Comparative Study of the Mineral Composition and Its Connection with Some Properties Important for the Sludge Flocculation Process-Examples from Omarska Mine

Article Comparative Study of the Mineral Composition and Its Connection with Some Properties Important for the Sludge Flocculation Process-Examples from Omarska Mine

Ljiljana Tankosić 1,*, Pavle Tanˇcić 2 ID , Svjetlana Sredić 1 and Zoran Nedić 3 1 Faculty of Mining Prijedor, University of Banja Luka, Save Kovacevica bb, 79101 Prijedor, Bosnia and Herzegovina; [email protected] 2 Geological Survey of Serbia, Rovinjska 12, 11000 Belgrade, Serbia; [email protected] 3 Faculty of Physical Chemistry, University of Belgrade, Studentski Trg 16, 11000 Belgrade, Serbia; [email protected] * Correspondence: [email protected]

Received: 6 February 2018; Accepted: 15 March 2018; Published: 20 March 2018

Abstract: Studied sludge samples are composed of major goethite and quartz; less clay minerals; and minor magnetite, hematite, clinochlore and todorokite. They have quite similar qualitative, but different semi-quantitative compositions. There are similar particle size distributions between the samples, and the highest contents of ~50% belongs to the finest classes of <6 µm. Among size classes within the samples, almost identical iron contents are present; indicating their similar mineral compositions, which make these systems very complex for further separation processes. Sludge II has a higher natural settling rate, due to its higher density and mineral composition. With addition of the flocculant, settling rates increase significantly with the increase of the liquid component in both of the samples. The effect of flocculant on the settling rate is different between samples, and depends on their mineral composition. The time of settling does not play a role in selectivity, to the ratio of the mass of floating and sinking parts, and iron content does not change with time. The content of iron partially increases by flocculation; therefore, this method should be considered as an appropriate one. Zeta potential values for sludge are mostly between those for goethite and quartz, indicating their particle mixture and intricately association.

Keywords: sludge; characterization; goethite; quartz; clays; particle size distribution; ﬂocculation properties; zeta potential

1. Introduction The iron ore deposit “Omarska” (Bosnia and Herzegovina) is located in the northeast part of the “Sana paleozoic”, or the Ljubija metalogenetic region; and in the Omarska-Prijedor field between the cities of Prijedor and Banja Luka [1,2]. Iron ore from Omarska mine was previously assigned as limonite ore in association with clay minerals, quartz and some manganese minerals [1]. As it is well known, limonite is one of the two principal iron ores (the other being hematite, Fe2O3), and has been mined for the production of iron. It should be classified mainly as a mixture of hydrated iron oxides and hydroxides, with a chemical formula which could be written as Fe2O3 × H2O. Furthermore, it is porous and often wrapped with fine-grained clay and other minerals. Because of that, X-ray powder diffraction (XRPD), Fourier transform infrared (FTIR) and scanning electron microscope-X-ray microanalysis (SEM-EDS) analyses were recently performed for obtaining more precise mineral determination [2]. These studies showed that primary natural mineral raw materials from this deposit are generally composed of major goethite-FeO (OH), quartz and clay minerals; in association with more or less minor

Minerals 2018, 8, 119; doi:10.3390/min8030119 www.mdpi.com/journal/minerals Minerals 2018, 8, 119 2 of 19 contents of hematite, magnetite, feldspars, amphiboles, chloritoid, etc. Clay minerals were identified as illite-sericite which prevails over kaolinite, and with chlorites which appear only sporadically [2]. According to the data from [3], the average content of Fe from this ore is 36.12%, but it is highly variable and ranges from 18.19% to 54.71%. Processing of ore in the Omarska mine is carried out by the following methods: washing, sieving, grading and magnetic concentration [1,2]. Crude ore is firstly washed in the washing drum, and then continues to wet sieving, grinding and grading by spiral classifier and the mechanical hydro-cyclones. The larger classes from hydro-cyclone go to the thickener and further to the magnetic separation. The smaller classes (<25 µm) overflow from hydro-cyclone to the tailing in the form of sludge. On the one hand, this sludge is an environmental problem; but on the other hand, if it contains significant amounts of limonite, we believe that it could be economically useful as iron ore, if an adequate technique for its sufficient concentration could be experimentally approved and applied. Conventional techniques for the preparation of iron ore, such as magnetic and wet separation, give good results only in the case of high-quality coarse ore; however, in the case of low grade or fine ores, they are often insufficiently effective [4–9]. An important economic and ecologic problem in the mineral processing of fine ores, is the large quantity of sludge disposed as final waste. Fine iron ores most often contain clays, quartz and other minerals as gangue material along with iron minerals; therefore, such sludge is a complex system. This is the reason why many studies are conducted in order to select the best technique for the evaluation of complex ores with large amounts of fine particles. One of the most studied techniques for removing gangue from fine iron ores is selective flocculation [10–14]. However, knowledge about fine limonite ore processing is still relatively poor. The selective separation of useful minerals from gangue in a complex system such as sludge, is difficult in any case, and depends on the individual components of the sludge and their behavior [15–17]. Some of the most important criteria for the selection of the most adequate processing method are related to the sludge mineralogy, its particle size distribution and flocculation behavior properties. The aim of this study is mineralogical characterization of two sludge samples with different initial content of iron derived by hydro-cyclone overflow generated during the processing of iron ore in the Omarska mine. In order to determine the mineral phases present and, in particular, their major impurities, XRPD, FTIR, chemical and SEM-EDS analyses were performed. Also, the particle size distributions and iron contents by size class were compared. Some other properties important for the sludge flocculation process, such as: natural settling; settling with the addition of flocculant; effect of solid concentration; and zeta potential variations were studied, as well. Hence, a comparative study of the mineral composition and its connection with some properties important for the sludge flocculation process, was processed.

2. Materials and Methods

2.1. Materials Iron ore in the Omarska mine is abundant in large quantities of small classes, and such as that those that come to the magnetic concentration plant. The overflow from spiral mechanical classifier (classes 0–0.5 mm) goes to the hydro-cyclone. The larger classes, namely “sand” from hydro-cyclone (classes 0.025–0.5 mm) go to the thickener; whereas the smaller classes, namely, overflow from hydro-cyclone (classes <0.025 mm), go to the tailing in forms of sludge. Two such characteristic sludge samples with different content of Fe (i.e., 29.43%—marked as sludge I; and 41.19%—marked as sludge II) were chosen and taken for further analysis. All reagents used were of analytical grade, and they were prepared as solutions in distilled water. The sodium hexametaphosphate (SHMP; Na6P6O18), manufactured by Lach-Ner, s.r.o. (Neratovice, Czech Republic), was used as dispersant. As flocculant, anionic polyacrylamide (PAM) type SUPERFLOC A100, manufactured by Kemira (Helsinki, Finland), was used. Preparation of flocculant Minerals 2018, 8, 119 3 of 19 for all experiments was carried out in the same way according to the instructions provided by the manufacturer of reagents [18]. As pH modifier, 0.1 M NaOH was used.

2.2. Methods

2.2.1. Characterization Methods Chemical analyses were performed by wet analysis according to BAS ISO 2597-1:2012 standard for determination of iron ore. Density of sludge samples was determined by the pycnometer-using method. Samples were dried at 105 ◦C to constant weight prior to determination, and distilled water was used at room temperature (20 ◦C). For pH measurements, laboratory pH-meter type SENSION TM pH31, HACH Lange GmbH (Dusseldorf, Germany), was used. The X-ray powder diffraction (XRPD) patterns of sludge I were obtained by a Philips PW-1710(Amsterdam, the Netherlands)automated diffractometer using a Cu tube (λ = 1.54060Å) operated at 40 kV and 30 mA. The instrument was equipped with a curved graphite monochromatic diffraction beam, and Xe-filled proportional counter. The diffraction data were collected in the 2θ a Bragg angle ranges from 4◦ to 65◦, counting for 0.25 s at every 0.02◦ steps. The divergence and receiving slits were fixed at 1◦ and 0.1 mm, respectively. The XRPD measurements were performed ex situ at room temperature in a stationary sample holder. The alignment of the diffractometer was checked by means of a standard Si powder material. Identification of the present mineral phases was done by comparison of the inter-planar spacings (d) and relative intensities (I) with the literature data, which is a corresponding card from the International Centre for Diffraction Data, the Powder Diffraction File ICDD-PDF database (https://www.icdd.com). Fourier Transform Infrared (FTIR) spectroscopy of both sludge samples was performed using Thermo Nicolet 6700 (Thermo Fisher Scientific, Waltham, MA, USA), at the spectral area 4000–400 cm−1, with resolution of 2 cm−1 and with 32 scans. Samples were powdered in agate mortar. For the spectra recording, KBr was used as following: in 150 mg of powdered KBr, 1 mg of the studied sample was added; then, it was homogenized and tablets were made under specific pressure and vacuum. Identification of the present mineral phases was done by comparison with the IR spectra from reference data of the corresponding mineral phases. Scanning Electron Microscope-SEM analyses were carried out in a JEOL JSM 6610LV (JEOL Ltd., Tokyo, Japan) coupled with Energy Dispersive Spectrometry-EDS by EDS detector model X-Max Large Area Analytical Silicon Drift connected with INCA Energy 350 Microanalysis System for sludge I. XRPD and SEM-EDS analyses of sludge II were performed in the laboratory of Global Research and Development, Mining and Mineral Processing, Maizières-lès-Metz, France. XRPD data were obtained on BRUKER D2 PHASER by using software DIFFRAC.EVA V4.0 (Bruker, Billerica, MA, USA); whereas SEM-EDS data were obtained on HITACHI “TM 3000” Tabletop Microscope (Tokyo, Japan) with High Sensitivity BSE Detector.

2.2.2. Particle Size Distribution Analysis For particle size distribution, Beaker decantation analysis was used for both samples, according to the following procedure: (i) 100 g of the limonite sludge, previously dried at 105 ◦C; (ii) two cups of 2 L; and (iii) six cups of 1 L volume. For dispersant, 0.002 M of sodium hexametaphosphate (SHMP) was used. The pulp was gently stirred in order to disperse the particles through the whole volume, and then it was allowed to stand for the calculated time. The water which was above the end of the tube was syphoned off, then dried and weighed. Velocity of the particles and time of settling was calculated according to forms from the literature [19]. Minerals 2018, 8, 119 4 of 19

2.2.3. Settling and Flocculation Experiments The natural settling and settling with the addition of flocculant were determined for both samples. Also, different solid/liquid relationships (marked as S:L) of: 1:7; 1:9.7; and 1:19.7; at the same conditions were used. For settling, a graduated glass cylinder with volume of 100 mL was used. The flocculation experiments were performed at a pulp density with three different solid/liquid ratios (i.e.,: 1:7; 1:9.7; and 1:19.7). All of the tests were performed at pH = 7, which is an average pH value in real conditions. The tests of selectivity were carried out according to the following procedure: (i) Beaker of 1000 mL volume for better visualization of the dispersion; (ii) 69 g of dry sample in 500 mL of distilled water for the experiment (which is adequate to the simulation of natural industrial conditions); (iii) 50 g/t of flocculant A100; and (iv) 1000 g/t of dispersant SHMP.

2.2.4. Zeta Potential Measurements Zeta potential measurements were performed using a ZM3-D-G meter, Zeta Meter system 3.0+, with direct video imaging from Zeta Meter Inc., Staunton, VA, USA; at Universidad Federal de Minas Gerais, Belo Horizonte, Brazil. The measurements were carried out according to the following procedure: samples were classiﬁed through the sedimentation in 250 mL test tubes, with a mineral concentration of 80 mg/L to reach a particle size below 10 µm. Distilled water was used during the sedimentation procedure with the natural minerals, and the dispersant reagents solutions were used for the remaining sedimentation tests. The pH of the mineral suspensions, with or without dispersant reagents, was adjusted at the beginning of the sedimentation procedure. Before each test, the completely opened Zeta Meter cell was ﬁrst washed intensively with tap water, and after that, with distilled water. Before each measurement, the platinum and the molybdenum electrodes were washed with distilled water. The voltage used in the test was always the highest possible voltage that did not generate vortex due to the heating of the suspension during the measurements.

3. Results and Discussion

3.1. Physicochemical Characterization Basic physicochemical characteristics were given in Table1. The chemical analyses of two sludge samples show significant differences, and therefore, that was the main reason why they were selected for further comparisons. Sludge I is significantly poorer in terms of desirable Fe content, and contains a significantly higher amount of undesirable gangue impurities, such as Si and Al. On the other hand, sludge II is richer in terms of Fe; and with lesser content of the Si and Al impurities than the sludge I, but still relatively high. Determined LOI contents, Si/Al ratios and pH values are very similar in both samples. The Si/Al ratios indicated the presence of free quartz and alumosilicates in both of the samples. Sludge II have higher density and S/L ratio than sludge I.

Table 1. Basic physicochemical properties of two sludge samples.

Chemical Compositions (in Mass %) Sample Si/Al Ratio S/L Ratio pH Value Density (g·cm−3) Fe Mn SiO2 Al2O3 LOI Sludge I 29.43 2.56 30.05 9.66 9.32 3.11 1:9.1 6.96 3.000 Sludge II 41.19 1.98 18.85 6.03 10.68 3.13 1:7 7.02 3.526

3.2. XRPD Study The obtained results of the XRPD studies are shown at Figure1 and Table2. By comparison of these, it can be clearly seen that there are more or less similar qualitative mineral compositions, but with their different semi-quantitative contents. It can be concluded that sludge samples consist mostly of goethite-FeO(OH) and quartz (SiO2), which prevails over clay minerals. Minerals 2018, 8, 119 5 of 19

Also, the minor contents of magnetite, hematite, clinochlore and todorokite (with composition of: (Na,Ca,K,Ba,Sr)1−x(Mn,Mg,Al)6O12 × 3–4H2O), were determined. Identiﬁcation of clay minerals indicates that they are mostly of illite-sericite and kaolinite composition, and with chlorites, which appears only sporadically. Sericite is mainly of the muscovite mica type.

Table 2. Identiﬁed qualitative and semi-quantitative mineral compositions by XRPD studies. Minerals 2018, 8, x 5 of 19 Sample Sludge I Sludge II Table 2. Identified qualitative and semi-quantitative mineral compositions by XRPD studies. Qualitative and Quartz, goethite, clays Goethite, quartz, illite-sericite, clinochlore semi-quantitativeSample (of illite-sericite-kaoliniteSludge I Sludge II and todorokite. mineralQualitative composition and Quartz,composition), goethite, magnetiteclays (of illite- and hematite. Goethite, quartz, illite-sericite, clinochlore and semi-quantitative sericite-kaolinite composition), todorokite.Goethite (~65%) which prevails over mineral composition magnetite and hematite. Quartz (~44%) and goethite (~38%) quartz (~21%) and illite-sericite (~10%). Goethite (~65%) which prevails over quartz Quartzwhich (~44%) prevails and over goethite clay minerals In the sample, there are also most Comment (~21%) and illite-sericite (~10%). In the sample, (~38%)(~10%), which magnetite prevails (~5%)over clay probably present clinochlore (chlorite Comment there are also most probably present mineralsand hematite (~10%), (~3%).magnetite (~5%) group) and todorokite with about clinochlore (chlorite group) and todorokite and hematite (~3%). ~2% of each. with about ~2% of each.

Figure 1.FigureX-ray 1. powderX-ray powder diffraction diffraction (XRPD) (XRPD) patterns patterns of the of thesamples. samples. (a) Sludge (a) I; Sludge (b) Sludge I; ( bII.) Sludge II. The results of determined densities (Table 1), obviously confirm the XRPD results (Table 2), and Thethe results correlation of determined between sample densities mineral composition (Table1), and obviously their densities. conﬁrm Namely, the increase XRPD of resultsgoethite (Table2), (4.27–4.29 g/cm3) content increases the sample density, whereas increase of quartz (2.65–2.66 g/cm3) and the correlation between sample mineral composition and their densities. Namely, increase and clays (2.79–2.80 g/cm3) contents decrease it (https://www.mindat.org). 3 of goethite (4.27–4.29 g/cm ) content increases the sample density, whereas increase of quartz (2.65–2.66 g/cm3) and clays (2.79–2.80 g/cm3) contents decrease it (https://www.mindat.org).

Minerals 2018, 8, 119 6 of 19

Minerals 2018, 8, x 6 of 19 3.3. FTIR Study 3.3. FTIR Study The obtained results of the FTIR studies are shown in Figure2 and Table3. Identiﬁcation of the present mineralThe obtained phases results was doneof the usingFTIR studies adequate are shown references in Figure [20– 222 and]. The Table obtained 3. Identification results are of the in very present mineral phases was done using adequate references [20–22]. The obtained results are in very good accordance, and mainly conﬁrm XRPD results of the previously determined qualitative and good accordance, and mainly confirm XRPD results of the previously determined qualitative and semi-quantitativesemi-quantitative mineral mineral compositions. compositions.

Figure 2. Fourier transform infrared (FTIR) patterns of the samples. (a) Sludge I; (b) Sludge II. Figure 2. Fourier transform infrared (FTIR) patterns of the samples. (a) Sludge I; (b) Sludge II. Table 3. Identified qualitative and semi-quantitative mineral compositions by FTIR studies. Table 3. Identiﬁed qualitative and semi-quantitative mineral compositions by FTIR studies. Sample Sludge I Sludge II Quartz (798 cm−1), goethite (3130, 908 and Goethite (3118, 899 and 798 cm−1), quartz (798 SampleIdentified Sludge I Sludge II 798 cm−1) and clay minerals (clays: 3624, cm−1), and clay minerals (clays: 3622, 1654, mineral − − Quartz1029 (798 and cm 9081), cm goethite−1; illite-seri (3130,cite: 908 534 and cm−1; 1030,Goethite 1008 and (3118, 899 899cm− and1; illite-sericite: 798 cm 1), 534 quartz Identiﬁedcomposition −1 −1 798 cm ) and clay minerals (clays:−1 3624, 1029 −(7981 cm ), and clay minerals (clays: 3622,−1 1654, mineral and kaolinite: 3701 and 473 cm ). cm ; and kaolinite: 3696, 470 and 470 cm ). and 908 cm−1; illite-sericite: 534 cm−1; 1030, 1008 and 899 cm−1; illite-sericite: 534 cm−1; composition and kaolinite: 3701 and 473 cm−1). and kaolinite: 3696, 470 and 470 cm−1).

Minerals 2018, 8, 119 7 of 19

3.4. SEM-EDS

SEM-EDSMinerals was2018, 8 used, x for the morphological, structural and chemical composition study7 of 19 of sludge samples, and results are shown at Figures3 and4, and Tables4 and5. For defining a more precise 3.4. SEM-EDS elemental and mineral composition, analyses of selected points on the surface were done. These points were primarilySEM-EDS selected was by used choosing for the morphological, different grains structur withal and various chemical colors, composition brightness, study of types sludge of crystals, samples, and results are shown at Figures 3 and 4, and Tables 4 and 5. For defining a more precise shapes andelemental textures, and andmineral with composition, the main analyses purpose of ofselected analyzing points on as the many surface different were done. mineral These species as possible. Itpoints is obvious were primarily that majority selected by of choosing the visible different grains grains are with very various fine orcolors, ultra-fine, brightness, with types sizes of less than about 10 µcrystals,m; bigger shapes grains and textures, than these and with are the with main at purpose less quantity, of analyzing and as is many contrary different to themineral much bigger grains determinedspecies as possible. for natural It is obvious mineral that raw majority samples of the [2 visible]. grains are very fine or ultra-fine, with sizes less than about 10 μm; bigger grains than these are with at less quantity, and is contrary to the SEMmuch images bigger and grains EDS determined chemistry for ofnatural sludge mineral I samples raw samples (Figure [2]. 3 and Table4) show heterogeneity of mineral compositionSEM images withand EDS the chemistry predominant of sludge oxide I samples (and/or (Figure hydroxide) 3 and Table 4) minerals show heterogeneity consisting mainly of iron, siliconof mineral and composition aluminum; with and the predominant with some oxide Mn, (and/or Na and hydroxide) K, as well. minerals Also, consisting only rarely,mainly there are some Ti andof iron, Cu impurities.silicon and aluminum; Furthermore, and with Si/Al some ratioMn, Na indicates and K, as the well. presence Also, only of rarely, free silicathere are (i.e., quartz), some Ti and Cu impurities. Furthermore, Si/Al ratio indicates the presence of free silica (i.e., quartz), with or withoutwith or without Al-silicates. Al-silicates.

Figure 3.Scanning electron microscope (SEM) image of the sludge I sample with analyzed selected points 1–5. Figure 3. Scanning electron microscope (SEM) image of the sludge I sample with analyzed selected pointsTable 4. 1–5. X-ray microanalysis (EDS) data from the selected points 1–5 of the sludge I shown in Figure 3. Element 1 2 3 4 5 Table 4. X-ray microanalysis (EDS) datawt % from the 54.08 selected 50.97 points 46.11 1–5 of59.02 the sludge48.38 I shown in Figure3. O at % 67.71 71.28 72.46 72.12 75.33 Elementwt % 1 44.11 2 11.80 4.20 3 17.20 41.85 5 Si wt %at 54.08% 31.46 50.97 9.40 46.11 3.76 11.97 59.02 1.64 48.38 O wt % 0.47 8.48 2.75 14.60 1.73 Al at % 67.71 71.28 72.46 72.12 75.33 at % 0.35 7.03 2.57 10.57 1.60 wt % 44.11 11.80 4.20 17.20 1.85 Si wt % 1.34 23.31 44.21 3.77 47.34 Fe at % 31.46 9.40 3.76 11.97 1.64 at % 0.48 9.34 19.90 1.32 21.12 wt %wt % 0.47 8.481.00 2.753.87 14.60 1.73 Al Na n.d. n.d. n.d. at %at % 0.35 7.03 0.97 2.57 3.29 10.57 1.60 wt %wt % 1.34 23.311.12 44.210.34 1.19 3.77 47.34 Fe K n.d. n.d. at %at % 0.48 9.34 0.64 19.900.22 0.59 1.32 21.12 wt % 1.00 3.87 Na n.d. n.d. n.d. at % 0.97 3.29 wt % 1.12 0.34 1.19 K n.d. n.d. at % 0.64 0.22 0.59 wt % 2.06 2.39 0.35 0.69 Mn n.d. at % 0.84 1.09 0.13 0.31 Minerals 2018, 8, 119 8 of 19

Minerals 2018, 8, x Table 4. Cont. 8 of 19

wt % 2.06 2.39 0.35 0.69 Mn wt % n.d.0.54 Ti at %at % n.d. 0.25 0.84 n.d.1.09 0.13 n.d. 0.31 n.d. Compound wt % 1.09 0.54 Ti % at % n.d. 0.25 n.d. n.d. n.d. wtCompound % % 0.73 1.09 at % 0.26 Cu wt %n.d. 0.73 n.d. n.d. n.d. Compound 1.11 Cu % at % n.d. 0.26 n.d. n.d. n.d. Compound %n.d.—not detected. 1.11 n.d.—not detected. X-ray microanalysis (EDS) data for selected points (Figure3 and Table4) conﬁrm heterogeneity of sludge I surfacesX-ray microanalysis with the following (EDS) data detected for selected possible points major (Figure phases, 3 and Table and which4) confirm were heterogeneity calculated from compoundof sludge % basedI surfaces on oxygenwith the byfollowing stoichiometry: detected possible quartz (pointmajor phases, 1); Fe minerals and which intricately were calculated associated with alumosilicatesfrom compound (point % based 2); Fe on minerals oxygen (pointsby stoichiometr 3 and 5);y: and quartz alumosilicates (point 1); Fe (point minerals 4). Small intricately quantities associated with alumosilicates (point 2); Fe minerals (points 3 and 5); and alumosilicates (point 4). of manganese minerals (or its impurities) have been also recorded at the points 2–5. Small quantities of manganese minerals (or its impurities) have been also recorded at the points 2–5.

Figure 4. Scanning electron microscope (SEM)image of the sludge II sample with analyzed selected Figurepoints 4. (aScanning–f). electron microscope (SEM)image of the sludge II sample with analyzed selected points (a–f). Minerals 2018, 8, 119 9 of 19

Table 5. X-ray microanalysis (EDS)data from the selected points a–f of the sludge II shown in Figure4.

Element a b c d e f wt % 38.12 53.13 43.16 41.90 28.20 36.71 O at % 67.74 66.07 57.14 55.92 57.44 52.08 wt % 1.17 22.85 56.84 48.06 0.64 29.74 Si at % 1.18 16.19 42.86 36.54 0.74 24.03 wt % 0.27 18.89 8.38 0.36 18.11 Al n.d. at % 0.29 13.93 6.63 0.44 15.23 wt % 58.67 64.55 5.09 Fe n.d. n.d. n.d. at % 29.87 37.67 2.07 wt % 3.38 Na n.d. n.d. n.d. n.d. n.d. at % 2.93 wt % 1.75 1.66 8.76 K n.d. n.d. n.d. at % 0.89 0.91 5.09 wt % 1.77 6.25 Mn n.d. n.d. n.d. n.d. at % 0.92 3.71 n.d.—not detected.

X-ray microanalysis (EDS) data for selected points of the sludge II sample (Figure4 and Table5), compared to sludge I sample, conﬁrm the same heterogeneity of sludge surfaces. In addition, the same major possible phases were detected, i.e., Fe minerals associated with manganese (spectrums a and e); alumosilicates with K, Na and rarely Fe (spectrums b and f); and quartz (spectrums c and d). Therefore, it is obvious that the chosen points are of various types of crystals, shapes, textures, colors and brightness, and are adequate to the determined minerals. For example, Fe minerals are bright, whereas quartz and clay minerals are gray or dark. One of the most limiting factors for determination of the mineral phases from EDS data is its inability to measure hydrogen, since goethite and clay minerals contain considerable amounts of - OH groups and/or H2O. According to that, we can only speculate about these possible phases. However, we believe that the obtained SEM-EDS results are also in very good accordance and that they mainly conﬁrms XRPD results of the previously determined qualitative and semi-quantitative mineral compositions presented in this paper (Figure1 and Table2). Besides that, all of the presented results are further in very good agreement with those previously obtained for the mineral raw material [2].

3.5. Particle Size Distribution Analyzes Comparative results of particle size distribution analyzes of two samples of sludge, given in Table6 and Figure5, show that there is no considerable difference in the classes of particle size distribution of these two samples. However, it is clearly noticeable that in both samples, the highest mass percentage (~50%) belongs to the ﬁnest classes, i.e., to those bellow 6 µm. Furthermore, almost 3/4 of the samples belong to the classes below 13 µm, conﬁrming the SEM-EDS results (Figures3 and4).

Table 6. Particle size distribution and iron content by size class for two investigated samples.

Sludge I Sludge II Fraction (µm) Mass % Fe * (Mass %) Fe ** (Mass %) Mass % Fe * (Mass %) Fe ** (Mass %) Initial 100 29.43 - 100 41.19 - >25 10.42 29.05 3.03 13.83 44.53 6.16 18–25 6.77 27.63 1.87 7.58 38.78 2.94 13–18 8.96 27.34 2.45 7.93 40.09 3.18 9–13 12.60 28.71 3.62 8.29 41.49 3.44 6–9 11.88 30.23 3.59 9.11 43.27 3.94 <6 49.37 28.73 14.18 53.26 37.38 19.91 Fe *—units of Fe; Fe **—units of Fe compared to initial. Minerals 2018, 8, 119 10 of 19 Minerals 2018, 8, x 10 of 19

Figure 5. Particle size distribution for two investigated samples. Figure 5. Particle size distribution for two investigated samples. In both samples, iron content was also determined for every size class separately (Table 6). The InFe content both samples, by particle iron size content classes wasis quite also uniform determined and generally for every is in size accordance class separately with the initial (Table 6). The Femeasurements. content by particle Calculated size units classes of Fe is compared quite uniform to initial and values generally show the isin largest accordance iron content with in the the initial measurements.size class bellow Calculated 6 μm. Although units of Fein comparedsome classes to a initialslightly values higher show iron content the largest is observed iron content by in the sizecomparison class bellow with the 6 µ initialm. Although one (such inas some6–9 μm; classes Table 6), a slightly these differences higher iron are not content sufficient is observed reason by for further research to be done on separate classes. Particularly interesting is that there is no big comparison with the initial one (such as 6–9 µm; Table6), these differences are not sufficient reason for difference in the classes of particle size distribution between the two samples, although the mineral furtherphases research are present to be done in various on separate contents classes. (Tables Particularly 1–3). This indicates interesting that isall that of the there identified is no big mineral difference in thephases, classes both of particleuseful (such size as distribution goethite and between other Fe theminerals) two samples, and useless although (i.e., gangue the mineralminerals, phases such are presentas quartz in various and clays), contents are present (Tables 1in– 3the). Thissludge indicates samples thatin very all ofsimilar the identifiedcontents in mineral all of the phases, particle both usefulsize (such classes. asgoethite As these andminerals other mainly Fe minerals) appear as and fine useless and ultra-fine (i.e., gangue particles, minerals, this makes such the as system quartz and clays),very are complex present for in further the sludge separation samples processes. in very similar contents in all of the particle size classes. As these mineralsSome other mainly recently appear presented as fine results and ultra-fineof particle particles,size distribution this makes of the the limonite system sludge very that complex for furtheroccurs separationas a hydro-cyclone processes. overflow in the Omarska iron mine [23] are also in accordance with those Somelisted here. other recently presented results of particle size distribution of the limonite sludge that occurs3.6. as Initial a hydro-cyclone Settling and Flocculation overflow Studies in the Omarska iron mine [23] are also in accordance with those listed here. The main goal of initial settling and flocculation studies was to compare the behavior of two 3.6. Initialsludge Settling samples and with Flocculation different S/L Studies ratios; and with or without flocculant. Additionally, the possibility of selective separation of iron minerals and quartz was studied, due to the fact that quartz was Thedetermined main goalas the of main initial impurity settling by andthe pres flocculationent research studies (Figures was 1–4 toand compare Tables 1–5). the behavior of two sludge samplesBased on with previous different experiments S/L ratios; of limonite and with and or clay without settling flocculant. [24], it can Additionally, be concluded the that possibility the of selectivedistribution separation of limonite of in iron settled minerals product and is signif quartzicantly was higher studied, than due the distribution to the fact of that clay, quartz with was determinedthe use of as any the applied main impurity flocculants, by regardless the present of researchthe time of (Figures settling,1 –and4 and the Tablestype and1– 5concentration). Basedof applied on reagents. previous Distributions experiments of oflimonite limonite in the and settled clay product settling in [ 24laboratory], it can experiments be concluded under that the distributiontest conditions of limonite were inwithin settled the product range of is 80–100%. significantly The distributions higher than of the clay distribution in the overflow of clay, were with the within 20–50%, which, translated into industrial conditions, means that the settled product with over use of any applied flocculants, regardless of the time of settling, and the type and concentration of 51–55% of Fe can be easily obtained, with mass utilization of over 80%, compared to the initial sludge. appliedThis reagents. is possible, Distributions because industrial of limonite sludge in in Omarska the settled contains product over 40%, in laboratory and sometimes experiments more than under test conditions50% of Fe. On were the within basis of the the range Fe content of 80–100%. in settled The product, distributions we believe of claythat it in would the overflow be, after were withinconsolidation, 20–50%, which, very interesting translated for intoapplication industrial in the conditions, field of metallurgy. means that the settled product with over 51–55%For ofinitial Fe cansettling be easily investigations, obtained, withunder mass best utilizationconditions, of the over same 80%, procedure compared used to the for initial sludge.investigations This is possible, of settling because rate on industrialnatural limonite sludge samples in Omarska [24] was contains applied. overThese 40%,experiments and sometimes aim moreto than demonstrate 50% of Fe. the On possible the basis impact of theof different Fe content input in settledsamples product, on the behavior we believe of settling that it wouldrate, be, afternaturally consolidation, and also very in the interesting presence of for the application flocculant. Furthermore, in the field ofthe metallurgy. effect of solid concentration was Forexamined initial by settlingusing different investigations, ratios of solid/liquid. under best In such conditions, manner, we the tried same to define procedure the behaviors used for of these samples, as well as to determine if there is a difference between samples from plants for the investigations of settling rate on natural limonite samples [24] was applied. These experiments aim to demonstrate the possible impact of different input samples on the behavior of settling rate, naturally and also in the presence of the flocculant. Furthermore, the effect of solid concentration was examined by using different ratios of solid/liquid. In such manner, we tried to define the behaviors of these samples, as well as to determine if there is a difference between samples from plants for Minerals 2018, 8, 119 11 of 19 Minerals 2018, 8, x 11 of 19 preparationthe preparation of mineral of mineral raw rawmaterials materials “Omarska” “Omarska” (which (which S:L ratios S:L ratios are usually are usually of 1:7; of 1:8; 1:7; or 1:8; 1:9), or and 1:9), possibleand possible influence influence of mineralogical, of mineralogical, chemical chemical or particle or particle size sizedistribution distribution analyses. analyses.

3.6.1. Natural Settling with Different S/L Ratio Ratio As it is well known, forms for calculating the grain fall rate cannot accurately determine the rate of precipitation,precipitation, duedue to to the the defects defects in thein the correct correct values values for correction for correction coefficients coefficients that take that into take account into accountthe irregular the irregular shape of shape the grain, of the the grain, changings the changings of fluid of density fluid density and in particular,and in particular, the questions the questions related relatedto the flocculation to the flocculation or dispersion or dispersion of the smallest of the smalle classesst resultingclasses resulting from the from charge the ofcharge the grain. of the grain. At the beginning of precipitation, in diluted pulp, and under free fall conditions, deposition rate is relatively large and constant. The smaller the an anglegle between the straight line of the curve and the ordinates, the greatergreater thethe speedspeed ofof compaction.compaction. When When crossing crossing into into the the area area of of plunged plunged fall, fall, the the rate rate is israpidly rapidly reduced reduced and and the the graph graph gets gets a a rectilinear rectilinear character. character. As Asthe the densitydensity increasesincreases inin the thickening zone, the curve is asymptotically approaching a constant value that depends on the thickness of the precipitate layer of the precipitated product. A A defective defective point point is is called called a a critical critical deposition deposition point. point. The prevailing opinion is that for the critical point of precipitation, the beginning of the precipitation curve should be taken. The critical rate (V ) in this paper is calculated as: V = H/t (mm/min); curve should be taken. The critical rate (Vav)av in this paper is calculated as: Vav = H/tav cr (mm/min);cr where where H is height of the clear zone during critical time (in mm), and t is deposition time required to H is height of the clear zone during critical time (in mm), and tcr is depositioncr time required to achieve aachieve critical a deposition critical deposition point (in point minutes). (in minutes). In Figure Figure6 6,, naturalnatural settlingsettling curvescurves ofof sludgesludge II andand sludge IIII with different ratios of S:L are shown. Generally, there is no big difference in settling behavior between these samples. However, sludge II sample has a higher settling rate as expected due to its higher density and higher contents of Fe minerals in its composition (Tables 11–3).–3). IfIf wewe comparecompare thethe effecteffect ofof solidsolid concentration,concentration, inin allall threethree cases, sludge II has a higherhigher depositiondeposition rate.rate. Also,Also, samplessamples withwith smallestsmallest solid/liquidsolid/liquid ratio have the highest settling rates in both cases,cases, which is in accordance with some otherother studiesstudies [[25].25].

Figure 6.Natural settling with different ratios of S:L. (a) Sludge I; (b) Sludge II. Figure 6. Natural settling with different ratios of S:L. (a) Sludge I; (b) Sludge II. 3.6.2. Settling with Different S/L Ratios with Additional Flocculant A100 3.6.2. Settling with Different S/L Ratios with Additional Flocculant A100 In Figure 7, settling curves of both samples with different ratios of S/L, and with additional anionicIn Figureflocculant7, settling A100, curves are shown. of both Figure samples 7 shows with different that there ratios is relatively of S/L, and small with difference additional between anionic sludgeflocculant I and A100, sludge are shown.II. In all Figurecases 7settling shows starts that there fast, isi.e., relatively only after small 30 seconds difference from between the beginning, sludge I regardlessand sludge of II. the Inall S/L cases ratio; settling or the starts type fast, of samp i.e., onlyle. However, after 30 seconds by comparison from the beginning,with natural regardless settling (Figuresof the S/L 6 ratio;and 7), or it the can type be ofclearly sample. seen However, that the bysettling comparison rate is withobviously natural improved settling (Figures with additional6 and7), flocculant.it can be clearly seen that the settling rate is obviously improved with additional flocculant. In Table 7 7 and and Figure Figure8 ,8, comparisons comparisons of of critical critical settling settling rates rates are are shown. shown. From From these, these, it it can can be be clearly seen that in the case of natural settling, there is no significantsignificant increase in the critical settling rates, regardlessregardless of of the the S:L S:L ratios. ratios. The The differences differences between between the ratesthe rates in the in relationship the relationship of S:L areof S:L greater are withgreater higher with percentage higher percentage of the liquid of the phase liquid in phase both samples. in both samples. It is important It is important to emphasize to emphasize here that suchhere thatdiluted such pulp diluted is unusual pulp is for unusual plants for from plants which from the which samples the were samples taken were (i.e., taken the overflow (i.e., the ofoverflow the hydro of thecyclone), hydro but cyclone), it was used but init was theexperiment used in the for experime easier detectionnt for easier of the detection behavior of thethe mineralbehavior particles of the mineraland the influenceparticles and of the the solid/liquid influence of ratios. the solid/liquid ratios.

Minerals 2018, 8, 119 12 of 19 Minerals 2018, 8, x 12 of 19

Minerals 2018, 8, x 12 of 19

Figure 7. Settling with different S:L ratios and with flocculant A100. (a) Sludge I; (b) Sludge II. Note: Figure 7. Settling with different S:L ratios and with flocculant A100. (a) Sludge I; (b) Sludge II. The division on the x-axis have made a smaller partition than at Figure 6, since the height of the Note: The division on the x-axis have made a smaller partition than at Figure6, since the height of the Figuresediment 7. Settlingof settling with sample different after S:L 180 rati secondsos and is with the same.flocculant A100. (a) Sludge I; (b) Sludge II. Note: sediment of settling sample after 180 seconds is the same. The division on the x-axis have made a smaller partition than at Figure 6, since the height of the sedimentHowever, of with settling the sample addition after of 180 the seconds flocculant, is the thes same.e rates increase significantly with the increase of theHowever, liquid component with the additionin both of of the the samples. flocculant, Fu theserthermore, rates increasethe differences significantly in critical with settling the increase rates betweenof theHowever, liquid samples component with in the the addition inpresence both ofof theofthe the samples.flocculant, flocculant Furthermore, thes aree rates also increaseobvious. the differences significantly These indifferences critical with settling the obviously increase rates ofdependbetween the liquid on samples the component various in the mineral presence in both compositions, of thethe flocculantsamples. because Fu arerthermore, also sludge obvious. IIthe contains Thesedifferences differences significantly in critical obviously higher settling content depend rates betweenofon iron the variousminerals samples mineral than in sludgethe compositions, presence I (Tables of 1 the becauseand flocculant 2). Obvi sludgeously, are II contains alsothe effect obvious. significantly of floccu Theslante higherdifferences in the contentheterogeneous obviously of iron dependsystemminerals dependson than the sludge various both Ion (Tablesmineral the mineral1 andcompositions,2). composition Obviously, because the and effect sludgetheir of quantity; flocculantII contains as inwell significantly the as heterogeneous on the higher S/L ratio. systemcontent ofdepends iron minerals both on than the sludge mineral I composition(Tables 1 and and2). Obvi theirously, quantity; the effect as well of asfloccu on thelant S/L in the ratio. heterogeneous system depends both onTable the 7. mineral Critical compositionsettling rate (mm/s) and their of Sludge quantity; I and asSludge well II.as on the S/L ratio. Table 7. Critical settling rate (mm/s) of Sludge I and Sludge II. Sludge I Sludge II Table 7. Critical settling rate (mm/s) of Sludge I and Sludge II. S:L (% of Solid) Natural Settling Sludge Settling I with Flocculant Natural Settling Sludge Settling II with Flocculant 1:7 (12.5) 0.05 Sludge I 2.83 0.07 Sludge II 1.61 S:L (% of Solid) Natural Settling Settling with Flocculant Natural Settling Settling with Flocculant S:L 1:9.7(% of (9.4) Solid) Natural0.10 Settling Settling with2.95 Flocculant Natural0.11 Settling Settling with3.33 Flocculant 1:19.71:7 (12.5) (4.8) 0.050.050.28 2.832.833.95 0.070.07 0.30 1.611.61 3.95 1:9.7 (9.4) 0.100.10 2.952.95 0.110.11 3.333.33 1:19.7 (4.8) 0.28 3.95 0.30 3.95 1:19.7 (4.8) 0.28 3.95 0.30 3.95

Figure 8. Critical settling rate. (a) Sludge I; (b) Sludge II.

Figure 8. Critical settling rate. (a) Sludge I; (b) Sludge II. Effect of solid/liquidFigure ratio on 8. Critical settling settling rate was rate. confirmed (a) Sludge I;by (b the) Sludge results II. of hindered settling rate which was calculated according to the Richardson–Zaki equation [25]. This equation describes a methodEffect for of calculating solid/liquid the ratio sedimentation on settling raterate inwas a liquid-solid confirmed bysystem the results as a function of hindered of the settling free falling rate Effect of solid/liquid ratio on settling rate was conﬁrmed by the results of hindered settling whichrate of wasa single calculated particle according and the concentration to the Richardson of particles.–Zaki Fromequation Table [25]. 8, itThis can equationbe seen that describes samples a rate which was calculated according to the Richardson–Zaki equation [25]. This equation describes methodwith the for smallest calculating solid/liquid the sedimentation ratio have the rate highest in a liquid-solid settling rates system in both as a cases. function of the free falling ratea method of a single for calculating particle and the the sedimentation concentration rate of in particles. a liquid-solid From system Table as8, ait functioncan be seen of the that free samples falling rate of a single particle and the concentration of particles. From Table8, it can be seen that samples with the smallest solid/liquidTable 8. Hindered ratio have settling the highest rate (mm/s) settling of Sludge rates Iin and both Sludge cases. II. with the smallest solid/liquid ratio have the highest settling rates in both cases. Table 8. Hindered Sludge settling I rate (mm/s) of Sludge I and Sludge Sludge II. II S:L (% of Solid) Terminal Settling Hindered Settling Terminal Settling Hindered Settling 1:7 (12.5) 0.184 Sludge I0.148 0.203 Sludge II 0.193 S:L 1:9.7(% of (9.4) Solid) Terminal0.184 Settling Hindered0.157 Settling Terminal0.203 Settling Hindered0.203 Settling 1:19.71:7 (12.5) (4.8) 0.184 0.1480.170 0.203 0.1930.216 1:9.7 (9.4) 0.184 0.157 0.203 0.203 1:19.7 (4.8) 0.184 0.170 0.203 0.216

Minerals 2018, 8, 119 13 of 19

Table 8. Hindered settling rate (mm/s) of Sludge I and Sludge II.

Sludge I Sludge II S:L (% of Solid) Terminal Settling Hindered Settling Terminal Settling Hindered Settling 1:7 (12.5) 0.184 0.148 0.203 0.193 1:9.7 (9.4) 0.184 0.157 0.203 0.203 1:19.7 (4.8) 0.184 0.170 0.203 0.216

3.6.3. Testing Selectivity of Flocculation with Flocculant A100 and Dispersant SHMP As noted above, the dispersant and the flocculant were selected on the basis of results obtained earlier of the effects of certain reagents on the behavior of limonite and clay [24]. Namely, it was then concluded that the distribution of limonite in settled product is significantly higher than the distribution of clay, with the use of applied flocculants, regardless of the time of settling. However, at that time, behavior of quartz was not studied, due to the supposition of its insignificant contents and role. Some earlier studies showed that hydrolyzed polyacrylamide may be used for selective flocculation of hematite from quartz [25–27]. In this chapter, the influence of settling time on the selectivity was tested only at the iron richer sample (sludge II) with addition of sodium hexametaphosphate (SHMP) as dispersant. Initially, settling behavior was tested by monitoring the weight distribution and the content grade and recovery of Fe and SiO2 in floating and sinking part. The results of the distribution of masses and grade and recovery of Fe and SiO2 contents between the sinking and floating part in the function of settling time are presented in Table9.

Table 9. Selective ﬂocculation of sludge II with different time of settling.

Time of Settling Product M (%) Fe Grade (%) SiO2 Grade (%) Fe Recovery (%) SiO2 Recovery (%) Float 18.00 43.31 15.40 18.93 14.71 30” Sink 82.00 40.90 20.0 81.42 87.00 Total 100.00 - - - - Float 11.35 44.52 12.85 12.27 7.74 10 Sink 88.65 40.98 20.20 88.20 95.00 Total 100.00 - - - - Float 13.50 45.81 11.80 15.01 8.45 20 Sink 86.50 40.53 20.30 85.11 93.15 Total 100.00 - - - - Float 17.70 44.78 12.75 19.24 11.97 30 Sink 82.30 40.41 20.20 80.74 88.19 Total 100.00 - - - - Float 10.33 46.66 10.25 11.70 5.62 40 Sink 89.67 40.84 19.95 88.91 94.90 Total 100.00 - - - - Float 14.70 45.51 12.10 16.24 9.44 50 Sink 85.30 40.61 20.30 84.10 91.86 Total 100.00 - - - - Float 14.07 45.23 11.50 15.45 8.58 100 Sink 85.93 40.28 20.10 84.03 91.63 Total 100.00 - - - -

According to the obtained results, settling time does not play a significant role in the distribution of masses between the sinking and floating parts. Partial increasing of iron ore content after flocculation by comparison to the initial sample can be seen especially in the floating part. However, taking into account the distribution of masses, recovery of iron ore is relatively low, i.e., within a few Fe%; and, therefore, it is not quite satisfactory. However, as the content of iron partially increases by flocculation, this method should be considered as appropriate. Minerals 2018, 8, 119 14 of 19

Separation indices are not calculated, because it is obvious from Table9 that the selectivity has not been achieved. Theoretically, selective ﬂocculation depends on many parameters, such as: surface chemistry of particles, particle size distribution, degree of dispersion of particles, functional groups of the polymer, etc. Speciﬁcally, heterogeneity of the mineral surface is one of the important factors in these cases. According to the cited papers, we expected that polyacrylamide is much less adsorbed on quartz. Unfortunately, in this case, this did not happen.

3.7. Zeta Potential Study In order to better understand the influence of mineral composition on dispersion, in this chapter, results of zeta potential (ζ-potential) measurements were analyzed for the sludge itself, as well as for the individual goethite and quartz phases, which were previously identified as major minerals in the sludge samples (Figures2 and3; Tables1–3). AnMinerals important 2018, 8, x step in the selective flocculation process is to achieve stable dispersion14 of of 19 mineral particles.the Dispersion individual goethite stability and of quartz mineral phases, particles which is were dependent previously on identified the zeta potentialas major minerals of present in the particles. The knowledgesludge samples of zeta (Figures potential 2 and plays 3; Tables a significant 1–3). role in understanding dispersion and interfacial processes, suchAn important as flocculation. step in the Zetaselective potential flocculation is a pr measureocess is to of achieve the magnitude stable dispersion of the of electrostatic mineral or charge repulsion/attractionparticles. Dispersion stability between of mineral particles, particles and it is is dependent one of the on fundamental the zeta potential parameters of present known to affect stability.particles. ItsThe measurement knowledge of zeta brings potential detailed plays insight a significant into therole causesin understanding of dispersion, dispersion aggregation and or interfacial processes, such as flocculation. Zeta potential is a measure of the magnitude of the flocculation,electrostatic and it or can charge be used repulsion/attraction to improve the betw formulationeen particles, of dispersions, and it is one emulsions of the fundamental and suspensions. Fromparameters a plot ofknown zeta to potential affect stability. as a functionIts measurem of pH,ent brings a number detailed of insight important into the data causes can of be noted. Namely,dispersion, isoelectric aggregation point (IEP) or flocculation, represents and ait can condition be used to when improve the the value formulation of zeta of dispersions, potential is zero. At suchemulsions conditions, and suspensions. the dispersion is unstable, and there are no reflective forces present among the particles.From A higher a plot of zeta zeta potential potential producesas a function dispersion. of pH, a number Also, of particles important with data an can IEP be belownoted. pH = 7 Namely, isoelectric point (IEP) represents a condition when the value of zeta potential is zero. At have an acidic character, whereas those with an IEP above pH = 7 are basic (i.e., alkaline). This study such conditions, the dispersion is unstable, and there are no reflective forces present among the was doneparticles. by measuring A higher ofzeta zeta potential potential produces in the dispersion. various combinationsAlso, particles with of absence/presence an IEP below pH = 7 of have dispersant (SHMP),an and acidic also character, in the absence/presencewhereas those with an of IEP anionic above polyacrylamidepH = 7 are basic (i.e., A100 alkaline). (PAM) This flocculant. study was Zeta-potentialdone by measuring curves of forzeta sludgepotential II, in and the variou also fors combinations natural mineral of absence/presence raw samples of of dispersant goethite II and quartz II,(SHMP), which and were also previously in the absence/presence determined of [anionic2], are polyacrylamide shown at Figure A1009. (PAM) The zeta flocculant. potential curves for natural quartzZeta-potential and goethite curves are for similar sludge toII, variousand also curvesfor natural available mineral inraw the samples literature of goethite [28], withII and the IEPs quartz II, which were previously determined [2], are shown at Figure 9. The zeta potential curves for at pH = 2.6 and pH = 6.6 values, respectively. The IEP for sludge II that was found at pH = 4.5 confirms natural quartz and goethite are similar to various curves available in the literature [28], with the IEPs mineralogicalat pH = 2.6 heterogeneity and pH = 6.6 values, on surfaces respectively. of particles. The IEP for Namely, sludge II from that was the found results at pH obtained, = 4.5 confirms it is obvious that themineralogical measured zeta heterogeneity potential on and surfaces IEP value of particles. of sludge Namely, II mostly from the lie results between obtained, the values it is obvious of individual mineralthat goethite the measured and quartz zeta potential phases. and Therefore, IEP value thisof slud mostge II probablymostly lie between indicates the tovalues the presenceof individual of both of these individualmineral goethite phases and on quartz the surface phases. of Therefore, sludge particles. this most probably The magnitude indicates of to thethe negativepresence of zeta both potential of these individual phases on the surface of sludge particles. The magnitude of the negative zeta of all mineral particles is maximized at pH = 10 value. potential of all mineral particles is maximized at pH = 10 value.

Figure 9. Zeta-potential curves for sludge II, goethite and quartz samples. Figure 9. Zeta-potential curves for sludge II, goethite and quartz samples. The presence of sodium-hexametaphosphate (SHMP) dispersant obviously causes the increase of negative zeta potential magnitudes in all of the investigated cases, as it can be seen from Figure 10. Also, from these, it can be clearly seen that the surface charge remains negative throughout all of the investigated pH values, and for all of the samples. According to the well-known DLVO (Derjaguin, Landau, Verwey, Overbeek) theory, the stability of the suspension of colloidal particles is determined by the balance between the electrostatic interaction and the van der Waals interaction between particles. The addition of SHMP increases the negative charges on particle surfaces, causing better dispersion.

Minerals 2018, 8, 119 15 of 19

The presence of sodium-hexametaphosphate (SHMP) dispersant obviously causes the increase of negative zeta potential magnitudes in all of the investigated cases, as it can be seen from Figure 10. Also, from these, it can be clearly seen that the surface charge remains negative throughout all of the investigated pH values, and for all of the samples. According to the well-known DLVO (Derjaguin, Landau, Verwey, Overbeek) theory, the stability of the suspension of colloidal particles is determined by the balance between the electrostatic interaction and the van der Waals interaction between particles. The addition of SHMP increases the negative charges on particle surfaces, causing better dispersion. Minerals 2018, 8, x 15 of 19

Minerals 2018, 8, x 15 of 19

Figure 10. Zeta potential in absence/presence of SHMP. (a) Sludge II; (b) Goethite and quartz. Figure 10. Zeta potential in absence/presence of SHMP. (a) Sludge II; (b) Goethite and quartz. After addition of SHMP dispersant, the biggest difference of zeta potential magnitudes between goethiteAfter addition Figureand quartz 10. of Zeta SHMP are potential also dispersant, at inpH absence/presence = 10 (Figure the biggest 10b). of SHMP. For difference example, (a) Sludge of such zeta II; ( bzeta potential) Goethite potential and magnitudes valuesquartz. at pH between = goethite10 increase and quartz from are−30 alsomV (without at pH = SHMP) 10 (Figure to − 4510 b).mV For(with example, SHMP) for such goethite, zeta potential but only valuesfrom −29 at mV pH = 10 increase(without fromAfter SHMP) −addition30 mV to of (without−31 SHMP mV (with dispersant, SHMP) SHMP) to the− for45 biggest quartz. mV (withdi fferenceAfter SHMP) adding of zeta for SHMP, potential goethite, goethite magnitudes but is only mostly from between more−29 mV negatively charged than quartz. Also, behavior of sludge surface charge is very similar to goethite, (withoutgoethite SHMP) and quartz to −31 are mV also (with at pH SHMP) = 10 (Figure for quartz.10b). For After example, adding such SHMP,zeta potential goethite values is mostly at pH =more and10 increase the surface from charge −30 mV remains (without negative SHMP) throughout to −45 mV (withall of SHMP)the investigated for goethite, pH butregion only (Figure from − 10a,b).29 mV negatively charged than quartz. Also, behavior of sludge surface charge is very similar to goethite, The(without particles, SHMP) regardless to −31 mVof their (with mineralogy, SHMP) for with quartz. negative After zetaadding potentials SHMP, of goethite significant is mostly magnitude more and thewillnegatively surfaceremain charged chargedispersed than remains due quartz. to negativethe Also, electrostatic behavior throughout re ofpulsion. sludge all of surfaceThe the energy investigated charge barrier is very pHcan similar regionbe eliminated to (Figure goethite, by 10 a,b). The particles,neutralizationand the surface regardless of charge surface ofremains charge their mineralogy,duenegative to the throughout adsorption with negative all of ofa suitable the zeta investigated flocculant potentials pH onto ofregion signiﬁcantthe particle (Figure surface magnitude10a,b). will remainandThe bridgingparticles, dispersed betweenregardless due the of to particles. their the electrostaticmineralogy, Flocculant with repulsion.adso negativerption can Thezeta be potentials energy measured barrier of by significant changing can be magnitudeof eliminated the zeta by neutralizationpotentialwill remain after of dispersed surface adsorption. charge due If to the duethe polymer toelectrostatic the is adsorption adsorbed, repulsion. there of a The suitableis a energychange ﬂocculant inbarrier the surface can onto be of theeliminated the particle particles, by surface and bridgingandneutralization thus the between change of surface thein zeta particles.charge potential. due Flocculant to the adsorption adsorption of a suitable can be flocculant measured onto by the changing particle surface of the zeta potentialand bridgingIn after Figures adsorption. between 11 and 12,the If zeta theparticles. polymerpotential Flocculant curves is adsorbed, adsoas a rptionfunction there can isof be apH changemeasured for the in same theby changing surfacestudied samplesofof the zeta particles, in the presence of anionic polyacrylamide (PAM, A100), and with/without SHMP dispersant, are and thuspotential the changeafter adsorption. in zeta potential. If the polymer is adsorbed, there is a change in the surface of the particles, shown. These curves are presented in two different ways: as mutually compared samples (Figure 11); Inand Figures thus the 11 change and 12 in, zetazeta potential. potential curves as a function of pH for the same studied samples in the and asIn eachFigures individual 11 and 12, sample zeta potential with/without curves reagents as a function (Figure of 12).pH forThis the offers same a studiedbetter view samples of the in presence of anionic polyacrylamide (PAM, A100), and with/without SHMP dispersant, are shown. situation.the presence of anionic polyacrylamide (PAM, A100), and with/without SHMP dispersant, are Theseshown. curves These are curves presented are presented in two in different two different ways: ways: as as mutually mutually compared compared samples samples (Figure (Figure 11); 11); and asand each as each individual individual sample sample with/withoutwith/without reagents reagents (Figure (Figure 12). 12This). offers This offersa better a view better of the view of the situation.situation.

Figure 11. Zeta potentials of sludge, goethite and quartz. (a) With PAM (A100); (b) With PAM (A100) and SHMP.

ItFigure is obvious 11. Zeta that potentials the used of sludge, anionic goethite polyacrylamide and quartz. ((PAM)a) With causesPAM (A100); changes (b) Withon the PAM surface (A100) of all Figure 11. Zeta potentials of sludge, goethite and quartz. (a) With PAM (A100); (b) With PAM (A100) testedand samples SHMP. (Figures 11a and 12). The relatively high zeta potential values indicate a fairly stable dispersion,and SHMP. regardless of the mineral composition. The further additional inclusion of a SHMP dispersantIt isIt obvious is obvious significantly that that the the usedreduces used anionicanionic the zeta polyacrylamide polyacrylamide potential (Figures (PAM) (PAM) causes11b causesand changes 12), changes indicating on the onsurface thefavorable surfaceof all of all testedconditionstestedsamples samples for flocculation, (Figures (Figures 11a 11 particularly anda and 12). 12 The). for Therelatively goethite relatively athigh pH zeta = high 10 potential (Figure zeta potential12b). values The indicate difference values a indicatefairly of the stable zeta a fairly potentialsdispersion, between regardless quartz of theand mineralgoethite composition.indicates a more The pronouncedfurther additional flocculation inclusion for goethite of a SHMP than fordispersant quartz. Itsignificantly is also evident reduces that zeta the potential zeta potential values of (Figures sludge in 11b this andcase are12), only indicating partially favorable between thoseconditions for the for individual flocculation, oxides, particularly and depends for goethite on the atpH pH range. = 10 (Figure 12b). The difference of the zeta potentials between quartz and goethite indicates a more pronounced flocculation for goethite than for quartz. It is also evident that zeta potential values of sludge in this case are only partially between those for the individual oxides, and depends on the pH range.

Minerals 2018, 8, 119 16 of 19 stable dispersion, regardless of the mineral composition. The further additional inclusion of a SHMP dispersant significantly reduces the zeta potential (Figures 11b and 12), indicating favorable conditions for flocculation, particularly for goethite at pH = 10 (Figure 12b). The difference of the zeta potentials between quartz and goethite indicates a more pronounced flocculation for goethite than for quartz. It is also evident that zeta potential values of sludge in this case are only partially between those for the individual oxides, and depends on the pH range. Minerals 2018, 8, x 16 of 19

Figure 12. Zeta potentials without reagents (1); with PAM (2); and with PAM and SHMP (3). (a) FigureSludge 12. Zeta II; (b) potentials Goethite; (c) without Quartz. reagents (1); with PAM (2); and with PAM and SHMP (3). (a) Sludge II; (b) Goethite; (c) Quartz. The mechanism of flocculation takes place in two stages, i.e., adsorption of reagent onto surface of particles and formation of aggregates. Polymer adsorption onto mineral surface takes place due to The mechanism of flocculation takes place in two stages, i.e., adsorption of reagent onto surface typical chemical bonds, i.e., ionic, dispersive (van der Waals), covalent, semi-polar, hydrogen of of particleshydrophobic and formation bonds, as ofwell aggregates. as their combination Polymer [29]. adsorption onto mineral surface takes place due to typical chemicalThe nature bonds, of the mechanisms i.e., ionic, dispersiveinvolved in flocculation (van der Waals),of mineral covalent, suspensions semi-polar, is very complex, hydrogen of hydrophobicand depends bonds, on as many well parameters, as their combination such as: surface [29 chemistry]. of particles, particle size distribution, Thedegree nature of ofdispersion the mechanisms of particles, involved functional in grou flocculationps of the polymer, of mineral etc. suspensions The flocculation is very in the complex, presence of polymer molecules can be attributed to charge neutralization and inter-particle bridging. and depends on many parameters, such as: surface chemistry of particles, particle size distribution, Electrostatic bonding is the predominant mechanism when it comes to oppositely charged particles. degreePolymer of dispersion molecules, of by particles, adsorption functional on oppositely groups charged of oxide the polymer, particles, can etc. neutralize The flocculation the charge in the presenceon of particle polymer surfaces molecules and reduce can bethe attributedinter-particle to repulsive charge neutralization forces. A polymer and can inter-particle also flocculate bridging. Electrostaticsimilarly bonding charged is particles the predominant if the electrostatic mechanism repulsive when forcesit are comes not strong to oppositely enough to chargedprevent the particles. Polymerparticles molecules, from bridging by adsorption by the long on chains oppositely of the polymer. charged The oxide mechanism particles, of flocculation can neutralize negatively the charge charged oxide with anionic polyacrylamide cannot be explained by electrostatic bonds. Flocculation

Minerals 2018, 8, 119 17 of 19 on particle surfaces and reduce the inter-particle repulsive forces. A polymer can also flocculate similarly charged particles if the electrostatic repulsive forces are not strong enough to prevent the particles from bridging by the long chains of the polymer. The mechanism of flocculation negatively charged oxide with anionic polyacrylamide cannot be explained by electrostatic bonds. Flocculation of negatively charged oxide particles by anionic polymer is probably due to the hydrogen bonding [10]. Electronegative MO− groups are predominant at high pH values on oxide surfaces, and some authors believe that there is a possibility that it can bond with the weakly acidic NH2 function, forming a weaker bond [30]. PAM has flocculating effects on both goethite and quartz, as well as on their mixture (i.e., sludge). Our study of the possibility of selective activity was based on the fundamental differences of surface charge of quartz and goethite. According to the zeta potential measurements observed, there is a difference in the surface charge of quartz and goethite, even in presence of the PAM and SHMP. However, the behavior of the mixture is quite different, and their zeta potential values are mostly between those for the individual oxides (Figures9–12). This could also further indirectly indicate that goethite and quartz (and/or clays) are most probably more or less intricately associated or even included in each other, in accordance with the SEM-EDS study (points 2, 3, 5, a, e and f; Figures3 and4, and Tables4 and5). Initial settling experiments presented in this paper indicate that despite some differences in the behavior of individual phases, these are not sufficient enough to achieve selective PAM activity under the studied conditions. In the case of such low-grade limonite ore, gangue minerals (which are usually of quartz and clay composition) are finely distributed in a porous matrix of goethite, or intricately associated and even included in each other, as zeta potential and SEM-EDS studies indicated. This makes interfacial particle surfaces heterogeneous, even when they are well dispersed, as our results have shown. In such a case, the presence of another mineral can activate the quartz surface and make it much more reactive to the polyacrylamide chain, than would be expected [26]. Another reason may be the relatively high solid concentration. All cited positive results have been obtained with a low solid concentration of 1–5%. We selected an S/L ratio of 1:7 (i.e., of 12.5%), because it corresponds to the industrial conditions. Under such conditions, the flocculant more easily bridges the iron mineral particles simultaneously with quartz particles in this heterogeneous system. This leads to the hetero flocculation and reduces selectivity.

4. Conclusions The mineral phases present in the sludge generated during the processing of iron ore in the Omarska mine were studied. According to the obtained results of XRPD, FTIR, chemical, SEM and EDS analyses, it can be concluded that sludge samples were composed mostly of goethite and quartz, which prevails over clay minerals. Magnetite, hematite, clinochlore and todorokite are minor determined phases. Identification of clay minerals indicate that they are mostly of illite-sericite and kaolinite composition, and with chlorites, which appears only sporadically. Sericite is mainly of the muscovite mica type. Although the phase composition of two analyzed sludge samples are almost the same, their content varies. The results presented indicate quartz as the main impurity. The results obtained from the investigations of area fractions for selected points agree well with the data of mineralogical compositions of these samples and indicate high heterogeneity of the surface. Particle size distribution analyses confirm the complexity of the system. All of the identified mineral phases, both useful (iron minerals) and useless (quartz and clays), are present in the sludge as fine and ultra-fine particles. In both samples, the largest part of the mass of the finest particles, and large iron content is concentrated just in the smallest fractions. Also, similar mineral compositions are present among every size class within the samples, which makes this system very complex for separation processes. It is obvious that the majority of the visible grains are very fine or ultra-fine, with sizes less than about 10 µm; bigger grains than these are with much less quantity, and also in contrast to the much bigger grains determined for natural mineral raw samples. Minerals 2018, 8, 119 18 of 19

Settling and flocculation examinations show that there is no significant difference in natural settling behavior between the two sludge samples. Samples with the smallest solid concentration of the sludge have the highest settling rates in both cases. The settling rate is obviously improved by using anionic polyacrylamide flocculant in both cases, with differences between the two samples. The effect of flocculant depends both on the mineral composition and on their quantity, as well as on the S/L ratios. The initial test of selectivity of anionic polyacrylamide did not show results, probably because of the large heterogeneity of the mineral surfaces present in the mixture and relatively high solid concentration. Fe grade increases in the floating part, but Fe recovery is too small. The time of settling does not play a significant role in selectivity; the ratio of the mass of floating and sinking parts, and iron content does not change with time. These data suggest that it is necessary to further examine the individual components and their behavior in a complex system such as sludge, in order to determine the best parameters (conditions, reagents, etc.) for selective flocculation. The influence of the mineral composition of the sludge on the surface charge can be seen from the zeta potential values. IEP was obtained at the pH values of 2.6, 6.6 and 4.5 for quartz, goethite and sludge, respectively. After the addition of SHMP as dispersant, and A100 as flocculant, the surface charge remains negative throughout all of the investigated pH values, and for all samples. The biggest difference of zeta potential magnitudes between goethite and quartz are obtained at pH = 10. The behavior of the mixture varies with respect to the individual components, and their zeta potential values are mostly between those for the individual oxides. In this paper we have shown that the behavior of sludge from mineral processing, as a natural mixture of oxides, depends on its mineral composition. In the case of limonite sludge, in which the goethite and quartz are predominant, the heterogeneity of the surface areas of the particles present in the sludge is particularly pronounced.

Acknowledgments: The authors are grateful to three anonymous reviewers for their useful comments and suggestions. The authors are also grateful to the ArcelorMittal company in Prijedor (Bosnia and Herzegovina) for technical support and enabling the study visit of L.T. to the laboratory Global Research and Development, Mining and Mineral Processing, Maizières-lès-Metz (France), for the purpose of preparation of her PhD; to Armando Araujo for the zeta potential measurements at Universidad Federal de Minas Gerais, Belo Horizonte (Brazil); to the SEM LAB of Faculty of Mining and Geology, University of Belgrade (Serbia), for the SEM and EDS measurements; and to Aleksandra Cirićfor´ technical assistance. Author Contributions: Ljiljana Tankosić, Pavle Tanˇcić, Svjetlana Sredić and Zoran Nedić conceived and designed the experiments; Ljiljana Tankosić, Pavle Tanˇcić and Zoran Nedić performed the experiments; Ljiljana Tankosić,Pavle Tanˇcić,Svjetlana Sredićand Zoran Nedićanalyzed the data; Ljiljana Tankosić,Pavle Tanˇcić and Svjetlana Sredićwrote the paper. Conflicts of Interest: The authors declare no conflict of interest.

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