INTERACTION OF FEED MASS FLOW AND FEED GRADE IN ROUGHER SCAVENGER FLOTATION AT THE MIDUK CU COMPLEX

Transcription

1 INTERACTION OF FEED MASS FLOW AND FEED GRADE IN ROUGHER SCAVENGER FLOTATION AT THE MIDUK CU COMPLEX Mohammad Rezaee-Rad & Hassan Haji Amin Shirazi Shahid Bahonar University of Kerman, Iran Reza Ebrahimi & Sedighe Zeidabadi National Iranian Copper Industries Company (NICICO), Iran ABSTRACT Based on design criteria at the Miduk copper complex, about 75% of the final concentrate was supposed to be produced in the roughing section, consisting of 5 flotation columns. However, when the columns are being installed at the early stage of the circuit, nearly all of the rougher feed is reported to 10 rougher scavengers, 50 m 3 mechanical cells. This implies that residence time in rougher scavenger section is shortened considerably. In an attempt to define the optimum operational conditions in the rougher scavenger section, interaction of feed-mass-flow-rate and feed-grade was investigated. Kinetic flotation experiments were conducted on collected samples with varying Cu content. Recovery vs. Flotation Time curves were generated and the optimum residence time for lab (batch) scale and afterwards for full size machines were calculated. Eventually, two models were presented for defining the variation of the optimum feed mass flow rate and the optimum recovery vs. feed grade.

2 CHAPTER 7 INTRODUCTION At the Miduk copper complex, ground feed is introduced to 5 flotation columns as roughing section. The tailings from the rougher columns are reported to 10 scavenger mechanical cells (Figure 1). Figure 1: Miduk flotation circuit. While the columns were designed to produce about 75% of the total concentrate, any attempt to float off the more readily floatable particles, mainly liberated chalcocite, chalcopyrite and covellite, in the rougher columns has been unsuccessful. Thus nearly all of the rougher feed is reported to scavenger cells. Therefore rougher scavenger (RS) cells actually play the rougher roll. There should therefore be a way available to change this. Clearly, optimum recovery could only be achieved providing sufficient residence time in this stage, which is affected by the following parameters: Feed flow rate Feed grade Type, dosage and distribution of reagents Particle size distribution Air flow rate and pulp density [5]. Due to lack of a proper feed blending system, feed grade variation is inevitable. With all of the other parameters being fixed, the feed flow rate to the flotation circuit was the only variable for the adjustment. Therefore, considering the grade of the feed, feed mass flow rate should be regulated. METHODOLOGY Kinetic batch flotation experiments were conducted on 15 samples varying feed grade, 8 of which were collected from the feed stream to the RS cells, and the remainder, which were low grade samples (less than 0.8%), from the low grade site of the mine. Low grade samples were ground to the same size of the feed to RS cells (80% passing 100 μm for 87% liberation of the valuable mineral, Figure 2). 378

3 interaction of feed mass flow and feed grade in... Figure 2: Size distributions of ground low grade samples and samples collected from feed stream to RS cells. The flotation tests conditions were kept the same for all of the samples, with the feed grade being the only variable effective parameter on the flotation kinetic. Concentrates were collected in 0.5, 1, 2, 4, 7 and 13 minutes. Kinetic coefficients determination was simply accomplished by regression analysis of the test results, in light of the Morris [4]. modified first order rate Equation (1): R = R I x [1 - exp (-K ( t + Ø))] (1) Optimum residence time (the time in which the rate of gang recovery to concentrate equals the rate of valuable mineral recovery to tailings) was then calculated using Equation (2) [1, 2]. (2) Applying topt in equation (1), optimum recovery Rd was obtained. Considering short- circuiting effects in full size (continuous) flotation machines, the optimum calculated recovery is achievable with greater residence time [3]. This can be determined using Equation (3). (3) In commercial flotation processes that use a number of flotation cells in series, the metallurgical objective is, primarily, to achieve optimum recovery obtained in lab (batch) tests, with a residence time approximating the optimum batch flotation time. With a total fixed volume in the RS cells at the Miduk concentrator equaling 500 m 3, the only variable for residence time adjustment would be feed flow rate to the cells. The volumetric flow rate to RS circuit was determined using Equation (4). Finally, the feed mass flow rate of the feed was found using Equation (5). (4) (5) PROCEMIN Santiago, Chile 379

4 CHAPTER 7 Thus, the optimum mass flow rate of the feed was determined for each sample by varying feed grade. By regression analysis of the test results, the relationship between optimum feed mass flow rate and feed grade was then established. RESULTS AND DISCUSSION It is shown in Figure 3 that an increase in feed grade contributes to a decrease in optimum residence time in batch experiments, obtaining a certain recovery. However, due to short circuiting effects, targeting the same recovery in plant operations requires greater residence time. Figure 3: Batch Flotation vs. Full Size Optimum residence time targeting the same recovery. Based on the batch experiments that were conducted on samples with varying feed grade, optimum residence time and optimum recovery for plant operation were determined. Then, the optimum feed mass flow rate was obtained. Variations of resulting optimum recoveries and the optimum feed mass flow rates against feed grade, are shown in Figure 4. Figure 4: Variations of optimum recovery and optimum feed mass flow rate against feed grade. As is shown, an increase in feed grade and consequently an increase in optimum residence time in plant operation, results in a decrease in the optimum feed mass flow rate to obtain optimum recovery. Meanwhile, any reduction in throughput having a fixed grade would contribute to an increase in recovery. With the variable speed SAG mill and the ball mill 380

5 interaction of feed mass flow and feed grade in... circuit being flexible, the grind size 80% passing 100 μm for 87% liberation of the valuable mineral can be fixed. It can be seen that the optimum mass flow rate vs. feed grade is defined applying a second order regression curve, while for optimum recovery vs. feed grade, a logarithmic regression curve is adequately fitted on the results (Equations (6 & 7)). M ( t / h ) = ƒ ƒ (6) R d % = Ln (ƒ) (7) The presented models square errors, associated with the experimental results, are tabulated in Table 1. From the table it can be seen that the sum of the square errors for optimum throughput and optimum recovery, are 2490 and 42.1 respectively. Whilst the throughput model has 3 and the recovery model has 2 coefficients, standard deviations were calculated considering 3 and 2 degrees of freedom, respectively. As a result, standard deviations of the optimum throughput and the optimum recovery were found to be 14.4 and 1.8, respectively. Meanwhile, determination coefficients are and 0.990, suggesting that with 95% confidence level, having 14 degrees of freedom, t 2.5% being 2.145, optimum throughput and optimum recovery can be determined in the range ± 8 t / h and ± 0.1% respectively. Results indicated that under optimum metallurgical conditions, 5% increase in the RS cells recovery, associated with 0.08% decrease in tailings grade, could be obtained. It should be noticed that the presented models are specific to the RS circuit at Miduk copper complex, for which they were established, and are specific for the mineralogical and operational factors at the plant. Thus the procedure that was outlined, should be followed from the fist step on, for each specific case. Table 1: Experimental and modeling results varying feed grade to RS cells Grade % M( t / h ) Rd % t opt (min) T (min) M m R d(m) [M (e) -M (m) ]2 [R d(e) -R d(m) ] SSE=2490 SSE= 42.1 CONCLUSION The variations of the optimum throughput and the optimum recovery,against feed grade in the rougher scavenger section were studied at the Miduk copper complex. It was observed that a second order curve can be used to define the variation of the optimum throughput, and a logarithmic curve can be utilized to specify the variation of optimum recovery vs. feed grade. PROCEMIN Santiago, Chile 381

6 CHAPTER 7 It is concluded that applying the established models, the optimum throughput and the optimum recovery can be determined with 95% confidence level in the range of ± 8 t/h and ± 0.1%, respectively. Thus, 5% increase in the Rougher Scavenger cells recovery, associated with 0.08% decrease in tailings grade, could be obtained. ACKNOWLEDGEMENTS The authors would like to thank the National Iranian Copper Industries Company (NICI- CO) for supporting this research. The contribution of Reza Shamsadini, Mahmood Hekmati, Ali Mahdavi, Mohammad Reza Yarahmadi, Mohammad Reza Garmsiri and Vahid Mirshekari is appreciated. NOMENCLATURE R lab (batch) recovery at time t, % R I lab (batch) recovery at infinite time, % K lab (batch) kinetic constant, min -1 t lab (batch) residence time, min Ø time correction factor K a valuable mineral kinetic constant, min -1 K b gang kinetic constant, min -1 R Ia lab (batch) valuable mineral recovery at infinite time, % R Ib lab (batch) gang recovery at infinite time, % Ø a valuable mineral time correction factor Ø b gang time correction factor T plant practice optimum residence time, min t opt lab (batch) optimum residence time, min R d optimum recovery, % n number of cells in a row Q volumetric flow rate of pulp V required total volume in rougher scavenger cells S volume scale-up factor A aeration factor M mass flow rate (throughput) ρ pulp density ϕ solids concentration % F feed grade % REFERENCES Agar, G. E., Chia, J. & Requis, L. (1998) Flotation Rate Measurements to Optimize an Operating Circuit. Mineral Engineering, Vol. 11, No. 4, pp [1] Agar, G. E., Stratton-Crawley, R. & Bruce, T. J. (1980) Optimizing the Design of Flotation Circuits. Cim Bulletin, 73(824), pp [2] Bourassa, M., Barbery G., Broussaud, A. & Conil, P. (1988) Flotation Kinetics Scale-Up, Comparison Laboratory Batch Test to Pilot Plant Processing, XVI International Mineral Processing Congress, Stockholm, pp [3] 382

7 interaction of feed mass flow and feed grade in... Morris, T. M. (1952) Measurement and Evaluation of the Rate of Flotation as a Function of Particle Size. Trans AIME, 193, pp [4] Ucurum, M. & Bayat O. (2007) Effects of Operating Variables on Modified Flotation Parameters in the Mineral Separation. Separation and Purification Technology 55, pp [5] PROCEMIN Santiago, Chile 383

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