Effect of Slurry Density on Load Dynamic and Milling Performances in an Iron Ore Ball Mill - On-line Estimation of In-mill Slurry Density.

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1 Effect of Slurry Density on Load Dynamic and Milling Performances in an Iron Ore Ball Bernard de Haas *#, Alfred Van den Bosch #, Axel Köttgen &. # Magotteaux, Belgium & University of Liège, Belgium Keywords: Ball Mill, Slurry Density, Optimisation, Model. Abstract In ball milling, the transmission of grinding energy from the steel balls to the particles in the slurry is closely linked to the way the media and the slurry are mixed. One of the key parameters that influence this mixing is related to the pulp rheological properties, and more precisely its viscosity. As the slurry viscosity is difficult to measure on regular basis in concentrator operations, the pulp density is often assessed instead as a convenient substitute. For the current work, extensive surveys have been performed on an industrial overflow ball mill, processing iron ore. The slurry density was set to different values by adapting the water addition flow at the mill inlet. The mill is equipped with a Sensomag which provides information about the ball load and pulp volume, values and positions. For each density tested, mill discharge slurry samples were taken and assessed for density and Blaine. The load position and mill absorbed power were recorded by the Sensomag. The ball level was kept constant throughout the surveys with the help of a Magoload. The main outcomes of the work that has been performed are: the highlight of an optimal pulp density leading to optimal mill performances, the quantification of the slurry density effect on the mill load position, and the elaboration and validation of a density model able to estimate the in-mill density while the mill is operation. Most of the results presented in the article are corresponding to what has been found previously in platinum ore milling. * Corresponding author. Equipment description The circuit studied is processing iron ore. The ball mill dimensions are 4.8 x m. The latter is filled with 30 mm balls. It is equipped with an overflow discharge and a smooth wave rubber liner. It is running in close circuit on a bank of 5 hydrocyclones. Comminution Conference 2012 Cape Town 1/9

2 Cyclones Water addition Ball mill Mill discharge sample collection Figure 1: Circuit flow sheet. The mill is equipped with a Sensomag and its ball filling degree is kept constant with the help of a Magoload. Both equipments have been described, in details, in previous articles such as [Clermont, B., et al. 2010] and [Keshav, P., et al.]. The Sensomag raw data is available in terms of ball and pulp angular positions inside the mill (toe and shoulder). The data analysis, performed in this article, is also based on ball average lift angle and total ball angle, as defined in the figure below (figure 2). 0 Ball shoulder Ball toe Total ball angle 180 Ball average lift angle Figure 2: Ball angles definitions. Comminution Conference 2012 Cape Town 2/9

3 Total ball angle = Ball shoulder Ball toe = Ball width Surveys description Ball average lift = Ball shoulder + 2 Ball Toe During the tests, the ball filling degree was kept constant in the mill, while the inlet water addition was adapted to change the slurry density in the range of [ ] kg/dm³. Mill discharge samples were collected and their density was measured with a Marcy scale. The particles fineness, in terms of specific surface area (Blaine cm²/g), was also measured. The graph below shows the densities variations over one day of test. 3.2 Slurry density (kg/dm³) :00:00 08:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 Figure 3: Slurry density variations during one day of surveys. As the mill is under permanent Sensomag monitoring, the slurry and ball angular positions have also been collected during the survey period. The graph below shows the ball average lift angle (left scale - recorded by the Sensomag) as well as mill absorbed power (right scale) and slurry density (scaled to fit in the graph) Average Lift Mill Power Density (* ) :00 05:00 07:00 09:00 11:00 13:00 15:00 17: Figure 4: Ball load average lift angle, mill absorbed power and slurry density variations during one day of surveys. Comminution Conference 2012 Cape Town 3/9

4 As one can notice, the mill power variations are extremely well correlated with the average lift angle variations. The correlation is also very strong (inverted) with the slurry density. This shows that the mill power variations due to slurry density changes are mainly the result of ball load shift, up and down, in the tumbling mill. The graph below shows the ball load width (total angle). It is visible that the ball load is slightly expanding (total angle increasing) when the slurry density is increasing. This load expansion with slurry density has also been observed in several other occasions [Clermont, B., et al. 2008] and [Keshav, P., et al.] Total Ball Angle Mill Power Density (* ) :00 05:00 07:00 09:00 11:00 13:00 15:00 17: Figure 5: Ball load width (total angle), mill absorbed power and slurry density variations during one day of surveys. The increase of slurry density leads to a ball load drift down and a resulting power decrease. A load expansion is also observed. In an overflow mill, this combination of drift and expansion conducts the ball load to enter the slurry pool with the direct effect of a better pulp and ball mixing [Keshav, P., et al.]. This improved efficiency can be seen in the graph below, showing the performance in terms of particle surface produced: Blaine (cm²/g) produced by unit of specific energy (kwh/t). Comminution Conference 2012 Cape Town 4/9

5 3900 Particles surface produced per unit of energy 120 kw absorbed Mill Power Blaine/(kWh/T) Blaine/(kWh/T) Density Figure 6: Particles surface produced by unit of energy. As shown on the graph, the mill absorbed power is decreasing as the slurry density is increasing. The slope changes around 2.9 kg/dm³. This corresponds to a maximum of the surface produced curve. - Below 2.9 kg/dm³, the ball and pulp mixing is not optimal. On one hand, the balls are lifted higher which is energy consuming and on the other hand, the pool volume (where there is only slurry) is large. There are fewer balls to particles contacts (although they may be more energetic). - Above 2.9 kg/dm³, the ball load is sliding more down to the bottom of the mill and is experiencing more expansion. This decreases the number of efficient ball to particle contacts which decreases the overall mill grinding performances. There might be more balls to particles contacts but there are definitely less efficient. - At 2.9 kg/dm³, the balls to particles contacts are both numerous and efficient. The observed effect of slurry density variations on ball load and mill absorbed power can be used to build a slurry density estimation model. Comminution Conference 2012 Cape Town 5/9

6 Density model Based on the data collected during the surveys, a slurry density model was build. It has the following format: DensityModel = K1. α Toe + K 2. α Shoulder + K 3. Power Where K 1 = and α Toe is the ball toe angle (in, measured by the Sensomag) K 2 = and α Shoulder is the ball shoulder angle (in ) K 3 = and Power is the mill absorbed power (in kw) The graph below shows the density model and its correlation with the measured slurry density. The fitting is fairly good (R² = 0.931) Density Model Linear (Density Model) y = x R 2 = Figure 7: Slurry density model (ordinate) compared to measured density (abscissa). This model was validated by a second survey perform a few months later. The test procedure was repeated for 3 different slurry densities (2.65, 2.93 and 3.11 kg/dm³). The graph below shows the results of the validation. Comminution Conference 2012 Cape Town 6/9

7 Density Model 2nd survey Linear (Density Model) y = x R 2 = Figure 8: Slurry density model (ordinate) compared to measured density (abscissa). The 3 magenta points correspond to validation performed after several months. Thanks to the density model, it is now possible to monitor its evolution minute by minute. With this valuable tool, the operator is able to take corrective actions to keep the density at its optimal value. Further developments In order to deliver comprehensive updated information to the operator, a user interface was developed. It includes: A cross section view of the mill displaying the actual ball and pulp volume, Current actual spot values and updated graphs of the last hour of operation of the main process data: - ball mill filling degree (among which dry ball volume = ball not in contact with pulp), Comminution Conference 2012 Cape Town 7/9

8 - in-mill slurry density (model described above), - pool volume, - mill pulp content (both in % of mill volume and in % of voids in between balls) Historical charts for the main Sensomag process data. Comminution Conference 2012 Cape Town 8/9

9 Conclusion Originally, the Sensomag was providing information on the ball load and pulp volume as toe and shoulder angles from which was derived the ball filling degree (in % of mill volume). These data, although very complete, had very few practical meaning for the plant personnel in their day to day operations. For a few years, tremendous developments have been performed which lead to: quantification of customer benefits, pulp density model and user friendly operator interface. These developments lead to a unique and comprehensive tool directly available for the plant operators and engineers in their search for continuous milling process optimisation. References [Clermont, B., et al. 2008] Benoît Clermont, Bernard de Haas, Olivier Hancotte. Real time mill management tools stabilizing your milling process. Third International Platinum Conference. Sun City (2008). [Clermont, B., et al. 2010] Benoît Clermont, Bernard de Haas. Optimize mill performances by using on-line ball and pulp measurements. SAIMM Journal (2010). [Keshav, P., et al.] Pratish Keshav, Bernard de Haas, Benoit Clermont, Aubrey Mainza, Michael Moys. Optimisation of the secondary ball mill using an on-line ball and pulp load sensor The Sensomag. Mineral Engineering (2011). [Köttgen, A., at al.] Axel Köttgen, Bernard de Haas, Pratish Keshav, Aubrey Mainza, Michael Moys, Stoyan Gaydardzhiev. Improving the grinding performance in ball mills with a conductive and inductive sensor. IFPRI Meeting Poster Session (2010). [Makokha, A., et al] Augustine B. Makokha, Michael H. Moys. Multivariate approach to on-line prediction of in-mill slurry density and ball load volume based on direct ball and slurry sensor data. Mineral Engineering (2012). Comminution Conference 2012 Cape Town 9/9