroscopy (XPS) measurements. In the presence of nitrogen, floatability of pyrite increased. The recovery of pyrite in the presence of air was 22%, while in the mixture with galena (ratio 1:4) the recovery increased to 43%. The results indicated that the presence of galena improved floatability of pyrite

A new approach in separation process evaluation. Efficiency ratio and upgrading curves

In mineral processing separation efficiency (SE), operation efficiency (OE), selectivity index (SI) and other indices have been used to evaluate the separation process. Up to now, no study has been conducted on the relationship between the SE, OE and SI indices. In this research, two upgrading curves are proposed based on the above indices for process and selectivity evaluation. The first upgrading curve is based on recovery R, SE, and OE as a function of concentrate grade. This curve has three background lines, including no upgrading line, ideal upgrading line and the ideal mixing line. The proposed upgrading curve is applicable not only for process evaluation by specification of OE and SE, but also for selectivity evaluation with the lowest difference between SE and OE. The curve showed that the recovery value is always greater than the SE and OE values. The parameters of OE, SE and R were used for plotting the upgrading curve as a function of concentrate grade taking into consideration all of them at a time. A new selectivity indicator, namely Efficiency Ratio (ER) as the selectivity parameter, is proposed as the ratio of OE to SE. The ER values fluctuate between 1 and . It can be presented as a function of concentrate and tailing grades (ER = [c(1-t)]/[1(c-t)]). The results showed that ER is insensitive to the feed grade and has the inverse relationship with SI. To measure the separation selectivity, another upgrading curve is proposed based on ER and SI parameters. This curve is divided into seven separation classes for evaluation the class of a separation process from ideal class to no separation one. The results of this research can be useful for separation process evaluation

Prediction of Co(II) and Ni(II) ions removal from wastewater using artificial neural network and multiple regression models

In this research, carboxymethyl chitosan-bounded Fe3O4 nanoparticles were synthesized and used for removal of Co(II) and Ni(II) ion metals from wastewater. The capability of magnetic nanoparticles for metal ions removal was investigated under different conditions namely pH, initial concentration of metal ions and adsorbent mass. The assessment of adsorbent performance for metal ions removal under different conditions requires cost and time spending. In this regard, the capability of artificial neural network (ANN) and nonlinear multi-variable regression (MNLR) models were investigated for predicting metal ions removal. The values of operational parameters such as pH, contact time, initial concentration of metal ions and adsorbent mass were applied for simulation by means of ANN and MNLR. A back propagation feed forward neural network, with one hidden layer (4:8:2), was proposed. Two criteria, including mean square error (MSE) and coefficient of determination (R2) were used to evaluate the performance of models. The results showed that two models satisfactorily predicted the adsorbed amount of metal ions from wastewater. However, the ANN model with higher R2 and lower MSE than the MNLR model had better performance for predicting the adsorbed amount of metal ions from wastewater

Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm – artificial neural network (GA-ANN)

In this study, a back propagation feed forward neural network, with two hidden layers (10:10:10:4), was applied to predict Cu grade and recovery in industrial flotation plant based on pH, chemical reagents dosage, size percentage of feed passing 75 μm, moisture content in feed, solid ratio, and grade of copper, molybdenum, and iron in feed. Modeling is performed basing on 92 data sets under different operating conditions. A back propagation training was carried out with initial weights randomly mode that may lead to trapping artificial neural network (ANN) into the local minima and converging slowly. So, the genetic algorithm (GA) is combined with ANN for improving the performance of the ANN by optimizing the initial weights of ANN. The results reveal that the GA-ANN model outperforms ANN model for predicting of the metallurgical performance. The hybrid GA-ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the metallurgical performance prediction