Optimisation of a Multi-Gravity Separator with Novel Modifications

Article Optimisation of a Multi-Gravity Separator with Novel Modiﬁcations for the Recovery of Ferberite

Robert Fitzpatrick 1,*, Patrick Hegarty 1, Keith Fergusson 1, Gavyn Rollinson 1, Weiguo Xie 1 and Treve Mildren 2

1 Camborne School of Mines, University of Exeter, Penryn Campus, Exeter TR10 9FE, UK; [email protected] (P.H.); [email protected] (K.F.); [email protected] (G.R.); [email protected] (W.X.) 2 Gravity Mining Ltd., Glencarrow, New Road, Stithians TR3 7BL, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-(0)1326-254-141

Received: 15 February 2018; Accepted: 20 April 2018; Published: 2 May 2018

Abstract: Tungsten is considered by the European Union as a critical raw material for future development due to its expected demand and scarcity of resource within Europe. It is therefore, critical to optimize European tungsten operations and maximise recoveries. The role of enhanced gravity/centrifugal concentrators in recovering tungsten from ultra-ﬁne fractions should form an important part of this aim. Reported herein are the results of investigations to improve efﬁciency of Wolf Minerals’ Draklends mine, a major European tungsten mine, by recovering saleable material from a magnetic waste stream of a low-intensity magnetic separator using an enhanced gravity concentrator. The mine hosts wolframite and ferberite as the main tungsten bearing mineral species. A Mozley multi-gravity separator (MGS) C-900 was selected as it is suited to exploiting small variations in mineral density to affect a separation. Working with a current manufacturer, a novel scraping blade system was tested. To assess the MGS in a statistically valid manner, a response surface methodology was followed to determine optimal test conditions. The test programme showed that the most important parameters were drum speed and wash water rate. Under optimal conditions the model predicted that 40% of the tungsten could be recovered above the required grade of 43% WO3.

Keywords: centrifugal gravity separation; tungsten-bearing minerals; quantitative mineralogy; response surface method; central composite rotatable design

1. Introduction

1.1. Background Tungsten is recognised by the European Union as one of 27 critical raw materials for future economic growth and prosperity. Tungsten has a wide range of properties due to its unique physical properties which include extremely high melting point and hardness when alloyed with carbon. The principle application of tungsten is as a cemented carbide followed by alloys with various other metals. This economic importance combined with a high reliance on imports into the EU, have led to the classiﬁcation as a critical metal, see Figure1. The European Union imported 15,000 tonnes of tungsten concentrate in 2012, relying on Russia for 98% of the total. World supply of tungsten is dominated by China which in 2012 produced 85% of global supply and consumed 51% [2]. To help reduce the reliance on import of tungsten the European Union has providing funding for the OptimOre project (Horizon 2020 research and innovation programme grant 642201). One objective of this project is to optimise the processing of tungsten ores to help diversify global supply and make European deposits more competitive. To support this objective

Minerals 2018, 8, 191; doi:10.3390/min8050191 www.mdpi.com/journal/minerals Minerals 2018, 8, 191 2 of 18 this article reports the results of using an enhanced gravity concentrator to improve the recovery of tungsten at the Drakelands mine, a major European mine site operated by Wolf Minerals Ltd. Specifically, investigations were undertaken to optimise recovery of tungsten-bearing minerals using a Mozley MGS with novel modifications. The objective of the work was to assess the feasibility of producing a saleable product from this stream and to compare the novel modifications to the MGS withMinerals the 2018 conventional, 8, x FOR PEER system. REVIEW 2 of 17

Non-critical raw materials 5.0 LREEs Critical raw materials HREEs 4.5 Antimony Magnesium 4.0 Phosphorus Bismuth 3.5

Niobium 3.0 Borate Natural graphite Scandium

2.5 PGMs Indium Beryllium Supply Risk Supply 2.0 Bauxite Germanium Tungsten Helium Vanadium Cobalt Baryte 1.5 Sapele wood Hafnium Gallium Phosphat e rock Natural cork Coking coal Silicon Fluorspar Natural Rubber Rhenium Manganese 1.0 Lithium metal Tantalum Natural Teak… Tellurium Tin Molybdenum Chromium Feldspar Magnesite Iron ore Gypsum Sulphur Potash 0.5 Talc Silver Perlite Kaolin Titanium Selenium Aluminium Silica sand Nickel Gold Aggregates Diatomite Zinc Copper 0.0 Bentoni… Limestone Lead 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 Economic Importance

FigureFigure 1. 1. European Commission criticality assessment for raw materials with with critical critical materials materials highlightedhighlighted inin red,red, tungstentungsten hashas beenbeen furtherfurther highlightedhighlighted byby thethe authorsauthors [[1]1]..

The European Union imported 15,000 tonnes of tungsten concentrate in 2012, relying on Russia The feed to the LIMS separator is gravity concentrate which has been roasted under reducing for 98% of the total. World supply of tungsten is dominated by China which in 2012 produced 85% conditions to convert hematite (Fe O ) to magnetite (Fe O ). The LIMS magnetic stream is currently of global supply and consumed 2 51%3 [2]. To help reduce3 4 the reliance on import of tungsten the treated as waste but has a high tungsten grade (over 20%) and so represents a measurable loss within European Union has providing funding for the OptimOre project (Horizon 2020 research and the operation. If saleable material can be recovered from this stream in an efficient manner it will innovation programme grant 642201). One objective of this project is to optimise the processing of increase profitability at the mine site and help to decrease the energy per tonne of concentrate produced. tungsten ores to help diversify global supply and make European deposits more competitive. To Both of these outcomes are important to help ensure the competitiveness of European based mines in a support this objective this article reports the results of using an enhanced gravity concentrator to global market dominated by a small number of countries. improve the recovery of tungsten at the Drakelands mine, a major European mine site operated by 1.2.Wolf Multi-Gravity Minerals Ltd Separator. Specifically, Device investigations were undertaken to optimise recovery of tungsten- bearing minerals using a Mozley MGS with novel modifications. The objective of the work was to assessThe the multi-gravity feasibility of separator producing (MGS) a saleable also knownproduct as from the enhanced-gravitythis stream and to separator compare (EGS) the novel was designedmodifications in the to late the 1980’sMGS with to early the 1990’sconventional for the system. concentration of fine and ultra-fine heavy minerals, withThe early feed applications to the LIMS focusing separator on cassiterite,is gravity concentrate chromite, celestite which has and been magnetite roasted [3 under]. The reducing machine operates on similar principles to a shaking table but utilises rotational motion to generate centrifugal conditions to convert hematite (Fe2O3) to magnetite (Fe3O4). The LIMS magnetic stream is currently forcestreated greater as waste than but those has a of high gravity. tungsten This grade increase (over in force20%) allowsand so forrepresents more efficient a measurable separation loss atwithin finer the operation. If saleable material can be recovered from this stream in an efficient manner it will increase profitability at the mine site and help to decrease the energy per tonne of concentrate produced. Both of these outcomes are important to help ensure the competitiveness of European based mines in a global market dominated by a small number of countries.

Minerals 2018, 8, x FOR PEER REVIEW 3 of 17

1.2. Multi-Gravity Separator Device The multi-gravity separator (MGS) also known as the enhanced-gravity separator (EGS) was designed in the late 1980’s to early 1990’s for the concentration of fine and ultra-fine heavy minerals, with early applications focusing on cassiterite, chromite, celestite and magnetite [3]. The machine Minerals 2018, 8, 191 3 of 18 operates on similar principles to a shaking table but utilises rotational motion to generate centrifugal forces greater than those of gravity. This increase in force allows for more efficient separation at finer sizes. The main main design and operating variables of the MGS MGS are are summarised in Figure 2 2 alongsidealongside thethe principle forces acting on a particle.

Figure 2. MGS operating and design (denoted with *) variables and principle forces (Force Diagram modifiedmodiﬁed after [[4]).4]).

Due to the large number of variables and interactions, most modelling work for the MGS has consisted of determining optimal conditions using regression analysis. A A review review of of this research literature reveals that the rotational velocity of the drum has been identifiedidentified as the most important factor on grade and recovery [[55–88].]. Both [[5,7]5,7] concluded that rotational velocity and pulp density were the most important factors with regards regards to to optimisation. optimisation. In In [6 ] [6] the the conclusion conclusion was was that rotationalthat rotational velocity velocity had a significanthad a significant impact whereasimpact whereas inclination inclination and wash and water wash flowrate water flowrate had a trivial had effecta trivial on effect performance. on performance. In [8] it was In [8] observed it was observedthat wash that water wash had anwater important had an interaction important with interaction performance with performance at high wash at water high flowrates wash water and flowrateshigh rotational and highvelocity. rotational The authors velocity. of [5 ] The also authors observed of an [5] important also observed interaction an important between wash interaction water betweenand pulp wash density. water and pulp density. To quantify the results reported in literature, a boxplot was constructed to illustrate the relative importance ofof operatingoperating variables variables on on grade grade and and recovery recovery when when using using a Mozley a Mozley MGS MGS (Figure (Figure3). It 3). was It was constructed by analysing the results published in six research articles ([5,8–12]). constructed by analysing the results published in six research articles ([5,8–12]). 2 The level of influenceinfluence for each variable was judged using the relative change in the adjusted RR2 measure of model fitfit when a variable was removed. Figure 3 3 shows shows that that the the rotational rotational velocity velocity is is the the most most important important operating operating variable variable for for the the performance of the MGS. Shake amplitude was found to have a wide variation in importance. All the performance of the MGS. Shake amplitude was found to have a wide variation in importance. All the research articlesarticles used inclination as a variable factor though it’s influenceinfluence on performance appears relatively small. There was only one one paper paper which which investigated investigated the effect effect of of the the pulp pulp density density (% (% solids) solids) on MGS performance and it was found to have a significant influence on performance. on MGS performance and it was found to have a significant influence on performance. Although a good deal has been reported on the effect of operating variables on MGS Although a good deal has been reported on the effect of operating variables on MGS performance performance much less attention has been paid to design variables. The author could find no much less attention has been paid to design variables. The author could find no published research on publishedthe influence research of changes on the to designinfluence variables of changes on MGS to design performance. variables The on cone MGS angle, performance. the shape The or profile cone of scraping blades and the relative velocity between drum and scraping blade would all be expected to have an influence on performance but have not been properly investigated. It was noted in [4] that it was ‘odd’ that the relative velocity of blades had not previously been investigated as the force of the blades (FSC) is the only one acting in the direction of the concentrate outlet (see force diagram in Minerals 2018, 8, x FOR PEER REVIEW 4 of 17 Minerals 2018, 8, x FOR PEER REVIEW 4 of 17 Mineralsangle, the2018 ,shape 8, x FOR or PEER profile REVIEW of scraping blades and the relative velocity between drum and scraping4 of 17 angle,blade wouldthe shape all or be profile expected of scraping to have blades an influence and the on relative performance velocity butbetween have drum not been and properlyscraping angle,Minerals the2018 shape, 8, 191 or profile of scraping blades and the relative velocity between drum and scraping4 of 18 bladeinvestigated. would It all was be noted expected in [4] tothat have it was an ‘odd’ influence that the on relative performance velocity but of blades have nothad beennot previously properly blade would all be expected to have an influence on performance but have not been properly investigated.been investigated It was asnoted the in force [4] that of theit was blades ‘odd’ (FthatSC) the is therelative only velocity one acting of blades in the had direction not previously of the investigated. It was noted in [4] that it was ‘odd’ that the relative velocity of blades had not previously Figurebeenconcentrate investigated2). It can outlet be asfurther(see the force force stated diagram of that the the in blades profileFigure (F SC of2).) the is It canthe scraping onlybe further one blade acting stated will also in that the have the direction an profile important of the been investigated as the force of the blades (FSC) is the only one acting in the direction of the effectconcentratescraping on theblade outlet force will directed (seealso have force towards andiagram important the inconcentrate Figureeffect on 2). outlet.the It force can be directed further towards stated thatthe concentrate the profile outlet. of the concentrate outlet (see force diagram in Figure 2). It can be further stated that the profile of the scrapingA new blade scrapingscraping will also blade have design an important has been effect developeddeveloped on the force by GravityGravity directed Mining towards Ltd.Ltd. the (Cornwall,(Cornwall, concentrate UK) outlet. and scraping blade will also have an important effect on the force directed towards the concentrate outlet. fittedfittedA to new a Mozley scraping C-900 blade MGS design unit has located been atdeveloped the Camborne by Gravity School Mining of Mines, Ltd. University (Cornwall, of ofUK) Exeter, Exeter, and A new scraping blade design has been developed by Gravity Mining Ltd. (Cornwall, UK) and UK.fitted The to a scraping Mozley bladesC-900 are MGS low unit profile profile located in comparison at the Camborne to the traditionalSchool of Mines, blades. University In theory, less of Exeter, dense fitted to a Mozley C-900 MGS unit located at the Camborne School of Mines, University of Exeter, particlesUK. The scraping not pinned blades to the are drum low willprofile weir in overcomparison the blades to the so thattraditional only the blades. densest In materialtheory, less is carried dense UK. The scraping blades are low profile in comparison to the traditional blades. In theory, less dense toparticles thethe concentrateconcentrate not pinned outlet. to the Photos Photosdrum willof of the theweir fitted fitted over scraping the blades blade so that systems only theare densestshown materialin Figure is4 4 carriedwhilst whilst particles not pinned to the drum will weir over the blades so that only the densest material is carried schematicsto the concentrate demonstrating outlet. Photos their theoretical of the fitted operatingoperating scraping principleprinciple blade systems isis shownshown are inin shown FiguresFigures 5in 5 and and Figure6 .6. 4 whilst to the concentrate outlet. Photos of the fitted scraping blade systems are shown in Figure 4 whilst schematics demonstrating their theoretical operating principle is shown in Figures 5 and 6. schematics demonstrating their theoretical operating principle is shown in Figures 5 and 6.

Figure 3.3. Boxplot of relative importance of operational variables on grade and recovery based on relativeFigure 3. change Boxplot in of thethe relative adjustedadjusted importance RR22 measure of ofoperational model ﬁtfit variables onon removingremoving on grade aa variable.variable. and recovery Brackets based indicate on Figure 3. Boxplot of relative importance of operational variables on grade and recovery based on numberrelative change of data points.in the adjusted R2 measure of model fit on removing a variable. Brackets indicate relative change in the adjusted R2 measure of model fit on removing a variable. Brackets indicate number of data points. number of data points.

(a) (b) (a) (b) Figure 4. (a) Photograph of( aMozley) C-900 MGS with conventional scraping(b) blades; (b) photograph of FigureMozley 4. C-900 (a) Photograph MGS with ofnovel, Mozley low C-900 profile MGS scraping with conventionalblades. scraping blades; (b) photograph of Figure 4. ((aa)) Photograph Photograph of of Mozley Mozley C-900 C-900 MGS MGS with with conventional conventional scraping scraping blades; blades; (b)) photograph photograph of of Mozley C-900 MGS with novel, low profile scraping blades. Mozley C-900 MGS with novel, low profile proﬁle scraping blades.

Figure 5. Cross-sectional view of scraping blades. Conventional scraping blade system is shown on Figurethe left 5.and Cross-sectional the novel, low view profile of scraping bladesblades. on Conventional the right. scraping blade system is shown on Figure 5. Cross-sectional view of scraping blades. Conventional scraping blade system is shown on Figurethe left 5.andCross-sectional the novel, low view profile of scraping blades.blades on Conventional the right. scraping blade system is shown on thethe left and the the novel, low profile proﬁle scraping blades on the right.

Minerals 2018, 8, 191 5 of 18 Minerals 2018, 8, x FOR PEER REVIEW 5 of 17

Figure 6.6. Plan view of scraping blades. Conventional scraping blade system is shown on the left and thethe novel,novel, lowlow proﬁleprofile scrapingscraping bladesblades onon thethe right.right.

2. Materials and Methods

2.1. Ore Sample Approximately 25 kg of material was collected in early 2016 from the magnetic product stream from a a low low intensity intensity magnetic magnetic separator separator used used in the in processing the processing plant of plant Drakelands of Drakelands mine. This mine. material This hadmaterial a grade had of a grade about of 23% about WO 23%3 and WO the3 minimumand the minimum acceptable acceptable grade for grade blending for blending is 43% WOis 43%3 which WO3 indicatedwhich indicated that the that LIMS the magnetic LIMS magnetic stream wasstream a waste was fractiona waste butfraction only abut modest only a upgrading modest upgrading would be requiredwould be to required produce to aproduce saleable a product. saleable product. This would This increase would increase plant recovery plant recovery and potentially and potentially reduce energyreduce inputenergy per input tonne per of tonne concentrate of concentrate produced. produced. Gravity separation Gravity separation was an attractive was an optionattractive to process option thisto process material this as material the plant as is the already plant familiaris already with familiar the technology with the technology and gravity and separation gravity separation represents arepresents low capital a low cots, capital low energy cots, low and energy low environmental and low environmental impact solution impact in comparisonsolution in comparison to alternative to techniquesalternative suchtechniques as froth such flotation. as froth flotation. Prior knowledge knowledge ofof the the plant, plant, indicated indicated that that the the material material would would likely likely contain contain ferberite ferberite (S.G. (S.G. 7.45) as7.45) the as principle the principle ore mineral ore mineral and reduced and reduced hematite/magnetite hematite/magnetite (S.G. 5.1–5.3) (S.G. 5.1 as– the5.3) principle as the principle gangue mineral.gangue mineral. As the ore As andthe ore gangue and gangue minerals minerals are expected are expected to have to similar have similar densities densities the separation the separation would bewould expected be expected to be difficult. to be difficult. The MGS The is MGS suited is to suited exploiting to exploiting small variations small variations in mineral in mineral density todensity affect ato separation. affect a separation. For example, For example, the MGS the has MGS been has used been historically used historically for the for separation the separation of chromite of chromite from goethitefrom goethite [3]. [3]. Based on the above, the material is a useful test material as it is a good example of material which is difficultdifficult toto recover using conventional approachesapproaches andand isis suitedsuited to the MGS. The testwork testwork described described herein was undertaken with the aim of optimising the separation of this material and further, to test whether thethe novelnovel scrappingscrapping bladesblades improveimprove performanceperformance forfor thisthis material.material. From the the as as received received sample, sample, representative representative sub-samples were collected collected for chemical chemical and mineralogical analyses. The remaining material was stage-ground using a stainless-steel batch rod mill until 100% of the material passed through a 90 µμmm aperture sieve.sieve. This size range is suited to the MGS which operates most effectively on finefine material with optimum performance suggested to occur at lessless thanthan 5353µ μmm [ 13[13].]. This This size size range range also also made made material material handling handling and and pumping pumping more more practical. practical. Size analysisSize analysis of the of ground the ground material material was completed was completed using a Malvern using a MasterSizer Malvern MasterSizer 3000 (Malvern 3000 Panalytical (Malvern Ltd.,Panalytica Malvern,l Ltd UK),., Malvern, a laser UK), diffraction a laser particle diffraction analyser. particle analyser.

2.2. Chemical and Mineralogical Analysis All chemical and mineralogical analyses were undertaken in the Camborne School of Mines chemical and imaging mineralogical facility, Penryn,Penryn, UK. BulkBulk geochemical analysis was carried out using an OlympusOlympus DP-6000C portable XRF analyser (Olympus UK & Ireland, Southend-on-sea,Southend-on-sea, UK with calibrationcalibration usingusing aa BrukerBruker S4S4 PioneerPioneer WDSWDS X-rayX-ray FluorescenceFluorescence (XRF)(XRF) instrumentinstrument (Bruker(Bruker AXS Ltd.,Ltd., Coventry, UK).UK). Quantitative mineralogical analysis was conducted using a QEMSCAN® 4300 (Thermo-Fisher, formally FEI Company, Eindhoven, The Netherlands) which is based on a Zeiss EVO 50 series SEM

Minerals 2018, 8, 191 6 of 18

Quantitative mineralogical analysis was conducted using a QEMSCAN® 4300 (Thermo-Fisher, formally FEI Company, Eindhoven, The Netherlands) which is based on a Zeiss EVO 50 series SEM and consists of four light elements Bruker SDD (Silicon Drift Droplet) Energy Dispersive X-ray Spectrometers (EDS) and an electron backscatter detector. Samples were prepared as resin blocks impregnated with carbon to minimize settling. The whole sample block was analysed in fieldscan measurement mode with 10 µm X-ray spacing, then granulated into 11,848 particles. The software iMeasure v. 4.2 was used for data acquisition, and iDiscover v. 4.2 and 4.3 were used for the data processing. Further manipulation of the QEMSCAN data was undertaken using Mathworks Matlab 2017b (MathWorks, Natick, MA, USA). Electron microprobe analysis was completed using a JEOL JXA-8200 microprobe (Joel USA Inc., Peabody, MA, USA) using a 15 nA electron beam with a probe current of 3.1 × 10−8 accelerated to 15 kV and focused to a 5 µm beam diameter using wavelength dispersive X-ray spectrometers only. The microprobe was calibrated to five elements using a ZAF matrix correction routine. The elements analysed were bismuth, tungsten, tin, iron and manganese measured at 87 points identified as ferberite by QEMSCAN.

2.3. Experimental Approach for Selective Concentration by Enhanced Gravity Separator To test the effectiveness of the new scraping blade system a series of tests were undertaken in the Mozley C-900 MGS modified to include the new blades. Three operating variables were selected based on the review of previous studies to optimise the separation of LIMS waste material with the new blades. These were the rotational velocity, wash water flowrate and pulp density. All other operating variables were maintained at constant levels throughout the experimental program. Initial tests were completed to determine the length of time required to reach a steady state with regards to mass pull, grade and recovery during an experimental run. To investigate the effect of this procedure on the separation and the effects of further changes to wash water flowrate, samples were collected at regular intervals from the beginning of an experimental run and after a further change in flowrate. Screening experiments were undertaken with the LIMS waste material to determine suitable ranges for the operating variables. The screening experiments indicated that only a small range of rotational velocities was suitable. It was not possible to recover any concentrate under the most favourable conditions with velocities less than 130 RPM and above 150 RPM it was not possible to upgrade the material as hematite was recovered as well as ferberite. It was found that slurries with less than 30% solids by mass would not produce a concentrate product under high levels of wash water. Based on these screening experiments, the ranges chosen for the experiment were 130 to 150 RPM, 2 to 7 L per minute wash water and pulp density of 25% to 45% solids. A set of experiments informed by a Central Composite Rotatable Design (CCRD) was then undertaken using the Response Surface Methodology (RSM) to create a model for the processing of the LIMS waste material through a MGS C-900 with new blades. The three operating variables selected were varied according to the experimental design whilst all other operating variables were maintained at constant levels throughout the experimental program. This approach was selected as it has been shown to be beneficial for optimisation for the MGS [6,9] as well as other gravity separation and mineral processing equipment [14]. The CCRD is based on a 2 level factorial design with its origin at the centre and additional axial points, β, at set distances from the centre. Repeat runs were undertaken at the central point to estimate experimental error. The axial points were determined with the use of Equation (1) and the number of test runs required is determined by Equation (2) where k is the number of factors and n0 the number of centre point runs [15]. Six centre point runs were selected for this model which was processed using the statistical analysis software, Minitab 17.

α = 2k/4 (1) Minerals 2018, 8, 191 7 of 18

k number of runs = 2 + 2·k + n0 (2) Based on (1) and (2) the value of α is 1.682 (23/4) and the number of runs is 20 (23 + 2·3 + 6). The variable levels used are summarised in Table1, whilst the full experimental design is shown in Table2. The order of the experiments was randomised before testing.

Table 1. Summary of values used for variable levels in CCRD experimental design programme.

Coded Variable Value −β −1 0 +1 +β

xn,max + xn,min xn,max−xn,min xn,max + xn,min xn,max + xn,min xn,max−xn,min xn,min 2 − 2α 2 2 + 2α xn,max Rotational velocity, 130 134.1 140 145.9 150 rpm (x1) Wash water 2 3 4.5 6 7 ﬂowrate, lpm (x2) Pulp density, % 25 29.1 35 40.9 45 solids (x3)

Table 2. Summary of experimental runs in CCRD experimental design programme.

Coded Uncoded Std Order x1 x2 x3 Rotational Velocity, rpm Wash Water Flowarate, lpm Pulp Density, % Solids 1 −1 −1 −1 134.1 3 29.1 2 1 −1 −1 145.9 3 29.1 3 −1 1 −1 134.1 6 29.1 4 1 1 −1 145.9 6 29.1 5 −1 −1 1 134.1 3 40.9 6 1 −1 1 145.9 3 40.9 7 −1 1 1 134.1 6 40.9 8 1 1 1 145.9 6 40.9 9 −β 0 0 130.1 4.5 35 10 +β 0 0 149.9 4.5 35 11 0 −β 0 140 2 35 12 0 +β 0 140 7 35 13 0 0 −β 140 4.5 25.1 14 0 0 +β 140 4.5 44.9 15 0 0 0 140 4.5 35 16 0 0 0 140 4.5 35 17 0 0 0 140 4.5 35 18 0 0 0 140 4.5 35 19 0 0 0 140 4.5 35 20 0 0 0 140 4.5 35

Due to the relatively low mass of material it was necessary to recycle the 25 kg sample between experimental test runs. To minimise loss of fines, test products were left to settle for a minimum of 4 h before decantation and preparation of feed slurries. Some of the decanted water was then added to create the required pulp density for the next test run. The feed grade was monitored during the test programme to assess loss of ferberite which is known to be friable and most likely to breakdown during experimentation and accumulate in the finer fractions. Any decrease in feed grade during testing could affect the reliability of trends in the results. Tests using conventional scraping blades were undertaken both before the CCRD experimental design programme and afterwards. This was done to minimise any bias resulting from deterioration of material or loss of fines. These tests were undertaken using the same methodology as during the CCRD programme.

3. Results and Discussion

3.1. Sample Characterisation by Chemical and Mineralogcial Analysis The Chemical composition of the elements of interest in the LIMS magnetic fraction are summarised in Table3. Minerals 2018, 8, 191 8 of 18

Table3 shows that the major elements within the LIMS magnetic waste stream are tungsten and iron which is expected given that the material is the magnetic fraction of a gravity pre-concentrate. The values obtained by QEMSCAN agree well with the results obtained by XRF which is good evidence that the QEMSCAN sample was suitably representative. Mineralogical analysis of the LIMS magnetic waste material was undertaken to better understand the composition and better qualify the appropriateness of using an MGS for separation. A summary of the modal mineralogy reported by QEMSCAN analysis is shown in Figure7. Minerals 2018, 8, x FOR PEER REVIEW 8 of 17 Table 3. Distribution of elements of interest in the LIMS magnetic waste material as measured by XRF The and values QEMSCAN. obtained by QEMSCAN agree well with the results obtained by XRF which is good evidence that the QEMSCAN sample was suitably representative. Analysis Type WO Fe Mn Sn Si Mineralogical analysis3 of the LIMS3 magnetic waste material was2 undertaken to2 better understandXRF the composition 22.95 and better 53.09qualify the appropriateness 0.72 of using 1.33 an MGS for separation. 0.18 QEMSCAN 20.45 49.71 0.39 0.15 0.34 A summary of the modal mineralogy reported by QEMSCAN analysis is shown in Figure 7.

Hubnerite , 0.02 Cassiterite, 0.2 Others, 4.5 Wolframite , 3.3 Quartz, 0.8

Ferberite - no alteration , 9.4

Ferberite, Ferberite - low Hematite , 39.7 alteration , 28.2 51.4 Ferberite - high alteration , 2.1

Ferberite - no alteration Ferberite - low alteration Ferberite - high alteration Hematite Wolframite Hubnerite Cassiterite Quartz Others

FigureFigure 7. 7.Summary Summary of of modal modal mineralogy mineralogy of LIMS of LIMS waste waste stream stream material material based on based QEMSCAN on QEMSCAN analysis. analysis. The data in Figure7 agrees with prior knowledge of the material. It shows that the LIMS magnetic wasteTable material 3. Distribution contains 43.1%of elements tungsten-bearing of interest in the minerals LIMS magnetic by mass waste and material that ferberite as measured is the by dominant XRF tungsten-bearingand QEMSCAN. species accounting for 92% of these minerals. As expected, the other major mineral present is hematite whichAnalysis accounts Type for 51.4% WO of3 theFe mass.3 Combined,Mn Sn2 theSi tungsten-bearing2 minerals and hematite account for 94.5%XRF of the sample22.95 mass.53.09 The0.72 QEMSCAN 1.33 0.18 analyses showed that the chemistry of ferberite grainsQEMSCAN was varied because20.45 of weathering.49.71 0.39 Specifically,0.15 0.34 the ferberite showed signs of hematisation which is the replacement of tungsten by iron in the grain. This was accounted for in QEMSCANThe data by dividingin Figure ferberite 7 agrees arbitrarily with prior into knowledge three categories of the based material. on the It degree shows of that hematisation the LIMS asmagnetic indicated waste by EDS material chemistry contains (Figure 43.1%7). Thetungsten-bearing majority of the minerals ferberite by in mass the LIMS and wastethat ferberite stream (75%)is the isdominant weathered tungsten-bearing with a relatively species low level accounting of alteration. for 92% Approximately of these minerals. 5% is As highly expected, weathered the other and altered.major mineral The extent present of hematisation is hematite waswhich further accounts quantified for 51.4% using of EPMA.the mass. The Combined, results of thesethe tungsten- analyses arebearing summarised minerals in and Figure hematite8. account for 94.5% of the sample mass. The QEMSCAN analyses showedFigure that8 shows the chemistry the relative of ferberite molar proportions grains was of varied W, Fe because and Mn of for weathering. the three sub-categories Specifically, the of ferberite.ferberite showed It can be signs seen of that hematisation the tungsten which content is the varies replacement from the of theoretical tungsten by maximum iron in the of grain. 50% or This 1:1 molarwas accounted ratio of W:(Fe,Mn) for in QEMSCAN to a pseudo-hematite by dividing ferberite containing arbitrarily less than into 5% three tungsten. categories There based is evidence on the indegree Figure of8 hematisation that data points as indicated with high by manganeseEDS chemistry content (Figure generally 7). The have majority little of tungsten the ferberite depletion in the (circledLIMS waste points stream in Figure (75%)8). is It weathered would be expected with a relatively that this low identified level of trend alteration. would Approximately continue if particles 5% is classifiedhighly weathered by QEMSCAN and altered. as wolframite The extent and hubneriteof hematisation were also was analysed further byquantified EPMA. Thisusing observation EPMA. The is results of these analyses are summarised in Figure 8. Figure 8 shows the relative molar proportions of W, Fe and Mn for the three sub-categories of ferberite. It can be seen that the tungsten content varies from the theoretical maximum of 50% or 1:1 molar ratio of W:(Fe,Mn) to a pseudo-hematite containing less than 5% tungsten. There is evidence in Figure 8 that data points with high manganese content generally have little tungsten depletion (circled points in Figure 8). It would be expected that this identified trend would continue if particles classified by QEMSCAN as wolframite and hubnerite were also analysed by EPMA. This observation is in line with the theory that the wolframite is not directly weathered but first undergoes ferberitisation before hematisation, i.e., replacement of tungsten with iron [16]. Hematisation of the ferberite and the resultant variation in tungsten to iron ratios will affect the specific gravity of particles. This problem is compounded by cavitation and an increased porosity which further reduces

Minerals 2018, 8, 191 9 of 18 in line with the theory that the wolframite is not directly weathered but first undergoes ferberitisation before hematisation, i.e., replacement of tungsten with iron [16]. Hematisation of the ferberite and the resultantMinerals 2018 variation, 8, x FOR inPEER tungsten REVIEW to iron ratios will affect the specific gravity of particles. This problem9 of 17 is compounded by cavitation and an increased porosity which further reduces the specific gravity duethe specific to trapped gravity gangue due mineralsto trapped air. gangue The depletion minerals of air. tungsten The depletion combined of withtungsten added combined buoyancy with is problematicadded buoyancy for gravity is problematic separation for gravity as the specificseparation density as the is specific linked density to these is factors.linked to The these expected factors. recoveryThe expected using recovery physical separationusing physical is also separation limited as is some also of limited the tungsten as some bearing of the mineral tungsten grains bearing fall belowmineral the grains required fall below grade forthe blending.required grade for blending. HavingHaving establishedestablished thatthat variationsvariations inin thethe densitydensity ofof tungsten bearing minerals werewere expected, to betterbetter understandunderstand thethe feasibilityfeasibility ofof separatingseparating thesethese minerals the mineral liberations and associations werewere analysed.analysed. TheThe tungsten-bearingtungsten-bearing mineralsminerals areare wellwell liberatedliberated withwith 93.2%93.2% ofof particlesparticles beingbeing atat leastleast 90%90% liberated.liberated. There isis onlyonly aa 20%20% associationassociation betweenbetween ferberiteferberite andand hematitehematite asas shownshown inin TableTable4 .4.

Fe 0 100 No Alteration 10 90 Low Alteration High alteration 20 80 30 70 40 60 50 50 60 40 70 30 80 20 90 10 100 0 W 0 10 20 30 40 50 60 70 80 90 100Mn

FigureFigure 8.8. Ternary plotplot from EPMAEPMA analysis ofof points identiﬁedidentified asas ferberite byby QEMSCAN. TheThe circled regionregion highlightshighlights pointspoints withwith relativelyrelatively highhigh manganesemanganese contentcontent whichwhich showshow littlelittle tungstentungsten depletion.depletion.

Table 4. Mineral associations within the LIMS waste stream sample as reported by QEMSCAN, each Tablecolumn 4. totalsMineral to associations100% association. within the LIMS waste stream sample as reported by QEMSCAN, each column totals to 100% association. Background Wolframite Ferberite Hubnerite Cassiterite Fe Oxide Quartz Background 0.00 8.3 47.1 4.1 24.3 59.5 32.3 Background Wolframite Ferberite Hubnerite Cassiterite Fe Oxide Quartz Wolframite 1.5 0.0 27.2 73.0 3.3 0.5 1.0 BackgroundFerberite 0.0027.5 8.386.4 47.10.0 4.120.2 24.330.5 11.2 59.5 13.3 32.3 WolframiteHubnerite 1.50.0 0.00.8 27.20.1 73.00.0 3.30.0 0.0 0.5 0.0 1.0 FerberiteCassiterite27.5 0.2 86.40.2 0.00.4 20.20.0 30.50.0 11.20.2 0.5 13.3 HubneriteFe Oxide 0.060.9 0.82.9 0.119.7 0.00.9 0.025.0 0.0 0.0 43.3 0.0 CassiteriteQuartz 0.22.2 0.20.4 0.41.5 0.00.0 0.04.2 2.8 0.2 0.0 0.5 Fe Oxide 60.9 2.9 19.7 0.9 25.0 0.0 43.3 QuartzThe potential2.2 for gravity 0.4 separation 1.5 can be illustrated 0.0 by 4.2 dividing particles 2.8 analysed 0.0 by QEMSCAN into size and density categories. This was achieved by importing data from false colour imagesThe generated potential by for QEMSCAN gravity separation in to Mathworks can be Matlab illustrated 2017b. by Within dividing the particlessoftware, analysedthe diameter by QEMSCANof each particle into was size calculated and density as categories.the minor axis This of was the achieved ellipse that by had importing the same data normalized from false second colour imagescentral generatedmoment as by the QEMSCAN particle. The in todensity Mathworks of particles Matlab was 2017b. calculated Within as the a software,weighted the average diameter of the of eachminerals particle distributed was calculated within as the the particle; minor axis where of the mineral ellipse that densities had the were same taken normalized as standard second mineral central values. Some caution needs to be used when interpreting these data as the density of the weathered minerals is not standard and it is difficult to measure directly. Densities were estimated for these mineral categories using a linear interpolation based on the average proportion of tungsten in the mineral structure and the standard densities of ferberite and hematite. The particles were then classified into size and density categories. Figure 9 shows the mass distribution in the size and density categories generated.

Minerals 2018, 8, 191 10 of 18

moment as the particle. The density of particles was calculated as a weighted average of the minerals distributed within the particle; where mineral densities were taken as standard mineral values. Some caution needs to be used when interpreting these data as the density of the weathered minerals is not standard and it is difﬁcult to measure directly. Densities were estimated for these mineral categories using a linear interpolation based on the average proportion of tungsten in the mineral structure and Mineralsthe 2018 standard, 8, x FOR densities PEER REVIEW of ferberite and hematite. The particles were then classiﬁed into size and density 10 of 17 categories. Figure9 shows the mass distribution in the size and density categories generated.

7-10 6-7 5.5-6 15 5-5.5 10 4-5 5 <4 Mass 0 <25 distribution, % distribution, 25-63 63-90 90-125 125-180 180-250 250-355 355-500 500-1000

FigureFigure 9. Mass 9. Mass distribution distribution of of particles particles inin size size and and density density categories categories generated generated using Mathworks using Mathworks Matlab 2017b. Matlab 2017b.

In Figure9 there are two distinct peaks at S.G. 5–5.5 and S.G. 6–10. This can be interpreted as Inthe Figure relatively 9 there liberated are two hematite distinct and wolframitepeaks at S.G. series 5– minerals.5.5 and TheS.G. concentration 6–10. This can criterion be interpreted for this as the relativelyseparation liberated (Equation hematite (3)) is 1.4, and based wolframite on an average series particle minerals. density in The the S.G. concentration 5–5.5 range of S.G.criterion 5.28 for this separationand in (Equation the S.G. 6–10 (3)) ranges is 1.4, of S.G.based 6.98 on and an assuming average the particle fluid density density to be S.G.in the 1.00. S.G. 5–5.5 range of S.G. 5.28 and in the S.G. 6–10 ranges of S.G. 6.98 and assuming the fluid! density to be S.G. 1.00. ρheavy − ρ f luid concentration criterion = (3) ρ 휌ℎ푒푎푣푦− ρ− 휌푓푙푢𝑖푑 푐표푛푐푒푛푡푟푎푡푖표푛 푐푟푖푡푒푟푖표푛 = light( f luid ) (3) 휌푙𝑖푔ℎ푡 − 휌푓푙푢𝑖푑 This value would suggest that the separation is very difficult [17] which agrees well with the prior Thisknowledge value would of the material suggest and that in fact the shows separation that the taskis very is more difficult difficult [17] than which initially agrees anticipated well with the prior knowledgeas the concentration of the criterion material for aand separation in fact of shows un-weathered that the ferberite task from is more hematite difficult is 1.5. This than initially anticipatedsuggests as thatthe aconcentration centrifugal separator criterion is likely for to a be separation required and of that un-weathered MGS would be ferberite a good candidate. from hematite is

1.5. This3.2. suggests Particle Size that Analysis a centrifugal separator is likely to be required and that MGS would be a good candidate. The size distribution of the stage ground material is reported in Figure 10. The size analysis was undertaken on completion of the test programme. The P of the material was 61 µm with 10% less 3.2. Particle Size Analysis 80 than 4 µm. The sizeThe distribution particle size distribution of the stage of the ground material material is in a suitable is reported range for in efﬁcient Figure separation 10. The bysize MGS. analysis was There is a signiﬁcant proportion of minus 10 µm material which would be expected to adversely affect undertaken on completion of the test programme. The P80 of the material was 61 µm with 10% less the separation. than 4 µm.

6 100 90 5 80 70 4 60 3 50 40 2 30 Volume distribution, % distribution, Volume 20 1

10 % distribution, volume Cumulative 0 0 0.1 1 10 100 1000 Particle size, μm Figure 10. Size distribution of feed material for testwork as measured by a Malvern Mastersizer 3000 laser diffractometer. Particle size is reported as equivalent spherical diameter.

Minerals 2018, 8, x FOR PEER REVIEW 10 of 17

7-10 6-7 5.5-6 15 5-5.5 10 4-5 5 <4 Mass 0 <25 distribution, % distribution, 25-63 63-90 90-125 125-180 180-250 250-355 355-500 500-1000

Figure 9. Mass distribution of particles in size and density categories generated using Mathworks Matlab 2017b.

In Figure 9 there are two distinct peaks at S.G. 5–5.5 and S.G. 6–10. This can be interpreted as the relatively liberated hematite and wolframite series minerals. The concentration criterion for this separation (Equation (3)) is 1.4, based on an average particle density in the S.G. 5–5.5 range of S.G. 5.28 and in the S.G. 6–10 ranges of S.G. 6.98 and assuming the fluid density to be S.G. 1.00.

휌ℎ푒푎푣푦 − 휌푓푙푢𝑖푑 푐표푛푐푒푛푡푟푎푡푖표푛 푐푟푖푡푒푟푖표푛 = ( ) (3) 휌푙𝑖푔ℎ푡 − 휌푓푙푢𝑖푑 This value would suggest that the separation is very difficult [17] which agrees well with the prior knowledge of the material and in fact shows that the task is more difficult than initially anticipated as the concentration criterion for a separation of un-weathered ferberite from hematite is 1.5. This suggests that a centrifugal separator is likely to be required and that MGS would be a good candidate.

3.2. Particle Size Analysis The size distribution of the stage ground material is reported in Figure 10. The size analysis was undertaken on completion of the test programme. The P80 of the material was 61 µm with 10% less Mineralsthan 4 µm.2018, 8, 191 11 of 18

6 100 90 5 80 70 4 60 3 50 40 2 30 Volume distribution, % distribution, Volume 20 1

10 % distribution, volume Cumulative 0 0 Minerals 2018, 8, x FOR PEER REVIEW 11 of 17 0.1 1 10 100 1000 The particle size distribution of the materialParticle is size,in a suitableμm range for efficient separation by MGS. There is a significant proportion of minus 10 µm material which would be expected to adversely Figure 10 10.. SizeSize distribution distribution ofof feed feed material material forfor testwork testwork as measured as measured by aby Malvern a Malvern Mastersizer Mastersizer 3000 affect the separation. 3000laser laserdiffractometer. diffractometer. Particle Particle size is size reported is reported as equivalent as equivalent spherical spherical diameter. diameter.

3.3. Selective Concentration by Enhanced Gravity Concentrator 3.3. Selective Concentration by Enhanced Gravity Concentrator

3.3.1.3.3.1. Experimental Experimental Results Results TheThe results results of of the the initial initial test test work work to to determine determine the the time time required required to to reach reach a a steady steady state state and and consequentlyconsequently appropriate appropriate sampling sampling times times to ensure representative representative samples. samples. Experiments Experiments were were undertakenundertaken sequentially sequentially and and so so it was it was necessary necessary to determine to determine the time the timerequired required to reach to reachsteady steady state onstate machine on machine start-up start-up and and after after changes changes to feed to feed conditions. conditions. The The results results of of these these experiments experiments are are summarisedsummarised in in Figures Figures 1111 andand 12.12.

grade / recovery / mass pull, % pull, mass / / recoverygrade 5 3 0 WO 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 Elapsed time, mm:ss

Figure 11. Results of experiments to monitor variation in concentrate grade, recovery and mass pull to Figure 11. Results of experiments to monitor variation in concentrate grade, recovery and mass pull concentrate outlet over time. to concentrate outlet over time.

10 grade / recovery / mass pull, % pull, mass / / recoverygrade 3

WO 0 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 Elapsed time, mm:ss

Figure 12. Results of experiments monitoring variation in concentrate grade, recovery and mass pull to concentrate outlet over time when changing wash water flowrate.

Minerals 2018, 8, x FOR PEER REVIEW 11 of 17

The particle size distribution of the material is in a suitable range for efficient separation by MGS. There is a significant proportion of minus 10 µm material which would be expected to adversely affect the separation.

3.3. Selective Concentration by Enhanced Gravity Concentrator

3.3.1. Experimental Results The results of the initial test work to determine the time required to reach a steady state and consequently appropriate sampling times to ensure representative samples. Experiments were undertaken sequentially and so it was necessary to determine the time required to reach steady state on machine start-up and after changes to feed conditions. The results of these experiments are summarised in Figures 11 and 12.

grade / recovery / mass pull, % pull, mass / / recoverygrade 5 3 0 WO 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 Elapsed time, mm:ss

Figure 11. Results of experiments to monitor variation in concentrate grade, recovery and mass pull Minerals 2018, 8, 191 12 of 18 to concentrate outlet over time.

10 grade / recovery / mass pull, % pull, mass / / recoverygrade 3

WO 0 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 Elapsed time, mm:ss

Figure 12. Results of experiments monitoring variation variation in in concentrate concentrate grade, recovery recovery and mass pull Mineralstoto concentrate 2018, 8, x FOR outlet PEER over REVIEW time when changing wash water ﬂowrate.flowrate. 12 of 17

FigureFigure 11 11 shows shows that that a a steady steady state state is is reached reached after after about about 4–5 4– min5 min from from machine machine start-up. start-up. Figure Figure 12 shows12 shows that that after after further further changes changes to washto wash water water a steady a steady state state is reached is reached after after about about 1–2 1 min.–2 min. Due Due to limitationsto limitations in the in the mass mass of feed of feed material material available available it was it notwas possible not possible to run to beyond run beyond 11 min 11 at min the testedat the solidtested feed solid rate. feed Based rate. Based on the on above the above results, results, for each for run each of run the of experimental the experimental design, design, samples samples were collectedwere collected at 6 min at 6 and min 7 and min 7 after min setting after setting the desired the desired conditions. conditions. FigureFigure 1313 showsshows thethe grade-recoverygrade-recovery curvecurve forfor thethe experimentsexperiments undertakenundertaken asas aa partpart ofof thethe CCRDCCRD design. The figure shows that the maximum recovery above the required grade of 43% WO3 was design. The ﬁgure shows that the maximum recovery above the required grade of 43% WO3 was approximatelyapproximately 31%.31%.

60 R² = 0.915

50 3 40

30 Grade, % WO % Grade, 20

0 0 5 10 15 20 25 30 35 40 45 50 55 60 Recovery, % WO3

FigureFigure 13. 13. Grade-recovery Grade-recovery curve curve for for experimental experimental runs. runs. The dashed line line indicates indicates the the minimum minimum 3 requiredrequired WOWO3 grade gradeset set by by the the mine mine operator operator and and the the solid solid line line is is a 2-degreea 2-degree polynomial polynomial line line of bestof best ﬁt withfit with R2 valueR2 value of 0.915.of 0.915.

The MGS was then modelled with the experimental results using the response surface The MGS was then modelled with the experimental results using the response surface methodology within Minitab 17. The resultant equation linking the input variables to recovery and methodology within Minitab 17. The resultant equation linking the input variables to recovery grade are shown in Equations (4) and (5). The response surfaces generated are shown in Figures 14 and 15.

Recovery = − 2515 + 33.6∙x1 + 34.0∙x2 − 4.52∙x3 − 0.1102∙x1∙x1 + 0.727∙x2∙x2 (4) − 0.0394∙x3∙x3 − 0.349∙x1∙x2 + 0.0485∙x1∙x3 + 0.140∙x2∙x3

Grade = 1095 − 13.42∙x1 +13.5∙x2 − 1.68∙x3 + 0.0420∙x1∙x1 − 0.351∙x2∙x2 (5) + 0.0087∙x3∙x3 - 0.0606∙x1∙x2 + 0.0063∙x1∙x3 + 0.0249∙x2∙x3 The graphs for recovery presented in Figure 14 show an increasing recovery with increasing rotational velocity (x1) and decreasing wash water flowrate (x2). The pulp density (x3) has a positive effect on recovery between 36–38% solids with a decrease in recovery the further from this density the slurry becomes. The recovery graphs also suggest that at high wash water levels an increase on % solids in the slurry will add to recovery. The graphs for the grade model presented in Figure 15 show that grade increases with decreasing rotational velocity. Increasing wash water flowrate and pulp density have very little effect on the produced grade. Using ANOVA, it was found that the rotational velocity had the most effect on recovery followed by wash water flowrate with these two variables also having the strongest interaction effect. The importance of rotational velocity agrees well with the literature. However, the importance of wash water was more than that of pulp density which is contrary to [6,8]. The strong interaction effect between rotational velocity and wash water is in agreement with [9].

Minerals 2018, 8, 191 13 of 18 and grade are shown in Equations (4) and (5). The response surfaces generated are shown in Figures 14 and 15.

Recovery = −2515 + 33.6·x + 34.0·x − 4.52·x − 0.1102·x ·x + 0.727·x ·x 1 2 3 1 1 2 2 (4) − 0.0394·x3·x3 − 0.349·x1·x2 + 0.0485·x1·x3 + 0.140·x2·x3

Grade = 1095 − 13.42·x + 13.5·x − 1.68·x + 0.0420·x ·x − 0.351·x ·x 1 2 3 1 1 2 2 (5) + 0.0087·x3·x3 − 0.0606·x1·x2 + 0.0063·x1·x3 + 0.0249·x2·x3 MineralsMinerals 2018 2018, ,8 8, ,x x FOR FOR PEER PEER REVIEW REVIEW 1313 of of 17 17

FigureFigure 14. 14. Response ResponseResponse surface surface surface plots plots plots of of WO WO of33 WO recovery recovery3 recovery to to MGS MGS to concentrate concentrateMGS concentrate stream stream at stream at fixed fixed at variable variable ﬁxed variablepositions.positions. positions.

FigureFigure 15. 15. 15. Response ResponseResponse surfacesurface surface plots plots plots of of WOWO of3WO3 grade grade3 grade in in thethe in MGSMGS the concentrate concentrate MGS concentrate streamstream at stream at fixedfixed at variable variable ﬁxed variablepositions.positions. positions.

3.3.2.3.3.2.The ValidationValidation graphs of forof ResultsResults recovery presented in Figure 14 show an increasing recovery with increasing rotationalToTo test test velocity the the validity validity (x1) and of of decreasing the the generated generated wash model water model ﬂowrate it it was was examined examined(x2). The pulp using using density a a number number (x3) has of of a statistical statistical positive measures.effectmeasures. on recovery Firstly,Firstly, the betweenthe experimentalexperimental 36–38% errors solidserrors were withwere measured.ameasured. decrease inItIt was recoverywas foundfound the thatthat further thethe standardstandard from this deviationdeviation density the slurry becomes. The recovery graphs also suggest that at high wash water levels an increase on forfor WO WO33 grade grade for for the the repeated repeated central central point point experimental experimental runs runs was was 1.4%. 1.4%. For For the the recovery recovery the the %standardstandard solids in deviation deviation the slurry was was will 4.2%. 4.2%. add to This This recovery. error error is isThe a a combination graphs combination for the of of grade the the analytical analytical model presented error error and and in Figure errors errors 15 in in obtainingshowobtaining that the gradethe samesame increases pulppulp densitydensity with decreasing andand feedfeed rate rotationalrate betweenbetween velocity. testtest runs.runs. Increasing TheThe standardstandard wash water deviationdeviation ﬂowrate betweenbetween and pulp density have very little effect on the produced grade. repeatrepeat analyticalanalytical readings readings waswas 0.6%0.6% WOWO33, , suggestingsuggesting thatthat most most ofof thethe errorerror waswas duedue toto inaccuracyinaccuracy inin experimentation.experimentation. ToTo ensureensure thatthat therethere werewere nono significantsignificant variationsvariations inin feedfeed gradegrade duringduring thethe experimentalexperimental runs,runs, whichwhich wouldwould maskmask thethe effectseffects ofof operatingoperating variables,variables, thethe calculatedcalculated feedfeed gradegrade waswas plottedplotted againstagainst experimentalexperimental run run datedate (Figure(Figure 1616).). TheThe chartchart showsshows thatthat mostmost datadata pointspoints fitfit withinwithin thethe averageaverage standardstandard deviationdeviation ofof analyticalanalytical errorerror (±0.6%)(±0.6%) withwith twotwo outliersoutliers identifiedidentified andand removed.removed. ForFor thethe testtest runsruns containingcontaining outliers,outliers, resultsresults areare based based on on a a single single sample sample not not an an average average of of both both samples. samples. There There is is no no discernible discernible trend trend in in the the feed feed gradegrade over over the the course course of of the the experimentation experimentation which which suggests suggests a a lack lack of of bias bias over over time. time. This This also also indicatesindicates that that the the lossloss ofof finesfines during during the the test test programme programme waswas minimal. minimal. TheThe fitfit ofof thethe modelsmodels generatedgenerated usingusing thethe ResponseResponse SurfaceSurface MethodMethod cancan bebe assessedassessed usingusing thethe adjustedadjusted RR22 values values (Table (Table 55).).

Minerals 2018, 8, 191 14 of 18

Using ANOVA, it was found that the rotational velocity had the most effect on recovery followed by wash water ﬂowrate with these two variables also having the strongest interaction effect. The importance of rotational velocity agrees well with the literature. However, the importance of wash water was more than that of pulp density which is contrary to [6,8]. The strong interaction effect between rotational velocity and wash water is in agreement with [9].

3.3.2. Validation of Results To test the validity of the generated model it was examined using a number of statistical measures. Firstly, the experimental errors were measured. It was found that the standard deviation for WO3 grade for the repeated central point experimental runs was 1.4%. For the recovery the standard deviation was 4.2%. This error is a combination of the analytical error and errors in obtaining the same pulp density and feed rate between test runs. The standard deviation between repeat analytical readings was 0.6% WO3, suggesting that most of the error was due to inaccuracy in experimentation. To ensure that there were no signiﬁcant variations in feed grade during the experimental runs, which would mask the effects of operating variables, the calculated feed grade was plotted against experimentalMinerals 2018 run, 8, x dateFOR PEER (Figure REVIEW 16 ). 14 of 17

30 Mean of feed grades 29 Standard deviation of feed grades 28 3 27

22 Calculated Feed grade, % WO % grade, Feed Calculated 21

20 01/08 02/08 03/08 04/08 05/08 06/08 07/08 08/08 09/08 10/08 11/08 12/08 13/08 14/08 Date of experiment

FigureFigure 16. Variation16. Variation in WOin WO3 feed3 feed grade grade overover all experimental experimental runs. runs. X denotes X denotes outliers outliers removed removed from from the dataset.the dataset.

Table 5. Adjusted R2 values for fitted models of grade and recovery. The chart shows that most data points fit within the average standard deviation of analytical error (±0.6%) with two outliers identifiedModel andAdjusted removed. R2 Measure For the of test Fit runs containing outliers, results are based on a single sample notWO an3 Grade average of both95.4% samples. There is no discernible trend in the feed grade over the course of theRecovery experimentation which91.5% suggests a lack of bias over time. This also indicates that the loss of fines during the test programme was minimal. Table 5 shows that the models account for most of the variation in the grade and recovery. The gradeThe fit model of the is models better fittedgenerated to the using data the which Response is not unexpected Surface Method due to can the be lower assessed measured using the 2 adjustedexperimental R values error. (Table Model5). fits showed that most points fit within the 95% confidence interval for the mean response and all fall within the 95% confidence interval for predicting values. Table 5. Adjusted R2 values for fitted models of grade and recovery. 3.3.3. Comparison to Conventional Scraping Blades Model Adjusted R2 Measure of Fit To assess the influence of the novel low profile scraping blades, suitable data points for comparison were takenWO from3 Grade a dataset of results for both conventional 95.4% and low profile scraping blades. The dataset for conventionalRecovery blades was formed from 91.5% test work undertaken both before and after the CCRD-RSM experimental programme. The tests undertaken using conventional blades represent a range of test conditions with the wash water flow rate, pulp density and drum velocity varied to obtain a range of values. The dataset for the low profile scraping blades combined the results of the CCRD-RSM experiments with further tests undertaken afterwards. Suitable data points for comparison were selected based on equivalence in WO3 feed grade and stability of mass flow rate to ensure the validity of the comparison. This was important as it was found that the residence time for concentrate material using the conventional scraping blades was longer than for the low profile blades and it was not possible in all tests to reach a steady state due to limitations in the feed available. This tended to result in lower reported feed grades or mass flow rates. There was a slight trend towards lower feed grades in later test-work. This is expected to be a result of general loss of material during testing (spillages, etc.) which reduced the feed mass and so maximum experiment run time but also due to some degradation of the ferberite over time. Further QEMSCAN analyses would allow for better understanding of this issue but this was not undertaken as a part of the study. The methodology used to select valid data points was to firstly reject tests in which the measured mass flow rate was less than 85% of the or where the feed grade was less than 20% WO3. The average feed grade of the data remaining in the conventional scraping blade dataset was then calculated and

Minerals 2018, 8, 191 15 of 18

Table5 shows that the models account for most of the variation in the grade and recovery. The grade model is better fitted to the data which is not unexpected due to the lower measured experimental error. Model fits showed that most points fit within the 95% confidence interval for the mean response and all fall within the 95% confidence interval for predicting values.

3.3.3. Comparison to Conventional Scraping Blades To assess the influence of the novel low profile scraping blades, suitable data points for comparison were taken from a dataset of results for both conventional and low profile scraping blades. The dataset for conventional blades was formed from test work undertaken both before and after the CCRD-RSM experimental programme. The tests undertaken using conventional blades represent a range of test conditions with the wash water flow rate, pulp density and drum velocity varied to obtain a range of values. The dataset for the low profile scraping blades combined the results of the CCRD-RSM experiments with further tests undertaken afterwards. Suitable data points for comparison were selected based on equivalence in WO3 feed grade and stability of mass flow rate to ensure the validity of the comparison. This was important as it was found that the residence time for concentrate material using the conventional scraping blades was longer than for the low profile blades and it was not possible in all tests to reach a steady state due to limitations in the feed available. This tended to result in lower reported feed grades or mass flow rates. There was a slight trend towards lower feed grades in later test-work. This is expected to be a result of general loss of material during testing (spillages, etc.) which reduced the feed mass and so maximum experiment run time but also due to some degradation of the ferberite over time. Further QEMSCAN analyses would allow for better understanding of this issue but this was not undertaken as a part of the study. MineralsThe 2018 methodology, 8, x FOR PEER used REVIEW to select valid data points was to firstly reject tests in which the measured15 of 17 mass flow rate was less than 85% of the or where the feed grade was less than 20% WO3. The average feeddata gradepoints of from the both data datasets remaining rejected in the where conventional the relative scraping difference blade to dataset this average was then value calculated was greater and datathan points2 standard from deviations both datasets (1.65%). rejected In total where 20 the data relative points difference were rejected to this and average 21 data value points was accepted. greater thanFrom 2 the standard dataset deviations of conventional (1.65%). scraping In total 20blade data tests points accepted, were rejected 7 tests and were 21 completed data points before accepted. the FromCCRD-RSM the dataset experimentation of conventional programme scraping and blade 1 test tests afterwards. accepted, 7 tests were completed before the CCRD-RSMFigure experimentation 17 shows the WO programme3 grade andrecovery 1 test curveafterwards. for the accepted data points valid for comparison. Figure 17 shows the WO3 grade recovery curve for the accepted data points valid for comparison.

70 Conventional scraping 60 blades

50 3 40

30 Grade, % WO % Grade, 20

0 0 10 20 30 40 50 60 Recovery, %

FigureFigure 17.17. Grade-recoveryGrade-recovery plotplot forfor comparablecomparable datadata pointspoints ofof lowlow proﬁleprofile andand conventionalconventional scrapingscraping bladeblade tests.tests. TheThe solidsolid lineslines indicateindicate thethe ﬁttedfitted regressionregression lines.lines.

Analysis of co-variance (ANCOVA) was undertaken using Mathworks Matlab 2017b to determine if there was a statistically significant difference in the regression lines for the two datasets of comparable data points. The results of this analysis indicated that the difference in the slope of the two regression lines was not significant at 95% confidence level (p-value = 0.47). The difference between the regression lines was significant (p-value = 0.002) at a 95% confidence level. Further comparison was drawn by creating enrichment ratio-recovery curves for data points within 2 standard deviations and 3 standard deviations. Where the enrichment ratio is taken as the ratio of concentrate grade to feed grade. These comparisons are summarised in Figure 18.

3 Conventional scraping blades Low profile scraping blades 3 2.5

1.5 Enrichment ratio, WO ratio, Enrichment

1 0 10 20 30 40 50 60 Recovery, % Figure 18. Enrichment ratio-recovery plot for comparable data points of low profile and conventional scraping blade tests. The solid lines indicate the fitted regression lines for points within 2 standard deviations of the mean. The dotted lines indicate the fitted regression lines for the points within 3 standard deviations.

Minerals 2018, 8, x FOR PEER REVIEW 15 of 17

data points from both datasets rejected where the relative difference to this average value was greater than 2 standard deviations (1.65%). In total 20 data points were rejected and 21 data points accepted. From the dataset of conventional scraping blade tests accepted, 7 tests were completed before the CCRD-RSM experimentation programme and 1 test afterwards. Figure 17 shows the WO3 grade recovery curve for the accepted data points valid for comparison.

70 Conventional scraping 60 blades

50 3 40

30 Grade, % WO % Grade, 20

0 0 10 20 30 40 50 60 Recovery, % Figure 17. Grade-recovery plot for comparable data points of low profile and conventional scraping Minerals 2018, 8, 191 16 of 18 blade tests. The solid lines indicate the fitted regression lines.

AnalysisAnalysis of of co-variance co-variance (ANCOVA) (ANCOVA) was undertaken was undertaken using Mathworks using Mathworks Matlab 2017b Matlab to determine 2017b to ifdetermine there was if a there statistically was a significantstatistically difference significant in thedifference regression in the lines regression for the two lines datasets for the of two comparable datasets dataof comparable points. The data results points. of this The analysis results of indicated this analysis that the indicated difference that in the the difference slope of the in the two slope regression of the linestwo wasregression not significant lines was at 95% not confidence significant level at 95% (p-value confidence = 0.47). level The difference (p-value = between 0.47). The the regression difference linesbetween was the significant regression (p-value lines was = 0.002) significant at a 95% (p confidence-value = 0.002) level. at a 95% confidence level. FurtherFurther comparisoncomparison was was drawndrawn byby creatingcreating enrichment enrichment ratio-recoveryratio-recovery curvescurves for for data data pointspoints withinwithin 22 standardstandard deviationsdeviations andand 33 standardstandard deviations.deviations. WhereWhere thethe enrichmentenrichment ratioratio isis takentaken asas thethe ratioratio ofof concentrateconcentrate gradegrade toto feedfeed grade.grade. TheseThese comparisonscomparisons areare summarisedsummarised inin FigureFigure 18 18..

3 Conventional scraping blades Low profile scraping blades 3 2.5

1.5 Enrichment ratio, WO ratio, Enrichment

1 0 10 20 30 40 50 60 Recovery, %

FigureFigure 18.18. EnrichmentEnrichment ratio-recoveryratio-recovery plotplot forfor comparablecomparable datadata pointspoints ofof lowlow profileprofile andand conventionalconventional scrapingscraping bladeblade tests.tests. TheThe solid lineslines indicateindicate thethe fittedfitted regressionregression lineslines forfor pointspoints withinwithin 22 standardstandard deviationsdeviations of of thethe mean. mean. The The dotted dotted lines lines indicate indicate the the fitted fitted regression regression lines lines for for the the points points within within 3 standard3 standard deviations. deviations.

Figure 18 shows that when differences in feed grade are accounted using an enrichment ratio the low profile scraping blades shows improved performance of conventional blades for the tests compared. For points within 2 and 3 standard deviations there was not sufficient statistical evidence that the regression lines slopes were different at the 95% level but there was sufficient evidence that the low profile blades made a difference to performance. However, at a 90% level there was sufficient evidence of different slopes for data points within 3 standard deviations. This can be seen in Figure 18 at low recoveries where the upgrade using conventional scraping blades may perform better than for the low profile blades. Further experimentation is required to investigate this improvement to increase statistical reliability and ensure optimal conditions for the conventional blades. Further, the applicability of the result to other materials must be tested. With these caveats, it can be said that the results show that the low-profile blades offer significant potential and merit further investigation to optimise the design. Further experimentation was not possible in this instance due to insufficient material remaining to reach steady state using conventional blades, potentially compounded by degradation of the material.

4. Conclusions The results of the test work using a Mozley MGS with novel scraping blades has been reported. The LIMS magnetic fraction used for testing contained valuable, liberated ferberite indicating the potential for recovery by physical separation. QEMSCAN data showed that the ferberite in the stream was weathered and depleted in tungsten and that the main gangue mineral was hematite, limiting the options for equipment to separate the material. The Mozley MGS is well suited for this type of separation. Minerals 2018, 8, 191 17 of 18

Using the response surface methodology with CCRD design it was possible to model the grade and recovery of WO3 when varying the rotational velocity, wash water flow rate and feed pulp density. The models generated by RSM fitted the data well with adjusted R2 of 95.4% and 91.5% for the grade and recovery of WO3, respectively. This indicated that the variables selected accounted for over 90% of the observed variation in results. Of the remaining variability in each model approximately half was a result of experimental errors. Test work showed that it was possible to recover ferberite from the LIMS stream which meets the required minimum grade. The maximum reported recovery which met the required grade was 32.7%. Although relatively low, the tungsten recovered would otherwise be lost as waste. Comparison of the new blades with traditional ones indicate that for this material there was a significant improvement in separation performance in the region of the required minimum grade. Further experimentation is required to investigate this improvement but the results indicate that the low-profile blades offer significant potential and merit further investigation to optimise the design.

Author Contributions: R.F. and K.F. conceived and designed the experiments; K.F., P.H., T.M. performed the experiments; R.F., P.H., G.R. and W.X. analyzed the data; G.R. and P.H. contributed analysis tools; T.M. contributed prototype low profile blades; R.F. wrote the paper with contributions from K.F and G.R. Acknowledgments: This work is part of the OptimOre project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 642201. Authors are thankful to Wolf Minerals for providing material for experimentation and to Gravity Mining Ltd. for support in undertaking experiments and providing the opportunity to test the modified low profile blades. Conflicts of Interest: The authors declare no conflict of interest.

References

1. European Commission. Study on the Review of the List of Critical Raw Materials; European Commission: Brussels, Belgium, 2017; pp. 33–35. 2. European Commission. Report on Critical Raw Materials for the EU—Critical Raw Materials Profiles; European Commission: Brussels, Belgium, 2015; pp. 193–202. 3. Chan, B.S.K.; Mozley, R.H.; Childs, G.J.C. Extended trials with the high tonnage multi-gravity separator. Miner. Eng. 1991, 4, 489–496. [CrossRef] 4. Glass, H.J. Separating small particles with the multi-gravity separator. In Innovation in Physical Separation Technologies: Richard Mozley Memorial Volume. Papers Presented at the “Innovation in Physical Separation Technologies” Conference; IMM: London, UK, 1998; pp. 161–171. ISBN 978-1-870706-33-9. 5. Ahmadabadi, A.D.; Hejazi, R.; Saghaeian, S.M. Statistical evaluation and optimization of multi-gravity separator (MGS) parameters for upgrading of chadormalu ultra-fine haematite tailings. In Proceedings of the International Mineral Processing Congress—IMPC 2012, New Delhi, India, 23–28 September 2012; pp. 2016–2025. 6. Aslan, N. Application of response surface methodology and central composite rotatable design for modeling the influence of some operating variables of a Multi-Gravity Separator for coal cleaning. Fuel 2007, 86, 769–776. [CrossRef] 7. Turner, J.W.G.; Hallewell, M.P. Process improvements for fine cassiterite recovery at Wheal Jane. Miner. Eng. 1993, 6, 817–829. [CrossRef] 8. Venkateswara, G.; Sharma, S.K.; Markandeya, R. Modeling and optimization of Multigravity separator for recovery of Iron values from Sub Grade Iron Ore. In Proceedings of the International Mineral Processing Congress—IMPC 2014, Santiago, Chile, 20–24 October 2014. 9. Aslan, N. Application of response surface methodology and central composite rotatable design for modeling and optimization of a multi-gravity separator for chromite concentration. Powder Technol. 2008, 185, 80–86. [CrossRef] 10. Chaurasia, R.C.; Nikkam, S. Optimization Studies on a Multi-Gravity Separator Treating Ultrafine Coal. Int. J. Coal Prep. Util. 2017, 37, 195–212. [CrossRef] Minerals 2018, 8, 191 18 of 18

11. Ozgen, S. Modelling and optimization of clean chromite production from ﬁne chromite tailings by a combination of multigravity separator and hydrocyclone. J. South. Afr. Inst. Min. Metall. 2012, 112, 387–394. 12. Rao, G.V.; Markandeya, R.; Sharma, S.K. Process amenability studies of sub grade iron ore from Bacheli complex, Bailadila sector, India. In Proceedings of the International Mineral Processing Congress—IMPC 2012, New Delhi, India, 23–28 September 2012; pp. 1840–1860. 13. Pascoe, R.D.; Power, M.R.; Simpson, B. QEMSCAN analysis as a tool for improved understanding of gravity separator performance. Miner. Eng. 2007, 20, 487–495. [CrossRef] 14. Obeng, D.P.; Morrell, S.; Napier-Munn, T.J. Application of central composite rotatable design to modelling the effect of some operating variables on the performance of the three-product cyclone. Int. J. Miner. Process. 2005, 76, 181–192. [CrossRef] 15. Napier-Munn, T.J. Statisitcal Methods for Mineral Engineers—How to Design Experiments and Analyse Data; Julius Kruttschnitt Mineral Research Centre: Indooroopilly, QLD, Australia, 2014, ISBN 978-0-9803622-4-4. 16. Keats, W. Alteration and Replacement of Wolframite in the Hemerdon Tungsten Deposit, Devon. Ph.D. Thesis, Camborne School of Mines, Camborne, UK, 1981. 17. Wills, B.A.; Finch, J. Wills’ Mineral Processing Technology, Eighth Edition: An Introduction to the Practical Aspects of Ore Treatment and Mineral Recovery, 8th ed.; Butterworth-Heinemann: Oxford, UK, 2015, ISBN 978-0-08-097053-0.

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