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    Wyświetlanie 1-3 z 3
    Tytuł:
    The effect of ball size distribution on power draw, charge motion and breakage mechanism of tumbling ball mill by discrete element method (DEM) simulation
    Autorzy:
    Panjipour, R.
    Barani, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/110020.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    discrete element method
    ball mill
    ball size distribution
    mill power
    breakage mechanism
    Opis:
    In this research, the effect of ball size distribution on the mill power draw, charge motion regime and breakage mechanism in a laboratory ball mill was studied using the discrete element method (DEM) simulation. The mill shell and crushing balls were made of Plexiglas® and compressed glass, respectively. Modeling was performed using Particle Flow Code 3D (PFC3D). Model parameters were back-calculated by comparing the power draws and images obtained from simulation and laboratory test works. After determining the model parameters, the mill was simulated in mill fillings of 15, 20, 25, 30, 35 and 40% with ball media of 2 and 2.5 cm in diameter. For every mill filling, the numbers of big and small balls were changed and 11 scenarios were chosen. The results showed that at a constant mill filling, the power draw was changed with changing the ball size distribution and for all mill fillings the maximum power draw occurred when the fraction of small balls was between 30-40%. The effect of ball size distribution increased with increasing mill filling and for the mill filling of 35%, the ball size distribution had the maximum effect on the power draw. When the mill charge contained mono-sized balls, the ball flow regime inside the mill transited to the cataracting and impact breakage was the main breakage mechanism. Increasing the fraction of big balls from 0 to 70% led the flow of balls into the cascading regime and breakage mechanism to attrition.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2018, 54, 2; 258-269
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improvement of the accuracy of Hogg and Fuerstenaus model in predicting the power draw of ball mills based on determining the grinding media’s dynamic voidage
    Autorzy:
    Golpayegani, Mohammad Hasan
    Rezai, Bahram
    Powiązania:
    https://bibliotekanauki.pl/articles/2175427.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    voidage
    grinding media
    ball mill
    Hogg model
    Fuerstenau's model
    power draw
    Opis:
    In the development of tumbling mills' power models, the voidage of grinding media is assumed to be static and equal to 40%. While the grinding media’s voidage is dynamic; and hence is varied by changing the operating parameters. In this paper, to improve the Hogg and Fuerstenau model's accuracy in predicting the ball mills' power draw, the grinding media's static and dynamic voidage was studied for Bond's proposed ball size distributions (BSD) for the ball mills' first filling. To this end, by scaling down balls to one-tenth of actual size, developing a novel method to measure the dynamic voidage, and employing the three-level factorial method, a separate empirical model was developed for determining the dynamic voidage of each Bond's BSD with respect to mill's fractional filling and rotating speed. Moreover, using the multiple regression method, a general empirical model was derived to determine the dynamic voidage of each supposed BSD based on calculating the mean absolute deviation of balls diameter (MAD). Results indicated that grinding media's dynamic voidage increases with an increase in rotating speed and a decrease in fractional filling and balls diameter's MAD. The maximum and minimum static and dynamic voidage occurred for the seventh and first Bond's BSDs. By employing an industrial database and analyzing the mean absolute percentage error (MAPE) of predicted ball mills' power draw, it was found that the Hogg and Fuerstenau model's accuracy enhances by calculating the load's bulk density based on the grinding media's dynamic voidage.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2022, 58, 6; art. no. 153380
    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 1-3 z 3

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