# Agglomeration potential evaluation of industrial Mn dusts and sludges based on physico-chemical characterization

J.L. Dubos, Beate Orberger, J.M. Milazzo, S. Blancher, T. Wallmach, J. Lützenkirchen, J. Banchet

► To cite this version:

J.L. Dubos, Beate Orberger, J.M. Milazzo, S. Blancher, T. Wallmach, et al. Agglomeration potential evaluation of industrial Mn dusts and sludges based on physico-chemical characterization. Powder Technology, 2020, 360, pp.1079-1091. 10.1016/j.powtec.2019.10.101. hal-02360168

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# Agglomeration potential evaluation of industrial Mn dusts and sludges based on physico-chemical characterization

Dubos, J.L. ¹/², Orberger, B. ²/³, Milazzo, J.M.,¹ Blancher, S.¹, Wallmach, T. ¹,

Lützenkirchen, J. ⁴ Banchet, J. ⁵

1. Eramet IDEAS, 1 Avenue Albert Einstein, 78190 Trappes-en-Yvelines, France

2. GEOPS, Université Paris Sud, Université Paris Saclay, Bâtiment 504, 91405 Orsay, Cedex
France

3. CATURA Geoprojects, 2 rue Marie Davy, 75014 Paris

4. INE, Karlsruhe Institute of Technology-KIT, 76344 Eggenstein-Leopoldshafen, Germany

5. EUROTAB, ZAC des Peyrardes, St Just St Rambert,, France

## ABSTRACT

Fine particles (<3 mm) are important, environmentally hazardous wastes of the mining and metallurgical industries that still contain valuable metals. Agglomeration, mainly using binders, is a common process to safely recycle those materials. This study aims at mastering cold agglomeration through understanding natural binder properties of the material. For the first time, a comprehensive physico-chemical characterization was performed on 13 samples from a manganese processing chain including ore fines. The main parameters to predict the agglomeration potential of the materials used in the present study are the presence of layer- and/or tunnel-structured phases along with the particle and grain size distribution, hardness and shapes, and particle surface charges. The Gabon ore fines show the best agglomeration potential. The soft, layer-structured plastic minerals (clays, lithiophorite) present naturally coat the hard grains (pyrolusite, cryptomelane) forming particles with enhanced binding potential. Blending these ore fines with low agglomeration potential dusts avoids binder use in the agglomeration process.

Keywords: Manganese oxides, tunnel structure minerals, layered structured minerals, agglomeration, pyrometallurgy, pollution, recycling
## 1. Introduction

Industrial dusts, here defined as particles sized below 3 mm, and sludges, composed of fine particles (<100 µm) suspended in aqueous solution, are the most important wastes of mining and pyrometallurgical industries. For example, the wastes related to manganese production amount to 15 kt/y of dusts and 10 kt/y of dried sludges from all the treatment processes of the sole Eramet Norway production plants. These numbers exclude dusts produced during mining, beneficiation and handling of ore fines. The installed pelletization capacity worldwide estimated at about 481 million tons per year [1] translates well the scale of the fines issue.

Until the late 20th century, those dusts were mostly released into the atmosphere. Technologies to filter out dust **have** evolved since 1975 [2]. The fines also had to be handled and were at first stored in landfills. However, this solution was costly and could sometimes cause environmental issues [3], due to the lixiviation of metals still present in the dusts. The metal content amounted up to 70 wt.% [4], i.e. 30 wt.% higher than in the original ores. Liebman [5] estimated that only 35 wt.% of the Electric Arc Furnace (EAF) dusts were recycled, part of it through agglomeration.

Reintroducing those dusts into the metal producing processes would increase the overall yield and decrease the waste. However, this **would** require an increase in particle sizes. Indeed, the direct introduction of loose dust and water bearing sludges into furnaces will decrease the gas permeability in the burden, **where it** may cause gas accumulation, leading to pressure build-up, which may in turn end up in a furnace explosion. Particle size upscaling can be achieved through high temperature agglomeration (sintering) [6], [7], cold agglomeration (briquetting, tableting) or with both cold and hot agglomeration steps (pelletization). The latter is widely used in the iron ore industries [8].
Each method outputs different products, each with different chemical and mechanical properties.

The predominantly considered properties are the mechanical resistance of the agglomerates and the generation of fines of the latter. Most technical approaches involve binders and/or moisture to increase agglomeration performance. Moisture enhances capillary forces and surface tension, but the impact may decrease when particles dry. Therefore, binders are used. The most common binder is bentonite for iron ore pelletization. Organic binders (e.g. lignosulfonate [9] or KemPel [10]) are becoming a popular replacement for inorganic binders as only low amounts of impurities are added to the furnace. Presently, companies try to agglomerate their own dusts and sludges based on empirical experiments, as no universal agglomeration process is available.

In our case, the following agglomeration requirements must be met: (i) sufficient mechanical strength in order to allow packing, transporting and storage, without generating new dust, (ii) a chemical and mineralogical composition that is easily reducible in the furnace and (iii) properties that will not deteriorate gas permeability (e.g. [11]).

Cold agglomeration without using a binder is the best economical and ecological option for complex industrial dusts but it has not yet been studied to sufficient detail. The process was essentially investigated for monophasic powders in pharmaceutical [12], [13], food [14] and material industries [15], [16].

Pietzsch [11] and Schulze [17] outlined that the most important forces controlling cold, binderless agglomeration are capillary bridges linked to moisture, and interparticular (Van der Waals and electrostatic) forces. Consequently, the physico-chemical characteristics (hardness, nature, structure, surface states...) of the materials and the mechanics/physics of the agglomeration method have all to be taken into account.
72 Industrial dusts and sludges are complex materials. Being multiphased and heterogeneous in 73 particle size, they provide variable physico-chemical properties during agglomeration. Particle size 74 distribution is an important factor for agglomeration: for example, during granulation particles of 75 about 1-3 mm act as nucleation site of the granules, while particles smaller than 0.2 mm act as an 76 adhesive layer [18]. The mineralogy and its associated textures, the crystallography and the various 77 grain/phase morphologies are important factors. Bulk and phase chemistries of the materials and 78 physical parameters (e.g. Cation Exchange Capacity (CEC), Specific Surface Area (SSA)) are also 79 to be taken into account. All these parameters are rarely investigated in this context, and no 80 comprehensive systematic study **currently** exists.

81 **This paper is the first part of a larger project.** The objective of this study is to define 82 key parameters for cold agglomeration of dusts from the manganese pyrometallurgical processing 83 chain, **based on characterization work and theoretical consideration. Future communications 84 will address agglomeration experiments and a predictive modeling of agglomeration 85 potential based on material characteristics, respectively.** In the present study and for the first 86 time, a systematic mineralogical, chemical and physical characterization of 13 representative 87 samples from ore feed, processing dusts and sludges from the ferromanganese process chain of the 88 Sauda plant (Eramet NORWAY) was performed. A multi technique approach was chosen for 89 mineralogy and chemistry (XRD, XRF, SEM-EDS, QEMSCAN), particle size distribution (laser 90 granulometer, QICPIC) in order to compare and evaluate the performance of each technique. In 91 addition, parameters such as CEC, SSA, using the Brunauer-Emmet-Taylor (BET) method) and 92 zeta potential, at present rarely studied for those materials [13], are presented.
Thirteen samples from the manganese pyrometallurgical plant of Eramet in Norway (Sauda; Fig. 1) were studied. Six samples represent the feed material: (i) lateritic manganese ore and its sintered (reduced) product from the Mn-mine of Moanda, Gabon (Eramet COMILOG; 1 and 2 in Fig. 1, respectively), (ii) mixed carbonate-oxide ore from Gloria and Asman, both from South Africa (ASMANG; 3, 4 Fig. 1), (iii) recycled Metal Oxygen Refining (MOR) slag and onsite material spilled during handling (respectively remelt, 5, and spillmix+metal, 6, Fig. 1). The raw materials are referred to as: GO (Gabon ore), AO (Asman ore) and GIO (Gloria ore). Those materials are converted into manganese alloy in the furnaces. Other samples originate from (i) the environmental hood (located above the furnaces hall, 7, Fig. 1), (ii) dust generated during metal tapping from the furnace (8), and (iii) sludges produced in a water treatment plant processing dusts collected in Venturi filters (9). Samples 7, 8 and 9 will be referred to as smelting dusts hereafter. MOR low density ultrafines (<100 µm) do not contain metal droplets (10) while, two types of Cooler dusts (medium density ultrafines, <100 µm, and high density medium sized fines, 100-500 µm) do (11, 12). Samples 10, 11 and 12 will hereafter be referred to as refining dusts. The last sample is ferromanganese dust, generated during the crushing of the alloy.

## 2. Methodology

### 2.1 *Sampling & handling*

According to Lyman, a sampling operation must be mechanically and methodologically correct, which means that every particle in the material feed have an equal probability to be sampled [19]. In the present study, our specimens were not acquired according to these principles, hence we can't certify their representativity. However, they fit the objective, which is to obtain a
qualitative characterization of the different products across the production chain. Each material line has a very specific mineralogy, as is later shown later in this paper (See part 3.2). The mineralogy assessed in this paper is similar to the specifications defined in the literature ([20]–[23]) and the proportions are within natural, limited variations. Once a deeper comprehension is obtained, it will be possible to recreate the properties of the different variations of a given dust to predict their behavior.

All the materials were manually taken inline of the process: from the feeding lines of the furnace for the feeds, and from their collecting recipient (big bags) for the process dusts.

All subsequent division of homogenized samples were performed using a riffle split to keep sample representativity.

All samples were wet sieved to separate the following fractions: C (Coarse: >1 mm), M (Medium: 1-0.1 mm), and F (Fines: <100 µm). Chemical and mineralogical analyses were performed at Eramet IDEAS, Trappes (France).

The scale used to determine the results of the sieving have an accuracy of 0.05 g.

## 2.2 Bulk chemistry

For chemical analyses, all samples were grinded to <100 µm and subsequently dried at 105°C for about 12h. The Loss On Ignition (LOI) was determined through a 2h calcination at 1000 °C to ensure that each element reaches its highest oxidation state. LOI may **assume** both positive and negative values. A positive value (weight loss) indicates volatile loss, while negative values indicate oxidation of highly reduced material (metals, monoxides, spinel-type oxides). Such highly
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reduced materials are metastable at ambient temperatures, but will revert to their stable state when heated, which *involves* higher oxidation states.

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All bulk samples and their different size fractions were analyzed for major, minor and trace elements by means of X-Ray Fluorescence spectroscopy (XRF; PANalytical AXIOS Minerals equipped with a Co-anode). Total sulphur and carbon contents were analyzed by combustion (C/S Horiba EMIA 320V analyzer). Chlorine was determined by ion chromatography (881 Compact IC Pro. Colonne anionique Metrosep A Sup3- 250/4.6, Metrohm) and Boron by ICP-OES (720, Agilent).

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All the detection limits and uncertainty are reported along with the analytical results in the research data linked to this paper.

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## 2.3 X-Ray Diffraction (XRD)

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Qualitative mineralogical analyses were performed using X-Ray Diffraction (XRD, PANalytical X'PERT PRO, with a Cu-anode, measuring time about 1h per sample). Prior to measurements, samples were grinded to about 40 µm. The obtained diffractograms were evaluated using HighscorePlus software and the Crystallography Open Database [24]

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## 2.4 Mineralogical quantification estimations

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Based on bulk chemistry and qualitative mineralogy, the modal mineralogy of each sample fraction was calculated. The theoretical compositions of mineral endmembers were used to perform these calculations. This approach is naturally erroneous as **the present** phases are frequently composed of solid solutions. The mineralogy varies in the different samples. The methodology is described in Fig. 2.
157 When no discriminant element exists (e.g. between calcite CaCO₃ and kutnohorite (Ca, Mn)(CO₃)₂ in Fig. 2), a "repartition coefficient" is introduced to distinguish between both minerals. This 158 coefficient is calculated for each case, following two rules: (i) involvement of at least 2 wt.% in 159 both minerals to fit the XRD detection limit (~2 vol.%), (ii) total mineral quantification closest to 160 100 wt.%. The calculation does not consider the oxidation state of the elements (e.g. Mn) as they 161 are not available. Because this method does not consider chemical variations in each mineral phase, 162 it remains approximative and may lead to misinterpretation if used without additional information. 163

164 **Its accuracy is also dependent on the chemical analyses uncertainties.** 164

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166 2.5 *Polished section preparation*

167 Dusts and block samples were prepared to fit into molds. Fluid resin, methyl methacrylate, was 168 poured over the samples. Samples were hardened in an autoclave at 45 °C and 80 bars for 48h: 169
170 Since small (<1 mm) gaz bubbles are produced during the hardening, smaller/less dense particles
171 (e.g. rhodochrosite: 3.69, pyrolusite: 4.80) may have been displaced upwards. **Although** most of
172 the material is concentrated at the bottom of the sample, some of the samples may exhibit an under-
173 polished (Rotoforce 3/Rotopol 31, Struers, France). All samples were coated with an 174
175 approximately 25 nm thick carbon layer for SEM/QEMSCAN investigations.
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176 2.6 *Scanning electron Microscopy (SEM) and QEMSCAN*

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A high-resolution SEM-FEG Zeiss Supra 55 equipped with a 30 mm² Bruker SDD EDS detector was used in the Back Scattered Electron (BSE) and Secondary Electron (SE) modes to study the morphologies and textures of the dust particles at an accelerating voltage of 15 kV, with a diaphragm dimension of 120 µm, and a working distance of 8.6 mm. Energy Dispersive Spectroscopy (EDS) was used to obtain qualitative elemental composition of the grains.

A FEI Quanta 650F FEG, equipped with 2 Bruker Xflash 30 mm silicon drift EDX detectors, was used in BSE mode. The results were automatically treated with an associated QEMSCAN® system (Quantitative Evaluation of Minerals by SCANning electron microscopy, at 25kV and 10 nA, average counting time 5h per section). The iMeasure v5.4 software allows the collection of statistical data on mineralogy, and quantitative textural analyses of samples, which are classified and processed using the iDiscover v5.4 software suit. Due to the very fine particle size, the field image measurement option was selected. X-Ray data were collected with a 2.5 µm resolution, and total X-Rays count of 2000 counts per spectrum. The software compares the analyses to a database to identify the mineralogy of each pixel. It then calculates the mineralogical quantification based on the assumptions that (i) the surface proportions of the minerals are representative of their 3D distribution and (ii) the theoretical density of each mineral can be applied in the samples. The classification used in this study was verified by the XRD and bulk chemistry results to ensure the best fit between the different methods.

The challenge in studying fine particles such as industrial dusts with the QEMSCAN®, is two-fold:
199 (i) The resolution of the equipment defined by the spot size (about 1 µm) resulting in
200 interferences of analyses (2.44 µm). This makes it difficult to have a clear-cut signature
201 of very small particles or complex textures, thus complicating phase identification. As
202 each microanalysis could be a mixture of several phases, mineral quantification needs
203 to be statistically resolved. This was done by classifying EDS analyses to the dominant
204 phase of mixes.

205 (ii) The characterization of a wide range of materials (semi-carbonated to oxide ores,
206 products along the pyrometallurgical processing chain) with one single database.
207 Usually a database is built for a specific purpose on a given sample type. A universal
208 database demands larger constraints, as there are phases with quasi-identical chemical
209 compositions. In order to overcome this issue, the boundaries between minerals were
210 identified by statistical data treatment of raw EDS Qemscan data. The (Mn+Fe)/O
211 elemental ratio was used to discriminate between Mn oxides (pyrolusite, haussmanite),
212 allowing a good but not perfect distinction. Iron was included as it substitutes for Mn
213 in the crystal structures.

214 The shape factor used in this paper is a built-in function of the QEMSCAN software. It calculates
215 the ratio of the square of the perimeter to the area of a particle and gives an estimation of the
216 roundness of particles.

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218 2.7 Granulometry and granulomorphology

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Apart from the QEMSCAN software, two methods were used to assess the Particle Size Distribution of the samples: laser granulometry, using a CILAS 1064L, and optical granulometry, with a Sympatec QICPIC apparatus. The analytical cycle is similar for both machines: a few milligrams of sample material are injected into the apparatus in an aqueous medium, and ultrasound is used to separate the particles from each other. Water is set to flow through the apparatus for the analyses. While the CILAS relies on laser diffraction to measure particle sizes, assuming spherical particles, the QICPIC uses optical measurement and image analysis to measure and calculate morphological properties of the particles (e.g. Equal Projection Circle (EQPC), aspect ratio, convexity, sphericity). The theoretical calculations applied by the software are presented on the Sympatec website [25]. The CILAS ideally analyzes fine particles with a resolution of 0.1 µm, while the QICPIC is ideal for larger particles (up to 1 mm) with a resolution of ~5 µm for size analyses, and ~ 20 µm for calculated properties. For both machine, each analysis was repeated until three consecutive measurements showed similar curves. The values were then averaged to obtain the curves presented in this paper. Both methods were used and compared in this study. The QICPIC and QEMSCAN results were also compared.

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## 2.8 Specific Surface Area (SSA) and Cation Exchange Capacity (CEC)

Specific Surface Areas (SSA) were analyzed for fractions <100 µm (except the Cooler dusts) in using the Brunauer-Emmett-Taylor (BET) method [26]. Measurements were performed at the Études Recherches Matériaux laboratory (ERM, Poitiers, France) using a TRISTAR II 3020 from Micromeritics®. The CEC was analyzed at BRGM (Orléans, France) using cobaltihexamine following the NF X-130 (1999) norm. Since this reactant is a chloride compound, it may artificially
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increase its reactivity with a sample containing chlorides such as sylvite (KCl) in the hood dust and sludges. The obtained CEC values therefore have to be considered inconclusive for these kinds of samples.

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The uncertainty of those tests is reported in the research data linked to this paper.

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## 2.9 Zeta potential

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Zeta potential is the electric potential at the shear boundary, the interface between the outer Helmholtz plane and the bulk of the solution [27]. Zeta potential measurements (Nanobrook 9000plus PALS with green laser) were performed at the Karlsruhe Institute of Technology (KIT, Germany). These analyses require particles ≤ 1 µm. The AO and GIO ores did not generate sufficient amount of fines, hence a suitable fraction is only available among the ores for GO. Analyzed samples included the <100 µm fraction of GO, sinter, tapping dust, sludges, MOR dust and metal dust. The material was suspended in MilliQ (ultrapure) water. The suspension material was extracted for measurements. The zeta potential was calculated from the electrophoretic mobility using the Smoluchowski model [28]. A single result is the average value of ten sets, composed of ten measurements each.

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In a first series, the measurements were performed by injecting the suspended material into MilliQ water and measuring the pH. This was applied to all the above-mentioned samples.

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However, the zeta potential is pH dependent, and the IsoElectric Point (IEP), which corresponds to the pH value where the zeta potential reaches 0 mV, is considered the most favorable for particle agglomeration [29]. To find the IEP, the pH was monitored and adjusted between two
measurements of the same material. This enables **to obtain** the correlation between pH variations and zeta potential variations. The adjustment was made through the addition of a base (NaOH) to increase, or an acid (HCl) to decrease the pH. This **second series involving pH-variation** is time consuming and was therefore applied to only four samples: GO, sludges, MOR dusts and metal dusts.

## 3. Results

Due to the important number of samples, and the volume of data generated by the XRF and XRD analyses, this paper will present synthetic charts. The exhaustive tables can be found in the research data linked to this paper.

### 3.1 Bulk chemistry

The complete results, including the minor elements (Ba, Cu, Na, P, Sr, Ti, Zn, S, B and Cl) are presented in research data linked to this paper. Figure 3 shows the elemental composition of the sample specific granulometric fractions. It illustrates the differences in major elements, major changes during the pyrometallurgical processing, and the variations of the minor elements (See research data data).

The element contents are reported in weight percent. Manganese is the main element of interest. Si, C, K, Al, Mg and Ca impact the pyrometallurgical processes. Therefore, we **discuss** the elements showing major changes.

Manganese and iron are present in different oxidation states from feed material to final product (Mn 0, 2+, 3+, 4+; Fe: 0, 2+, 3+). The chemical analyses are presented as elements, since we do not have exact information regarding the oxidation state (see research data). The LOI of the
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analyzed samples shown in Tab 1 vary between +15 to -26 wt.%, representing a gradual decrease
in oxidation state of the samples **along the processing chain**. The values presented for the ores are
averaged across all fractions.

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Remelt (recycled material) seems to have similar ratios between its elements (Mn excepted), but
an increasing oxidation state with decreasing fraction size (LOI: -15.49 (C), -11.77 (M), -3.19
wt.% (F)). The sinter fractions exhibit similar compositions (3 wt.% Mn variation being the highest
observed in Fig. 3), while the spillmix has more variable composition.

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For the fine fractions, the elemental composition and the oxidation state appear to be controlled by
the pyrometallurgical process (Fig. 1). Since the process transforms oxides into metal, this seems
obvious. However, sludges and hood dust are more oxidized than the tapping dust, although the
three are extracted at the same process step.

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As expected during Fe-Mn production, the Mn content increases from about 40 wt.% in the feed
to ~70 wt.% in the final products. This is related to the decrease in contents of both oxygen and
minor elements. The feed materials also contain Al, K, Ca and minor elements (as listed above),
which are void or diminished in content in the final products (metal/MOR).

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Some dust types generated along the ferromanganese production process act as elemental traps.
For example, K is present in small amounts in GO and the sinter feed. It is most concentrated in
the hood dust. A small amount of K is also present in sludges and tapping dust, but not in the
subsequent material. Minor elements having low evaporation temperature (Zn, Pb...) are also

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concentrated in the smelting furnace dusts. Silica is concentrated in the smelting and refining slags, but some silica is still present in the metal dusts (Fig. 3).

GIO and AO contain Ca and C. The carbon content in GIO (~4.3 wt.%) is about twice as high as in AO. Sludges and hood dusts contain higher C than in the ores (12.7 and 10.8 wt.% respectively).

Carbon is related to Ca-Mg carbonates in the AO and GIO and Mn carbonates in the smelting dusts. The subsequent material contains C <1.6 wt.%, (Fig. 3, Research data).

Sulfur is present to a significant extent only in the AO F fraction (0.48 wt.%), remelt (~0.23 wt.%) and smelting dusts (hood dusts: 0.98 wt.%, tapping dusts: 1.14 wt.%, sludges: 0.32 wt.%). Chlorine is present in significant amounts in hood (1.56 wt.%) and tapping (0.33 wt.%) dusts.

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314 3.2 *Mineralogy*

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Based on XRD analyses, the feed material is composed of two types of ores (oxidized and semi-carbonated; Fig. 4). GO hosts oxides (pyrolusite, cryptomelane), hydroxides (lithiophorite, goethite, gibbsite), clay minerals (kaolinite and illite), and quartz. Clays and gibbsite are concentrated in the *F* fraction. Cryptomelane is present in the *C* and *M* fractions. The semi-carbonated AO and GIO ores contain oxides (AO: hausmannite, bixbyite, hematite; GIO: hausmannite), hydroxides (AO: manganite), silicates (AO, GIO: braunite) and carbonates (AO, GIO: kutnohorite, calcite, dolomite). Contrary to GIO, the only difference between the fractions is the presence of dolomite solely in the *F* fraction of the GIO. The sinter, produced from GO, is only composed of spinel-type oxides (manganoosite, hausmannite, galaxite) and silicates (tephroite,
quartz). The remelt contains oxides (manganosite, hausmannite, galaxite), silicates (glaucochroite, quartz, forsterite) and carbonates (dolomite, calcite). The fraction-related variations are linked to the carbonates. Calcite is absent from the *C* fraction, dolomite is absent from the *M* fraction. The spillmix contains metals, oxides (manganosite and hausmannite), carbonates (dolomite and calcite) and silicates (forsterite). The smelting dusts also show different typologies. Sludges mostly contain carbonates (rhodochrosite, kutnohorite, smithsonite), while tapping dust mainly consists of oxides (hausmannite, manganosite) and traces of quartz. Hood dust mostly displays manganosite and hausmannite, but also rhodochrosite and kutnohorite. All three samples contain sylvite (KCl). The mineralogical simplification continues in the refining dusts composed of manganosite, hausmannite and ferromanganese. Forsterite is present in the *C* fraction of the cooler dusts. Metal dusts are composed of FeMn and forsterite.

Fig. 4 presents the quantitative mineralogy from QEMSCAN analyses and modal calculations performed based on XRD-XRF correlation. QEMSCAN analyses allowed the distinction and quantification of the solid solution in the same mineral group (e.g. calcite, dolomite, kutnohorite). The quantification of the Mn-bearing and non-Mn-bearing phases by QEMSCAN shows the chemical complexity of those structures, with Mn substituting for Ca, Mg and Fe. Classification was simplified into Mn-bearing and non-Mn-bearing phases (threshold at about 20 wt.%), presented in Figures 4 and 5. Furthermore, QEMSCAN indicates mineral phases below 2 vol.% in the sample (below detection limit of XRD), such as FeMn in the MOR dusts and hydroxides in remelt. The mixed phases, such as kutnohorite or galaxite (MnAl₂O₄) are considered as Mn-bearing phases.
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In general, the mineral group estimates between the two quantification methods are consistent.

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Differences are observed for GO involving the absence of clay minerals in the QEMSCAN
analysis. This is related to the small particles size (see section 2.1). In this sample, the estimated
Mn silicate content signifies mixing of silicates and clays with Mn-bearing phases (oxides and
hydroxides) due to the low resolution of QEMSCAN with respect to particle size (Fig. 5).

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QEMSCAN analyses of the sinter show notable differences with the modal calculated mineral
content (Fig. 4), by displaying metal and carbonate phase quantification. This may be due to
variable pore sizes and high porosity of the material, unlike the other feeds. Such porosity increases
the probability of analyzing resin or void along with the sample. This “perturbated” signal will
create an overestimation of certain phases (e.g. carbonate if mixed with resin, or metal if mixed
with void). These overestimations are corrected for by chemical assessment between QEMSCAN
and XRF, and by QEMSCAN data base calibration using specific microprobe analysis [30].

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3.3 *Morphology*

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GO (Fig. 5A and 6A) includes particles (10-200 µm) composed of angular Mn-oxides (pyrolusite) and goethite, which are surrounded by a mixture of Mn-hydroxides (lithiophorite) and clays, forming composite rounded particles (Fig. 5A). Rare angular quartz particles of ~100 µm are present. Neoformed lithiophorite and clay minerals occur as needles and/or web-like porous structures. Fibrous illite involves aggregates of linear grains mixed with kaolinite (Fig .7).

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The AO (Fig. 5B) shows highly variable mineralogy, bimodal grain sizes for the Mn carbonates (~1 and ~100 µm), Mn-silicates and clays. The large angular particles are composed of kutnohorite

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mixed with hausmannite, and angular grains of galaxite. The latter form elongated grains (40 to 20 µm in length), with slightly smoother edges. Micron-size carbonates (kutnohorite, calcite, dolomite, and magnesite) are intergrown with kaolinite and quartz, forming angular to rounded aggregates (about 35 µm). Those composite aggregates form a matrix surrounding the angular grains. Rare barite grains (~10 µm) occur.

The sinter dusts show grain sizes below 100 µm (Fig. 5C and 6C). While QEMSCAN represents particles around 20 µm, SEM shows higher variability. Grains are mainly angular, where particles are either rounded or angular. Tephroite particles are composed of microcrystalline needles while hausmannite particles consist of ~10 µm large, euhedral crystals. Hausmannite may also form grains of variable sizes (1 to 80 µm).

GIO is dominated by 2 types of particles: (1) silicate-carbonate mixes which are most abundant, and (2) Mn oxides-hydroxides composites. Interestingly, “particles in particles” are observed in the silicate-carbonate mixtures, with the included particles being non-Mn-bearing silicate-rich. This is probably related to the impregnation process. Figures 6D and 7D show that particles dominate over individual grains of various sizes (< 70 µm), presenting angular hausmannite and garnet (pyrope).

The tapping dust is composed of rounded particles (~100 µm) hosting spherical hausmannite grains (1-10 µm; Fig 7C). This matrix embeds 10-60 µm long forsterite needles and half-spherical periclase (MgO). Large particles composed of rhodochrosite and hausmannite (~600 µm long, 150 µm large) occur, as well as angular quartz grains (Fig. 6C). The bigger particles (20-40 µm) are porous. The EDS analyses show pure Mn oxides for the bigger ones (20-40 µm), but Mn-Fe oxides
388 with traces of Zn for the smaller ones. The QEMSCAN image (Fig 5C) displays more cohesive
389 material (Mn-oxides dominated) including some angular quartz grains (1-100 µm).

390 Sludges are composed of rounded Mn-carbonate particles a few 100 µm large which are sometimes
391 in contact (Fig. 5D). These particles are composed of rhodochrosite grains (1-20 µm; Fig. 7D).

392 Smaller particles are rare, and constitute a mixture of smithsonite/rhodochrosite or Mn-Fe oxides,
393 most likely spinels based on EDS results (Fig. 6D).

394 The matrix of both sludge and tapping dusts samples is made of very fine spherical grains, being
395 (i) a mix of dark grains with slag signature (O, Al, Si, K, Mn) and light grey grains presenting a
396 mix of hausmannite/rhodochrosite in the tapping dusts, and (ii) pure dark grey rhodochrosite, with
397 K traces in the sludges.

398 MOR represents the last step of the pyrometallurgical process. Nearly all particles are spherical
399 and < 100 µm and are Mn-Fe oxides (spinel) (Fig. 6G and 7G). Some rare occurrences are white
400 and elongated grains with rounded edges. Based on EDS spectra normalized to 100%, this is a
401 FeMn alloy, composed of about 84 wt.% Mn, 16 wt.% Fe. The matrix is composed of dark grey
402 submicrometric pure hausmannite (Mn₃O₄) particles.

403 MOR is composed of similar rounded particles as the sludges, but the fraction of intermediate
404 particle sizes (a few tens of µm) is more important (Fig. 5, 6, 7E). Mn-oxides (hausmannite) are
405 present instead of Mn-carbonates. Similarly, to the tapping dusts, hausmannite forms spherules.
406 Those grains range from submicrometric to 40 µm, forming both the matrix and the bigger grains
407 of the sample.
408 Metal dust (Fig. 5, 6, 7 F) includes homogenous and angular particles where Mn-metal dominates.

409 Mn oxides (manganoosite) show elongated shapes and occur interstitial to the Mn metal. Forsterite
410 is present as large angular grains, interstitial to the Mn-metal.

411 Figure 8 presents the shape factor analyzed by the QEMSCAN with respect to the mineralogy for
412 the samples presented in figures 5, 6 and 7. The shape factor is the ratio between the squared
413 perimeter and the surface area. It describes the complexity of the shape of a particle: the more
414 complex the shape (convoluted particles, jagged edges...), the higher the ratio, hence the shape
415 factor. A perfect circle and a square have shape factors of 12 and 16, respectively. The
416 morphological parameters of particles < 15 µm tend to be erroneous, due to their size being
417 comparable to QEMSCAN resolution, and were filtered out. This explains the absence of the
418 classes "clays" and "others" which were identified with other methods. This is shown on the
419 previous graphs (Figures 4 and 5). All the mineral groups are considered as gangue material, when
420 not specifically selected. We then calculate the shape factor for each particle. The weighted
421 averages of the shape factors are presented below.

422 The shape factor for each mineral type varies within one single sample. GO has similar shape
423 factors (~35) for Mn silicates, Mn oxides and Mn-free hydroxides. However, Mn hydroxides have
424 a complex shape factor around 60. The sinter sample presents two main shape factor groups: (1)
425 metal, Mn hydroxides and silicates (18-25), and (2) Mn silicates and Mn oxides (32-40). For AO,
426 the oxide shape factors are around 25. Mn silicates, on the other hand, have much more complex
427 shapes, with a shape factor of around 52. The GIO presents two shape factor populations: (1) very
428 smooth surfaced particles for CaMg carbonates and Mn carbonates, around 15, and (2) more
429 complex shapes (e.g. serrated surfaces), between 32-37 for the other mineral groups (Mn silicates,
430 Mn oxides and oxides).

431 Tapping dust exhibits a similar shape factor for the Mn silicates and oxides (~20). The Mn oxides
432 present shape factor values around 52. Sludges show 3 distinct groups of shape factors, i.e.: 12
433 (oxides), 28 (Mn oxides), and 43 (Mn carbonates). QEMSCAN analyses cannot distinguish the
434 different grains (crystals) for Mn carbonates, since the mineralogy is entirely dominated by the Mn
435 carbonates, forming a bonding matrix. The MOR dust presents higher average shape factors: the
436 lower ones are at 28 for the Mn silicates. Shape factors are 50 for the metal particles and 53 for
437 the Mn oxides. The metal sample has low values for both Mn silicates (21) and Mn oxides (25),
438 but a very high one (57) for the metal itself.

439 While QICPIC analyses also allow to determine the morphological properties of particles, it
440 involves fundamental differences with respect to QEMSCAN: (1) it will analyze the
441 morphological properties of the particles regardless of their mineralogy, (2) it involves a dynamic
442 water flow, without cluster formation and (3) it acquires 2D projections of particles, while
443 QEMSCAN acquires planar images across the particles that are numerically recalculated in
444 volume.

445 Three values were calculated during QICPIC measurements: the convexity (amount of concave
446 areas at particle surfaces), the sphericity (ratio between the EQPC (diameter of a circle of Equal
447 Projection area) perimeter and the real perimeter of the particles), and the aspect ratio (minimum
448 Feret diameter over maximum Feret diameter of the particles [31]). For each of these metrics, the
449 higher the value, the simpler particle morphology.
The obtained values (Fig. 9) confirm the QEMSCAN results, i.e. that the processed dusts tend to exhibit “simpler” morphology. Across all samples, sphericity values vary between 0.49 and 0.62, with higher values found in tapping, sinter, MOR dusts and GLO, and lower values in AO and GO. Convexities vary between 0.74 and 0.86, with the same distribution, with the sludges yielding high values. Aspect ratio varies between 0.68 and 0.81, with tapping, MOR and sinter showing higher values, and AO, GO and metal dusts showing lower values.

## 3.4 Grain and particles size distribution

Each material was wet screened, and results are shown in Fig. 10. Laser granulometry was performed on the *F* fraction for all samples. The amount of fines in GLO and AO was very low (Fig. 10). This amount was used for thin section preparation and chemical analysis but unfortunately the material left was not sufficient for granulometric measurements.

The studied samples exhibit two types of particle size distributions (Fig. 11): (A) samples with a main population between 1 and 10 µm (GO, sludges, hood, tapping, MOR and cooler fine dusts), and (B) samples with main population between 10 and 100 µm (sinter, remelt, spillmix, metal dust).

The type 1 (Fig. 11A) samples show a multimodal distribution. All dusts peak at 0.3 µm, except the sludges (0.6 µm). GO, tapping and hood dust show populations peaking at 30, 22 and 18 µm, respectively. The type 2 sample (Fig. 11B) also has a population peaking at 0.3 µm and a peak at 3.4 µm. For remelt and spillmix, as well as sinter and metal, the main population can be subdivided with peaks (i) at 13 and 20 µm, respectively and (ii) at 36 µm. Sinter and metal samples are more homogeneous, but do show two populations peaking at (i) around 20 µm, and (ii) at 36 µm. Similar observations are made for the spillmix and remelt samples (Fig. 10B).
## 3.5 CEC and Zeta potential

The CEC value (Tab. 2) measured for GO is about 8 times higher than for the other ores. The AO (0.3 meq/g) has only half the CEC value of the GIO (0.66 meq/g), although both ores have similar qualitative composition. However, AO has a slightly higher hydroxide content than GIO. The CEC values of the remelt and spillmix, at 4.4 and 3.4 meq/g, respectively are higher than for GO. Among the process dusts, the hood dust reaches 22.2 meq/g, the highest value of all samples. While the sludge is characterized by a CEC of 5.5 meq/g, the tapping dust has a low CEC (0.1 meq/g). The CEC of the metal is the second highest (9.1 meq/g) of all samples, while that of MOR is low (0.5 meq/g).

The GO has a zeta potential value of -18.5 mV, while the sinter exhibits -8 mV (Tab. 2). The tapping dust has a slightly positive value of +7 mV when suspended in MilliQ water. The two tests performed on MOR show zeta potential values of -3.3 and +2.7 mV, i.e. close to zero. Cooler, metal dusts and sludges have negative values. The values determined in MilliQ water pertain to varying pH conductivity values among the samples. The observed linear relationship with CEC may hence be coincidental (Fig.12) and would require further study.

The Isoelectric Point (IEP) was determined for four samples along the processing chain: GO fines, sludges, MOR and metal dusts. The GO zeta potential shows a strong dependence with respect to pH changes, from 27 mV at pH 3.1 to -21 mV at pH 4.8. This is a typical curve for oxide minerals with low ionic strength. The IEP is at ~pH 4.1. For sludges, the zeta potential is -18 mV at pH ~4. It seems to increase with decreasing pH, reaching zero zeta potential at pH 2. Although the metal dust zeta-potential reaches zero around a similar pH (~2.5), its evolution is not linear. The slope is steeper between pH 2.5 and 3 (0 to -12 mV) than for sludges (around -7 to -11 mV). The MOR
dust behavior is peculiar. The points at pH 3.9/2.7 mV and pH 4.7/-3.3 mV, close to zero, are measured by simply suspending the sample in milliQ water. However, when acid or base is added, the values drastically change and align themselves on a curve that would yield an estimated IEP at pH ~2, and a zeta potential that decreases with increasing pH value.

The BET measurements yield a high variation among the feed material. GO has the highest value at 27 m²/g while AO (2 m²/g), GIO (2 m²/g) and sinter (below detection limit) show the lowest BET (Tab. 3). The recycled materials remelt and spillmix, have higher values at 13 and 20 m²/g, respectively. The values reached by the process dusts are decreasing with increasing degree of transformation, with the metal reaching below the measurable limit.

## 4. Discussion

### 4.1 Efficiency and assessment of the methodologies

For the studied samples, ten different analytical techniques were coupled to obtain maximum information on each sample. Those methods and their performances regarding the different aspects of characterization are summarized in Tab. 4.

The bulk chemistry (in this study investigated via XRF, LOI, ICP-OES, combustion, ion chromatography) is essential to access the major, minor and trace element contents. Chemical characterization is also a key parameter to calibrate other methods (e.g. XRD, QEMSCAN) and the ensuing quantification. In the present study, high volatile (high LOI) bearing samples (GO, GIO, sludges and Hood dusts) are mainly composed of soft, plastic and sectile materials, such as carbonates and hydroxides but also Mn/Fe oxides formed in soils [32]. In contrast, highly negative LOIs were measured in metal dusts composed of hard, ductile and malleable materials. In our
specific system, this information can hence be used as a first indicator of their oxidation state and
mechanical properties.

XRD is essential for identification of the major minerals (>2 wt.%). However, the minor phases
(e.g. swelling clays), which may act as binders during agglomeration, may be missed. Thus,
carbonates were not detected in remelt, nor the Mn silicates in the tapping dusts (Fig. 4).

Furthermore, routine XRD lacks distinction between the solid solution of minerals of the same
crystallographic system. To access such information, mineral separation and high counting time
would be necessary. This would considerably increase the analyses time in industrial setting, and
thus decision-making [33].

For high-resolution SEM-EDS and QEMSCAN, sample preparation is a key issue [34]. Section
preparation and the use of viscous resins leads to agglomeration due to adhesive forces [35]. The
preparation protocol caused a size-based segregation may occur due to different particle properties
[36], [37]. Preparation with methylmetacrylate coupled to degassing enhanced segregation.

High-resolution SEM-EDS is performed to access qualitative phase chemistry and morphology.
Although achieving representativity is time-consuming, SEM is mandatory to calibrate
QEMSCAN results regarding µm-scale grain morphologies.

QEMSCAN analyses then allow quantification of the defined phases, as well as morphological
information of particles (e.g. shape factor, grain sizes). When studying dusts <15 µm, as in the
present study, the low resolution gives poorer morphological estimations.

For instance, when considering quasi-monomineralic dust samples (e.g. sludge, metal, MOR), the
software is not able to deconvolute very fine grained particle aggregates. This leads to erroneous
particle or grain sizes as well as shape factors. QICPIC involves less agglomeration issues as it relies on loose, ultrasound-separated sample material. However, it is unable to correlate the different properties of a particle.

CEC represents the cation adsorption potential of a material at a given pH. Higher values are usually considered to be correlated with the clay content. However, Kabata-Pendias [38] also discusses the possibility that the Mn oxides content affects the CEC value. Indeed, Mn oxides such as pyrolusite or cryptomelane (present in GO) have tunnel structures, which may increase CEC values [39]. This is consistent with the results obtained for GO, AO and GLO, whereas CEC of GO is about ten times higher compared to the other two (Tab. 2). The CEC of metals has not been previously studied. The negative surface charge can explain the high CEC value obtained (~9 meq/g).

The BET surface area, shown in Tab. 3, assesses the external and internal surface area of a sample, which depends on both granulo-morphology and mineralogy. The presence of clays is usually assumed to be the main factor in increasing the BET. However, impact must also be attributed to layer-structured and tunnel-structured phases. Lithiophorite for example has a structure very close to clay minerals, although it is a hydroxide. This fits with the values observed in our samples. The lithiophorite, pyrolusite and cryptomelane content of GO can explain the high value (27 m²/g). Process dusts have intermediate values. Their formation occurs between the furnace bath and the atmosphere, where temperature decreases rapidly. This prevents the same compact crystallization that could occur in a slowly cooling system, and results in a more porous material, which increases the BET value despite the absence of clay minerals. The variation observed among process dusts is due to differences in crystallization kinetics [40]. First evaluation of QICPIC results for MOR
and metal dusts lead to think that metal would have higher BET values. However, this is not the case, which may be explained by mineralogical differences. The resulting variation may hence be due to grain size and material properties.

The above methods, coupled with the understanding of agglomeration mechanisms, allow a critical extraction of information, such as metal content, mechanical behavior and packing density (Tab. 4). Those are key parameters affecting agglomeration processes [11].

## 4.2 Agglomeration parameters for the studied dusts

The main forces occurring during agglomeration are capillary, electrostatic, and Van der Waals forces [17]. There are two ways to impact these forces: modifying (i) directly one of the terms of the equation (e.g. [17], [41]) and (ii) indirectly one of the terms of the equation by altering a phenomenon occurring during the agglomeration cycle. One of the prime examples concerning the latter is the breakage of brittle particles under uniaxial pressure, leading to a more compact agglomerate. Shorter separation distances between particles must be achieved to obtain a hard agglomerate. The key parameter seems to be the mineralogy of a given material, including its variable crystallography and mutual interaction since they are linked in the material brittleness. Particle size distribution is also an important parameter for agglomeration. Pan et al [18] found that a good mixture of particles < 200 µm and particles of 1-2 mm is most favorable, with little or no intermediate particle sizes. The reason is that the small particles will “fill the gaps” left by the large particles that undergo breakage. The following section will discuss the agglomeration
potential of each material, estimated by the authors from a theoretical point of view. The main
physical properties of each sample are shown in the Tab. 5.

GO has the highest percentage of layered minerals (kaolinite, illite, lithiophorite, birnessite), with
perfect cleavages, intrinsic Van der Waals bonding, and tunnel-structured minerals (pyrolusite and
cryptomelane). The layer structure minerals occur as very fine (nm to µm) platy (clays) or needle-
shaped (hydroxides) particles of low hardness (~2 on Moh's scale). They usually form soft
coatings around the much harder (~6 on Moh's scale) tunnel-structured minerals (pyrolusite,
cryptomelane). The layer structure minerals exhibit negative charges on their basal surfaces, and
positive or negative charges on their edges depending on the pH. All those properties favor their
adsorption on the tunnel-structured surfaces, and bonding among themselves when compressed
(Fig. 14). Tunnel-structured minerals may incorporate cations and/or water molecules [42]. This
will in turn help increase the surface charges for the electrostatic and/or Van de Waals forces
and/or the moisture content increasing capillary bridges. In a comparable fashion, nickel laterite
ore from New Caledonia was successfully agglomerated using cold, binder-free briquetting
(internal report ERAMET Ideas). QEMSCAN analyses (Fig. 15) show abundant layer-structured
minerals (smectite). These swelling clays have properties similar to commercial bentonite, and
thus naturally increase adhesive potential. The GO and nickel ores both have high amounts of
tunnel and layer structure minerals. Considering the behavior of the material in Fig. 15, the overall
adhesive potential of GO is assumed to be high. However, due to the coatings on the hard minerals
(pyrolusite, cryptomelane...) by softer material, breakage is less likely, which in turn limits the
creation of fresh surfaces during compaction.
600 AO and GIO have similar properties. These samples are mostly composed of hard minerals (braunite, hausmannite, respectively 6.0 and 5.5 on Moh's scale) with low content of fine particles.
601
602 They also contain soft carbonates (about 3 on Moh's scale) with perfect cleavages, which are brittle
603 under compaction. However, particle breakage may be less efficient to reduce the separation
604 distance between the particles than the clay deformation. Consequently, the adhesive potential of
605 AO and GIO is lower than the one of GO. Instead, their low fine production reduces the need for
606 agglomeration.

607 The sinter is composed of hard cubic spinels (hausmannite, galaxite) and tephroite having a hardness of 6.0 and 7.5 on the Mohs scale, respectively. All three minerals exhibit perfect cleavage.
608
609 At room temperature (i.e. cold agglomeration temperature), spinels show rigid behavior [43]. The
610 resulting low breakability of the particles coupled to the low fine production of the sample will
611 yield important separation distance between particles [17]. The complex shape of the particles may
612 lead to physical interlocking of particles/grains. Spinels have good sorption capacities [44], used
613 for industrial remediation through nano-sized spinels [45]. In our samples, the spinels are tens of
614 microns in size, resulting in a smaller interaction surface area. Despite their good sorption capacity,
615 the lack of good sorbent material in the sample suggests that the sinter will exhibit low adhesive
616 potential under pressure.

617 The sludges have an essentially carbonated mineralogy (Moh's hardness around 3), but narrow
618 PSD. They will hence exhibit low adhesive potential, although higher than that of the MOR.
619 Indeed, the high hardness of the minerals (hausmannite, about 6 on Moh's scale), simple shape
620 (spherical particles) and narrow PSD of the MOR dust suggest negligible adhesive potential. The
tapping dusts have similar properties as the MOR. However, the broader PSD increases its
adhesive potential.

The metal dust is the only sample with ductile behavior. Its high deformation threshold makes
interlocking of particles difficult. Additionally, the shape of the particles is angular though simple.

The PSD is narrow, and the mineralogy is homogeneous. This suggests rather low adhesive
potential.

## 4.3 Increasing agglomeration potential

### 4.3.1 Moisture

The presence of moisture amidst the grains improves the cohesiveness of the material through an
increase of the capillary forces. This will regulate the flow and deformation of the material [46].

However, the presence of moisture also reduces Van der Waals forces. At liquid saturation, the
material becomes a slurry (Fig 16; [47]) which requires a balance between the tensile strength
(capillary forces) and the compressive strength (Van der Waals forces).

The surface charges mainly depend on the pH of the environment. It is therefore possible to control
the surface state (physical and chemical) of the particles by conditioning the moisture added to the
compacted material [48], to manipulate the electrostatic attraction forces. For example, if the
material is complex, with different phases, it would be possible to modify and adapt the pH so that
different phases have opposite surface charges that cause attraction.
640
4.3.2 Blending

641
In order to optimize the overall agglomeration process for all dust types ensuring a constant
642
valuable metal content of the agglomerates, blending could be necessary [49]. In-depth material
643
characterization allows to model optimum properties to achieve cold binder free agglomeration.
644
In the present study, GO shows the best agglomeration potential. Future work will benchmark the
645
blending proportion GO with the other materials and the compaction parameters.

646
5. Conclusions

647
1. A proper characterization of dusts must imperatively be pluridisciplinary, with a strong
648
emphasis on mineralogy. Mineralogy and crystallography define the key parameters for
649
agglomeration. Tunnel-structured must be considered along with layered-structure
650
minerals for their helping roles in agglomeration. Further systematic study, relating Mn
651
mineralogies and CEC would be required. Firstly, a laboratory study on the different dust
652
materials should include SEM, QEMSCAN, XRD, XRF, CEC, BET and zeta potential
653
studies. Then based on these results, key parameters of the material can be extracted and
654
quantified (e.g. percentage of layer and tunnel structure minerals), to be used in the
655
industrialized process scheme.
656

656
2. Considering dust generation along the processing chain, simplification of the mineralogy
657
and the morphology was noticed.

658
3. Ores, especially GO in the present study, have the highest agglomeration potential due to
659
their high amount of layer- and tunnel-structure minerals, highest among the analyzed
660
materials. As GO is the most abundant dust and its agglomeration potential is high,
661
blending of this high agglomeration potential matrix with the other materials should be
662 studied and benchmarked further, along with the specificity of each agglomeration method.

663 The two other ores (AO and GIO) have slightly lower agglomeration potential than GO.

664 The process and sinter dusts have low agglomeration potentials, with the exception of
665 MOR dust, of which exhibit negligible potential.

666 4. Additional work considers actual agglomeration experiments. Inclusion of such results will
667 allow to design a predictive model for the agglomeration potential of samples.

668 **6. Acknowledgements**

669 We thank Eramet IDEAS, the GEOPS laboratory, the KIC-EIT RawMaterials Go4.0. The PhD is
670 supported by a CIFRE project N° 2017/0326 and the ANRT for their logistic and financial support.

671 A. Salaün, S Lafon and Q. Jallet (ER) are thanked for their help handling the samples. We thank
672 the staff of ERAMET Norway SAUDA (B. Ravary, E.-D. Fatnes, D.-O. Hjertenes and E.-V.
673 Hansen) for their help during the sampling mission.

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797

798
799

800

801 Fig. 1: Schematic sampling location across the treatment process of the ferromanganese production
802 plant Sauda (Eramet Norway).

803

804 Fig. 1. Modal mineralogy calculation methodology. The minerals with exclusive elements are
805 calculated first, and then the next until all elements have been accounted for.

806

807 Fig. 3: Elemental concetrations (wt.%) of the samples from Mn-ores (top) to metal (bottom).
808 Granulometric fractions were analyzed separately, represented as C (Coarse, 500 µm – 2 mm), M
809 (Medium, 100-500 µm) and F (Fine, < 100 µm). ME (Minor elements): Ba, Cu, Na, P, Sr, Ti, Zn,
810 S, B and Cl. The difference to 100% represents oxygen (not measured).

811

812 Fig. 4: Mineralogical quantification performed on the samples during this study. The bottom line
813 (Q) presents QEMSCAN data. The top line (N) presents the modal quantification based on the
814 combination of XRD phase identification and XRF chemical quantification, normalized to 100%.

815

816 Fig. 5. QEMSCAN classification of *F* fractions of samples across the FeMn production. A)
817 Gabon ore, B) Asman ore, C) Sinter dusts, D) Gloria Ore, E) Tapping dusts, F) Sludges, G)
818 MOR dusts and H) Metal dusts.
819

820

Fig. 6: SEM-BSE pictures of samples across the FeMn production, with HV = 15 kV and a working distance of 8.3 mm. A) Gabon ore, B) Asman ore, C) Sinter dusts, D) Gloria ore, E) Tapping dusts, F) Sludges, G) MOR dusts and H) Metal dusts.

821

822

823

824

Fig. 7: SEM pictures at higher magnification than Fig. 6 of samples across the ferromanganese production sample with HV = 18 kV and a working distance of 8.3 mm. A) Gabon ore, B) Asman ore, C) Sinter dusts, D) Gloria ore, E) Tapping dusts, F) Sludges, G) MOR dusts and H) Metal dusts.

825

826

827

828

829

Fig. 8: Shape factor calculated by the QEMSCAN software for each sample and each mineral group, reflecting the shape complexity of the particles: higher shape factor means higher complexity.

830

831

832

833

Fig. 9: QICPIC measurements results with Aspect ratio, sphericity and Convexity.

834

835

Fig. 10: Feed material separated through wet sieving in three fractions: F; Fines <100 µm, M; Medium 1-0.1 mm, C; Coarse >1 mm. Results are expressed in wt. %.

836

837
838 Fig. 11: Particle Size Distribution (PSD) of the measured samples. A) type 1 Samples: main population between 1 and 10 µm. B) type 2 Samples: main population between 10 and 100 µm.

840
841 Fig.12: Graph presenting zeta potential versus CEC using the data presented in table 2.

842
843 Fig. 13: Zeta potential evolution according to pH variations, for the Gabon ore, sludge, MOR dusts and Metal dusts samples.

845
846 Fig. 14: Schematic of the hydroxide coating behavior under compression of the sample. The layered structured minerals behave plastically and fill the gaps, hence reducing the separation distance between the particle.

849
850 Fig. 15: QEMSCAN false color image of a nickel laterite ore pellet realized by ERAMET.

851
852 Fig. 16: Impact of moisture on the intensity of Van der Waals and Capillary forces. Agglomeration processes look for a high compressive strength, but capillary bridges help reduce the fine generation of an agglomerate. A balance is hence required. Modified from [47].

855
856 Table captions

857
Tab. 1: Loss On Ignition (LOI) for each sample, averaged across all samples. The positive values show an effective mass loss due to volatile elements loss, while the negative values correspond to a mass gain due to an oxidation of highly reduced material.

Tab. 2. Surface charges measurement across the samples. pH was measured during the zeta potential measurement in order to assess the modification induced by the dust on the pH of the added milliQ water. The results presented here are from the first set of measurements.

Tab. 4. Each method has specific strengths and weaknesses when dealing with fine material. The main aspects of each methods are summarized here, along with the agglomeration metrics each method allows to investigate.

Tab. 5: Summary of the shape properties of the samples investigated in this paper. The hardness values written X/Y* indicate the presence of two very distinct mineralogical family (e.g. Oxides and Clays in the GO) in terms of hardness.
<figure><img src="image_2.png" /></figure>

Blended feed dust

① Gabon ore

② Sinter (from Gabon ore)

③ Asman ore

④ Gloria ore

⑤ Remelt

⑥ Spill mix + metal

⑦ Hood dust

⑧ Tapping dust

⑨ Sludges

⑩ MOR dust

⑪ Cooler dust fines

⑫ Cooler dust coarse

⑬ Metal dust

⑩ Ore feed

⑪ Smelting dust

⑫ Recycled feed

⑬ Refining dust

⑭ Casting and crushing dust
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

Calculation order

Si, Mn

Elements composing the mineral (O excluded)

Fe

Elements taken as limiting for the calculation
<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /></figure>

Legend:

<table><tr><td>Mn / Oxides</td><td>Mn / Silicates</td><td>Metal</td></tr><tr><td>Mn / Hydroxides</td><td>Mn / Carbonates</td><td>Clays</td></tr></table>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

Legend:

Cc: Calcite

Ba: Barite

Do: Dolomite

FeMn: Ferromanganese

Fo: Forsterite

Ga: Galaxite

Go: Goethite

Hs: Hausmannite

He: Hematite

Ka: Kaolinite

Ku: Kutnohorite

Lp: Lithiophorite

Ma: Manganoosite

Ox: Oxides

Py: Pyrolusite

Qz: Quartz

Rh: Rhodochrosite

Sm: Smithsonite

Sl: Slag-like matrix

Te: Tephroite
<figure><img src="image_18.png" /></figure>

**Legend:**

Cc: Calcite

Ba: Barite

Do: Dolomite

FeMn: Ferromanganese

Fo: Forsterite

Ga: Galaxite

Go: Goethite

Hs: Hausmannite

He: Hematite

Ka: Kaolinite

Ku: Kutnohorite

Lp: Lithiophorite

Ox: Oxides

Py: Pyrolusite

Rh: Rhodochrosite

Si: Siderite

Sl: Slag-like matrix

Te: Tephroite
<figure><img src="image_19.png" /></figure>

Size fraction (wt.%)

F

M

C

Gabon

Sinter

Asman

Gloria

Spillmix

Remelt
<figure><img src="image_20.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_21.png" /></figure>
<figure><img src="image_22.png" /></figure>

$R^2 = 0.9655$
<figure><img src="image_23.png" /></figure>
A

<figure><img src="image_24.png" /></figure>

B

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

Mn hydroxides

<figure><img src="image_27.png" /></figure>

Mn oxides
<figure><img src="image_28.png" /></figure>

Smectite

Serpentine

Other minerals

Smectite

Serpentine

Other minerals
<table><tr><td>Van der Waals forces</td><td></td></tr><tr><td>Capillary bridges</td><td></td></tr><tr><td>Compressive strength</td><td></td></tr></table>

$S_L$=Liquid saturation

Pendular

<figure><img src="image_29.png" /></figure>

$S_L=0$

Funicular

<figure><img src="image_30.png" /></figure>

$S_L=0.25$

Capillary

<figure><img src="image_31.png" /></figure>

$S_L=0.9$

Slurry

<figure><img src="image_32.png" /></figure>

$S_L=1?$

<figure><img src="image_33.png" /></figure>

Solid State

<figure><img src="image_34.png" /></figure>
<table><thead><tr><th>Sample</th><th>GO</th><th>AO</th><th>GIO</th><th>Sinter</th><th>Remelt</th><th>Spillmix</th><th>Sludges</th><th>Hood dust</th><th>Tapping dust</th><th>MOR dust</th><th>Metal dust</th></tr></thead><tbody><tr><td>LOI (wt.%)</td><td>14.3</td><td>8.1</td><td>15.7</td><td>-0.3</td><td>-5.0</td><td>-9.6</td><td>35.4</td><td>22.0</td><td>3.9</td><td>-0.1</td><td>-26.1</td></tr></tbody></table>
<table><thead><tr><th>Sample</th><th>GO</th><th>AO</th><th>GIO</th><th>Sinter</th><th>Remelt</th><th>Spill mix</th><th>Sludges</th><th>Hood dust</th><th>Tapping dust</th><th>MOR dust</th><th>Cooler dust</th><th>Metal dust</th></tr></thead><tbody><tr><td>CEC (meq/g)</td><td>4.03</td><td>0.3</td><td>0.66</td><td>0.49</td><td>4.39</td><td>3.35</td><td>5.52</td><td>22.2</td><td>0.1</td><td>0.48</td><td></td><td>9.11</td></tr><tr><td>Zeta potential (mV)</td><td>-18.5</td><td></td><td></td><td>-8.0</td><td></td><td></td><td>-17.4</td><td></td><td>7.0</td><td>-3.3</td><td>-25.0</td><td>-23.4</td></tr><tr><td>pH Zeta</td><td>4.8</td><td></td><td></td><td>6.7</td><td></td><td></td><td>5.2</td><td></td><td>7.8</td><td>4.7</td><td>5.8</td><td>6.2</td></tr></tbody></table>
<table><thead><tr><th>Sample</th><th>GO</th><th>AO</th><th>GIO</th><th>Sinter</th><th>Remelt</th><th>Spill mix</th><th>Sludges</th><th>Hood dust</th><th>Tapping dust</th><th>MOR dust</th><th>Metal dust</th></tr></thead><tbody><tr><td>BET (m²/g)</td><td>27</td><td>2</td><td>2</td><td>0</td><td>13</td><td>20</td><td>7</td><td>7</td><td>4</td><td>3</td><td>0</td></tr></tbody></table>
<table><thead><tr><th></th><th>Information</th><th>Strength</th><th>Weakness</th><th>Associated metric</th></tr></thead><tbody><tr><td>Bulk chemistry</td><td>Chemistry</td><td>Calibration for other methods</td><td></td><td>Metal content</td></tr><tr><td>XRD</td><td>Qualitative mineralogy</td><td>Reference method</td><td>Need chemical calibration<br>Difficult for complex material</td><td>Mechanical behavior</td></tr><tr><td>SEM</td><td>Textural information</td><td>High resolution In-Situ chemistry</td><td>Low representativity<br>or<br>Highly time consuming</td><td>Mechanical behavior</td></tr><tr><td>Qemscan</td><td>Quantitative chemistry<br>Quantitative mineralogy<br>Granulomorphology</td><td>High representativity<br>In-depth analysis<br>Mineral based analyses</td><td>Sample preparation<br>dependant<br>High calibration time</td><td>Mechanical behavior<br>Interaction surface</td></tr><tr><td>QICPIC</td><td>Granulomorphology</td><td>Low preparation time<br>Good representativity</td><td>Low resolution<br>No parameters crosscorrelation</td><td>Packing density<br>Interaction surface</td></tr><tr><td>Cilas</td><td>PSD</td><td>High representativity<br>High resolution</td><td>500 µm diameter max</td><td>Packing density</td></tr><tr><td>BET</td><td>SSA</td><td>Reference method for SSA</td><td></td><td>Interaction surface</td></tr><tr><td>CEC</td><td>Reactivity</td><td></td><td>Only targets ultrafine particles</td><td>Van der Waals forces<br>Electrostatic forces</td></tr><tr><td>Zeta potential</td><td>Reactivity</td><td>Variable condition applicable</td><td>High preparation time<br>Only targets ultrafine particles</td><td>Van der Waals forces<br>Electrostatic forces</td></tr></tbody></table>
<table><thead><tr><th rowspan="2"></th><th rowspan="2">Hardness Moh</th><th colspan="3">PSD</th><th rowspan="2">Sphericity</th><th rowspan="2">Convexity</th><th rowspan="2">Aspect ratio</th><th rowspan="2">Agglomeration potential</th></tr><tr><th>1<sup>st</sup> decile</th><th>Average</th><th>10<sup>th</sup> decile</th></tr></thead><tbody><tr><td>GO</td><td>6/2*</td><td>0.247</td><td>4.347</td><td>21.623</td><td>0.53</td><td>0.75</td><td>0.70</td><td>High</td></tr><tr><td>AO</td><td>6/3*</td><td></td><td></td><td></td><td>0.49</td><td>0.74</td><td>0.70</td><td>Average</td></tr><tr><td>GIO</td><td>6/3*</td><td></td><td></td><td></td><td>0.60</td><td>0.81</td><td>0.75</td><td>Average</td></tr><tr><td>Sinter</td><td>6.5</td><td>2.568</td><td>17.884</td><td>51.314</td><td>0.61</td><td>0.81</td><td>0.79</td><td>Low</td></tr><tr><td>Tapping</td><td>6.0</td><td>0.284</td><td>2.793</td><td>12.763</td><td>0.62</td><td>0.84</td><td>0.80</td><td>Low</td></tr><tr><td>Sludge</td><td>3.0</td><td>0.645</td><td>2.068</td><td>4.849</td><td>0.69</td><td>0.81</td><td>0.73</td><td>Low</td></tr><tr><td>MOR</td><td>6.0</td><td>0.202</td><td>1.990</td><td>4.648</td><td>0.58</td><td>0.86</td><td>0.81</td><td>Negligible</td></tr><tr><td>Metal</td><td>6.5</td><td>4.892</td><td>23.609</td><td>66.386</td><td>0.56</td><td>0.78</td><td>0.68</td><td>Low</td></tr></tbody></table>
Mn ore fines

<figure><img src="image_35.png" /></figure>

Complex mineralogy & shapes

<figure><img src="image_36.png" /></figure>

Successful agglomeration

<figure><img src="image_37.png" /></figure>

High adhesive forces

Compaction process - Blending

<figure><img src="image_38.png" /></figure>

Mn process dusts and sludges

<figure><img src="image_39.png" /></figure>

Simple mineralogy & shapes

<figure><img src="image_40.png" /></figure>

Unsuccessful agglomeration

<figure><img src="image_41.png" /></figure>

Low adhesive forces

Pyrolusite/Cryptomelane

Lithiophorite/Clays

Hausmannite