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Flotation tree analysis for recovery of sillimanite from red sediments
International Journal of Mining Science and Technology xxx (2015) xxx–xxx
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International Journal of Mining Science and Technology journal homepage: www.elsevier.com/locate/ijmst
Flotation tree analysis for recovery of sillimanite from red sediments Laxmi T. a,⇑, Rao R. Bhima b a b
Advanced Materials Technology Department, Institute of Minerals & Materials Technology, Bhubaneswar 751013, India Aryan Institute of Engineering and Technology, Bijupatnaik University of Technology, Bhubaneswar 752050, India
a r t i c l e
i n f o
Article history: Received 2 August 2014 Received in revised form 18 January 2015 Accepted 15 March 2015 Available online xxxx Keywords: Tree analysis Flotation Red sediment Sillimanite
a b s t r a c t In this paper an attempt is made to recover sillimanite by flotation tree analysis process and conventional flotation process from non magnetic fraction of red sediments. The experimental results of both the processes are presented. The data reveal that the deslimed sample contains 33.2% (by weight) total heavy minerals and out of which the sillimanite mineral content is 3.6% (by weight). It is observed that flotation tree analysis needs 10 cells to get five output products and where as conventional flotation process needs 15 cells to recover similar grade of five output products. Thus, flotation tree analysis is not only economic process but also efficient process (to say efficient process, the tree analysis product should be higher grade). Ó 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
1. Introduction Tree analysis is widely used to evaluate the graderecovery-boundary by generating a grade-recovery-curve, and is used in evaluating froth flotation circuit separation efficiency [1,2]. It is a visual representation of all the events which can occur in a system. As the number of events increases, the picture fans out like the branches of a tree. Tree analysis can be used to analyze systems in which all components are continuously operating, or for systems in which some or all of the components are in standby mode-those that involve sequential operational logic and switching. The starting point (referred to as the initiating event) disrupts normal system operation. The tree analysis displays the sequences of events, involving success and/or failure of the system components. Nicol suggested a new procedure, known as tree analysis. Under this approach, a laboratory batch flotation cell was used to separate a coal sample into a concentrate and tailings fraction. Each concentrate and tailings fraction was subsequently refloated, and the procedure was repeated so that the testing sequence branched out in the form of a tree [1]. Nicol introduced a tree analysis technique, involving repeated branching of the flotation steps (Fig. 1), in which the resulting concentrate and tailings are subjected to a number of successive scavenger and cleaner flotation in a laboratory flotation cell [2]. In Fig. 1, C stands for concentrate and T stands for tailing obtained ⇑ Corresponding author. Tel.: +91 9470363995. E-mail address: [email protected] (T. Laxmi).
from first stage flotation of feed. An initial rougher flotation is conducted at a low reagent dosage and the resulting concentrate and tailings are refloated with an addition of reagents in each step. The accuracy of overall separation performance can be improved by increasing the number of subsequent cleaning and scavenger steps [2]. Dell outlined a simplified version of his release analysis procedure which was experimentally less difficult than the original version [3]. In his experimental study, the sample was fractionated by changing in operating conditions (i.e. aeration rate and impeller speed) as opposed to changing in flotation time. Pattern studied the characteristics of fundamental response of a coal to flotation and compared the release and tree analysis [4]. The release analysis produced different floatability curves depending on reagents consumption where as the tree analysis points consistency converged on the same curve. Hence, the tree analysis procedure is demonstrated to be superior in that it more finely discriminates between particles of relatively high but different floatability and that its results are independent of collector dosage [3]. Meloy reported that tree analysis is a non-feedback flotation circuit, which may be visualized as a triangular array of cells, with feed at the orthogonal corner [5]. It also believes that such a flotation circuit can be built for experimental verification. The number of equivalent paths by which a particle can reach a particular output cell is described by the coefficient of a particular term in the binomial expansion, F C T/n (it shows the relationship between outputs after flotation concentrate (F) and tailing (T), where n is array size of flotation). Here the power of F in each
http://dx.doi.org/10.1016/j.ijmst.2015.07.021 2095-2686/Ó 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx Red sediments
Feed
C
CC
T
CT
Slimes (rejected)
Hydrocyclone
TC
TT
Light minerals (rejected)
Spirals CCC
CTT
TCC
TTT
Total heavy minerals
Fig. 1. Schematic diagram of the step0-by-step tree analysis procedure [4]. Magnetic minerals (stock pile)
WHIMS
expansion term is the number of float outputs, the power of T is the number of tailing outputs along each given path to that particular output cell, n is the number of flotation levels. Also, the exponent n identifies the number of cells through which such a particle must travel prior to reaching its output cell [5]. Routray and Rao studied the magnetic separation tree analysis for recovery of magnetic minerals from beach sand [6]. In this study, four-level magnetic separation tree analysis was used for obtaining more than 96% of total magnetic minerals [6]. The first output of magnetic separation tree analysis contains 96.79% magnetic minerals which accounts to 64.31% ilmenite mineral and 32.48% garnet. The schematic presentation of tree analysis used for separation of magnetic minerals from beach sand using rare earth drum (1.4 T) is shown in Fig. 2. In Fig. 2, M stands for magnetic and N stands for non magnetic. The red sediments of badlands topography contain 3.33% of sillimanite in the total heavy minerals. Thus, it needs flotation process to recover sillimanite. In view of this, an attempt has been made to recover maximum possible grade of sillimanite by flotation process in tree analysis method as well as in conventional flotation process.
1.4 T
Non magnetic minerals (feed to flotation)
Fig. 3. Experimental set up to recover flotation feed from the red sediments of badlands topography.
TTTT Circuit out put 4
TTTC
3
4
2
1
TTCC
3
4
TCCC
2
3
4
CCCC
Feed
2. Experimental
Fig. 4. Tree analysis for separation of sillimanite concentrate from non magnetics using flotation cell.
2.1. Material The red sediment samples were collected from badlands topography of Basanputti village, Ganjam Dist, Odisha, India (Lat. 19°210 N and Long. 85°030 E) in form of grid pattern up to the water table level during rainy season. These red sediment samples were thoroughly mixed and a composite sample was prepared. Initially, the representative red sediment sample was scrubbed and deslimed by using hydrocyclone. The slimes were rejected and the sand was used for further studies to recover total heavy minerals. The deslimed bulk sample was subjected to series of rougher, cleaner and scavenger spiral concentrators (stage spirals) to recover Total Heavy Minerals (THM). THM was subjected to magnetic separation using Wet High Intensity Magnetic Separator
Feed
MMMM
NMMM NNMM NNNM
Circuit output NNNN
Fig. 2. Tree analysis for separation of magnetic minerals from beach and by using rare earth drum (1.4 T) [6].
(WHIMS) to recover ilmenite in magnetic fraction and quartz, sillimanite, rutile and zircon in non magnetic fraction. The non magnetic fraction was then subjected to flotation cell to recover high grade sillimanite in tree analysis method as well as in conventional method. The details on recovery of sillimanite feed for flotation is shown in Fig. 3. 2.1.1. Recovery of sillimanite mineral 2.1.1.1. Flotation tree analysis method. Initially about 15 kg of non magnetic fraction sample was subjected to flotation cell batch wise. The concentrate obtained from the experiment was further subjected to flotation cell and non tailing was kept as it was. Again the concentrate obtained was subjected to flotation cell for two more times to separate tailings from it. At the end of the first level experiment, a product obtained which was concentrate in all stages of the first level experiment and named CCCC. The tailing obtained from the first level and first stage experiment was subjected to flotation cell for three times to separate the concentrate from it. At the end of the fourth level and first stage experiment, the product obtained was TTTT. The tailing from first level and second stage of experiment mixed with concentrate from second level and first stage experiment and subjected to flotation cell. The concentrate obtained from the experiment mixed with tailing from first level and third stage experiment and subjected to flotation cell. The concentrate obtained in the product named as CCCT. The tailing from second level second stage experiment mixed with concentrate from 3rd
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
3
T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx Circuit output units CCCC CCCT
CCTC
CCTT
CTCC
CTCT CTTC CTTT TCCC TCCT TCTC TCTT
CCT
TCT
CTT
CCC
TTC
TCC
Level 4
TTTT
TTCC TTCT TTTC
CTC
TTT
CT
TC
Level 3 TT
CC T Level 2 C
Level 1 Feed
Fig. 5. Four level tree analysis representing with number of ways that concentrate and tailing distributed in different sixteen outputs (identical units can be seen from the output).
Table 1 Physical properties of deslimed red sediment sample.
80000 I: Ilmenite S: Sillimanite R: Rutile Z: Zircon G: Gamet
I
Value
Bulk density (g/cm3) True density (g/cm3) Porosity (%) Angle of repose (°) d80 passing size (lm) Total heavy mineral (THM) (%) Total magnetic heavy minerals (TMHM) (%) Total non-magnetic heavy minerals (TNHM) (%) Very heavy minerals (VHM > 3.3 g/cm3) (%) Light heavy minerals (LHM < 3.3 g/cm3) (%) Slimes (%) Total iron (Fe) in slimes (%)
1.9 2.8 38.4 21.6 290.0 33.2 29.3 3.9 29.6 3.6 29.6 9.7
70000 60000 A.U.
Detail
50000 40000 I
30000 Z
G Z S R
G S I Z S I
I
20000
II
10000 0
10
20
30 40 Two theta
50
60
70
Fig. 7. XRD of total heavy minerals (THM). Zircon 0.32 % Sillimanite 3.33 %
Rutile 0.23 %
Garnet 0.28 %
Others 0.32 %
Ilmenite 28.71 %
Fig. 6. Modal analysis of total heavy minerals (THM).
level 1st stage experiment and subjected to flotation cell. The concentrate obtained mixed with tailing from 2nd level 3rd stage experiment and named CCTT. The tailing from 3rd level and 2nd stage experiment mixed with concentrate from 4th level 1st stage and named CTTT. Tree analysis flotation circuit gives five separate outputs such as CCCC, TCCC, TTCC, TTTC and TTTT. The schematic representation of four level tree analysis procedures on non magnetic fraction of red sediment sample is shown in Fig. 4.
2.1.1.2. Conventional flotation process. The conventional method is a series of branching batch tests on the feed to and from a flotation cell. In four-level tree analysis, there appear four different paths,
each leading to different output units and the same four different paths leading to the one circuit output unit (Fig. 5). Initially about 15 kg of non magnetic fraction sample was subjected to flotation cell batch wise. There are two outputs from first level flotation test, i.e. C and T (C stands for concentrate and T for tailing). In the second level of tree analysis, the product from the first level of tree analysis is again floated into a second level concentrate and a tailing. Also, at this second flotation level, the tailing from the first level test is also refloated into a second level concentrate and a tailing. Thus, after the second level test in the tree analysis there are four outputs: TT, TF, FT and FF. Similarly, in the third level of tree analysis the four outputs from the second level of tree analysis are again floated-each into a concentrate and a tailing component. Thus, after the third level test in the tree analysis there are eight outputs: CCC, CCT, CTC, CTT, TCC, TCT, TTC and TTT. At last, in the fourth level of tree analysis, the eight outputs from the third level of tree analysis are again floated—each into a concentrate and a tailing component. Thus, after the fourth level test in the tree analysis there are sixteen outputs: CCCC, CCCT, CCTC, TTCT, TTTC and TTTT. Because of the symmetry obtained by assuming the same time for flotation in every unit, the number of stages required for feed to reach the desired output product (high grade of concentrate and rejectable grade of tailings) can be expressed by binomial expansion of (C + T)n.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx
59.8
90.5
30.4
81.9
A
92.3
15.9
42.4
B 40.2
29.9
22.6
88.8
90.3
88.6
27.2
5.8
92.1
37.9
37.3
35.9
22.7
12.3
6.5
75.3
82.8
5.4
7.2
H 14.4
91.9
89.4
7.3
42.7
93.8
60.6
66.1
4.4
11. 6
82.1
10.0 CCTT
14.4
6.9 3.6
2.0
CCCC
15.3
7.4
I 31.6
96.5
53.3 CCCT
4. 4 4. 3
94.5
G
F
E
100.0
19.8 5.4
4.3
Rec. (%)
66.1
D 14.5
39.5
Silli. (%)
100.0
10.5
C 29.4
18.1
94.1
Wt. (%)
90.5
51.4
2.8
2.7
J
CTTT
5.5
64.9
5.5
TTTT 29.6
9.4
4.2
Fig. 8. Flow sheet with material balance on recovery of sillimanite from non magnetic fraction of red sediment sample by flotation tree analysis method.
Now, if one assumes that for a flotation, the probability of recovery of sillimanite in one level is same as that of another level, then a C1C2C3T4 path has the same probability as a T1C2C3C4 path or another TiCjCkCl path. Thus, in a four by four-triangular array there are four equally probable ways that a particle may reach the next to the upper circuit output unit (Fig. 5). Moreover, in a four-by-our array there are six equally probable ways that a particle may reach the middle circuit output unit. In a four-step tree analysis there are sixteen different products in the sixteen output units. So, using symmetry, there are many paths but only five with different probabilities-those five different probabilities are shown in Eq. (2). Those five different probabilities are the five different probabilities of getting into five circuit output cells. The outputs of tree analysis and conventional method procedures were evaluated by sink-float methods to estimate the total heavy minerals contains. In order to ensure the accuracy of mineral
Table 2 Results of flotation tree analysis on recovery of sillimanite from non magnetic fraction of red sediment sample. Item
CCCC
CCCT
CCTT
CTTT
TTTT
Total
Weight (%, by weight) Sillimanite (%, by weight) Recovery (%, by weight)
10.5 96.5 15.3
42.7 93.8 60.6
11.6 82.1 14.4
5.6 64.9 5.5
29.6 9.4 4.2
100.0 66.1 100.0
Thus, for a tree of three steps one has the expansion:
ðC þ TÞ3 ¼ CCC þ 3CCT þ 3CTT þ TTT
ð1Þ
Similarly, for a tree of four steps one has the expansion:
ðC þ TÞ4 ¼ CCCC þ 4CCCT þ 6CCTT þ 4CTTT þ TTTT
ð2Þ
Feed
C
59. 5 90.3 81.3
CC
CT
28. 7 92.2 39.2
31. 4 88.6 42.1
40. 5 30.5 18.7
CTC
15. 0 94.5 21.4
13. 1 89.8 17.8
27. 6 89.5 37.4
10. 2 96.7 14.9
4. 8 89.5
CTCC
CCTC
CCTT
9. 2 92.0 12.8
3. 9 84.7
CTTC 5.0
3. 0 88.1
100.0
66. 1
100.0
T
3. 8 81.8
2. 0 59.5
CTTT 4.0
0. 8 57.8
2. 8 89.2
4.7
TCCC 1.8
2. 6 91.1
3.6
0. 8 87.6
3.8
1. 4 84.9
0. 2 66.1
0. 6 77.1
TTC 1.8
5. 1 82.5
TTCC 0.2
TCTT 1.1
36. 3 23.6 13.1
TCT
TCCT
TCTC 0.7
5.6
TCC
CTT
CTCT
25. 6 91.8 35.6
6.5
Rec. (%)
TT
4. 2 88.4
CCT
CCCT
Silli. (% )
TC
CCC
CCCC
Wt. (%)
4. 1 89.4
1. 9 90.3
6.7
TTCT 5.5
1. 0 59.5
2.6
29. 3 9.2
TTTC 0.7
TTT 31. 2 14.2
6.4
0.9
TTTT 4.1
Fig. 9. Flow sheet with material balance on recovery of sillimanite from non magnetic fraction of red sediment.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx Table 3 Results of conventional flotation process on recovery of sillimanite from non magnetic fraction of red sediment sample. Product
Weight (%, by weight)
Sillimanite (%, by weight)
Recovery (%, by weight)
(a) Details of all conventional cell results CCCC CCCT CCTC CCTT CTCC CTCT CTTC CTTT TCCC TCCT TCTC TCTT TTCC TTCT TTTC TTTT Total
10.2 4.8 9.2 3.9 25.6 2.0 3.0 0.8 2.6 0.2 0.8 0.6 4.1 1.0 1.9 29.3 100
96.7 89.5 92.0 84.7 91.8 59.5 88.1 57.8 91.1 66.1 87.6 77.1 89.4 59.5 90.3 9.2 66.1
14.9 6.5 12.8 5.0 35.6 1.8 4.0 0.7 3.6 0.2 1.1 0.7 5.5 0.9 2.6 4.1 100
Detail
Weight (%, by weight)
Sillimanite (%, by weight)
Recovery (%, by weight)
96.7 91.7 88.0 61.8 9.2 66.11
14.9 54.6 22.1 4.3 4.1 100.0
(b) Summary of results obtained from conventional flotation process Product-1 (CCCC) 10.2 Product-2 (CCTC + CTCC + TCCC + TTTC) 39.3 Middling-1 (CCCT + CCTT + CTTC + TCTC + TTCC) 16.6 Middling-2 (CTCT + CTTT + TCCT + TCTT + TTCT) 4.6 Tailing (TTTT) 29.3 Total 100.0
count, all the concentrate and tailing product were subjected to sequential sink-float tests by using bromoform (2.89 g/cm3) and methylene iodide (3.3 g/cm3). Mineralogical studies were carried out both microscopic and X-ray diffraction. The mineralogical phase analysis of products obtained (sink fraction) were carried out using PANalytical X-Pert X-ray powder diffractometer with 0
Mo Ka radiation (k = 0.709 Å A) from 6° to 40° scanning angle at a scanning rate of 0.02°/s. 3. Results and discussion 3.1. Physical properties of sample The physical properties of deslimed red sediment sample of Basanputti are given in Table 1. The bulk density of the sample is 1.9 g/cm3. The d80 passing size of the sample is 290 lm obtained from the size analysis of deslimed feed. The slime content in sediment sample is 29.6% (by weight). The deslimed sample contains 33.2% THM, out of which 29.6% Very Heavy Minerals (VHM) and 3.6% of Light Heavy Minerals (LHM). It is also observed that the sample contains magnetic heavies of 29.3% and non magnetic heavies of 3.9%. The mineralogical modal analysis of THM of typical red sediment samples shown in Fig. 6 indicates that the sample mainly contains ilmenite (28.71%, by weight) followed sillimanite (3.33%, by weight), zircon (0.32%, by weight), rutile (0.23%, by weight), garnet (0.28%, by weight), and other heavy minerals (0.32%, by weight). The mineralogical data indicate that the specific gravity difference between heavy minerals such as ilmenite, rutile, zircon, and sillimanite, and gangue mineral quartz is very much significant. The XRD pattern of the THM of deslimed feed sample shown in Fig. 7 indicates that the sample contains ilmenite followed by sillimanite, zircon, rutile, etc. 3.2. Flotation tree analysis method In the present study, the tree analysis for forth flotation has been attempts when 10 unit operations are involved to recover five
products, out of which one total concentrate (CCCC), one rejectable tailings (TTTT) and three intermediate products. The results of four level tree analyses with number of ways that concentrate and tailings distributed and its mass balance in each path and five output units, such as CCCC, TCCC, TTCC, TTTC and TTTT, are shown in Fig. 8. The summary of results on recovery of sillimanite by way of tree analysis is given in Table 2. The data indicate that the first output of froth flotation tree analysis representing CCCC contains 96.5% grade of sillimanite with 10.5% yield and 15.3% recovery. In the subsequent second output (CCCT) of the unit, a product contains 93.8% sillimanite with 42.7% yield and 60.6% recovery. The rejectable tailings (TTTT) contains 9.4% grade of sillimanite with 29.6% yield and 4.2% recovery.
3.3. Conventional flotation method In conventional flotation method, 15 unite operations are involved in such a way that concentrates and tailings are distributed in 16 outputs, such as CCCC, CCCT and TTTT. The flow sheet with material balance on recovery of sillimanite from non-magnetic fraction of red sediment sample by conventional flotation method is shown in Fig. 9. The details of all conventional cell results are given in Table 3a, and the summary of results obtained from above conventional flotation process is listed in Table 3b. The data indicate that the first output of flotation representing CCCC (Product-1) contains 96.7% grade of sillimanite with 10.2% yield and 14.9% recovery. The subsequent second output (Product-2) of the unit contains 91.7% grade of sillimanite with 39.3% yield and 54.6% recovery. The rejectable tailings contain 9.2% grade sillimanite with 29.3% yield and 4.1% recovery. The sillimanite grade of intermediate products (Middling-1 & 2) varies from 88.0% to 61.8%. In both the flotation methods, the surface chemistry of the cell changes with time. Surface active reagents such as collectors and frothers concentrate in the froth, depleting the flotation cell of these reagents. As the surface chemistry of the cell changes, the probability of a given particle floating in a cell also changes.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx 600
S
12000
Q S
S
500
10000
S S S Q SS
300 SQ
200
Q
S
SQ
S Q
Intenalty (a.u.)
Intenalty (a.u.)
400 S SQ
CCCC CCCT CCTT CTTT
100 0 0
10
20
30
40
50
60
70
80
90
8000 Q
6000 S
4000
S Q
Q
2000
S
QS
S Q
Q Q
QQ
Q S
0 0
10
20
30
40
Two theta (°)
Two theta (°)
(a) Products
(b) Tailings
50
60
70
Fig. 10. XRD of flotation tree analysis.
Four level conventional cleaning and scavenging flotation process need 15 cells to recover sillimanite with similar grade of five products as obtained from flotation tree analysis process. Thus, flotation tree analysis reduces the cost of number of cells and chemical reagents, and minimizes the time, man power and space. 3.4. XRD of flotation tree analysis products Fig. 10 shows the XRD pattern of flotation tree analysis products and tailings. The data of Fig. 10a indicate that the intensity of sillimanite peaks is gradually increasing toward CCCC from CTTT. All products contain maximum sillimanite peaks followed by quartz peaks. The only difference in individual products is the intensity of peaks (sillimanite), whereas the XRD pattern of Fig. 10b indicates that the rejectable tailings of flotation tree analysis contain maximum quartz peaks followed by sillimanite peaks. 4. Conclusions The following conclusions are drawn from the four-level tree analysis of flotation for recovery of sillimanite from non magnetic fraction of red sediments. (1) The deslimed sample contains 33.2% THM, out of which 29.6%VHM and 3.6% of LHM (LHM-Sillimanite mineral). (2) It is observed that there are five outputs of four-level tree analyses CCCC, TCCC, TTCC, TTTC and TTTT needs only 10 flotation cells. (3) The first output of non magnetic fraction of four-level tree analysis contains 10.5% (by weight) with 96.5% grade of sillimanite (CCCC). (4) The second output of tree analysis contains 93.8% sillimanite grade with 42.7% yield.
(5) The rejectable tailing fraction contains 29.6% (by weight) with 9.4% grade of sillimanite (TTTT). In four levels, conventional cleaning and scavenging flotation process need 15 cells to recover sillimanite with similar grade of five products as obtained from flotation tree analysis process. Thus, flotation tree analysis reduces the cost of number of cells, chemical reagents as well as minimizes the time, man power and space. Hence, flotation tree analysis is a novel approach for recovery of sillimanite minerals from red sediments. However, the number of levels varies depending on the feed containing light heavy minerals (sillimanite). Acknowledgments The authors are thankful to the Director, Institute of Minerals and Materials Technology (CSIR), Bhubaneswar for giving permission to use laboratory facilities. One of the authors Ms. T. Laxmi is thankful to BRNS for granting senior research fellowship. References [1] Nicol SK, Bensley CN, Teh KC, Firth BA. The estimation of coal flotation response. In: Membrey W, editor. In: Proceedings of improving froth flotation of coal. Australian Coal Industry Research Laboratories; 1983. p. 116–34. [2] Nicol S. Measurement of coal flotation efficiency using the tree flotation technique. In: Proceedings of 12th international coal preparation congress. Cracow; 1994. p. 1159–66. [3] Dell CC. An improved release analysis procedure for determining coal washability. J Inst Fuels 1964;37:149–50. [4] Pratten Stephen J, Bensley Colin N, Nicol Stuart K. An evaluation of the flotation response of coals. Int J Miner Process 1989;27(3-4):243–62. [5] Meloy TP, Whaley DA, Williams MC. Flotation tree analysis-reexamined. Int J Miner Process 1998;55:21–39. [6] Routray Sunita, Rao Raghupatruni Bhima. Magnetic separation tree analysis for recovery of magnetic minerals from beach sand. Turkish J Sci Technol 2012;7(1):37–48.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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International Journal of Mining Science and Technology journal homepage: www.elsevier.com/locate/ijmst
Flotation tree analysis for recovery of sillimanite from red sediments Laxmi T. a,⇑, Rao R. Bhima b a b
Advanced Materials Technology Department, Institute of Minerals & Materials Technology, Bhubaneswar 751013, India Aryan Institute of Engineering and Technology, Bijupatnaik University of Technology, Bhubaneswar 752050, India
a r t i c l e
i n f o
Article history: Received 2 August 2014 Received in revised form 18 January 2015 Accepted 15 March 2015 Available online xxxx Keywords: Tree analysis Flotation Red sediment Sillimanite
a b s t r a c t In this paper an attempt is made to recover sillimanite by flotation tree analysis process and conventional flotation process from non magnetic fraction of red sediments. The experimental results of both the processes are presented. The data reveal that the deslimed sample contains 33.2% (by weight) total heavy minerals and out of which the sillimanite mineral content is 3.6% (by weight). It is observed that flotation tree analysis needs 10 cells to get five output products and where as conventional flotation process needs 15 cells to recover similar grade of five output products. Thus, flotation tree analysis is not only economic process but also efficient process (to say efficient process, the tree analysis product should be higher grade). Ó 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
1. Introduction Tree analysis is widely used to evaluate the graderecovery-boundary by generating a grade-recovery-curve, and is used in evaluating froth flotation circuit separation efficiency [1,2]. It is a visual representation of all the events which can occur in a system. As the number of events increases, the picture fans out like the branches of a tree. Tree analysis can be used to analyze systems in which all components are continuously operating, or for systems in which some or all of the components are in standby mode-those that involve sequential operational logic and switching. The starting point (referred to as the initiating event) disrupts normal system operation. The tree analysis displays the sequences of events, involving success and/or failure of the system components. Nicol suggested a new procedure, known as tree analysis. Under this approach, a laboratory batch flotation cell was used to separate a coal sample into a concentrate and tailings fraction. Each concentrate and tailings fraction was subsequently refloated, and the procedure was repeated so that the testing sequence branched out in the form of a tree [1]. Nicol introduced a tree analysis technique, involving repeated branching of the flotation steps (Fig. 1), in which the resulting concentrate and tailings are subjected to a number of successive scavenger and cleaner flotation in a laboratory flotation cell [2]. In Fig. 1, C stands for concentrate and T stands for tailing obtained ⇑ Corresponding author. Tel.: +91 9470363995. E-mail address: [email protected] (T. Laxmi).
from first stage flotation of feed. An initial rougher flotation is conducted at a low reagent dosage and the resulting concentrate and tailings are refloated with an addition of reagents in each step. The accuracy of overall separation performance can be improved by increasing the number of subsequent cleaning and scavenger steps [2]. Dell outlined a simplified version of his release analysis procedure which was experimentally less difficult than the original version [3]. In his experimental study, the sample was fractionated by changing in operating conditions (i.e. aeration rate and impeller speed) as opposed to changing in flotation time. Pattern studied the characteristics of fundamental response of a coal to flotation and compared the release and tree analysis [4]. The release analysis produced different floatability curves depending on reagents consumption where as the tree analysis points consistency converged on the same curve. Hence, the tree analysis procedure is demonstrated to be superior in that it more finely discriminates between particles of relatively high but different floatability and that its results are independent of collector dosage [3]. Meloy reported that tree analysis is a non-feedback flotation circuit, which may be visualized as a triangular array of cells, with feed at the orthogonal corner [5]. It also believes that such a flotation circuit can be built for experimental verification. The number of equivalent paths by which a particle can reach a particular output cell is described by the coefficient of a particular term in the binomial expansion, F C T/n (it shows the relationship between outputs after flotation concentrate (F) and tailing (T), where n is array size of flotation). Here the power of F in each
http://dx.doi.org/10.1016/j.ijmst.2015.07.021 2095-2686/Ó 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx Red sediments
Feed
C
CC
T
CT
Slimes (rejected)
Hydrocyclone
TC
TT
Light minerals (rejected)
Spirals CCC
CTT
TCC
TTT
Total heavy minerals
Fig. 1. Schematic diagram of the step0-by-step tree analysis procedure [4]. Magnetic minerals (stock pile)
WHIMS
expansion term is the number of float outputs, the power of T is the number of tailing outputs along each given path to that particular output cell, n is the number of flotation levels. Also, the exponent n identifies the number of cells through which such a particle must travel prior to reaching its output cell [5]. Routray and Rao studied the magnetic separation tree analysis for recovery of magnetic minerals from beach sand [6]. In this study, four-level magnetic separation tree analysis was used for obtaining more than 96% of total magnetic minerals [6]. The first output of magnetic separation tree analysis contains 96.79% magnetic minerals which accounts to 64.31% ilmenite mineral and 32.48% garnet. The schematic presentation of tree analysis used for separation of magnetic minerals from beach sand using rare earth drum (1.4 T) is shown in Fig. 2. In Fig. 2, M stands for magnetic and N stands for non magnetic. The red sediments of badlands topography contain 3.33% of sillimanite in the total heavy minerals. Thus, it needs flotation process to recover sillimanite. In view of this, an attempt has been made to recover maximum possible grade of sillimanite by flotation process in tree analysis method as well as in conventional flotation process.
1.4 T
Non magnetic minerals (feed to flotation)
Fig. 3. Experimental set up to recover flotation feed from the red sediments of badlands topography.
TTTT Circuit out put 4
TTTC
3
4
2
1
TTCC
3
4
TCCC
2
3
4
CCCC
Feed
2. Experimental
Fig. 4. Tree analysis for separation of sillimanite concentrate from non magnetics using flotation cell.
2.1. Material The red sediment samples were collected from badlands topography of Basanputti village, Ganjam Dist, Odisha, India (Lat. 19°210 N and Long. 85°030 E) in form of grid pattern up to the water table level during rainy season. These red sediment samples were thoroughly mixed and a composite sample was prepared. Initially, the representative red sediment sample was scrubbed and deslimed by using hydrocyclone. The slimes were rejected and the sand was used for further studies to recover total heavy minerals. The deslimed bulk sample was subjected to series of rougher, cleaner and scavenger spiral concentrators (stage spirals) to recover Total Heavy Minerals (THM). THM was subjected to magnetic separation using Wet High Intensity Magnetic Separator
Feed
MMMM
NMMM NNMM NNNM
Circuit output NNNN
Fig. 2. Tree analysis for separation of magnetic minerals from beach and by using rare earth drum (1.4 T) [6].
(WHIMS) to recover ilmenite in magnetic fraction and quartz, sillimanite, rutile and zircon in non magnetic fraction. The non magnetic fraction was then subjected to flotation cell to recover high grade sillimanite in tree analysis method as well as in conventional method. The details on recovery of sillimanite feed for flotation is shown in Fig. 3. 2.1.1. Recovery of sillimanite mineral 2.1.1.1. Flotation tree analysis method. Initially about 15 kg of non magnetic fraction sample was subjected to flotation cell batch wise. The concentrate obtained from the experiment was further subjected to flotation cell and non tailing was kept as it was. Again the concentrate obtained was subjected to flotation cell for two more times to separate tailings from it. At the end of the first level experiment, a product obtained which was concentrate in all stages of the first level experiment and named CCCC. The tailing obtained from the first level and first stage experiment was subjected to flotation cell for three times to separate the concentrate from it. At the end of the fourth level and first stage experiment, the product obtained was TTTT. The tailing from first level and second stage of experiment mixed with concentrate from second level and first stage experiment and subjected to flotation cell. The concentrate obtained from the experiment mixed with tailing from first level and third stage experiment and subjected to flotation cell. The concentrate obtained in the product named as CCCT. The tailing from second level second stage experiment mixed with concentrate from 3rd
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx Circuit output units CCCC CCCT
CCTC
CCTT
CTCC
CTCT CTTC CTTT TCCC TCCT TCTC TCTT
CCT
TCT
CTT
CCC
TTC
TCC
Level 4
TTTT
TTCC TTCT TTTC
CTC
TTT
CT
TC
Level 3 TT
CC T Level 2 C
Level 1 Feed
Fig. 5. Four level tree analysis representing with number of ways that concentrate and tailing distributed in different sixteen outputs (identical units can be seen from the output).
Table 1 Physical properties of deslimed red sediment sample.
80000 I: Ilmenite S: Sillimanite R: Rutile Z: Zircon G: Gamet
I
Value
Bulk density (g/cm3) True density (g/cm3) Porosity (%) Angle of repose (°) d80 passing size (lm) Total heavy mineral (THM) (%) Total magnetic heavy minerals (TMHM) (%) Total non-magnetic heavy minerals (TNHM) (%) Very heavy minerals (VHM > 3.3 g/cm3) (%) Light heavy minerals (LHM < 3.3 g/cm3) (%) Slimes (%) Total iron (Fe) in slimes (%)
1.9 2.8 38.4 21.6 290.0 33.2 29.3 3.9 29.6 3.6 29.6 9.7
70000 60000 A.U.
Detail
50000 40000 I
30000 Z
G Z S R
G S I Z S I
I
20000
II
10000 0
10
20
30 40 Two theta
50
60
70
Fig. 7. XRD of total heavy minerals (THM). Zircon 0.32 % Sillimanite 3.33 %
Rutile 0.23 %
Garnet 0.28 %
Others 0.32 %
Ilmenite 28.71 %
Fig. 6. Modal analysis of total heavy minerals (THM).
level 1st stage experiment and subjected to flotation cell. The concentrate obtained mixed with tailing from 2nd level 3rd stage experiment and named CCTT. The tailing from 3rd level and 2nd stage experiment mixed with concentrate from 4th level 1st stage and named CTTT. Tree analysis flotation circuit gives five separate outputs such as CCCC, TCCC, TTCC, TTTC and TTTT. The schematic representation of four level tree analysis procedures on non magnetic fraction of red sediment sample is shown in Fig. 4.
2.1.1.2. Conventional flotation process. The conventional method is a series of branching batch tests on the feed to and from a flotation cell. In four-level tree analysis, there appear four different paths,
each leading to different output units and the same four different paths leading to the one circuit output unit (Fig. 5). Initially about 15 kg of non magnetic fraction sample was subjected to flotation cell batch wise. There are two outputs from first level flotation test, i.e. C and T (C stands for concentrate and T for tailing). In the second level of tree analysis, the product from the first level of tree analysis is again floated into a second level concentrate and a tailing. Also, at this second flotation level, the tailing from the first level test is also refloated into a second level concentrate and a tailing. Thus, after the second level test in the tree analysis there are four outputs: TT, TF, FT and FF. Similarly, in the third level of tree analysis the four outputs from the second level of tree analysis are again floated-each into a concentrate and a tailing component. Thus, after the third level test in the tree analysis there are eight outputs: CCC, CCT, CTC, CTT, TCC, TCT, TTC and TTT. At last, in the fourth level of tree analysis, the eight outputs from the third level of tree analysis are again floated—each into a concentrate and a tailing component. Thus, after the fourth level test in the tree analysis there are sixteen outputs: CCCC, CCCT, CCTC, TTCT, TTTC and TTTT. Because of the symmetry obtained by assuming the same time for flotation in every unit, the number of stages required for feed to reach the desired output product (high grade of concentrate and rejectable grade of tailings) can be expressed by binomial expansion of (C + T)n.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx
59.8
90.5
30.4
81.9
A
92.3
15.9
42.4
B 40.2
29.9
22.6
88.8
90.3
88.6
27.2
5.8
92.1
37.9
37.3
35.9
22.7
12.3
6.5
75.3
82.8
5.4
7.2
H 14.4
91.9
89.4
7.3
42.7
93.8
60.6
66.1
4.4
11. 6
82.1
10.0 CCTT
14.4
6.9 3.6
2.0
CCCC
15.3
7.4
I 31.6
96.5
53.3 CCCT
4. 4 4. 3
94.5
G
F
E
100.0
19.8 5.4
4.3
Rec. (%)
66.1
D 14.5
39.5
Silli. (%)
100.0
10.5
C 29.4
18.1
94.1
Wt. (%)
90.5
51.4
2.8
2.7
J
CTTT
5.5
64.9
5.5
TTTT 29.6
9.4
4.2
Fig. 8. Flow sheet with material balance on recovery of sillimanite from non magnetic fraction of red sediment sample by flotation tree analysis method.
Now, if one assumes that for a flotation, the probability of recovery of sillimanite in one level is same as that of another level, then a C1C2C3T4 path has the same probability as a T1C2C3C4 path or another TiCjCkCl path. Thus, in a four by four-triangular array there are four equally probable ways that a particle may reach the next to the upper circuit output unit (Fig. 5). Moreover, in a four-by-our array there are six equally probable ways that a particle may reach the middle circuit output unit. In a four-step tree analysis there are sixteen different products in the sixteen output units. So, using symmetry, there are many paths but only five with different probabilities-those five different probabilities are shown in Eq. (2). Those five different probabilities are the five different probabilities of getting into five circuit output cells. The outputs of tree analysis and conventional method procedures were evaluated by sink-float methods to estimate the total heavy minerals contains. In order to ensure the accuracy of mineral
Table 2 Results of flotation tree analysis on recovery of sillimanite from non magnetic fraction of red sediment sample. Item
CCCC
CCCT
CCTT
CTTT
TTTT
Total
Weight (%, by weight) Sillimanite (%, by weight) Recovery (%, by weight)
10.5 96.5 15.3
42.7 93.8 60.6
11.6 82.1 14.4
5.6 64.9 5.5
29.6 9.4 4.2
100.0 66.1 100.0
Thus, for a tree of three steps one has the expansion:
ðC þ TÞ3 ¼ CCC þ 3CCT þ 3CTT þ TTT
ð1Þ
Similarly, for a tree of four steps one has the expansion:
ðC þ TÞ4 ¼ CCCC þ 4CCCT þ 6CCTT þ 4CTTT þ TTTT
ð2Þ
Feed
C
59. 5 90.3 81.3
CC
CT
28. 7 92.2 39.2
31. 4 88.6 42.1
40. 5 30.5 18.7
CTC
15. 0 94.5 21.4
13. 1 89.8 17.8
27. 6 89.5 37.4
10. 2 96.7 14.9
4. 8 89.5
CTCC
CCTC
CCTT
9. 2 92.0 12.8
3. 9 84.7
CTTC 5.0
3. 0 88.1
100.0
66. 1
100.0
T
3. 8 81.8
2. 0 59.5
CTTT 4.0
0. 8 57.8
2. 8 89.2
4.7
TCCC 1.8
2. 6 91.1
3.6
0. 8 87.6
3.8
1. 4 84.9
0. 2 66.1
0. 6 77.1
TTC 1.8
5. 1 82.5
TTCC 0.2
TCTT 1.1
36. 3 23.6 13.1
TCT
TCCT
TCTC 0.7
5.6
TCC
CTT
CTCT
25. 6 91.8 35.6
6.5
Rec. (%)
TT
4. 2 88.4
CCT
CCCT
Silli. (% )
TC
CCC
CCCC
Wt. (%)
4. 1 89.4
1. 9 90.3
6.7
TTCT 5.5
1. 0 59.5
2.6
29. 3 9.2
TTTC 0.7
TTT 31. 2 14.2
6.4
0.9
TTTT 4.1
Fig. 9. Flow sheet with material balance on recovery of sillimanite from non magnetic fraction of red sediment.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx Table 3 Results of conventional flotation process on recovery of sillimanite from non magnetic fraction of red sediment sample. Product
Weight (%, by weight)
Sillimanite (%, by weight)
Recovery (%, by weight)
(a) Details of all conventional cell results CCCC CCCT CCTC CCTT CTCC CTCT CTTC CTTT TCCC TCCT TCTC TCTT TTCC TTCT TTTC TTTT Total
10.2 4.8 9.2 3.9 25.6 2.0 3.0 0.8 2.6 0.2 0.8 0.6 4.1 1.0 1.9 29.3 100
96.7 89.5 92.0 84.7 91.8 59.5 88.1 57.8 91.1 66.1 87.6 77.1 89.4 59.5 90.3 9.2 66.1
14.9 6.5 12.8 5.0 35.6 1.8 4.0 0.7 3.6 0.2 1.1 0.7 5.5 0.9 2.6 4.1 100
Detail
Weight (%, by weight)
Sillimanite (%, by weight)
Recovery (%, by weight)
96.7 91.7 88.0 61.8 9.2 66.11
14.9 54.6 22.1 4.3 4.1 100.0
(b) Summary of results obtained from conventional flotation process Product-1 (CCCC) 10.2 Product-2 (CCTC + CTCC + TCCC + TTTC) 39.3 Middling-1 (CCCT + CCTT + CTTC + TCTC + TTCC) 16.6 Middling-2 (CTCT + CTTT + TCCT + TCTT + TTCT) 4.6 Tailing (TTTT) 29.3 Total 100.0
count, all the concentrate and tailing product were subjected to sequential sink-float tests by using bromoform (2.89 g/cm3) and methylene iodide (3.3 g/cm3). Mineralogical studies were carried out both microscopic and X-ray diffraction. The mineralogical phase analysis of products obtained (sink fraction) were carried out using PANalytical X-Pert X-ray powder diffractometer with 0
Mo Ka radiation (k = 0.709 Å A) from 6° to 40° scanning angle at a scanning rate of 0.02°/s. 3. Results and discussion 3.1. Physical properties of sample The physical properties of deslimed red sediment sample of Basanputti are given in Table 1. The bulk density of the sample is 1.9 g/cm3. The d80 passing size of the sample is 290 lm obtained from the size analysis of deslimed feed. The slime content in sediment sample is 29.6% (by weight). The deslimed sample contains 33.2% THM, out of which 29.6% Very Heavy Minerals (VHM) and 3.6% of Light Heavy Minerals (LHM). It is also observed that the sample contains magnetic heavies of 29.3% and non magnetic heavies of 3.9%. The mineralogical modal analysis of THM of typical red sediment samples shown in Fig. 6 indicates that the sample mainly contains ilmenite (28.71%, by weight) followed sillimanite (3.33%, by weight), zircon (0.32%, by weight), rutile (0.23%, by weight), garnet (0.28%, by weight), and other heavy minerals (0.32%, by weight). The mineralogical data indicate that the specific gravity difference between heavy minerals such as ilmenite, rutile, zircon, and sillimanite, and gangue mineral quartz is very much significant. The XRD pattern of the THM of deslimed feed sample shown in Fig. 7 indicates that the sample contains ilmenite followed by sillimanite, zircon, rutile, etc. 3.2. Flotation tree analysis method In the present study, the tree analysis for forth flotation has been attempts when 10 unit operations are involved to recover five
products, out of which one total concentrate (CCCC), one rejectable tailings (TTTT) and three intermediate products. The results of four level tree analyses with number of ways that concentrate and tailings distributed and its mass balance in each path and five output units, such as CCCC, TCCC, TTCC, TTTC and TTTT, are shown in Fig. 8. The summary of results on recovery of sillimanite by way of tree analysis is given in Table 2. The data indicate that the first output of froth flotation tree analysis representing CCCC contains 96.5% grade of sillimanite with 10.5% yield and 15.3% recovery. In the subsequent second output (CCCT) of the unit, a product contains 93.8% sillimanite with 42.7% yield and 60.6% recovery. The rejectable tailings (TTTT) contains 9.4% grade of sillimanite with 29.6% yield and 4.2% recovery.
3.3. Conventional flotation method In conventional flotation method, 15 unite operations are involved in such a way that concentrates and tailings are distributed in 16 outputs, such as CCCC, CCCT and TTTT. The flow sheet with material balance on recovery of sillimanite from non-magnetic fraction of red sediment sample by conventional flotation method is shown in Fig. 9. The details of all conventional cell results are given in Table 3a, and the summary of results obtained from above conventional flotation process is listed in Table 3b. The data indicate that the first output of flotation representing CCCC (Product-1) contains 96.7% grade of sillimanite with 10.2% yield and 14.9% recovery. The subsequent second output (Product-2) of the unit contains 91.7% grade of sillimanite with 39.3% yield and 54.6% recovery. The rejectable tailings contain 9.2% grade sillimanite with 29.3% yield and 4.1% recovery. The sillimanite grade of intermediate products (Middling-1 & 2) varies from 88.0% to 61.8%. In both the flotation methods, the surface chemistry of the cell changes with time. Surface active reagents such as collectors and frothers concentrate in the froth, depleting the flotation cell of these reagents. As the surface chemistry of the cell changes, the probability of a given particle floating in a cell also changes.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021
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T. Laxmi, R.B. Rao / International Journal of Mining Science and Technology xxx (2015) xxx–xxx 600
S
12000
Q S
S
500
10000
S S S Q SS
300 SQ
200
Q
S
SQ
S Q
Intenalty (a.u.)
Intenalty (a.u.)
400 S SQ
CCCC CCCT CCTT CTTT
100 0 0
10
20
30
40
50
60
70
80
90
8000 Q
6000 S
4000
S Q
Q
2000
S
QS
S Q
Q Q
Q S
0 0
10
20
30
40
Two theta (°)
Two theta (°)
(a) Products
(b) Tailings
50
60
70
Fig. 10. XRD of flotation tree analysis.
Four level conventional cleaning and scavenging flotation process need 15 cells to recover sillimanite with similar grade of five products as obtained from flotation tree analysis process. Thus, flotation tree analysis reduces the cost of number of cells and chemical reagents, and minimizes the time, man power and space. 3.4. XRD of flotation tree analysis products Fig. 10 shows the XRD pattern of flotation tree analysis products and tailings. The data of Fig. 10a indicate that the intensity of sillimanite peaks is gradually increasing toward CCCC from CTTT. All products contain maximum sillimanite peaks followed by quartz peaks. The only difference in individual products is the intensity of peaks (sillimanite), whereas the XRD pattern of Fig. 10b indicates that the rejectable tailings of flotation tree analysis contain maximum quartz peaks followed by sillimanite peaks. 4. Conclusions The following conclusions are drawn from the four-level tree analysis of flotation for recovery of sillimanite from non magnetic fraction of red sediments. (1) The deslimed sample contains 33.2% THM, out of which 29.6%VHM and 3.6% of LHM (LHM-Sillimanite mineral). (2) It is observed that there are five outputs of four-level tree analyses CCCC, TCCC, TTCC, TTTC and TTTT needs only 10 flotation cells. (3) The first output of non magnetic fraction of four-level tree analysis contains 10.5% (by weight) with 96.5% grade of sillimanite (CCCC). (4) The second output of tree analysis contains 93.8% sillimanite grade with 42.7% yield.
(5) The rejectable tailing fraction contains 29.6% (by weight) with 9.4% grade of sillimanite (TTTT). In four levels, conventional cleaning and scavenging flotation process need 15 cells to recover sillimanite with similar grade of five products as obtained from flotation tree analysis process. Thus, flotation tree analysis reduces the cost of number of cells, chemical reagents as well as minimizes the time, man power and space. Hence, flotation tree analysis is a novel approach for recovery of sillimanite minerals from red sediments. However, the number of levels varies depending on the feed containing light heavy minerals (sillimanite). Acknowledgments The authors are thankful to the Director, Institute of Minerals and Materials Technology (CSIR), Bhubaneswar for giving permission to use laboratory facilities. One of the authors Ms. T. Laxmi is thankful to BRNS for granting senior research fellowship. References [1] Nicol SK, Bensley CN, Teh KC, Firth BA. The estimation of coal flotation response. In: Membrey W, editor. In: Proceedings of improving froth flotation of coal. Australian Coal Industry Research Laboratories; 1983. p. 116–34. [2] Nicol S. Measurement of coal flotation efficiency using the tree flotation technique. In: Proceedings of 12th international coal preparation congress. Cracow; 1994. p. 1159–66. [3] Dell CC. An improved release analysis procedure for determining coal washability. J Inst Fuels 1964;37:149–50. [4] Pratten Stephen J, Bensley Colin N, Nicol Stuart K. An evaluation of the flotation response of coals. Int J Miner Process 1989;27(3-4):243–62. [5] Meloy TP, Whaley DA, Williams MC. Flotation tree analysis-reexamined. Int J Miner Process 1998;55:21–39. [6] Routray Sunita, Rao Raghupatruni Bhima. Magnetic separation tree analysis for recovery of magnetic minerals from beach sand. Turkish J Sci Technol 2012;7(1):37–48.
Please cite this article in press as: Laxmi T, Rao RB. Flotation tree analysis for recovery of sillimanite from red sediments. Int J Min Sci Technol (2015), http://dx.doi.org/10.1016/j.ijmst.2015.07.021