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    Tytuł:
    Significance of reagents addition sequence on iron anionic reverse flotation and their adsorption characteristics using QCM-D
    Autorzy:
    Hou, Ying
    Sobhy, Ahmed
    Wang, Yue
    Powiązania:
    https://bibliotekanauki.pl/articles/1448929.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    iron oxide
    adsorption
    pH modifier
    starch
    reverse flotation
    QCM-D
    Opis:
    To explore the influence of reagents addition sequence of the pH regulator and the starch depressant on the anionic reverse flotation of iron oxide, flotation conditional experiments were performed on mixed low-intensity and high-gradient magnetic concentrates which is the flotation feed acquired from the iron processing plant. Besides, quartz crystal micro-balance with dissipative (QCM-D) was conducted to detect the adsorption phenomena of the flotation reagent on iron oxide sensors at different addition orders. The outcomes showed that the flotation performance using the pH regulator prior to the depressant was the best. For example, at 1.6 kg/Mg starch dosage, the recovery and separation efficiencies were improved by 18.3% and 21.2%, respectively, with keeping the concentrate Fe grade as high as 69.5%. Also, QCM-D frequency shifted by -41 Hz from 17 Hz to -24 Hz with increased dissipation from -2.6 x 10-6 to 8.2 x 10-6, indicating an increase in the mass of slightly-rigid starch adsorption layer on the surface of iron oxide under a strong alkaline condition with adsorption density of about 0.46 mg/cm2. On the other hand, under weak alkaline conditions, starch was adsorbed, and then the starch was desorbed upon the addition of the strong alkaline solution. Whereas, adding the pH modifier to create a strong alkaline condition enhanced the starch adsorption significantly with coordination and hydrogen bonds, and prevented the following adsorption of the anionic collector for more efficient reverse flotation of iron oxide minerals.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2021, 57, 1; 284-293
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network
    Autorzy:
    Wang, Ziyang
    Hou, Ying
    Sobhy, Ahmed
    Powiązania:
    https://bibliotekanauki.pl/articles/27323660.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    tower mill
    grinding power consumption
    energy saving
    genetic algorithm
    BP neural network
    Opis:
    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.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2023, 59, 6; art. no. 172096
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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