Processing of Alumina-Rich Iron Ore Slimes: Is the Selective Dispersion–Flocculation–Flotation...

TECHNICAL PAPER TP 2701

Processing of Alumina-Rich Iron Ore Slimes: Is the SelectiveDispersion–Flocculation–Flotation the Solution We Are Lookingfor the Challenging Problem Facing the Indian Iron and SteelIndustry?

Vinay Jain • Beena Rai • Umesh V. Waghmare •

Venugopal Tammishetti • Pradip

Received: 10 January 2013 / Accepted: 5 May 2013

� Indian Institute of Metals 2013

Abstract Beneficiation of alumina rich iron ore slimes is

a major challenge for the Indian iron ore industry. Con-

sidering the limits of gravity and magnetic separation

processes in the relatively finer size range in terms of

achieving adequate separation efficiency, selective flotation

(with and without selective flocculation) of iron ore slimes,

which is being used commercially in several countries for

the beneficiation of iron ores, is worth exploring for the

beneficiation of Indian iron ores. Based on the extensive

work carried out in our laboratories, we have concluded

that the design and development of highly selective

reagents to achieve satisfactory separation of hematite and

goethite from alumina containing minerals (gibbsite or

kaolinite) in the ore and ore slimes, is the key to solving the

challenging problem of processing alumina rich iron ores.

Accordingly our research work has been focused on find-

ing/designing selective reagents for iron oxide–gibbsite–

kaolinite separation based on a molecular modeling com-

putational approach developed by us for the design of

mineral processing reagents. We present in this paper the

results of our density functional theory computations to

evaluate the interaction energies of a wide variety of dif-

ferent reagent functional groups such as carboxylic acid,

hydroxamic acid, phosphonic acid, iminobismethyl phos-

phoric acid, xanthate and starch with hematite, gibbsite and

kaolinite surfaces. Among all the reagents investigated so

far, starch exhibits the highest selectivity towards the

hematite surface with a difference in interaction energy of

*63 kcal/mol between hematite and gibbsite surfaces.

Based on our earlier work which indicated polyvinyl pyr-

rolidone (PVP) to be more selective dispersant for kaolinite

compared to conventional sodium silicate and sodium

hexametaphosphate, we have investigated selective floc-

culation–dispersion of natural iron ore slimes (three dif-

ferent samples obtained from three different mines in

India) with PVP and starch reagent combination. The

results are promising. While the work is still in progress,

the implications of our recent results are discussed in the

context of the challenging problem of processing of alu-

mina rich iron ore slimes in India.

Keywords Hematite � Gibbsite � Goethite � Kaolinite �Selective flocculation � Starch � Iron ore slimes

1 Introduction

India is endowed with rich iron ore deposits. The long term

sustainability of our iron and steel industry depends on the

judicious utilization of this precious asset. Presence of

relatively higher content of alumina in Indian iron ores has

been a cause of concern and a challenging problem for the

industry without a satisfactory solution thus far. India is

currently the fifth largest producer of crude steel in the

world. The steel production in our country is expected to

double within next few years. Our annual iron ore pro-

duction will soon reach more than 300 million tonnes. Out

of a total production of 218.6 million tonnes of iron ore

produced in the year 2009–10, a record 117.4 million

tonnes consisting of 13.2 million tonnes of lumps and

104.2 million tonnes of sinter fines, were exported [1].

With a production capacity of 32 million tonnes per anum

and an annual production of over 20 million tonnes, India

V. Jain � B. Rai � U. V. Waghmare � V. Tammishetti �Pradip (&)

Tata Research Development and Design Centre

(A Division of Tata Consultancy Services Ltd), 54B,

Hadapsar Industrial Estate, Pune, India

e-mail: [email protected]

123

Trans Indian Inst Met

DOI 10.1007/s12666-013-0287-1

is also the largest producer of sponge iron (directly reduced

iron, DRI). Iron ores and ore fines concentrates are thus

needed to satisfy our increasing domestic demand of blast

furnace (BF) and DRI grade products. In order to maintain

the competitive edge of Indian iron and steel industry,

which is beset with extremely serious problems of shortage

of land and water in those states where iron ore deposits are

found, it is absolutely imperative that the state-of-the-art

mineral processing technology is employed to achieve the

desirable target of zero waste, that is, converting the mined

ore into a variety of marketable grade products.

2 Beneficiation of Indian Iron Ores

Iron ores are being beneficiated all around the world using

a wide variety of separation techniques and combinations

thereof such as spiral, floatex density separator, jig, multi-

gravity separator, low and high intensity magnetic sepa-

rator, conventional as well as column flotation and selec-

tive dispersion–flocculation. Recent advances include

Batac jigs, packed flotation column, packed column jigs

and centrifugal concentrators like Falcon Concentrator,

Kelsey jigs and Knelson Concentrator for the beneficiation

of iron ore slimes [2–6]. Until very recently the processing

of hematitic ores in India did not involve any beneficiation

except for whatever rejection of silica (and to some extent

alumina in the form of clays) occurs during washing and

classification of crushed ores. More recently however, with

the successful commissioning of a beneficiation-cum-pel-

letization plant by Essar Steel, the beneficiation of fines

and slimes followed by pelletization of concentrates has

become an economically attractive option for Indian iron

ores [6].

The advantages of beneficiating iron ore fines and slimes

are obvious. It will lead to (a) better utilization of natural

resources (b) higher mine output in terms of marketable

products (c) reduction in the environmental impact of iron

ore mining as a consequence of less residue material

(tailings) for storage and disposal and (d) production of

high value added products leading to higher BF and sinter

plant productivity [5, 6].

Based on extensive research conducted in our labora-

tories, we have proposed that it is possible to come up with

an integrated innovative solution to the processing of

Indian iron ore fines and slimes aimed at achieving zero

waste production [5, 6]. In order to develop a commercially

viable process flow sheet for a given ore deposit and/or

accumulated fines/slimes resource the systematic investi-

gation would necessarily involve establishing (i) the nature

of occurrence, association and liberation characteristics of

the alumina containing minerals available in the deposit (ii)

a comparison of the separation efficiency of various unit

operations for both hematite–goethite/kaolinite/gibbsite

separation in terms of recovery-grade plots (separation

characteristics) and also recovery as a function of particle

size (iii) a preliminary techno-economic assessment of the

various alternate separation flow sheets. It is worthwhile

exploring a beneficiation strategy aimed at the production

of the following three marketable grade products (with no

waste to dispose of ultimately):

• Iron rich concentrate (which can be further converted

into pellets, briquets or sinter) meeting the specifica-

tions of BF grade and/or direct reduction (DRI) grade

marketable product.

• Alumina rich concentrate acceptable as the feed (or an

additive) to Bayer’s process of producing smelter grade

alumina.

• A residue which can be utilized in the production of

value added products such as glass ceramics and

cements.

In the worst case, if the residue (which should be min-

imized to a bare minimum) needs to be stored, one should

employ the semi-dry disposal technology so that there are

no tailings dams created but the residue is stored on a

reclaimable land area which is converted into green forest

in a reasonable period of time [6, 7].

We have earlier articulated and presented the basic

technological elements of an integrated strategy to utilize

alumina rich iron ore deposits [1–6]. A critical review of

the earlier R&D investigations on the reduction of alumina

in Indian iron ores clearly indicates that in addition to

magnetic separation and gravity separation, there is a need

to examine if froth flotation and selective dispersion–floc-

culation are likely to be more effective in the separation of

alumina containing minerals in Indian iron ores, certainly

for those ores having liberation size below 75 l. Consid-

ering the particle size distribution of Indian iron ore slimes

(which are likely to be even finer, if finer crushing is

resorted to for the production of even lower alumina con-

tent in lumps and fines), these two processes appear to be

extremely promising, provided the appropriate reagents are

available [5, 6].

One of the more important findings of earlier investi-

gations is that alumina in Indian iron ore slimes occurs in

the form of two distinct mineral constituents namely,

gibbsite (hydrated aluminum oxides) and kaolinite (and

other clay minerals in minor quantities). Even though not

adequately quantified, the liberation studies also indicate

that a substantial proportion of alumina is present in the

liberated form in the slimes and hence amenable to sepa-

ration by physical means [4–6]. Relative occurrence of

gibbsite and kaolinite differs from deposit to deposit and

hence ore mineral characterization on a representative

sample of the particular deposit is absolutely essential. The


123

final flow sheet may differ depending on the relative

occurrence of these two gangue minerals.

The occurrence of alumina in the matrix of iron ore

minerals, in particular goethite has also been reported. The

iron ores containing significant proportion of goethite are

thus more difficult to beneficiate because (a) relatively less

iron content in the goethite (62.9 % Fe) as compared to

hematite (70 % Fe) and magnetite (72.4 % Fe), bringing

down the concentrate grade with respect to iron content and

(b) the alumina present in the goethite matrix appears to be

less amenable to physical separation (the scientific reasons

underlying this observation are not yet established) which

causes problems in achieving the final concentrate grade

with respect to alumina content as well.

2.1 Froth Flotation and Selective Flocculation of Iron

Ores and Ore Slimes

Flotation process for concentrating iron ores received a big

impetus in USA immediately after the Second World War

due to the dwindling resources of direct shipping iron ores

in the Lake Superior District. Flotation of iron ores

essentially for silica removal has been reviewed exten-

sively in literature [8–12]. The iron ore industry in Min-

nesota and Michigan in US uses cationic flotation of silica

from magnetic taconites at a rate of 40 million tonnes

annually [8]. Column flotation technology for rejecting fine

silica using a variety of cationic amines was also com-

mercialized in iron ore industry including at Kudremukh

(plant is now however closed due to environmental rea-

sons) in India [6, 7, 9–22].

Cleveland Cliffs is the largest producer of iron ore pellets

in North America with a combined production capacity of 38

million tonnes and operating six mines located in Michigan,

Minnesota and Eastern Canada. In order to process finely

disseminated large deposits of oxidized taconites containing

predominantly hematite and goethite, US Bureau of Mines in

the late sixties developed a process involving selective

flocculation and desliming followed by cationic flotation of

coarse silica. It was commercialized for the first time in 1974

at the Cleveland Cliffs Iron Co’s Tilden Concentrator in USA

[8–10]. The 4.1 million tonnes per year capacity plant was

later expanded to produce 8.2 million tonnes per year of

pellets assaying 64 % Fe. The plant flow sheet involves

dispersion of minerals using sodium hydroxide in combina-

tion with sodium silicate/lignosulfonates/hexametapho-

sphate or tripolyphosphates during grinding followed by

selective flocculation of iron minerals using starches (for

example, tapioca flour starch). The settled (flocculated)

concentrate is then subjected to reverse flotation with cat-

ionic amine reagents in order to remove coarse silicates.

Starch thus works both as a selective flocculant and as a

depressant for iron minerals [8–10].

It is interesting to note that the hard taconites are ground

to 80 % minus 74 l in autogenous mills in Tilden Mine

concentrator. In addition to silica, the phosphate minerals

are also rejected during selective flocculation–flotation of

Tilden ore [9, 10]. Similar flotation plants are operating

in Sweden to remove phosphate impurities from iron

ores [18].

Flotation (with and without selective flocculation) is

thus an established and highly successful commercial

process in iron ore industry for removing silica and phos-

phate impurities. The reduction of alumina containing

minerals (kaolinite and gibbsite) by flotation is however

not yet investigated adequately and thus remains a chal-

lenging problem yet to be solved. The key to developing a

successful flotation separation process for Indian iron ores

and ore slimes is thus to find selective reagents for the

separation of iron ore minerals (hematite and goethite)

from alumina containing minerals (gibbsite and kaolinite).

We have systematically investigated and reported earlier,

the possibility of achieving selective separation amongst

hematite–alumina–kaolinite–montmorillonite minerals, the

mineral constituent representative of Indian iron ore slimes

by a selective dispersion–flocculation route [4–6, 23–27].

We have successfully utilized first principles quantum

chemical computations for the design of reagents (floccu-

lants, dispersants and flotation collectors) for a wide variety

of mineral separation problems [28–35]. We present our

most recent results on the design of reagents for separation

of hematite from associated kaolinite and gibbsite minerals

in the following section.

3 Design and Development of Highly Selective

Reagents for the Beneficiation of Indian Iron Ore

Slimes

We have employed first-principles density functional the-

ory (DFT) as implemented in the PWscf code within the

Quantum Espresso package [36] running on the EKA

supercomputer at the Computational Research Laborato-

ries, Pune to model the reagent–mineral interactions. The

generalized gradient approximation of Perdew, Burke and

Ernzerhof [37] is used for the exchange–correlation func-

tional. Vanderbilt ultrasoft pseudo-potentials [38] are used

for describing the ionic cores. The Kohn–Sham wave

functions are expanded using a plane–wave basis-set up to

a kinetic energy cutoff of 25 Ry and charge density with a

cutoff of 180 Ry. Structural relaxations are performed until

the total force on each atom is less than 0.01 eV/Bohr. The

bulk hematite, gibbsite and kaolinite structures are fully

optimized with Brillouin zone integrations sampled on

Monkhorst-pack grids of 3 9 3 9 2, 2 9 2 9 2, and,

5 9 3 9 4 k-points, respectively. The principal cleavage


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surfaces namely (0001) and (001) are modeled for hematite

and gibbsite/kaolinite, respectively. The surfaces are cre-

ated by introducing a 10 A vacuum along the c-axis in the

bulk structure of minerals. The starting configurations of

the functional groups on the mineral surfaces are created

using Graphical Visualizer of Materials Studio [39]. The

interaction energies are computed using the expression:

DE ¼ Ecomplex � Esurface þ Emoleculeð Þ

where Ecomplex is the total energy of the optimized

complex, and Esurface and Emolecule are the total energies

of the isolated mineral surface and reagent, respectively.

The more negative magnitude of interaction energy (DE)

indicates stronger interactions between the reagent and

the mineral surface. The readers are referred to our

earlier papers on molecular modeling computations for

the design of mineral processing reagents for more

details [40].

3.1 Hematite [a-Fe2O3], Gibbsite [a-Al(OH)3]

and Kaolinite [Al2Si2O5(OH)4] Crystal Structures

The crystal structures of hematite and gibbsite are modeled

and the results are compared with literature. The conven-

tional hexagonal unit cell of hematite contains 30 atoms

(18-O and 12-Fe) with oxygen atoms occupying the hex-

agonal close packed lattice sites and the iron atoms filling

two-thirds of the octahedral voids (Fig. 1a). The ideal

stacking of atomic layers along the c-axis can be described

by the sequence ��Fe–O3–Fe–Fe–O3–Fe�� (the subscript

denotes the number of atoms per unit cell in that particular

layer). Hematite, in its ground state, is antiferromagnetic

with Fe-atoms within Fe–Fe double layers having parallel

spins while those between adjacent double layers (sepa-

rated by an O3 layer) having antiparallel spins. The spins

are aligned parallel to the c-axis. The spin polarized cal-

culations used in this work successfully predict this

Fig. 1 Bulk structures of

a hematite, b gibbsite, and

c kaolinite. Red, indigo, blue,

pink and orange spheres

indicate O, Fe, H, Al and Si

respectively.

(Color figure online)


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antiferromagnetic structure to be the most stable ground

state structure with lattice parameters a = 5.013 A and

c = 13.801 A [40] which are within 0.5 % of the experi-

mental values of a = 5.035 A and c = 13.747 A [41].

Unlike hematite, gibbsite has a fairly open, monoclinic

structure consisting of sheets of Al(OH)3 bound together by

weak hydrogen bonds (Fig. 1b). Each sheet consists of a

OH-double layer with Al-atoms occupying two-thirds of

the octahedral voids within this double layer. The com-

puted lattice parameters for gibbsite are also in good

agreement with experimental and previous calculated DFT

results [40].

Kaolinite is a layered aluminosilicate mineral possessing a

triclinic structure with C1 (centered symmetry) space group

(Fig. 1c). Within each layer, there is an aluminate sublayer,

wherein each aluminum atom coordinates octahedrally with six

oxygen atoms, connected through oxygen atoms to a silicate

sublayer, wherein each silicon atom coordinates tetrahedrally

with four oxygen atoms. The O-atoms connected to Al-atoms

are hydroxylated. The layers are held together by hydrogen

bonds across the (001) cleavage plane. The calculated lattice

parameters: a = 5.184 A, b = 9.002 A, c = 7.387 A,

a = 91.89�, b = 105.13� and c = 89.48� agree well with

those determined experimentally [42] by X-ray single crystal

diffraction: a = 5.154 A, b = 8.942 A, c = 7.401 A, a =

91.69�, b = 104.61�, c = 89.82�. The results are also in good

agreement with previous DFT calculations by Hu and Mi-

chaelides [43].

3.1.1 Hematite (0001) Surface

The Fe-terminated (0001) surface, the most stable surface

under ultra high vacuum conditions [44–46] is character-

ized by large relaxations caused by the breakage of three

Fe–O bonds. As described in our earlier work [40], the

calculated relaxations compare well with the previous

theoretical and experimental results. We computed the

relaxations for both 18-layer (full unit cell) as well 9-layer

slabs (half unit cell). The magnitudes of relaxations for the

9-layer slab are very close to that for the 18-layer slab.

Hence, the subsequent adsorption studies are conducted on

the 9-layer slab only.

3.1.2 Gibbsite (001) Surface

The (001) surface is the basal and the predominant cleav-

age plane for gibbsite. The gibbsite (001)-terminated sur-

face is created cleaving the optimized crystal along the

c-axis through the interlayer hydrogen bonds. The gibbsite

(001) surface has (OH)-terminated Al-layers with 2/3rd of

the OH-groups aligned almost vertical and 1/3rd aligned

almost parallel to the (001) surface.

3.1.3 Kaolinite Surface

The (001) surface, the predominant cleavage plane in

kaolinite [47, 48], was created by adding a vacuum of 10 A

between adjacent (001) layers of the optimized unit cell so

as to induce the breakage of the interlayer hydrogen bonds.

Relaxation of this slab structure did not lead to any sig-

nificant changes in the atomic arrangements compared to

that of the bulk, unlike in the case of hematite. The Al–O

and Si–O bond lengths did not change much (\0.1 A) and

Fig. 2 Structures of a amylose and b amylopectin molecules

Fig. 3 Optimized glucose dimer. Red, fluorescent, and blue spheres

represent O, C and H respectively. (Color figure online)


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O–H bond lengths were within 0.005 A of the bulk struc-

ture. These results compare well with similar observations

made by Hu and Michaelides [43].

As evident from the bulk structure, kaolinite slab has

two different surface terminations—an octahedral surface

terminated by hydroxyl (or Al–OH) groups, and a tetra-

hedral surface terminated by basal oxygen atoms (or Si–O).

We have investigated adsorption of starch on both these

surfaces.

3.2 Reagent Molecules

We have computed interaction energies for a wide variety

of reagents. The discussion however is confined to starch in

this paper. Starch, being a large and complex polymer,

cannot be modeled as it is through DFT. Since the starch

Table 1 Comparison of calculated bond-lengths in a-D-glucose

dimer with what is reported in the literature

Bond distance This work BLYP

(Ibrahim et al. [50])

C–C (CH2O) 1.532 1.532

C–C (ring) 1.529 1.559

C–H 1.107 1.108

O–H 0.978 0.980

C–O 1.430 1.447

Fig. 4 Optimized structures of

starch complexes with

a hematite (0001), b gibbsite

(001), c kaolinite Al–OH

terminated and d kaolinite Si–O

terminated surfaces


123

molecule consists of linear (amylose) and branched (amy-

lopectin) polymeric fractions of the a-D-glucose monomer

(Fig. 2), for the adsorption studies, a smaller glucose dimer

molecule consisting of two a-D-glucose monomers joined

together by the C1–C4 linkage is modeled as representative

of starch polymer. It is assumed that the glucose dimer will

adsorb in a similar way as the starch molecule. This

assumption is supported by Pavlovic and Brandao [49] who

have reported identical infrared spectra for the adsorbed

glucose dimer and starch on the hematite surface indicating

similar adsorption mechanisms.

The structure of glucose dimer was optimized using a

cubic box of 13.23 A (25 Bohr) (Fig. 3). As shown in

Table 1, the calculated bond lengths compare well with that

of a-D-glucose obtained using DFT BLYP method [50].

3.3 Mineral–Starch Complexes and Interaction

Energies

The DFT optimized structures of mineral–starch complexes

are shown in Fig. 4 for hematite, gibbsite and kaolinite. The

corresponding computed interaction energies for starch are

compared in Table 2 along with those for adsorption of

water. Since the absolute magnitude of interaction energy is

higher for starch and hence it implies that it would replace

water at the surface. It is also evident that starch shows

relatively higher interaction energy for the hematite surface

as compared to gibbsite and kaolinite, suggesting starch to

be selective towards hematite. This can also be seen in the

optimized complexes (Figs. 4, 5) where strong Omolecule–Fe

complexes are formed with Fe-atoms on the hematite sur-

face as opposed to only weak hydrogen bonds (shown by the

dotted lines) on the gibbsite and kaolinite surfaces.

The highlight of these computations is the large differ-

ence in the magnitude of interaction energies between

starch–hematite and starch–gibbsite as well as the starch–

kaolinite complexes. This observation thus suggests the

possibility of starch being relatively more selective towards

the hematite surface in hematite–gibbsite–kaolinite mix-

tures. Our computations are consistent with FTIR spec-

troscopy results reported by Subramanian et al. [51] which

indicate chemical interactions between the Fe-atom of

hematite and starch adsorbate.

3.3.1 Templating Effect for Starch Interactions

with Hematite Surface

We have further examined the initial and final structures of the

starch–hematite complex and found that the O–O distances in

the dimer are very close to the corresponding Fe–Fe distances

on the hematite surface which possibly facilitates very strong

Fe–Ostarch interactions. As shown in Fig. 5, the initial O–O

distance of 5.30 A in the dimer is similar to the corresponding

Fe–Fe distance of 5.01 A on the hematite surface. With pro-

gress in optimization, the O–O distance relaxes from 5.30 to

5.03 A without straining the molecule much, but involving

energetically less expensive rotations of different molecular

parts. As a result, the O–O distance in the optimized complex

becomes remarkably close to the corresponding Fe–Fe distance

on the hematite surface (5.00 A). Such a perfect match between

Table 2 DFT computed interaction energies of starch and water on

hematite, gibbsite and kaolinite surfaces

Reagent Interaction energy (-kcal/mol)

Hematite (0001)

surface

gibbsite (001)

surface

Kaolinite (001) surface

Fe-terminated Al–OH

terminated

Al–OH

terminated

Si–O

terminated

Starch 74 11 11 4

Water 21 13 14 1

Fig. 5 a Initial and b optimized

starch–hematite complexes

showing templating effect

between O–O atoms in starch

and Fe–Fe atoms on hematite

surface


123

the O–O (starch) and Fe–Fe distances gives rise to a templating

effect responsible for the high interaction energy for starch–

hematite. This binuclear complexation mechanism based on

strong affinity between Fe and Ostarch atoms aided by a tem-

plating effect, arising out of a close correspondence between

Fe–Fe distance on the hematite surface and O–O distance in

starch, is the most plausible mode for starch adsorption on

hematite. In fact, our findings are in good conformity with the

hypothesis proposed by Ravishankar et al. [23] to explain the

experimentally observed stronger adsorption of amylopectin

over amylose on hematite. Amylopectin, being a branched

molecule will have more number of end groups (C1–O and

C4–O) which would lead to higher adsorption. The observed

templating effect from our results, which also involves a C4–O

group, substantiates their hypothesis.

4 Experimental Studies on the Selective Dispersion–

Flocculation of Natural Iron Ore Slimes from Indian

Mines

4.1 Experimental Materials and Methods

Polyvinylpyrrolidone (PVP) of average molecular weight

360,000 is procured from Aldrich chemical company and

corn starch (73 % amylopectin and 27 % amylose) is

procured from Sigma chemical company. Analytical

grade HNO3 and NaOH are used for the pH adjustment.

Iron ore slime samples are obtained from three different

mines of India. The slime sample was characterized by

sieve analysis, wet chemical analysis and X-ray diffrac-

tion (XRD).

Flocculation experiments are performed using PVP as

dispersant and starch as flocculent. PVP solution (1 ppm) is

prepared by dissolving PVP in distilled water. The pH of

PVP solution was further adjusted to a desired value.

Causticized starch stock solution (10,000 ppm) was pre-

pared by dissolving causticized corn starch in distilled

water. The dry slime sample was added to the PVP solution

(at 1–5 % pulp density) and ultrasonicated for 5 min. This

slime dispersion was further conditioned for 30 min in a

flocculator at an impeller speed of 100 rpm. The pH was

measured again and adjusted to desired value. The required

amount of starch was then added and the impeller speed

was reduced to 40 rpm for conditioning the slurry for

additional 3 min. After conditioning with the starch agi-

tation was stopped and the suspension was allowed to settle

for desired time and the flocculated portion was separated

by decantation. Flocculated and suspended portions were

dried and analyzed by wet chemical method and XRD. To

determine the best conditions for the dispersion–floccula-

tion experiment for the reagent combination of PVP dis-

persant and starch flocculent, a step by step procedure is

followed. In each step effect of one parameter is tested by

keeping the other parameters constant. The effect of pH,

reagents and their dosages, pulp density and settling time,

were studied and then experiments were performed at

optimum condition.

4.2 Results

The natural iron ore slimes samples obtained from three

different mines in India were characterized by XRD. As

illustrated in Fig. 6, the samples do contain all the four

minerals namely, hematite, goethite, kaolinite and gibbsite

but in differing proportions. It is also important to note that

while sample I and II are minus 37 microns, the sample III

is extremely fine, that is, 100 % minus 8 l.

Table 3 Selective dispersion–flocculation results obtained with three natural ore slimes samples (experimental conditions: pH 11.5, 1 ppm PVP,

20 ppm starch, 1 % pulp density, settling time 15 min)

Samples Feed Concentrate Performance

%Fe %Al2O3 %LOI %Fe %Al2O3 %LOI %Fe recovery Yield (%)

Sample I 58.2 7.2 5.6 66.4 3.4 3.2 70.6 61.7

Sample II 49.1 12.1 8.9 54.7 7.7 5.9 58.9 52.9

Sample III 44.9 11.0 9.5 51.8 8.2 9.2 70.0 60.5

Fig. 6 X-ray diffraction patterns of slime samples (H hematite, Go

goethite, G gibbsite and K kaolinite)


123

A wide variety of experimental conditions were studied

as per the standard procedure to arrive at a combination of

pH, flocculant dosage, pulp density and settling time for the

best results [52]. Highly alkaline pH, that is, above 10.5 is

preferred. The results obtained for all three samples with

starch–PVP combination, under the preferred experimental

conditions (pH 11.5, 1 % pulp density, 1 ppm PVP and

20 ppm starch) are compared in Table 3. It was possible to

upgrade all the three samples of iron ore slimes but the best

results were obtained with Sample I. We were able to

produce a concentrate assaying 66 % Fe and 3.4 % Al2O3

at a yield of 67 % starting from a feed assay of 58.2 % Fe

and 7.2 % Al2O3. These preliminary results are indeed

promising and do validate our molecular modeling com-

putations. A lot more work is needed to quantify the dis-

persion–flocculation of each mineral constituent. The work

is in progress to ascertain the extent of liberation of dif-

ferent minerals as well as the extent of Al substitution in

the matrix of goethite, if any.

5 Concluding Remarks

It is important to design highly selective reagents to

achieve the desired level of separation efficiency in the

hematite–goethite–kaolinite–gibbsite separation system.

Molecular modeling computations are of great value in the

screening/identification/design of promising reagents.

Starch–PVP combination has shown promise in the bene-

ficiation of alumina rich Indian iron ore slimes.

Acknowledgments The authors sincerely thank Computational

Research Laboratories, Pune for providing access to the EKA High

Performance Supercomputer facility for molecular modeling com-

putations. The authors are grateful to M/s Tata Steel, Steel Authority

of India Ltd and JSW Steel Ltd for providing the samples of natural

iron ore slimes for our investigations. The help, support and

encouragement received from Mr K. Ananth Krishnan, Chief Tech-

nology Officer (CTO), Tata Consultancy Services (TCS) during the

course of this work is gratefully acknowledged.

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