Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl
Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 2

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
|
|
tom Vol. 58, iss. 1
37--49
EN
Most iron reserves are low in grade with quartz as the main gangue mineral, and anionic reverse flotation has become the most crucial separation method in the processing plants of iron ore. Thus, a flotation feed sample that is a mixture of low-intensity and high-gradient magnetic separators concentrates was acquired from a processing plant. The sample characterizations with X-ray diffraction (XRD), X-ray fluorescence (XRF), laser particle size analyzer, and mineral liberation analysis (MLA) confirmed that the sample consists of iron oxide as a valuable mineral and quartz as a gangue mineral with adequate liberation degree. In the anionic reverse flotation, the interaction of the flotation reagents with the constituents of the feed makes the flotation a complex system. Thus, the selection and optimization of regent dosages were performed using a uniform experimental design to estimate the optimum separation efficiency. The optimum reagent system was 1.6 kg/Mg starch depressant, 1.0 kg/Mg calcium oxide (lime) activator, and 0.8 kg/Mg TD-II anionic collector. At the optimum, 68.90% iron grade with 92.62% recovery was produced.
|
2023
|
tom Vol. 59, iss. 6
art. no. 172096
EN
Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.