Applied Science, Faculty of
Mining Engineering, Keevil Institute of
DSpace
UBCV
Tuchscherer, Michele
Master of Applied Science - MASc
University of British Columbia
The application of insoluble mineral flocculation-flotation on an ultrafine, high insoluble mineral (insol) content stream utilizing flotation column technology was evaluated as an alternate method to dissolution in separating insoluble minerals from residual salts in fine potash tailings. This study was a novel application of insoluble flocculation-flotation due to the high insol composition, the ultrafine particle size distribution, and the use of column flotation technology, which has yet to be implemented in a slimes flotation duty on an industrial scale. The feed insol content was 5-25 times higher than typical slimes flotation applications with a P80 of 30 µm compared to 3350 µm. A twelve-inch diameter by five-meter-tall pilot column with an external cavitation sparging system was installed in the plant to trial slimes flotation of an ultrafine, high clay stream with an ethoxylated amine collector. The column operating parameters were optimized, and a statistical approach was taken to evaluate the flotation response and determine the flotation column sizing parameters. The results conclude that collector-less insol flotation utilizing column technology from a high clay, ultrafine stream was successful, with a modeled optimum of 88.2% insol rejection and 86.7% KCl recovery; however, the patented method of flocculation-flotation is invalid in this application, due to the observed destruction of flocs in the flotation pulp arising from the power intensity of the column external sparging system and the significant column residence time. Direct comparison of the pilot separation efficiencies with a more power-intensive flotation machine revealed that the selectivity performance of the column was equipment limited. Complete liberation of the insoluble minerals, a fundamental characteristic of the ultrafine stream, resulted in near-ideal separation efficiencies from sylvite at high insol rejection rates when sufficient power intensity was provided; however, individual insol mineral flotation was found to be unselective. Column flotation of the ultrafine high clay stream was characterized by a low carrying capacity of 0.9 t/h/m2 and a long residence time of 17 minutes. Sparger energy intensity, collector addition, pulp density, feed composition, and brine temperature were found to be statistically significant predictors of insol rejection (R2 = 80.47%).
APPLICATION OF SELECTIVE-FLOCCULATION FLOTATION ON A HIGH CLAY, ULTRAFINE POTASH STREAM by Michele Tuchscherer B.A.Sc., The University of Saskatchewan, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mining Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2022 © Michele Tuchscherer, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Application of selective flocculation flotation on a high clay, ultrafine potash stream submitted by Michele Tuchscherer in partial fulfillment of the requirements for the degree of Master of Applied Science in Mining Engineering Examining Committee: Dr. Sanja Miskovic, Mining Engineering, UBC Supervisor Dr. Marek Pawlik, Mining Engineering, UBC Supervisory Committee Member Dr. Bern Klein, Mining Engineering, UBC Supervisory Committee Member iii Abstract The application of insoluble mineral flocculation-flotation on an ultrafine, high insoluble mineral (insol) content stream utilizing flotation column technology was evaluated as an alternate method to dissolution in separating insoluble minerals from residual salts in fine potash tailings. This study was a novel application of insoluble flocculation-flotation due to the high insol composition, the ultrafine particle size distribution, and the use of column flotation technology, which has yet to be implemented in a slimes flotation duty on an industrial scale. The feed insol content was 5-25 times higher than typical slimes flotation applications with a P80 of 30 µm compared to 3350 µm. A twelve-inch diameter by five-meter-tall pilot column with an external cavitation sparging system was installed in the plant to trial slimes flotation of an ultrafine, high clay stream with an ethoxylated amine collector. The column operating parameters were optimized, and a statistical approach was taken to evaluate the flotation response and determine the flotation column sizing parameters. The results conclude that collector-less insol flotation utilizing column technology from a high clay, ultrafine stream was successful, with a modeled optimum of 88.2% insol rejection and 86.7% KCl recovery; however, the patented method of flocculation-flotation is invalid in this application, due to the observed destruction of flocs in the flotation pulp arising from the power intensity of the column external sparging system and the significant column residence time. Direct comparison of the pilot separation efficiencies with a more power-intensive flotation machine revealed that the selectivity performance of the column was equipment limited. Complete liberation of the insoluble minerals, a fundamental characteristic of the ultrafine stream, resulted in near-ideal separation efficiencies from sylvite at high insol rejection rates when sufficient power intensity was provided; iv however, individual insol mineral flotation was found to be unselective. Column flotation of the ultrafine high clay stream was characterized by a low carrying capacity of 0.9 t/h/m2 and a long residence time of 17 minutes. Sparger energy intensity, collector addition, pulp density, feed composition, and brine temperature were found to be statistically significant predictors of insol rejection (R2 = 80.47%). v Lay Summary Environmental and social governance has become an increasing priority for the Canadian mining industry. Improving operational sustainability requires optimum extractive resource management and minimizing water and land usage. The innovative application of proven technologies in potash circuit design provides an opportunity to rapidly improve sustainability practices while mitigating operational risk. This study aims to evaluate the feasibility of flocculation-flotation in a novel application on a high clay, ultrafine tailings stream as an alternative to dissolution in managing fine potash tailings. Successful implementation of slimes flotation in this application would increase operational sustainability, not only by reducing fine tailings costs and environmental impact but also by providing an opportunity to recover the ultrafine potash lost to tails in the current desliming process. vi Preface This thesis is a result of experimental work identified by the author and Courtney Rohachuk and completed by the author at Nutrien facilities. Pilot equipment installation and maintenance as well as sampling and analytical support were provided by the relevant departments at Nutrien. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii Acknowledgments ........................................................................................................................xv Chapter 1: Introduction ................................................................................................................1 1.1 Background ..................................................................................................................... 1 1.2 Thesis objectives ............................................................................................................. 5 Chapter 2: Literature review ........................................................................................................6 2.1 Potash processing ............................................................................................................ 6 2.1.1 The Saskatchewan potash mining industry ................................................................. 6 2.1.2 Potash processing ........................................................................................................ 8 2.1.3 Reverse flotation in potash applications ................................................................... 13 2.1.4 Slimes flotation process as implemented at Agrium Vanscoy in PLM ore body ..... 15 2.2 Fine particle flotation .................................................................................................... 16 2.2.1 Flotation fundamentals.............................................................................................. 16 2.2.2 Ultrafine flotation...................................................................................................... 19 2.2.2.1 Flotation cell design to enhance fine particle flotation ..................................... 22 2.2.3 Separate flotation of coarse and fine particles for fine particle recovery ................. 23 2.2.4 Selective-flocculation flotation to improve fine particle flotation ............................ 23 viii 2.2.4.1 Polymer flocculation ......................................................................................... 24 2.2.4.2 Shear-flocculation ............................................................................................. 25 2.2.5 Rheology in fine particle flotation ............................................................................ 26 2.3 Column flotation ........................................................................................................... 28 2.3.1 Cavitation in column flotation .................................................................................. 30 2.4 System characteristics ................................................................................................... 32 2.4.1 Clay minerals ............................................................................................................ 32 2.4.2 Flotation implications of a high ionic strength suspension ....................................... 34 2.4.2.1 Viscosity effects of structure-making and breaking ions in solution ................ 37 Chapter 3: Methodology..............................................................................................................40 3.1 Pilot installation ............................................................................................................ 40 3.2 Experimental procedures .............................................................................................. 44 3.3 Data analysis ................................................................................................................. 46 3.3.1 Fitting system curves with the Vera function ........................................................... 46 3.3.2 Comparing two trendlines ......................................................................................... 47 Chapter 4: Results........................................................................................................................49 4.1 Feed characterization .................................................................................................... 49 4.2 Pilot column .................................................................................................................. 51 4.2.1 Selectivity curves – Insol rejection and KCl recovery.............................................. 51 4.2.2 The effect of slurry density ....................................................................................... 51 4.2.3 Recirculation pressure ............................................................................................... 53 4.2.3.1 Mineralogical composition of insoluble fraction of feed, concentrate, and tailings samples at high and low recirculation intensity ................................................................ 56 ix 4.2.3.2 Power intensity.................................................................................................. 58 4.2.4 Residence time & carrying capacity ......................................................................... 59 4.2.5 Froth characteristics .................................................................................................. 61 4.2.6 Regression modelling of overall insol rejection and KCl recovery responses ......... 62 4.2.6.1 Response optimization: insol recovery to concentrate and KCl rejection to tailings 63 4.2.7 Quantification of experimental error through regression modelling ........................ 64 Chapter 5: Discussion ..................................................................................................................67 Chapter 6: Conclusions ...............................................................................................................77 Chapter 7: Recommendations ....................................................................................................79 References .....................................................................................................................................80 Appendices ....................................................................................................................................85 Appendix A ............................................................................................................................... 85 A.1 Comparing two trendlines to determine the effect of collector on insol rejection at high pulp density ................................................................................................................... 85 A.2 Comparing two trendlines to determine the effect of collector on KCl recovery at high pulp density ........................................................................................................................... 86 A.3 Comparing two trendlines to determine the effect of collector on insol rejection at low pulp density ........................................................................................................................... 87 A.4 Comparing two trendlines to determine the effect of collector on KCl recovery at low pulp density ........................................................................................................................... 88 Appendix B ............................................................................................................................... 89 B.1 Particle size distribution of slimes flotation streams ................................................ 89 x Appendix C ............................................................................................................................... 89 C.1 Regression model of baseline slimes flotation data .................................................. 90 C.2 Regression model of insol rejection for overall optimization data ........................... 92 C.3 Regression model of KCl recovery for overall optimization data ............................ 95 C.4 Minitab response optimizer results ........................................................................... 97 xi List of Tables Table 1 Typical potash product specifications (Nutrien, 2021) .................................................... 12 Table 2 Particle size distribution of slimes flotation feed from Malvern Particle Size Analyzer 49 Table 3 XRD analysis of slimes flotation feed ............................................................................. 49 Table 4 Low insol rejection mass balance and composition of insoluble mineral fraction from XRD analysis .......................................................................................................................................... 57 Table 5 High insol rejection mass balance and composition of insoluble mineral fraction from XRD analysis ................................................................................................................................ 57 Table 6 Descriptive statistics of predictors and responses used to develop regression models (count = 20) .............................................................................................................................................. 65 Table 7 Summary of results for comparison of two trendlines to determine effect of collector on insol rejection at 10% w/w pulp density ....................................................................................... 85 Table 8 Summary of results for comparison of two trendlines to determine effect of collector on KCl recovery at 10% w/w pulp density ........................................................................................ 86 Table 9 Summary of results for comparison of two trendlines to determine effect of collector on insol rejection at 5% w/w pulp density ......................................................................................... 87 Table 10 Summary of results for comparison of two trendlines to determine effect of collector on KCl recovery at 5% w/w pulp density .......................................................................................... 88 xii List of Figures Figure 1 Thickness of the Prairie Evaporate Formation and area of potash distribution within these salts (Fuzesy, 1982) ........................................................................................................................ 7 Figure 2 Schematic cross-section across southern Saskatchewan of the Prairie Evaporate Formation showing relative position of potash beds (Fuzesy, 1982). ............................................ 8 Figure 3 Block flow diagram of potash processing ........................................................................ 9 Figure 4 Effect of particle size on flotation recovery (Lynch et al., 1981; Mankosa et al., 2018) 20 Figure 5 Example of a carrying capacity plot for a pilot scale test (Kohmuench et al., 2009) ..... 30 Figure 6 Depiction of the classification of phyllosilicate minerals (Hurlbut & Klein, 1977) ...... 34 Figure 7 Equilibrium diagram of KCl-NaCl-H2O system (Linke & Seidell, 1958) .................... 35 Figure 8 Viscosity of alkali halide solutions as a function of concentration (Haynes et al., 2016)....................................................................................................................................................... 38 Figure 9 Simplified process flow diagram of pilot circuit installation ......................................... 44 Figure 10 Selectivity curve of KCl recovery with insol rejection with no collector and at an optimum pulp density ................................................................................................................... 51 Figure 11 Selectivity curve of KCl recovery with insol rejection for slimes flotation without collector at optimal and low pulp densities................................................................................... 52 Figure 12 Effect of increasing recirculation pressure on insol rejection to the concentrate at high and low pulp densities with and without collector addition ......................................................... 53 Figure 13 Effect of increasing recirculation pressure on KCl recovery to the concentrate at high and low pulp densities with and without collector addition ......................................................... 54 Figure 14 Effect of increasing recirculation pressure on separation efficiency to the concentrate at high and low pulp densities with and without collector addition ................................................. 55 xiii Figure 15 Selectivity curve of KCl recovery with insol rejection at an optimum pulp density ... 59 Figure 16 Column concentrate solids rate with increasing feed loading rates ............................. 60 Figure 17 The effect of tailings residence time on separation efficiency ..................................... 60 Figure 18 Image of slimes flotation concentrate under optimal operation conditions ................. 61 Figure 19 The effect of increasing insol rejection to the concentrate on brine recovery .............. 62 Figure 20 Modelled and baseline data insol rejection by observation number............................. 66 Figure 21 Comparing two trendlines to determine the effect of collector on insol rejection at 10% w/w pulp density ........................................................................................................................... 85 Figure 22 Comparing two trendlines to determine the effect of collector on KCl Recovery at 10% w/w pulp density ........................................................................................................................... 86 Figure 23 Comparing two trendlines to determine the effect of collector on insol rejection at 5% w/w pulp density ........................................................................................................................... 87 Figure 24 Comparing two trendlines to determine the effect of collector on KCl recovery at 5% w/w pulp density ........................................................................................................................... 88 Figure 25 Particle size distribution of slimes flotation feed, con and tails ................................... 89 Figure 26 Normality plot of baseline insol rejection model residuals. With P-Value >0.05 we accept the null hypothesis that the data is normally distributed. .................................................. 90 Figure 27 Residual plots for regression model of baseline insol rejection data ........................... 91 Figure 28 Observed insol rejection and predicted insol rejection vs observation number ........... 93 Figure 29 Pareto chart of standardized effects for overall insol flotation model .......................... 93 Figure 30 Residual plots for regression model of overall insol rejection to concentrate data ...... 94 Figure 31 Pareto chart of standardized effects for overall KCl rejection model .......................... 96 Figure 32 Residual plots for regression model of overall KCl recovery data .............................. 96 xiv Figure 33 Response optimizer results for KCl recovery and insol rejection ................................ 97 xv Acknowledgments I would like to express my gratitude to my academic supervisor, Dr. Sanja Miskovic, and my supervisory committee, Dr. Bern Klein, and Dr. Marek Pawlik for their support in developing my thesis. Thank you to Nutrien for funding this research and my graduate studies. Special thanks to my current supervisor, Luke Broughton, for his support, respect, and feedback, without whom this completed work would not have been possible. It cannot be overstated how appreciative I am of the Allan metallurgical team for their patience and dedication to this project. In no particular order, thank you to Michael Schell, Kira Hertlein, Kent Scharback, Braden Kirwan, Roland Wollman, Sean Fenrich, Leni Udell, Justin Hayunga, and Derek Hamm, as well as previous students, who have patiently supported this work, completed hundreds of assays by hand, and have made themselves available on irregular shifts to ensure the success of this research. I would also like to recognize the contributions of Kristen Gunter, a former Process Engineer intern, who went above and beyond the expectations of her role to support the operation of the pilot work contained in this thesis. An additional thank you to the many other departments at the site for their patience as I learned the ropes of project execution through this work (apologies, Electrical team). This research would not have left infancy without the support of Courtney Rohachuk. Thank you, Courtney, for agreeing to meet with me for our first pizza and wine night to discuss this absurd idea, and for your continued friendship. Finally, thank you to my partner, Alex Miket, for his endless patience and support throughout my graduate studies. Thank you for forcing me to learn statistics in our covid Nerd Den, and for listening to me rant about bubbles for the past three years. 1 Chapter 1: Introduction 1.1 Background The inefficient separation of insoluble minerals from fine sylvite and halite underlies two of the largest processing challenges facing the conventional potash mining industry: the recovery of ultrafine potash and the high capital cost and environmental impact of fine tailings management. Conventional potash concentrators have historically struggled to break the 90% recovery barrier due to separation inefficiencies resulting in fine sylvite losses in the desliming process. The fine tailings handling requirements are subsequently compounded by the misdirected fine halite and sylvite reporting with the insoluble minerals to fine tailings rather than with the desired salt tailings stream. Most potash mines in Saskatchewan with significant ore clay content transitioned away from the co-mingling of tailings, beginning in the mid-1990s, to address tailings pile stability concerns caused by fines migration to the bottom of the pile, operational issues with return brine clarity, and remediation plans upon mine closure. Several methods for managing fine potash tailings have been explored; however, none have proved to be more economically feasible than decant structures, which have become the dominant technology for fine tailings storage in Saskatchewan. The capital cost and construction time associated with decant structure earthworks are substantial, and dyke raises occur at a high frequency due to poor consolidation of clays in the decant ponds. Operations began supplementing brackish water addition to the fine tailings stream with up to 50% freshwater to dissolve the residual fine salts and extend the life of the fine tailings cell thereby minimizing decant structure costs. Approximately 35% of overall freshwater usage may be consumed in the 2 dissolution approach to fine tailings management. Additional brine generated by freshwater usage is managed by injection wells. While freshwater addition is a cost-effective solution to minimize fine tailings management costs and land usage requirements, it does not address the fundamental issue of recovery losses arising from mechanical desliming circuit separation inefficiencies. The alternate approach to tailings dissolution proposed in this study is the novel application of flocculation-flotation of insoluble minerals on an ultrafine, high clay content stream. Flocculation-flotation of insoluble (insol) minerals is a reverse flotation process defined by the targeted recovery of gangue minerals to the flotation concentrate. In potash processing applications of selective flocculation-flotation of insoluble minerals, the insol gangue is floated to the concentrate to achieve separation from sylvite and halite which report to the flotation tailings. This thesis evaluates the feasibility of flocculation-flotation utilizing column technology on this “unfloatable” stream to increase operational sustainability, not only by reducing fine tailing costs and environmental impact but also by providing an opportunity to recover the ultrafine potash lost to tails from the mechanical desliming process. Flocculation-flotation of insoluble minerals to separate fine insol gangue from coarse sylvite and halite in upstream desliming potash processing is a well-established technology that has been used in potash operations globally; however, insol flotation is rarely applied to ultrafine streams, likely due to selectivity concerns. Insoluble minerals are often collectively referred to as “slimes” in potash processing. Slimes flotation is typically incorporated into the flowsheet after comminution to deslime the ore prior to sylvite flotation. Consequently, the slimes flotation feed insol content 3 is relatively low and the particle size distribution is broad with the liberated insoluble minerals falling to the finest fractions. Slimes flotation feed insol content ranges from approximately 2% to 10% w/w, with 10% considered to be quite high. The slimes flotation feed is conditioned with a flocculant followed by optional collector addition. The conditioned feed is then floated in mechanical Denver-style flotation cells. An exception to this typical application is the use of a Jameson Cell slimes flotation circuit for tertiary desliming at Cleveland Potash (McConnell & Gilchrist, 2001). Published insol rejections to flotation froth range from approximately 65-85%, often with several stages of cleaning and scavenging to achieve high flotation efficiencies. Corresponding potash recovery to slimes flotation tailings ranges from the low 80’s to nearly 100%, in some applications (Banks, 1979; Perucca & Cormode, 1999; Thompson & Huiatt, 1979; Titkov et al., 2016). Potash recovery has been shown to be inversely proportional to insol rejection in slimes flotation applications. The Vanscoy potash operation, which processes ore from the same ore body as the piloted process, successfully operated slimes flotation in a primary desliming application for 40 years. The typical metallurgical performance of the Vanscoy slimes flotation circuit, processing ore with 5% insol content, was 65% insol recovery to froth with 2.8% of the potash in the mill feed lost to the slimes froth (Perucca & Cormode, 1999). The Vanscoy slimes flotation performance was assumed to be the minimum expected slimes flotation separation efficiency for the ultrafine, high insol stream, with improvements anticipated to be achieved using column flotation technology in place of mechanical cells. 4 The feed stream characteristics of the studied unconventional application of slimes flotation were significantly different from the typical slimes flotation feed: on average, half of the solid content was comprised of insoluble minerals in contrast to the normal operating range of 2-10%. The particle size distribution was ultrafine and narrow, with a P80 of approximately 30 µm compared to a P100 of 3350 µm in a primary desliming application (Perucca & Cormode, 1999). The amenability of this ultrafine stream to column flotation was unknown, with potential operational challenges arising from the rheological effects of the high clay content and fine particle size distribution, the high ionic strength flotation system, and the significant mass pull associated with reverse flotation. Column flotation technology was selected to investigate slimes flotation due to the ultrafine flotation advantages of column cells and familiarity with the technology in potash operations in Canada. Flotation column technology has yet to be applied to any known slimes flotation application on an industrial scale; therefore, the suitability of the technology in this duty was also unknown. 5 1.2 Thesis objectives This study aimed to evaluate the application of flocculation-flotation on an ultrafine, high insoluble mineral content stream utilizing flotation column technology. Sampling campaigns on a pilot-scale flotation column were completed to evaluate the main research objective, with the following sub-objectives: 1. Determine the effect of column operating parameters (froth wash, air rate, froth depth, pulp density, energy intensity, etc.) on slimes flotation performance 2. Evaluate the effect of ethoxylated amine slimes collector addition on separation efficiency 3. Complete a mineralogical characterization of the insoluble minerals to determine the floatability behavior of individual components 4. Determine flotation column design parameters: carrying capacity and residence time 5. Generate a regression model using pilot data to predict flotation performance at optimum operating conditions 6. Evaluate the suitability of column flotation technology in an insoluble mineral flotation application 6 Chapter 2: Literature review 2.1 Potash processing 2.1.1 The Saskatchewan potash mining industry Potassium is one of the three essential plant nutrients, in addition to nitrogen and phosphorus. Potassium is a key component of fertilizer and is irreplaceable in maintaining and expanding global crop production. Potash is the colloquial term for potassium-bearing minerals used in agriculture, typically applied to potassium chloride. Over 90% of the potassium minerals used in fertilizer are potassium chloride (muriate of potash) although other compounds such as potassium sulfate, potassium nitrate, or double salts of potassium and magnesium sulfate are also used (Trivedi). When referring to ore, all of the naturally occurring potassium salts are called potash ores (Garrett, 2012). Saskatchewan hosts some of the world’s best underground potash deposits which are extracted by either conventional mining or solution mining. In Saskatchewan, all conventional potash mines operate between 900-1200m below the surface using continuous mining machines to extract the ore using either stress-relief or room-and-pillar mining methods (Funk, 2019). As shown in Figure 1, the Prairie Evaporate Formation lies diagonally over southern Saskatchewan and contains the Western world’s largest reserve of potash (Funk, 2019). The predominant mineralization in Saskatchewan is sylvinite, which consists of halite (NaCl), potassium chloride (KCl), a minor amount of anhydrite, and carnallite (KCl · MgCl2 · 6H2O), and a small percentage of water-insoluble minerals. The insoluble minerals occur as seams and disseminations within the ore zone and consist principally of non-clay minerals such as dolomite, anhydrite, and quartz, and 7 clay minerals including illite, and chlorite (Perucca & Cormode, 1999). The potash extracted from the sylvinite ore is primarily sold as fertilizer products, which have specific product particle sizing specifications and a minimum grade requirement of 60% K2O. Figure 1 Thickness of the Prairie Evaporate Formation and area of potash distribution within these salts (Fuzesy, 1982) As shown in Figure 2, there are three mineable Potash Members within the Prairie Evaporate: The Patience Lake Member (PLM), the Belle Plaine Member (BPM), and the Esterhazy Member (EM). Conventional mines close to Saskatoon operate in the Patience Lake Member while the Rocanville and Esterhazy mines in southeastern Saskatchewan operate in the Esterhazy member. The solution mines near Regina extract from both the Patience Lake and Belle Plaine potash members. As described by Perucca (2003), the PLM is a medium-grained mixture of sylvite and halite with a 90% liberation size of approximately 1.2mm. The average ore grade is 36% KCl and contains a moderate amount of insoluble clay content, around 5.5% on average. The EM is a coarse-grained 8 mixture of sylvite and halite with some carnallite (1-5%) and minimal insoluble content. Liberation (90%) is very coarse at 9.5mm with an average ore grade of 30% KCl (Perucca, 2003). Figure 2 Schematic cross-section across southern Saskatchewan of the Prairie Evaporate Formation showing relative position of potash beds (Fuzesy, 1982). 2.1.2 Potash processing The majority of the fertilizer grade potash in Saskatchewan is produced by conventional froth flotation plants. Crystallization is used to produce high purity white muriate including industrial and specialty fertilizer grades. Mosaic’s Esterhazy plant utilizes dense media separation in addition to flotation. The conventional flotation refining process is shown in Figure 3 and will be described below. 9 Figure 3 Block flow diagram of potash processing The run of mine ore is produced by continuous miners with a top size of approximately 1500 mm and is typically crushed to reduce transport issues from large lumps before it is fed either directly to the mill or transferred to ore storage. Potash ore is considered a soft rock with a Bond Work Index of 7 to 9 (Perucca, 2003). The objective of crushing is to maximize liberation from the sodium chloride and expose the clays for scrubbing and rejection while reducing fines generation, as fine potash generated during crushing will be lost to tailing in the subsequent desliming stage. The ore is fed into the mill on a series of conveyors and passed over a coarse grizzly screen to remove and crush oversize lumps before the ore is contacted with saturated brine in the pulper tank at the start of the primary crushing circuit. Primary crushing circuits are typically closed-circuit with vibrating screens and utilize either wet or dry crushing. Dry crushing circuits are operationally 10 simple but require dust management, while wet crushing facilitates higher screening efficiency and increased insol release. The crushing circuit discharge is sent to the scrubbing and desliming stage where the slurry is split into coarse and fine fractions to increase desliming efficiency and prepare materials for conditioning. It is critical to achieve a high removal efficiency of the slimes before flotation as undepressed insoluble minerals in the flotation feed will compete with sylvite for collector adsorption thereby increasing reagent cost and reducing KCl recoveries. Two stages of scrubbing are often used in desliming circuits where the ore insol content is substantial, such as the PLM. Scrubbers are a series of highly agitated cells processing slurry at high densities designed to increase desliming efficiency by mechanically dispersing insoluble minerals coating the surface of the salt minerals by abrading the particles against one another. After scrubbing, desliming is performed by either size separation utilizing hydrocyclones, screens, and hydroseparators or by slimes flotation. The conventional flotation plants in Saskatchewan currently utilize size separation to remove the insoluble minerals before flotation. Desliming by slimes flotation is practiced at potash facilities around the world and has been part of previous circuit designs at plants in Saskatchewan. Conditioning for slimes flotation typically requires a selective flocculant to increase the size of the slimes particles prior to flotation and reduce the required collector dosage rates (Thompson & Huiatt, 1979). Slimes flocs are then conditioned with a collector and floated in conventional mechanical flotation cells. A chemical 11 dispersant is sometimes added to increase desliming efficiency, for either method, prior to the hydrosepartor or slimes flotation stage. Conditioning before sylvite flotation is typically performed separately on the fines and coarse fractions to optimize reagent usage. The coarse and fine streams are first conditioned with a depressant, to blind the residual clays, followed by a collector, to render the sylvite hydrophobic. An extender oil is also utilized in coarse conditioning to increase coarse particle recovery. Frother, at low dosage rates, is added immediately prior to the flotation cells to promote froth stability. The function of frother in potash brines is significantly different than in dilute aqueous solutions used in other flotation processes. Conditioning also occurs whenever new surfaces are exposed before flotation e.g. in the regrinding circuit after the flotation tails have been crushed to liberate the middlings. No further reagent addition is required prior to cleaner flotation of the rougher concentrate or scavenger flotation of the centrifuge effluent. Flotation is performed separately on the coarse and fines fractions or the streams are recombined and floated together in a rougher flotation stage. Combined flotation is carried out in Denver DR-style flotation cells followed by screening of the concentrate and cleaner flotation with either columns or mechanical cells to upgrade the concentrate screen undersize. Separate coarse and fines flotation is achieved using the Eriez HydrofloatTM or Denver DR-type flotation cells on the coarse fractions, and flotation columns for the fine fractions. The rougher flotation tails consist of unliberated middlings, coarse particles including liberated KCl that was too coarse to float, and minerals fines comprised primarily of NaCl. Vibratory screens are utilized on the rougher flotation 12 tails to reject the salt fines to final tails while the coarse solid fraction is re-ground with impactors or rod mills and re-floated in the regrind circuit. The combined flotation concentrate is leached with freshwater or NaCl-undersaturated brine to obtain desired product grade and is then centrifuged to obtain 3-5% product moisture. The centrifuge cake is dried using natural gas-fired rotary dryers or fluidized bed dryers. The dryer discharge is sized to desired product specifications by rotary multideck screens. The screened flotation product may be sold as standard or fines or further processed by compaction into coarse granular material and sold at a premium. Potash fines are managed by compaction to produce a granular product or by dissolution and recrystallization to produce standard-sized product. Fines management by crystallization is typically carried out in a co-saturated brine circuit with the crystallizer product blended into the standard-sized material to improve product grade. Crystallization is also used to obtain industrial-grade soluble muriate of potash, which has a specification of 1% NaCl maximum and low iron content (<5 ppm). Typical product specifications for standard, granular, and crystallizer products are shown in Table 1. Table 1 Typical potash product specifications (Nutrien, 2021) Product Minimum Grade (%K2O) P80 (micron) Standard 60.0 1409 Granular 60.0 3689 Soluble Fines 62.95 397 Chicklets 62.95 Greater than 90% of product retained on fractions between 25 mm - 3 mm. 13 The flotation tailings consist mainly of coarse halite and sylvite, while the desliming tailings are ultrafine with high insol content. Due to the difference in material handling characteristics of the two tailings streams, the coarse and deslime tailings are managed separately at most Saskatchewan potash operations with high ore insol content. Brine recovery before tailings disposal is critical to maximize plant recoveries, as any process brine lost to tailings is substituted with undersaturated brine. High return brine volumes required to maintain the plant brine balance may result in significant recovery losses as KCl is dissolved in the process of achieving full brine saturation at plant operating temperatures. To increase brine recovery, the coarse and deslime tailings are debrined and thickened using cyclones, centrifuges, and thickeners. The coarse salt tails report to the Tails Management Area (TMA) while deslime tailings report to the Fine Tailings Management Area (FTMA). 2.1.3 Reverse flotation in potash applications The process of desliming of potash ore by selective flocculation followed by flotation of the insoluble minerals was developed as an alternative to mechanical desliming processes in the 1950s and received commercial use in 1974 (Banks, 1980). Selective-flocculation flotation of potash slimes has since become a proven technology and has been implemented in potash operations globally, including in Canada, the U.K., Russia, and Spain (Aliferova et al., 2005). Globally, the slimes flotation process is much the same despite alternatives for chemical surfactants tailored to the mineral composition of the insoluble material. First, a selective polyacrylamide flocculant is added to the flotation feed to increase the apparent particle size of the 14 fine slimes. The aggregation of the slimes particles is deemed critical for two proposed reasons: (1) flocculation of slimes flotation feed reduces the available surface area and therefore, reduces collector consumption (Thompson & Huiatt, 1979), and (2) ultrafine slimes particles must be aggregated to acquire enough kinetic energy to overcome the liquid flux around an air bubble to make contact and successfully be recovered (Titkov et al., 1999). The efficiency of slimes flotation is increased when the flocculant is followed by a surfactant (Titkov et al., 1999). According to Titkov et al. (1999), the most efficient collector is a nonionogenic surfactant such as ethoxylated fatty acids, alcohols, phenols, and amines. Ethoxylated amines produce a high slimes flotation efficiency but are less selective as they also recover sylvite to the flotation froth. Titkov et al. (1999) also proposed the use of biodegradable ethoxylated phenols which have shown to have higher selectivity in slimes flotation than fatty acids. All patented processes and published data on commercial applications of slimes flotation utilize the conditioning process of selective flocculation followed by slimes collector addition. Two notable exceptions are a 1960 US patent filed by Dow Chemical Company, which proposes the use of an acrylamide flocculant as a collector and a frothing agent free of specific collector activity, such as MIBC to float insoluble slimes from low-insol slimes potash ore (Williams, 1960), and slimes flotation of ore from the Starobin deposit at the Belaruskali flotation plants which is performed with a polyacrylamide flocculant and no surfactant addition (Titkov et al., 1999). 15 2.1.4 Slimes flotation process as implemented at Agrium Vanscoy in PLM ore body Although slimes flotation has been successfully implemented at several potash operations, the effect of insoluble mineralogy in the various potash ores may produce variable flotation efficiencies. The Vanscoy potash operation, which processes ore from the Patience Lake Member close to Saskatoon, SK, commissioned slimes flotation for primary desliming in 1974, and successfully operated slimes flotation for 40 years (Perucca & Cormode, 1999). As described by Banks (1980), Cominco Ltd.’s Vanscoy plant was originally designed with a mechanical desliming circuit to treat ore with an insol content of 4.5%. However, after commissioning, wide variability in the mill feed insol content was encountered, ranging from 4.5 -8% insol. This variability led to operational difficulties in the desliming circuit resulting in substantial recovery losses from two primary sources: (1) high insol content in the sylvite flotation feed causing significant recovery losses, and (2) excessive fine KCl losses to the tertiary cyclone overflow, in the order of 12-15% of the potash in the mill feed (Banks, 1979). A project was initiated to install a hydroseparator in place of the tertiary cyclones when slimes flotation by selective flocculation was proposed as an alternative desliming method (Banks, 1979). Vanscoy began onsite laboratory testing of selective flocculation-flotation of slimes following the American Cyanamid process in 1972 (Banks, 1979). The American Cyanamid Company’s patented process for desliming sylvinite ores by selective flocculation and froth flotation of the slimes utilized a high molecular weight acrylamide polymer for selective flocculation followed by a cationic collector for the flocculated slimes (Brogiotti, 1974). The research progressed to a successful, large-scale pilot trial which processed approximately 25% of the mill feed through the 16 selective flocculation-flotation process. The plant conversion from mechanical desliming to slimes flotation was completed in 1974. The original slimes flotation circuit design was configured as follows: ore was crushed to minus 3.35mm and pulped to 70% solids with saturated process brine. The high-density slurry was scrubbed in attrition scrubbers for 5 to 10 minutes. The scrubber discharge was gently conditioned in a mixing launder with S 3731, a high molecular weight non-ionic polyacrylamide flocculant at 90 g/ton mill feed followed by Aero 870, a proprietary ethoxylated alkylamic alkylguanidine complex, also at 90 g/ton mill feed dosage rate (Banks, 1979). The slimes were floated in two banks of four Denver DR 300 cubic foot cells. Further optimization and expansion of the slimes flotation circuit in the following decades included the addition of a secondary stage of insol flotation to remove the insols liberated by the rotating action of the impellers in the primary slimes flotation stage (Perucca & Cormode, 1999). Typical metallurgical performance of the slimes flotation circuit processing ore with 5% insol content was 65% insol recovery to froth with 2.8% of the potash in the mill feed lost to the slimes froth (Perucca & Cormode, 1999). 2.2 Fine particle flotation 2.2.1 Flotation fundamentals Froth flotation is the process of physically separating and collecting valuable minerals in a slurry system based on the natural and induced wettability of the particles. A particle surface is hydrophilic if it is readily wetted by water, and if the surface repels water, it is hydrophobic. The collector mechanism is driven by the natural tendencies of air bubbles to selectively adhere to hydrophobic particles. The bubble-particle aggregates are transported to the top of the flotation 17 cell due to buoyancy forces and form a froth. The valuable mineral is recovered by the removal of the froth concentrate. Barring entrainment, the hydrophilic particles remain in the pulp where they are discarded or sent for further processing. Flotation concentration is referred to as direct flotation when the desired mineral is transferred to the froth; reverse flotation refers to the flotation of the gangue mineral from the desired minerals, which remain in the pulp. Flotation is a complex, three-phase system of air, water, and solids governed by physical and chemical factors. Chemical variables govern the hydrophobic and hydrophilic behavior of particles. Physical factors are a combination of ore characteristics such as particle size, composition, and liberation as well as machine-dependent factors such as air rate and bubble size (Wills & Finch, 2015). Flotation systems are generally comprised of two distinct zones, the collection zone, and the froth zone, with a separation interface. Overall flotation recovery can be understood as the summation of collection zone recovery and froth recovery, of which froth recovery has been found to be the limiting factor (Franzidis & Harris, 2010). Elimination of the lower efficiency of froth recovery is the basis of the design of reactor-style frothless flotation machines such as Woodgrove’s Direct Flotation Reactor (DFR). The process of a material being recovered to the flotation froth may be driven by three mechanisms: true flotation, hydraulic entrainment, or physical entrapment between bubble-particle aggregates. The separation efficiency between the targeted mineral and the gangue is dependent on the degree of entrainment or entrapment. True flotation is a selective process based on mineral surface properties, while entrainment and entrapment are unselective processes that can recover 18 both valuable and gangue minerals. Entrainment is a mechanical process by which fine particles are entrained in the wake of rising bubbles in the pulp but are not physically attached. Prior to flotation, the ore is comminuted to liberate the valuable mineral and conditioned with the necessary reagents to produce the desired flotation behavior. Possible flotation reagents include collectors, frothers, dispersants, and regulators (depressants, pH modifiers, and activators). Collectors increase the hydrophobicity of the desired mineral by forming a hydrophobic layer on the mineral surface, reducing the induction time for bubble-particle attachment. The purpose of regulators is to modify the action of collectors on mineral surfaces thereby governing the selectivity of the flotation process (Bulatovic et al., 2007). Depressants are used to suppress the flotation of gangue minerals by reducing the potential for hydrophobization (i.e., blinding of mineral surface to prevent collector adsorption) or rendering the mineral hydrophilic. Activators facilitate the interaction of the collector with the desired mineral surface. Frothers are heteropolar surface-active reagents that reduce the surface tension of water to enable the generation of smaller bubbles and the formation of a stable froth phase. The role of frothers in high ionic strength aqueous mediums is unique compared to typical water systems as the high salt content retards bubble coalescence. As described by Tomlinson (1963), the probability of particle flotation is governed by four probability factors, as shown in Equation (1): the probability of bubble-particle collision (Pc), the probability of attachment upon collision (Pa), the probability of emergence of bubble-particle aggregate to the froth (Pe), and the probability of recovery of the bubble-particle aggregate in the 19 froth to the concentrate (Pf). The probability of emergence is often expressed in terms of the probability of detachment defined as Pe = (1-Pd) (Yoon, 2000). 𝑃 = 𝑃𝑐𝑃𝑎(1 − 𝑃𝑑)𝑃𝑓 (1) The probability of collision is dependent upon particle size, bubble size, and hydrodynamic conditions in the cell (Yoon, 2000). Pa is also affected by hydrodynamics but is largely governed by surface chemistry. For bubble-particle attachment to occur upon collision, the thin water film separating the particle and bubble must be ruptured (Ye & Miller, 1989). The time required for disruption of the film is referred to as the induction time. The induction time is inversely proportional to the particle hydrophobicity/contact angle. For a strongly hydrophobic surface, the induction time is short, and if it is less than the time that the bubble and particle are together, then bubble-particle aggregation occurs, and the particle is floated. With a hydrophilic particle, the induction time is large and exceeds the bubble-particle contact time. Both contact angle and induction time are used to characterize surface properties related to flotation. Particles with a higher contact angle will demonstrate a stronger attachment to the bubble and more resilience to disruptive forces (Wills & Finch, 2015). 2.2.2 Ultrafine flotation The effect of particle size on flotation recovery is a well-studied process. It has been established that any flotation process will follow the “elephant curve” which qualitatively correlates flotation recoveries to a particular particle size distribution, presented by Lynch et al. (1981). The optimal size range has been shown to be approximately between 15-150um (Trahar & Warren, 1976). The 20 effect of particle size on flotation recovery is shown in Figure 4. Although the shape of the curve may stay the same, the optimum may shift for minerals of varying densities (Song et al., 2001; Trahar & Warren, 1976). Figure 4 Effect of particle size on flotation recovery (Lynch et al., 1981; Mankosa et al., 2018) Flotation performance decreases at both the coarse and fine end of the particle size distribution. The decline in the coarse fraction can be attributed to stresses acting on the loaded bubble particle aggregates including excessive turbulence, buoyancy limitations, and particle dropback from froth coalescence (Mankosa et al. (2018), Schubert (2008)). The decrease in ultrafine flotation recovery is attributed to slow flotation kinetics arising from insufficient collision rates and poor adhesion characteristics. In systems where the particle size is significantly smaller than the bubble size, bubble-particle collision is inhibited as particles are likely to be hydraulically swept past the bubble by flow streamlines near the bubble surface. Therefore, the mechanisms for low recovery efficiencies of coarse and fine particles can be attributed to the high probability of particle detachment and low probability of bubble collision, respectively. 21 Low efficiencies of fine particle flotation may be overcome with sufficient residence time or by increasing the power input to the flotation cell (Manouchehri & Farrokhpay, 2016; Pease et al., 2006; Schubert, 2008). Schubert (2008) argued that the ultrafine flotation kinetics are improved by intensifying the impeller specific power input to generate increased turbulence thus improving the probability of bubble particle contact. An increase in the recovery of finely ground Cu-Ni ore has been when reported when the impeller speed was increased from 450 to 1200 rpm (Farrokhpay et al., 2021). However, non-selective entrainment of finest particles into the froth lamellae is intensified by increasing power input which may result in decreased concentrate grade (Schubert, 2008). Two types of bubble-particle aggregation models have been proposed. Dobby and Finch (1987) proposed the kinetic model discussed above, in which attachment only occurs if the contact time of a particle on a bubble surface is greater than the induction time. In the thermodynamic model, attachment only occurs if the kinetic energy of a particle approaching a bubble is larger than the energy needed to disrupt the intervening water film and form a stable three-phase contact line (Scheludko et al., 1976; Yoon, 2000). The kinetic energy of a particle is dependent on the particle mass and system agitation/turbulence. The kinetic energy of particles can be increased by increasing the agitation rate or increasing apparent particle size by selective flocculation to promote successful bubble-particle attachment. The bubble-particle detachment probability in Equation (1) is negligibly small due to low inertia for fine particles less than 80um (Farrokhpay et al., 2021; Yoon, 2000). Sutherland (1948) was the 22 first to derive an expression of Pc from a stream function, which was valid only for bubbles much larger than those used in flotation practice. Gaudin (1957) also derived an equation for Pc using the stream function for the Stokes flow condition; however, this approach was only valid for very small bubbles. Recognizing the limitations of these two models, Reay and Ratcliff (1973) adapted the approach of Flint and Howarth (1971) to derive the following relationship, which has been verified experimentally for the flotation of fine particles (Yoon, 2000): 𝑃𝑐 ∝ (𝐷𝑝𝐷𝑏)2 (2) where Dp is the particle diameter and Db is the diameter of the bubble. As can be seen in Eq. (2), the flotation kinetics of fine particles may be increased by decreasing the bubble size, enlarging the apparent particle size through selective aggregation or carrier flotation (Ahmed & Jameson, 1985). The kinetics may also be increased by increasing the frequency of collision instances by intense mixing via specific cell design or increased energy input. 2.2.2.1 Flotation cell design to enhance fine particle flotation The development of reactor-separator type cells utilizes the design principle of separation of each stage of flotation – bubble-particle collision, separation of bubble-particle aggregates from the pulp, and froth collection – enabling the flotation stages to be optimized individually without impacting the optimal operating conditions of the other. The bubble-particle collision frequency 23 and intensity are increased by restricting the volume of collision and providing sufficient energy to ensure collision and attachment occur. Examples of this type of reactor-separator flotation machine include the Jameson cell, StackCellTM, and DFR (Direct Flotation Reactor). 2.2.3 Separate flotation of coarse and fine particles for fine particle recovery Flotation pulp usually contains particles with a wide size distribution which are subsequently floated in the same chemical and physical environment. Under the same conditions, ultrafine particles float more slowly than coarse particles making it more difficult to achieve high ultrafine flotation recoveries while still maintaining high flotation selectivity to gangue (Pease et al., 2006; Trahar & Warren, 1976). However, if the coarse and fine size fractions are separated prior to flotation to generate an ultrafine flotation feed with a narrow size distribution, suitable conditions can be established to increase fine particle flotation including extended residence time, increased energy input, and/or chemical modifier dosages. 2.2.4 Selective-flocculation flotation to improve fine particle flotation Fine particles in the flotation feed can be selectively aggregated by coagulation or chemical flocculation to enlarge the apparent particle size by controlling the surface chemistry with the addition of polymers, electrolytes, or changes in pH (Kitchener, 1972). The flocculated particle (floc) is then rendered hydrophobic by the addition of a collector followed by flotation of the aggregate (Brogiotti, 1974; Song et al., 2001). Floc flotation has been used successfully to recover copper from low-grade ores (Aruna & Shende, 2006). Selective floc flotation is the patented method for reverse flotation of insoluble minerals from sylvinite ore, which has been applied on a plant scale at Carlsbad and Vanscoy operations (Banks, 1979; Brogiotti, 1974). 24 2.2.4.1 Polymer flocculation Polymer flocculation involves the use of long-chain, high molecular weight organic polymer molecules that adsorb simultaneously onto many sites on a solid surface and forms bridges between particles. If the polymer dosage is low enough that the mineral surfaces are only partly covered, then bridging flocculation will occur as the polymer adsorbs onto several particles at once creating large aggregates (Fleer et al., 1993). However, if the polymer concentration is high enough such that the absorbed polymers cannot find adsorption sites on other particles the effectiveness of flocculants drops dramatically resulting in steric stabilization and poor settling characteristics (Napper, 1970). The densely adsorbed polymer creates a mechanical barrier (steric stabilization) that repels other particles and prevents particles from aggregating. Flocculants are most efficient at adsorption densities corresponding only to a fraction of the complete surface coverage (Kitchener, 1972). The addition of high molecular weight flocculants destabilizes the system of dispersion towards aggregation resulting in a mechanical network of flocculated particles that produce non-Newtonian behavior with a measurable yield stress and increases apparent viscosity (Klein & Pawlik, 2005). Polyacrylamide-based polymers are the most widely used industrial flocculants (Klein & Pawlik, 2005). Polyacrylamides are usually non-ionic or anionic, with anionic polymers being the most commercially available as they are often a higher molecular weight and lower production cost (Wills & Finch, 2015). Physical conditions that impact shearing, such as pumping and agitation, will cause permanent redispersion by breaking down flocs. The high ionic strength medium of potash processing will also affect flocculant adsorption behavior. Ma and Pawlik (2005) found the flocculation of quartz by guar gum, a non-ionic polysaccharide - high molecular weight flocculant, 25 was more efficient in solutions of structure breaking cations (KCl) and steric stabilization is more difficult to achieve even at high polymer doses. Their study showed that the higher the polymer molecular weight, the more significant the effects of the alkali metal cations on the adsorption efficiency (Ma & Pawlik, 2005). 2.2.4.2 Shear-flocculation Shear-flocculation, or hydrophobic flocculation, has been identified as another possible aggregation method suitable for fine flotation (Forbes, 2011). Hydrophobic flocculation arises as a result of hydrophobic interaction between particles and kinetic energy input (Song et al., 2001). With the exception of naturally hydrophobic minerals, selective hydrophobic flocculation is induced by special collectors adsorbing to the targeted mineral surfaces. Hydrophobic flocs are formed through mechanical conditioning, in which sufficient kinetic energy is provided to hydrophobic particles to overcome the energy barrier (Warren, 1975). This energy barrier between particles may be lower in high ionic strength solutions in which the electrical double layer (EDL) is compressed, and repulsive forces are screened by cations in the solution. The extent of hydrophobic flocculation is determined by the degree of hydrophobicity of the targeted particles and is enhanced by the addition of air bubbles. Shear aggregation has also been shown to occur with particles that have a natural degree of hydrophobicity (Bradshaw & Connor, 1996). A commercial application of shear-flocculation has not been used to date due to the extended residence time and power requirements of the method (Forbes, 2011). 26 2.2.5 Rheology in fine particle flotation The rheology of flotation has received growing attention as ore quality declines and more low-grade minerals are processed, which often include clays. Clays have complex rheological behavior that may affect some flotation slurries resulting in decreased concentrate grades and recoveries. Most mineral slurries are non-Newtonian (Klein & Pawlik, 2005). It has been shown that as the slurry solid content increases, the rheological behavior moves from Newtonian to non-Newtonian with a progressive appearance of a yield stress and an exponential increase in the slurry viscosity (Prestidge, 1997). Hydrodynamic interactions in the flotation cell may be impeded by high pulp viscosity and yield stress including gas dispersion, particle suspension, and bubble-particle contact, collision, and attachment (Cruz et al., 2019). Rheology is also an important froth property as it can affect both froth stability and froth mobility (Farrokhpay, 2012). The rheological behavior of slurry has been shown to increase gangue entrainment and decrease flotation recovery (Zhang et al., 2015). Effective fine particle flotation requires a stable, dispersed mineral suspension to meet the prerequisites for effective collision events between bubbles and single hydrophobic particles (Schubert, 2008). Ultrafine flotation processes are often run at low pulp densities (<10% solids w/w) to achieve the required state of dispersion and reduce entrainment into the flotation froth. As previously discussed, a dilute flotation pulp reduces the probability of collision, impeding flotation kinetics. However, interparticle forces dominate the rheological behavior of fine mineral suspensions such that a moderate volumetric solid content may significantly increase pulp viscosity. An increase in slurry viscosity will dampen turbulence intensity and therefore, also reduce the probability of collision, hindering flotation kinetics (Schubert, 2008). 27 Suspended solid particles increase the slurry viscosity and, consequently, decrease the turbulence intensity of a fluid system. Weiß and Schubert (1988) demonstrated the influence of increasing the volumetric concentration of fine particles on the pulp turbulent energy dissipation rate. Clear damping effects were observed which increased with increasing volumetric solid content. The magnitude of the reduction in turbulent energy dissipation rate was significantly greater for fine particles (1-4µm) than for 100-200 µm particles. From this work, Weiß and Schubert (1988) concluded that for a given power input and pulp density, every viscosity reduction directly increases the bubble-particle collision rate and thus improves the flotation kinetics. To validate the effect of rheology on turbulence damping, a comparison study was conducted of the suspensions with and without dispersant which found the use of dispersant was able to restore the turbulent energy dissipation rate (Weiß & Schubert, 1988). Gas dispersion may also be negatively affected by increased slurry viscosity (Cruz et al., 2019). For cavitation to occur in a liquid, it must overcome its natural cohesive forces maintaining the liquid continuum (Gogate & Pandit, 2001). Any increase in these forces will tend to increase the threshold pressure required to generate the cavitational event and hence the cavitational energy required. For liquids of higher viscosities, the number of cavitation events is reduced due to the increased pressure threshold, and therefore the overall cavitation effect is lower. The impedance of increased viscosities on cavitation is greater for non-Newtonian systems (Gogate & Pandit, 2001). 28 2.3 Column flotation Conventional column flotation technology was developed in Canada in the early 1960s and was first implemented in the cleaning of molybdenum concentrates (Boutin & Wheeler, 1967). Columns are ideally suited for the flotation of fine particles, typically less than 100 µm (Kohmuench et al., 2009). Column cells are most often designed with a height to diameter ratio greater than unity, giving the technology its name. Flotation columns are distinguished from other flotation machines by two features: the bubble generation system and the use of wash water (Finch & Dobby, 1990). Feed slurry enters the upper section of the flotation column and is distributed over the cross-sectional area using a feed distribution system. The feed descends counter-current to a rising swarm of fine bubbles that are generated by an internal or external air sparging device. The rising bubbles attach and collect the hydrophobic particles in the collection zone. The collection zone is analogous to the recovery zone in a mechanical flotation machine. The collected particles report to a froth that is stabilized by wash water. Wash water is added to improve the concentrate grade by washing down nonselective carryover of gangue that has been entrained by the bubbles into the froth. For this reason, the froth region is also sometimes referred to as the cleaning zone. Particles that do not attach to bubbles settled down through the collection zone and report to the column tailings. Flotation columns allow the development of a deep froth which, in combination with wash water, produces a higher grade product than conventional technologies (Kohmuench et al., 2009). Key operating and design variables of column cells include residence time (H/D ratio), froth carrying capacity, aeration rate, and wash water rate. The height to diameter ratio defines the 29 collection zone height in a column. The recovery zone is the volume above the spargers and below the froth depth where the bubble particle contact and attachment occur. From the design residence time, the volume of the flotation column is calculated based on the desired throughput. According to Ityokumbul (1996), a height to diameter (H/D) ratio of at least 5 is typically maintained, however, H/D ratios may be as high as 10 (Kohmuench et al., 2009). Columns in potash flotation applications often have H/D ratios less than 2 while achieving excellent flotation performance. The carrying capacity of a column is defined as the mass flow rate of concentrate divided by the cross-sectional area of the column and is a key design parameter for column flotation as it determines the cell diameter (Kohmuench et al., 2009). Maximum carrying capacity is achieved when all available bubble surface area has been consumed. Above this limit, any available floatable solids in the feed will report to the tailings. The carrying capacity is linearly related to the particle size and density and decreases with smaller feed particle size distributions due to the increased required bubble surface area. The maximum carrying capacity is typically determined empirically by increasing the dry solids’ feed rate to the column and measuring the corresponding concentrate mass flow rate. The product yield rate will increase linearly until the maximum froth capacity is reached, at which point the curve will plateau and may even decrease due to heavy froth loading (Figure 5). The required column cross-sectional area (diameter) can then be determined by dividing the expected froth tonnage by the max carrying capacity (Kohmuench et al., 2009). Carrying capacity is often the limiting factor on recovery and capacity for fine particle, high yield systems such as cleaner circuits (Finch & Dobby, 1990). 30 Figure 5 Example of a carrying capacity plot for a pilot scale test (Kohmuench et al., 2009) Aeration rate is measured as superficial gas velocity, Jg: the volumetric flow rate of gas divided by the column cross-sectional area, typically in units of cm/s. The use of Jg allows the comparison of aeration conditions for columns of different diameters. Industrial flotation cells typically operate at superficial gas velocities between 0.5-2.5cm/s (Dahlke et al., 2005; Finch & Dobby, 1990). Similarly, the wash water rate is referred to as the bias rate: the net flow of water through the cleaning zone over the cross-sectional area (Finch & Dobby, 1990). The bias flow is equivalent to the difference in the flow of water between tailings and feed. The positive direction of flow is downwards. 2.3.1 Cavitation in column flotation Several methods have been used to generate micron bubbles for flotation including hydrodynamic cavitation, ultrasonic oscillation, and electrolysis (Li et al., 2015). Cavitation is the process of in situ formation, growth, and subsequent collapse of gas/and or vapor-filled cavities in a fluid under the influence of external forces (Tao, 2005; Zhou et al., 2009). The first stage of cavitation i.e. the 31 creation of cavities is defined as nucleation (Zhou et al., 2009). Cavitation generates micron-sized bubbles on the surface of hydrophobic particles in a mineral suspension which has been shown to be beneficial to both coarse and fine particle flotation (Fan & Tao, 2008; Sobhy & Tao, 2013; Yoon & Luttrell, 1989). The nucleation of bubbles on the hydrophobic particle surface removes the rate-limiting steps of bubble collision and attachment for fine particle flotation. The microbubbles on the particle surface act as a secondary collector by attaching to larger, flotation-sized bubbles which generate the buoyancy forces required to quickly recover the aggregate to the concentrate (Li et al., 2015). Hydraulic cavitation refers to in-situ gas nuclei generation due to local pressure reduction associated with increasing fluid velocity. In mechanical cells, the strong shear induced by impeller agitation favors bubble formation by cavitation (Zhou et al., 2009). Hydraulic cavitation in column flotation is generated using external recirculation systems, such as the CISA/Microcel™ sparging system. The system pumps slurry from the base of the collection zone into a recirculation ring and through an inline contactor. Air (and water, in the case of potash flotation) are added before the sparging device, which shears the air and mixes it with the slurry into an aerated bubble dispersion that is injected back into the column (Lahey et al., 1998). Slurry addition during cavitation may result in the nucleation of fine bubbles on the hydrophobic mineral surface. Static mixers or venturis, such as the CavTube™, are typically used with external slurry recirculation sparging systems. The CavTube consists of an hour-glass-shaped tube through which a slurry/gas mixture is pumped using a centrifugal pump. The high flow velocity and specific nozzle geometry of the CavTube shear the air into flotation-sized bubbles and produces hydraulic cavitation to generate fine bubbles. Flotation columns with sparging slurry recirculation systems are better modeled as 32 perfectly-mixed reactor, rather than the traditional plug flow model, or as a combination of both models (Lahey et al., 1998). 2.4 System characteristics 2.4.1 Clay minerals Insoluble minerals in potash ores are typically a mixture of non-clay minerals such as dolomite, anhydrite, hematite, and quartz, and clay minerals including illite, chlorite, and septechlorite (Perucca, 2003). The upper size limit for clay minerals is defined as 2 microns, so virtually all clay particles can also be classified as colloids and are therefore dominated by inter-particle forces (Caenn et al., 2011). Clay minerals belong to a group of minerals known as phyllosilicates. The wettability, aggregation, dispersion, flotation, and thickening of phyllosilicates are strongly affected by their crystal structure and surface properties (Yin et al., 2012). Phyllosilicate content in slurries can strongly affect the rheology due to their characteristic non-spherical morphology and anisotropic surface charge properties (Ndlovu et al., 2011). In a reverse flotation application, flotation kinetics will be strongly influenced by the ultrafine properties of clay minerals. Reagent consumption may be high due to the large surface area of colloidal particles, and reagent adsorption may be affected by anisotropic features of their crystal structure. Phyllosilicates are composed of layers of silica tetrahedral sheets and alumina (or magnesium) octahedral sheets joined together in different proportions. The silica tetrahedron (T-layer) is comprised of a central Si4+ atom coordinated to four surrounding O2- atoms. The octahedral (O-layer) sheet consists of a central Al3+ or Mg2+ cation coordinated to either O2- or OH- (Burdukova et al., 2008). The sheets are tied together by sharing common oxygen atoms. The unit layers are 33 stacked face to face in what is known as the crystal lattice (Caenn et al., 2011). Phyllosilicates are built successively with increasing layers of T and O groups, with the addition of metal cations and water molecules for some groups (Figure 6). Phyllosilicates derive their name not only from how the silica tetrahedra are bonded together in sheets but also from the “platy” or “leaf-like” morphology of many of the minerals in this family (Greek: phyllon, leaf) (Hurlbut & Klein, 1977). The sheets of the unit layer are formed by stable, covalent bonds while the layers in the crystal lattice are held together by van der Waals forces and secondary valences (Caenn et al., 2011). Consequently, the lattice cleaves readily along the basal surfaces, forming the characteristic tiny mica-like flakes. There are three main modes of particle aggregation for platy minerals: face-face (F-F), edge-face (E-F), and edge-edge (E-E) (Luckham & Rossi, 1999). F-F associations lead to the formation of laminar structured aggregates with a low yield stress, while E-E and E-F form voluminous ‘house of cards’ structures that have significant rheological implications including persistent yield stress and thixotropic (time-dependent shear thinning) behavior. Phyllosilicates can be classified into the following groups given in Hurlbut and Klein (1977): Serpentine, Clay, Swelling Clay, Mica group. As described by Burdukova et al. (2008), the simplest scenario consists of a single T-layer bonded to an O-layer e.g. kaolinite, which falls under the serpentine group. The addition of another T-layer forms minerals in the clay group which includes talc. The successive bonding between two clay layers (T-O-T) by a metal cation results in the mica group minerals, e.g., muscovite. The swelling clay group is derived by the addition of a metal cation and water molecules between successive T-O-T layers. 34 Figure 6 Depiction of the classification of phyllosilicate minerals (Hurlbut & Klein, 1977) The wetting characteristics of phyllosilicates vary significantly with composition. The edge surfaces are hydrophilic with significant sites for hydrogen bonding of water molecules. The silica face surfaces can have low polarity in some cases and exhibit a hydrophobic state (Miller et al., 2016). In a non-potash application of clay flotation, Yuehua et al. (2004) showed significant improvement in the flotation of kaolinite by aggregating kaolinite plates with a specific polyacrylamide flocculant designed to adsorb on the hydrophilic, octahedral plane to expose the hydrophobic Si-O tetrahedral plane thus increasing the overall aggregate hydrophobicity. 2.4.2 Flotation implications of a high ionic strength suspension Potash processing is conducted in a co-saturated KCl-NaCl-H2O brine. The ionic strength of co-saturated KCl-NaCl brine is approximately 6M at 25℃. The flotation behavior in a highly concentrated electrolytic system is distinct from that of conventional flotation processes in dilute water-based systems. Figure 7 depicts the solubility equilibrium diagram for a KCl-NaCl-H2O 35 system, developed using data from Linke and Seidell (1958). KCl concentration decreases sharply with decreasing temperatures while the NaCl solubility increases marginally with lower brine temperature. This difference in solubility is what drives the crystallization process for the production of white muriate of potash. Figure 7 Equilibrium diagram of KCl-NaCl-H2O system (Linke & Seidell, 1958) The composition of the brine is determined by the mineralogical composition of the ore being processed and the system temperature. The presence of other salts in the solution affects the system equilibrium. Magnesium cations may be present in solution at operations with carnallite ore but are at negligible concentrations in the system considered. Mg2+ concentrations over 2-4% w/w increase brine viscosity, which may affect the flotation system behavior (Perucca, 2000). KCl saturation is most affected by other salts in solution as less hydrated ions are salted out by more hydrated ones in a competition for free water. The potassium ion is characterized by negative hydration and is therefore particularly sensitive to any changes in the ionic composition of the 0501001502002503003500 10 20 30 40 50 60 70 80 90 100 110Grams per litre of solutionTemperature (℃)NaCl KCl36 brine. Brine composition determines brine density and viscosity and influences the gas dispersion behavior and colloidal chemistry of flotation reagents. Strong electrolyte solutions compress the electrical double layer (EDL) by altering the zeta potential which will, in turn, change the suspension state of aggregation-dispersion. The adsorption of alkali metal cations, such as K+ and Na+, on negatively charged surfaces takes place through electrostatic attraction without forming permanent chemical bonds and therefore negatively charged surface sites are not neutralized but only partially screened (Ma & Pawlik, 2005). The extent of the screening is a function of the electrolyte concentration. The higher the concentration of the electrolyte, the more efficient the screening of the negative surface charge and therefore the lower the zeta potentials. Lower zeta potentials present less repulsive energy around charged particles thereby promoting aggregation by attractive van der Waals forces and allowing hydrophobic forces to dominate bubble-particle interaction behavior. Yoon and Sabey (1989) observed that compression of the EDL around hydrophobic particles increases bubble-particle adhesion, resulting in a higher flotation response. Laskowski and Iskra (1970) found lower induction times and improved attachment efficiency for bubble-particle aggregates in the presence of KCl in solution are due to the destabilization of wetting films on hydrophobic minerals in salt solutions. The effects of a high ionic strength medium on flotation are complex. Dissolved species may reduce or increase the surface charge of particles consequently altering states of aggregation and electroviscous effects. Hydration forces are also altered by dissolved ions resulting in changes to the suspending fluid viscosity. Ions in solution have also been shown to affect the adsorption 37 efficiency of polymers including flocculants and dispersants (Ma & Pawlik, 2005). Inorganic salts have been shown to suppress coalescence and can substitute for frother if at sufficient concentrations (Quinn et al., 2007). Quinn et al. (2014) found the critical ionic concentration for NaCl solution to be 0.31M to achieve minimum bubble size. The study found that 0.4M NaCl was approximately equivalent to a frother dosage of 10ppm. However, although high concentrations of organic salts can inhibit coalescence and increase gas holdup, they generally cannot form a stable froth without the presence of fine hydrophobic particles (Lekki & Laskowski, 1975). Xstrata’s Raglan concentrator and the reverse flotation system considered in this paper both operate successfully without frother addition. 2.4.2.1 Viscosity effects of structure-making and breaking ions in solution Seasonal plant brine operating temperatures range from 25-36℃ and the process brine is always at complete KCl-NaCl co-saturation. NaCl saturation increases with lower brine temperature while KCl saturation concentration decreases. Conversely, in the warmer brine conditions the KCl saturation increases while the NaCl concentration decreases. Potassium and sodium ions in the solution have an opposite effect on the solution viscosity (Figure 8). Sodium ions increase the relative viscosity with increasing concentration while potassium cations marginally decrease the solution viscosity. These viscosity effects can be understood in terms of KCl and NaCl ions’ water structure-making and structure-breaking capabilities (Ma & Pawlik, 2005). Frank and Wen (1957) and Gurney (1953) analyzed the behavior of ions in terms of their water structure making and water structure breaking capabilities. As summarized by Ma and Pawlik (2005), water structure-making ions are 38 small, strongly hydrated ions which reinforce the normal structure of water resulting in the disappearance of the structure breaking-region around the ion. Solution viscosity increases significantly as the concentration of structure-making ions increases. NaCl is an example of a structure-making ion. Conversely, structure-breaking ions are large, poorly hydrated ions that disturb the ice-like structure of water and generate an extensive region of structure-breaking around the ion. Structure-breaking ions decrease the viscosity of aqueous solutions. KCl is an example of a water structure-breaking ion. Structure-makers/breakers may affect the efficiency of polymer attachment; therefore, reagents in brine systems may behave differently than expected. Ma and Pawlik (2005) found structure-breakers control the system response to polymer adsorption, but structure-makers have the largest impact on fluid viscosity. Figure 8 Viscosity of alkali halide solutions as a function of concentration (Haynes et al., 2016) 39 Kestin and Shankland (1984) found that the viscosity of a saturated NaCl brine decreased with increasing temperature (20-65C). This work also found that increasing concentrations of sodium ions increase the solution viscosity. Kestin et al. (1981) completed a second study with varying KCl concentrations over the same temperature range and found that the kinematic viscosity decreased with increasing temperature. Based on the findings of Kestin and Shankland (1984), as well as the water structure making the information presented in the previous section, plant brine can be assumed to display seasonal variability in apparent viscosity which may also impact flotation efficiencies. 40 Chapter 3: Methodology 3.1 Pilot installation A twelve-inch diameter by five-meter-tall pilot column with a cavitation tube sparger system was installed in the plant to trial slimes flotation of an ultrafine, high clay stream. The column skid was equipped with automatic PID control of column level (froth depth) using a control valve on column tailings in combination with a level transmitter. Manual control and local flow indication of wash brine and sparger airflow were provided. The sparging system configuration included a rubber-lined centrifugal slurry pump with variable speed drive and local pressure indication of backpressure before the cavitation tube. The circuit consisted of two 45-gallon plastic tanks with 0.5 hp agitators utilized as mixing tanks. The slurry transfer was completed using a series of variable speed, Masterflex I/P peristaltic pumps. The collector was prepared to a 3% w/w strength solution by melting the ethoxylated amine, weighing the required mass, and then mixing the amine with condensate (water at 60℃) for one hour. Mixing was performed using a 0.5 hp agitator in a 45-gallon plastic drum to ensure complete dispersion into the solution. As per the vendor recommendation, the prepared collector was stable in water at room temperature, so the collector was prepared in 30-gallon batches and stored for up to two weeks at a time. To validate the assumption of storage life of the collector, the effect of aged collector solution against fresh collector was trialed and no measurable difference in performance was observed. The pilot circuit was fed from a slipstream of the process via a flush-out installed on the side of a vertical pipe. The feed sample was assumed to be representative as segregation was not expected 41 to have occurred due to mixing in the pump and the viscous nature of the high-density slurry resulting in slow settling characteristics. Wall effects were deemed to be negligible. A simplified process diagram of the circuit configuration is shown in Figure 9. The circuit feed flow was manually controlled with a ball valve. The flotation feed was first added to a dilution tank where it was mixed with fully saturated process brine to achieve the desired pulp density. Slurry dilution before conditioning was required due to operational constraints. The required slurry feed and corresponding brine flowrates to the dilution tank were significantly higher than the column operational targets to prevent sand-out issues in the feed line. The high flow rate in the sample line also ensured the column feed was representative of the process by preventing settling in the line. The required flotation column feed flowrate was pumped ahead to the conditioning tank while the excess slurry overflowed the dilution tank to the plant sump. The ethoxylated amine was added to the diluted feed in the conditioning tank using a Masterflex L/S peristaltic pump. The required reagent flow was set manually using a graduated cylinder and stopwatch to determine the required pump speed to achieve the correct collector dosage. The level in the conditioning tank was maintained to ensure approximately five minutes of conditioning time. Conditioning was performed at lower densities (5-15% w/w) which may be typically sub-optimal for reagent-particle contact; however, due to the high ultrafine content and viscous nature of the slurry, the conditioning system was likely superior to conditioning at high pulp density. 42 The conditioned feed was pumped into the column feed distributor at an average rate of 19 lpm. Froth wash, froth depth, recirculation pressure, and air rate were set at desired setpoints. The process was given 45 minutes (3x residence time) to reach steady-state between changes to operating conditions. All relevant operation data was recorded before sampling at each test condition. Samples of the column tailings (tails) and concentrate (con) were collected simultaneously followed by the column feed, as this sample collection disrupts the steady-state operation. The concentrate and tailings samples were a composite of two cuts, collected two minutes apart to minimize the effect of process variations i.e., normal fluctuation in the column level. The time for sample collection was recorded for each stream to determine the mass flow rate for material balancing. The slurry temperature of each stream was recorded during sample collection to determine the KCl and NaCl composition of the process brine. The samples were collected in insulated thermoses and pressure filtered immediately after sampling to minimize sample contamination from crystalized KCl due to cooling of the brine. The filter cakes were dried in an oven at 130℃ then pulverized using a Retsch ZM200 centrifugal mill to ensure sample homogenization prior to assaying. Compositional analysis of the solids for % KCl, % NaCl, and % insolubles was completed on the feed, concentrate, and tailings solids. Percent solids by weight were also determined for each stream. Brine temperature, wet cake weight, and known KCl-NaCl-H2O saturation curves were utilized to correct the compositional analysis and percent solids for residual KCl and NaCl in the remaining brine in the filter cake. The samples were brine-corrected rather than alcohol-washed to 43 minimize analytical errors arising from incomplete brine displacement due to the high moisture content of the filter cakes. Wet-chemistry methods were utilized to complete compositional analysis for all samples due to interference from the high insol concentration using typical X-ray fluorescence (XRF) methods. KCl concentration was determined by sodium tetraphenylborate (STPB) titration and NaCl content by silver nitrate titration. Insoluble content was determined gravimetrically following the Saskatchewan Potash Producers Association standard procedure. The assays and the mass balance data were reconciled using Algosys Bilmat software. Samples collected for particle size analysis were processed differently to ensure accurate results. Feed, concentrate, and tails samples were collected following the procedure outlined above. The samples were alcohol-washed during filtration to prevent caking from moisture evaporation, then bagged without drying and sent for analysis. The samples were re-slurried in alcohol, and an aliquot was pipetted while agitating the slurry for analysis using a Malvern Mastersizer 2000 Particle Size Analyzer. X-ray diffraction (XRD) analysis was performed in triplicate using an Xpert PRO MPD to determine the composition of the insoluble minerals on retained samples of feed, concentrate, and tailings. Retained samples from high and low separation efficiency operating conditions were analyzed to determine if certain minerals were selectively concentrated. The feed samples were washed before analysis as the residual salts interfere with the XRD conclusions. 44 Figure 9 Simplified process flow diagram of pilot circuit installation 3.2 Experimental procedures Pilot flotation was run with minimal reagent addition to reduce operating costs and prevent froth stability issues. Flocculant was not added as part of the pilot reagent scheme but is utilized upstream as part of the typical processing of the feed stream to the pilot flotation column. The flocculant used in this process is Magnafloc 155, a high molecular weight anionic polyacrylamide flocculant at a dosage rate of 17 g/T. Frother was not added to any tests, as the froth was deemed to be sufficiently stable. No depressants or dispersants were added to the flotation process. Collector was only used at 60 g/T in an on-off capacity; dosage optimization was not completed as part of this sample campaign. 45 Initial exploratory test work was completed to evaluate the effect of froth depth, and froth wash on flotation performance. Froth wash was found to significantly decrease the insol rejection with little increase in selectivity and was difficult to operate as the wash manifold would salt off every 30 minutes. Froth depth was found to have no impact on flotation performance. Froth depth was kept constant at 10 inches and the froth wash was not used for the optimization campaign. The air rate was set at a Jg of 0.35 cm/s due to operational constraints. The exploratory results are not included in this report. The optimization campaign investigated the relationship between recirculation pump pressure, slimes collector addition, and feed percent solids. Recirculation pressure can be interpreted as an indication of the magnitude of pressure drop across the cavitation tube A one-factor-at-a-time experimental design was used to complete the optimization test work. Tests with high/low pulp density and with/without slimes collector were run in triplicate at each pressure setpoint (40, 30, 20, 12 psi). The slimes collector dosage was maintained at 60 g/T. Baseline samples to determine column operation at optimum operating conditions were collected at the start of each day of testing. The sustained collection of baseline flotation data facilitated the assessment of seasonal variations in plant performance on insoluble flotation performance. After the optimization campaign was completed, the required residence time was determined by increasing the feed flowrate systematically at optimum pulp density. Maximum carrying capacity was determined by increasing the feed solids rate to the column through both increases in pulp density and flow rate and determining the maximum concentrate mass flow rate. 46 3.3 Data analysis The reconciled sample data was analyzed using Excel and Minitab to determine the effect of the variables listed above as well as the influence of uncontrolled factors. 3.3.1 Fitting system curves with the Vera function System curve data including grade-recovery curves and selectivity curves were fit using the Vera function developed by Vera et al. (2000) following the method described by Napier-Munn (2014): 𝑅 = 𝑅𝑚𝑎𝑥 − asinh[𝑏(𝐽)] (3) where R = KCl recovery to the tailings (%) J = Insol rejection to the concentrate (%) Rmax = maximum KCl recovery at J = 0 a, b = parameters that describe the shape of the curve. Solver was used to minimize the sum of squares (SST) to fit the equation to the pilot data. The standard error (Eq. (4)) of the fit suggests the uncertainty of the prediction of KCl recovery by the model: 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐸𝑟𝑟𝑜𝑟 (𝑆𝐸) = √𝑆𝑆𝑇/(𝑛 − 3) (4) where SST = sum of the squared residuals n = number of data points 3 = number of parameters 47 3.3.2 Comparing two trendlines The method of comparing two trendlines, as described by Napier-Munn (2014), was utilized to determine the effect of collector addition on KCl recovery and insol rejection with increasing recirculation pressure at high and low pulp densities. The method involves the statistical comparison of two recovery-grade linear regression lines corresponding to two operating conditions (Napier-Munn, 1998). The method explicitly tests the separation of two lines in the y-direction if they are shown to be statistically parallel. The procedure can be summarized as follows, after Napier-Munn (2014): 1. Test for linear correlation. Determine if the correlation coefficients are statistically significant at the required confidence level. If P(R) < 0.05, then the correlation coefficients are statistically significant. 2. Using an F-test, compare the residual variances between the two trendlines. The null hypothesis is that the variances are the same; P(F) < 0.05 indicates failure of the test therefore, we reject the null hypothesis. If the variances are different, this violates the requirement for homoscedasticity required for the t-test. However, the t-test is robust to departures from this requirement so the analysis may proceed, using caution to examine the feasibility of the method’s conclusion. 3. Test the differences of the gradients using an F-test. The null hypothesis is that gradients are the same. If P(F) > 0.05, then we cannot reject the null hypothesis and must therefore accept it. If the null hypothesis is accepted, this means the lines are statistically parallel. 4. Test the differences in the intercepts using an F-test. The null hypothesis is that the intercepts are the same. If P(F) < 0.05, then we must reject the null hypothesis. If the 48 intercepts are not the same, this suggests there is a real, statistically significant separation between the regressions. 5. If the gradients are the same but the intercepts are different (parallel lines) then we can estimate and test the separation between the lines. This is done by estimating the separation of the lines, assuming they have the same mean gradient. The significance is determined using a t-test and confidence intervals are calculated on the separation. 49 Chapter 4: Results 4.1 Feed characterization The average flotation feed composition over the 78 test conditions was found to be 46.6% insol, 19.9% KCl, and 33.5% NaCl. The average slimes flotation feed particle size key indicators are shown in Table 2. The average XRD analysis of the insoluble mineral fraction of the slimes flotation feed is shown in Table 3. The typical slimes flotation feed, con, and tails particle size distribution is shown in Appendix B.1. Table 2 Particle size distribution of slimes flotation feed from Malvern Particle Size Analyzer P80 (um) 27.5 P50 (um) 10.9 Passing 7 µm (%) 38.3 Table 3 XRD analysis of slimes flotation feed Mineral Chemical Formula Quantitative (%) Dolomite CaMg(CO3)2 43.8 Quartz SiO2 13.85 Orthoclase KAlSi₃O₈ 1.9 Halite NaCl 1.4 Sylvite KCl 0.9 Anhydrite CaSO4 9.15 Muscovite KAl2(AlSi3O10)(F,OH)2 18.5 Clinochlore (Mg,Fe++)5Al(Si3Al)O10(OH)8 9.35 Kaolinite Al2O3 2SiO2·2H2O 1.9 Approximately 50% of the solid content in the flotation feed is insoluble minerals and of the insoluble minerals, the XRD analysis revealed that approximately 30% are clays. The clay concentration aligns closely with the particle size analysis which shows approximately 40% passing 7 µm in the feed stream, given experimental error and analytical detection limits when 50 analyzing clay minerals. It is expected that the XRD analysis and the colloidal fraction of the particle size distribution align as clays are classified by their mineral composition and colloidal particle size. Additional insoluble minerals may also fall into the colloidal size fraction contributing to the observed difference between the methods. The particle size analysis (Table 2) reflects an ultrafine particle size distribution with a P80 of 27.5 µm and 40% passing 7 µm and retained on 1 µm (detection limit for Malvern). The particle size distribution is narrow with 40% of the solid content falling into the colloidal particle range. However, the measured particle size distribution may not represent the true feed to the column as flocs will be degraded by the handling process. The major mineral phases were identified as dolomite, and clay phyllosilicates, with lesser amounts of quartz and anhydrite. Muscovite, which falls into the mica group, makes up most of the phyllosilicate minerals with the remainder being clinochlore (chlorite) and trace amounts of kaolinite (serpentine group). Notably, swelling clays and significant amounts of serpentine clays were not found. 51 4.2 Pilot column 4.2.1 Selectivity curves – Insol rejection and KCl recovery The selectivity curve of KCl recovery with insol rejection under optimal pulp density conditions with no collector addition is shown in Figure 10. The KCl recovery to tailings is predicted to be 83.4% at 85.0% insol rejection to the concentrate. The standard error is 6.4% indicating the uncertainty of prediction in the model is approximately ± 12.6%. Figure 10 Selectivity curve of KCl recovery with insol rejection with no collector and at an optimum pulp density 4.2.2 The effect of slurry density The effect of pulp density on collector-less slimes flotation selectivity is shown in Figure 11. Low pulp density is defined as less than 6.0% w/w solid content. Optimal pulp density is defined as solid content greater than 7.5% w/w but less than 12.0% w/w. 52 Figure 11 Selectivity curve of KCl recovery with insol rejection for slimes flotation without collector at optimal and low pulp densities The low percent solids and high percent solids data points were fit using the Vera model, as described above. The standard error of the low percent solids model was 2.9%. At low pulp density, 85.0% insol rejection to the concentrate corresponds to a 69.3 ± 5.8% KCl recovery to tailings. The standard error of the optimal percent solids model was 2.6%. At optimum percent density, 85.0% insol rejection to the concentrate corresponds to 84.2 ± 5.2% KCl recovery to tailings. Low pulp densities resulted in decreased selectivity resulting from lower overall insol rejection with increased KCl entrainment to the froth. Higher flotation feed densities were trialed but generated inoperable conditions in the pilot. Higher feed densities (15% w/w) resulted in the loss of level control due to high mass pull to the concentrate at optimum recirculation pressure. The air rate was not able to be reduced to bring 53 down the mass pull due to instrument constraints; therefore, high pulp density analytical results are not available. 4.2.3 Recirculation pressure The averaged pilot data showing the effect of increasing recirculation pressure, pulp density, and collector addition on insol rejection, KCl recovery, and separation efficiency is shown in Figure 12, Figure 13, and Figure 14, respectively. Separation efficiency is defined as the recovery of valuable mineral minus the recovery of gangue. Figure 12 Effect of increasing recirculation pressure on insol rejection to the concentrate at high and low pulp densities with and without collector addition 54 Figure 13 Effect of increasing recirculation pressure on KCl recovery to the concentrate at high and low pulp densities with and without collector addition As shown in Figure 12 and Figure 13, insol rejection increases with increasing recirculation pressure (pressure drop across cavitation tube) across all operating conditions tested, at high and low density, and with and without collector addition. KCl recovery decreases with increasing recirculation pressure for all conditions tested. Statistical analysis was conducted on flotation performance at high and low pulp density to determine the effect of the collector on the response variables across various recirculation pressures. The average results are presented in the graphs above and the unaveraged data used to compare trendlines is shown in Appendix A. 55 Using the method of comparing two trendlines it can be concluded that collector addition increases insol rejection and decreases KCl recovery at both high and low pulp density for all recirculation pressures. The insol rejection improvement from collector addition is greater at lower percent solids. At high pulp density, collector addition increases insol rejection by 6.1 ±4.0% but decreases KCl recovery by 3.7 ± 3.4% across all recirculation rates. We can be 90% confident that collector addition increases insol rejection by at least 3.0% and decreases KCl recovery by at least 1.1%. At low pulp density, the collector increases insol rejection by 24.6 ± 7.6% but decreases KCl recovery by 13.4 ± 5.8% across all recirculation rates. We can be 90% confident that collector addition increases insol rejection by at least 18.8% and decreases KCl recovery by at least 9.0%. A summary of the results from the statistical analysis of comparing two trendlines is shown in Appendix A. Figure 14 Effect of increasing recirculation pressure on separation efficiency to the concentrate at high and low pulp densities with and without collector addition 56 Separation efficiency increases with increasing recirculation pressure with a local maximum around 30 PSI, for all trends except the low pump density without collector addition. From Figure 14, it can be observed that pulp density has the largest impact on separation efficiency with a 30-45% increase in separation efficiency at higher pulp density. Collector addition significantly increased the separation efficiency, by approximately 22%, at low pulp density. At high slurry density, the effect of collector addition on separation efficiency is significantly less, with a 5% increase in separation efficiency observed at 30 PSI. Although the KCl recovery is reduced with collector addition, the magnitude of the increase in insol rejection is larger than the decrease in KCl recovery, resulting in higher separation efficiency. 4.2.3.1 Mineralogical composition of insoluble fraction of feed, concentrate, and tailings samples at high and low recirculation intensity XRD analysis was performed on feed, concentrate, and tailings samples from a high insol rejection test and a low insol rejection test to determine the compositional analysis of only the insoluble mineral fraction. Algosys Bilat was used to reconcile the XRD results with the remainder of the sample and operating data to generate a mass balance. The corresponding mass balances for the low insol rejection efficiency tests are shown in Table 4 and Table 5, respectively. 57 Table 4 Low insol rejection mass balance and composition of insoluble mineral fraction from XRD analysis Feed (%) Con (%) Tails (%) Recovery to Con (%) Rejection to Tails (%) KCl 24.1 0.3 32.8 0.4 99.6 NaCl 21.9 0.9 29.6 1.2 98.8 Insolubles: 46.7 90.5 30.7 51.8 48.2 Dolomite CaMg(CO3)2 44.8 48.7 40.6 56.3 43.7 Quartz SiO2 13.5 13.6 13.4 52.3 47.7 Anhydrite CaSO4 11.9 11.0 12.9 47.8 52.2 Muscovite KAl2(AlSi3O10)(F,OH)2 21.1 21.4 20.7 52.6 47.4 Kaolinite Al2O3 2SiO2·2H2O 0.9 0.1 1.7 6.1 93.9 Orthoclase KAlSi₃O₈ 1.7 1.4 2.0 43.1 56.9 Clinochlore (Mg,Fe2+)5Al(Si3Al)O10(OH) 6.3 3.9 8.8 32.3 67.7 Table 5 High insol rejection mass balance and composition of insoluble mineral fraction from XRD analysis Feed (%) Con (%) Tails (%) Recovery to Con (%) Rejection to Tails (%) KCl 8.3 0.9 31.5 8.3 91.7 NaCl 20.9 8.1 60.6 29.6 70.4 Insolubles: 68.5 86.4 12.5 95.6 4.4 Dolomite CaMg(CO3)2 44.2 44.1 46.5 95.3 4.7 Quartz SiO2 14.0 14.1 12.6 96.0 4.0 Anhydrite CaSO4 6.9 6.7 12.7 91.9 8.1 Muscovite KAl2(AlSi3O10)(F,OH)2 19.0 18.9 20.8 95.2 4.8 Kaolinite Al2O3 2SiO2·2H2O 1.7 1.7 1.1 97.2 2.8 Orthoclase KAlSi₃O₈ 1.5 1.5 2.3 93.2 6.8 Clinochlore (Mg,Fe2+)5Al(Si3Al)O10(OH) 11.5 11.8 3.9 98.5 1.5 Chamosite (Fe2+,Mg)5Al(AlSi3O10)(OH)8 1.1 1.1 0.1 99.6 0.4 58 4.2.3.2 Power intensity The flotation performance of the pilot column was qualitatively assessed during operation by collecting a sample of the column tailings, waiting for the solids to settle, then decanting the brine, and visually observing the content of the remaining solids. During this process, it was noted that there were flocs in the tailings of the pilot column and that the solids settled quickly allowing the brine to be decanted within minutes. In a separate experimental program, a flotation machine with greater power intensity was trialed on the same feed stream and operational conditions (reagents, pulp density, air rate, etc.) as the pilot column. When attempting to perform the same qualitative assessment on the high energy system, it was noticed that the tailings were fully dispersed and would not settle, even after extended periods of time (15-30 minutes). A comparison of the KCl recovery with insol rejection for both flotation systems is shown in Figure 15. The relationship between recirculation pressure and separation efficiency was investigated further by determining the change in power intensity with recirculation pressure. The power intensity, defined as watts per cubic meter of slurry, was calculated for the pilot and the separate equipment trial to compare the effect of energy intensity on separation efficiency. For the trend shown in orange in Figure 15, the sparger system pressure was kept constant at 30 psi, corresponding to a power intensity of 11,000 W/m3. In comparison, the maximum power intensity of the pilot unit, at 40 psi recirculation pressure, was only 1500 W/m3. 59 Figure 15 Selectivity curve of KCl recovery with insol rejection at an optimum pulp density 4.2.4 Residence time & carrying capacity To determine the maximum carrying capacity, the product solids mass flow rate was plotted against the feed solids tonnage divided by the column cross-sectional area. The feed tonnage was normalized for insoluble grade to account for any variations in column feed grade. As shown in Figure 16, the maximum carrying capacity was found to be 0.9 tph/m2. 60 Figure 16 Column concentrate solids rate with increasing feed loading rates The separation efficiency was plotted against the tailings residence time under optimum conditions to determine the required residence time to maximize insol rejection and KCl recovery (Figure 17). Tails residence time was used instead of feed residence time due to the high mass pull to the concentrate. A polynomial regression was fit to the data points. The tailings residence time corresponding to the maximum separation efficiency was found using Excel’s Solver tool to be approximately 17 minutes. Figure 17 The effect of tailings residence time on separation efficiency y = 1/(0.6047(1/x)2 - 1.1803(1/x) + 1.6979) 61 4.2.5 Froth characteristics The insol flotation concentrate was observed to be wet and mobile, with small, well-coated bubbles. Under optimal operating conditions, the froth was not voluminous or tenacious and was able to be redispersed into the mineral suspension with moderate agitation after approximately 30 minutes. Figure 18 shows the slimes flotation concentrate under optimal operating conditions. Figure 18 Image of slimes flotation concentrate under optimal operation conditions The increase in brine recovery to the froth with increasing insol rejection is shown in Figure 19. No relationship was found between brine recovery and NaCl or KCl recovery to the concentrate. The findings indicate that the brine is associated with the clay minerals, and results in a wet, mobile froth, as observed. However, despite the significant brine recovery to the froth, the entrainment of NaCl and KCl was not shown to increase with brine recovery. 62 Figure 19 The effect of increasing insol rejection to the concentrate on brine recovery 4.2.6 Regression modelling of overall insol rejection and KCl recovery responses Multiple linear regression in Minitab was performed on the optimization data to validate the findings of the effect of recirculation pressure, collector, and pulp density on insol rejection and KCl recovery. Appendices C.2 and C.3 contain the relevant analysis. The regression models are shown in Eq. (5) and Eq. (6): 𝐼𝑛𝑠𝑜𝑙 𝑟𝑒𝑗. = 0.1471[𝐶] + 0.0105[𝑃] − 0.0125[𝑇] + 0.0263[𝐶𝑤] + 0.0092[𝑓𝑖] + 0.0089[𝑓𝑘𝑐𝑙] − 0.0589 (5) 𝐾𝐶𝑙 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 = −0.1014[𝐶] − 0.0046[𝑃] + 0.0105[𝐶𝑤] − 0.0033[𝑓𝑖] − 0.0127[𝑓𝑘𝑐𝑙] + 1.3341 (6) where C = slimes collector (off/on; 0/1) P = sparger back-pressure (PSI) T = Brine temperature (℃) Cw = feed % solids (% w/w) fi = feed % insol (% w/w) fkcl = feed % KCl (% w/w) 63 The insol recovery model adjusted R2 was 80.47%. In order of influence on the response, recirculation pump pressure, slimes collector, feed % solids, feed % insol, feed % KCl, and brine temperature were found to be statistically significant predictors. The standard error of the model was 7.67%. The pilot insol rejection and the model prediction track closely together against the observation number (Figure 28). The KCl rejection model adjusted R2 was 53.84%. The statistically significant predictors, in order of influence, were recirculation pump pressure, slimes collector, feed % KCl, feed % solids, feed % insol. KCl rejection is independent of brine temperature. The standard error was 7.58%. 4.2.6.1 Response optimization: insol recovery to concentrate and KCl rejection to tailings Minitab’s response optimizer tool was used to simultaneously optimize the insol rejection and KCl recovery models. The feed composition was constrained to the average composition of KCl and insol values from the pilot trial and the brine temperature was constrained to 30℃, the annual average value during plant operation. Appendix C.4 contains the response optimizer results under these constraints. The model predicts the maximum insol recovery to be 88.2% with a corresponding KCl rejection of 86.7% under these constraints. The optimized response is extremely close to the predicted response of 85.0% insol recovery with 83.4% KCl rejection from the initial analysis of the pilot data (Selectivity curves – Insol rejection and KCl recovery). The optimized response accounts for the influence of covariates, such as increasing brine temperature, that the best-fit selectivity curve modeled using the Vera equation does not capture. 64 4.2.7 Quantification of experimental error through regression modelling Multiple linear regression was completed in Minitab to explain the observed variance in baseline data insol rejection and KCl recovery responses despite constant operating conditions. The model development would also consider the effect of uncontrollable variables such as feed grade. Table 6 summarizes the descriptive statistics for the baseline samples. All baseline samples were collected under the same operating conditions: no collector addition, target 10% w/w pulp density, no froth wash, Jg of 0.35 cm/s, froth depth of 10 in, and recirculation pressure of 40 psi. However, as can be seen in Table 6, significant variability was seen in the flotation response, despite constant operating conditions. The variability of covariates including flotation feed composition was also significant. It was observed that the insol rejection seemed to decrease throughout the year as the test campaign continued and the higher recoveries observed at the beginning of the testing were no longer attainable (Figure 20). The 10% standard deviation in insol rejection should be primarily comprised of analytical and sampling error. However, a 10% error in insol rejection is larger than expected if attributed solely to sample error. Sampling and analytical best practices were followed with diligence. Therefore, it was suspected that other factors were likely contributing to the baseline flotation performance that could not be controlled. 65 Table 6 Descriptive statistics of predictors and responses used to develop regression models (count = 20) Predictors Response Brine Temp (℃) Volumetric Feed Flow Rate (lpm) Pulp Density (% w/w) Feed Insol Conc. (% w/w) Feed KCl Conc. (% w/w) Insol Rejection (%) KCl Recovery (%) Min 22.7 11.69 2.94 42.18 7.58 59.6 75.8 Max 30.40 19.40 8.96 70.70 26.38 96.2 96.2 Median 25.30 15.25 6.72 50.38 19.15 84.4 84.5 Mean 26.20 15.60 6.56 51.72 18.28 83.3 84.6 Std dev 2.41 2.33 1.72 8.17 4.57 9.9 5.3 The baseline insol rejection regression statistics are shown in Appendix C.1 along with the corresponding residual plots. The regression model is shown in Eq. (7): 𝐼𝑛𝑠𝑜𝑙 𝑟𝑒𝑗𝑒𝑐𝑡𝑖𝑜𝑛 = − 0.0105[𝑇]2 + 0.5351[𝑇] + 0.0024[𝑓𝑖] – 6.0528 (7) where T = plant brine temperature (℃) and fi = feed insoluble concentration (% w/w). From the P-values of the regression model, it can be concluded that feed density, feed flowrate, and feed % KCl had no statistically significant impact on insoluble rejection at the 95% confidence level. The adjusted R2 was 0.845 suggesting that 85% of the variance in the baseline insol flotation can be attributed to changes in two factors: feed % insol and brine temperature. This model suggests that the remaining 15% of the observed variance can be attributed to sampling and analytical error. This sampling error is interpreted as the standard error of the regression at 3.8% insol rejection. It is observed by comparing the baseline model standard error (model standard deviation) to the baseline sample data standard deviation that the variance has been reduced by approximately 6 absolute percent. Using the model to capture the effects of brine 66 temperature and feed % insol produces a more reasonable 3.8% standard deviation, which is a logical value for inherent sampling error. Figure 20 Modelled and baseline data insol rejection by observation number Although a seasonal effect on KCl recovery was not observed, a regression analysis was completed to evaluate potential covariates. A statistically significant regression was not found between any of the predictors and the KCl recovery response. Therefore, it can be concluded that the variance in the KCl recovery data is likely due to sample and analytical error, and it is not responding to changes in the process in the same way as insol flotation performance. The sample error for the insol rejection and KCl recovery are quite similar at 3.8%, and 5.3%, respectively. 67 Chapter 5: Discussion The ultrafine particle size distribution and high clay content of the flotation feed in this slimes flotation application were expected to produce a slow flotation response and strong sensitivity to slurry rheology which would have significant implications for flotation efficiency and optimal column design and operation. The flotation kinetics were expected to be governed by the probability of collision as ultrafine particles may be significantly smaller than the mean bubble size and therefore, likely to be swept past the bubble without making contact. In alignment with the literature, it was found that low fine particle flotation efficiency may be overcome with long residence times and increasing power input to the flotation cell. It was found that a residence time of 17 minutes was required to maximize separation efficiency. This residence time is significantly longer than typical potash applications of 3-5 minutes and reflects the ultrafine nature of the flotation feed. The significant increase in insol rejection at high cavitation pressure exemplifies the increased collision efficiency with higher energy input. This effect is attributed to intensified mixing and likely increased nucleation of air on hydrophobic particles and/or increased generation of fine bubbles of appropriate size for the particles in the pulp. The effect of sparger pressure will be discussed in further detail below. The low maximum froth carrying capacity of 0.9 t/h/m2, compared to the typical potash carrying capacity of 3-4 t/h/m2, also arises from the ultrafine particle characteristics. The high surface area of fine particles will consume bubble surface area resulting in high bubble surface loading at low solid mass rates and a low carrying capacity. 68 Finally, although grade by size data was not provided, from the ultrafine particle size distribution it can be assumed that the insoluble minerals are fully liberated from the fine sylvite and halite, as 40% of the particles are retained on the 1µm detection limit of the Malvern. Therefore, limitations of mineral liberation should not be present in this system and a near perfection separation efficiency could theoretically be obtained, limited by entrainment and entrapment of fine salt particles in the bubble-particle aggregates. As shown in Figure 15, the selectivity curve is effectively horizontal for the more intense flotation system under the same operating conditions as the pilot column i.e the change in KCl recovery is negligibly affected by large changes in insol rejection, and a near-perfect separation is obtained. From this observation, we can conclude that the separation efficiency obtained with the pilot column was equipment limited and not system limited, due to the complete liberation of the ultrafine stream. The effect of increasing solid content on flotation performance and the high energy input to the cavitation system required to achieve maximum insol rejection are explained by rheological phenomena arising from the ultrafine, high clay slurry behavior. The particle size distribution was narrow, with 40% of the solid content falling into the colloidal particle range; therefore, interparticle forces will dominate slurry rheological behavior. Significant changes in viscosity are expected from small fluctuations in solid content due to the large surface area associated with colloidal particles. In addition, the use of flocculant in this application may have a significant impact on slurry rheological properties as half of the solid content in the flotation feed is insoluble minerals. The increased state of aggregation, generated by polymer flocculation, may result in the development of a yield stress and higher slurry viscosity, and subsequently, influence mineral entrainment and gas dispersion behavior. The increase in viscosity with increasing pulp density is 69 theorized to result in a high gas hold-up and consequently, the mass pull may have been the cause of the unstable operating conditions observed at 15% w/w pulp density, in which the bulk of the flotation feed reported to the concentrate. The effect of slurry rheology on gas dispersion and flotation kinetics in a potash system should be investigated. The effect of chemical or mechanical redispersion of the flocculated feed stream on gas dispersion and flotation selectivity should also be investigated. Regression modelling of the baseline pilot data identified brine temperature as a critical factor in determining insol flotation efficiency. Brine temperature displays seasonal variability which ranges from 25℃ in the winter to 35℃ in the summer. Sylvite flotation recovery is strongly affected by brine temperature, but this effect is largely attributed to collector solubility at increased brine temperatures (Laskowski, 2013). The strong influence of brine temperature on slimes rejection was unexpected as slimes flotation at the baseline conditions was operated without a collector. It should be noted that brine temperature was also a statistically significant factor for the overall insol rejection model that was developed using the full optimization test data. The rheological implications of the high clay, ultrafine feed stream, dominated by inter-particle interactions and without modifying reagents, may amplify the influence of temperature-dependent brine characteristics on gas dispersion. Seasonal changes in brine temperature will correspond to different operating points on the solubility equilibrium curve which results in changes to the ionic strength of the brine and the ratio of structure-maker to breaker cations. The changes in brine composition will in turn affect the fluid viscosity and surface tension, factors that directly impact gas dispersion behavior. The findings of this research suggest that gas dispersion may play critical a role in sylvite flotation response that has yet to be considered in sylvite or slimes flotation. An 70 investigation into the influence of changes in brine composition and temperature on gas dispersion is recommended. Flotation feed pulp density was shown to strongly influence insol rejection and separation efficiency. The optimum pulp density was found to be 10% w/w which resulted in high insol rejection and separation efficiency due to the increased probability of particle collision. Insol rejection was significantly reduced by low pulp density, which was an expected finding, due to the reduced probability of collision in dilute systems; however, the substantial decrease in separation efficiency was unexpected. The decrease in separation efficiency at low pulp density may be attributed to a weak attachment of KCl particles to an unloaded bubble surface and is not thought to be due to increased entrainment as no correlation was found between brine recovery and KCl and NaCl recovery to the concentrate. Without reagent addition, an unstable, light pink froth was observed to develop during equipment start-up with only brine or when feed was lost to the pilot unit during process upsets. The froth was assumed to be potash floating in the column due to its colour and observable froth characteristics. The column froth would return to a dark brown colour once proper feed was reinstated. Sample assays validated the visual observation that the dark froth was primarily comprised of insoluble minerals. Lekki and Laskowski (1975) found that although the high ionic strength of potash brine retards coalescence, a froth could only be formed in the presence of hydrophobic minerals. As a link pink froth was observed at low insol concentrations, it can be concluded that sufficient bubble-particle attachment of unconditioned KCl particles occurred to such an extent that a froth was able to develop. This phenomenon may be understood by the work 71 of Hancer et al. (2001), who found that in the case of sylvite wetting by brine is not complete and a contact angle of a few degrees (12–15°) is found. It is hypothesized that separation efficiency is high when sufficient insoluble mineral particles are present and able to form bubble-particle aggregates to fully satisfy bubble surface loading requirements, effectively blinding the bubble surface. However, when bubbles are poorly loaded, KCl particles may weakly attach to the bubble surface, thus reducing separation efficiency. As shown in Figure 19, brine recovery is directly correlated with insol rejection to the concentrate; therefore, sub-optimal operating conditions resulting in lower separation efficiency will produce a froth with a higher solid content. There are two implications of this finding: (1) water is strongly associated with the insoluble minerals, potentially due to the anisotropic wettability of the clay particle surfaces, and (2) the potential for increased froth tenacity arising from a lower insoluble mineral concentration in the froth. Froth handling during slurry transport is a constant challenge in potash operations. Although it was identified as a potential processing concern in this application, froth stability was not an issue during the slimes flotation pilot on the ultrafine high clay stream. The solid content of the froth was quite low, typically less than 15% w/w. While froth factor measurements were not collected, it was observed that the froth would collapse and go back into suspension easily under moderate agitation, retaining a small froth cap. The high froth mobility may be due to the deliberate operation of slimes flotation with minimal reagent addition to reduce operating costs and prevent froth stability issues. However, significant changes in froth behavior were not observed with collector addition. In comparison, when attempting to float KCl from a 10-20% insol stream at very low pulp densities with a typical depressant, collector, and 72 frother reagent scheme, an extremely poor separation was achieved, and the resulting froth was highly voluminous and tenacious. In summary, low flotation pulp density was found to significantly reduce separation efficiency resulting in denser froth; therefore 10% w/w pulp density should be targeted to achieve optimal slimes flotation performance. The proposed mechanism of reduced separation efficiency at low pulp densities is due to the attachment of weakly hydrophobic KCl particles when the surface bubble loading is low. If the opportunistic attachment of fine KCl particles during low bubble loading conditions is a mechanism for reduced KCl recovery in slimes flotation, there are implications for conventional slimes flotation in primary desliming applications in which low insol feed content may provide a similar opportunity for KCl particles to report to the froth. An investigation of the effect of solid content, composition, particle size, and reagent addition on froth characteristics could provide insight into how to improve flowsheet design to better manage froth in potash operations. The insol rejection and KCl recovery responses were evaluated with the addition of an ethoxylated amine collector to the ultrafine, high clay slimes flotation process. The collector addition significantly increased the insol rejection at low pulp densities, indicating a reduction in induction time as the bubble-particle attachment occurred more instantaneously with an increase in particle hydrophobicity induced by the collector. A statistically significant decrease in KCl recovery occurred at both high and low pulp densities with the addition of a collector, indicating that some collector was adsorbing to the KCl particles. The collector used for the pilot was an ethoxylated amine which, according to Titkov et al. (1999), produces a high slimes flotation efficiency but is 73 less selective as it also recovers sylvite to the flotation froth. Collector alternatives could be investigated in an attempt to reduce the magnitude of the KCl recovery loss with collector addition. The overall separation efficiency with collector addition increased over slimes flotation without a collector as the magnitude of the insol rejection increase was less than that of the KCl recovery loss. However, collector addition in this slimes flotation application is not recommended due to the additional operating costs of collector addition, the marginal increase in insol rejection, and the decrease in KCl recovery. The flotation of insoluble minerals without collector addition appears to be non-selective, as shown by the XRD analysis of the feed, con, and tails samples at various slimes flotation efficiencies. A similar XRD analysis could be performed to compare the effect of the collector on insoluble mineral flotation response, although it is expected that ethoxylated amine works broadly by increasing existing hydrophobicity and reducing induction time for bubble-particle aggregation. Although some phyllosilicates have been shown to demonstrate both hydrophobic and hydrophilic surface characteristics, muscovite, which was the dominant clay mineral in this system, has been shown to be completely hydrophilic (Yin et al., 2012). Furthermore, the mechanism of collector-less flotation of non-clay minerals such as dolomite, quartz, and anhydrite cannot be explained by anisotropic surface characteristics and is not understood. Insoluble minerals have shown a similar flotation response when floated on a lab-scale with synthetic dilution brine addition, eliminating the possible mechanism of residual flotation reagents in the brine working as collectors. As Hancer et al. (2001) have shown, structure-breaking ions (K+) in concentrated brines will displace interfacial water molecules from a hydrophilic solid surface to such an extent that the surface becomes slightly hydrophobic. As previously discussed, this is the mechanism behind the contact 74 angle of KCl particles developed in a saturated brine solution. This same mechanism may be driving the ability to unselectively float insoluble minerals in potash systems without collector addition, despite mineral hydrophilic tendencies in low ionic strength aqueous systems. Further research to understand the mechanism of slimes flotation without collector addition is recommended. High energy input, in the form of cavitation pressure, was found to be the key to achieving high insol rejection and separation efficiency. The pilot sparging system’s optimal operating condition was found to be between 30-40 psi in contrast to the recommended operating pressure of 12-15psi. The energy input required to achieve high insol rejection reflects the ultrafine particle size and high slimes content of this stream. Three mechanisms are proposed to explain the increase in insol rejection and separation efficiency observed with increasing sparger pressure: 1. Increase in kinetic energy generates a higher probability of collision 2. Generation of a greater volume of bubbles at appropriate bubble to particle size ratio and/or increased nucleation of air on hydrophobic slimes particles 3. Mechanical shearing of flocs to: a. Reduce slurry viscosity resulting in appropriate conditions for cavitation b. Destroy unselective agglomerations of flocs with polymer molecules attached to KCl surfaces or fine KCl entrained in the aggregate The effect of increased energy input on the probability of collision has been previously discussed. The findings of Li et al. (2015) support the proposed mechanism of increased generation of fine bubbles driving the step-change in insol rejection. Their work has shown that increasing the fluid 75 velocity through the venturi nozzle increases the number of micron-sized bubbles, resulting in a significant improvement in the recovery and flotation kinetics of fine coal (Li et al., 2015). The use of an external sparging system with slurry recirculation may reduce the impact of a low probability of collision by nucleating air on hydrophobic clays through cavitation. However, cavitation may be impeded by an increased pressure threshold for cavitation arising from non-Newtonian rheological behavior dominated by the interparticle forces of the ultrafine high clay stream. Therefore, higher sparger pressures would be required to generate the same gas dispersion as a lower viscosity slurry (Gogate & Pandit, 2001). The high efficiency of insol rejection achieved at 3x higher than normal sparger pressure likely arises from a combination of the rheological behavior of this high clay feed stream and the gas dispersion requirements to achieve high ultrafine particle recovery. The proposed mechanism for the increase in separation efficiency with sparger pressure is the destruction of unselective flocs by shearing and cavitation forces, allowing the selective attachment and recovery of fine insoluble particles. In addition to the moderate improvement in separation efficiency observed with increasing sparger pressure for the pilot column system, a substantial improvement in separation efficiency was obtained utilizing a flotation machine with greater power intensity (11,000 W/m3) as compared to the pilot unit (1500 W/m3). This finding, in conjunction with the observation of flocs remaining the in tailings of the pilot column while a complete state of dispersion was seen in the high-intensity flotation tailings, further supports the hypothesis that the destruction of polymer flocs increases separation efficiency. 76 This conclusion may be extended to infer that the addition of flocculant before slimes flotation utilizing column technology is unnecessary and possibly detrimental to flotation efficiency. A higher separation efficiency may be possible without flocculant addition as more selective aggregates may be formed through cavitation than polymer flocculation. Hydrophobic clay particles with nucleated surfaces may attach to each other or to flotation-sized bubbles resulting in increased buoyancy forces and rise rates. The possible occurrence of shear flocculation of insoluble minerals through cavitation in a high ionic strength medium should also be investigated. In conclusion, the patented method of selective flocculation-flotation for slimes flotation of an ultrafine, high clay slurry is not valid if utilizing column flotation technology, provided that the flocculant is not performing a collector function, even if polymer molecules are destroyed. Due to the high-power input per slurry volume of the cavitation system, it is assumed that all flocs are destroyed in the sparging process. This assumption is confirmed by the visual observations of the state of dispersion of the flotation column tailings. Flocculation prior to flotation is unnecessary if a sufficient volume of fine bubbles is generated or nucleated on the insol particles, as shown by the research findings. In the case of flocculation of a high slimes ultrafine stream prior to flotation, the viscosity of the high clay slurry is increased requiring additional energy input to generate required gas dispersion conditions. A higher separation efficiency may be possible without flocculant addition as more selective aggregates may be formed through cavitation than by polymer flocculation. 77 Chapter 6: Conclusions The application of column flotation technology for slimes flotation of an ultrafine high clay stream was successful in generating high separation efficiencies. An insol rejection of 88.2% with a corresponding KCl recovery of 86.7% was achieved in one stage of flotation without collector addition. High cavitation pressure was found to be the critical operating parameter to achieve superior separation efficiencies. Despite the favorable flotation performance with the pilot equipment configuration, a comparison of the pilot separation efficiency to a more energy-intensive flotation machine revealed that the selectivity performance of the column was equipment limited. Therefore, the patented method of selective flocculation-flotation for slimes flotation of an ultrafine high clay slurry is not applicable if utilizing column flotation technology with an energy-intensive sparging system. The addition of flocculant is negated by the intense shearing conditions generated through cavitation and may be detrimental to achieving high separation efficiency. The slimes flotation feed was characterized by a P80 of 27 µm and the insoluble mineral composition was found to be over 40% dolomite, with significant quartz and anhydrite as well as approximately 30% clays, primarily comprised of muscovite and chlorite. Insol mineral flotation was found to be unselective. Column flotation of the ultrafine high clay stream was characterized by a low carrying capacity of 0.9 t/h/m2 and a long residence time of 17 minutes, in alignment with the literature. In this application, the maximum carrying capacity is driven by mineralogy and is not expected to be improved by alternate flotation technologies. High pulp density, defined as 10% w/w compared to low pulp density (5% w/w), was found to be optimal to achieve high insol rejection and separation efficiency. Low pulp density resulted in reduced insol rejection and 78 separation efficiency. At the 90% confidence level, the addition of an ethoxylated amine as a slimes collector at optimum pulp density produced a minimum increase in slimes rejection of 3.0% corresponding to a minimum decrease in KCl recovery of 1.1%. Collector addition at low pulp densities resulted in an 18.8% increase in insol rejection with at least a 9.6% decrease in KCl recovery (90% confidence level). Brine temperature was identified as having a significant influence on insol flotation response through regression modeling of the baseline column flotation data, suggesting possible rheological implications on gas dispersion. 79 Chapter 7: Recommendations Multiple avenues for additional research have been identified through this study. The following research is recommended to improve the understanding of both slimes and sylvite flotation systems: 1. Evaluate the flotation of fine KCl from the slimes flotation tailings stream to quantify the potential improvement in mill recovery 2. Determine the mechanism of slimes flotation without collector addition including the possible role of flocculant as a collector 3. Investigate the influence of slurry rheology, including the effect of chemical or mechanical dispersion, on gas dispersion and flotation kinetics in a high ionic strength brine system 4. 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Minerals engineering, 22(5), 419-433. https://doi.org/10.1016/j.mineng.2008.12.010 85 Appendices Appendix A A.1 Comparing two trendlines to determine the effect of collector on insol rejection at high pulp density Figure 21 Comparing two trendlines to determine the effect of collector on insol rejection at 10% w/w pulp density Table 7 Summary of results for comparison of two trendlines to determine effect of collector on insol rejection at 10% w/w pulp density Test Collector No Collector R 0.8303 0.8403 P(R) 0.0056 0.0012 Residual Variance F = 1.3622 P(F) = 0.3495 Gradients F = 0.7038 P(F) 0.4139 Intercepts F=6.9682 P(F) = .0172 Separation Diff. = 6.05% t = 2.6166 P(t) = 0.0093 90% conf. int. = +/- 4.03% 90% lower conf. limit = 2.96% 86 A.2 Comparing two trendlines to determine the effect of collector on KCl recovery at high pulp density Figure 22 Comparing two trendlines to determine the effect of collector on KCl Recovery at 10% w/w pulp density Table 8 Summary of results for comparison of two trendlines to determine effect of collector on KCl recovery at 10% w/w pulp density Test Collector No Collector R -0.7258 -0.9025 P(R) 0.0175 0.0009 Residual Variance F = 2.4862 P(F) = 0.1237 Gradients F = 0.2269 P(F) = 0.6407 Intercepts F= 3.8155 P(F) = 0.0685 Separation Diff. = 3.74% t = 1.9055 P(t) = 0.0380 90% conf. int. = +/- 3.44% 90% lower conf. limit = 1.11% 87 A.3 Comparing two trendlines to determine the effect of collector on insol rejection at low pulp density Figure 23 Comparing two trendlines to determine the effect of collector on insol rejection at 5% w/w pulp density Table 9 Summary of results for comparison of two trendlines to determine effect of collector on insol rejection at 5% w/w pulp density Test Collector No Collector R 0.8676 0.8051 P(R) 0.0024 0.0088 Residual Variance F = 4.3429 P(F) = 0.0358 Gradients F = 1.5393 P(F) = 0.2351 Intercepts F= 31.158 P(F) = 0.0001 Separation Diff. = 24.6% t = 5.6814 P(t) = 0.0000 90% conf. int. = +/- 7.62% 90% lower conf. limit = 18.8% 88 A.4 Comparing two trendlines to determine the effect of collector on KCl recovery at low pulp density Figure 24 Comparing two trendlines to determine the effect of collector on KCl recovery at 5% w/w pulp density Table 10 Summary of results for comparison of two trendlines to determine effect of collector on KCl recovery at 5% w/w pulp density Test Collector No Collector R -0.7696 -0.675 P(R) 0.0153 0.0663 Residual Variance F = 1.4005 P(F) = 0.03487 Gradients F = 0.9546 P(F) = 0.3464 Intercepts F= 16.803 P(F) = 0.0011 Separation Diff. = 13.4% t = -4.0925 P(t) = 0.0006 90% conf. int. = +/- 5.79% 90% lower conf. limit = 8.96% 89 Appendix B B.1 Particle size distribution of slimes flotation streams Figure 25 Particle size distribution of slimes flotation feed, con and tails 90 Appendix C C.1 Regression model of baseline slimes flotation data Figure 26 Normality plot of baseline insol rejection model residuals. With P-Value >0.05 we accept the null hypothesis that the data is normally distributed. 91 Figure 27 Residual plots for regression model of baseline insol rejection data -0.1-0.0500.050.10.00 20.00 40.00 60.00 80.00ResidualsFeed % insolFeed % insol Residual Plot-0.1-0.0500.050.10 10 20 30 40ResidualsBrine Temp ©Brine Temp © Residual Plot-0.08-0.06-0.04-0.0200.020.040.060.0840% 60% 80% 100%Residualspredicted insol recoveryResiduals vs predicted insol rejection-0.1-0.0500.050.10 200 400 600 800 1000Residualstemp^2Temp^2 Residual Plot-2-1.5-1-0.500.511.52-2 -1 0 1 2RR -1R vs R-1-0.08-0.06-0.04-0.0200.020.040.060.080 5 10 15 20ResidualObservation NumberResiduals vs Observation Number92 C.2 Regression model of insol rejection for overall optimization data 93 Figure 28 Observed insol rejection and predicted insol rejection vs observation number Figure 29 Pareto chart of standardized effects for overall insol flotation model 0%10%20%30%40%50%60%70%80%90%100%0 10 20 30 40 50 60 70Insol RecoveryObservation NumberPredicted Insol Recovery to O/F Insol Recovery to O/F94 Figure 30 Residual plots for regression model of overall insol rejection to concentrate data 95 C.3 Regression model of KCl recovery for overall optimization data 96 Figure 31 Pareto chart of standardized effects for overall KCl rejection model Figure 32 Residual plots for regression model of overall KCl recovery data 97 C.4 Minitab response optimizer results Figure 33 Response optimizer results for KCl recovery and insol rejection
Thesis/Dissertation
eng
Mining Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Miskovic, Sanja
Application of selective-flocculation flotation on a high clay, ultrafine stream
Text