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.

Mineral Processing of Silica Sand in Hanout Area/South of Jordan

White sandstone samples from Hanout area of Late Cambrian-Early Ordovician sandstone in south of Jordan were studied and assessed as a source of glass sand. Upgrading the sand included removing or reducing the content of the contaminant oxides and the heavy minerals. The aim of this research was to achieve this upgrading by examining the best-suited and cost-effective processing method(s) with sufficient product recovery. Following the initial sample characterisation at “bench scale”, a pilot study was performed. A high-grade Glass Sand product of 500 - 125 μm size fraction was produced by wet screening, attrition scrubbing and the separation of heavy minerals using spirals. The high quality Glass Sand product compared well with Grade-A of the British Standard for glass sand. Due to the relatively low level of impurities in the raw material, a substantial silica sand recovery was produced with a high silica grade. The silica sand product was capable to be used in the high quality glass industry and in many other applications where pure silica is required. The mass flowrate of the feeds and the products in the spiral was calculated for the bulk sample as well as the amount of water required operating the process.

Estimation of Copper and Molybdenum Grades and Recoveries in the Industrial Flotation Plant Using the Artificial Neural Network

In this paper, prediction of copper and molybdenum grades and their recoveries of an industrial flotation plant are investigated using the Artificial Neural Networks (ANN) model. Process modeling has done based on 92 datasets collected at different operational conditions and feed characteristics. The prominent parameters investigated in this network were pH, collector, frother and F-Oil concentration, size percentage of feed passing 75 microns, moisture content in feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A multilayer perceptron neural network, with 10:10:10:4 structure (two hidden layers), was used to estimate metallurgical performance. To obtain the optimal hidden layers and nodes in a layer, a trial and error procedure was done. In training and testing phases, it achieved quite correlations of 0.98 and 0.93 for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction, respectively. The proposed neural network model can be applied to determine the most beneficial operational conditions for the expected Copper and Molybdenum grades and their recovery in final concentration of the industrial copper flotation process.

Beneficiation of White Kaolinitic Sandstone to Produce Kaolin Concentrate from Wadi Siq-Rakyia Area in Wadi Araba, Jordan

Kaolinitic sandstone samples of Lower Cretaceous from Wadi Siq-Rakyia area in Wadi Araba/south of Jordan were studied and assessed as a source of Kaolin. Three channel samples and a composite bulk sample were studied for their mineralogical, geochemical, and grain size distribution analysis. The aim of this research work was to achieve kaolin concentration by examining the best-suited and cost-effective processing method(s) with appropriate product recovery. Following the initial sample characterisation at “bench scale”, a pilot study was performed on the bulk sandstone sample. Kaolin was accumulated in the fine size fraction (−125 μm) after agitating and wet screening of the sample. The −125 μm size fraction sample was used to produce kaolin concentrate. Hydrocyclone classification was applied in the pilot study for this purpose. The mass flowrate of the feeds and the products in the hydrocyclones was calculated for the bulk sample as well as the amount of water required operating the process. A kaolin-enriched product was produced following the use of hydrocyclones. A kaolin concentrate at a grade of 71% and a recovery of 78% was produced which could be used in the ceramic industry for tableware and sanitaryware.