Stream Gate

DEVELOPING SIMPLE REGRESSIONS FORPREDICTING GOLD GRAVITY RECOVERY IN

GRINDING CIRCUIT

Zhixian Xiao

A thesis submitted to theFaculty of Graduate Studies and Research

In partial fulfillment of the requirement for the degree ofMaster of Engineering

Department of Mining, Metals and Materials EngineeringMcGill UniversityMontral, Canada

September 2001

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Abstract

Determining whether or not a gold gravity circuit should be installed in a gold

plant requires a prediction of how much goId will be recovered. This has always been a

difficult task because recovery takes place from the grinding circulating load, in which

gold's behavior must be described.

A population-balance mode! (PBM) to predict gold gravity recovery wasdeveloped at McGill University in 1994 (Laplante et al, 1995). The objective of thisresearch was to make this PBM user friendly. This was achieved in two different ways.

First, the behavior of gravity recoverable gold (GRG) in secondary ball mills andhydrocyclones was described by two parameters, 't and R..25Ilm, and these parameters

were linked to the circulating load of ore and the fineness of the grinding circuit

product, for easy estimation. Second, the database of simulations produced by the PBM

was represented by two multilinear regressions (one for coarse GRG, the other for fineGRG) linking the predicted GRG recovery to the naturallogarithm of 't, R-25Ilm, the sizedistribution of the GRG and the recovery effort (Re), defined as the proportion, in %, ofthe GRG in the circulating load recovered by gravity. Re was found to be the most

significant parameter, 't the least. The GRG size distribution, represented either by two

(coarse GRG) or three (fine GRG) points on the cumulative passing curve, has asignificant impact on recovery. A total of twenty different GRG size distributions were

used to generate the simulation database.

The multilinear regressions were tested on four case studies, and found topredict GRG recovery well within the precision with which the GRG content can be

measured, a relative 5%. Whenever size-by-size recovery data are available, the PBM

itself would be used; if not, the simpler regressions would be preferred.

11

Rsum

Pour justifier l'installation d'un circuit gravimtrique dans un concentrateur, ondoit, au minimum, pouvoir estimer la quantit d'or qui sera rcupre. Cette tche est

ardue, car la rcupration se fait de la charge circulante au broyage, dans laquelle le

comportement de l'or doit tre dcrit.

Un modle d'quilibrage de population (MEP) permettant d'estimer larcupration gravimtrique de l'or a t dvelopp l'universit McGill en 1994

(Laplante et al, 1995). Le but de cette thse tait de rendre ce modle convivial. Letravail s'est fait en deux tapes. D'abord, nous avons dcrit le comportement de l'or

rcuprable par gravimtrie (ORG) dans les broyeurs boulets secondaires et leshydrocyclones l'aide de deux paramtres, 1 et R-25J.lm, pour ensuite faire le lien entre

ces paramtres, la charge circulante et la finesse de broyage, afin de faciliter leur

estimation. Par la suite, nous avons reprsent la base de donnes obtenues du MEP par

deux rgressions multilinaires (une pour l'ORG grossier, l'autre pour l'ORG fin)faisant le lien entre la rcupration de l'ORG et le logarithme naturel des variables

indpendantes, soient 1, R..25J.lffi, la distribution granulomtrique de l'ORG et l'effort de

rcupration (Re), dfini comme tant le pourcentage de l'ORG de la charge circulantequi est rcupr. De tous les paramtres, Re a le plus d'impact et 1 le moins. La

distribution granulomtrique de l'ORG, reprsente soit par deux paramtres pour

l'ORG grossier ou trois pour l'ORG fin, a un impact majeur sur la rcupration, qui at dtermin en simulant la rcupration de 20 granulomtries diffrentes.

Les rgressions multilinaires, utilises pour quatre tudes de cas, ont pu estimer

la rcupration en ORG avec une prcision au moins gale de celle avec laquelle la

quantit d'ORG peut tre estime, soit environ 5% (relatif). Nous recommandonsl'utilisation du MEP lorsqu'un estim de la rcupration de l'ORG en fonction de la

taille des particules est disponible; sinon, les rgressions doivent tre utilises.

111

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IV

Acknowledgements

1 would like to thank Professor A. R. Laplante for his keen insight, WIseguidance, enthusiasm and constant support during this program. 1 'd like to thank him

for allowing me to work at my own pace and his invaluable help in technical writing

skills and oral skills in the discussion, especially for his correction of the thesis during

his sabbaticalleave.

1 would also like to thank Professor J. A. Finch for his inspiring lectures andsuggestions about the presentation.

1 wish to thank my friends and colleagues in the Mineral Processing group,

especially the Gravity Separation group: Mr. R. Langlois for his instruction in computer

skill; Dr. Liming Huang for his valuable technical discussions and endless help in my

daily life.

1 also wish to thank the Natural Sciences and Engineering Research Council of

Canada for their research funding.

Last but not least, 1 extend my warmest thanks to my parents and parents-in-Iawfor their support and encouragement, my sweet daughter Jessica Xiao for her

cooperation and the fun she gives me and my wife for her continued support,

encouragement and love.

Table of Contents

Abstract

Rsum 11Zhaiyao 111

Acknowledgements IV

Table of Contents V

List of Figures VI

List of Tables Vll

List of Abbreviations Xl

v

Chapter 1: Introduction1.1 Background

1.1.1 Oravity Recoverable Oold and

Predicting the Oold Recovery

1.1.2 Oold Behaviour in Orinding Circuits

1.1.3 Advantages of Recovering Oold by Gravity

1.2 Objectives of the Study1.3 Thesis Structure

Chapter 2: Gravity Recoverable Gold: A Background2.1 Introduction

2.2 Gravity Recoverable Gold

2.2.1 ORO Potential of Ores

2.2.2 ORO Available in Streams

2.3 Unit Processes

1

1

2

3

56

7

9

9

9

10

13

15

2.3.1 Comminution and Classification 162.3.1.1 The Breakage Function 162.3.1.2 The Selection Function 182.3.1.3 Investigation of Go1d's Behaviour in Comminution 182.3.1.4 Go1d's Behaviour in Cyclone 20

2.3.2 Recovery Dnits 23

2.3.2.1 Knelson Concentrator 23

2.3.2.2 Table 26

2.3.2.3 Jigs 27

Chapter 3: Simulating Gold Gravity Recovery 323.1 Introduction 323.2 The GRG Population Balance Model 32

3.2.1 A Simplified Approach 32

3.2.2 The Full PBM 393.3 Input Data for the PBM 43

3.3.1 GRG Data (F Matrix) 433.3.2 Dnits Matrices 45

Chapter 4: Simulation Results 524.1 Introduction 524.2 Simulation Results 52

4.2.1 Basic Case Study 524.2.2 Gravity Recovery Effort 554.2.3 Impact of Operating Variables 56

4.3 Representing Results with Mu1tilinear Regressions 614.3.1 Criteria and General Approach for Representing

the Simulated Database

61

VI

4.3.2 Regressions for Fine and Coarse GRG Size Distributions 62

4.3.3 Comparing the Regressions and Original PBM and

Testing for Phenomenological Correctness 64

4.4 Estimation of't and R.25 ~m 694.4.1 Representing the Grinding Circuit Design

Parameters with 't and R.25 ~m 694.4.2 Case Study 72

Chapter 5: Model Reliability and Validation 745.1 Introduction 74

5.2 Model Reliability 74

5.2.1 GRG-25~m, GRG-75~m, GRG-150~m and F Matrix 745.2.2 R-25~m and C Matrix 775.2.3 't and B Matrix 79

5.2.4 Re and R Matrix 795.3 Model Validation 80

5.3.1 Campbell Mine Case Study 80

5.3.2 Northem Qubec Cu-Au Ore Case Study 835.3.3 Case Study: Snip Operation 84

5.3.4 Case Study: Bronzewing Mine 86

5.4 Model Extrapolation and Applications 88

5.4.1 Model Extrapolation 88

5.4.2 Model Applications 89

Chapter 6: Conclusions and Future Work 916.1 Introduction 91

6.2 General Conclusions 91

6.3 Strengths and Weaknesses ofProposed Protocol 93

Vll

6.5 Future Work 94

References 97

Appendix A: Breakage and selection function used for GRG and ore 103

Appendix B: Grinding matrix for GRG and ore 105

Appendix C: GRG used for simulation 110

Appendix D: An example for simulation 112

Appendix E: Database used for generation ofregressions 117

Appendix F: Regression ANOVA Table for Coarse and Fine GRG 131

Appendix G: Database for generating the relationship between 1, R-25~m andcirculating load, fineness of grind. Regression ANOVA Table 135

Vlll

IX

List of Figures

Figure 2-1 Procedure for measuring GRG content with a KC-MD3 Il

Figure 2-2 Cumulative GRG recovery of three stages as function of particle size 13

Figure 2-3 Typical partition curve for gangue, goId and GRG 21

Figure 2-4 Partition curves of the secondary cyclones ofNew Britannia 22

Figure 2-5 Schematic cross-section of a Knelson Concentrator MD3 24

Figure 2-6 Basic Jig construction 29

Figure 2-7 Comparing size-by-size recovery of a KC and a Duplex Jig 30

Figure 3-1 Simple circuit of gravity recovery from the baIl mill discharge 33

Figure 3-2 Simple circuit of gravity recovery from the cyclone underflow 35Figure 3-3 Circuit of gravity recovery from the cyclone underflow using

a size-by-size approach 37

Figure 3-4 Recovery from the second mill discharge 40

Figure 3-5 Recovery from the cyclone underflow 41Figure 3-6 Recovery from the primary cyclone underflow 42

Figure 3-7 Normalized GRG distributions of the original data set 43

Figure 3-8 Coarse and fine GRG size distributions (down to 25 !-lm) used forsimulation (Hatched lines: fine GRGs; solid lines: coarse GRGs) 45

Figure3-9 Partition curves of GRG and ore for the three classification cases

(fine, intermediate, coarse) 51

Figure 4-1 GRG recovered in various size class when treating bleeds of

5 and 12% 55Figure 4-2 GRG recovery as function of recovery effort with coarse,

Intermediate and fine classification 57Figure 4-3 the impact of GRG size distribution to GRG recovery 58

Figure 4-4 %GRG in size fractions as a function of the Pso for the

phoenix NNX3 sample 59Figure 4-5 GRG recovery as a function of the recovery effort for fine

(Pso =75 !-lm) coarse (Pso=150 !-lm) grinding, NNX-3 sample 60Figure 4-6 Comparison of PBM and regression for fine GRG 65Figure 4-7 Comparing the PBM and the regression for a coarse

GRG distribution 66Figure 4-8 Effect of GRG size distribution of GRG recovery 67Figure 4-9 GRG recovery decreases with the increasing dimensionless

retention time in the mill 68Figure 4-10 Gravity recovery as a function of the recovery effort for fine

GRG and for coarse, medium and fine classification curves 69

Figure 4-11 't as a function of the ore circulating load and product size 71

Figure 4-12 R-25!-lm as a function of the ore circulating load and product size 71Figure 4-13 GRG recovery as a function ofthe recovery effort (Cu-Au ore) 73

x

Figure 5-1 Partition curve for ore*, gold* and GRG* with a saprolitic component 77

Figure 5-2 Campbell Mine cumulative GRG as function of particle size 80

Figure 5-3 GRG content retained as function of particle size 84

Figure 5-4 Cumulative GRG retained in each size class for Bronzewing Mine 86Figure 5-5 Measured and predicted gold gravity recoveries of the case studies 88

List of Tables

Table 2-1 Differences between GRG determination for ores and streams

Table 2-2 Coefficient used to correct the grinding matrix

Table 2-3 Typical values of Knelson Concentrator's recovery

Table 2-4 Typical values of Shaking Table's recovery used for simulation

Table 2-5 Evolution of the use of Jigs and KC at certain Canadian sitesTable 2-6 Typical values of Jig' s recovery used for simulation'

Table 3-1 Normalized GRG distributions used for the simulation

Table 3-2 Typical B matrix for GRG

Table 3-3 Parameters used to calculate the partition curves

Table 4-1 GRG size distribution ETable 4-2 The recovery matrix P*R

Table 4-3 Grinding matrix B (for a 't value of 1)Table 4-4 Classification matrix C (for a R..25J.lm value of82.8%)Table 4-5 Variables of regression analysis

Table 4-6 Actual and normalized GRG size distribution for Midas sample

Table 4-7 Actual and normalized GRG size distribution for Campbell

Table 4-8 Effect of changing product fineness from 65 to 85% minus

at a circulating load of 250%, for Re =5%

Table 5-1 Basic data from the Campbell grinding circuit

Table 5-2 Experimental and estimated data used for predicting GRGRecovery in Campbell Mine

Table 5-3 Predicted and reported gold recovery for Campbell Mine

Xl

14

20

25

27

28

31

44

49

50

54

54

55

55

646667

73

81

81

82

XlI

Table 5-4 Sensitive analysis of the impact of relative change of Re , 'r and R251lID 82Table 5-5 Data used for predicting GRG recovery on Northern Qubec Cu-Au Ore 83Table 5-6 Data used for predicting GRG recovery on Snip 85Table 5-7 Parameters used for goId recovery prediction 87Table 5-8 Predicted and reported gold recovery of Bronzewing Mine 87

GRG

KC

LKC

CL

PM

GRG_x

ANOVA

PBM

int.

KC-CD3

KC-CD30

Pso

g/min

g/t

Gs

Kg/min

L!min

SAG

List of Abbreviations and Acronyms

Gravity Recoverable Gold

Knelson Concentrator

Laboratory Knelson Concentrator

Circulating Load

Perfect Mixer

Gravity Recoverable Gold content below certain size

Analysis of Variances

Population-Balance Model

intermediate (used in table)

3 in Center Discharge Knelson Concentrator

30 in Center Discharge Knelson Concentrator

the particle size at which 80% of the mass passes

grams per minute

grams per tonne

times of gravity acceleration

kilogram per minute

litre per minute

semi-autogenous

Xl1l

CHAPTERONE

CHAPTERONE

INTRODUCTION

1.1 Background

INTRODUCTION 1

Gravity concentration of gold remained the dominant mineraI processing method

for thousands of years, and it is only in the twentieth century that its importance

declined, with the development of the froth flotation and cyanidation. However, in

recent years, gravity systems have been reevaluated due to increasing flotation costs, the

environmental and health hazards associated with cyanide, and the relative simplicity

and low cost of gravity circuits, and the fact that they produce comparatively little

pollution. Particularly over the past twenty years, goId gravity recovery has evolved

significantly because of the advent of the new technologies, such as Knelson and Falcon

Concentrators.

Treatment methods for the recovery of gold from ores depend on the type of

mineralization. Gold ores in which sulphides are largely oxidized are best treated by

cyanidation; gold ores that contain their major values as base metals, such as copper,lead and zinc, are generally treated by flotation; gold that is intimately associated with

pyrite and arsenopyrite, and usually with non-sulphide gangue mineraIs, is frequentIy

treated with the combination of flotation, sulphides oxidation and cyanidation (Marsdenand House, 1992). However, no matter in which form gold exists, sorne is liberated ingrinding circuits where it accumulates because of its density and malleability (Basini et

CHAPTERONE INTRODUCTION 2

al 1991). Therefore, gravity concentration can be incorporated in the recoveryflowsheet. Dorr and Bosiqui (1950) emphasized the importance ofrecovering gold fromthe grinding circuit and advocated gravity concentration, especially for those ores in

which a significant proportion of the goId is associated with base metal sulphides. In

flotation and cyanidation plants, a gravity circuit is often used within grinding circuits,

after a baIl mill discharge or cyclone underflow (Agar, 1980; Anon, 1983).

1.1.1 Gravity Recoverable Gold and Predicting the Gold Recovery

The term "Gravity Recoverable Gold" (GRG) is easily confused with the term"free gold". "Free gold" refers to gold that is readily extracted by cyanide at reasonable

grinds, typically when the ore is ground to a size of 80% below 75 Ilm. It can represent

a measure of the degree of 1iberation of the gold. "Gravity Recoverable Gold" (GRG)refers to that portion of gold present in ores or mill streams that can be recovered by

gravity into a very small concentrate mass 1%) under ideal condition. GRG includesgold that is not totally liberated. Generally, the amount of gold that can be recovered by

cyanidation is much higher than the GRG content.

The McGill University research group has already developed a method of

characterizing GRG in an ore. The details will be discussed in chapter two. The research

group has also proposed the use of Population-Balance Model (PBM) to predict GRGbehaviour in grinding circuits, either with or without gravity recovery. In this thesis, the

characterization of GRG and prediction of gravity recovery will be presented as two

different concepts. Characterizing the GRG content of an ore is not in itself a prediction

of how much gold will be recovered by gravity. Since GRG accumulates in the

circulating load of grinding circuits, predicting gravity recovery must incorporate a

description of this behaviour, as it determines how often a GRG particle or its progeny

can be presented to a recovery unit that treats either all or part of the circulating load.

Most methods of predicting gold gravity recovery fail to take into account this dynamic


component of gold recovery. For example, a pilot centrifuge unit installed in the

circulating load of an existing circuit may weIl recover gold effectively but its

performance reveals little about (a) how much gold will be left in the circulating loadonce a full scale unit is installed or (b) how much goId will be recovered at steady-stateby a full-scale, similar recovery unit.

Earlier Knelson Concentrator applications were largely retrofits, in plants where

gravity recovery was either not used or implemented with older equipment, typically

jigs in North America and spirals in Australia. Retrofitting one or many centrifuge unitsin an existing plant is generally a low-risk, low-retum endeavor. Few operating savings

can be generated from downstream recovery circuits (e.g. flotation, cyanidation), ascapital costs have already been sunk. For such applications, predicting how much gold

will be recovered by gravity is often not critical.

Many green field projects, on the other hand, rely heavily on gravity recovery toreduce the downstream processing effort, resulting in significant savings in capital and

operating costs. For example, a gold-copper ore can be treated by a combination of

gravity-flotation for a much lower cost than flotation-cyanidation. As much as 25% ofcapital and operating costs can thus be truncated, and the resulting flowsheet would be

environmentally more attractive, if only for political reasons. For such projects, theeconomic and metallurgical impact of gravity is such that reliable prediction of how

much gold will be recovered is critical. Even for projects where gravity plays a lesserrole, predicting how much gold can be recovered by gravity is desirable, if only to

justify the cost of gravity.

1.1.2 Gold Behaviour in Grinding Circuits

Gold's malleability and high specific gravity in grinding circuits are unusual and

affect aIl important mechanisms: breakage, liberation and classification. The specific


rate of breakage (selection function) of gold is 5 to 20 times lower than that of itsgangue (Banisi, et. al. 1991); therefore, it moves slower from its natural grain size intofiner size classes than its gangue. Gold, and particularly GRG, also has a distinctbehaviour in hydrocyclones, whereby typically more than 98% of all GRG fed to

cyclone reports to its underflow. Even below 25 !J.m, between 65% and 95% of GRG

still reports to underflows (depending on the fineness of grind). For example, atAgnico-Eagle, despite the very high density of the gangue (more than 50% sulphides),the Dso of gold was three times smaller than that of the gangue (Buonvino, 1994). Thisyielded recoveries to the underflow of 98% and more for all size classes above 371lm.

Generally speaking, in the absence of gravity recovery gold particles above 75 !J.m (ortheir progeny) circulate between 50 and 100 times in a grinding circuit and build up tovery high circulating loads, 2000-8000%, and often leave the grinding circuit only oncethey are overground (Laplante, 2000. Basini et al, 1991). Thus, in the absence ofgravity, free gold disappears slowly from coarser size classes through grinding, and

most of it reappears as GRG in finer size classes. In finer size classes, grinding kinetics

is very slow, and GRG disappears much more by classification to the cyclone overflow

(Laplante et al, 1994). This can cause losses due to overgrinding or surface aging orpassivation, difficulties in the estimation of the head grade or high gold inventories.

In a grinding circuit, the streams that contain a significant portion of the gold forgravity concentration are the ball mill discharge, the primary cyclone underflow and

perhaps the SAG mill discharge (Agar, 1992). In most gold mines, the primary gravityconcentrator usually treats part or all of the primary cyclone underflow or ball mill

discharge to recover liberated gold. The primary gravity concentrate is then upgradedwith a shaking table to obtain a final goId concentrate, which is directly smelted to

produce bullion containing 90-98% gold plus silver (Huang, 1996).


1.1.3 Advantages of Recovering Gold by Gravity

Recovering gold from the circulating load of grinding circuits yields significant

benefits from both design and operating perspectives: (i) the payment for gold bullionis more than 99% and is received almost immediately, while gold in flotationconcentrate is only paid 92-95% three or four months later (Wells and Patel, 1991;Huang, 1996); (ii) gold overgrinding is reduced and the amount of gold locked upbehind mill liners is minimized*; (iii) the removal of some of the gold by gravityconcentration can reduce the number of stages and the lock-up of goId in the CIP plant

(Loveday et al, 1982); (iv) the overall goId recovery can be improved by reducingsoluble losses and recovering large or slow leaching gold particles that would otherwise

be incompletely leached (Loveday et al, 1982); (v) for flotation, the risk of goldparticles advancing to flotation that are too coarse to float is reduced and the floatability

may be increased because of reduced surface aging and (vi) overall gold recovery canalso be increased by recovering gold smeared cnte other particles or embedded by other

particles (Banisi, 1990; Darnton et al, 1992; Ounpuu, 1992).

Due to the diversity of gold ore types and performance of gravity recovery units,

different levels of success have been reported. For example, Goldcorp's Red Lake Mine

processes a high-grade goId ore and recovers a high proportion (+50%) of the golddirectly from the grinding circuit with a Knelson CD20 Concentrator that improves

leaching efficiency and helps to maintain high overall plant recovery. The recovery of

coarse goId in the grinding circuit of the Tsumeb mill by using high-tonnage gravityseparation equipment (a Reichert cone) has resulted in significant decreases in theconsumption of reagents in the oxide flotation circuit (Venter et al, 1982). Gravity goldrecovery at the Homestake mill in the United States changed an unacceptable overall

In South Africa, it is estimated that 8% of the gold mined is stolen, much ofit from the holdup behind millliner


recovery to acceptable levels and in the OK Tedi project in New Guinea, a one percentincrease in the overall recovery was obtained (Hinds, 1989; Lammers, 1984).

Despite the many advantages of gold gravity recovery it is equally obvious that

not everyone is convinced of the benefits of installing and operating a gravity

concentration circuit. The most important reason perhaps is the lack of a reliable,

proven method for predicting, on a laboratory scale, whether or not the ore is amenable

to gravity recovery and what the recovery of gold in a concentrate would be if the

gravity separation were used (Gordon, 1992). A methodology for characterizing gravityrecoverable gold (GRG) was used successfully to estimate the gold liberation of over 75samples (Laplante et al, 1993). The main stumbling block in the application of gravityseparation of gold appears to be the lack of a suitable technique to predict from the

GRG data what the recovery of goId would be in a grinding circuit. In this study, gold

gravity recovery from grinding circuits is first represented by a population-balance

mode! (PBM). Second, the inputs of the PBM are linked to the predicted goId recoveryusing multi-linear regressions. Third, the developed regressions are linked to a new

concept, the recovery effort, the Pso of gravity recoverable gold (GRG), the retentiontime in the mill and the partition curve of GRG. These concepts are represented by

regression parameters that are easy to measure or calculate.

1.2 Objectives of the Study

The objectives ofthis study are as follows:

1). To simulate the gravity recoverable goId recovery in grinding circuits using apopulation balance model.

2). To develop simple regressions for predicting GRG recovery using the gravityrecovery effort (Re), the GRG content, the dimensionless retention time in the


mill (1") and the partition curve of GRG (the fraction of GRG below 25 /lmreports to the cyclone underflow, R..25Ilm)'

3). To assess the sensitivity of predicted goId recovery to the parameters of thePBM.

4). To test the reliability of the method using real case studies.

1.3 Thesis Structure

This thesis consists of six chapters. This chapter introduces the background of

this program, which includes briefly describing gold's behaviour in grinding circuits,

the advantages of recovering gold from grinding circuit and the rationale behind this

research. The objectives ofthis study and the thesis structure are also presented here.

Chapter two provides the background on what gravity recoverable gold (GRG)is and how to measure the GRG potential of ores and the GRG available in the various

streams of a grinding circuit. The most relevant units for comminution, classification

and goId recovery will be presented.

Chapter three introduces the GRG population balance model (PBM), first usinga simplified approach, then as it is actually used to predict gravity recovery. How to

estimate or generate the input data for the PBM will be presented in this chapter. The

various unit matrices used in the PBM will be described at the end of this chapter.

Chapter four introduces typical simulation results. It also explores howimportant operating parameters affect the circulating load and recovery of GRG. A new

concept, the gravity recovery effort, is presented. Results are then summarized into

multilinear regressions for coarse and fine GRG. A dimensionless grinding retention


time, 't, and the recovery of GRG in the 25 /lm fraction to the underflow of cyclone, K

25!1m, are then linked to the circulating load of ore and the product size of the grinding

circuit. Finally, a case study is presented.

The reliability of the model is discussed in Chapter five. Several case studies are

used to validate the model. Finally, mode! extrapolation and applications are briefly

discussed.

General conclusions and suggestions for the future work are presented ln

Chapter six.

CHAPTER2 GRAVITY RECOVERABLE GOLD: A BACKGROUND 9

CHAPTERTWO

GRAVITY RECOVERABLE GOLD:A BACKGROUND

2.1 Introduction

Predicting goId gravity recovery from grinding circuits has always been a

difficult task. To address this problem, a population-balance model (PBM) wasproposed by Laplante (1992) (more details will be presented in Chapter three). Themodel includes the necessary concepts of gold liberation, grinding and classification

used in the simulation in later chapter. In this chapter, sorne of important concepts used

in the PBM will be reviewed; gravity recoverable gold (GRG) characterization will bedescribed and GRG behaviour in comminution, classification and recovery units

presented.

2.2 Gravity Recoverable Gold

Gravity recoverable goId (GRG) is a concept used to characterize ores for theirgravity recoverable goId content. The amenability of an ore to gravity recovery is the

single most important parameter to justify the installation of a gravity circuit (Laplante,et al., 1993). Therefore, the ore must be characterized for its gravity recovery potential,as it is ground and progressively liberated. This is the most common definition of GRG,


and the GRG test is designed to address this question. GRG behaviour in ooits must also

be characterized, particularly in the units used to grind and classify and ooits to recover

GRG. If the GRG content of an ore is to be fully used, its behaviour in the various units

of a grinding circuit must be measured, then modeled. This is achieved by measuring

the GRG content in the streams entering or exiting the ooits. From this point of view,

there is a difference between characterization of GRG in an ore and in a stream. The

characterized GRG of an ore measures a potential for gravity recovery. The relevant

GRG content of a stream, by contrast, is the GRG content that is already liberated and

available for gravity recovery.

Gravity Recoverable Gold (GRG) refers to the portion of gold in an ore orstream that can be recovered by gravity at a very low yield 1%). It includes gold thatis totally liberated, as well as gold in particles that are not totally liberated but with such

density that they report to the gravity concentrate. Conversely, it excludes fine,

completely liberated gold that is not recovered by gravity because of the improper

characteristics such as shape factor and size or gold contained in gold carriers in such

small quantities that the specific gravity of the particle is not affected. Information

about the GRG in an ore or stream can be used for different purposes: if gravity

concentration exists in the circuit, the GRG information can be used to either determine

if the circuit is optimized or assist in its optimization. If there is no gravity

concentration in the existing circuit, the amount and size distribution can be used as one

of factors to justify whether a gravity concentration circuit should be installed and thebenefit of installing it.

2.2.1 GRG Potential of Ores

Despite advances in competing technologies, gravity concentration remains an

attractive option due to its low capital and operation cost, even at the beginning of the

third millennium. This continued interest has spurred research in new technologies,


most of which rely on separation in centrifugaI field (sometimes called enhancedseparation). The Kneison Concentrator (KC) has been by far the most commerciallysuccessfui centrifuge unit used for goId recovery. It was therefore appropriate to choosea Iaboratory scaie KC to measure GRG content.

The procedure is shown in Figure 2-1.

Samples

(50 kg)

45-55% -751!m

-~l~

tailing

Main tail

tailing

stage 3

stage 1

850 to -20 I!m

850 to -20 I!m

Pulverizing +105 I!m

stage 2

conc.

r6

Figure 2-1 Procedure for Measuring GRG Content with a KC-MD3

The test is based on the treatment of a sample mass of typically 50-70kg with a

KC-MD3. Usually, three stages are used: for the first stage, the representative ore


sample is crushed and pulverized to 100% -850 J.lm and then processed with a 7.5 cm

KC-MD3. The entire concentrate is screened from 20 to 600 J.lm and each size fraction

fire-assayed for extraction. The same procedure is performed on a 600g sample of thetailing. For the second stage, the tailing of stage 1 are split, and approximately a 27kg

sub-sample is ground in rod mills to a finer size, 45-55% -75 J.lm, and processed with

the KC-MD3 unit. The third stage repeats the above process with the tailing of stage 2,

usually a mass of 24 kg, ground to 80% -75 /lm. Both concentrates and 600 g samples

of the tailing are screened and assayed as for stage 1. The assays of the three

concentrates and the tailing of stage 3 are used to estimate the ORO content. The tailing

assays of stage 1 and 2 are used to estimate stage recovery and assess assaying

reproducibility.

The Knelson tests are carried out at feed rates and fluidization water flow rates

adjusted to match the feed size distribution, typically 1200 g/min and 7 L!min for stage1 to 400 g/min and 5 L!min for stage 3. These correspond to optimal settings asdetermined by extensive test work with both gold ores and synthetic feeds, but must be

adjusted for gangue density (Laplante, et al., 1996, Laplante, et al., 1995). Because thetest is optimized in laboratory, it yields the maximum amount of ORO; actual plant

ORO recoveries will be lower because of limitations in equipment efficiency and in the

usual approach of processing only a fraction of the circulating load.

Results are normally presented as size-by-size recoveries for each stage and

overall recovery. By plotting the cumulative retained recovery as a function of particle

size, from the coarsest to the finest size class, a graphie presentation is obtained. Figure2-2 shows the results of a test for sample from the Campbell mill feed (Balmertown,Ontario) (Laplante, 1999). For stage 1, recovery cumulates to 33% (for the finest sizeclass, the minus 20 J.lm fraction, the lower limit is arbitrarily set at a 15 J.lm). Resultsare also cumulated from stage 1 to stage 3, from 33%, the amount of ORO recovered

after one stage, to 68%, the total ORO content.


1000~ 1 Ji i

100Particle size (IJm)

4-~~~~~~~~~~~~~----I--"- stage 1"'i-~~~~~~~~~~-~~----I""""'-stage 24-~'----~--~~~~~~-~~---1--'- stage 3

100~ 900~Q) 80>0 700! 60C)0::: 50C)

40Q).::: 30-..!!!

~ 20E~ 100

010

'--~-~~~~~~~~~~~~-~~~~~---~-~-

Figure 2-2 Typical Cumulative Gold Recovery of a GRG Test as a Function of

Particle Size

2.2.2 GRG Available in Streams

For measuring the GRG content in streams, representative samples are extractedand processed with a KC-MD3 operated to maximize gravity recovery. As only GRG

that is already liberated is of importance, no grinding is used, and each sample is

processed only once to simplify the procedure and minimize the risk of recovering non-GRG. Putz (1994) and Vincent (1997) also used a modified procedure to maximizeGRG recovery for difficult separations, typically with high-density gangue. Typically,

a finer top size is used, or, for finer feeds, silica flour is added to the sample to decreaseits overall specifie density.


There are several other laboratory methods to measure the GRG content of a

stream. AH methods "recover" GRG in a concentrate stream. TraditionaHy,

amalgamation has been the conventional methods of measuring GRG, but the health

risk associated with the use of mercury has prompted commercial and research

laboratories to discontinue its use. More recently other units, such as the Mozley

Laboratory Separator, the superpanners or lab flotation ceHs, have been used. Most

methods yield irreproducible results, often not enough mass is used or not aH the GRG

is recovered.

Table 2-1 summanzes the difference between the ore and stream GRG

determination.

Table 2-1 Differences between GRG Determination for Ores and Streams

Ore Characterization Stream Characterization

Objective: Objective:To measure how much To measure how much

GRG is liberated as the ore is GRG is already liberatedground to finalliberation size in streams

Procedure: Procedure:Sequentialliberation and Removal of +850 /lm fraction,

recovery at 100% -850 /lm, recovery of GRG in a single stage from50% -75 /lm and 80% -75 /lm -850 /lm fraction. Procedure modifiedMinimum mass used: 24 kg for high s.g. samples

Product Treatment: Product Treatment:AH three concentrates and Same as the one of ore

600 g sample of three tailings characterization, but for singleare screened from 20 to 600 /Jm concentrate and tailing products

The GRG content of streams and performance of gravity units have been

difficult to evaluate for a number of reasons. One of the reasons is that slurry sampling


is an essential tool for the job but is error prone, especially when GRG is present, as it isless likely to be uniformly dispersed in the flowing slurry. Precision and accuracy are

difficult to achieve due to the occasional occurrence of coarse gold, called the nuggeteffect. Therefore, when sampling, great care must be taken to obtain a truly

representative sample of adequate mass. Large samples are often required to make the

assessment ofgold content statistically sound (Putz, 1994. Woodcock, 1994.)

For the purpose of estimating the minimum sample mass needed to achieve a

glven accuracy, the occurrence of GRG can be assumed to follow a Poisson

distribution. Consider a sample that contains n gold flakes on average. Actual samples

will indeed average n gold flakes, but with a standard deviation of j;;. The relativestandard deviation will be Jlj";;. This describes the fundamental sampling error anddoes not include assaying and screening errors or systematic errors stemming from

inappropriate sampling methodology. For the same grade and mass, finer feeds yield an

increasing number of gold particles and thus a lower fundamental sampling error. If all

the coarse goId particles could be removed, assayed separately, then recombined

mathematically with the grade of the material from which the coarse particles were

removed, the error associated with the overall grade of the sample would be lower. Ithas been proposed (Putz, 1994) that around 10 to 50 kg of material would be sufficientfor plant stream samples and the maximum size class for which reliable GRG content

information could be thus generated would generally be below 850/-lm. Actual sample

size requirements vary according to gold grade and the size distribution of GRG.

2.3 Unit Processes

Usually, gold gravity circuits are inserted in grinding circuits consisting of SAGor rod mill for primary grinding and ball mills for the secondary grinding, and

cyclopacks for classification. In most plants gold is recovered most frequently from


cyclone underf1ows, and less frequently from baIl mill discharges. Knelson

Concentrators are most frequently used for primary recovery, although jigs in NorthAmerica and spirals in Australia were more common sorne twenty years ago. Primary

concentrates are generally upgraded to smelting grade by shaking tables, although more

recently intensive application is gaining acceptance, with automated units such as

Oekko's In-Leach reactor and AngloOold's Modified Acacia process. In this section,

more details about the above gravity units and their modeling will be given.

2.3.1 Comminution and Classification

BalI mills are the only comminution units studied thus far with the ORO

approach. The study of a grinding operation as a rate process has becorne a well-

established practice (Kelsall et al., 1973a, 1973b; Hodouin et al., 1978). It enablesmineraI processors to simulate the grinding process more accurately. It can dramatically

facilitate control and optimization of the grinding circuits. Usually, the development

and refinement of baIl mill models use the concepts of breakage and selection functions.

Due to its malleability, gold behaves differently than other mineraIs in baIl mill or

grinding circuits. Banisi (1990) investigated in a laboratory mill the grinding behaviourof gold by means of breakage and selection functions and contrasted it with that of

silica.

2.3.1.1 The Breakage Function

When a single brittle particle breaks into smaller pieces, a range of particle sizes

will be produced. Conceptually, the breakage function, bij, is a mathematical description


of the fragments distribution into a number of size classes. It is defined as the

proportion of material which appears in size class i when broken once in size class j.The cumulative breakage function, Bij , is the proportion of broken material which, upon

single breakage from size class j, is finer than size class i (Austin et al., 1971). Therelationship between breakage function and cumulative breakage function is defined by:

bij = Bij - B (i+l)j i>j

When the fragment distribution is geometrically similar for all size classes, the

breakage function is defined as normalizable; otherwise, it is called non-normalizable

(Austin, et al., 1971a). In most simulations the breakage function is assumed to benormalizable. Although it appears that this assumption is not very realistic, it has been

found that most simulators are not sensitive to this simplification (Laplante et al, 1985).In the simulation of this paper the breakage functions for the gold and gangue are

assumed to be normalizable.

Many methods have been proposed to estimate the breakage function. Herbst

and Fuerstenau (1968) have devised a laboratory method whose basis is that zero orderproduction of fines should be apparent. Dividing its rate constant for each size i by the

selection function ofthe original size class j yields the value Bij;

B=F;!l S

J

j=l toi-l

where Bij is the cumulative breakage function, Fi is the fines production rate constant of

size class i and Sj is the selection function of original size class j (the parent class).


2.3.1.2 The Selection Function

The selection function or specific rate of breakage is a measure of grindingprocess kinetics. In other words, it is an indication of how fast the material breaks.There is ample experimental evidence that batch grinding kinetics follows first orderwith respect to the disappearance of material from a given size class due to breakage

(Kelly et al., 1982):

dM;(t) =-Set) *M(t)dt 1 1

where

Mj(t) : mass in size class i after a grinding time oftSj(t): rate constant for size class i (fI)

The rate constant has been described as the "selection function" by early investigators

(Herbst et al., 1968).

2.3.1.3 Investigation of Gold's Behavior in Comminution

Banisi (1990) compared the breakage and selection functions of gold and silicaby grinding approximately 50 g silica and 4.88 g (consisting of 1240 flakes) of goldfrom a single size class, 850-1200 ~m, respectively in a ball mill. Before grinding, thesamples were screened to determine the initial size distribution. Grinding was then doneincrementally for total times of 15, 30, 60, 90, 150, and 210 seconds. After eachgrinding increment, the samples were screened for 20 minutes to determine the size

distribution and then returned to the mill for the next cycle.


After the calculation and analysis of the breakage and selection function of goId

and silica, Banisi found that grinding of single size class of goId and silica in a baIl mill

followed tirst order kinetics. The selection function of silica was more than four times

that of gold. Further investigating at plant scale (Golden Giant Mine) found that goldgrinds six to twenty times slower than its gangue. Although Banisi's work was

important to identify the behavior of gold, there were still sorne potential improvements.

Noaparast (1996) investigated the breakage and recoverability of gold. He triedto generate a characterization of how goId fragments and their progeny respond to

gravity recovery. To understand the grinding and recoverability of gold, Noaparast

measured a) the rate of disappearance from the parent class, b) the distribution offragments in the other size classes, and c) what proportion of the progeny and unbrokenmaterial is gravity recoverable. Actually, the tirst concept corresponds to the selection

function, the second to the breakage function, and the third is more important when

simulating the GRG recovery in grinding circuit. A basic methodology was developed

based on three steps, namely the isolation of GRG, its incremental grinding and

recovery. First, samples were processed with a LKC to maximize and isolate GRG in

certain size classes. Second, each sample was first combined with silica sand of the

same size class to a total mass of 200 g. Material was then incrementally ground in mill.

After each increment, the ground product was dry screened and aIl material other than

that in the original size class was set aside and replaced with silica sand, to make up the

original 200 g for the next increment. Third, after incremental grinding, aIl samples

were mixed and silica from the initial size class was added to obtain a 3 kg sample. The

sample was then processed with a KC-MD3 to recover GRG. Based on the differentsamples tested, it was found that most of the gold in the original size class remains

gravity recoverable; even when broken to finer size classes. Generally gravity

recoverability decreased the finer the parent size class, or for the progeny classes much

finer than the parent class. Finally, based on the modified Rosin-Rammler equation, a

equation was obtained to model the gold recoverability of each size class:

CHAPTER2 GRAVITY RECOVERABLE GOLD: A BACKGROUND

[

( X )4]-0693* -RGRG = 98.5 * 1-e' 22

20

Where X is geometric mean size of size class (!lm)

This equation yields the GRG data shown in Table 2-2, used to model GRG

recovery in grinding circuit. A column matrix that includes the data in Table 2-2 is used

to correct each element of the breakage matrix below the main diagonal.

Table 2-2 Coefficient used to correct the grinding matrix

Size C1ass

(flm) -25" +25-37 +37-53 +53-75 +75-106 +106-150 +150-212 +212

RaRG 0.371 0.895 0.985 0.985 0.985 0.985 0.985 0.985

(* means size of the -25 !lm assumed to be 20 !lm)

2.3.1.4 Gold's Behavior in Cyclones

Gold's behavior in grinding circuits, both in comminution and classification

units, is the result of its malleability and specifie gravity, which combine to yield high

circulating loads. For gold or GRG classification, the only data available are cyclonepartition curves. The curve can be obtained by analysis of three or four of the projectedcyclone streams, typically the underfiow, overfiow, and one or two feeds. Each sample

is processed with KC-MD3 to determine GRG content. Studies in various mills

(Laplante, Liu and Cauchon, 1989; Banisi, Laplante and Marois, 1991; Laplante andShu, 1992; Putz, Laplante and Ladoucer, 1993; Woodcock, 1994; Putz, 1994) have


shown that the partition curve of GRG is above that of the ore. A typical gold, GRG and

ore partition curve is shown in Figure 2-3 (Laplante, 2000).

-1100

80

LI.. 60-~0- 40~0

20

010 100

Partiela Size, tm1000

'---------------------_._--

Figure 2-3 Typical Partition Curve for Gangue, Gold and GRG

Clearly, most goId and GRG, even below 25 /lm, still report to the cyclone

underflow. This explains why very large circulating loads build up, especially in the

fine size classes, which exhibit slower grinding kinetics. However, there is still

considerable uncertainty as to how the partition curve of goId or GRG in the fine size

range is affected by parameters such as rheology or the cut-size of gangue. Although

much remains to be done to understand the factors affecting gold's behavior in

cyclones, a link between GRG and ore (i.e. gangue) partition curves was used in thesimulation (more detail will be discussed in Chapter three). Plitt' s model (1976) wasalso used to calculate the full partition curve of GRG.

Ci = R f + (l-Rf)*{ l-exp [-O.693*(d/dso) m}

Where

Ci is fraction of material in size class i which reports to the underflow


Rf is bypass which is mass fraction of cyclone feed water recovered in theunderflow stream (it is usually called bypass)di is characteristic particle size of size class i

dso corrected cut size

m sharpness of separation coefficient

Although the partition curves of GRG and ore can be calculated with the above

parameters, there are still sorne problems. Part of the problem is that the traditional

approach to estimate Dsoc cannot be used for GRG, because recoveries for GRG in the

finest size class, typically the minus 25 !-lm fraction, tend to be very high, 70 to 90%. In

this case they are around 40%, thus, no "8' curve is generated. Figure 2-4 shows the

coarsest classification ever documented for GRG, at the New Britannia Mill. Note that

although the ore partition curve fits Plitt's model weIl, that of GRG and gold is very

difficult to fit, with no clear "8" shape and a bypass fraction that does not equal that of

the gangue.

u.-::::>.8'#.

100908070605040302010o

10 100Particle Size, j.Jm

--Gold

-tr- GRG

1000

Figure 2-4 Partition Curves of the Primary Cyclones of New Britannia


2.3.2 Recovery Units

2.3.2.1 Knelson Concentrator

The Knelson Concentrator is an innovative centrifugaI separator commissioned

in the early 1980s. With successful installations in major gold producing regions of theworld, it has become the most widely used unit to recover GRG. One unique feature of

the Knelson Concentrator is its groove construction and tangential fluidization water

flow in the separating bowl, which partially fluidizes the concentrate bed. As a result,

the unit can achieve high GRG recovery over a wide size range, typically 20 to 850 flm,with recovery falling around below 25 to 37 flm, due to low terminal settling velocitiesof finer particles and the relatively short retention times used in the unit.

Although the standard Knelson Concentrator is designed as a roughing

concentrator for gold ores, it can be used in slightly different ways. First and foremost,

it can be used to recover gold from the main circulating load of grinding circuits. Thereare many successful industrial applications for KC to recover gold in grinding circuits.

For example, in 1995, the Campbell gold mill at Ontario installed two Knelson

Concentrator CD 76 cm to replace the existing jigs in a rod/ball mill grinding circuit (itis now using a single unit and a smaller Knelson Concentrator in the gold room). Thischange has increased gravity recovery from 35% to 50%, which translates into

economic value through a reduced gold inventory in the plant process and an ability to

increase mill throughput. Second, it can be used to treat flash flotation concentrates.

Flash flotation can recover gold bearing sulphide mineraIs from hard-rock ores. Sincethe product of flash flotation from the circulating load of grinding circuits contains a

significant amount of GRG, gravity recovery of the GRG from these products before

smelting has been increasingly accepted. For example, the Lucien Bliveau mill used a

Knelson MD30 to treat its flash flotation concentrate (Putz et al, 1993; Putz, 1994). Themill was then moved to the Chimo mine, and recovery from the flash flotation


concentrate was then supplemented by a second Knelson treating a bleed from the

cyclone underflow, for coarser gold (+300 !lm) that would not readily float in the flashcell (Zhang, 1998).

Separation in the Knelson Concentrator is based on the difference in centrifugaI

forces exerted upon gold and gangue particles and on the fluidization of injected water.It utilizes the principles of hindered settling and a centrifugaI force that theoreticallyaverages 60 Gs. For the KC-MD3, water is injected tangentially at high pressure intothe rotating concentrating cone through a series of fluidization holes to keep the bed of

heavy particles fluidized (Figure 2-5). The feed is introduced as slurry whose densitycan be up to 70% to the base of the rotating inner bowl through the stationary feed tube

(called downcomer).

Feed

'l~'lils

COllC't,::utrate

Figure 2-5 Schematic cross-section of a Knelson Concentrator MD3


When the slurry reaches the bottom of the cone, it is forced outward and up the

cone wall depending on the size and specifie gravity by centrifugaI force. The slurrythen fills each ring to capacity to create a concentrating bed. Compaction of theconcentrating bed is prevented by the fluidizing water that enters tangentially into the

concentrate bed opposite to the rotation, at a flow rate controlled to achieve optimum

fluidization. Under the effect of centrifugaI force and water fluidization, high specifie

gravity particles such as gold are then retained in the concentrating cone. Gangue

particles are washed out of the inner bowl due to their low specifie gravity. When a

concentrating cycle is completed, the feed must be stopped or diverted, then

concentrates are flushed from the cone into the concentrate launder (Knelson et al.,1994). For the small units (e.g. KC-MD3 and KC-MD7.5), concentrate removal isusually accomplished by releasing the inner bowl from the outer bowl and washing the

concentrate out. For larger units, concentrate removal can be achieved automatically by

mechanically flushing the concentrate to the concentrate launder through the multi-port

hub.

For this work, size-by-size recovenes for a KC-MD 30 generated at mme

Camchib will be used (Vincent, 1997). These are shown in Table 2-3.

Table 2-3 Typical Values of Knelson Concentrator's Recovery

Size 25 37 53 75 106 150 212 300 425 +600

(/lm) -25 -37 -53 -75 -106 -150 -212 -300 -425 -600

RKC 0.6 0.65 0.72 0.75 0.78 0.77 0.73 0.68 0.65 0.58 0.48

Generally, Knelson size-by-size recoveries are relatively size independent: it is

frequent to observe a ratio of 1.5:1 to 3:1 in the recovery of the coarsest to the finestsize classes. This ratio usually increases with increasing feed rate and gangue specifie

gravity.

CHAPTER 2

2.3.2.2 Table

GRAVITY RECOVERABLE GOLD: A BACKGROUND 26

The shaking table is perhaps the most metallurgically efficient form of gravity

concentrator, being used to treat the smaller, more difficult streams, and to produce final

concentrates from the products of other forms of gravity system (Wills, 1997). ManygoId plants use a shaking table to upgrade Knelson concentrates, achieving recoveries

that vary between 40% and 95%.

A shaking table consists of a slightly inc1ined deck on to which the feed is

introduced at the feed box and distributed along part of the upper edges and spread over

the riffled surface. Wash water is distributed along the balance of the feed side from the

launder. The table is vibrated longitudinally cause the partic1es to "crawl" along the

deck parallel to the direction of motion. Thus the motion causes the partic1es move

diagonally across the deck from the feed end and finally to fan out according to their

size and the density. The larger lighter partic1es are washed into the tailing launder

while the smaller, denser partic1es riding highest towards the concentrate launder. Sorne

fines, inc1uding fine GRG, are immediately washed into the tail discharge upon feeding

(Putz, 1994).

Sivamohan and Forssberg (1985b) have reviewed the significance of manydesign and operating variables. The separation on a shaking table is controlled by a

number of operating variables, such as wash water, feed pulp density, deck slope,

amplitude, and feed rate. Partic1e shape and size range play an important role in the

table separation. There is sorne confusion as to what the table is most capable of

recovering, but the work of Huang (1996) c1early shows that most gold losses are fine,liberated goId that can be recovered with a KC MD-3. Sorne of the lost gold is flaky,

and generally reports in the middling fraction, generally intermingled with pyrite. Much

of this goId will not be recovered well by gravity, because it is not fully liberated. This


pattern appears to hold irrespective of the nature of the table surface, flat, riftled or

grooved.

When usmg a conventional shaking table to process KC concentrates, a

significant fraction of the finer gold may be lost to the table tails (Huang, Laplante andHarris, 1993). Because of the different forces (60 Gs for KC, IG for the table) acted onparticles, incomplete liberation particles and gold flakes. Thus, the table tailings that

contain significant amounts of GRG should be recycled to the KC for scavenging to

recover more GRG, as practiced at Lucien Bliveau. The shaking table recovery data

used for simulation in Chapter 3 are shown in Table 2-4. It shows that in the finest size

class the recovery is much lower than that of other size classes. Although recovery

drops significantly with decreasing particle size, it remains relatively high even for theminus 25-llm fraction, typically above 50%.

Table 2-4 Typical Values ofShaking Table's Recovery Used for Simulation

Size 25 37 53 75 106 150 212 300 425 +600(~m) -25 -37 -53 -75 -106 -150 -212 -300 -425 -600

%Rt 60 80 90 95 96 96 94 92 90 85 80

2.3.2.3 Jigs

Jigs used to be the recovery unit of choice for gold in North America. Sorne arestill used, although they have been replaced by Knelson Concentrator in a large number

of plants. Table 2-5 lists a selected number of Canadian sites where jigs have beeninstalled.


At plant start-up, most recent mill designs have incorporated Knelson units. Jigs

are discussed here because they offer, at low yield, a much different relationshipbetween GRG recovery and particle size.

Table 2-5 Evolution of the Use of Jigs and KC at Certain Canadian Sites

Case 1 Case 2 Case 3

Casa Berardi Campbell Mine Jolu

Golden Giant MSV, Dome Mine, Snip Operation

Lucien Bliveau Est Malartic

Mine Camchib Sigma

Case 1: Jigs used at start-up, then removed; KC installed later.

Case 2: Jigs used and then replaced by KC.

Case 3: Jigs used until mine shut-down

The jig is one of the most widely applied gravity concentrating devices. Jiggingis the process of sorting different specifie gravity mineraIs in a fluid by stratification,

based on the movement of a bed of particles. The particles in the bed are arranged by

the stratification in layers with increasing specifie gravity from the top to the bottom.

The jig is normally used to concentrate relatively coarse material, from 200 mm toO.lmm. When the specifie gravity difference is large, good concentration is possible

over a wider size range (Wills, 1997), which explains its earlier role in gold recovery.

The basic construction of a jig is shown in Figure 2-6. Essentially it is an opentank, filled with a fluid, normally water, with a horizontal jig screen near the top, andprovided with a spigot in the bottom, or hutch compartment, for concentrate removal.

The jig also includes means to continuously receive raw ore feed, a drive mechanismand methods of separating the stratified bed into two or more product streams (Burt,


1984). The jig bed consists of layer of coarse, heavy particles called ragging. In the jig,separation of mineraIs of different specifie gravity occurs in a fluidized bed by a

Tailing

Jig bed

Ragging

Jig Screen

Concentrate

Discharge spigot

Figure 2-6 Basic Jig Construction

pulsating current of water which produces stratification. On the upstroke the bed of

ragging and slurry are normally lifted as a mass, and then dilated as the velocity

decreases, while the suction stroke slowly closes the bed. The purpose of jigging is todilate the bed of material so that the denser and smaller particles penetrate the

interstices of the bed.

Jig capacity is described as the optimum throughput that produces an acceptablerecovery and is determined by the area of sereen bed. In other words, different

capacities result in different recoveries. Jig capacity varies depending on the jigconfiguration, ore feed size, and adjustments of stroke length and speed. Coarser grainscan usually be fed in larger volumes than fine grains in relation the area of the jig bed.

CHAPTER 2 GRAVITY RECOVERABLE GOLD: A BACKGROUND 30

For gold, jigs are generally used as the primary recovery unit to treat the full circulatingload at the expense of low stage recovery.

1000100Particle size,J,lm

-o-KCL Jig

100 .."........---------~8060 "'l-~~~~~~~~~.._______~

40

20

o+---IIBIIIBMeil-=tl;:10

Figure 2-7 Comparing Size-by-Size Recovery of a Knelson Concentrator and a

Duplex Jig (Based on Putz, 1994, and Vincent, 1997)

Both Putz (1994) and Vincent (1997) have studied jig circuits, although onlyVincent generated size-by-size GRG recovery data. Both reported very low stage

recoveries, about 2%. Overall gravity recoveries were in both cases in the forties,

because (a) the full circulating load was treated and (b) the amount of GRG circulatingload was around 2000% (Le. 2%* 2000%/100% = 40%). Table 2-6 shows the size-by-size recoveries that will be used for simulation (from Vincent, 1997). Figure 2-7 showsthat compared to KC, the relative effect of partic1e size on recovery is extremely high.


Table 2-6 Typical Values of Jig's Recovery Used for Simulation

Size 25 37 53 75 106 150 212 300 425 +600

(/lm) -25 -37 -53 -75 -106 -150 -212 -300 -425 -600

% Rjig 0.3 0.7 1.6 2.1 3.8 4.2 5.6 9.3 10.5 10.1 18.9

CHAPTER THREE SIMULATING GOLD GRAVITY RECOVERY 32

CHAPTER THREE

SIMULATING GOLD RECOVERY

3.1 Introduction

A methodology to estimate gold recovery by gravity was developed by McGillgravity research group (Laplante et al., 1994). It makes use of a population-balancemodel (PBM) that represents gold liberation, breakage and classification behaviour tosimulate gold gravity recovery in a grinding circuit. In this chapter, how the PBMmakes use of the characterization of GRG and its behaviour in unit processes will bepresented. This chapter is divided into three sections. In section 3.2, the derivation ofthe PBM is shown, starting from a very simple, single class model, to progress to athree-size class model and finally the full model. A limited number of circuits arepresented and for each, the matrix equation for calculating the GRG recovery is derived.In section 3.3, the extraction of plant data for GRG (as opposed to total gold) isdescribed; values for the matrices that will be used in the PBM are given in sections3.3.1 and 3.3.2, respectively.

3.2 The GRG Population Balance Model

3.2.1 A Simplified Approach

Consider the following circuit in a gold plant (Figure 3-1): fresh feed is fed to aball mill, and the total GRG is assumed to appear in the mill discharge as F. Brepresents the proportion of the GRG in the ball mill feed which is still gravity


recoverable in the mill discharge1 AlI or sorne of the ball mill discharge is sent to the

primary recovery and gold room recovery units. Primary recovery is based on the full

amount of GRG discharged from the ball mill, not that part of the discharge actually fedto the primary recovery unit (if the full discharge is not treated). Primary and gold roomrecovery can be represented separately (e.g. as P and G), but if both tailings arecombined, it is more expedient to represent their overall recovery, R. The tailings from

the primary unit and gold room are recycled as the feed to the cyclone, with any portion

of the mill discharge that was not treated. The overflow of cyclone goes to the next

recovery stage, such as flotation or cyanidation. The underflow of the cyclone is sent

back to the ball mill to regrind. C is the proportion of GRG in the cyclone feed that

reports to the cyclone underflow.

Ove flow

c clone

'-------.. ..

.. B. ..'----' F

BalI Mill Recovery Unit and Gold Room

Figure 3-1 Simple Circuit of Gravity Recovery from the BalI Mill Discharge

Let us define X as the amount of GRG in the ball mill discharge, which includesboth GRG freshly liberated (i.e., F) and GRG that was present in the cyclone underflowand was not ground into non-GRG in the ball mill. Then gravity recovery, D, is equal to

R*X, and the GRG directed to the cyclone is (l-R)*X, of which a proportion C is

1 The proportion is high, as gold is highly malleable and only a small fraction is ground into non-recoverable particles


classified to the underflow of cyclone, i.e. C*(l-R)*X. This ORO is then ground in theball mill, and a proportion B survives. Thus, at the ball mill discharge, the amount of

ORO is equal to B*C*(1-R)*X as ORO that has "survived" grinding, plus an amount Fthat has beenjust liberated, for a total ofB*C*(1-R)*X + F, which is also equal to X:

Re-arranging:

X= B*C*(1-R)*X + F Equation 3-1

x F[1- B *C *(1- R)] Equation 3-2

or X = [l-B*C*(l-R)] -1* F

And the ORO recovery, D, is equal to:

D R*F[l-B*C*(1-R)] Equation 3-3

As a numerical example, let us use values of 0.8 (80%) for F, 0.95 for B, 0.98for C and 0.1 for R. The value ofR can be obtained by taking the product ofhow much

of the circulating load is treated, how much is recovered in the primary recovery unit,

and how much of the ORO in the primary concentrate is recovered in the gold room.

Thus a R value of 0.1 could be obtained if 25% (0.25) of the circulating load is treated,with a primary recovery of 50% (0.5) and a gold room recovery of 80% (0.8).

The total ORO recovery, calculated with the above formula, is 0.494 or 49.4%.

Of a total of 80% ORO in the feed, approximately five eighth, or 49.4% of the total

gold, is recovered. These data are reasonably realistic, although B is slightly low. This

is the simplified PBM derived for this circuit configuration.


Consider another circuit, shown Figure 3-2 below:

Overflow,---,

KCand

GoldRoom

RD

Figure 3-2 Simple Circuit of Gravity Recovery from the Cyclone Underflow

As ore is ground and discharged from the baIl mill, GRG is generated as E. The baIlmill discharge is then sent to the cyclone. The cyclone overflow goes to the recovery

circuit, and from the underflow one fourth of the circulating load (Pl = 0.25) is bled andrecovered by Reichert Cones (P2 = 0.85), the concentrate is upgraded by KnelsonConcentrator. The Knelson concentrate is further upgraded in the goId room. For

simplicity's sake, the Knelson and gold room recoveries are lumped in a single

parameter, R (= 0.5). The tailings from the Reichert cones, Knelson Concentrator andgold room are recycled to the baIl mill. Using the same approach as for the first circuit,

the following equation is obtained, where X is the amount of GRG in the cyclone

underflow:

Equation 3-4

Rearranging Equation 3-4 results in the following PBM for this circuit:


P.*P*R*C*FD= 1 21- B *C *(1- ~ *P2 *R) Equation 3-5

Using the above values and equations, the GRG recovery, D, is equal to 0.434 or

43.4%, and the circulating load X, to 4.085 or 408.5%.

Although a size-by-size approach is not used in the above PBMs, the results

suggest that reasonable answers can be obtained for understanding how gold gravity

recovery from a grinding circuits works.

If gold recovery from the grinding circuit needs to be predicted, a size-by-size

approach is necessary for deriving the PBM. This approach will now be demonstrated

with three size classes, using simple recovery from the cyclone underflow (Figure 3-3).Part of the underflow is bled and fed to a screen, with the screen undersize to the

primary recovery unit, and the oversize back to baIl mil!. The concentrate from primary

recovery is treated in the gold room. Primary and gold room recovery will be lumped in

a single recovery matrix. Material not selected for primary recovery and the Knelson

and gold room tailings will be directed to the baIl mill for further grinding. The relevant

matrices are shown here:

[0.2]

E= 0.30.2

0.99

[

0.1

P=

0.2

[

0.5

R=

0.35

o.u [

0.9B = 0.06

0.03

0.950.04


Overflow....-----,

pPrimary and Gold room

D +- R

Cyclone Ilr--~

BalI Mill

T L..- ..

BFresh Feed--~" .. F

Figure 3-3 Circuit of Gravity Recovery from the Cyclone Underflow Using

a Size-by-Size Approach

The matrix E shows the amount of GRG of the ore in each size class. In theexarnple above, 0.2 (20%) is in the coarse size class, 0.3 (30%) in the interrnediate sizeclass and 0.2 (20%) in the fine size class2, for a total GRG content of 70% (30% of thegold in the ore in non-GRG). For the classification matrix C, shown above, we assumethat 100% (l.0) of the coarse GRG reports to the cyclone underflow, as do 99% (0.99)of the interrnediate size GRG and 90% (0.9) of the fine GRG. For primary screening, P,20% of the circulating load is screened, and half of the coarse GRG is rejected to thescreen oversize, whereas aIl of the GRG in the second and third size class report to thescreen undersize (henee a fraction of 0.2 of the cyclone underflow stream). For primaryrecovery P, it is assumed the primary recovery units is better at recovering coarse GRG

(50%) than GRG in the medium and finest size class (35%). AlI the above materialtransfer matrices are diagonal, because they represent units in which GRG is not ground

(i.e. does not migrate from one size to a finer one).

2 Column vectors that identify flows of GRG (i.e. E, Xand .Q) are underlined for easier identification


For the grinding matrix B, it is assumed that upon going through the ball millonce, 90% of the coarse GRG "survives" grinding, as opposed to 95% of the

intermediate size GRG and 98% of fine GRG (these values are found on the maindiagonal of B). Of the 10% of the coarse GRG that breaks into finer size classes, 6%reports as GRG in the second size class and 3% in the third (l% becomes non-GRG).Similarly, of the 5% of the intermediate size GRG that is ground, 4% reports as fine

GRG (also 1% becomes non-GRG). The 2% of the fine GRG that "disappears" becomesnon-GRG. With these descriptions, the grinding matrix can be expressed as a lower

triangular matrix. Note that the matrices used are either column matrices (underlined foreasier identification) or square matrices. With the exception of the B matrix, the squarematrices are diagonal, because they represent unit processes in which GRG does not

transfer from one size class to another. The B matrix is lower triangular, to represent the

migration of sorne of the GRG into finer size class by breakage.

The circulating load of GRG and how much is recovered in each size class

recovery can be derived as for the previous non-matrix approach:

x = [I-B * C * (I-P * R)] -1 * C *E

And the GRG recovery is:

D =P * R * (I-B * C * (I-P * R)) -1 * C *E

Equation 3-6

Equation 3-7

Note that division in the scalar model becomes matrix inversion in the matrix model.

The GRG circulating load and recovery are as follows:


[1.379]

X= 2.5961.932 [

0.0690]D= 0.1817

0.1353

Summing X and D yields, respective1y, total goId recovery, 0.3859 (38.6%) andthe circulating load of GRG, 5.91 (591%). From the GRG recovery matrix, it can beobserved that recovery is highest in the intermediate size class, because the coarsest size

class grinds more rapidly and is partially screened out of the gravity circuit feed; the

circulating load and recovery is highest in the intermediate size class; the finest size

class, despite receiving progeny from the two coarser size classes, does not have the

largest circulating load or recovery, because a significant proportion reports to the

cyclone overflow and its size makes gravity recovery less effective. These trends

mirror actually circuit performance.

For the derivation of the above PBM, sorne assumptions were made. First, the

GRG first appears at the discharge of the ball mill with the size distribution generated

by the GRG test, E. Second, no GRG will be rejected to the cyclone overflow beforebeing liberated. The validity of these assumptions was discussed by Laplante et al

(1995).

3.2.2 The Full PBM

The full PBM is very similar to the three-size-class model presented above;

typically, 10 to 12 size classes are used. Consider a grinding circuit made of the blockdiagram shown in Figure 3-4 (Laplante, Woodcock and Noaparast, 1994). As materialis ground and discharged, GRG is generated as E. A primary concentration step yields aproportion Pi of each size class (forming a diagonal matrix P) that is then presented to agravity separator for upgrading. From each size class, a GRG recovery of ri (forming adiagonal matrix R) is achieved.


Ball Mill

Primary concentration

SecondaryRecovery

DGravityConcentrates

Figure 3-4 Recovery from the Second Mill Discharge

Material not presented to the gravity unit or not recovered from all gravity units

is then classified by cyclone, a fraction Ci (forming a diagonal matrix C) being returnedto the mill. In the mill, a fraction of the GRG in each size class remains in the same size

class in the mill discharge (the main diagonal of Matrix B), but sorne GRG reports tofiner size classes (the lower triangular submatrix of B). Given the above description, itcan be derived the same way as simple PBM as the following formula:

D = PR* [I-BC (I-PR)r1 * E Equation 3-8

where D is a column matrix of the GRG flowrate into the concentrate for each sizeclass. Each di corresponds to the amount of GRG recovered in size class i. The sum of

the diS gives the total GRG recovery.

This circuit is relatively common in gold plants, and will be used to generate an

extensive database that will be summarized with multi-linear regressions.


When simulating GRG recovery, parametric estimation for the variouscomponents of the model is case specifie. For example, retrofit applications will be ableto take advantage of the existing grinding circuit to generate much of the data in a

reliable way (C and B) and validate the algorithm. Greenfield applications will use data"borrowed" from other operations, with a corresponding decrease in reliability. For

optimization studies, the usual approach will be used to generate C, B, P and R from the

existing circuit, tune the model to achieve a D consistent with observed circuit

performance, and test changes in recovery by modifying P and C.

Although equation 3-8 is specifie to the circuit shown in Figure 3-4, similar

equations representing different circuits can readily be derived. Figures 3-5 and 3-6

show two such circuits, represented by the folIowing equations, respectively:

Fresh Feed

Primary

Mill

Classification

Concentrate

B

BalI Mill

...-----1. SecondaryRecovery

Cyclone

Figure 3-5 Recovery from the Cyclone Underf10w


Secondary.----~~-., Recovery

SecondaryClassification

B

First Classification

Primary

Mill Concentrate

Fresh Feed

BaU Mill

Figure 3-6 Recovery from the Primary Cyclone Underflow

D = PR * [1-BC * (I-PR)] -1 * C *E Equation 3-9

D = PR * CI* [1 + B * (I-MB) -1 * M] *E Equation 3-10

where M = (I-PR) * CI + (I - CI) * C2 Equation 3-11

where CI and C2 are two matrices that describe the partition curves of the

primary and secondary cyclones, respectively. Figure 3-5 represents most gravitycircuits, where gold is recovered from the primary cyclone underflow. Figure 3-6represents goId recovery from the primary cyclone underflow, as practiced at Casa

Berardi (CB), Qubec.


3.3 Input Data for the PBM

3.3.1 GRG Data (F Matrix)

OriginaHy, five different GRG distributions, normalized to 100% and shown in

Figure 3-7 as cumulative, were used as Ematrix (Table 3-1) in the simulation.

-+-- very fine_fine-.- interrrediate

~coarse--.- int. less fines

1000100Particle Size, J.Il1

o+--J.---J.....J~~~~k""II-J-~10

100 iIII~~-----------,~ lCl)

~ 80 +--\-~.--\-~~~.oCl)

0::: 60 +-~-\---lIIIl~~--'..----~~~~-j~~ 40 +---~---'~~--~-----I::::JEc3 20 +-~~------'~----'~"w-~------'~~-J?ft

Figure 3-7 Normalized GRG Distributions of the Original Data Set(1: Coarse; 2: Intermediate; 3: Fine; 4: Very Fine; 5: Intermediate --fewer fines)

They represent different contributions of coarse and fine GRG. Because aH final

simulation results are expressed in terms of the recovery of GRG, as opposed to total

gold, the actual quantity of GRG is 100% for aH simulations. The end-user simplymultiplies the GRG recovery by the GRG content to obtain the total gold recovery. For

example, if the simulated GRG recovery is 50%, and the total GRG content is 80%,then the total goId recovery by gravity is 40%.


Table 3-1 Normalized GRG Distributions Used for the Simulation

Size, IJ,m Very fine Fine Intermediate Coarse int. less fines+600 0 0 0 16 0

425-600 0 0 1 8 0300-425 0 1 4 9 2212-300 0 1 4 10 6150-212 2 3 9 12 10106-150 2 7 12 Il 1075-106 4 17 15 11 1453-75 8 15 10 8 1337-53 10 18 15 6 1425-37 22 12 10 5 23

-25 52 26 20 4 8Sum 100 100 100 100 100

First, gravity recovery was simulated for the five GRG size distributions,

systematically varying other operating conditions, such as classification, grinding and

the recovery matrix (details will be given in the next Chapter). One regression mode!was then generated and found to fit the five original GRG size distributions weIl, but

fared poody with other GRG distributions, because the five original GRG size

distributions did not yield an adequate number of degrees of freedom for the regression

coefficients describing the effect of the GRG size distribution (i.e. only five differentsize distributions, which were fitted with four parameters). The problem was correctedby using a wider database of GRG distributions, twenty in total, aIl shown in Figure 3-8.

The last point (i.e. the contribution of the -25 /-lm fraction) has been deleted from eachcurve, for the sake of clarity.

Further fitting efforts indicated that mode1 accuracy was generally poor for the

coarsest size distributions. As a result, the twenty GRGs, including the original five,

were split into two subsets, fine and coarse GRG, based on the cumulative GRG content

coarser than 150 /lm, with a 25% transition limit between coarse and fine GRG.


1

100) 11!

A CoarseGRGs

100Partide size (~m)

0+------.---.-------,-------,-----,--,--,-,--,-----="'"---""1""""-"

10

"'0~ 80 +----~d.co+-'Q)~ 60Q) +--------"IIl'-.-~~~>

+:ico::J 40 -+-----~-____1t:-"11


For the present work, the value of P*R depends on the treated fraction of

circulating load (ball mill discharge) and the units used to recover gold in the primarystage and gold room. It was decided to use a single relationship between particle and

primary/gold room recovery, and to vary the proportion of the circulating load being

treated to vary P*R, -i.e. the gravity recovery effort. This proportion was set equal for

each size class.

The size-by-size primary and gold room recovery shown in Chapter 2 were used

for simulation. The primary recovery came from the performance of a MD30 KC with a

conventional bowl used at Les Mines Camchib (Laplante, Liu and Cauchon, 1990) andthe goId room recovery from generally observed goId room practice (Huang, 1996).

Grinding (B Matrix)

The grinding B matrix is probably the most difficult to estimate for the

simulation, because GRG particles are ground at a rate noticeably lower than the overall

ore due to their malleability (Banisi, Laplante and Marois, 1991).

A typical population balance grinding model is one that relates the size

distribution of the discharge of the mill, mg, to the size distribution of the feed, mf, the

residence time distribution in the mill, the breakage function, bij, and selection function,S. Ofthese parameters, S and bij are the most critical. The model can be resolved into asimple equation 3-11 of the type (Austin et. al, 1984):


Hl! 0 0 0 0H 21 H 22 0 0 0

M d = H 31 H 32 H 33 0 0 *M Equation 3-12f0

H n1 H n2 H n3 H n4 H nn

Each Hjj (on the main diagonal) is the fraction which remains in the original sizeclass j. the terms Hij (i>j) below each mail diagonal Hjj are the fraction which enter thefiner size class i from the original size class j. The assumption that no material can exitthe mill in a size class coarser than the one in which it entered is the reason the upper

triangle of the matrix is null (Hij=O for i


For this w

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