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EN
In this research, the effect of different sources of process water on the flotation efficiency of copper sulfide ore prepared from the Sarcheshmeh copper mine was investigated. For this purpose, samples of fresh water to the plant, overflows of copper-molybdenum concentrate thickener, copper concentrate thickener, and recycled water pool as well as a mixture of fresh water and recycled water were prepared and characterized. Flotation tests were performed under the same conditions as the plant’s rougher circuit and were kept constant during all experiments. Grade and recovery of copper, iron, molybdenum, and silica were selected as the metallurgical response of flotation tests. The results were subjected to statistical analysis to assess the relative significance of which water source affects the flotation performance as evaluated from the experimental results. The results showed that the copper concentrate thickener overflow had the greatest effect on the flotation efficiency, so the grade and recovery decreased by about 10% and 75% for copper, and 10% and 6% for iron in the concentrate, respectively, while the grade and recovery increased up to 0.1% and 12% for silica, and 3% and 25% for molybdenum, respectively. The reason for this effect was attributed to the high content of suspended solid particles, and Cu2+, Mo2+, and Fe2+ cations in this water source that increased the coating effect over gangue minerals and entrainment rate. The improvement of molybdenum flotation was also ascribed to the possible presence of residual diesel oil from the flotation process in the plant. Due to the relatively equal amount in all sources of process water, the effect of anions and ions of dissolved salts was difficult.
EN
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.
PL
Na powierzchni zbiornika odpadów po flotacji rud miedzi przeprowadzono doświadczenie polowe, w którym przeanalizowano właściwości fizyczne i chemiczne gruntu z dodatkiem odpadów mineralnych kamieniołomu, piaskowni oraz zwałowiska skały płonnej z udostępnienia złoża rud miedzi. Dodatek tych minerałów w niewielkim stopniu poprawił analizowane właściwości.
EN
At the surface of tailing impoundment in Wartowice (Lover Silesia) four experimental sites were covered with decimeter thick layer of chosen mineral compounds. Those mineral substances were taken from stone-pit, sand-pit and vain rock stockyard located in the neighborhood of flotation pound. Minerał compounds were mixed with flotation sediments and left four eight weeks for stabilization of physical and chemical properties. The influence of used compounds on the properties of obtained mixtures were examined. Critical properties of flotation sediments such as salinity, pH, content of macronutrients, water and air capacity, and concentration of copper, zinc and lead were examined in those mixtures.
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