Evaluation of Scale-Up Model for Flotation with Kristineberg Ore

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Evaluation of Scale-up Model for Flotation with Kristineberg Ore

Adam Isaksson

Chemical Engineering, master's level 2018

Luleå University of Technology Department of Civil, Environmental and Natural Resources Engineering

Evaluation of Scale-up Model for Flotation with Kristineberg Ore

Adam Isaksson

2018

For degree of MASTER OF SCIENCE

Luleå University of Technology Department of Civil, Environmental and Natural Resources Engineering Division of Minerals and Metallurgical Engineering Printed by Luleå University of Technology, Graphic Production 2018 Luleå 2018 www.ltu.se Preface

As you may have figured out by now, this thesis is all about mineral processing and the extraction of metals. It was written as part of my studies at Luleå University of Technology, for a master’s degree in Chemical Engineering with specialisation Mineral and Metal Winning. There are many people I would like to thank for helping me out during all these years. First of all, my thanks go to supervisors Bertil Pålsson and Lisa Malm for the guidance in this project. Iris Wunderlich had a paramount role during sampling and has kindly delivered me data to this report, which would not have been finished without her support. I would also like to thank Boliden Mineral AB as a company. Partly for giving me the chance to write this thesis in the first place, but also for supporting us students during our years at LTU. Speaking of which, thanks to Olle Bertilsson for reading the report and giving me feedback. The people at the TMP laboratory deserves another mention. I am also very grateful for the financial support and generous scholarships from Jernkontoret these five years. At last, but certainly not least, I would like to thank my family for always supporting me in my decisions. I have got the chance to express my inherent curiosity, for which I am truly grateful. This thesis was written in memory of my grandmother, who was one of my greatest supporters. It is said that the strongest metals are forged in the greatest of stellar hearts. Even the brightest of stars will one day set, but their warmth echoes through the vastness of eternity. I am now beginning a new phase of my life, for whatever it may hold. With a different sense of purpose, I am entering a realm of the unknown.

Adam Isaksson Boliden, May 2018

iii Abstract

The objectives of this project were to survey the flotation circuit of the Boli- den concentrator, mass balance collected data and evaluate a scale-up model for laboratory flotation results. The model assumes that half of the recovery to cleaner middlings in a standard laboratory test would report to the final concentrate if it were done in closed circuit, as is the case in a full-scale plant. It has been used by Boliden Mineral AB since 1982 but its accuracy had not been studied since 1986. The model can be categorised as of open circuit type with scale-up factors. The project was based on a complex Ag-Au-Cu-Pb-Zn sulphide ore from the Kristineberg mine. Laboratory tests were done to produce concentrates of CuPb, Cu, Pb and Zn with pulp samples from the concentrator as feed material. The software HSC 9.3 was used to mass balance data from the plant survey. It was decided that the model would be deemed usable if it was able to predict the plant results with the same accuracy as in the survey of 1986. A simulated locked cycle test with split factors (Agar & Kipkie, 1978) was identified as an alternative scale-up model. The results showed that the model was able to predict the plant results with the same accuracy as in 1986. It was especially good at predicting grade and recovery of the main element in a concentrate. For example, it predicted an 18 % higher grade and 11 % lower recovery of Cu to the CuPb concentrate, while a 3 % lower grade and 11 % lower recovery of Zn was predicted to the Zn concentrate. The locked cycle model gave much worse predictions on grades, but more accurate recoveries. It was also better at predicting the behaviour of minor impurity elements such as As and Bi. A recommendation is to combine the two alternatives in a type of “mixed cycle” model. In this study, it would have predicted an 18 % higher grade and 7 % lower recovery of Cu to the CuPb concentrate, as well as a 3 % lower grade and 1 % higher recovery of Zn to the Zn concentrate compared with plant results. Such a model seems to give better figures, but should be put to the test on more samples and ores to confirm this belief. It could at the very least be used to check the reliability of results predicted by the current scale-up model.

Keywords: ﬂotation, HSC, Kristineberg, locked cycle, mass balancing, mixed cycle, open circuit, scale-up

iv Sammanfattning (SWE)

Syftet med det här examensarbetet var att utföra en detaljprovtagning av flotationskretsen i Bolidens anrikningsverk, massbalansera data och sedan utvärdera en modell för uppskalning av resultat från laboratorieflotationer. Modellen antar att hälften av utbytet till returgodset i ett satsvis laborato- rieförsök skulle rapportera till det slutliga koncentratet om det återcirkuler- ades, såsom i ett anrikningsverk. Den har använts av Boliden Mineral AB sedan 1982 men utvärderades senast 1986. Kategoriskt kan den ses som en uppskalningsmodell av typen öppen krets med skalfaktorer. Projektet baserades på en komplex Ag-Au-Cu-Pb-Zn sulfidmalm från gru- van i Kristineberg. Laboratorieförsök utfördes för att ta fram koncentrat av CuPb, Cu, Pb och Zn, med pulpprover från driften som utgångsmaterial. Programmet HSC 9.3 användes för att massbalansera datan från provtagnin- gen. Det bestämdes att modellen skulle anses som godtagbar ifall den kunde förutspå driftresultatet med samma noggrannhet som 1986. Ett simulerat försök av typen sluten krets (Agar & Kipkie, 1978) identifierades som den mest intressanta alternativmodellen och även den utvärderades. Resultaten visade att modellen än idag ger godtagbara förutsägelser med samma noggrannhet som 1986. Modellen var särskilt bra på att förutspå halt och utbyte av den huvudsakliga metallen till dess eget koncentrat. Den förutspådde exempelvis en 18 % högre halt och 11 % lägre utbyte av Cu till CuPb-koncentratet, samt 3 % lägre halt och 11 % lägre utbyte av Zn till Zn-koncentratet. Den alternativa modellen gav sämre förutsägelser med avseende på halter, men bättre med avseende på utbyten. Den var bättre på att förutspå beteendet hos låghaltiga föroreningar såsom As och Bi. Rekommendationen är att kombinera de två modellerna till en “bland- kretsmodell”. I den här undersökningen hade ett sådant alternativ förutspått en 18 % högre halt och 7 % lägre utbyte av Cu till CuPb-koncentratet, samt 3 % lägre halt och 1 % högre utbyte av Zn till Zn-koncentratet jämfört med driftresultatet. En sådan modell tycks ge bättre förutsägelser, men bör tes- tas på fler prover och malmtyper. Den borde åtminstone kunna användas för att kontrollera trovärdigheten hos resultaten förutspådda av den nuvarande modellen.

Nyckelord: blandkrets, ﬂotation, HSC, Kristineberg, sluten krets, massbal- ansering, öppen krets, uppskalning

v Examination objectives

The author’s contribution to this work and requirements for the degree of Master of Science in Engineering according to the Swedish Higher Education Ordinance.

Higher Education Ordinance Motivation §1 Demonstrated knowledge of the disci- Chapters 1 and 2. Presentation of plinary foundation of and best practice published flotation scale-up methods in his or her chosen field of technology and the Boliden practise. as well as insight into current research and development work. §2 Demonstrated both broad knowledge of Chapters 1, 2 and 3. Flotation the- his or her chosen field of technol- ory and circuit design. Mathematical ogy, including knowledge of mathemat- knowledge required for mass balanc- ics and the natural sciences, as well ing and flotation kinetics. Specialised as a considerable degree of specialised knowledge in mass balancing of flota- knowledge in certain areas of the field. tion circuits and flotation test work. §3 Demonstrated the ability to identify, Chapters 3 and 4. Planning of flota- formulate and deal with complex issues tion test work and sampling of the con- autonomously and critically and with centrator. Mass balancing with HSC a holistic approach and also to partici- software. Developed an alternative pate in research and development work scale-up procedure. and so contribute to the formation of knowledge. §4 Demonstrated the ability to create, Chapters 2, 3 and 4. Identification of analyse and critically evaluate various scale-up models in literature and plan- technological solutions. ning of test work to evaluate said models. Developed an alternative scale-up procedure. §5 Demonstrated the ability to plan and Chapter 3. Considerations in the mass use appropriate methods to undertake balance such as flow rate estimations advanced tasks within predetermined and which elements to assay. Which parameters. flows to sample and tests to do.

vi EXAMINATION OBJECTIVES vii

Higher Education Ordinance Motivation §6 Demonstrated the ability to integrate Chapters 3 and 4. Only relatively sim- knowledge critically and systematically ple models were considered or devel- as well as the ability to model, simu- oped since a standard ﬂotation test late, predict and evaluate sequences of generally gives a limited amount of in- events even with limited information. formation. §7 Demonstrated the ability to develop Chapter 4. An alternative scale-up and design products, processes and model was developed. Brief discussion systems while taking into account the of the importance of mineral process- circumstances and needs of individuals ing and sustainability in chapter 1 but and the targets for economically, so- generally not applicable for this thesis. cially and ecologically sustainable development set by the community. §8 Demonstrated the capacity for team- Sampling was done together with oth- work and collaboration with various ers and required teamwork. Discus- constellations. sions with external supervisor and laboratory personnel. §9 Demonstrated the ability to present his Written report in English. Oral pre- or her conclusions and the knowledge sentation in Boliden for process and and arguments on which they are based development engineers. Another oral in speech and writing to diﬀerent au- presentation for fellow students and diences in both national and interna- teachers at the university in Luleå. tional contexts. §10 Demonstrated the ability to make as- Ethical aspects generally not applica- sessments informed by relevant disci- ble. Brief discussion of the impor- plinary, social and ethical aspects as tance of good grade-recovery estima- well as awareness of ethical aspects of tions (chapter 1) to prevent bad in- research and development work. vestments and environmental hazards related to high reagent dosages or poor recoveries. §11 Demonstrated insight into the possibil- Discussion of mineral processing and ities and limitations of technology, its its place in a society that requires met- role in society and the responsibility of als. Brief discussion of grade versus re- the individual for how it is used, in- covery and how they limit each other. cluding both social and economic as- Sampling required health and safety pects and also environmental and oc- considerations, as did the laboratory cupational health and safety consider- work. ations. §12 Demonstrated the ability to identify Suggestions for further work are pre- the need for further knowledge and un- sented in the report. Presentation of dertake ongoing development of his or an alternative scale-up procedure and her skills. how it can be made even better. Contents

1 Introduction 1 1.1 Project aim ...... 2 1.1.1 Limitations ...... 2 1.2 Boliden Mineral AB ...... 3 1.2.1 Kristineberg mine ...... 4 1.2.2 Boliden concentrator ...... 4 1.2.3 Current scale-up model ...... 5 1.3 Technical background ...... 7 1.3.1 Flotation theory ...... 7 1.3.2 Flotation circuits ...... 10 1.3.3 Mass balancing ...... 11

2 Review of scaling practises 14 2.1 Open circuit tests ...... 14 2.1.1 Boliden practise ...... 15 2.2 Locked cycle tests ...... 18 2.3 Kinetic models ...... 19 2.3.1 First order approach ...... 20 2.3.2 Second order approach ...... 20 2.3.3 Klimpel approach ...... 21 2.3.4 Kelsall approach ...... 21 2.4 Final thoughts ...... 22

3 Method 23 3.1 Sampling and sample preparation ...... 23 3.1.1 Flow rate estimation ...... 26 3.2 Flotation tests ...... 27 3.2.1 Cyclone overflow – roughing test ...... 28 3.2.2 Cyclone overflow – cleaning test ...... 29 3.2.3 Cyclone overflow – cleaning test with regrind ...... 30 3.2.4 CuPb mp – roughing test ...... 30

viii CONTENTS ix

3.2.5 CuPb mp – cleaning test ...... 31 3.2.6 CuPb mp – cleaning test with regrind ...... 31 3.2.7 CuPb concentrate – roughing test ...... 31 3.2.8 CuPb concentrate – cleaning test ...... 32 3.3 Analysis procedure ...... 32 3.4 Mass balancing in HSC ...... 33 3.5 Model evaluation ...... 33 3.5.1 Locked cycle simulation ...... 34 3.5.2 Comment on kinetic models ...... 34

4 Results 36 4.1 Current model results ...... 36 4.2 Locked cycle results ...... 37 4.3 Combining models – “Mixed cycle” ...... 38 4.3.1 Worked example ...... 39 4.4 Model comparison ...... 39 4.5 Mass balance - key ﬁgures ...... 44

5 Discussion 46 5.1 Alternative methods ...... 47 5.1.1 Combining models ...... 47 5.1.2 Regrinding of concentrates ...... 49 5.2 Applicability issues ...... 49 5.3 Mass balance review ...... 50 5.4 Experimental validity ...... 51

6 Conclusions 52

7 Recommendations 53 7.1 Further work ...... 53

8 References 55

A Product balances

B Stream data

C Scale-up models Figures

1.1 Annual world mine production of Cu, Zn and Pb in 2007-2016. 1 1.2 Boliden’s European production sites...... 3 1.3 Overview of processes in the Boliden concentrator...... 5 1.4 Typical laboratory procedure for flotation testing...... 6 1.5 Working principle of a flotation cell...... 8 1.6 Structures of some common collectors...... 9 1.7 The principle of a typical flotation circuit...... 10 3.1 Streams in the flotation circuit sampled in this study...... 25 3.2 Summary of the laboratory flotation tests...... 28 3.3 Flow sheet used to evaluate the simulated locked cycle model. 34 4.1 Model comparison for the CuPb concentrate grades...... 40 4.2 Model comparison for the CuPb concentrate recoveries. . . . . 40 4.3 Model comparison for the Cu concentrate grades...... 41 4.4 Model comparison for the Cu concentrate recoveries...... 41 4.5 Model comparison for the Pb concentrate grades...... 42 4.6 Model comparison for the Pb concentrate recoveries...... 42 4.7 Model comparison for the Zn concentrate grades...... 43 4.8 Model comparison for the Zn concentrate recoveries...... 43 4.9 Comparison between measured and balanced values in HSC. . 45

x Tables

1.1 Error models available in HSC for data reconciliation...... 13 1.2 Different degrees of sample quality available in HSC...... 13 2.1 Predicted versus plant results for the Levi ore...... 16 2.2 Predicted versus plant results for the Pinos Altos ore...... 16 2.3 Predicted vs plant results for the Holmtjärn ore...... 17 2.4 Predicted versus plant results for a Zn mineralisation...... 17 3.1 Streams sampled in this study...... 24 3.2 Streams where the flow rates were measured...... 26 3.3 Summary of flotation tests performed...... 27 3.4 Reagents in the cyclone overflow CuPb roughing test...... 29 3.5 Reagents in the cyclone overflow Zn roughing test...... 29 3.6 Reagents in the CuPb mp Zn roughing test...... 30 3.7 Reagents in the Cu roughing test...... 31 4.1 Results from the current model...... 36 4.2 Locked cycle model results...... 38 4.3 Mixed cycle model results...... 38 4.4 Some key values from the mass balance in HSC...... 44 4.5 WSSQ values from the mass balance in HSC...... 45 5.1 Holmtjärn predicted results versus plant results...... 48

xi Abbreviations

General conc concentrate D507 Danaﬂoat™ 507, an aqueous solution of thionocarba- mate and dialkyl dithiophosphate. D871 Danaﬂoat™ 871, an aqueous solution of mercaptoben- zothiazole and dialkyl dithiophosphate. IBX isobutyl xanthate ICP-AES inductively coupled plasma atomic emission spectroscopy. mp middlings product, material remaining in pulp and not reporting to froth. PAX potassium amyl xanthate rpm revolutions per minute RSD relative standard deviation WSSQ weighted sum of squares wt% weight percentage

Chemical formulae

Ag silver As arsenic Au gold

xii ABBREVIATIONS xiii

Bi bismuth

Ca(OH)2 calcium hydroxide 2 – CrO4 chromate ions Cu copper Cu+ copper(I) ions Cu2+ copper(II) ions

CuFeS2 copper(I) iron(III) sulphide (chalcopyrite)

Cu2S copper(I) sulphide (chalcocite)

CuSO4 · 5 H2O copper sulphate pentahydrate Fe iron FeAsS iron(III) arsenic sulphide (arsenopyrite)

FeS2 iron(II) disulphide (pyrite) HCl hydrochloric acid

HClO4 perchloric acid HF hydroﬂuoric acid

HNO3 nitric acid

H2SO4 sulphuric acid NaCN sodium cyanide

Na2CO3 sodium carbonate NaOH sodium hydroxide OH hydroxi groups OH – hydroxide ions Pb lead

PbCrO4 lead chromate PbS lead sulphide (galena) S sulphur Sb antimony 2 – SO3 sulphite ions Te tellurium Zn zinc Zn2+ zinc ions (Zn,Fe)S zinc (iron) sulphide (sphalerite)

Zn(OH)2 zinc hydroxide

ZnSO4 zinc sulphate Symbols

Roman

A matrix that holds all the assays of all product streams in a circuit. A> transpose of matrix A. c fraction of element in a stream.

Cp particle concentration d diameter of bucket D dilution ratio, weight liquid divided by weight solids.

Fw weight percent solids k1 first order rate constant k2 second order rate constant kf fast-floating rate constant in a Kelsall equation. ks slow-floating rate constant in a Kelsall equation. kK Klimpel rate constant L length of flotation cell

Mb sampled mass ﬂow mf feed mass ﬂow

Mtot total mass flow n number of flotation cells in a bank p mass flow in a product stream ~p product vector, which holds all products in a circuit. R recovery

R∞ maximum achievable recovery

xiv SYMBOLS xv

S split factor, the fraction of a component that reports to tailings in a separation. t time w weight in a weighted least squares regression. W matrix that holds all the weights in a least squares regression. ~y feed vector, which holds the mass ﬂows of all elements in the circuit feed. yˆ vector that holds all the mass ﬂows provided by a weighted least squares regression.

Greek

ρp pulp density

ρs solid’s density τ residence time φ slow-ﬂoating percentage in a Kelsall equation.

Chapter 1 Introduction

Base metals are some of the most important commodities in the world today and their rate of production shows an increasing trend. Even though recycling and utilisation of secondary raw materials have gained more focus over the last years, primary mine production is still very important. The annual world mine production and reserves of Cu, Zn and Pb during the last decade are shown in ﬁgure 1.1. The estimated reserves have generally grown or remained constant in size, despite the increase in production.

20 800 18 Cu 700 16 Cu 600 14 500 12 Zn 10 400 8 300 Zn 6 200 4 Pb

World mine reserves [Mtonne] reservesWorldmine Pb World mine production [Mtonne] productionWorldmine 100 2 0 0

Figure 1.1: Annual world mine production of Cu, Zn and Pb in 2007-2016 [1]. The general trend is an increase in both production and estimated reserves.

Sustainable mining and processing of low-grade ores will become more important in the future to meet an increasing metal demand, as the high- grade deposits are mined out [2]. It means that larger quantities of rock must be processed if the metal output is to be maintained. To achieve this, the

1 2 CHAPTER 1. INTRODUCTION machinery and equipment must be able to handle the higher throughput and therefore be larger. The achievable metal grade in and recovery to a concentrate produced at the mine site can be estimated with laboratory tests, where the beneficiation conditions are simulated. The procedure is very important since it forms the basis for whether metals can be extracted in a justifiable way with respect to both economy and environment. Flotation is one of the most common processes for separation of valuable from non-valuable minerals. It can be highly selective and is used to separate a variety of minerals. Unfortunately, factors such as optimal particle size and reagent dosages can only be determined experimentally [3]. Flotation tests in the laboratory are usually done batch-wise and in comparatively small cells. A typical laboratory cell has a volume several orders of magnitude smaller than the cells used in continuous, full-scale operations. Streams are often redirected to an earlier flotation stage to improve recoveries, which is difficult to do in the laboratory. Due to differences in operating conditions, flotation results may differ considerably between laboratory and plant. The laboratory results need to be adjusted to account for differences in parameters. This is not easily done, since flotation involves both physical and chemical phenomena in a three- phase system of gas, liquid and solids. It is however an important procedure, since unexpected results may lead to both economic and environmental issues due to poor recoveries, low grades or high reagent dosages.

1.1 Project aim

The purpose of this degree project was to survey the Boliden concentrator and later evaluate a model for upscaling of laboratory flotation results to plant flotation. The model is used by Boliden Mineral AB and was developed more than three decades ago in 1982. Changes in process technology, reagent additions and ore mineralogy may affect how accurately the model predicts plant results.

1.1.1 Limitations Some limitations had to be set due to time constraints and for practical reasons. The most important limitations were that only Kristineberg ore in a single production line was included in the project and that diﬀerences between size fractions were neglected. 1.2. BOLIDEN MINERAL AB 3

1.2 Boliden Mineral AB

The largest andIntroduction most importantMarket base metal producerOperations in SwedenCorporate Governance is BolidenFinancial reports Mineral AB. Figure 1.2 shows the diﬀerent production sites in Northern Eu- rope as of spring 2018 [4]. BOLIDEN’S MINES AND SMELTERS Boliden’s history began in 1925, when prospectors found a signiﬁcant amount of native Au at Fågelmyran outside Skellefteå. This was the start of what would later become one of the greatest Au mines in Europe. The town of Boliden and its company namesake had been born [4].

SMELTERS ● MINES ● SMELTERS ● HEADQUARTER Harjavalta AITIK ● ● KEVITSA Copper, nickel and precious metals Harjavalta produces copper, nickel matte, gold, silver and sulphuric acid. The raw ma- BOLIDEN AREA ● terials comprise copper concentrate from RÖNNSKÄR ● the Kylylahti mine and nickel concentrate ● KOKKOLA from the Kevitsa mine and external mines.

KYLYLAHTI ● P Copper 129 (126) ktonnes ● HARJAVALTA P Nickel in matte 31 (17) ktonnes O SEK 704 m (736) ● GARPENBERG A 495 (387) FTE ODDA ● STOCKHOLM ●

Odda

Zinc and zinc alloys for Europe’s BERGSÖE ● steel industry Odda produces pure zinc, zinc alloys, and sulphuric acid. The zinc production is ● TARA primarily exported to the European steel industry. A number of alloys have been developed in partnership with leading steel Figure 1.2: companies for the European automotive European production sites, located in Sweden, Finland, Norway andindustry. Ireland The aluminium fluoride producing [4]. Reproduced by courtesy of Boliden. unit of Odda was divested in 2016.

P Zinc 171 (163) ktonnes O SEK 314 m (390) SMELTERS The company started to grow and in 1935 were 2500 people in theA 285 work- (289) FTE force. Several other mines in the area such as Kristineberg, Adak, Bjurfors, Lainejaur andRönnskär Rävliden opened in theKokkola following years, before andBergsöe during World War II. Establishment of the Laisvall mine was especially important Copper smelter with electronic One of the world’s biggest zinc smelters Contributing to the lead metal ecocycle to secure the crucialscrap recycling Pb production in SwedenKokkola produces throughout zinc and zinc alloys, the war.Bergsöe For is theone of Europe’s biggest rest of the 20thRönnskärcentury, produces Boliden copper, gold, silver modernised and sulphuric its acid, production and silver in concentrate. technologies, recycling facilities infor lead batteries and lead, along with sulphuric acid, zinc clinker, The majority of the zinc concentrate the only secondary lead smelter in the frastructure andand several acquired other metals several as by-products. other The companies.comes from Boliden’s A mines large in Sweden, deal wasNordic made region. in The main products are lead 2003 when theraw Tara materials mine mainly comprise in Ireland concentrates and variousIreland and Finland. Nordic Kokkola facilities is the eighth wereand boughtlead alloys. The majority of the lead from Boliden’s mines and electronic scrap largest zinc smelter and the second production is sold to the European battery from Finnish Outokumpu.for recycling. The smelter The has an newe-scrap concernlargest in name Europe. became “New Boliden”.industry, with a smaller percentage used recycling capacity of 120 ktonnes per year, for lead sheet, amongst other things. making it one of the biggest in the world. P Zinc 291 (306) ktonnes Bergsöe works closely with Rönnskär and P Silver in concentrate 17 (16) tonnes Odda to handle certain materials. P Copper 207 (206) ktonnes O SEK 572 m (739) O SEK 852 m (727) A 539 (534) FTE P Lead alloys 46 (45) ktonnes A 775 (800) FTE O SEK 109 m (18) A 71 (71) FTE

Boliden Annual Report 2016 | 23 4 CHAPTER 1. INTRODUCTION

1.2.1 Kristineberg mine Kristineberg is one of Sweden’s deepest underground mines, with an estimated depth of 1300 metres. It is located in the Boliden area in Lycksele municipality and the annual production was 700 ktonne in 2013 [5]. The ore is of complex sulphide type with 5.6 % Zn, 0.5 % Cu, 0.3 % Pb, 34 g/t Ag and 0.5 g/t Au based on the probable reserve of 4.88 Mtonne in 2017 [6]. The most important minerals of value are sphalerite ((Zn,Fe)S), chalcopyrite

(CuFeS2) and galena (PbS). Ag and Au are found as electrum, amalgam, telluride and in native form [7]. Ore is primarily mined with the cut-and-ﬁll method [8] and transported 93 km by truck to the concentrator outside the town of Boliden [4]. Geologically, Kristineberg is a polymetallic volcanogenic massive sulphide ore deposit. Important non-valuable, so-called gangue minerals are pyrite (FeS2) and silicates like quartz, muscovite, chlorite, phlogopite, cordierite and andalusite. Several diﬀerent ore zones exist in the area, where zone A is the largest one and rich in Zn. It contains small amounts of Cu and pyrite is the dominating sulphide mineral. Zone B is also dominated by pyrite, but contains less Zn and more Cu compared with zone A. Another important site is called zone E, which has a more disseminated or vein-like structure and is richer in Cu and Au [7].

1.2.2 Boliden concentrator Ores from the mines of Kristineberg, Renström, Maurliden, Kankberg and slag from the copper smelter in Rönnskär are taken to the concentrator in Boliden. Concentrates of Au, Cu, Pb and Zn are produced by autogenous grinding followed by wet gravity separation and flotation, which is shown in figure 1.3. Crude Te (tellurium) and Au doré bars are produced with hydrometallurgical methods. In 2013, the concentrator treated 1.6 Mtonne of ore and 250 ktonnes of slag [5]. Three production lines exist: A, B and C. The first line is used to treat Rönnskär slag, while the other two treat ore in parallel processes. Ground ore is screened and the fines stream is directed to a Knelson separator. There, a wet gravity Au concentrate is produced before the stream enters a hydrocyclone battery. Flash flotation produces a Cu concentrate from the hydrocyclone underflow before it is reground. The hydrocyclone overflow enters the flotation circuit, where chalcopyrite and galena are floated together as a CuPb concentrate. They are later separated in a new series or flotation cells, where Cu and Pb concentrates 1.2. BOLIDEN MINERAL AB 5 are produced. A Zn concentrate is produced by flotation of sphalerite in a separate series of cells. Flotation residues, tailings are leached with air and NaCN at alkaline pH to dissolve Au and Ag, which are later adsorbed on activated carbon with the carbon-in-leach method. Elution is done according to the Anglo Ameri- can Research Laboratories’ process with HCl washing and NaOH-NaCN pre- soaking. Au and Ag are extracted from the eluate by electrowinning. The metals precipitate on the cathodes, which are later on calcined and smelted. Impure doré bars of Au and Ag are cast and sold for further refining.

BOLIDEN CONCENTRATOR

Feed ore Leaching

Flotation

Au conc. Au

Grinding Tailings pond Zn conc.

Pb conc. Sand to mine site Cu conc.

Figure 1.3: Principal ﬂow sheet. Note! The ﬂow sheet does not entirely depict the current process as described in the text. Reproduced by courtesy of Boliden.

1.2.3 Current scale-up model The scale-up model used today by the company was developed in 1982. After laboratory ﬂotation in several stages followed by sample preparation, metal grades in the products are determined. A typical procedure is illustrated in ﬁgure 1.4. The method is as follows [9]:

(i) A target concentrate grade is set and the material is ﬂoated in a number of cleaning steps to achieve this said grade. 6 CHAPTER 1. INTRODUCTION

(ii) Metal recovery to the cleaner middlings is calculated.

(iii) Half of the calculated recovery is added to the ﬁnal concentrate instead.

(iv) Concentrate weight is calculated based on achieved grade and mass of the metal in the concentrate after the cleaner material has been added.

(v) Recoveries of other metals to the concentrate are calculated in the same way, while their grades are calculated based on the concentrate mass.

If the aim is to get a concentrate with 25 % Cu, the material may be ﬂoated as seen in ﬁgure 1.4. In this imaginary case, 50 grams of Cu ended up in the concentrate and 20 grams in the cleaner middlings. The procedure is then to move 10 grams and get a concentrate with 60 grams of Cu instead. Since the grade was 25 %, the scaled concentrate mass is 240 grams. A minor element might be Pb, whose mass in the concentrate would be calculated in the same way. Since the concentrate mass has been calculated, it is now possible to estimate the plant grade of Pb in the concentrate.

Feed Tailings

Cleaner middlings

Concentrate

Figure 1.4: Typical laboratory ﬂow sheet. The cleaner middlings would normally be recirculated to an earlier ﬂotation stage, which alters grades and recoveries.

The method is used for Cu, Pb, Au, Ag, and Sb in Cu concentrates, as well as Pb and Zn in their own concentrates. It has been assumed that the grades of other metals in Pb and Zn concentrates can be calculated in the same way as for the Cu concentrate, but it has not been verified. The results are validated by comparing the Zn grade in final tailings between flotation test and the model. The grade should normally range between 0.1 and 1 %, often around 0.3-0.6 % [9]. 1.3. TECHNICAL BACKGROUND 7

1.3 Technical background Flotation is a complex process that involves diﬀerent adsorption mechanisms on the mineral surfaces. It is often carried out in diﬀerent cell banks aimed to increase either recovery or grade. The metallurgical performance of a circuit is investigated by sampling the streams, analysing their metal content followed by mass balancing.

1.3.1 Flotation theory To liberate grains of a valuable mineral from non-valuable gangue minerals, an ore must be crushed and ground to a very fine particle size. Even though the mineral of interest often is considerably heavier than other minerals, separation with wet gravity or similar methods is quite difficult. It is not an option to send all material directly to a smelter, since many metals and impurities are quite difficult to separate in the pyrometallurgical refining. Mineral processing is a necessary step to lower transportation and smelting costs, decrease slag volumes and minimize losses of metal that would otherwise cause economic or environmental issues [3]. Flotation is a mineral processing technique originally patented in 1877. It is the most important process for separation of valuable minerals in the world today and can be used to separate fine particles. Froth flotation is the process described here, but other types of flotation can be used for treatment of wastewater or water clarification. Froth flotation is also used for paper deinking [3][10]. The fundamental principle of flotation is that hydrophobic particles ad- here to air bubbles, while hydrophilic particles do not. If the valuable minerals can be made hydrophobic, they may be separated if air is introduced to an ore pulp. Bubbles rise to the surface above the pulp, where they form a mineralised froth layer enriched in the valuable mineral. Figure 1.5 shows a typical flotation cell, with air introduced from the bottom and a froth layer that is continuously removed. It is also possible to float the non-valuable material, which is then called reverse flotation. Mineral surfaces are made hydrophobic by addition of compounds known as collectors. In flotation of metallic sulphide minerals, these compounds are typically anions with polar and non-polar ends as shown in figure 1.6. The polar ends adsorb on the mineral surface, while the non-polar parts render the surface itself hydrophobic. Common collectors used in sulphide flotation are xanthates and dithiophosphates such as potassium amyl xanthate (PAX), isobutyl xanthate (IBX) and commercial mixtures as Danafloat™, although 8 CHAPTER 1. INTRODUCTION some minerals like molybdenite and native Au are hydrophobic by nature [3]. Adsorption of collector on the surface is always rivalled by adsorption of OH- ions, which means that the pH value is of paramount importance in flotation. The relative stability of metal-collector and metal-hydroxide compounds varies for different minerals, which means they can be separated selectively. If OH- ions are adsorbed on the surface, it will become hydrophilic and resist flotation as shown in reaction 1.1 where X- denotes an arbitrary collector anion. A higher pH will favour adsorption of OH- ions according to Le Chatelier’s principle.

Me−OH(ads) + X−(aq) )−−−−* Me−X(ads) + OH−(aq) (1.1) Some metal xanthates are too unstable and will directly dissolve in the pulp. In this case, compounds known as activators may be added before flotation. A typical example is addition of Cu2+ salts before sphalerite flota- + tion [7], which will react and form Cu2S on the surface. The Cu ions can then interact with the collector and enable flotation of sphalerite particles [3].

Air

Froth Froth

Bubbles

Pulp

Mineral

Rotor

Figure 1.5: Typical cell used in froth ﬂotation. Minerals are selectively made hydrophobic through addition of diﬀerent reagents. Air bubbles are produced at the bottom and bring hydrophobic particles to the surface.

As it sometimes can be diﬃcult to achieve suﬃcient selectivity in the separation, it is common practice to add so-called depressants to the pulp. These compounds may adsorb on certain minerals and make their surfaces hydrophilic. They can also change the redox potential of the pulp and make 2– hydrophobic species less stable [11]. CrO4 is a potent depressant of galena due to formation of insoluble and hydrophilic PbCrO4 on the surface. 1.3. TECHNICAL BACKGROUND 9

Since Cr(VI) is hazardous to both health and environment, an alternative is to use dextrin as a galena depressant during CuPb separation [12]. OH groups in dextrin interact with OH– ions adsorbed on the surface and form a hydrophilic layer that prevents ﬂotation [13]. Other depressants include 2– SO3 ions, which form insoluble species with many metals and can be used to depress pyrite and sphalerite. It is however believed to be a poor depressant

of chalcopyrite and the depressing action on galena is questioned [11]. ZnSO4 is another common depressant for sphalerite. The Zn2+ ions react with OH–

ions to form Zn(OH)2 that adsorbs on the sphalerite surfaces and hinders adsorption of the collector [12].

Amyl xanthate Isobutyl xanthate Amyl xanthate

Diethyl dithiophosphate Diethyl dithiophosphate

Figure 1.6: The structures of some common collectors and an example of how they may adsorb on a mineral surface.

For flotation to work properly, it requires formation of a relatively stable froth layer that prevents valuable minerals from re-entering the pulp, but is still easy to break down and transport once removed from the cell. Frothers are added to form the froth layer, typically in form of long and branched alco- hols or ethers. They should be able to adsorb on the water-air interface and stabilise bubbles, while having negligible collector properties toward gangue minerals. Methyl isobutyl carbinol, 1,1,3-triethoxybutane and the commercial product Dowfroth™ are some examples of common frothers used within the industry [12]. The frother influences many important process parameters such as bubble size, froth layer height and bubble strength. There is also a secondary enrichment in the froth layer, when water drains physically 10 CHAPTER 1. INTRODUCTION entrained gangue minerals. This enrichment is greatly affected by the froth height [14] and thus by the choice of frother. In conclusion, it can be said that flotation is a complex process with interaction between three different phases. Surface chemistry is important to get good recoveries and selectivity. The reactions are difficult to model and may involve both chemi- and physisorption, ionic and covalent bonding as well as other surface phenomena [12].

1.3.2 Flotation circuits A typical flotation circuit is shown in figure 1.7, although they often vary somewhat between different plants. The standard flotation process starts with one or several conditioning steps, when the different reagents are mixed with the pulp. This procedure is important to ensure that pulp chemistry is correct before separation begins. pH is often adjusted with either Ca(OH)2 or Na2CO3 and cheap acids, which translates to H2SO4 in practice.

ROUGHERS SCAVENGERS Feed Final tail

CLEANERS RECLEANERS Concentrate

Figure 1.7: The principle of a typical ﬂotation circuit.

After conditioning, the pulp enters the first bank of flotation cells known as roughers. Their purpose is to maximise the recovery and float off the main part of valuable material. Pulp is continuously fed to the rougher cells. Tailings continue to the next bank of cells called scavengers, whose purpose is to recover the last amount of floatable material before the pulp leaves the circuit as final tailings. The scavenger concentrate often contains mixed grains and is of low grade. Since this material is barely able to float, the froth layer is much thinner in these cells compared with the roughers. It means that material is rather easily recovered to the concentrate stream [3]. 1.3. TECHNICAL BACKGROUND 11

Scavenger concentrates are typically recirculated to the rougher feed after further liberation of valuable minerals by regrinding. Concentrates produced by rougher cells are usually of too low grade to be dewatered and directly sold to a smelter. The material needs to be floated one or several times again in series of cells known as cleaners and recleaners. Their purpose is to increase the concentrate grade, but it is important to re- member that an increase in grade gives a lower overall recovery. Cleaner cells add to investment and operating costs and a higher-value product of better quality may not always justify the extra expenses. The number of flotation steps should be just enough to give a grade and recovery that maximise the economic profit. The number of cleaner stages may vary between plants and for different ores. Flash flotation is a special type of process that sometimes can be beneficial to implement in a concentrator. It is used to prevent over-grinding of valuable minerals, since they often are both softer and heavier than gangue minerals. These properties may lead to high circulating loads in for example a closed grinding circuit. The underflow from a classifier such as a hydrocyclone may then be floated to remove liberated grains of valuable minerals before the stream is returned to the mill. The concentrate produced is often of sufficiently high grade to be directly dewatered and sold [15].

1.3.3 Mass balancing When samples have been taken from a concentrator, such as the flotation circuit, flow rates need to be mass balanced in order for the measurements to converge. Sampling and analytical errors lead to contradictory results, such as more material leaving than entering the circuit. This is not physically possible in steady-state operation and the system is said to be overdetermined. In order for the measurements to make sense, they are often adjusted with aid of a powerful reconciliation software such as MatBal or HSC. The procedure makes it possible to investigate unit performances, locate bottle necks and enables process simulation [16]. The simplest type of mass balance can be performed by solving equation 1.2, with n number of products pi, feed mass mf and elemental assays c(i−1)i. Put differently, a matrix row represents fractions of an element in products 1 → n. By multiplying with a product vector, the total mass of said element leaving the circuit is obtained. This should then equal the amount entering the circuit. If the matrix is called A, with product vector ~p and feed vector ~y, equation 1.3 can also be used to describe the process. 12 CHAPTER 1. INTRODUCTION

      1 1 ... 1 p1 mf                    c11 c12 . . . c1n  p2  c1f mf      =   (1.2)  . . . .   .   .   . . .. .   .   .              c(n−1)1 c(n−1)2 . . . c(n−1)n pn c(n−1)f mf

A~p = ~y (1.3) As earlier concluded, the procedure is often unsuitable since measurements rarely agree with each other and the system is then unsolvable. An important question is which elements to include, because assays often contain a lot of them. The main elements, such as Zn in a Zn concentrate are generally regarded as the most trustworthy in these cases [17]. In order to solve an overdetermined system of equations, some type of regression model must be used. An ordinary least-squares regression by mul- tiplication with the transpose A> of matrix A as seen in equation 1.4 is a simple way to do this. However, it is difficult to account for known differences in analytical precision and sample quality. It is a good idea to include as much information about the process as possible, but some elements are notoriously difficult to sample or analyse and should be given less importance in the regression model. When these types of adjustments are included, it is often called weighted least squares regression.

A>A~p = A>~y (1.4)

The weights wi are put in a matrix W and multiplied with equation 1.4 as seen in equation 1.5.

A>W A~p = A>W~y (1.5) Error estimation is done as in equation 1.6, where the total weighted sum of squares WSSQ is calculated. The estimates of vector ~y entries by equation 1.5 are here put in a solution vector yˆ with entries yˆi. A lower value should be read as a better ﬁt with the weighted regression model.

n X 2 WSSQ = wi(y î − yi) (1.6) i=1 In softwares such as HSC, weights are typically calculated from standard deviations set by the user. Table 1.1 presents the five different models available in HSC [16]. Error can either be calculated based on fixed standard 1.3. TECHNICAL BACKGROUND 13 deviations given as absolute or relative numbers, but also based on sample properties like mineral grade and particle size or analytical precision. As previously stated, some elements are notoriously difficult to analyse. Metals such as Cu, Zn and Ag are rather easy to analyse, but Pb may be more difficult due to low concentrations in the circuit. Au is an element easy to analyse with proper equipment, but difficult to sample since it exists in low concentrations and often as free-milling particles [17] suffering from nugget effects, which may give abnormally high assay grades.

Table 1.1: Error models available in HSC for data reconciliation [16].

Error model Description Fixed absolute Standard deviation set by the user. Fixed relative Standard deviation set by the user as a relative percentage. Mineral grade Relative standard deviation is calculated based on mineral grade. Size fraction Standard deviation is set diﬀerently based on the particle size. S-shape Combines relative standard deviation, detection limit and maximum possible standard deviation. Often used to estimate analytical errors.

In HSC, it is also possible to account for differences in sample quality [16]. If a sample is taken from a pump sump or another place where it is difficult to take material from the entire stream, it could be associated with a higher sampling error. Table 1.2 shows the three different classifications available in HSC to describe the quality of a sample as a relative standard deviation (RSD). A so-called “good” sample has material from the entire stream and is taken over a long time period to minimise problems with sudden fluctuations.

Table 1.2: Diﬀerent degrees of sample quality available in HSC [16].

Categorisation RSD Good 0.10 Moderate 0.15 Bad 0.30 Chapter 2 Review of scaling practises

Laboratory flotation tests are performed prior to pilot plant tests to get a first indication of achievable grade and recovery [18]. The tests can also be used to estimate parameters such as optimum grind size, reagent additions, flotation time and which type of equipment to use. Laboratory tests have the advantage of being cheap and rather simple, but pilot plant testing is often required to get a better long-term understanding of the process at a greater scale. Tests in the laboratory can be of both open circuit and locked cycle type, where only the last type is able to simulate the influence of recirculating material in the process. The scale-up model used by Boliden has the advantage of being simple compared with other methods, which will be seen here. Several models based on both experimental and theoretical approaches have been reported in literature [19] and are summarised in this section.

2.1 Open circuit tests

Simple laboratory tests involve one or several roughing stages followed by a number of cleaning stages to further remove gangue minerals from the concentrate. Open circuit tests are often the first ones performed to get information about achievable grade and recovery [20]. There is no recirculation of material in the cleaning stages and the valuable mineral has only a single chance to end up in its concentrate. The model evaluated in this project is of open circuit type and a typical procedure is illustrated in figure 1.4. Material that did not end up in the concentrate but would do so if it were recirculated, can be accounted for with a scale-up factor as done by Boliden. Open circuit tests are the least time-consuming and can be used to optimise cleaner efficiencies, regrinding mills and to indicate where problems with high circulating loads

14 2.1. OPEN CIRCUIT TESTS 15 may occur [21]. It has not been possible to find so much information about open circuit tests and exactly how they are used within the industry. Since they often are used to get a first indication of possible results and scale-up factors are based on proven in-house experience rather than scientific models, it might explain why research on this topic is rare.

2.1.1 Boliden practise As previously stated, the model investigated here can be regarded as of open circuit type. The overall procedure is described in section 1.2.3 and a more in-depth description is presented here. The model has previously been tested, verified and reported by Nils Johan Bolin in 1986 [9]. The abstract concludes with (translated from Swedish): “The scaled results are generally in good agreement with full-scale results, but the model should not be used blindly.” The report states that the model had been used since 1982 with good results. Levi, Pinos Altos (New Mexico) and Holmtjärn ores were used for the verification. The Holmtjärn case is especially interesting here, since it comes from the same area as the ore studied in this project. Levi ore from the Stekenjokk area typically contained 1.6 % Zn, 1.2 % Cu, 0.1 % Pb and 16 % S with 20 g/t Ag and 0.1 g/t Au as of 1984 when the mine was active [22]. The studied material had a similar composition, but with slightly higher Zn and Ag content of 1.9 % and 33 g/t respectively. A test was performed in 1981, with grinding of ore for 30 and 40 minutes in two separate tests, followed by flotation with cleaning in three stages. Table 2.1 presents relative deviations between results predicted by the scale-up model and the actual plant results. A negative number implies an underestimation by the model compared with full-scale. For example: The model predicted a Cu grade of 20.0 % compared with the actual plant value of 18.9 %. Corre- sponding values for the Cu recovery are 90 and 91 %. This gives a relative deviation of 20.0−18.9 ≈ +6 % for the grade and 90−91 ≈ −1 % for the recovery. 18.9 91 It can clearly be seen that the largest deviations between predicted and actual results were for elements of low grade. For example, Ag and Pb were of low grade in both flotation feed and Cu concentrate. Zn is usually of low grade in a Cu concentrate. The model quite accurately predicted the grades of Cu and Zn in their respective concentrates, but a seemingly better prediction was made for the recoveries. These overall conclusions were also presented by Bolin in 1986. 16 CHAPTER 2. REVIEW OF SCALING PRACTISES

Table 2.1: Relative deviations between predicted and full-scale plant results for the Levi ore in 1986.

Levi results ∆wt% Ag Cu Zn Pb Feed ∆Grade [%] +59 -13 -1 +113 ∆Grade [%] +86 +6 +94 0 Cu conc -19 ∆Recovery [%] -4 -1 +67 -63 ∆Grade [%] -4 Zn conc +3 ∆Recovery [%] -1

In the Pinos Altos case, ore was retrieved as drill cores. Since there was no full-scale operation, it was instead substituted with a pilot plant test. Little is known of this material, but the grades were 3.9 % Zn, 2.4 % Cu with 72 g/t Ag and 0.45 g/t Au based on the feed sample in 1983. Laboratory ﬂotation was done the same year with three cleaning stages and table 2.2 shows the diﬀerences between laboratory and pilot plant.

Table 2.2: Relative deviations between predicted and plant results for the Pinos Altos ore in 1983.

Pinos Altos results ∆wt% Au Ag Cu Zn Feed ∆Grade [%] +56 +22 +18 -2 ∆Grade [%] +10 -4 -7 +13 Cu conc +28 ∆Recovery [%] +1 +1 +48 ∆Grade [%] +8 Zn conc ∆Recovery [%] -5

The same basic type of pattern can be seen for Pinos Altos as for the Levi case. Behaviour of higher grade elements is more easily predicted, while the same is true for recoveries compared with grades. Holmtjärn is the last case reported by Bolin in 1986. The ore contained 3.2 % Zn, 0.40 % Cu and 0.30 % Pb with 81 g/t Ag and 6.9 g/t Au based on the plant feed. Au existed partly in free-milling native form. The site is known to have relatively high grades of arsenopyrite (FeAsS) [23]. Table 2.3 shows the deviations between actual plant and predicted results from 1984. It is written that the material was ground in a laboratory rod mill with 8 kg 2.1. OPEN CIRCUIT TESTS 17 of steel rods and then ﬂoated in a 2.5 litre WEMCO® cell, with cleaning in two stages for both Cu and Zn concentrates.

Table 2.3: Relative deviations between predicted and full-scale plant results for the Holmtjärn ore in 1984.

Holmtjärn results ∆wt% Au Ag Cu Zn Pb Feed ∆Grade [%] +36 +53 +20 +66 +70 ∆Grade [%] -34 -21 -29 +82 -30 Cu conc +62 ∆Recovery [%] -7 0 -4 +80 ∆Grade [%] -13 Zn conc ∆Recovery [%] -10

A pilot plant test was also performed with old waste rock from the Holmtjärn area, but the results were not particularly good in this case. Bolin’s conclusion is that weathered material probably is unsuitable for this type of test. The scale-up model has sometimes produced strange results. It has for example been used to approximate the metallurgical behaviour of a Zn mineralisation in the Kristineberg area in 2001 [24], but the results deviated a lot when the ore was eventually processed in 2006. As can be seen in table 2.4, the feed compositions are quite diﬀerent and the results should therefore be questioned.

Table 2.4: Deviations between predicted and full-scale plant results in 2006 for the new Zn mineralisation in Kristineberg ore.

Kristineberg Zn mineralisation results ∆wt% Ag Cu Zn Pb Feed ∆Grade [%] +154 -61 +221 +230 ∆Grade [%] +19 -64 +169 +338 CuPb conc -17 ∆Recovery [%] -61 -21 -49 +12 ∆Grade [%] +1 Zn conc +72 ∆Recovery [%] -46

Diﬀerences in ore mineralogy, poor analyses or test errors might explain these results, but it can also be a sign that the scale-up model is not always to be trusted. 18 CHAPTER 2. REVIEW OF SCALING PRACTISES

In conclusion, it can be said that the scale-up model is quite good at predicting the recoveries of a specific metal to its own concentrate. The largest deviations are for Zn in Cu concentrates, as identified by Bolin. It was probably easier to depress sphalerite in Cu flotation than predicted by the model. As previously stated, it should not be used blindly and it is written that adjustments may be required after the results have been reviewed.

2.2 Locked cycle tests

The problem with an open circuit test is that material from cleaner middlings are recirculated back to the process in a full-scale operation, which is not the case in those types of tests. The open circuit test may include a scale-up factor to account for this material, but another option is to do a locked cycle test. The cleaner middlings are then collected and mixed with fresh material in some way. The cleaning procedure is repeated to produce new middlings that are once again recirculated. This is done until the composition of the different streams are somewhat constant and steady-state is reached [25]. The test is able to simulate the results from a continuous, full-scale operation in a much better way but is also more time-consuming than an open circuit test. Material may be recirculated in different ways to find an optimal flotation setup [26]. Locked cycle tests are commonly used in mineral processing laboratories to confirm the results from an open circuit test. As previously stated, it is very time-consuming but maximises the amount of information possible to retrieve from a laboratory procedure. It may require five or more repetitions, which dramatically increases the amount of flotation tests needed to get a single result [26]. Since it is not known how many steps that are actually required, chemical assays after each repetition may also be needed. The difficulty to reach steady-state during a locked cycle test is a well- known problem that has been pointed out for a long time. Pilot plant testing is several orders of magnitude more expensive than laboratory experiments and the results are often questionable even in those types of tests. Previous studies have shown that split factors determined in a laboratory test can be used to simulate the steady-state results of a locked cycle test [27]. A split factor is defined as the fraction of an element reporting to tailings. Agar and Kipkie [27] analysed a flotation set-up with total recirculation of material from the cleaning stages. The simulation was based on drill cores and split factors were determined in a batch test. A computer program was constructed to ease the calculations. The results were then compared with 2.3. KINETIC MODELS 19 the results from an actual locked cycle test with eight cycles. The results from the simulation were generally in good agreement with the locked cycle results and could be used as an alternative procedure. This held true for both sulphidic copper-nickel ores and silver-bearing rock, which were the ones tested in the studies. It is an interesting method, since locked cycle tests are superior to open circuit tests, but in this way it was possible to combine the easiness of an open circuit test with the better results from a locked cycle test. The possibility to simulate a locked cycle test based on results from a batch, open circuit test as done by Agar and Kipkie [27] has been widely referenced in literature. The procedure was further investigated theoretically by Nishimura, Hirosue, Shobu and Jinnai in 1989 [28]. First order kinetics as presented in section 2.3 was used instead of split factors, with an assumption that the rate constants are unaffected by recycled cleaner middlings. It was found that the flotation time and value of rate constants are paramount for the number of iterations required to achieve steady state. Çilek [29] highlighted the fact that split factors may change if process chemistry and ore mineralogy cannot be kept constant. Split factors can for instance be different between the first and last iterations, since a lot of particles and chemicals have been recirculated. Such fluctuations mean that the test must be run several times to get reliable results, which increases the cost and is more time consuming. Çilek showed that an artificial neural network, which is a type of machine learning, can be used to account for such fluctuations. Split factors were determined experimentally for flotation of a complex sulphide ore with chalcopyrite and pyrite as the main valuables. Ten different types of flotation circuits were investigated. The model was able to account for fluctuations successfully and predict the metallurgical performance with a <4 % error without additional experiments, which must be seen as a very good result. It should however be noted that such a model is rather complex.

2.3 Kinetic models

Kinetic flotation models are some of the most widely used models for upscaling of flotation processes [19]. Most researchers agree that flotation performance is governed by kinetic factors, at least if the particles are kept in suspension and the pulp is perfectly mixed [30]. 20 CHAPTER 2. REVIEW OF SCALING PRACTISES

2.3.1 First order approach A common way to model the flotation process is to view it as particle-bubble collisions, similar to collisions between molecules in a chemical reaction. Since the airflow is kept constant, the decrease in particle concentration can be formulated as in equation 2.1. Cp describes the particle concentration of a mineral to be floated at time t, while k1 is a flotation rate constant. dC p = −k C (2.1) dt 1 p The differential equation can be solved and expressed in terms of recoveries as seen in equation 2.2.

R = 1 − e−k1t (2.2) Although this type of model is commonly used to describe flotation processes and they in general can be assumed to be of first order, problems arise when the rate constant k1 is to be determined. This is usually done with a laboratory batch test [19], but it has been reported that the value may change dramatically throughout the test. Studies on laboratory flotation of apatite have shown that the rate usually decreases with time, which can be explained by the fact that some part of the material is impossible to float. There exists an ultimate recovery R∞ that cannot be overcome. Equation 2.3 shows a better way to describe the recovery in a laboratory batch test for first order kinetics [30].

−k1t R = R∞ 1 − e (2.3) For operating plant cells, ideal mixing is often assumed and the recoveries can then be calculated with equation 2.4. Here, R is the recovery at a residence time of τ. k τ R = 1 (2.4) 1 + k1τ The residence time can be estimated from the flow rate of tailings and cell volume. Several programs including HSC Sim [31] use the first order approach to simulate flotation processes.

2.3.2 Second order approach A limited number of studies have concluded that second order kinetics with respect to particle concentration in some cases can be applicable [19]. Equa- 2.3. KINETIC MODELS 21

tion 2.5 shows this type of kinetics, where k2 is a second order ﬂotation rate constant. dC p = −k C2 (2.5) dt 2 p If integrated and rewritten in terms of recoveries, the result is equation 2.6 [19].

R2 k t R = ∞ 2 (2.6) 1 + R∞k2t Refractory, native Au associated to pyrite is one example of a mineral that is reported to follow second order kinetics [32].

2.3.3 Klimpel approach For first order kinetics, the rate constant is assumed constant. In practice, it depends on several operating parameters. This is something that the Klimpel model presented in equation 2.7 tries to address [33], where kK denotes the modified first order rate constant.

 1  −kK t R = R∞1 − 1 − e  (2.7) kKt

2.3.4 Kelsall approach An interesting approach is presented in equation 2.8, which divides the mineral particles into fast-floating and slow-floating material. φ denotes percentage of slow-floating material, ks slow-floating rate constant and kf a fast-floating rate constant. This equation is known as the Kelsall model.

R = (100 − φ)(1 − e−kf t) + φ(1 − e−kst) (2.8) The equation has for example been used to predict full-scale results from a pilot plant flotation test of Cu ore with associated Mo and Au minerals, but could also be applied to laboratory tests in one study [34]. The plant was located at the Ernest Henry Mine in Australia. Both the concentrator and pilot plant were sampled and mass balanced with MatBal 8.0. The residence time in rougher cells was estimated from the tailings flow rate and cell volume. A plot of the recoveries as functions of flotation time was made and fitted with equation 2.8 using KinCalc software. The ratios between constants φ, ks and kf for concentrator and pilot plant were calculated respectively. These ratios 22 CHAPTER 2. REVIEW OF SCALING PRACTISES were then used to estimate the parameters for the parallel flotation line in the plant based on results from another pilot test. The recoveries for a parallel line were calculated with equation 2.8 and then compared with actual, mass balanced data from that line. The results were seen as satisfactory.

2.4 Final thoughts Many different models exist for simulation and upscaling of flotation processes. Some of these are very simple and based on data easily retrieved from laboratory experiments. Others are more theoretically formulated and require determination of several parameters in order to be useful. Newcombe, Bradshaw and Wightman [35] have criticised the general laboratory procedure for estimation of full-scale metallurgical performance. It was wisely argued that many flotation circuits are equipped with a flash flotation stage that is often not accounted for during testing, but may remove up to 50 % of the valuable material before the pulp enters the actual flotation circuit. Newcombe et al pointed out that entirely different reagents often are used in flash flotation and with a coarser material, typically from a hydrocyclone underflow. This is completely different compared with the laboratory procedure, which commonly uses finer material and reagent dosages equiv- alent to the main flotation circuit. It was also criticised that most models require calibration against plant results to be successful. This is not ideal since many ores or mineralisations to be investigated are not processed today. It could therefore be difficult to predict their full-scale behaviour with some of the models presented in this chapter. Chapter 3 Method

The project was divided into two parts: sampling and laboratory tests, followed by mass balancing, simulations and calculations in HSC. In order to answer whether the scale-up model is still suitable, it had to be compared with other models of similar complexity. Even if a model is very accurate, it is not certain that values and parameters needed are easily retrieved. More complicated models that require a lot of diﬀerent input data and depend on many parameters were therefore not investigated. The model used by Boli- den would be seen as veriﬁed if it was able to predict the plant results with the same accuracy as presented in section 2.1.1. Even if the model was able to predict the results with the same accuracy, it would be unwise to use it if competing models give better results.

3.1 Sampling and sample preparation

Section B of the concentrator was sampled on 13 February 2018 and five people were involved. Each person was given a specific part of the circuit to sample, such as the Zn flotation or CuPb separation. At that time, Kristineberg ore had been processed for two days after a change from Maurliden ore on 11 February. The date was chosen to give the process a chance to reach steady-state before samples were taken. The procedure took four hours and a sample was taken every 30 minutes, which means that pulp was collected nine times for every sample. Pulp volumes between one and two litres in total per sample were collected in an as representative way as possible. All the streams sampled can be seen in figure 3.1 and are listed in table 3.1. Unfortunately, streams 14 and 15 were never sampled as planned and pulp had to be collected a couple of hours later in a single take. These streams may be redundant for the overall mass balance but were in any case included.

23 24 CHAPTER 3. METHOD

Eight pulp samples were taken for laboratory flotation tests. Two litres were collected respectively, except for the CuPb concentrate where only 1.2 litre samples were retrieved. The different streams from which pulp was taken to the flotation tests are listed in table 3.3.

Table 3.1: The sampled streams. Note that some numbers have been excluded. These streams simply do not exist. Some extra numbers were kept if more sampling points were to be added later in the planning phase. In total, 54 streams are listed.

No Stream name No Stream name 1 Cyclone overflow 30 Cu 1 cleaner concentrate 2 B3 mill recirculation 31 Cu 2 cleaner concentrate 3 CuPb flotation feed 32 Cu concentrate 4 CuPb rougher concentrate 33 Zn flotation feed 5 CuPb rougher mp 34 Zn 1 rougher concentrate 6 CuPb mp 35 Zn 2A rougher concentrate 7 CuPb scavenger concentrate 36 Zn 2B rougher concentrate 8 CuPb 1 cleaner concentrate 37 Zn rougher mp 9 CuPb 1 cleaner mp 38 Zn scavenger concentrate A 10 CuPb 2 cleaner mp 39 Zn scavenger concentrate B 11 CuPb 2 cleaner concentrate 40 Zn 1 cleaner feed 12 CuPb 3 cleaner mp 41 Zn 1 cleaner mp 13 CuPb concentrate 42 Zn 1 cleaner concentrate 14 B31 pump overflow 43 Zn 2 cleaner mp 15 B3 mill product 44 Zn 2 cleaner concentrate 18 Total Cu rougher concentrate 45 Zn 3 cleaner concentrate 19 Total recirculation 46 Zn 3 cleaner mp 20 CuPb separation feed 47 Zn 4 cleaner mp 21 Cu 1 rougher concentrate 48 Zn concentrate 22 Cu 2 rougher concentrate 49 To B41 pump 23 Cu scavenger concentrate 50 B41 pump overflow 24 Cu 1 rougher mp 51 B4 mill product 25 Cu 2 rougher mp 52 Zn mp 26 Pb concentrate 53 Zn 1 rougher mp 27 Cu 1 cleaner mp 54 Zn recirculation 28 Cu 2 cleaner mp 59 Knelson Au concentrate 29 Cu 3 cleaner mp 60 Flash Cu concentrate 3.1. SAMPLING AND SAMPLE PREPARATION 25 Simplified flowsheet. Stream names can be seen in table 3.1. Label “13, 20” shows that two samples were taken from the same stream at different places. Note! A single mill is used to denote the grinding circuit. Regrinding mills are not shown. Figure 3.1: 26 CHAPTER 3. METHOD

3.1.1 Flow rate estimation Since most of the concentrator was difficult to sample with respect to flow rates, they had to be measured where possible. The sampled streams seen in table 3.2 were in most cases concentrate froth collected directly from the flotation cells. A small 2 litre bucket with 0.14 metre diameter d was filled and clocked. From this procedure, the solids mass flow into the bucket could be calculated after the sample had been filtered and dried. In most cases, the procedure was repeated 3-4 times to get an average bucket flow rate Mb. The number of cells in series n and cell length L were used to estimate the total solids mass flow Mtot from the cell bank as seen in equation 3.1.

nL M = M (3.1) tot d b

Table 3.2: Streams where the ﬂow rates were measured.

No Stream name Comments 8 CuPb 1 cleaner concentrate Entire stream sampled 11 CuPb 2 cleaner concentrate 20 CuPb separation feed Entire stream sampled 21 Cu 1 rougher concentrate 22 Cu 2 rougher concentrate 23 Cu scavenger concentrate 31 Cu 2 cleaner concentrate 32 Cu concentrate 34 Zn 1 rougher concentrate 35 Zn 2A rougher concentrate 36 Zn 2B rougher concentrate

The purpose of this measurement was to obtain input values for the mass balance in HSC. The procedure was only able to give a rough estimate and several factors had to be considered. For example, it is not certain that froth was produced evenly over the bank. This was somewhat accounted for by moving the bucket along the bank instead of keeping it still in one place, but it still gives some errors. The method was used for all streams in table 3.2 except 8 and 20, where the entire stream could be directly sampled. Online measurements of feed ore and concentrate streams were also included to aid the program with the mass balance. 3.2. FLOTATION TESTS 27

3.2 Flotation tests

The different streams sampled for the flotation tests are listed in table 3.3, together with three different types of test performed. Mixing and flotation times in roughing tests were pre-determined, but cleaning was done until no more particles appeared in the froth and the bubbles seemed barren. A summary of all tests can be seen in figure 3.2. If, for example, the CuPb concentrate was sampled for a roughing test, it means that only the part labelled “Cu ROUGHING (×3)” was regarded in that case.

Table 3.3: The streams sampled for testing and tests performed. In some tests, the rougher concentrate was reground. The procedure is summarised in ﬁgure 3.2.

Test Stream name Roughing Cleaning Comment 1 Cyclone overflow 3× CuPb, 3× Zn 2 Cyclone overflow 3× CuPb, 3× Zn 4× CuPb, 4× Zn 3 Cyclone overflow 3× CuPb, 3× Zn 4× CuPb, 4× Zn Regrind 4 CuPb mp 3× Zn 5 CuPb mp 3× Zn 4× Zn 6 CuPb mp 3× Zn 4× Zn Regrind 7 CuPb concentrate 3× Cu 8 CuPb concentrate 3× Cu 4× Cu

All tests were performed in a 2.7 litre cylindrical WEMCO® laboratory cell, with self-aeration and 1200 rpm rotor speed. Froth was manually removed with 30-40 strokes per minute on average. Reagent dosages were estimated based on pulp weight and an educated guess of the dilution ratio in the diﬀerent streams. The pH value was adjusted with Ca(OH)2 in all cases. Reagent dosages in this section are presented as grams of reagent per tonne of solids in pulp. Conditioning time was generally 5 minutes unless otherwise stated. The reagent additions were based on dosages used in a similar laboratory study on Renström ore a few months earlier. NaHSO3 was added to a much lesser extent for the Kristineberg ore in full-scale operation, which meant that the dosages were lowered a bit compared with the Renström case. Roughing tests were used to check the reliability of results retrieved from the cleaning tests, which are the ones used in Boliden’s scale-up model. They also provide a limited amount of kinetic information, which will later be described in section 3.5. Tests that include a regrinding step provide information about the inﬂuence from the regrinding circuit. 28 CHAPTER 3. METHOD

Cyclone overflow

CONDITIONING

CuPb ROUGHING (×3)

CuPb CLEANING (×4)

CuPb mp

CONDITIONING CuPb concentrate

Zn ROUGHING (×3) CONDITIONING

Cu ROUGHING (×3)

Cu CLEANING (×4)

Zn CLEANING (×4) Zn mp Pb concentrate (Cu mp)

Zn concentrate Cu concentrate

Figure 3.2: Summary of the laboratory ﬂotation tests. Three diﬀerent streams were sampled as described in table 3.3.

3.2.1 Cyclone overﬂow – roughing test Pulp was added to the WEMCO® cell and conditioned for 5 minutes before

CuPb ﬂotation with 1000 g/t ZnSO4 and 100 g/t NaHSO3. pH was adjusted to 10.5 with 60 g/t Ca(OH)2. A total amount of 1.5 kg solids was assumed. Flotation was done in three stages with PAX and D871 collectors as presented in tables 3.4 and 3.5. Water and Dowfroth 250 were added when needed to maintain a constant pulp volume and froth layer. The concentrates were removed separately. 3.2. FLOTATION TESTS 29

Table 3.4: Flotation parameters and reagent dosages for the cyclone overﬂow CuPb roughing test.

Parameters CuPb rough 1 rough 2 rough 3 PAX [g/t] 10 5 D871 [g/t] 30 20 10 Dowfroth 250 [drops] 1 Mixing time [min] 1 1 1 Flotation time [min] 2 2 3

After CuPb ﬂotation, the pulp was conditioned once again for 5 minutes with 300 g/t CuSO4 · 5 H2O and pH was raised to 11.5 with 160 g/t Ca(OH)2. As for CuPb ﬂotation, Zn concentrates were removed separately.

Table 3.5: Flotation parameters and reagent dosages for the cyclone overﬂow Zn roughing test.

Zn rough 1 rough 2 rough 3 IBX [g/t] 30 20 10 Dowfroth 250 [drops] 1 Mixing time [min] 1 1 1 Flotation time [min] 2 3 3

3.2.2 Cyclone overflow – cleaning test In the cleaning test, three CuPb rougher concentrates were produced in the same manner as explained in section 3.2.1. However, this time the concentrates were mixed together and cleaned in four stages. The pulp was conditioned with 1000 g/t ZnSO4 and 100 g/t NaHSO3 prior to roughing. pH was adjusted to 10.5 with 50 g/t Ca(OH)2. Additions of collector and other reagents were identical to the roughing test as presented in tables 3.4 and 3.5. The concentrates were re-floated without reagent additions, except for small amounts of Ca(OH)2 needed to maintain pH at 10.5. Flotation times were 1 minute 43 seconds, 1 minute 29 seconds, 1 minute 31 seconds and 1 minute 19 seconds respectively. The non-floating material from the first cleaning stage was mixed with the non-floating material from the roughing flotations. A Zn concentrate was produced in a similar procedure, with three roughing and four cleaning stages. The pulp was first conditioned with 300 g/t

CuSO4 · 5 H2O and pH adjusted to 11.5 with 210 g/t Ca(OH)2. The rougher 30 CHAPTER 3. METHOD

concentrate was conditioned with 50 g/t CuSO4 · 5 H2O before the ﬁrst cleaning stage and Ca(OH)2 before every stage to keep pH at 11.5. The respective ﬂotation times were 1 minute 47 seconds, 2 minutes 32 seconds, 1 minute 58 seconds and 1 minute 43 seconds.

3.2.3 Cyclone overﬂow – cleaning test with regrind

This test was conducted in exactly the same way and with the same reagent additions as explained in section 3.2.2. The diﬀerence is that both CuPb and Zn rougher concentrates respectively were reground before the cleaning procedure. A laboratory rod mill with 8 kg stainless steel rods was used with a grinding time of 5 minutes at 48 rpm. Mill dimensions were 190 mm in diameter and 240 mm in length. The concentrates were put into the mill with small water additions. Cleaning times were 4 minutes 55 seconds, 3 minutes 48 seconds, 2 minutes 50 seconds and 3 minutes 17 seconds for the CuPb concentrate. One drop of Dowfroth 250 was added in the third stage. Corresponding times for the Zn concentrate were 5 minutes 18 seconds, 5 minutes 7 seconds, 3 minutes 0 seconds and 2 minutes 20 seconds with one drop of Dowfroth in the fourth stage.

3.2.4 CuPb mp – roughing test

This pulp was unfortunately conditioned with 40 g/t CuSO4 · 5 H2O instead of the aimed 400 g/t. The initial pH was 11.5, which means that no Ca(OH)2 was added. Table 3.6 describes the roughing procedure and other reagent additions. A total amount of solids around 1.1 kg was assumed.

Table 3.6: Flotation parameters and reagent dosages for the CuPb mp roughing test.

Parameters Zn rough 1 rough 2 rough 3 IBX [g/t] 30 20 10 Dowfroth 250 [drops] 1 Mixing time [min] 1 1 1 Flotation time [min] 2 3 3 3.2. FLOTATION TESTS 31

3.2.5 CuPb mp – cleaning test A Zn rougher concentrate was produced in the same way as explained in section 3.2.4, but the three concentrates were mixed together before cleaning.

The pulp was conditioned with 400 g/t CuSO4 · 5 H2O and pH raised to 11.5 with 97 g/t Ca(OH)2. pH was once again raised to 11.5 before each cleaning step with small additions of Ca(OH)2. Flotation times in the four cleaning stages were 1 minute 58 seconds, 2 minutes 20 seconds, 1 minute 55 seconds and 2 minutes 12 seconds.

3.2.6 CuPb mp – cleaning test with regrind The diﬀerence between this test and the one described in section 3.2.5, is that the rougher concentrate was reground before it was re-ﬂoated. The same laboratory rod mill with 8 kg stainless steel rods and 5 minutes grinding time described in section 3.2.3 was also used here. Cleaning times were 3 minutes 17 seconds, 4 minutes 44 seconds, 2 minutes 40 seconds and 3 minutes 7 seconds. Frother was added in the last two cleaning stages.

3.2.7 CuPb concentrate – roughing test Since this pulp contained a lot of floatable material, only 1.2 litres were sampled instead of the 2 litres as were otherwise the case. The pulp was conditioned for 3 minutes with 300 g/t dextrin and pH was raised to 12 with 620 g/t Ca(OH)2. Table 3.7 describes the flotation procedure and other reagent additions. Since chalcopyrite is the mineral that was floated, the froth produced a Cu concentrate while the non-floatable pulp formed a Pb concentrate instead. Reagents were added based on an estimated 700 grams of solids.

Table 3.7: Flotation parameters and reagent dosages for the CuPb concentrate roughing test.

Parameters Cu rough 1 rough 2 rough 3 D507 [g/t] 10 10 10 Mixing time [min] 1 1 1 Flotation time [min] 3 3 5 32 CHAPTER 3. METHOD

3.2.8 CuPb concentrate – cleaning test A rougher concentrate was produced as explained in section 3.2.7. About

690 g/t of Ca(OH)2 was needed to reach pH 12. Small amounts of Ca(OH)2 were added before each cleaning stage to keep pH at 12. Flotation times were 7 minutes 49 seconds, 5 minutes 58 seconds, 4 minutes 41 seconds and 4 minutes 28 seconds. No frother was added in the cleaning procedure.

3.3 Analysis procedure

All the samples listed in table 3.1 were ﬁltered under vacuum after the pulp weight had been measured. The solids were dried at 65 °C overnight. The dry material was weighed and the pulp’s dilution ratio D was calculated together with the solids weight percentage Fw. Smaller samples were obtained with a riﬄe splitter to ensure representativeness. Density of solids ρs was measured with an AccuPyc II 1340 He gas pycnometer from Micromeritics. About 30-40 grams were used for each analysis at 21 psi gas pressure. These measurements were partly done since it could be interesting to do a mass balance with minerals instead of elements in the future. Equation 3.2 could then be used to calculate the density of pulp ρp.

D + 1 3 ρp = 3 g/cm (3.2) D + 1 g/cm ρs

Samples of 100-150 grams were sent to ALS Geochemistry in Piteå for assays of Fe, Cu, Zn, Pb, Ag, Au, Bi, As and S. Procedure ME-OG62 was used for all elements except Au and S, which includes HNO3-HClO4-HF- HCl digestion of 0.4 gram material followed by analysis with inductively coupled plasma atomic emission spectroscopy (ICP-AES). For Au, the fire assay procedure Au-CON01 was used, which required 30 grams of material. In the cases where a metal was of too high grade to be determined with ME- OG62, procedure CON02 was used instead. It is developed for concentrates and includes titration of a 4 gram digested sample with gravimetric finish instead of ICP-AES. The LECO furnace technique according to S-IR08 with 0.01-0.1 grams of material per sample was used to analyse S content. Since only small amounts of material are produced in several of the laboratory flotation tests, analysis of Au was excluded for these samples. It means that the Au assays only were included to aid the mass balance and since it was of interest from a plant point-of-view. 3.4. MASS BALANCING IN HSC 33

3.4 Mass balancing in HSC

The assays and flow rates measured were used to mass balance the circuit in HSC 9.3 with weighted least squares regression. Since some elements were of too low grade to be measured, they had to be guesstimated by the program. Flow rates were generally given high relative standard deviation, or low weights if put differently. Low weights were also given to Au, Bi and As since they are either difficult to sample or near the detection limit. Higher weights were given to Zn and Cu, especially in their own concentrates and streams thereabout. Ag, Fe and S were assigned weights somewhere in between the two cases. The values had to be adjusted up and down in order to get the mass balance to converge and give reasonable values with a low WSSQ. Appendix B presents the actual weights used in HSC as seen in table B.5. The error model “fixed relative” was used for all elements in every stream. Standard deviations were set at 30 % for solids and Au, 20 % for Ag, 50-70 % for As and Bi, 1-5 % for Cu and Zn with 10-15 % for the others. Since it was more or less difficult to sample the different streams, they were given different sampling errors. If a sample was taken from a pump sump or large chute, it was classified as “moderate”. The two samples from streams 14 and 15 taken at a later time and a tube tap sample were seen as “bad”. The classification “good” was used for all other streams since they were relatively easy to sample.

3.5 Model evaluation

The current scale-up model was used as described in section 1.2.3. All the results from the cleaning tests were scaled, but only the most successful test for each of the three different flotations was compared with the plant results. Successful in this case means that the grade and recovery of the main element seemed reasonable and with an acceptable Zn grade in the final tailings. As described in section 1.2.3, it is assumed that the roughing tests give Zn grades in tailings that are close to the values in the plant and were therefore used as guidelines. The values should range from 0.1 to 1 % and normally between 0.3 and 0.6 %. This is also the main use of the roughing tests in this project although they could later be used for indicative, kinetic studies. The behaviour of minor elements in Zn concentrates was also evaluated, since the model has been assumed to work for them in the same way it does for Cu concentrates. An element not described by the model but included in this project was Bi, which was treated in the same way as As and Zn in Cu 34 CHAPTER 3. METHOD concentrates. It means that their grades are assumed to be unaffected by the scale-up process and are set by the laboratory test.

3.5.1 Locked cycle simulation A very simple model is the one presented by Agar and Kipkie, previously reviewed in section 2.2. An Excel sheet was constructed to evaluate the model. The three roughing stages were considered as a single separation unit, followed by four cleaning stages as seen in ﬁgure 3.3.

Tailings

Feed S2

S5 Concentrate

Figure 3.3: Flow sheet used to evaluate the simulated locked cycle model.

Split factor Si for separation i was defined as the fraction of a certain element that reports to the middlings fraction. Values for Si were calculated for all separations and all analysed elements based on the laboratory flotation results. Product weight was also treated in the same way as the elements, which made it possible to calculate the amount of material that reports to concentrate and tailings respectively. Equation 1.2 on page 12 could then be used to mass balance each fraction of every element that reports to the different streams seen in figure 3.3.

3.5.2 Comment on kinetic models It was decided to not evaluate the kinetics models, since the data received from the flotation experiments only gives a limited amount of kinetic information. They were also excluded due to time constraints of the project. The 3.5. MODEL EVALUATION 35 kinetic models would require more simulations in HSC and deserve a project on their own. The main problem with a kinetic simulation in this project is that it was not possible to retrieve kinetic information in the cleaning stages. There is also a limited number of data points since only three roughing stages were used. It is not possible to fit a kinetic model to the plant data, since it would require cell-by-cell sampling that was not done in this project. Every model ever made would give a perfect fit with only a single data point. Some kinetic model fitting was done however, based on the laboratory roughing tests. The Klimpel model was chosen since it requires relatively few data points to give valid results. The calculated parameters are found in table B.6 in appendix B. The results from the roughing tests were put in HSC Sim and its built-in function for equation-fitting with the Klimpel model was used to retrieve the parameters. Chapter 4 Results

The aim of this project was to investigate if the current scale-up model is still usable and gives reliable results. Table 4.1 shows the deviations between the predicted and actual results for CuPb, Cu and Zn concentrates in the same manner as described in section 2.1.1. Plant results are compared with the results from cleaning tests 3.2.2, 3.2.5 and 3.2.8 without regrind. A comparison with other tests is presented in section 4.4 and appendix C.

4.1 Current model results

Table 4.1 shows that the relative deviations are in the same order of magnitude as when the model was presented by Bolin in 1986.

Table 4.1: Relative diﬀerences in grades and recoveries with the current model.

Plant vs current model results ∆wt% Ag Cu Zn Pb As Bi Feed ∆Grade [%] +15 +28 -27 +15 -27 +67 ∆Grade [%] -12 +18 -62 -31 -52 -50 CuPb conc -3 ∆Recovery [%] -26 -11 -67 -42 -33 -72 ∆Grade [%] +1 +14 -37 +240 -67 -81 Cu conc -19 ∆Recovery [%] -13 -17 -35 +165 -61 -79 ∆Grade [%] -10 +518 -31 -50 +7 -19 Pb conc +39 ∆Recovery [%] +32 +850 +21 -34 +118 +51 ∆Grade [%] -16 -33 -3 -34 -85 -52 Zn conc -33 ∆Recovery [%] -16 -35 -11 -31 -90 -90

36 4.2. LOCKED CYCLE RESULTS 37

As earlier concluded, the largest deviations are seen for elements of low grade such as Pb, As and Bi in the Cu concentrate. On the other hand, the model more accurately predicts the grade and recovery of the main element to its concentrate. The Cu grade in the CuPb concentrate was 18 % higher and the recovery 11 % lower in the scaled laboratory results compared with plant results. Corresponding ﬁgures for the Cu concentrate is 14 % higher grade and 17 % lower recovery. For the Zn concentrate, Zn grade was 3 % lower and recovery 11 % lower compared with the plant results. The deviation in mass pull is small, 3 %, for the CuPb concentrate but a bit higher at 19, 39 and 33 % for the other concentrates. Largest deviations were seen for the low-grade impurities like As and Bi. The deviations were in a couple of cases almost as high as hundred per cent. The Pb concentrate clearly deviates from the described trend, since the predicted Pb grade is far worse compared with main metals in other concentrates. The calculation was however not the same in this case, since the Pb concentrate is just the tailings material from the chalcopyrite ﬂotation. In other words, the scale-up model was not used in the same way as for the other concentrates. Regrinding of the concentrates had a great impact on grade and recovery. This data is also presented in appendix B, but it can be mentioned that the Cu grade increased from 20.0 to 23.6 % and recovery from 74 to 82 % with respect to the CuPb concentrate.

4.2 Locked cycle results

If the results predicted by the locked cycle model are compared in the same way, it can be seen in table 4.2 that the deviations in grade are much higher although the recoveries are more accurately predicted. An exception from the general trend is once again the Pb concentrate, for which the locked cycle model made a worse prediction compared with the current model. The locked cycle model predicted a 62 % lower grade and 7 % lower recovery of Cu to the CuPb concentrate compared with the plant results. It also predicted a 19 % lower grade and 1 % higher recovery of Cu to the Cu concentrate. The same ﬁgures for Zn in the Zn concentrate are 49 % lower grade and 1 % higher recovery. It can also be seen that the deviations are smaller for the impurities As and Bi. The mass pull estimation was worse than predicted by the current model. 38 CHAPTER 4. RESULTS

Table 4.2: Relative diﬀerences in grades and recoveries with the locked cycle model.

Plant vs locked cycle results ∆wt% Ag Cu Zn Pb As Bi ∆Grade [%] -68 -62 -43 -74 -19 -74 CuPb conc +217 ∆Recovery [%] -11 -7 +133 -28 +233 -50 ∆Grade [%] -15 -19 +32 +163 -33 -27 Cu conc +39 ∆Recovery [%] +26 +1 +132 +253 +38 +36 ∆Grade [%] +63 +39 -9 +168 -8 +206 Pb conc -81 ∆Recovery [%] -68 -50 -78 -52 -74 -23 ∆Grade [%] -47 -60 -49 -68 -56 -48 Zn conc +45 ∆Recovery [%] +13 -18 +1 -28 -25 -76

4.3 Combining models – “Mixed cycle”

As is evident from tables 4.1 and 4.2, the current model is much better at predicting the concentrate grades, but the locked cycle model seems to give more accurate recoveries. If the respective model’s strengths are combined to a type of “mixed cycle” model, the results are those presented in table 4.3.

Table 4.3: Relative diﬀerences in grades and recoveries with the mixed cycle model.

Plant vs mixed cycle results ∆wt% Ag Cu Zn Pb As Bi ∆Grade [%] -1 +18 +76 -19 +150 -19 CuPb conc +3 ∆Recovery [%] -11 -7 +133 -28 +233 -50 ∆Grade [%] +19 +14 +85 +268 -6 -60 Cu conc -1 ∆Recovery [%] +26 +1 +132 +253 +38 +36 ∆Grade [%] -70 -74 -83 -50 -83 -43 Pb conc +1 ∆Recovery [%] -68 -50 -78 -52 -74 -23 ∆Grade [%] +1 -27 -3 -39 -17 +5 Zn conc -25 ∆Recovery [%] +13 -18 +1 -28 -25 -76

The mass pull is calculated in the same way as for the current model with aid of the main element grade, but with the main element mass as calculated 4.4. MODEL COMPARISON 39 by the locked cycle model. This gives a model that predicts a 18 % higher grade and 7 % lower recovery of Cu to the CuPb concentrate, 14 % higher grade and 1 % higher recovery of Cu to the Cu concentrate and ﬁnally 3 % lower grade and 1 % higher recovery of Zn to the Zn concentrate. These ﬁgures are better than those predicted by the two earlier models. The mass pull estimation is also far better, especially for the Cu concentrate.

4.3.1 Worked example To show how the mixed cycle model works, a hypothetical example is presented in this section. If a Zn cleaning test produces four products of cleaner middlings, the fraction of material entering the middlings product in each flotation is calculated and the amount of material in the final concentrate is calculated as the locked cycle model describes. This was earlier presented in sections 2.2 and 3.5.1. The next step is to use the concentrate grade retrieved in the laboratory test. In this case, it can be assumed that 58 % Zn was obtained. If the locked cycle simulation yielded 20 grams of Zn in the final concentrate, the concentrate mass can be calculated as seen in equation 4.1. This is a way to estimate the true amount of concentrate if the middlings would have been recirculated as in figure 3.3. Grades of other elements can now be calculated since the concentrate mass has been estimated.

20 g M = ≈ 34.5 g (4.1) conc 0.58

4.4 Model comparison

A full comparison of the different models with plant results is presented in figures 4.1-4.8. In contrast to the earlier results in tabular format, these figures present the actual grades and recoveries for the different concentrates. As earlier mentioned, a total comparison where all laboratory tests are included is found in appendix C. It is once again clear that the behaviour of higher-grade elements is more easily predicted. Figure 4.5 shows that all models gave deplorable predictions for the Pb concentrate. The locked cycle model is escpecially bad, since it predicted a grade of 86.4 % Pb as well as 18.2 % Zn. This is not physically possible but there is nothing that prevents this model from giving those kinds of results.

Cu concentrate - Recoveries

99 99

100 98

91 91 91 91

86 86 81

60 60 53

60 53

39 [wt%]

40 37

26 24

20 17 8

0 Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

40 CHAPTER 4. RESULTS

2018-05-04 CuPb concentrate - Grades

25.0 1500

1368

1356

20.6 20.6 CuPb concentrate - Grades 1202

20.0 18.3 1200 17.5

25.0 1500

1368 1356

15.0 900

12.7

20.6 20.6

1202

10.4 10.3

20.0 18.3 1200 17.5

10.0 8.8 600

437

As, Bi, Ag As, [g/t]

6.6

6.3

5.9 310

wt, Cu, wt, Zn, Pb[wt%] 15.0 900

279

12.7

4.0 226

5.0 3.3 300

140

124

2.0

1.9

10.4

10.3

100

73 60

10.0 8.8 600

0.0 437 0

As, Bi, Ag As, [g/t]

6.6 6.3

wt Cu Zn 5.9 Pb As Bi Ag

310

wt, Cu, wt, Zn, Pb[wt%]

279

4.0 226

5.0 3.3 300

Plant Current model Locked cycle Mixed140 cycle

124

2.0

1.9

100 73 60 Figure 4.1: Model comparison for the CuPb concentrate grades. 0.0 0 wt Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

CuPb concentrate - Recoveries

100

82 82 78

80 76

59 59 55

60 55

49 44

[wt%] 40

32 32

20 18

10 10

7 7

3 3

2 1 0 Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

Figure 4.2: Model comparison for the CuPb concentrate recoveries. Note! Recoveries are calculated based on the cyclone overﬂow.

1 (3)

Zn concentrate - Recoveries

96 96

100 95

79 76

80 76

59 59

60 56

44 44 42

[wt%] 40

19 19 15

20 15

8 2 0 Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

4.4. MODEL COMPARISON 41

Cu concentrate - Grades 1744

100.0 93.8 1800

1476 1464

80.0 1440

1244

67.3 66.9

60.0 54.5 1080

40.0 720

28.8 28.8

As, Bi, Ag As, [g/t]

25.3 20.5

wt, Cu, wt, Zn, Pb[wt%] 20.0 360

11.1

10.7

162

8.6

7.62

122

118

115

5.78

3.7

3.26

40 30 0.0 0 wt Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

Figure 4.3: Model comparison for the Cu concentrate grades.

Cu concentrate - Recoveries

99 99

100 98

91 91 91 91

86 86 81

60 60 53

60 53

39 [wt%]

40 37

26 24

20 17 8

0 Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

Figure 4.4: Model comparison for the Cu concentrate recoveries. Note! Recoveries are calculated based on the CuPb concentrate.

CuPb concentrate - Grades

25.0 1500

1368

1356

20.6 20.6 1202

20.0 18.3 1200 17.5

15.0 900

12.7

10.4 10.3

10.0 8.8 600

437

As, Bi, Ag As, [g/t]

6.6

6.3

5.9

310

wt, Cu, wt, Zn, Pb[wt%]

279

4.0 226

5.0 3.3 300

140

124

2.0

1.9

100

73 60 0.0 0 wt Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

42 CHAPTER 4. RESULTS

Pb concentrate - Grades

100.0 Pb concentrate - Grades 1906 2000 86.4

100.0 1906 2000 1593

80.0 86.4 1600 1593 80.0 1600

60.0 1171 1200

1057 1171

60.0 45.5 1200 1057

40.0 45.5 800

33.1

32.7

32.2 As, Bi, Ag As, [g/t]

40.0 521 800

33.1

423

32.7

32.2

wt, Cu, wt, Zn, Pb[wt%]

20.0

18.2

356

As, Bi, Ag As, [g/t]

16.1

298 521

20.0 13.8 400

423

wt, Cu, wt, Zn, Pb[wt%]

20.0

8.1

137

18.2

356

128

6.2

118

16.1

298 3.4

20.0 13.8 400

1.8

1.3

0.3

8.1

137

128 6.2

0.0 118 0

3.4

1.8

1.3 22 wt Cu 0.3 Zn Pb As Bi Ag 0.0 0 wt PlantCu CurrentZn model PbLocked cycleAs MixedBi cycle Ag Plant Current model Locked cycle Mixed cycle Figure 4.5: Model comparison for the Pb concentrate grades.

Pb concentrate - Recoveries

100 Pb concentrate - Recoveries 92

100

76 83

80 74 76

80 74

63 55

60 61

55 47

60 47

40 40

47 47

[wt%] 37

40 34

40 40

[wt%]

37 28

40 34

19 14

20 14

9 9 9 9 14

20 14

9 9 9 9

1 1

2 1 0 1 0 Cu Zn Pb As Bi Ag Cu Zn Pb As Bi Ag Plant Current model Locked cycle Mixed cycle Plant Current model Locked cycle Mixed cycle

Figure 4.6: Model comparison for the Pb concentrate recoveries. Note! Recoveries are calculated based on the CuPb concentrate.

2018-05-04 4.4. MODEL COMPARISON 43

Zn concentrate - Grades

72.0 150

137

59.1 57.6

60.0 57.6 125 114

48.0 100

80 79

36.0 66 75

30.1

60 42

24.0 50 Bi, Ag As, [g/t]

17.5

wt, Cu, wt, Zn, Pb[wt%]

12.1

20 9.1

12.0 8.1 25

0.4

0.3

0.2

0.1 0.1 0.0 0 wt Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

Figure 4.7: Model comparison for the Zn concentrate grades. Zn concentrate - Recoveries

120

96 96 100 95

Zn85 concentrate - Recoveries

76 76

80 72

96 96

100 95

59 59 56

60 85

[wt%]

44 44

76 76

80 42 72

40 67

59 59

20 19

60 19 15

20 15

44 44

2 [wt%] 40 0

Cu Zn Pb As Bi Ag

19 19 15 20 15

Plant Current model Locked cycle Mixed cycle8

2 0 Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

Figure 4.8: Model comparison for the Zn concentrate recoveries. Note! Recoveries are calculated based on the CuPb mp.

Cu concentrate - Grades 1744

100.0 93.8 1800

1476 1464

80.0 1440

1244 67.3 66.9 3 (3) 60.0 54.5 1080

40.0 720

28.8 28.8

As, Bi, Ag As, [g/t]

25.3 20.5

wt, Cu, wt, Zn, Pb[wt%] 20.0 360

11.1

10.7

162

8.6

7.62

122

118

115

5.78

3.7

3.26

40 30 0.0 0 wt Cu Zn Pb As Bi Ag

Plant Current model Locked cycle Mixed cycle

44 CHAPTER 4. RESULTS

4.5 Mass balance - key ﬁgures

A summary of the most important ﬁgures obtained from the mass balance and used for evaluation of the models is presented in table 4.4. A compilation of all data is found in appendix B, together with properties that may be important if it is of interest to do a mass balance or model evaluation by minerals instead of elements. The balance reﬂects that sphalerite is the main mineral of value, since the Zn concentrate mass pull is rather high compared with the other concentrates.

Table 4.4: Some key values from the mass balance in HSC.

Mass balance summary Grades No Stream name [t/h] Ag [g/t] Cu [%] Zn [%] Pb [%] 1 Cyclone overﬂow 112.7 41 0.40 7.60 0.33 6 CuPb mp 110.4 14 0.05 7.55 0.08 13 CuPb conc 2.2 1368 17.5 10.4 12.7 26 Pb conc 0.7 1171 1.31 20.0 32.2 32 Cu conc 1.5 1464 25.3 5.78 3.26 48 Zn conc 13.4 79 0.30 59.1 0.41 52 Zn mp 97.1 5 0.02 0.44 0.04 Recoveries No Stream name wt [%] Ag [%] Cu [%] Zn [%] Pb [%] 6 CuPb mp 98.0 34 12.5 97.3 23.9 13 CuPb conc 2.0 66 87.5 2.7 76.1 26 Pb conc 0.7 19 2.1 1.7 63.0 32 Cu conc 1.3 48 85.4 1.0 13.1 48 Zn conc 11.9 23 8.9 92.3 14.6 52 Zn mp 86.1 11 3.6 5.0 9.3

A complete water balance for the flotation circuit is presented in tables B.1-B.2, which are also found in appendix B. These values are not of any particular interest for the model evaluation, but may be interesting from other points-of-view. It can be seen in figure 4.9 that most measurements are in good agreement with the balanced values, with some outliers. These are mostly low-grade elements like Ag, As and Bi in flows that were difficult to sample, such as recirculating streams in pump sumps. 4.5. MASS BALANCE - KEY FIGURES 45

10000

1000 n = 463 2 R = 0.89 100

10 Balanced 1

0.1

0.01 0.01 0.1 1 10 100 1000 10000 Measured

Figure 4.9: Parity chart of measured and mass balanced data given as t/h, g/t or wt%.

Table 4.5 presents WSSQ values associated with the mass balance. Largest deviations between measurements and balanced values were seen for Cu and Zn, whose values were given high weights since these measurements usually are more trustworthy.

Table 4.5: Squared diﬀerences between measured and regression values. Lower is better.

Mass balance summary - Statistics Ag Cu Zn Pb As Bi WSSQ 7.8 46.3 41.6 26.1 11.0 2.2 Chapter 5 Discussion

The results in this study suggest that the current scale-up model is able to predict plant results with the same accuracy as in 1986, when it was last presented. As earlier concluded, the best predictions are made for the main element in a concentrate while lower grade elements are much more difficult to model. The most pronounced example of this is Bi, whose plant behaviour was far off what was predicted with the scale-up model. This is however something that could be expected, since just a small deviation from 1 g/t to 2 g/t gives a 100 % deviation. It makes modelling more difficult. A similar trend could be seen for As and Zn in Cu concentrates, which also exist in very small quantities. The current model says that As and Zn can be assumed to be of the same concentrate grade in the plant as found in the laboratory cleaning tests, which is a statement that may be questioned based on the findings in this study. There is a clear trend that the model underestimates the recovery of As in all froth concentrates and Zn in CuPb and Cu concentrates. The deviations would have been more accounted for if material in cleaner middlings had been added to the concentrate in the same manner as for the other elements. Since the predictions are so bad in these cases, this practice should be changed immediately. A potential reservation is that As is of very low grade in this particular ore, as evident from table C.1 in appendix C. It is possible that the current scale-up procedure for As is more accurate in material with higher grades of the element. Something that had not been investigated previously is whether minor elements in the Zn concentrate could be treated in the same way as for the Cu concentrate. Based on the findings in this study, there are no reasons to suspect that it is not possible. Ag, Cu and Pb show deviations with the same order of magnitude in the Zn concentrate as for Ag, Zn and Pb in the Cu concentrate.

46 5.1. ALTERNATIVE METHODS 47

5.1 Alternative methods

The competing model identified in literature was a simulated locked cycle test based on split factors retrieved from a standard, batch-wise laboratory procedure. This model gave deplorable predictions on grade, but interestingly better recovery predictions. At first glance, the grade predictions do not seem too bad since it actually gives better values for several minor elements such as As and Bi. On a closer look, it can however be seen that the grade predictions for major elements such as Cu in the CuPb concentrate and Zn in the Zn concentrates are far worse than the current model predicts. Since these elements are paramount, not least from an economic point-of-view, the locked cycle model should not be seen as a better alternative to the current scale-up procedure. It is hard to say why the locked cycle model gave bad grade predictions, but a difficult decision is how to estimate the concentrate weight. Agar and Kipkie argued that the total weight should be assigned split factors and be treated as the individual elements, but this approach clearly did not work in this case. An alternative could be to calculate the amount of non- sulphide gangue in the material, assign it split factors and treat it as the other elements. The concentrate mass could then be calculated by summarising all the components. Such a procedure would likely give just as poor results, since it is not too far off the used method. It can be seen in table 4.2 that the model overestimates the concentrate mass in all cases, with underestimated grades as a consequence. A potential reason is that recirculated material behaves rather differently compared to fresh material and reports to tailings in a larger extent. This should mostly hold true for gangue material, since recoveries of the valuable elements were accurately predicted.

5.1.1 Combining models When both models were combined, the results were superior to each individual model. The so-called mixed cycle model predicted recoveries better than the current model and grades better than the locked cycle model. The only difference between the locked and mixed cycles is how the concentrate mass is treated. It seems to be a good approach to calculate it from the main element grade and its predicted mass. To the author’s knowledge, this type of procedure has not been tested before although it is similar to the locked cycle simulation. It is not possible to conclude if it gives better predictions than the current model in the general view, but it was certainly the case in this study. The model should be tested 48 CHAPTER 5. DISCUSSION on more samples and different ore types. It should at least be used to check the reliability of the current model, especially for the recoveries. Another advantage with the mixed cycle model could be that it is less biased. It is told that the results from the current model sometimes need to be adjusted if they seem unreasonable. This is obviously something that may deviate between different people, at least to some degree. One should always review the predicted results critically, and that is also the case with those told by the mixed cycle model. However, all elements are treated equally and should require fewer adjustments if it is able to give more accurate predictions in the first place. There is no reason to suspect that other elements could not be treated by the model in the same way as the ones included in this project. In that sense, it is also more general. The current model treats for example As and Zn in Cu concentrates differently, and questions arise when elements not mentioned by it such as Bi are to be included. One should also be able to use the mixed cycle model on concentrates of more exotic elements than Cu, Zn and Pb. The simulated locked cycle model has been referenced and acknowledged in literature and could be expected to work even in those cases, which should mean the same for its mixed cycle cousin. However, this may also be true for the current scale-up model. Table 5.1 shows how much the results for the Holmtjärn case presented in section 2.1.1 would deviate if the mixed cycle is used instead. The numbers are more or less the same as for the current model, with a slightly better Pb prediction. The same trend with better predicted recoveries is also seen in this case, although the differences should be seen as statistically insignificant.

Table 5.1: Relative diﬀerences between results predicted by the mixed cycle model and plant results for Holmtjärn ore.

Holmtjärn Mixed cycle vs Plant results ∆wt% Au Ag Cu Zn Pb ∆Grade [%] -20 -5 -29 +121 +12 Cu conc +76 ∆Recovery [%] +5 +14 +4 +144 ∆Grade [%] -13 Zn conc ∆Recovery [%] +9

A similar comparison could be made for the other cases in section 2.1.1, but since those results were based on several laboratory tests it is more dif- ﬁcult to do a fair comparison. 5.2. APPLICABILITY ISSUES 49

5.1.2 Regrinding of concentrates As earlier stated, regrinding of concentrates had a quite large impact on grade and recovery. When a scale-up method is used, it is important to choose a suitable laboratory result as basis and it is not entirely clear if a reground concentrate would give better plant predictions. Table C.1 in appendix C shows that the non-reground Cu grade in the CuPb concentrate of 20.6 % is closer to the plant result of 17.5 %. From that point of view, it is likely that the model would overestimate the grade even more if results from the regrind tests had been used instead. It can also be noted that the recoveries are very similar when the reground laboratory concentrate is compared with the plant concentrate, with 82 % in the laboratory and 85 % in the plant. A similar result can be seen for Zn in the Zn concentrate. It therefore seems like a good idea to use the recovery to a reground concentrate to check the reliability of recoveries predicted by the scale-up model, at least for the major metal. The full eﬀect of a concentrate regrind can be seen in appendix A, were complete product balances for all ﬂotation tests are presented.

5.2 Applicability issues One interesting result was that all models failed to predict the composition of and recovery to the Pb concentrate. The deviations were larger compared with the other three concentrates, as evident in figures 4.1-4.8. The main difference between this and other concentrates is that it is not a froth product, but the tailings from chalcopyrite flotation. Even though it may seem that the models made acceptable predictions for several elements like As, Bi and Ag, it is the main element that is most important. The problem with the current model is that it overestimated the Pb grade in the Cu concentrates, which gives too high losses to the Cu concentrates. An effect is a lower recovery to the Pb concentrate and a too low grade prediction for Pb. The locked cycle model severely overestimates the mass pull to the Cu concentrate, which leaves too little material for the Pb concentrates and results in an unrealistically high Pb grade in the Pb concentrate. This shows that the locked cycle model is quite bad at predicting grades, since there is nothing that prevents it from giving sky-high grades that are mineralogically impossible. Since none of the models were able to predict the grades of and recoveries to the Pb concentrate satisfactory, it should be questioned if they should be used for concentrates that are not froth products. 50 CHAPTER 5. DISCUSSION

5.3 Mass balance review

The main problem with every mass balance is to get the data to reconcile. A large number of streams and elements were sampled and assayed, which made it relatively easy to balance the circuit since no crucial data was missing. All missing values in the flotation circuit could be calculated and the balance is in that sense complete. A problem with this part of the project was to get the grinding circuit to balance. As seen in figure 3.1, it had to be heavily simplified since only the outlet streams were sampled. Especially the Knelson concentrate could be expected to have a very uncertain composition since, being an underflow, it is coarse and heterogeneous. The Kristineberg ore contains very little Au and it is not certain that there even was a steady flow out of the Knelson separator. There are also uncertainties in how this material was sampled, since there is an abnormal amount of it although the flow rate is negligible according to online readings. It is possible it was taken directly from the sack under the separator which collects material from it. This sack is only changed a couple of times per week and one could not rule out that it was contaminated with, let us say, Maurliden ore that was processed a couple of days before the sampling campaign. It can be seen in both table 4.4 and C.4 (appendix C) that the Zn grades are quite high in most of the streams in the Zn circuit. The most noteworthy result is that the Zn grade in CuPb mp is as high as 7.55 % according to the mass balance, but only about 5.50 % in the laboratory tests. It is likely that HSC has slightly overestimated this grade, but one should also note that the laboratory samples were taken from the stream as a single batch. The material used for the mass balance was instead sampled nine times for several hours, which means that this could be a natural fluctuation. If the Zn grade in Zn concentrate was slightly overestimated by HSC, it can probably be said that the Cu grade in CuPb concentrate was underestimated. There is a clear difference between the measured value of 19.8 % compared with 17.5 % in the balance, as seen in appendix B. One should therefore regard the predicted values for the CuPb concentrate as slightly better than indicated in table 4.1. One of the main flaws in the mass balance is the abnormally high Cu grade of 34.1 % in stream 21, one of the rougher concentrates in the CuPb separation presented in table B.3, appendix B. This is about the stoichiomet- ric Cu grade of pure chalcopyrite, which may not be an entirely reasonable result. Small amounts of Zn, Pb and Fe are also present in the stream as seen in the table. Compare this Cu grade with the 10.0 % in stream 22, which 5.4. EXPERIMENTAL VALIDITY 51 is the other Cu rougher concentrate. It is likely that these streams have a similar composition, but it has not been taken into account by HSC. Since these streams are mixed and form stream 19, it should have a cancelling effect and not affect the end result too much. Problems with unrealistically high grades is a drawback with an element-based instead of mineral-based mass balance. It is possible these flaws would not have appeared if the balance had been based on minerals instead of elements.

5.4 Experimental validity One obvious difference between the flotation tests reported by Bolin in section 2.1.1 and this project is the pulp preparation. Here, pulp was taken directly from the concentrator while ores were crushed and ground in the earlier cases. Pulp taken from the concentrator could have had smaller particles and a narrower size distribution, since it had been classified in hydrocyclones before it was taken out. If the flotation tests had been based on ground ore rather than pulp, it would have been difficult to simulate the effect of flash flotation and Knelson separation in the laboratory. This material would have entered the flotation circuit and affected the results. But since these flows are rather small and the Knelson separator primarily removes Au and other elements of no interest in the evaluation, the effect should be quite small. As earlier mentioned, it is not even certain that the Knelson separator was in steady operation during sampling. A factor that might affect the results is whether the scaled results are compared with balanced or measured plant data. The program used for mass balancing alters the flow rates but more importantly the grades to make sure that input equals output. Some of these changes in grade may result in over- or underestimated values compared with what was de facto achieved. Chapter 6 Conclusions

In this project, a scale-up model used by Boliden Mineral AB to predict plant performance from laboratory ﬂotation results has been investigated. The major ﬁndings are described in this section.

(i) There is no reason to suspect that the current scale-up model gives worse results than when it was presented in 1986. The deviations between predicted and plant results are in the same order of magnitude today with respect to both grade and recovery.

(ii) It would be better to use the same approach for As and Zn in Cu concentrates as for the other elements. If it is assumed that these elements will be of the same grade as in the laboratory tests, the deviations may be exceptionally high. This also holds true for Bi and all other elements in every concentrate.

(iii) A simulated locked cycle test based on the laboratory results is just as easy to do as the current procedure. It gave better estimates of recoveries in this study, but deplorable grade predictions.

(iv) A so called “mixed cycle” test gave the best results in this study. It cal- culates the concentrate mass pull in the same way as the current model, but the recovery of every element is calculated as in the locked cycle simulation. Such a strategy gave superior predications with respect to both grade and recovery of almost every element.

(v) All models failed to make satisfactory predictions for the Pb concentrate. Their applicability on non-froth concentrates should be questioned.

52 Chapter 7 Recommendations

The main conclusion in this project is that the current model predicts the plant performance with the same accuracy as when it was presented in 1986. However, the recoveries seem to be better estimated with a simulated locked cycle test and a recommendation is therefore to use the following practise as a complement: (i) Set a target grade of the element of interest and a laboratory scheme with roughing and cleaning to achieve this grade. (ii) Calculate the split factors for each separation process and all elements to be included. (iii) Use equation 1.2 and a circuit as seen in ﬁgure 3.3 to calculate the mass of every element that ends up in the concentrate. This is preferably done with a simple Excel sheet. (iv) Use the mass of the main element and its concentrate grade from the laboratory tests to estimate the concentrate mass. (v) Calculate the scaled grade and recoveries based on the concentrate mass and the masses of each component. The new approach should at least be used to check the reliability of the results from the current model. One should question how reliable the results are if there are large deviations between the two models.

7.1 Further work Some suggestions for future projects are presented in this section. A few of them may form the basis for new degree projects at Boliden, while others

53 54 CHAPTER 7. RECOMMENDATIONS could be used to verify the results in this study.

(i) Test the suggested mixed cycle model with more types of ore. If it is able to make better predictions in these cases as well, it could replace the current model entirely.

(ii) Evaluate the kinetic models by a series of cleaning steps from which the necessary parameters can be retrieved. Simulate the process and compare it with the models presented in this thesis.

(iii) Repeat the procedure but with minerals instead of elements, which may prevent unrealistic results such as >100 % grades. Chapter 8 References

[1] USGS. Mineral Commodity Summaries, 2017. Available at: https:// minerals.usgs.gov/minerals/pubs/mcs/ [Last accessed 25 January 2018]. [2] G. Calvo, G. Mudd, A. Valero, and A. Valero. Decreasing Ore Grades in Global Metallic Mining: A Theoretical Issue or a Global Reality? Resources, 5(36):1–14, 2015. [3] A. B. Wills. Mineral Processing Technology. Burlington: Butterworth- Heinemann, 1997. [4] Boliden Group. 90 år av kunskap och innovation, 2017. Available at: https://www.boliden.com/sv/verksamhet/om-boliden/ bolidens-historia/ [Last accessed 25 January 2018]. [5] T. Söderqvist. Boliden Area CMD 20 November 2013. Boliden Mineral AB, 2013. [6] New Boliden. Årsredovisning 2017 - metaller för långsiktigt värdeska- pande, 2018. [7] H. Årebäck, T. J. Barrett, S. Abrahamsson, and P. Fagerström. The Palaeoproterozoic Kristineberg VMS deposit, Skellefte district, northern Sweden, part i: geology. Mineralium Deposita, 40:351–367, 2005. [8] W. A. Hustrulid and R. L. Bullock. Rock Support in Cut-and-Fill Mining at the Kristineberg Mine. Underground Mining Methods: Engineering Fundamentals and International Case Studies, page 325, 2001. Society for Mining, Metallurgy, and Exploration, Inc. [9] N. J. Bolin. Försöksutvärdering - Metodbeskrivning för Normalresultat (Internal report), 1986.

55 56 CHAPTER 8. REFERENCES

[10] T. F. Zabel and W. R. C. Marlow. Flotation in Water Treatment. Innovations in Flotation Technology, 208:431–454, 1992.

[11] P. Somasundaran and B. M. Moudgil. Reagents in Mineral Technology. Marcel Dekker Inc, 1988.

[12] S. M. Bulatovic. Handbook of ﬂotation reagents. Elsevier, 2007. [13] Q. Liu and J. S. Laskowski. The role of metal hydroxides at mineral surfaces in dextrin. International Journal of Mineral Processing, 27:147– 155, 1989.

[14] B. Pålsson. Mineral Processing Course - Flotation theory. Technical report, Luleå University of Technology, 2014.

[15] R. Coleman. Maximise your recoveries in a flash, 2010. Available at: https://www.outotec.com/globalassets/newsletters/output/2010-2/ output-december-2010---flash-flotation.pdf [Last accessed 9 February 2018].

[16] A. Remes, J. Tommiska, and P. Lamberg. HSC - Mass Balance Module. Technical report, Pori Research Center, 2016.

[17] B. Pålsson. Mineral Processing Course - Circuit Balancing. Technical report, Luleå University of Technology, 2015.

[18] S. R. Williams, M. O. Ounpuu, and K. W. Sarbutt. Bench and pilot plant programs for ﬂotation circuit design. SGS Minerals Services, pages 1–9, 2002.

[19] M. Gharai and R. Venugopal. Modeling of Flotation Process—An Overview of Diﬀerent Approaches. Mineral Processing and Extractive Metallurgy Review, 37(2):120–133, 2016. [20] R. C. Dunne, G. S. Lane, G. D. Richmond, and J. Dioses. Interpretation of ﬂotation data for the design of process plants. Technical report.

[21] Grinding Solutions Ltd. Froth ﬂotation services. Available at: https: //www.grindingsolutions.com/laboratory-services/ﬂotation/ [Last accessed 2 February 2018].

[22] G. Meurant. Northern Europe Including Examples from the USSR in Both Europe and Asia. Elsevier, 2013. 57

[23] M. Johansson and L. Bildström. Exkursionslokaler i norra Västerbotten, 2003.

[24] N. J. Bolin. Kristineberg a new zinc mineralisation. Beneﬁciation test. Technical report, Process Technology, 2001.

[25] SGS. Our ﬂotation services - Locked cycle test. Available at: http://www.sgs.com/en/mining/metallurgy-and-process-design/ unit-operations-and-metallurgical-services/ﬂotation/locked-cycle-test [Last accessed 2 February 2018].

[26] G. E. Agar. Calculation of locked cycle flotation test results. Minerals Engineering, 13(14-15):1533–1542, 2000. [27] G. E. Agar and W. B. Kipkie. Predicting Locked Cycle Flotation Test Results from Batch Data. CIM Bulletin, pages 119–125, 1978. [28] S. Nishimura, H. Hirosue, K. Shobu, and K. Jinnai. Analytical evaluation of locked cycle flotation tests. International Journal of Mineral Processing, 27:39–50, 1989. [29] E.C. Çilek. Application of neural networks to predict locked cycle flotation test results. Minerals Engineering, 15:1095–1104, 2002.

[30] R. P. King. Modeling and Simulation of Mineral Processing Systems. Society for Mining, Metallurgy, and Exploration, Inc., 2 edition, 2012.

[31] A. Remes. HSC - Sim MinPro Unit Models, 2017. Technical manual.

[32] E. Yalcin and S. Kelebek. Flotation kinetics of a pyritic gold ore. Inter- national Journal of Mineral Processing, 98:48–54, 2011.

[33] A. Gupta and D. S. Yan. Mineral Processing Design and Operation: An Introduction. 2006. [34] C. Greet and J. Kinal. Scaling laboratory results to the plant: A method- ology, in Procemin 2013 - 10th International Mineral Processing Confer- ence. 2013.

[35] B. Newcombe, D. Bradshaw, and E. Wightman. Development of a laboratory method to predict plant ﬂash ﬂotation performance. Minerals Engineering, 39:228–238, 2012.

Appendices

Appendix A Product balances

This appendix presents the complete product balances for the laboratory flotation tests, as seen in tables A.1-A.8. The three cleaning tests in tables A.2, A.5 and A.8 were used in the scale-up procedure and are therefore most important. The roughing tests were used to check the reliability of the scale-up procedure as mentioned in section 2.1.1 of the main report. It can be seen that two roughing tests were done for the cyclone overflow: CuPb and Zn, which means that chalcopyrite and galena were floated together followed by a flotation of sphalerite. These two concentrates were then cleaned separately in one of the tests, described in table A.3. In figure 3.2 of the main report, it can be seen that the first middlings produced in the CuPb cleaning procedure were taken to the Zn flotation. This explains why no “CuPb cleaner mp 1” exists. A general trend in the cleaning tests is that the mass pull to cleaner middlings goes down after each cleaning step. In the same time, the grade of the main metal in the middlings goes up. This should be expected since entrained or otherwise unfloatable gangue minerals should be removed quite easily. Later on mixed grains and slow-floating grains of the valuable mineral might be removed. All the cleaner middlings contain a lot of Fe and S, which is probably because a lot of pyrite is removed. This was also something that could be observed in the laboratory. Something that should be noted is the unreasonably low Zn grade of the final concentrate in table A.2. This could be due to experimental or analytical errors. For that reason it was decided to not use this test in the scale-up procedure.

I II APPENDIX A. PRODUCT BALANCES

al A.1: Table

rdc aac o h yln vro ogigtest. roughing overﬂow cyclone the for balance Product

III

Product balance for the cyclone overﬂow cleaning test.

Table A.2:

IV APPENDIX A. PRODUCT BALANCES

al A.3: Table

rdc aac o h yln vro laigts ihregrind. with test cleaning overﬂow cyclone the for balance Product

Product balance for the CuPb mp roughing test.

Table A.4:

VI APPENDIX A. PRODUCT BALANCES

al A.5: Table

rdc aac o h ubm laigtest. cleaning mp CuPb the for balance Product

VII

Product balance for the CuPb mp cleaning test with regrind.

Table A.6:

VIII APPENDIX A. PRODUCT BALANCES

al A.7: Table

rdc aac o h ubcnetaeruhn test. roughing concentrate CuPb the for balance Product

Product balance for the CuPb concentrate cleaning test.

Table A.8:

Appendix B Stream data

In this appendix, the complete stream data is presented. This includes dilution ratios D, wt% solids Fw, densities for both solids ρs and pulp ρp, solids and water ﬂow rates M and W as seen in tables B.1 and B.2. Note that all data is divided into separate sections of balanced and unbalanced values.

Balanced values

Table B.1: Stream properties. Densities as g/cm3, ﬂow rates in t/h.

PHYSICAL PROPERTIES

No Stream name D ρs ρp Fw MW 1 Cyclone overflow 1.0 3.29 1.5 50.6 112.7 109.9 2 B3 mill recirculation 5.6 3.79 1.1 15.1 7.2 40.8 3 CuPb flotation feed 1.3 3.30 1.4 44.3 119.9 150.7 4 CuPb rougher conc 4.1 4.05 1.2 19.7 7.7 31.2 5 CuPb rougher mp 1.1 3.31 1.5 46.7 112.3 127.9 6 CuPb mp 1.1 3.27 1.5 47.6 110.4 121.7 7 CuPb scavenger conc 5.6 3.98 1.1 15.2 1.8 10.2 8 CuPb 1 cleaner conc 2.0 4.21 1.3 33.5 2.9 5.7 9 CuPb 1 cleaner mp 5.6 3.74 1.1 15.0 5.4 30.6 10 CuPb 2 cleaner mp 5.3 4.00 1.1 15.8 0.6 3.4 11 CuPb 2 cleaner conc 2.1 4.46 1.3 32.3 2.4 5.0 12 CuPb 3 cleaner mp 6.7 4.00 1.1 13.1 0.1 0.8 13 CuPb conc 2.6 4.38 1.3 27.7 2.2 5.9 14 B31 pump overflow 8.4 3.77 1.1 10.6 3.0 25.0 15 B3 mill product 3.7 3.98 1.2 21.3 4.3 15.8 18 Total Cu rougher conc 5.3 4.40 1.1 15.7 0.2 1.2

X XI

PHYSICAL PROPERTIES (cont.)

No Stream name D ρs ρp Fw MW 19 Total recirculation 0.2 4.62 3.0 84.7 1.5 0.3 21 Cu 1 rougher conc 0.2 4.37 2.8 83.4 1.0 0.2 22 Cu 2 rougher conc 0.1 4.56 3.0 87.0 0.6 0.1 23 Cu scavenger conc 0.3 4.53 2.5 77.4 0.2 0.1 24 Cu 1 rougher mp 5.8 4.56 1.1 14.8 1.5 8.7 25 Cu 2 rougher mp 11.2 4.53 1.1 8.2 0.9 10.3 26 Pb conc 16.0 4.66 1.0 5.9 0.7 11.8 27 Cu 1 cleaner mp 31.2 4.73 1.0 3.2 0.04 1.2 28 Cu 2 cleaner mp 18.0 4.37 1.0 5.3 0.1 1.6 29 Cu 3 cleaner mp 5.6 4.24 1.1 15.0 0.1 0.3 30 Cu 1 cleaner conc 1.2 4.33 1.5 45.2 1.6 1.9 31 Cu 2 cleaner conc 1.2 4.30 1.5 45.8 1.6 1.9 32 Cu conc 2.1 4.29 1.3 32.5 1.5 3.1 33 Zn ﬂotation feed 1.3 3.37 1.5 43.3 142.0 185.8 34 Zn 1 rougher conc 0.9 3.97 1.6 52.1 17.9 16.4 35 Zn 2A rougher conc 0.9 3.97 1.6 51.4 9.8 9.3 36 Zn 2B rougher conc 1.6 3.97 1.4 37.7 9.6 15.9 37 Zn rougher mp 1.6 3.20 1.4 38.9 104.7 164.3 38 Zn scavenger conc A 3.0 3.85 1.2 24.9 3.8 11.6 39 Zn scavenger conc B 2.7 3.87 1.2 26.8 3.8 10.5 40 Zn 1 cleaner feed 0.9 4.01 1.6 51.4 100.3 94.9 41 Zn 1 cleaner mp 1.8 3.91 1.4 36.2 23.9 42.1 42 Zn 1 cleaner conc 0.8 4.04 1.7 56.7 76.3 58.4 43 Zn 2 cleaner mp 0.8 4.04 1.7 54.2 63.0 53.3 44 Zn 2 cleaner conc 1.0 4.03 1.6 50.4 20.7 20.4 45 Zn 3 cleaner conc 1.0 4.03 1.6 49.6 13.5 13.7 46 Zn 3 cleaner mp 1.0 4.03 1.5 43.1 7.4 9.8 47 Zn 4 cleaner mp 1.0 4.04 1.6 48.6 0.1 0.1 48 Zn conc 1.0 4.03 1.5 46.3 13.4 15.5 49 To B41 pump 2.0 3.95 1.3 33.0 31.6 64.1 50 B41 pump overﬂow 2.0 3.88 1.3 32.7 31.1 63.9 51 B4 mill product 0.5 4.08 2.0 68.3 0.5 0.2 52 Zn mp 1.6 3.21 1.4 38.0 97.1 158.4 53 Zn 1 rougher mp 1.4 3.27 1.4 42.3 124.1 169.4 54 Zn recirculation 2.0 3.49 1.3 33.0 31.6 64.1 59 Knelson Au conc 4.70 60 Flash Cu conc 4.11 XII APPENDIX B. STREAM DATA

Table B.2: Water additions in the ﬂotation circuit.

WATER ADDITIONS Unit Added water [t/h] CuPb roughers 8.3 CuPb scavengers 4.0 CuPb cleaners 1.8 CuPb prim recleaners 1.8 CuPb sec recleaners 1.7 Cu prim roughers 1.8 Cu sec roughers 1.7 Cu scavengers 1.5 Cu cleaners 1.2 Cu prim recleaners 1.2 Cu sec recleaners 1.6 Zn roughers 20.1 Zn scavengers 16.0 Zn cleaners 5.6 Zn prim recleaners 5.6 Zn sec recleaners 2.9 Zn tert recleaners 2.0

Table B.3: Elemental grades in the mass balance.

GRADES No Stream name Sol. [t/h] Ag [g/t] Cu [%] Pb [%] Zn [%] 1 Cyclone overflow 112.7 41 0.4 0.3 7.6 2 B3 mill recirculation 7.3 460 2.4 2.8 21.5 3 CuPb flotation feed 119.9 67 0.5 0.5 8.4 4 CuPb rougher conc 7.7 732 6.9 5.4 17.8 5 CuPb rougher mp 112.3 21 0.08 0.2 7.8 6 CuPb mp 110.4 14 0.05 0.08 7.6 7 CuPb scavenger conc 1.8 436 2.0 4.5 23.6 8 CuPb 1 cleaner conc 2.9 1225 14.9 10.1 13.5 9 CuPb 1 cleaner mp 5.4 468 2.6 2.3 20.8 10 CuPb 2 cleaner mp 0.6 725 5.7 0.8 24.2 11 CuPb 2 cleaner conc 2.4 1347 17.1 12.3 11.0 12 CuPb 3 cleaner mp 0.1 956 9.7 5.4 21.2 13 CuPb conc 2.2 1368 17.5 12.7 10.4 14 B31 pump overflow 3.0 522 3.0 3.4 20.8 15 B3 mill product 4.3 417 2.0 2.5 22.0 18 Total Cu rougher conc 0.2 1705 9.1 16.5 8.4 19 Total recirculation 1.5 1467 24.9 3.9 5.9 XIII

GRADES (cont.) No Stream name Sol. [t/h] Ag [g/t] Cu [%] Pb [%] Zn [%] 21 Cu 1 rougher conc 1.0 1336 34.1 1.6 4.2 22 Cu 2 rougher conc 0.6 1680 10.0 7.5 8.8 23 Cu scavenger conc 0.2 1729 9.2 14.0 7.7 24 Cu 1 rougher mp 1.5 1439 5.7 20.3 14.1 25 Cu 2 rougher mp 0.9 1285 2.9 28.5 17.5 26 Pb conc 0.7 1171 1.3 32.2 20.0 27 Cu 1 cleaner mp 0.04 1586 8.4 29.1 12.2 28 Cu 2 cleaner mp 0.09 1837 12.6 19.1 11.3 29 Cu 3 cleaner mp 0.06 1979 19.1 10.5 9.2 30 Cu 1 cleaner conc 1.6 1485 24.6 4.2 6.1 31 Cu 2 cleaner conc 1.6 1483 25.1 3.5 5.9 32 Cu conc 1.5 1464 25.3 3.3 5.8 33 Zn ﬂotation feed 142.0 37 0.1 0.3 12.4 34 Zn 1 rougher conc 17.9 134 0.4 1.1 42.1 35 Zn 2A rougher conc 9.8 84 0.3 0.6 43.0 36 Zn 2B rougher conc 9.6 78 0.2 0.5 42.9 37 Zn rougher mp 104.7 12 0.04 0.07 1.6 38 Zn scavenger conc A 3.8 103 0.3 0.6 16.8 39 Zn scavenger conc B 3.8 99 0.3 0.5 16.7 40 Zn 1 cleaner feed 100.3 111 0.3 0.8 50.7 41 Zn 1 cleaner mp 23.9 122 0.3 1.1 33.3 42 Zn 1 cleaner conc 76.3 108 0.3 0.7 56.2 43 Zn 2 cleaner mp 63.0 114 0.2 0.8 55.5 44 Zn 2 cleaner conc 20.8 80 0.3 0.4 57.6 45 Zn 3 cleaner conc 13.5 79 0.3 0.4 59.1 46 Zn 3 cleaner mp 7.4 83 0.2 0.5 54.7 47 Zn 4 cleaner mp 0.1 80 0.2 0.5 56.6 48 Zn conc 13.4 79 0.3 0.4 59.1 49 To B41 pump 31.6 117 0.3 1.0 29.3 50 B41 pump overﬂow 31.1 117 0.3 1.0 29.0 51 B4 mill product 0.5 115 0.4 1.0 47.8 52 Zn mp 97.1 5 0.02 0.04 0.4 53 Zn 1 rougher mp 124.1 23 0.07 0.1 8.1 54 Zn recirculation 31.6 117 0.3 1.0 29.3 59 Knelson Au conc <0.1 1375 1.3 7.3 3.4 60 Flash Cu conc <0.1 1385 18.9 7.9 4.7 XIV APPENDIX B. STREAM DATA

GRADES (cont.) No Stream name As [g/t] Bi [g/t] Fe [%] S [%] 1 Cyclone overflow 83 9 16.3 19.3 2 B3 mill recirculation 161 106 17.0 29.4 3 CuPb flotation feed 87 15 16.4 19.9 4 CuPb rougher conc 146 151 17.3 28.6 5 CuPb rougher mp 83 5 16.3 19.3 6 CuPb mp 14 3 16.3 19.1 7 CuPb scavenger conc 180 127 18.0 31.8 8 CuPb 1 cleaner conc 131 245 19.0 29.4 9 CuPb 1 cleaner mp 155 98 16.7 28.6 10 CuPb 2 cleaner mp 156 124 20.1 33.0 11 CuPb 2 cleaner conc 125 273 18.7 28.5 12 CuPb 3 cleaner mp 139 158 19.1 30.8 13 CuPb conc 124 279 18.7 28.4 14 B31 pump overflow 157 122 15.0 27.0 15 B3 mill product 165 94 18.4 31.1 18 Total Cu rougher conc 194 246 18.4 29.6 19 Total recirculation 124 169 23.4 31.5 21 Cu 1 rougher conc 109 124 26.5 32.2 22 Cu 2 rougher conc 149 242 18.5 30.3 23 Cu scavenger conc 193 207 19.0 30.2 24 Cu 1 rougher mp 144 373 13.7 26.2 25 Cu 2 rougher mp 141 457 10.7 23.5 26 Pb conc 128 521 8.5 21.8 27 Cu 1 cleaner mp 200 445 15.5 26.6 28 Cu 2 cleaner mp 191 296 20.6 29.7 29 Cu 3 cleaner mp 150 189 23.9 32.1 30 Cu 1 cleaner conc 126 169 23.4 31.5 31 Cu 2 cleaner conc 123 163 23.6 31.6 32 Cu conc 122 162 23.6 31.6 33 Zn flotation feed 134 11 15.9 21.7 34 Zn 1 rougher conc 3 47 10.7 31.3 35 Zn 2A rougher conc 410 21 11.4 33.1 36 Zn 2B rougher conc 413 21 11.3 31.9 37 Zn rougher mp 105 3 17.6 18.0 38 Zn scavenger conc A 522 31 17.2 30.2 39 Zn scavenger conc B 472 31 17.3 30.9 40 Zn 1 cleaner feed 349 27 8.6 33.3 41 Zn 1 cleaner mp 260 40 13.7 30.5 42 Zn 1 cleaner conc 377 23 7.0 34.1 43 Zn 2 cleaner mp 428 23 7.1 34.0 44 Zn 2 cleaner conc 190 20 6.4 34.0 XV

GRADES (cont.) No Stream name As [g/t] Bi [g/t] Fe [%] S [%] 45 Zn 3 cleaner conc 138 21 6.3 34.4 46 Zn 3 cleaner mp 286 20 6.7 33.2 47 Zn 4 cleaner mp 239 20 6.7 33.9 48 Zn conc 137 21 6.2 34.5 49 To B41 pump 317 38 14.6 30.5 50 B41 pump overﬂow 312 38 14.6 30.5 51 B4 mill product 646 30 11.5 34.5 52 Zn mp 74 1 17.7 17.0 53 Zn 1 rougher mp 153 6 16.6 20.3 54 Zn recirculation 317 38 14.6 30.5 59 Knelson Au conc 210 90 36.5 43.9 60 Flash Cu conc 40 120 24.8 28.2

Table B.4: Recoveries of elements in the mass balance.

RECOVERIES No Stream name Sol. [%] Ag [%] Cu [%] Pb [%] Zn [%] 1 Cyclone overflow 100 100 100 100 100 2 B3 mill recirculation 6.4 72 38.9 54.9 18.2 3 CuPb flotation feed 106.4 172 138.9 154.9 118.2 4 CuPb rougher conc 6.8 121 118.4 109.2 15.9 5 CuPb rougher mp 99.6 51 20.5 45.7 102.3 6 CuPb mp 98.0 34 12.5 23.9 97.3 7 CuPb scavenger conc 1.6 17 8.1 21.8 5.0 8 CuPb 1 cleaner conc 2.6 76 95.6 77.4 4.5 9 CuPb 1 cleaner mp 4.8 55 30.9 33.1 13.2 10 CuPb 2 cleaner mp 0.57 10 8.1 1.3 1.8 11 CuPb 2 cleaner conc 2.1 69 90.2 77.9 3.0 12 CuPb 3 cleaner mp 0.1 3 2.7 1.8 0.3 13 CuPb conc 2.0 66 87.5 76.1 2.7 14 B31 pump overflow 2.6 33 19.9 26.9 7.2 15 B3 mill product 3.8 39 19.0 28.0 11.0 18 Total Cu rougher conc 0.2 8 4.6 9.9 0.2 19 Total recirculation 1.4 49 86.1 16.1 1.1 21 Cu 1 rougher conc 0.9 28 72.9 4.2 0.5 22 Cu 2 rougher conc 0.5 21 13.1 11.8 0.6 23 Cu scavenger conc 0.2 7 3.9 7.0 0.2 24 Cu 1 rougher mp 1.3 47 19.2 81.8 2.5 25 Cu 2 rougher mp 0.8 26 6.0 70.0 1.9 26 Pb conc 0.7 19 2.1 63.0 1.7 27 Cu 1 cleaner mp 0.03 1 0.7 2.9 0.05 28 Cu 2 cleaner mp 0.08 4 2.5 4.6 0.1 XVI APPENDIX B. STREAM DATA

RECOVERIES (cont.) No Stream name Sol. [%] Ag [%] Cu [%] Pb [%] Zn [%] 29 Cu 3 cleaner mp 0.05 3 2.5 1.6 0.06 30 Cu 1 cleaner conc 1.4 51 87.9 17.7 1.1 31 Cu 2 cleaner conc 1.4 50 87.9 14.8 1.1 32 Cu conc 1.3 48 85.4 13.1 1.0 33 Zn ﬂotation feed 126.0 113 36.1 104.4 205.3 34 Zn 1 rougher conc 15.9 52 17.2 56.3 88.1 35 Zn 2A rougher conc 8.7 18 5.5 14.5 49.1 36 Zn 2B rougher conc 8.5 16 4.8 12.8 48.1 37 Zn rougher mp 93.0 28 8.7 20.8 20.0 38 Zn scavenger conc A 3.4 9 2.6 6.0 7.5 39 Zn scavenger conc B 3.4 8 2.6 5.6 7.5 40 Zn 1 cleaner feed 89.0 240 62.5 209.7 596.0 41 Zn 1 cleaner mp 21.2 63 18.5 69.0 93.0 42 Zn 1 cleaner conc 67.7 177 44.0 140.7 500.0 43 Zn 2 cleaner mp 55.9 154 35.0 126.1 408.0 44 Zn 2 cleaner conc 18.4 36 13.0 24.6 139.4 45 Zn 3 cleaner conc 12.0 23 9.0 14.8 93.2 46 Zn 3 cleaner mp 6.5 13 4.1 10.0 47.1 47 Zn 4 cleaner mp 0.1 0.2 0.08 0.2 0.9 48 Zn conc 11.9 23 8.9 14.6 92.3 49 To B41 pump 28.0 80 23.7 80.5 108.0 50 B41 pump overﬂow 27.6 79 23.2 79.3 105.3 51 B4 mill product 0.4 1 0.4 1.3 2.8 52 Zn mp 86.1 11 3.6 9.3 5.0 53 Zn 1 rougher mp 110.1 62 19.0 48.1 117.2 54 Zn recirculation 28.0 80 23.7 80.5 108.0 59 Knelson Au conc <0.1 <0.1 <0.1 <0.1 <0.1 60 Flash Cu conc <0.1 <0.1 <0.1 <0.1 <0.1

RECOVERIES (cont.) No Stream name As [%] Bi [%] Fe [%] S [%] 1 Cyclone overﬂow 100 100 100 100 2 B3 mill recirculation 13 78 6.7 9.8 3 CuPb ﬂotation feed 113 178 106.7 109.8 4 CuPb rougher conc 12 118 7.2 10.1 5 CuPb rougher mp 101 60 99.5 99.7 6 CuPb mp 97 36 97.7 97.1 7 CuPb scavenger conc 4 24 1.8 2.7 8 CuPb 1 cleaner conc 4 72 3.0 3.9 9 CuPb 1 cleaner mp 9 54 4.9 7.1 10 CuPb 2 cleaner mp 1 8 0.7 1.0 11 CuPb 2 cleaner conc 3 66 2.4 3.1 12 CuPb 3 cleaner mp 0.2 2 0.1 0.2 XVII

RECOVERIES (cont.) No Stream name As [%] Bi [%] Fe [%] S [%] 13 CuPb conc 3 64 2.3 2.9 14 B31 pump overflow 5 37 2.4 3.7 15 B3 mill product 8 41 4.3 6.1 18 Total Cu rougher conc 0.5 6 0.2 0.3 19 Total recirculation 2 27 2.0 2.2 21 Cu 1 rougher conc 1 12 1.4 1.4 22 Cu 2 rougher conc 1 15 0.6 0.8 23 Cu scavenger conc 0.4 4 0.2 0.3 24 Cu 1 rougher mp 2 58 1.1 1.8 25 Cu 2 rougher mp 1 43 0.5 1.0 26 Pb conc 1 39 0.3 0.7 27 Cu 1 cleaner mp 0.08 2 0.03 0.05 28 Cu 2 cleaner mp 0.2 3 0.1 0.1 29 Cu 3 cleaner mp 0.09 1 0.08 0.09 30 Cu 1 cleaner conc 2 28 2.0 2.3 31 Cu 2 cleaner conc 2 26 2.0 2.3 32 Cu conc 2 25 1.9 2.2 33 Zn flotation feed 205 158 122.9 141.4 34 Zn 1 rougher conc 0.6 85 10.4 25.8 35 Zn 2A rougher conc 43 21 6.1 14.9 36 Zn 2B rougher conc 43 20 5.9 14.1 37 Zn rougher mp 118 31 100.4 86.6 38 Zn scavenger conc A 21 12 3.6 5.3 39 Zn scavenger conc B 19 12 3.6 5.4 40 Zn 1 cleaner feed 376 275 46.8 153.3 41 Zn 1 cleaner mp 67 98 17.9 33.6 42 Zn 1 cleaner conc 309 176 28.9 119.7 43 Zn 2 cleaner mp 290 148 24.4 98.6 44 Zn 2 cleaner conc 42 43 7.2 32.4 45 Zn 3 cleaner conc 20 29 4.6 21.4 46 Zn 3 cleaner mp 22.7 14.9 2.7 11.3 47 Zn 4 cleaner mp 0.4 0.3 0.05 0.2 48 Zn conc 20 28 4.5 21.1 49 To B41 pump 108 122 25.0 44.3 50 B41 pump overflow 104 120 24.7 43.5 51 B4 mill product 3 2 0.3 0.8 52 Zn mp 77 8 93.2 75.9 53 Zn 1 rougher mp 204 73 112.3 115.6 54 Zn recirculation 108 122 25.0 44.3 59 Knelson Au conc <0.1 <0.1 <0.1 <0.1 60 Flash Cu conc <0.1 <0.1 <0.1 <0.1 XVIII APPENDIX B. STREAM DATA

Table B.5: Diﬀerent weights used in the mass balance given as a relative percentage.

MASS BALANCE WEIGHTS (RSD) No Stream name Sol. [%] Ag [%] Cu [%] Pb [%] Zn [%] 1 Cyclone overflow 30 10 5 10 5 2 B3 mill recirculation 30 10 5 10 5 3 CuPb flotation feed 30 10 5 10 5 4 CuPb rougher conc 30 10 5 10 5 5 CuPb rougher mp 30 10 5 10 5 6 CuPb mp 30 10 5 10 5 7 CuPb scavenger conc 30 10 5 10 5 8 CuPb 1 cleaner conc 30 10 5 1 5 9 CuPb 1 cleaner mp 30 10 5 1 5 10 CuPb 2 cleaner mp 30 10 5 10 5 11 CuPb 2 cleaner conc 30 10 5 5 5 12 CuPb 3 cleaner mp 30 10 5 10 5 13 CuPb con 30 10 1 1 5 14 B31 pump overflow 30 50 50 50 50 15 B3 mill product 30 50 50 50 50 18 Total Cu rougher conc 30 10 5 10 5 19 Total recirculation 30 10 5 10 5 21 Cu 1 rougher conc 30 10 5 10 5 22 Cu 2 rougher conc 30 10 5 10 5 23 Cu scavenger conc 30 10 5 10 5 24 Cu 1 rougher mp 30 10 5 5 5 25 Cu 2 rougher mp 30 10 5 10 5 26 Pb conc 30 10 5 1 5 27 Cu 1 cleaner mp 30 10 5 10 5 28 Cu 2 cleaner mp 30 10 5 10 5 29 Cu 3 cleaner mp 30 10 5 10 5 30 Cu 1 cleaner conc 30 10 1 10 5 31 Cu 2 cleaner conc 30 10 1 10 5 32 Cu conc 30 10 1 10 5 33 Zn flotation feed 30 10 5 10 5 34 Zn 1 rougher conc 30 10 5 10 1 35 Zn 2A rougher conc 30 10 5 10 1 36 Zn 2B rougher conc 30 10 5 10 1 37 Zn rougher mp 30 10 5 10 5 38 Zn scavenger conc A 30 10 5 10 5 39 Zn scavenger conc B 30 10 5 10 5 40 Zn 1 cleaner feed 30 10 5 10 5 41 Zn 1 cleaner mp 30 10 5 10 5 42 Zn 1 cleaner conc 30 10 5 10 1 43 Zn 2 cleaner mp 30 10 5 10 5 44 Zn 2 cleaner conc 30 10 5 10 1 XIX

MASS BALANCE WEIGHTS (RSD, cont.) No Stream name Sol. [%] Ag [%] Cu [%] Pb [%] Zn [%] 45 Zn 3 cleaner conc 30 10 5 10 1 46 Zn 3 cleaner mp 30 10 5 10 5 47 Zn 4 cleaner mp 30 10 5 10 5 48 Zn conc 30 10 5 10 1 49 To B41 pump 30 10 5 10 5 50 B41 pump overﬂow 30 10 5 10 5 51 B4 mill product 30 10 5 10 5 52 Zn mp 30 10 5 10 5 53 Zn 1 rougher mp 30 10 5 10 5 54 Zn recirculation 30 10 5 10 5 59 Knelson Au conc 100 100 100 100 100 60 Flash Cu conc 100 100 100 100 100

MASS BALANCE WEIGHTS (RSD, cont.) No Stream name As [%] Bi [%] Fe [%] S [%] 1 Cyclone overflow 20 70 10 10 2 B3 mill recirculation 20 70 10 10 3 CuPb flotation feed 20 70 10 10 4 CuPb rougher conc 20 70 10 10 5 CuPb rougher mp 20 70 10 10 6 CuPb mp 20 70 10 10 7 CuPb scavenger conc 20 70 10 10 8 CuPb 1 cleaner conc 20 70 10 10 9 CuPb 1 cleaner mp 20 70 10 10 10 CuPb 2 cleaner mp 20 70 10 10 11 CuPb 2 cleaner conc 20 70 10 10 12 CuPb 3 cleaner mp 20 70 10 10 13 CuPb conc 5 70 10 10 14 B31 pump overflow 50 70 50 50 15 B3 mill product 50 70 50 50 18 Total Cu rougher conc 20 70 10 10 19 Total recirculation 20 70 10 10 21 Cu 1 rougher conc 20 70 10 10 22 Cu 2 rougher conc 20 70 10 10 23 Cu scavenger conc 20 70 10 10 24 Cu 1 rougher mp 20 70 10 10 25 Cu 2 rougher mp 20 70 10 10 26 Pb conc 20 70 10 10 27 Cu 1 cleaner mp 20 70 10 10 28 Cu 2 cleaner mp 20 70 10 10 29 Cu 3 cleaner mp 20 70 10 10 30 Cu 1 cleaner conc 20 70 10 10 31 Cu 2 cleaner conc 20 70 10 10 XX APPENDIX B. STREAM DATA

MASS BALANCE WEIGHTS (RSD, cont.) No Stream name As [%] Bi [%] Fe [%] S [%] 32 Cu conc 20 70 10 10 33 Zn ﬂotation feed 20 70 10 10 34 Zn 1 rougher conc 20 70 10 10 35 Zn 2A rougher conc 20 70 10 10 36 Zn 2B rougher conc 20 70 10 10 37 Zn rougher mp 20 70 10 10 38 Zn scavenger conc A 20 70 10 10 39 Zn scavenger conc B 20 70 10 10 40 Zn 1 cleaner feed 20 70 10 10 41 Zn 1 cleaner mp 20 70 10 10 42 Zn 1 cleaner conc 20 70 10 10 43 Zn 2 cleaner mp 20 70 10 10 44 Zn 2 cleaner conc 20 70 10 10 45 Zn 3 cleaner conc 20 70 10 10 46 Zn 3 cleaner mp 20 70 10 10 47 Zn 4 cleaner mp 20 70 10 10 48 Zn conc 20 70 10 10 49 To B41 pump 20 70 10 10 50 B41 pump overﬂow 20 70 10 10 51 B4 mill product 20 70 10 10 52 Zn mp 20 70 10 10 53 Zn 1 rougher mp 20 70 10 10 54 Zn recirculation 20 70 10 10 59 Knelson Au conc 100 100 100 100 60 Flash Cu conc 100 100 100 100

Kinetic parameters according to the Klimpel model were retrieved and are presented in table B.6. The roughing tests were used for these calculations.

Table B.6: Estimated Klimpel constants from the roughing tests.

KLIMPEL CONSTANTS - CuPb ROUGHING Stream name R∞ kK Chalcopyrite 95.0 6.55 Sphalerite 25.2 0.68 Galena 74.5 1.30 Arsenopyrite 34.3 0.85 Bismuth 46.9 1.84 Silver 81.2 2.45 Pyrite 17.2 0.64 Silicates 26.9 0.56 XXI

KLIMPEL CONSTANTS - Cu ROUGHING Stream name R∞ kK Chalcopyrite 99.9 1.75 Sphalerite 99.9 0.49 Galena 98.2 0.21 Arsenopyrite 99.9 0.47 Bismuth 88.6 0.36 Silver 97.2 0.68 Pyrite 99.9 0.65 Silicates 99.9 0.75

KLIMPEL CONSTANTS - Zn ROUGHING Stream name R∞ kK Chalcopyrite 79.9 0.70 Sphalerite 98.6 4.68 Galena 89.7 0.76 Arsenopyrite 99.9 0.87 Bismuth 70.4 0.49 Silver 81.4 1.07 Pyrite 99.9 1.72 Silicates 10.6 0.35

Unbalanced values

Table B.7: Stream properties. Densities as g/cm3, ﬂow rates in t/h.

PHYSICAL PROPERTIES (UNBALANCED)

No Stream name D ρs ρp Fw 1 Cyclone overﬂow 0.86 3.29 1.60 53.9 2 B3 mill recirculation 8.50 3.79 1.08 10.5 3 CuPb ﬂotation feed 1.12 3.30 1.49 47.1 4 CuPb rougher conc 2.91 4.05 1.24 25.6 5 CuPb rougher mp 1.01 3.31 1.53 49.7 6 CuPb mp 1.12 3.27 1.49 47.2 7 CuPb scavenger conc 5.10 3.98 1.14 16.4 8 CuPb 1 cleaner conc 1.93 4.21 1.35 34.1 9 CuPb 1 cleaner mp 8.00 3.74 1.09 11.1 10 CuPb 2 cleaner mp 5.43 4.00 1.13 15.6 11 CuPb 2 cleaner conc 2.33 4.46 1.30 30.0 12 CuPb 3 cleaner mp 5.92 4.00 1.13 14.4 XXII APPENDIX B. STREAM DATA

PHYSICAL PROPERTIES (UNBALANCED, cont.)

No Stream name D ρs ρp Fw 13 CuPb conc 1.75 4.38 1.39 36.4 14 B31 pump overflow 9.97 3.77 1.07 9.1 15 B3 mill product 4.06 3.98 1.17 19.8 18 Total Cu rougher conc 2.38 4.40 1.30 29.6 19 Total recirculation 17.25 4.62 1.04 5.5 21 Cu 1 rougher conc 1.12 4.37 1.57 47.1 22 Cu 2 rougher conc 1.24 4.56 1.54 44.6 23 Cu scavenger conc 1.20 4.53 1.55 45.4 24 Cu 1 rougher mp 15.52 4.56 1.05 6.1 25 Cu 2 rougher mp 20.20 4.53 1.04 4.7 26 Pb conc 22.11 4.66 1.04 4.3 27 Cu 1 cleaner mp 17.09 4.73 1.05 5.5 28 Cu 2 cleaner mp 17.91 4.37 1.04 5.3 29 Cu 3 cleaner mp 12.31 4.24 1.06 7.5 30 Cu 1 cleaner conc 1.22 4.33 1.53 45.1 31 Cu 2 cleaner conc 0.88 4.30 1.69 53.3 32 Cu conc 1.75 4.29 1.39 36.3 33 Zn flotation feed 1.31 3.37 1.44 43.3 34 Zn 1 rougher conc 0.92 3.97 1.64 52.1 35 Zn 2A rougher conc 0.94 3.97 1.63 51.6 36 Zn 2B rougher conc 1.63 3.97 1.40 38.1 37 Zn rougher mp 1.55 3.20 1.37 39.2 38 Zn scavenger conc A 3.15 3.85 1.22 24.1 39 Zn scavenger conc B 2.84 3.87 1.24 26.0 40 Zn 1 cleaner feed 0.85 4.01 1.68 54.0 41 Zn 1 cleaner mp 2.69 3.91 1.25 27.1 42 Zn 1 cleaner conc 0.80 4.04 1.72 55.5 43 Zn 2 cleaner mp 0.84 4.04 1.69 54.2 44 Zn 2 cleaner conc 1.00 4.03 1.60 49.9 45 Zn 3 cleaner conc 0.92 4.03 1.64 52.0 46 Zn 3 cleaner mp 1.31 4.03 1.48 43.3 47 Zn 4 cleaner mp 1.36 4.04 1.47 42.3 48 Zn conc 1.37 4.03 1.46 42.2 49 To B41 pump 2.04 3.95 1.33 32.8 50 B41 pump overflow 3.15 3.88 1.22 24.1 51 B4 mill product 0.51 4.08 2.00 66.4 52 Zn mp 1.60 3.21 1.36 38.5 53 Zn 1 rougher mp 1.48 3.27 1.39 40.4 54 Zn recirculation 1.42 3.49 1.42 41.3 59 Knelson Au conc 0.27 4.70 2.63 78.7 60 Flash Cu conc 32.82 4.11 1.02 3.0 XXIII

Table B.8: Elemental grades in the mass balance.

GRADES (UNBALANCED) No Stream name Sol. [t/h] Ag [g/t] Cu [%] Pb [%] Zn [%] 1 Cyclone overflow 123.4 50 0.465 0.412 6.03 2 B3 mill recirculation 482 2.45 3.22 23.6 3 CuPb flotation feed 55 0.538 0.474 6.61 4 CuPb rougher conc 938 11.25 7.99 15.7 5 CuPb rougher mp 20 0.107 0.143 5.67 6 CuPb mp 11 0.034 0.059 5.58 7 CuPb scavenger conc 449 1.845 4.44 24.01 8 CuPb 1 cleaner conc 1.6 1085 14.0 9.15 14.4 9 CuPb 1 cleaner mp 449 2.36 2.29 23.5 10 CuPb 2 cleaner mp 739 5.72 3.10 23.68 11 CuPb 2 cleaner conc 3.4 1195 16.51 10.70 12.15 12 CuPb 3 cleaner mp 961 9.73 5.50 21.0 13 CuPb conc 2.2 1333 19.86 13.35 8.17 14 B31 pump overflow 705 4.84 5.24 20.99 15 B3 mill product 627 5.80 4.01 22.27 18 Total Cu rougher conc 1470 22.05 12.55 6.59 19 Total recirculation 1575 10.50 24.64 12.65 21 Cu 1 rougher conc 0.5 1460 22.93 11.05 5.97 22 Cu 2 rougher conc 0.1 1795 12.15 18.99 14.0 23 Cu scavenger conc 0.1 2140 11.55 19.4 17.25 24 Cu 1 rougher mp 1440 4.33 28.39 17.68 25 Cu 2 rougher mp 1290 2.19 29.96 19.35 26 Pb conc 0.5 1240 1.555 33.9 19.39 27 Cu 1 cleaner mp 1625 8.33 29.14 12.80 28 Cu 2 cleaner mp 1840 13.60 18.0 11.20 29 Cu 3 cleaner mp 1980 18.30 10.25 9.31 30 Cu 1 cleaner conc 1450 22.63 10.50 6.66 31 Cu 2 cleaner conc 1.1 1475 26.90 7.00 4.86 32 Cu conc 1.7 1430 27.90 6.60 4.37 33 Zn flotation feed 33 0.125 0.242 11.35 34 Zn 1 rougher conc 13.9 128 0.373 1.075 46.6 35 Zn 2A rougher conc 6.9 90 0.269 0.589 45.5 36 Zn 2B rougher conc 6.8 83 0.234 0.527 45.3 37 Zn rougher mp 10 0.030 0.063 1.495 38 Zn scavenger conc A 119 0.348 0.657 28.92 39 Zn scavenger conc B 114 0.353 0.605 29.05 40 Zn 1 cleaner feed 106 0.293 0.796 51.9 41 Zn 1 cleaner mp 124 0.370 1.13 38.0 42 Zn 1 cleaner conc 103 0.251 0.705 56.1 43 Zn 2 cleaner mp 123 0.249 0.735 56.2 44 Zn 2 cleaner conc 86 0.266 0.480 57.3 45 Zn 3 cleaner conc 83 0.288 0.420 57.7 XXIV APPENDIX B. STREAM DATA

GRADES (UNBAL. cont.) No Stream name Sol. [t/h] Ag [g/t] Cu [%] Pb [%] Zn [%] 46 Zn 3 cleaner mp 81 0.254 0.497 56.7 47 Zn 4 cleaner mp 80 0.249 0.491 57.0 48 Zn conc 11.7 78 0.320 0.406 58.1 49 To B41 pump 124 0.340 1.045 39.8 50 B41 pump overﬂow 134 0.354 1.10 35.5 51 B4 mill product 115 0.382 0.959 48.0 52 Zn mp 7 0.020 0.047 0.491 53 Zn 1 rougher mp 19 0.053 0.119 6.74 54 Zn recirculation 1575 10.50 24.64 12.65 59 Knelson Au conc 1225 0.830 7.27 3.34 60 Flash Cu conc 1385 18.87 7.94 4.66

GRADES (UNBAL. cont.) No Stream name As [g/t] Bi [g/t] Fe [%] S [%] 1 Cyclone overflow 16.95 19.85 2 B3 mill recirculation 200 100 17.10 29.00 3 CuPb flotation feed 20 17.05 19.65 4 CuPb rougher conc 130 150 19.30 29.60 5 CuPb rougher mp 140 16.75 20.50 6 CuPb mp 15.95 19.05 7 CuPb scavenger conc 170 110 17.95 31.9 8 CuPb 1 cleaner conc 120 190 21.20 30.80 9 CuPb 1 cleaner mp 150 80 15.60 28.30 10 CuPb 2 cleaner mp 160 130 19.70 32.7 11 CuPb 2 cleaner conc 110 210 21.20 30.70 12 CuPb 3 cleaner mp 140 160 19.00 30.70 13 CuPb conc 235 22.20 30.50 14 B31 pump overflow 160 160 15.10 26.60 15 B3 mill product 170 130 18.75 30.40 18 Total Cu rougher conc 190 24.90 31.10 19 Total recirculation 170 400 17.20 26.70 21 Cu 1 rougher conc 120 180 24.00 31.20 22 Cu 2 rougher conc 150 340 19.20 30.80 23 Cu scavenger conc 160 340 17.20 29.60 24 Cu 1 rougher mp 180 520 12.00 24.40 25 Cu 2 rougher mp 160 530 10.00 22.70 26 Pb conc 140 580 8.89 22.10 27 Cu 1 cleaner mp 190 480 15.25 26.50 28 Cu 2 cleaner mp 190 300 20.50 29.70 XXV

GRADES (UNBAL. cont.) No Stream name As [g/t] Bi [g/t] Fe [%] S [%] 29 Cu 3 cleaner mp 150 190 23.80 32.0 30 Cu 1 cleaner conc 130 150 25.60 32.2 31 Cu 2 cleaner conc 27.60 33.4 32 Cu conc 28.00 33.6 33 Zn ﬂotation feed 260 10 16.05 21.60 34 Zn 1 rougher conc 780 40 10.60 32.2 35 Zn 2A rougher conc 750 20 11.30 33.9 36 Zn 2B rougher conc 730 20 11.20 32.7 37 Zn rougher mp 16.60 17.60 38 Zn scavenger conc A 740 30 17.20 30.70 39 Zn scavenger conc B 630 30 17.30 31.40 40 Zn 1 cleaner feed 610 30 8.61 33.3 41 Zn 1 cleaner mp 1130 40 13.70 31.7 42 Zn 1 cleaner conc 340 20 6.98 34.4 43 Zn 2 cleaner mp 400 7.10 33.8 44 Zn 2 cleaner conc 220 20 6.61 34.1 45 Zn 3 cleaner conc 120 20 6.26 33.7 46 Zn 3 cleaner mp 270 20 6.67 33.2 47 Zn 4 cleaner mp 240 20 6.72 33.9 48 Zn conc 6.12 33.9 49 To B41 pump 1010 40 13.25 32.5 50 B41 pump overﬂow 1210 50 14.70 30.90 51 B4 mill product 650 30 11.50 34.5 52 Zn mp 17.05 17.25 53 Zn 1 rougher mp 200 16.60 19.05 54 Zn recirculation 170 400 17.20 26.70 59 Knelson Au conc 185 85 36.80 44.1 60 Flash Cu conc 40 120 24.80 28.20 Appendix C Scale-up models

A complete comparison between the scaled results from all flotation tests and the plant is presented in this appendix. Only standard cleaning tests were used for the scale-up procedure and the results with regrind were mainly used as a comparison. As mentioned in the main report, the recoveries in tests with regrind are often rather similar to the scaled recoveries. This might be because unliberated particles are reground in the plant, which should increase the recovery but especially the grade. Table C.1 shows the results from CuPb flotation, with recoveries calculated against the cyclone overflow. Note the high Zn feed grade in the plant compared with the laboratory tests. This could be a consequence of the mass balance, since the unbalanced value was almost identical to the laboratory values. It is clear that the locked cycle model gives despicable grade predictions but somewhat better predictions on recoveries. As expected, regrinding increased both Cu grade and recovery. Corresponding figures for the Cu flotation are found in table C.2. Re- coveries are calculated with the CuPb concentrate as reference stream. The plant recovery of Cu was exceptionally high, as predicted by the locked and mixed cycle models. At last, a comparison of the Zn flotations is presented in table C.4. This table contains a lot of data, but one should note the low Zn concentrate grade in test 3.2.2. This must be the result of an experimental or analytical error and the test was discarded in the comparison. It is also interesting that regrinding in test 3.2.3 decreased the Zn recovery. An explanation for this is poor froth recovery in the first cleaning procedure, since the cleaner middlings contained a lot of material with rather high Zn grade.

XXVI XXVII CuPb feed grade [%, g/t] CuPb conc grade [%, g/t] Recovery [%] 41 0.40 7.60 0.33 83 9 1.99 1368 17.5 10.4 12.7 124 279 66 88 3 76 3 64 Ag Cu Zn Pb As Bi wt% Ag Cu Zn Pb As Bi Ag Cu Zn Pb As Bi Comparison of scale-up models for the CuPb flotation. Ag, As and Bi grades are presented as g/t and the others as wt%. Lab tests 3.2.23.2.3 (regrind)Current model 443.2.2 0.44 47 5.633.2.3 (regrind) 0.36 0.51 5.54 62Locked cycle 0.38 13 613.2.2 15 1.633.2.3 (regrind) 1145 1.82 23.0Mixed cycle 2.96 1030 5.50 20.63.2.2 4.00 403.2.3 7.13 (regrind) 120 62ACTUAL 140 43 82 40 1 74 25 1.69 1 1 1302 34 1.93 23.0 15 2 2.96 1202 7.21 20.6 17 4.00 3.60 40 8.81 120 762 6.31 60 12.8 140 437 51 5.36 4.60 6.64 88 2.01 49 5.90 91 1 1367 3.32 78 111 2.04 23.0 34 100 1 9.63 1356 73 1 58 8.26 20.6 44 15 163 18.3 91 2 59 200 10.3 4 18 310 82 43 226 58 7 91 5 55 59 27 4 10 82 43 32 7 5 55 27 10 32 Table C.1: XXVIII APPENDIX C. SCALE-UP MODELS al C.2: Table .. 6914 881. 20156 19 66 153 91 60 86 53 91 99 60 91 8 86 26 99 65 45 91 115 24 12.0 81 10.7 118 63 28.8 82 1744 8.57 66.9 30 7.62 20.5 40 1244 11.1 93.8 3.65 6 28.8 20 1476 11 54.5 19 63 32 30 40 3.56 3.65 28.8 983 42.1 ACTUAL 209 3.2.8 84 cycle Mixed 13.4 8.28 3.2.8 19.4 cycle Locked 1285 3.2.8 m. Current 3.2.8 tests Lab oprsno cl-pmdl o h ufltto.A,A n igae r rsne sgtadteohr swt%. as others the and g/t as presented are grades Bi and As Ag, flotation. Cu the for models scale-up of Comparison 381. 041. 2 7 7316 5357 .612127 83 76 39 66 17 37 98 72 162 122 3.26 5.78 25.3 1464 67.3 279 124 12.7 10.4 17.5 1368 gC nP sB t gC nP sB gC nP sBi As Pb Zn Cu Ag Bi As Pb Zn Cu Ag wt% Bi As Pb Zn Cu Ag ufe rd % /]C ocgae[,gt eoey[%] Recovery g/t] [%, grade conc Cu g/t] [%, grade feed Cu XXIX Pb feed grade [%, g/t] Pb conc grade [%, g/t] Recovery [%] Ag Cu Zn Pb As Bi wt% Ag Cu Zn Pb As Bi Ag Cu Zn Pb As Bi 1368 17.5 10.4 12.7 124 279 32.7 1171 1.31 20.0 32.2 128 521 28 2 63 83 34 61 Comparison of scale-up models for the Pb concentrate (Cu mp). Ag, As and Bi grades are presented as g/t and the others Lab tests 3.2.8Cur. m. 3.2.8 1285 19.4Locked c. 8.28 13.43.2.8 84Mixed c. 2093.2.8 5.13ACTUAL 1755 2.10 14.0 54.1 80 850 7 1 45.5 9 1057 21 8.09 13.8 5 6.18 16.1 21 1906 137 1.82 18.2 423 33.1 86.4 356 118 37 0.34 1593 19 3.40 16.14 76 9 22 55 74 1 298 92 14 40 9 9 1 47 14 40 9 47 Table C.3: as wt%. XXX APPENDIX C. SCALE-UP MODELS al C.4: Table .. .38 .25. .5142 65 64 519 15 44 30 19 96 24 15 59 17 54 44 76 6 96 96 19 71 27 59 15 22 12 95 37 44 76 114 30 2 69 96 0.25 24 29 49 23 56 59 17 57.6 54 123 126 0.22 58 76 6 96 8 0.88 0.89 18 80 44 71 27 46.4 59.2 2 40 11 9.13 49 0.22 0.38 95 37 7 9 42 1.65 60 111 68 69 59.2 85 1 20 5.21 1 0.13 13 11.4 49 0.33 7 47 30.1 22 33 52 55 103 0.12 56 1 0.76 74 53 10 9.01 0.39 42 50.1 50 19 20.7 43 22 0.32 10 17.5 0.10 89 25 47 0.90 94 20 30 44 32.2 6.17 0.27 0.18 10 25.5 37 20 57.6 56 10 10 0.20 10 16.6 0.83 0.87 66 7 46.4 57.2 10 8.08 0.25 0.39 1.40 2 111 70 6 59.2 6 14 4.97 0.29 8.60 1 74 1 96 8 31 16 13 8.45 ACTUAL 36 24 1 55 (regrind) 3.2.6 24 38 10 3.2.5 10 22 86 14 (regrind) 20 3.2.3 43 20 0.10 3.2.2 10 30 57.6 10 cycle 10 Mixed 0.08 0.77 10 (regrind) 0.36 3.2.6 33 57.2 46.4 10 0.33 3.2.5 7.02 0.15 0.97 (regrind) 78 3.2.3 42 59.2 10 3.32 0.22 3.2.2 6.60 71 cycle 68 Locked 0.05 10 11 (regrind) 8.21 5.48 3.2.6 81 58 0.03 0.08 3.2.5 0.19 10 10 5.33 (regrind) 5.52 3.2.3 61 0.05 0.10 0.21 3.2.2 12 21 5.63 model Current 0.04 (regrind) 3.2.6 19 3.2.5 (regrind) 3.2.3 3.2.2 tests Lab oprsno cl-pmdl o h nfltto.A,A n igae r rsne sgtadteohr swt%. as others the and g/t as presented are grades Bi and As Ag, flotation. Zn the for models scale-up of Comparison gC nP sB t gC nP sB gC nP sBi As Pb Zn Cu Ag Bi As Pb Zn Cu Ag wt% Bi As Pb Zn Cu Ag 400 .500 231. 903 9104 3 16 29 12 79 20 61 95 72 67 21 137 0.41 59.1 0.30 79 12.1 3 82 0.08 7.55 0.05 14 nfe rd % /]Z ocgae[,gt eoey[%] Recovery g/t] [%, grade conc Zn g/t] [%, grade feed Zn